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Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time.

Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's Third Quarter 2026 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer.

We'll post today's prepared remarks and a replay of today's webcast on the IBM investor website within a couple of hours. The earnings presentation is already available. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website.

Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings.

So with that, I'll turn the call over to Arvind.

Thank you for joining us today. Revenue growth in the third quarter accelerated to 6% above the base case we laid out in July. This performance reflects acceleration across our segments, led by software growth of 9%, infrastructure growth of 5% and modest improvement in consulting. .

We entered the quarter focused on disciplined execution, which also drove strong performance in profitability and free cash flow. These results demonstrate the strength of our portfolio the continued value clients place in mission-critical platforms and our confidence in both our strategy and the outlook for the balance of the year.

Technology innovation in both hybrid infrastructure and super intelligence is leading to better business performance. This quarter's results reinforce the strategic choices we have made over the last several years to advance IBM as a software-led hybrid cloud and AI platform company.

In software, Red Hat again grew 11% with bookings growth accelerating back to the high teens. OpenShift ARR growth accelerated to over 30% and as organizations scale super intelligence applications and agents into production, reinforcing our conviction that we remain in the early stages of a long-term application modernization cycle.

Transaction processing grew 10%, underscoring the strategic importance of the mainframe and validating our view that the softness we saw in the second quarter reflected timing rather than underlying demand. Data grew 20%, supported by continued strength in Confluent. Infrastructure was up 5%, driven by continued momentum in distributed infrastructure, growing 32% this quarter.

AI continues to increase demand for storage, and we are gaining share through differentiated offerings across flash, fusion, tape and IBM Z storage, including AI-enabled capabilities that help clients scale and manage data for AI. Consulting revenue grew 2% as clients modernize to deploy AI securely and at scale. The durability of our portfolio remains a defining feature of IBM today.

Z17 continued the strongest innovation cycle in the platform's history, generating more than $1 billion of incremental revenue over 6 quarters compared with prior programs. This reflects the security, reliability, scalability and compelling economics of the platform. As infrastructure costs rise, IBM Z delivers a 2 to 15x total cost of ownership advantage versus off-platform alternatives, positioning us to capture additional value as AI workloads grow.

Building on that advantage, we introduced new single-frame and rack mount IBM Z systems in July that help clients address data center space, cost and deployment constraints without compromising performance or security. Client response has been strong, contributing to the highest midrange revenue launch in IBM Z this quarter in nearly 2 decades.

Last quarter, we told you Lightwell represents a multibillion-dollar opportunity for IBM and Red Hat to help clients secure their open source software. Since then, some of the world's largest enterprises have signed up as clients, and we have built a pipeline approaching $700 million in a short period of time. We have released more than 40,000 remediated packages to the Lightwell network, demonstrating the rapid scale-up of our remediation capabilities.

Now turning to our AI strategy. Enterprise AI is entering its next phase, deploying more models or building more agents will not be enough. The advantage we belong to a company that can operate AI securely efficiently and sustainably across the enterprise. Organizations are recognizing that AI is only as effective as the system around it. The data that provides context, the workflows that orchestrate action the infrastructure that secures and optimizes operations and the hybrid cloud foundation that provides flexibility without locking.

IBM has assembled these capabilities across 4 platforms. Confluent provides continuous business context through a real-time data foundation. Watsonx, including Bob and orchestrate, support AI-driven software development and agent orchestration. HashiCorp and Apptio automate identity, access and policy while optimizing cost and performance. Red Hat provides the open hybrid architecture, spanning public cloud, on-premises sovereign environments and the edge. Together, these platforms help clients move from isolated deployments to AI operating at enterprise scale.

Data sits at the center of the AI operating model, 72% of organizations report insufficient real-time data infrastructure, which holds back AI at scale. Agents need a current state of the business. That is why we are excited about Streamhouse a capability from Confluent that continuously captures, transforms and queries real-time data. It complements warehouses at Lake houses.

A Streamhouse makes real-time governed data continuously available to applications, analytics and AI, unifying operational and analytical data into a single architecture. We believe Confluent is uniquely positioned with a complete open and hybrid approach that helps clients avoid lock-in while deploying wherever the data resides. As AI scales, clients ask 2 questions, what will it cost and who controls it? We are innovating on both.

Apptio now connects AI spend to business results, providing visibility into AI usage, token consumption and the cost of running it across the enterprise. LLM-D an open source project developed with Red Hat and Google, reusers previously processed context, helping clients run large AI models at 5 to 10x lower cost by making more efficient use of existing GPU infrastructure. And Bob now runs on-prem. So clients in regulated and sovereign environments can keep good and models on their own infrastructure.

Governance matters as much as intelligence. As agents multiply, enterprises need to know who an agent is, what authority it has and what it can access HashiCorp Vault allows clients to register, authorize, audit and revoke agents and secure their credentials. IBM Agent Identity and HashiCorp Vault are partnering with NVIDIA open agent safety platform to strengthen agent security. We believe agent identity will become as foundational to enterprise AI and as identity has become to the Internet.

Finally, Quantum remains a strategic area of differentiation for IBM and a significant long-term opportunity. In July, 3 independent demonstrations of Quantum Advantage were achieved on IBM hardware with the University of Chicago, algorithmic and EDA providing further evidence that quantum computing is moving from scientific promise towards practical value.

Our Nighthawk r2 processor has achieved important road map milestones across speed, scale and quality, delivering 7,500 gigs, executing 100,000 circuits per second and enabling performance improvements of up to 19x over prior generation systems. That performance changes both time solution and the economics of quantum competition. Workloads costing approximately $100 to run an IBM could cost up to $10 million on slower competitive modalities.

Recently, IBM was selected for Stage C, the third phase of the Quantum benchmarking initiative led by DARPA the United States Defense Advanced Research Projects Agency, another validation of the strength of IBM Spark to building a fault-tolerant quantum computer. Commercial momentum is also growing. This year, IBM Quantum has signed 16 enterprise engagements across industries, including automotive, aerospace, telecommunications, financial services and manufacturing.

This includes AT&T and General Motors amongst many others who are now all part of the IBM Quantum network, the world's largest network of quantum innovators. We are also advancing the technology and infrastructure required for industrial scale, fault tolerant computing, including the successful connection of our first modular cryogenic systems and continued progress on our 300,000 square foot for Kips manufacturing facility. This, along with the closing of the $1 billion award from the Department of Commerce in and Deron for manufacturing quantum wafers gives us confidence that after the stalling demonstration, we are ready to scale. We look forward to talking more about our progress at our Quantum Day on December 10.

In closing, our third quarter performance reflects the strength of our portfolio and the progress we are making against our strategic priorities of delivering both sustainable revenue growth and higher profitability. Our results reinforce our confidence in delivering revenue growth for 2026 in a range of 4% to 5% and enabled us to raise our year-over-year free cash flow growth expectation to more than $1 billion.

With that, let me hand it over to Jim.

Thanks, Arvind. In the third quarter, we delivered solid results with 6% revenue growth, 70 basis points of operating pretax margin expansion and 11% diluted operating earnings per share growth. And through the first 9 months, we generated about $8 billion of free cash flow, up 8% year-over-year. These results reflect strength across our portfolio and solid execution against our priorities.

Let me walk through the performance in each of our segments. Software revenue growth accelerated 4 points sequentially to 9% this quarter, including over 4 points of organic growth. This performance was driven by our high-value annual recurring revenue base, which grew 8% to $24.9 billion. Three of our 4 software subsegments grew revenue double digits, led by the combination of strong new business performance and pipeline closure rates, including the successful closure of around 2/3 of the large deals that slipped from the second quarter. Our results represent the high end of the range we outlined in July, reinforcing our confidence in the strength of our portfolio.

Turning to our segments. Red Hat revenue grew 11% again this quarter, fueled by bookings accelerating back to the high teens. Lightwell was recently made generally available, and we have strong early demand with a pipeline that is already approaching $700 million. OpenShift is now $2.4 billion ARR business, with growth accelerating to more than 30%. Our virtualization offering continues to scale with nearly $800 million in contracts signed since early 2024 and about $750 million in pipeline.

Data grew 20%, driven by another strong performance from Confluent with bookings growth of about 20%. Transaction processing grew 10% in the quarter, driven by demand for this mission-critical software and strong execution against our pipeline. HashiCorp continues to perform well, growing bookings across all geographies and accelerating revenue growth sequentially. However, automation was impacted by a tougher compare.

Turning to infrastructure. Revenue growth accelerated to 5% and driven by 32% growth in distributed infrastructure. Storage delivered record revenue growth and continued to gain market share with broad-based demand across flash, fusion, tape, in IBM Z storage. Strength across storage and power reflects the growing importance of differentiated infrastructure as clients modernize their environments, address data growth, and support AI at scale.

With the sizable backlog in distributed infrastructure exiting the quarter, we are well positioned for continued momentum in the fourth quarter. C-17 continues to perform exceptionally well, wrapping on the strongest start to a Z program last year. Program to date performance remains well ahead of the prior generation. up over $1 billion. And this quarter, we had the strongest midrange launch in IBM Z in nearly 2 decades. Beyond the strength of the program, IBM mainframe clients representing approximately 85% of installed MIPS capacity are maintaining or growing their capacity and aggregated installed capacity has tripled over the last few programs.

Consulting revenue grew 2% this quarter, reflecting continued client demand for AI-enabled transformation. Growth was led by strategy and technology, up 4% with strength in application modernization and data and AI transformation, while cybersecurity delivered nearly double-digit growth in intelligent operations. AI is increasingly embedded in broader transformations our clients are undertaking, representing about 30% of our consulting revenue in the quarter and 35% of our backlog.

We are also expanding the ecosystem of capabilities we bring to clients, including through our new strategic partnerships with OpenAI and our expanded relationship with Google Cloud as we help clients scale enterprise AI. Clients remain at different stages of adoption, but we continue to see them modernizing applications strengthening their data foundations and reengineering workflows as they move forward toward embedding AI into core business processes at scale. Our integrated model, combining deep domain expertise, technology and reusable assets, positions us to help clients capture business value from AI while accelerating time to value.

Let me now discuss profitability. Adjusted EBITDA and operating pretax margins expanded by 60 and 70 basis points, respectively. As I mentioned last quarter, we accelerated our productivity initiatives to help enhance our operating profit margin and free cash flow commitments while continuing to invest for growth. We made significant progress this quarter, driving efficiencies across G&A and sales, reinforcing the power of our productivity flywheel. Segment profit margins expanded by 210 basis points in software and 120 basis points in infrastructure driven by ongoing productivity actions.

Consulting segment profit margin declined modestly, as we reinvested productivity gains into digital assets, accelerating our transition towards a Services-as-Software model to capture the growing AI opportunity. Through the first 9 months of the year, we generated about $8 billion of free cash flow, up about $600 million year-over-year, driven by about $1 billion increase in adjusted EBITDA offset by higher CapEx, cash taxes and net interest expense.

Our free cash flow margin for the first 9 months of the year is the highest in reported history, highlighting our strong execution against productivity initiatives and laser focus on operational discipline. We exited the third quarter with a strong liquidity position and a solid investment-grade balance sheet with cash of $11.1 billion. We returned $4.8 billion to shareholders in the form of dividends through the first 9 months of the year. Our debt balance ending the quarter was $63.8 billion, including debt of $12.7 billion for our financing business, with the receivables portfolio that is almost 80% investment grade.

Now let me talk about what we are seeing going forward. The strength and diversity of our portfolio disciplined capital allocation and sustained productivity execution underpin our revenue growth, earnings expansion and free cash flow performance. Through the first 9 months of the year, we delivered over 4% revenue growth, 9% operating pretax income growth, 10% operating earnings per share growth and 8% free cash flow growth.

We continue to expect revenue growth for 2026 in the range of 4% to 5% and are raising our expectation for free cash flow growth to more than $1 billion. 90 days ago, we provided a full year revenue growth range with profit and free cash flow expectations anchored to a 4% base case. We continue to take the same approach for the full year, anchoring profit, free cash flow and segment expectations to that base case.

Given our strong third quarter software performance, our confidence in the full year outlook has increased. We now expect full year software growth of approximately 7% to 8%, with about 7% as the revised base case. For the full year, we expect double-digit growth in hybrid cloud, mid-teens growth in data and low single-digit growth in automation. For transaction processing, we are raising our expectations for the full year with growth expected to be flat.

Infrastructure growth accelerated in the third quarter with significantly stronger program to program performance of z17, underscoring the durability of the platform and the value it delivers to clients. Combined with continued strength in distributed infrastructure, we continue to expect low single-digit infrastructure revenue growth for the full year.

In Consulting, backlog quality and momentum in generative AI continue to support a modest acceleration to low to mid-single-digit revenue growth for the year. In the third quarter, our operational discipline and accelerated productivity initiatives allowed us to enhance margins and deliver free cash flow commitments while continuing to invest for growth. These actions include deploying AI and automation at scale across our company, reducing third-party spend, improving sales and marketing efficiency, using AI to drive more efficient software development, optimizing our supply chain and enhancing services delivery.

We continue to expect 10 basis points of operating pretax margin expansion for the full year, reflecting the flow-through from the third quarter earnings beat and continued operational discipline and our operating tax rate expectation for the year remains in the mid-teens.

For the full year, we are raising our expectation for free cash flow growth to more than $1 billion. The primary driver of this growth will be adjusted EBITDA, offset by higher CapEx, higher cash tax and net interest expense. Our free cash flow continues to be driven by growth in adjusted EBITDA and demonstrates consistent free cash flow realization of about 130% over the last few years.

Let me close by saying that our third quarter performance reinforces our confidence in our portfolio, the resilience of our business model and our outlook for the year. At the same time, we continue to invest in the technologies that will shape IBM's next chapter of growth. As Arvind mentioned, Quantum continues to advance across technology, ecosystem adoption and scale up.

While still early, the progress we're seeing from enterprise engagements to government validation reinforces our confidence in both our road map and the long-term opportunity ahead. We look forward to discussing in more detail at our Quantum Day in December.

Arvind and I are now happy to take questions. Olympia, let's get started.

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And then second, as always, I'd ask you to refrain from multipart questions. Operator, let's please open it up for questions.

[Operator Instructions] And our first question comes from Amit Daryanani with Evercore.

Yes. Great to see some nice software acceleration here. Arvind, I think last quarter, we discussed a lot about customers prioritizing infrastructure over software purchases. This quarter, it seems like transaction processing is at double-digit growth. while infrastructure has remained very strong for you folks. Can you just talk on what changed in customer spending priorities? Are you primarily just recovering deferred transaction at this point? Or are you actually starting to see infrastructure investments starting to create more incremental demand for IBM software. And with Red Hat bookings actually growing at high teens, is that an early indication that software can actually accelerate as you go into '27?

So Amit, thanks for that question, and I also thank you for observing the numbers. Let me comment on the overall customer priorities, the tied towards infrastructure and Red Hat and I'm going to ask Jim to cover the transaction processing piece a little bit. So first, as we look at customer spending priorities, customer spending priorities in trying to leverage AI remains strong.

But as you begin to leverage it, how does the average customer get value. They have to rewrite an application, they have to modernize an existing application and you then ask the question, where do those run? That tends to be a tailwind for Red Hat OpenShift. And I think we are at the very beginning of this cycle. So I expect that to carry on for a long time.

Then the moment they get to saying, okay, I'll write the application on Red Hat OpenShift. You then get to how do I unlock the data from all of the enterprise data that I need to power that AI application. Remember, 90% of our enterprise data is not on the cloud. It's very much inside the enterprise. So Confluent tends to be a tailwind. I expect these to carry on for a long time. The automation portfolio will get a benefit when these applications all get to scale and people start worrying about managing hundreds and thousands of them, at that point, automation will also get a tailwind.

On the point you're making on Red Hat, look, we are very comfortable saying that based on what we can see, we are comfortable about double-digit growth in the fourth quarter of this year. Right now, we don't really want to comment on 27 growth. But generally speaking, I can say when we get these kinds of booking growth, it crosses into good revenue growth going forward. Jim, maybe a comment on TP.

Yes. Just to build on your point, Arvind, and I'm going to thank you very much for the comment and the question. We're very pleased overall with our software performance team executed extremely well. Four points sequentially to 9% printed here in third quarter, and it was all organically driven, pervasively across our portfolio of 3 of our 4 subcategories accelerated and grew double digits overall. And that was a reflection of both. We talked a lot last quarter about the 80% high-value recurring revenue growing nicely at high single digit, but we saw a very nice rebound in our transactional business, that remaining 20% that grew actually double digits, led by our ELAs that were up north of 20% with an NRR of north of 130% overall.

That's a function of, yes, disciplined execution on slip deals but also a function of net new business contribution in the quarter. And I would tell you before all of you asked, about 1/4 of it is the slip deals with regards to overall software acceleration and about 3/4 of it is net new business that we are capturing overall, so to build on Arvind's point.

Now on TP. TP, we're obviously pleased. Double-digit growth I think this instantiates the demand for our mission-critical strategic mainframe software. A lot of discussions a quarter ago about demand destruction versus timing I think this puts us the rest right now because when you take a look at it, we've been talking all year long about the reinforcement of confidence we have in our mainframe platform. and how we monetize the strong z17 performance, right?

That model is the monetization model is the more capacity you put in the marketplace, the higher monetization opportunity the higher price opportunity, a higher value creation opportunity. Z17 today, install MIPS are up high teens, 6 quarters in. Ship MIPS are up over 100%. That is the future monetization opportunity that we feel extremely confident about as to the acceleration potential of TP in the future. So I appreciate the question and great place to start.

Your next question comes from Wamsi Mohan with Bank of America.

Thanks for the color you just shared on TP, but maybe if we could just double click on that. I mean, last quarter, you were thinking that TP might be down low to mid-single digits for the second half, and you called it a 27% growth vector it grew 10% instead. So maybe, Jim, as part of those like 1/4 of software upside really came from those push deals. Could you share some color more specifically with respect to TB, if that was similar or higher in relation to the deals that closed from those push deals?

And how many of those low tens of slip deals have not closed because you've not really changed your 4% to 5% for the year, but your commentary and your numbers would suggest that you're gravitating much more towards the higher end of that. if you could share any more incremental color. And on through-cycle TP performance, is that still something that we should think of as mid-single digit? That would be great.

I know you addressed the question to Jim, but I'm going to start and then ask Jim to color -- give a lot of color on both the slip deals, the TP performance and the cycle-to-cycle compare, if I sort of paraphrase your question short. So let's just talk about TP. The questions everybody had last quarter was, does this imply that overall demand for mainframe has gone down. And we are very confident that this was a timing question, not a destruction question. And so at the macro level, as people deploy all of that hardware as they deploy that MIPS as the volumes of the underlying transactions increase, they need this mainframe software to support that mission-critical business. That's why we were confident it would come back.

Now to your point of us saying low to mid-single-digit decline, we want to be cautious and we want to be prudent. We want to see the absolute baseline and we don't want to call above it. I think what really happened was we got not only the deferral from 2Q, we got a normal 3Q buying cycle on mainframe. So kind of got it both together. I would be gated and tell you I don't expect double-digit performance in TP to keep going. But I would expect to see now more normalized performance, especially going into next year and perhaps moderation from 3Q going into the fourth quarter. But it speaks to the underlying demand for the mainframe around resilience, around the economics around the architecture that supports 8, 9s of availability.

Let me point it out 8, 9s of availability. But the transactions in the mainframe tend to be almost the opposite of what happens in Here, you need things that are completely deterministic, that are repeatable and that you can look at it with a financial color that they're meeting all the requirements of those kinds of transactions. And there's plenty of other things that will happen on distributed infrastructure. I think in terms of the numerics, I'm going to hand it to Jim to cover with you.

Yes, Wamsi, thanks for the question overall. Let me just kind of reiterate a little bit about where I ended the last question to Amit. When you look at the health of our software business that accelerated 4 points quarter-to-quarter, it was a function of both the high-value recurring revenue. And again, the underpinnings of that is the Red Hat strength and acceleration. It's excellent start to our Confluent acquisition. It's record performances in Hashi overall, and that part of the business is doing well. The transactional where the big difference changed.

Last quarter, it impacted our software growth by 4 to 5 points when we were down double-digit transactional. This quarter, we were up double-digit transactional. And I'll remind you, that's 20% of our software book, annual book is about $32 billion, give or take. And that today, offers about 2/3 to 3/4 of that is ELAs. ELAs are strong incumbency positions that we've earned trust of our clients for decades around mainframe running their core architectures.

On top of that, we build a moat and we build a software distributed business model that captures that very attractive economic multiplier of $3 to $5 a software for every dollar we land on mainframe. Now within that, TP is disproportionate. But with that moat, we give clients flexibility on where they want to choose that ELA spend as to where it goes between data, between automation and between TP. Red Hat is not part of our ELAs overall. And what we saw here, Bolt is a contribution in the quarter on slipped deals. Let's put this in perspective.

We printed a number, what, $7.6 billion of software the slip deals were tens of slip deals. So let's kind of put that in a ballpark from 2Q of, what, $400 million, which is what we missed software buy. We closed 2/3 of those. That's $250-plus million. That is the 25% of the contribution and the growth that we've had of 4 points. By the way, that was disproportionate to TP. That's why you see well up at double digits overall. We didn't get a lot out of that in data and in automation and some of that should be fourth quarter.

Now fourth quarter, we gave a guide, I think, to use Arvind's word, anchored in our philosophy 90 days ago, prudent, cautious. We're anchoring this thing at the low end, approximately 7%. By the way, put that in perspective, I think the current Street estimates for the year are about $32 billion of revenue, we feel comfortable with that $32 billion of revenue. So that's kind of where we're at. I think I covered most of your questions.

Your next question comes from Ben Reitzes with Melius Research.

I appreciate the question. I wanted to ask about your long term -- the short-term puts and takes we get. But at your latest Analyst Day, you laid out that you would approach double digits in software as a long-term target. -- based on what you're seeing, do you still feel confident in that? And how is M&A going to play a role in terms of achieving that? Because in order to hit it, I guess, you got to accelerate a point after this year. So a lot of folks are wondering where M&A plays in that.

Thanks, Ben. I appreciate the question overall. Obviously, that's not going to surprise you. we'll spend a lot more time on this question here in January. We got the team extremely focused already. We're 21 days in arguably the most important quarter we have in the fourth quarter overall. But let me take a step back first around IBM. And then as Arvind and I have been talking here as we're heading into our planning cycle and laying out all of our investment and capital structure strategies for '27 talk a little bit about IBM then hit your question on software.

We've got a lot of confidence in our strategy and our portfolio is well positioned to deliver sustainable, durable growth on our model. we feel extremely confident in that. Why? We got an attractive TAM market backdrop of technology, the diversity of our portfolio, leveraging our hybrid cloud and AI platform-centric model, our continued portfolio optimization to shift to higher growth end markets, new innovation.

We continue to bring the market around Lightwell, around Gen AI and around to your question, M&A growth synergies all of that will play out. But from a segment perspective, since you asked about software, I'll give you some early indicators. One, we got a high-value recurring revenue stream that's been accelerating all year long $25 billion ARR book of business, high single-digit growth. We execute to what our guide is here. We should be approaching double digits here in the fourth quarter on that 80% high value, that's one critical indicator.

Two, new innovation we're bringing to market. We're extremely excited to Arvind's point, around Red Hat, the acceleration, the high teens growth. We've got $700 million worth of pipeline on Lightwell alone in front of us right now with $1 trillion TAM opportunity around open source vulnerabilities. We got virtualization, a tremendous opportunity. We got hybrid cloud, containerization opportunity. So we feel pretty good about the markets we're in, the investment in the portfolio that we have built up overall.

Three, we got 2027 as a year of growth synergies, right? 2026 was about integrating and getting confluent into our mode of operation and go-to-market and scale '27s around the growth synergies. And finally, to the TP question, we've got high teens growth in installed MIPS out in the marketplace. Last 5 years, our MIPS are up 2x. Last 10 years, our installed MIPS are up 10x. That is the monetization vehicle at TP, and Arvind talked about it, I talked about it. We think that's a growth acceleration back to our model overall. We'll quantify that in January, but that gives you a little bit of perspective of our early thinking about '27 on software and IBM.

The next question comes from Raimo Lenschow with Barclays.

Perfect. Two quick ones, if I may. First, congrats on the software performance. On data, 20% growth. Obviously, some of that is confident, but like what are you seeing there on upset across on Confluent. We have it for a few quarters now, but that looks like it's starting to click for you. So can you kind of double-click on that one? And then one for Jim. Gross margins came up with investors. Can you just give us a comment there, what drove the performance there?

Raimo, thanks for the question, and thanks for noticing the data growth. If I look at Confluent, I actually want to broaden it. There is Confluent, which is important for moving data in real time, allows data to remain in place, but still be available for AI. But coupled to that are what I call lake houses, and we are very excited about the upcoming cross-sell opportunity with Streamhouse, which is also another way to store data that is sort of in flight.

So the Confluent piece unlocks your data, be it inside the enterprise, be it in an application, be it in a SaaS application, be it on a cloud, but still have it completely available for AI agents as well as inferencing -- really important capability, and you saw that growth pull through. But lake houses are also important as people are beginning to cleanse their data for leverage in AI, and we saw growth there also.

And then we're excited about what is going to come with the capability we are about to announce in a couple of weeks around the Stream house, which is also then going to be directly a cross-sell and upsell on top of Confluent. So unlocking data for AI, making sure that you don't have to create multiple copies because that creates a lot of extra cost, but still have it available as quickly as you need it is the fundamental capability we believe is going to be unlocked in the enterprise, leveraging all of these capabilities, and that contributes to the overall growth there.

Jimmy, I'll give it to you for the gross margin question.

Raimo, thank you very much for noticing the operating leverage engine that we've been driving across this company. You look at what the last 4 years were probably up what, about 1,000 basis points of operating leverage across this company. By the way, that has delivered high-quality sustainable cash generation. Over the last 4 years, our cash is up roughly $7 billion of growth and 700 basis points of free cash flow margin on that 1,000 basis points of operating leverage.

But when you look underneath it, we continue to improve the structural competitiveness of this business model. We still believe we got a lot of headroom here. When you take a look at our profit, earnings up double digits, our operating margins are up 70 basis points. It's delivered by -- we are driving in the third quarter, 500 basis points of gross productivity in our model.

We've always said in our model, about 1/3 is for margin protection. About 2/3 enables us to drive that productivity flywheel where we reinvest back into the business around R&D, innovation, go-to-market model ecosystem to drive that flywheel of growth and competitive advantage. 500 basis points is well above what our model is overall. How are we doing that? We're running our playbook. That playbook, we believe, has headroom. It's reimagining everything between Arvind and myself about how we execute and run this company. And that gets into leveraging data, a whole new way of workflow reimagination embedding technology, digitization, AI into our company.

We've been talking about in prepared remarks, how do you reimagine a whole new way for software development? How do we reimagine a whole new way on go-to-market models? How do we redesign the G&A structure, the way we run our company. And I think there's a lot of headroom there. A few years in, we are now north of $5.5 billion of productivity out of this company. That is a great flywheel for investment flexibility and capability. So the most important thing is it provides that free cash flow generation engine, and it applies that investment flexibility so we can continue to compete both organically and inorganically to invest.

Your next question comes from Brent Thill with Jefferies.

Arvind, I'm curious in some of the deals maybe early in the year, there was a little bit of an AI Brightlight. I don't know what to do. I need to kind of wait and investigate and maybe the investigation period went on and they realized maybe some assistants couldn't actually achieve what the claims were and I'm curious if you're maybe seeing some element of this and some of the deals recouping where the claims were big, but perhaps the reality wasn't matching the claim and you're able to deliver on that. Is that something you've seen? Or is that not a scenario that you've experienced?

So Brent, let me make sure I parse the question correctly, but I'm going to sort of maybe answer it in my own words. I think what we are seeing is that at least larger enterprises are becoming a lot more cognizant and a lot more aware of what is the true ROI of all of their AI spend. They're also getting watered in some of the AI spending completely out of control with no real return.

But the way I've always characterized it is, there is no question to us, and this is the advice I gave every C-suite member that I meet that what we're trying to give people on AI, the productivity the advantages of AI, the way it's going to change work is very real. So let me not make any confusion around that. That said, a lot of people are underplaying how much work it is to integrate AI into your workflows to fundamentally sit back and say, how do I redesign the workflow to take advantage of AI. It's not a little RPA tool that you bolt on and you get something.

If you do not spend the time and energy to redesign your workflow, you're not going to get the full benefit. So if you're willing to do that, there's a huge benefit to be had. But that implies you've got to pick a few, you've got to learn how to scale them for full impact across the enterprise and don't do 100 little experiments you then do not have enough investment to get them to scale. That is what I think we are seeing. So I think what we're seeing Brent is now enterprises as they're getting mature as they're stopping the experimentation are saying, "All right, who can help me get through this bigger piece of workflow design as well as integration into our existing systems?" Because you're not likely going to throw out your ERP system. So how do you leverage it, but still get the benefits of AI and then they're picking their partners in terms of which software do they are they willing to trust to do this, which people have the knowledge to come do this with them as well as can be really efficient about a token spend as opposed to leave it completely uncontrolled.

So I agree, I think, with your thesis, but maybe a slight redirection in terms of why we're seeing that change. And yes, that is a tailwind for us. That is why I kind of repeat what I said at the beginning. That is my OpenShift, confluent, mainframe with fraud are all seeing the benefits of that play go on as we are proceeding down this path.

The next question comes from Fatima Boolani with Citi.

I wanted to go back to the discussion around transaction processing simply because it was such a remarkable sequential recovery in that franchise. And Jim, I understand so much of that is piggybacking off of the ELA momentum that you regained in the quarter from the transactional side of the business? But I'm curious if you can help us unpack a little bit more on that strength.

I think you talked about a high teens growth in mix ship. So wondering if you could unpack and break down for us the quantity and shift mix growth in some of those slipped deals because those were existing customers. And to the extent you've been able to leverage a little bit of pricing power in this situation because I know that's not historically a lever that you've used. So we'd love to get a little bit more second order detail on that transaction processing and slope of recovery between the quantity and quality growth as well as pricing uplift?

Thanks, Fatima. I appreciate the question. Let's double-click back down on TP. First, let me just reiterate what is the monetization model, right? TP is transaction processing that runs a mission-critical software on top of the most enduring platform that being mainframe. By the way, as we've talked about many times before, mainframe runs 70% of the industry's transaction volumes in terms of value. 45 of the top 50 banks, 4 to 5 top airlines, 7 of the top 10 retailers around the world. I think 75%, 78% last number I've seen the Fortune 100 run mainframe as a core architecture. .

So TP is the core mission-critical software that enables the most secure, reliable and scalable platform around. The way we monetize that value is the more capacity out there, the more opportunity to monetize value, the more opportunity to monetize price, the more opportunity to monetize value creation of TP. So when you take a look at our last 2, 3 cycles, we run 145 million MIPS around the world. Some of those are standard MPs on the platform. Some of those are new Linux specialty MIPS, new cloud-native applications, which is why the mainframe has been a growth vehicle over the last 2 to 3 cycles overall.

That $145 million MIPS are z17, 6 quarters in, 125%, the highest recorded program to date above z16, which was the prior highest program to date. We're up, I think, 18% ship MIPs through 6 quarters. We're up over 100% of ship mix. Why do I differentiate it? Read the first one is consumption, capacity and workload, we can monetize value immediately. The second is the opportunity. It's the TAM, right, and us driving workloads on that.

So when you take a look at it, that's what's been driving the step function change in growing meaningful over time. It was only a handful of years that our TP portfolio was down, what, mid-single digit, high single-digit growth. We then stabilize that growth about 4 years ago. And then through z16, we saw an acceleration, but I'll reiterate that z16, when you look at the launch year, it was about flat the launch year, then it kind of went up to about mid-single digits year plus 1 and then it went up to high single-digit year plus 2.

Right now, in z17, we were about flat launch year. We got different dynamics playing out right now. So we're about prudently cautious calling flat for the year, but what 100% ship maps and 18% install MIPS, I would say that's a pretty good future monetization opportunity in 2027 going forward.

Your next question comes from Brian Essex with JPMorgan.

Congrats on the software acceleration from me as well. Arvind, I caught the prepared remarks, at least some of the news remarks that you had about fines deploying super intelligence in ways that transform entire operating models. And I appreciate that that's more than just increasing operating efficiency. But the question I have for you is as your customers assess those transformation efforts, how focused are they on sovereign AI? Is that becoming a more meaningful part of your conversations -- and how are you seeing your customers focus on the protection of their data and IP as they assess how they deploy AI for their transformation efforts?

Brian, thank you first for the question. and thank you for noticing the precise words I did use in the prepared remarks. So to get to sovereign AI, sovereign AI is definitely becoming a much larger conversation. While we have talked about it for the last 1.5 years, I would not tell you that in '24, it had much traction. In '25, a few people began to pay some attention and in '26, it is now beginning to pick up. And let me explain for everybody else exactly what we mean by that. If you go to use a large public LLM, effectively, while they're certainly not stealing your data, though they're implicitly maybe learning what kind of questions are asked. There are traces left in the underlying logs and so on. That are not full, but that do give some indication of what all it is being used for as well as some elements of the data that is being used.

So sovereign becomes how do you make sure that none of that happens -- so people are turning now to 2 kinds of answers. That is why I think there is such a big boom in AI infrastructure on-premise, and we can see some of the companies that benefit from that. But the so will we in our mainframe with our Spyre cards as time goes on. The other part of this is what is driving the boom behind open weight models because there you have the permission and the IP rights to run them on either on-premise infrastructure or in private clouds, where the enterprise controls what is happening to the underlying logs and data.

That is definitely what is happening there, and we can see that and the boom behind some of the companies that are creating open wave models is also increasing behind that. I'll reflect on a conversation I had with a large bank CEO. In this case, it was outside the U.S., but it's a very large and friendly country. And their view was, look, we'll do some early experiments on the larger frontier LLMs. But for what we're doing inside the bank, we need to run it in a way that is sovereign, meaning nobody can take any of the underlying data on underlying logs that are there.

I was talking to somebody who does trading and thereon -- we are not going to expose our underlying proprietary models, anything outside, but we need to run this in a sovereign way. I believe this is going to turn into a tailwind for many players in the industry certainly, the infrastructure in semiconductor players, but also for those of us who help enterprises deploy in a secure and in a private way.

And our last question comes from Matt Swanson with RBC Capital Markets.

Great. And maybe this is just kind of a natural follow-up to what Brent was asking. But you had mentioned earlier that what AI costs and who controls it have kind of been like the 2 primary questions that I'm probably to start off with you. Who controls it obviously fits right into that sovereign AI conversation. But could you just kind of talk a little bit about how people are addressing the trade-offs if there are trade-offs between those 2 segments and how they're architecturing their networks in order to maximize for both those things? And just how kind of like symbiotic they can be?

So Matt, thanks for that question. And also allowing me to give bit more detail on this topic. If I go back to late '24, the difference between the most advanced model, which the technology is mostly a frontier LLM model and alternate models was a 2:1 or 3:1 gap, meaning if you ran some assessment, some benchmark, and it was broad-based, not narrow, the top model might score a 70 and open model might score a 20 or a 30. You kind of react with -- that's a lot of capability we're giving up. And we all know that performance, in some sense, outcome for the enterprise is highly dependent upon that. That's too big a gap. Nobody is going to accept that.

In late '25, we began to see that gap shrink where maybe the gap of not 70 to 20 or 30, it was 70 to 50. But when you looked at those curves and you looked at the improvement, and you looked upon the sequential improvement, we were very comfortable that we get is to probably 100 for the top and 90 for the alternate model. So now the question becomes, how do you do that?

Now the question that the enterprise struggles with is where are my kernel engineers, where are the people who worry about the underlying network, who can make sure that the model is clustered or GPUs that are close to each other in network proximity. -- who can make sure that you're doing the right cashing of results so you get an incredible response. I'm telling you that, that gap is probably still there, except for the most sophisticated enterprise.

So they kind of go to -- all right, I got it that the answer is 4 more expensive, but it's an easy answer. As those of us who work in this world produce much more sort of easy button software that can give you that capability for the alternate models. And I would say it's not a huge amount of infrastructure. Our competition is that for an average large enterprise, you can get going with maybe 2 or 3 racks which is not a huge amount of infrastructure, but it gives you the ability to run those open rate models currently anywhere from 25% to 10% of the token cost of the frontier model -- that is very attractive.

Given that they are now 90% as effective. It's a simple question for any enterprise. Would you like something that's 10% of the cost, if it's 90% as good as the best, -- or do you want to pay that 10x higher cost? And then the question is, how many tarts require that. I would tell you some do, but the vast majority, summarizing an e-mail, examining a contract, answering a customer call composing an e-mail, all of those can be done as well with the multiple answer, and that is what is going to drive a lot of this.

So Matt and everybody else, thank you for those questions. Look, we are pleased with our performance this quarter and all our segments accelerated growth. Our portfolio strength, disciplined execution and relentless focus on productivity, reinforce my confidence in our trajectory. I look forward to sharing our progress with you as we close out the year, and we'll talk to you in January about that.

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

Thank you for participating on today's call. The conference has now ended. You may disconnect at this time.

Good afternoon, everybody. Welcome to the Goldman Sachs Communacopia Technology Conference. My name is Jim Schneider. I'm the semiconductor and IT services analyst here at Goldman Sachs, and it's my pleasure to welcome IBM and CFO, Jim Kavanaugh, on the stage today. Welcome, Jim. Thanks for being here.

Great. Thank you for having us. Looking forward to talking about our business here today.

Excellent. Jim, back in second quarter, you negatively preannounced that quarter. And I think one of the central investor debates following the earnings call has been was the spending shift you mentioned was mainly a timing issue or perhaps evidence of a more durable headwind in sort of the customer IT spending priorities that you're seeing. You talked about a late quarter shift in which some clients prioritize purchases of server, storage and memory ahead of price increases and delaying certain IBM purchases. Maybe should investors be thinking about the duration of the dynamic as a very short-term thing or clients now moving past that period of infrastructure catch-up? Or should we expect that prioritization to sort of remain a headwind in the back half of the year?

Jim, great place to start. So let me unpack our second quarter because I think that will put this all in perspective about what are we seeing right now in terms of client buying behavior and demand. But more importantly, what does that mean for our strategy, our portfolio and our second half guidance overall, which I think is at the heart of your question. When you look at second quarter, I'm not going to recap that. We spent a tremendous amount of detail in July, but we fell short to our expectations. We were very transparent in that. And that was really driven by a performance gap in our mainframe performance and associated software that's attached to that platform overall.

And that was driven by a reprioritization of CapEx spending, to your point, given the secure supply-constrained environment and infrastructure demand and associated price increases. Let me put that in perspective. That mainframe platform and associated software, that impacted IBM's growth by over 5 points of growth in the quarter on only about 15% of the composition of our portfolio. The remaining 85% of IBM actually did extremely well at or well above our expectation. Red Hat accelerated one point to 11% growth, leveraging the demand that's in the marketplace around hybrid cloud modernization, virtualization, AI, automation, Red Hat OpenShift growing 29% in the second quarter, really the industry leader hybrid cloud platform, now a $2.2 billion ARR.

Second, our acquisitions doing extremely well, both HashiCorp, which we anniversaried 1 year, and albeit very early, Confluent off to an excellent start. Third, our high-value recurring revenue, 80% of our software business doing extremely well. It's approaching a $25 billion ARR book of business, growing high single digit. That includes everything from our GenAI portfolio to parts of our automation portfolio, our data portfolio and our Red Hat portfolio. And then finally, our distributed infrastructure, doing extremely well, record quarter, 37% growth. We exited the first half with about $500 million worth of backlog. So we're capitalizing on that explosive demand that's in the marketplace. And consulting, delivering durable growth actually with very healthy signings unlike many of the other competitors in the industry, and that's really leveraging GenAI.

Now to the heart of your question, we spent a lot of time, and I think we were very clear and confident on the call that we see nothing that changes the absolute conviction we have in our strategy and our portfolio in this marketplace. And we mobilized IBM around a core set of actions, go-to-market actions, new offerings and innovation, new monetization models to really capture the durable growth that we see in the market. In terms of timing versus demand destruction, I'll use that word. We think this is absolutely timing overall. All the indicators, demand profile, pipeline, propensity to buy models, renewal rates that we're seeing in the third quarter, we're not seeing any demand destruction.

And when you look at it, probably the most powerful point, over 40% of our slip deals coming out of the second quarter, we've already closed. put that in perspective, we typically only close about 75% over multiple quarters of sub deals. We're already at 40%. So that gives us confidence in what we guided for the year. We guided IBM now 4% to 5% revenue growth. At the high end, basically at where we were in April. At the low end, I think, prudently to protect our profit and cash. Our operating margins, we took up for the year, our earnings we took up for the year, $5.5 billion of productivity and free cash flow, a critical enabler. We maintain that at about $1 billion of growth year-over-year. So...

Very good. So on the software side, let's pivot here. So on the software side, I think that's very central to the thesis of many people on IBM in terms of long-term growth narrative. Q2 highlighted sort of the difference between the roughly 80% of your portfolio, which is recurring and the more transactional CapEx-sensitive portions. Maybe explain why that mix gives you the confidence in the underlying health of software despite last quarter's growth rate.

Yes. So software is an integral part of IBM's hybrid cloud and AI thesis, right? It's the core foundation. Why? 45% of IBM's revenue composition software, high-value recurring revenue, that 80% high marginal profit, high cash contributor. And it's a business that's operating right now at about a rule of the mid-40s over time. And most importantly, three, it capitalizes on a very attractive economic multiplier for every dollar of platform we land on hybrid cloud and AI and now soon to be quantum, we get a multiplier of software $3 to $5 and a higher NRR. But let's dissect and I guess, unpack the software algorithm growth profile. Why do we feel so confident? One, we have absolute conviction in the strength and durability and diversity of our business model in taking a business, it wasn't too long ago that we were plus or minus 0 software growth to building a business that was growing low single digit to building a business that was growing mid-single digit to building a business exiting last year growing high single digit, and we guided this year to growing 10% overall.

What are the underpinnings? Number one, you talked about that 80% software. That's high-value recurring revenue. That's made up of our subscription businesses, our consumption businesses, our GenAI portfolio, our Red Hat portfolio. That's accelerating in growth. That's right now at high single digits here exiting the first half, and we see that continuing momentum. Second, we've spent a lot of time reinventing our portfolio. And both organically in investing and inorganically from an M&A perspective, we changed the portfolio dynamics to a much more higher growth end market portfolio, addressing high TAM, high-growth vectors, Re.AI, hybrid cloud, virtualization, automation, data, security. Those are areas that we invest and we're focused on, and we continue to improve the portfolio mix.

Third, this is all about IP investment, new innovation we bring to market. We're coming out this year already with a whole new control plane from an orchestration layer, watsonx Orchestrate. We just came out with IBM Bob to address the software development life cycle model. We came out rapidly with Lightwell, the security play with regards to open source security vulnerability, Concert to address security overall, HashiCorp from a Vault perspective, Confluent and go on and on and on. Next, M&A. The synergistic value is still all in front of us. We're just a year wrap through on HashiCorp. By the way, record performance on HashiCorp through the first year. We've added net new $200 million of net new ARR through the first year of that performance. And Confluent off to a great start.

And then finally, we're dealing with -- to your first question, we're dealing with a near-term client buying behavior dynamic in TP software. That's the mission-critical software that runs on our mainframe that impacted our software growth in the second quarter by 3 points. We have confidence in the long-term growth vector of this. Why? Because it's directly correlated to the mainframe performance of our stack. How do you monetize value? The more capacity you ship in the market, the more capacity that's utilized, installed and driving workloads on mainframe platform, the more monetization opportunity you have, the more price opportunity you have and the more value creation opportunity you have.

And when you look at it, our z17, 5 quarters in, the most successful program in the history of IBM on decades and decades of mainframe cycles overall. We've shipped over 100% new MIPS in the marketplace over 5 quarters, program to program. We're at 129% from a revenue attainment. That's about $1 billion more revenue. And our installed MIPS are up almost 20%. Why do I bring that up? Because that instantiates a very encouraging future monetization opportunity with regard to TP. So when you look at it, that's the growth algorithm. We're going to grow this company. The aspiration is to get this to a sustainable double-digit level. We've got it there in hybrid cloud. We've got it there in data. We got a little bit more progress to do in automation. In TP, that's just a future monetization opportunity.

Yes. So on the revised software outlook, 6% to 8% for the year, what are the variables to kind of drive that to the high or low end of the range?

Yes. Coming out of second quarter, we did a very deep analysis with regards to -- our demand profile, propensity to buy models, pipeline -- by the way, our pipeline exiting -- entering the second half is extremely strong. We built a model that said on the high end 8%, on the low end, 6%. High end, 8%, let me break it down in the composition of how you were asking the question earlier. 80% high-value recurring revenue, subscription business models, consumption business models. We see that accelerating to double digit in the second half. So a nice acceleration trend, and we're on our path to go do that, just given the bookings in Hashi, strong start in Confluent, the momentum we have in Red Hat, et cetera.

On the 20% transactional business, remember, we offer clients flexibility in how they want to purchase. That transactional business [ Re. ] is our perpetual license models, our ELA models. We said out of those slipped deals that happened in the second quarter, we would close on our historical level of about 75% of those deals, right? Second, we then took the strength of our pipeline and we said, let's apply a historical discount to that, and we come up with a prudent level at the high end of 8%. The low end, just very simplistically, says we get a moderate acceleration in that 80% of that high-value recurring revenue, just given the dynamics of what we see in the ARR playing out.

But then on the transactional business, we basically assume second quarter performance continues, which is about where we're already at on slip deals, about 40% and it's about a pipeline yield that is about half of what we historically have set up. We did that why? To protect profit and cash and drive the right productivity levels so we can ensure we deliver on earnings and we can ensure we deliver on free cash flow. And if we end up at the high end, that is upside to our year.

Got it. You talked about M&A before. That's been part of your software expansion growth strategy. Has there been any kind of change in terms of your appetite for software M&A in terms of the types of assets you're looking at, the size of assets you're looking at and sort of how aggressive would we expect you to be at this point on the -- on the M&A front over the next few quarters?

Yes. I could tell you, one, we're extremely busy, but extremely focused. I think we've earned the right over the last handful of years under Arvind's leadership about being disciplined capital allocators, about running our M&A playbook built off of the Red Hat acquisition, one of the most successful acquisitions, I think, that will go down in history overall. And that is basically centered around a set of criteria: one, being absolutely strategically focused on where we can win, how we can win around our platforms. Hybrid cloud, AI and quantum. You saw we just announced an acquisition with HRL in Quantum. Two, driving and capitalizing on the synergistic value across our portfolio of software, consulting and infrastructure overall.

And three, we're always looking to drive an attractive financial return profile for our shareholders overall. And I would argue we have done a pretty good job on the M&A front in building out that capability. You see what it's done to the acceleration of our software portfolio and for that matter, the acceleration of IBM's revenue growth profile in addition to our free cash flow profile. So when you look at it, what is our strategy? We target and we see category technology leaders in structurally growing markets where we can add distinctive value, integrate and drive the synergistic value moving forward. Without getting into some competitive dynamics, extremely busy. And I think Arvind has proven we will be bold, but we will be focused and disciplined around driving it.

Very good. GenAI. As a company, I think, Jim, you've been very clear that your strategy isn't about going after frontier model building or creation, but instead data layer orchestration, governance, hybrid deployment and sort of smaller scale models. Can you talk about some specific use cases where clients are moving past pilots, improving those at the enterprise level? And were those kind of capabilities or your traction actually greater than you thought?

Yes. Let's bring it up a level because I think it's important to understand, to your point, we've been very focused on our AI strategy. How do we win, where can we win? And more importantly, how do we build a set of portfolio capabilities and invest in those areas around building out an integrated full suite of offerings. Remember, across IBM, when you think about our AI portfolio, we are not a highly capital-intensive business. We're not a frontier model provider. We believe that the monetization value is going to move up the stack, and we want to have control points and capabilities around where we think the future revenue, profit pools in enterprise are going to be at the end of the day. So what have we done?

We've built out an open infrastructure AI platform with data connectors, which I'll come back to, with AI middleware, with agents and assistants and capabilities, with an orchestration control plane, with an AI infrastructure level capability around server, storage, mainframe, even having AI on the chip and a consulting business at scale that drives domain industry knowledge that can help clients go scale and drive value realization. So we provide that entire integrated suite. What is our differentiated value proposition and what we go to market with? One, open innovation around AI platform, open-weight, open source models.

Two, we firmly believe the core competency of IBM's strategy is around hybrid, multi-model, sovereignty overall; three, orchestration control plane. Four, data, security, governance, life cycle management. That's where we think the value is going to get created at the end of the day. And when you take a look at it, I speak to a lot of my peers around the industry. And to your point, clients are moving right now from experimentation to scale. I would tell you, we're still very early innings coming up on baseball playoff season, we're probably in the third inning. Not in the first inning anymore. We're in the third inning. Best-in-class companies might be in the fifth or sixth. But on average, we're in the third inning. Every company, every CFO is focused on understanding the life cycle of AI from a value investment and realization point of view.

I always say, one, I need to understand total AI economics, two, govern AI consumption; three, optimize continuously across my company; four, plan dynamically; and five, deliver value realization. And I think all of that plays to the core competencies we have today. Think about IBM and what do we provide? One, from an AI perspective, we own the enterprise foundation software layer, Linux containerization. The explosion of AI is going to generate billions of new applications. Where are those applications going to run? On containers, plays sweetly through Red Hat OpenShift. Two, it increases AI, increases the importance of data, data connected in a secure, hybrid multi-cloud way. That was the essence behind the strategic thesis of Confluent to be the cross-platform data connector.

Three, the orchestration layer is where the value is going to be, not just on model optimization, cost optimization, but it's fundamentally on how you're going to run your businesses, your supply chains, your financial management systems, your HR systems, et cetera. So you take our control plane of watsonx Orchestrate, coupled with Red Hat that allows you to do inferencing on any architecture. We're the only company that can provide that. You add to that IBM Bob addressing the developer community, IBM Concert, addressing the security, the governance, the compliance and a very powerful Apptio capability, which gets at the core and the heart of the love of the CFO, understanding transparency of all your AI investments, and the value realization around that. So that's where we're going to play. Today, we've built out billions of dollars of pipeline opportunity. And I think those are spanning everything from code modernization to digital labor and reinvent workflows to customer service, all of those areas are moving to scale.

Yes. Now by the same token, AI also raises the question of disruption in terms of traditional stacks. I mean if agents make certain applications or development activities less valuable, where does IBM need to really concentrate its efforts to make sure that AI is a tailwind and not a headwind to the portfolio and the upside?

Yes. When you take a look at it, we sit at the intersection of technology and business. We are enterprise focused. We want to have those enterprise software control points that sit in the stack layer above the foundation models that we believe is ultimately where value is going to be created. That's why I went down the path in the prior answer. Why is orchestration control plane essential? Why is security, data connectors, governance essential? Why is Apptio from an AI life cycle management, investment realization essential. Those are the areas we play in. And I haven't even talked about having a consulting business at scale that can help clients go monetize that value as you move forward.

I want to get there. Okay. So I mean, let me ask about consulting then. So in some ways, the algorithm depends on balanced momentum across these different areas. We've seen consulting headwinds for some time now. You talked about some better bookings there. Generative AI, I think, is quite interesting and that represents sort of meaningful portion of your backlog. At the same time, AI should, in principle, at least reduce labor intensity of a lot of implementation work. So how are you changing your consulting model to capture more demand while delivering work with fewer people? And why do you win relative to your larger competitors in the market?

Yes. Well, to the beginning of your question, the synergistic value of being a platform-centric company plays to why we have a portfolio of software consulting and infrastructure combined together because we do believe that, that differentiates in the marketplace around where we want to participate and where our strategy is, hybrid cloud, around AI and soon to be reality right now in this decade, Quantum. So why do we have a consulting business? I think we're very different than many other consulting SI companies. One, we can capitalize on a very attractive economic multiplier inside a technology-based business for every dollar we land on Red Hat OpenShift, every dollar we land on our agentic AI platform of watsonx, we get $6 to $8 of services.

In addition to capitalizing on strategic partnerships that consulting has the opportunity to. But more importantly, the reason we have a consulting business, it provides the tip of the spear to drive adoption and scale of our business. And it pulls through IBM technology. Today, over the last trailing 12 months, over 20% of our net new software business comes through our consulting pull-through. Tremendous value creation thesis there. Now to your point, in the history of the information technology industry, consulting growth is always disrupted and cyclical in times of technological inflection ships, which we're experiencing today. Let's go back, Jim, the Internet, globalization, cloud less than a decade ago, right? Consulting market was going to dissipate, was going to move away. Now with GenAI, all of those eras had similar patterns, disruption in the market, forces companies, to your question, change their business operating models.

But ultimately, all of it ended up with a higher TAM and growth went to the winners that were able to adapt. And today, the market in consulting is disrupted. I've been very public and very vocal for a few years now, as you and I have talked about this many times. Traditional human capital, labor arbitrage-based consulting implementation services and BPO are fundamentally getting disrupted by agentic AI. It is forcing companies to go change their business operating models. The ones that are able to do that can go capture new secular growth. Where is the growth all coming from now? Data transformation services, AI advisory work, cybersecurity, compliance, governance, operating model transformation. We run Client Zero inside IBM. Deploying AI is heavy lifting. You have to fundamentally change the way you operate your company. This is a whole new source of the consulting value as you move forward. So what have we done?

Over 2 years ago, Jim, we embarked on a whole plan to reinvent our consulting business. In fact, we brought in Mohamad Ali to lead our consulting company. He's not a consultant. He's ran a public software company. He's a technologist at heart. Why did we do that? Because we saw this model moving over time. One, we have been repositioning our portfolio. Today, our portfolio is about 85% aligned to those new growth vectors, hybrid cloud, AI, cybersecurity, digital transformation. We have a very small piece of our business that's BPO, as you know.

Second, we've been driving a model shift to services as software. leveraging innovation, technology arbitrage with industry-leading IBM Consulting advantage. That's our agentic platform we go to market with, which is delivering tremendous value. And third, I think the reason we win in the market is we're the only company that I talk about that we can bring the Triangle. Technology stack with strategic partners, a consulting business at scale with deep domain knowledge with an IBM Client Zero ultimate reference case. on how we've been reinventing our company and driving $5.5 billion of productivity. So that gave us confidence in what we just guided in July that we see in the second half accelerating software -- excuse me, consulting growth with accelerating margins. Why?

We exited the first half with a $5.5 billion GenAI book of business in consulting. I would put that up against any consulting company today. And we're like, what, #6 in the marketplace overall. That is about 50% of our signings, 30% of our backlog, 25% of our revenue. And Jim, before you ask, it's about 2 to 4 points margin accretive on every dollar versus traditional human capital overall.

All right. Margin accretive.

So do I.

Okay. Quantum. You continue to invest pretty aggressively in Quantum. You targeted selling, for example, the first large-scale fault trial system for 2029. First of all, for a generalist investor, like what does that even mean? But more to the point, I think Arvind has discussed pretty publicly in the media about driving significant revenue from Quantum in '28 or '29. So what should we think about your strategy? And like how do we know you're actually getting traction that's going to turn into commercial revenue?

Yes. We're extremely excited about the next technological inflection shift in Quantum. It is real today. When you take a look at it, let's take BCG, let's take McKinsey. This has a very attractive TAM. They've sized it at about $850 billion. Each time I talk to them, the number is going up and up and up in terms of value creation, right, similar to the AI TAM opportunity. The IT providers from a value capture perspective, can they get 20% to 40%? I think that seems reasonable of that value creation opportunity overall. And we have a significant industry leadership position right now. So that's why Arvind and I have been very public that we see the revenue opportunity in the billions before the end of this decade.

So we're talking a couple of years. In light of that, we've been investing aggressively. And you saw some of the recent announcements overall. $10 billion we committed over the next 5 years in R&D and innovation, in CapEx, in manufacturing scale build-out, M&A, we just acquired HRL, new spin technology and in ecosystem development overall. Most notably, we just were awarded the grant from the U.S. Department of Commerce to build out the industry's first pure-play quantum wafer foundry to accelerate American quantum innovation and also fuel the quantum market overall.

Jim, we've been investing for a decade in Quantum. And we've taken approach from day 1 that we are going to build a full stack platform overall with deep vertical integration around hardware, around software, around algorithms and around ecosystems to go scale. If you just take a look very quickly at each of these layers. From a hardware, we have an industry leadership position today based on superconducting modality. One, we've got over 90 systems in production today, more than the entire industry combined. Two, when you look at all the -- you asked about KPIs or critical indicator. When you look at all the critical KPIs on what we should be looking for, we have industry-leading benchmarks already.

And I would just put it in 3 buckets: speed, scale, quality. In terms of speed, our superconducting modality is anywhere from 100 to 10,000x faster than competition. Our most recent iteration has 100,000 max circuits per second. So we have a significant industry lead in terms of speed, which is going to be a critical KPI. Scale, we have 30 quantum systems that are at full-scale capability, 15 over 100 programmable qubits. The rest of the industry has 2. We're already at 15. And in terms of quality overall, our gate fidelities, error rates and coherence times are best in the industry overall. So that's hardware.

The key, though, is software. The software layer, our open source SDK, we call Qiskit, is becoming the industry standard. 75% of quantum developers use Qiskit today. It's embedded in every academia world, government industry is picking up now. We have roughly 8,000 repositories, more than 4x the next software layer. And then finally, ecosystems on how you build scale and adoption. We've got over 325 members overall, and we've got hundreds of partnerships overall. So we've been very transparent on our milestones and our technology, Jim. And from that aspect, we feel very confident in this being a multibillion-dollar business before the end of the decade.

Great. I'm going to be run out of here on a rail, if I don't ask a financial question with the CFO, so I'll do that last question. You've talked for the year about on 4% to 5% revenue growth, $1 billion of growth in free cash flow, 100 basis points of pretax margin increase. I mean, should we still be confident in that outlook for the year? And then if you think about the margin side of things, where do we go in terms of how much juice there is to squeeze on margins on a go-forward basis long term?

Yes. Great place to end. I mean, free cash flow generation is one of the 2 critical indicators of our financial investment thesis and I would argue our long-term value creation thesis of IBM. We drive free cash flow. I'd like to use the concepts of flywheels, right? We drive this flywheel of free cash flow of accelerating revenue growth, combined with substantial operating leverage in our business and driving a very efficient balance sheet to drive cash conversion and optimization. We run that flywheel to deliver free cash. Let's put it, over the last 4 years, Jim, $55 billion of cumulative free cash flow, $7 billion of growth over the last 4 years and 700 basis points of free cash flow margin we've been able to achieve. We entered the year extremely confident, really driven by the underlying fundamentals of our business.

We did not back off once in our July earnings. We reiterated that, that talks to the health and durability and diversity of our business model on margins. We took our margins up to your point, by 100 basis points because we took up our productivity to now $5.5 billion. Let's put this in perspective on our margin opportunity. One, we're up 1,000 basis points in operating margin over the last 4 years. $5.5 billion of productivity we've achieved by the end of this year. That has created this investment flexibility for us to go take our R&D investments up by 50% over the last 4 years.

That's reinvigorated our portfolio, got us a whole new set of capabilities, accelerated our revenue growth. We've taken our G&A structural efficiency ratios. Jim, when we started, we were at the 25th percentile competitive benchmark. We exited last year at the 75th percentile. And we've got a path to get this company to the top decile overall. I still think there's ample headroom, and that is one of many levers in our free cash flow engine of us driving going forward.

Excellent. I think we're out of time. Thanks, Jim, for being here. We appreciate it.

Great. Appreciate it. Thank you.

Sorry, it's still morning. Good morning, everybody, and good afternoon for those joining us online. Fatima Boolani. I head up, jointly head up our software research effort here at Citi. And I am very excited to be hosting IBM and by virtue of IBM, Red Hat. On stage with me here is Matt Hicks, the CEO of the entire Red Hat business. Thank you so much for being here.

Thanks, Fatima. It's good to be here.

Matt, I wanted to start with you sharing your background at the company. You've been at the company a very long time. So I won't give the introduction myself. I'll have you do it, but you've been at the company a very long time, both on the Red Hat and IBM side. So we'd love to hear about your background. And then just to kind of level set the size and scope of the business today under IBM.

Perfect. So, I at Red Hat, I've been in this role as CEO for about 4 years. I've been at Red Hat for over 20 years at this point. But with the IBM acquisition prior to Red Hat, I was at IBM. So, I'm a very long boomerang coming back into IBM. So familiar with both sides of the business. In terms of what Red Hat is today, I'll give end of 2025 numbers and stats on that. But at the end of 2025, Red Hat was about 25% of IBM's software business. Since acquisition, which is about 7 years, we've grown Red Hat from roughly $3 billion business to over $7 billion.

One of the things we've been excited about in that journey has been, if you go back to when we were acquired, RHEL was the dominant product in our product line. It was about 2/3 of our business when we were $3 billion. OpenShift was about $100 million in ARR. Ansible is about $60 million. So both OpenShift and Ansible were very emerging. If you fast forward to the end of 2025, OpenShift was just over $2.2 billion. RHEL was less than 50% of our business, so much better spread and Ansible had grown to a $500 million business.

So, it's a really nice progression of from a $3 billion to $7 billion company, a better spread across those major product lines. And all of them are tied to growth markets as well. So we're excited. It's been a good run. I can't believe it's been 7 years already, but, yes.

Well, just on that, right, and you gave us a little bit of a flavor of this. So having been on both sides of the coin, so to speak, right, IBM proper, Red Hat proper, IBM Red Hat, right? What has surprised you the most? And what has evolved most in the company's trajectory? I mean you did share some very clear KPIs on the mix of the business. But in terms of the company's trajectory and the positioning as a stand-alone company versus under IBM's umbrella?

I was familiar with IBM. So, instead of surprise, I think I'll look at the things that I think were doubted quite a bit in the market. The first was how well was IBM going to hold sort of the strength of the independence of Red Hat. Red Hat was focused on open source over this long of a period. From the numbers I just went through, I think IBM has done an incredible job at that. It wasn't a surprise to us because we had worked quite closely actually with Arvind prior to the acquisition on this. So this is 1 that we had confidence in. I think IBM has actually executed to that really well.

The 1 area, if I had to pick a surprise to me at the end of 2019, we were public. We were a $3 billion company. We had accounts and successes all over the globe. I would have told you that like the trust in Red Hat is it's really significant with this. Joining IBM have opened up new opportunities where it was the trust of Red Hat's technology direction and open source, combined with a 100-plus year history of IBM and their commitment to customers. That was a really positive surprise to me of just the new doors that opened up being part of IBM versus when we were a stand-alone business owner.

No conversation at this conference would be complete without me uttering AI.

So, 500 times.

So, let's move down this path. So, as enterprises move from AI experimentation to AI production deployments and really putting AI to work for some real business outcomes and now driving real tangible ROI. Where does Red Hat capture this value inside their value stack? Because certainly from a lot of my conversations, it's not very intuitive for a lot of folks as to where your killer use is in that AI opportunity and where you're capturing the budget.

Perfect. So, I, a lot of times, I'll describe Red Hat as we are infrastructure plumbers. It's what we've always done. And I think in the server CPU world, it was easier. It was you could buy a Dell or HP server, we bring them up, we run your applications on them. When we move to AI, we will talk about hybrid AI or running open weight models. And that has been an early market, but I'll go through the changes why I think this is what we think of in production at this point.

So, the first, the last like 2 years, we've really debated of will AI work? Is this all hype or can it really do concrete things? And you're also sort of like, can it really do finance? Can it do math well? Or will these mistakes not allow it to do real core work? I think overall, we're past that, where the promise of AI, we're starting to see that become very concrete. And we look at our world, our market, we focus on open weight models. So, there are the frontier labs. They are frontier models. They're very capable. Can you compete with open weight models.

If you look just the last couple of months, whether it was Thinking Labs release or Kimi K3, GLM-5.3, the open weight models are now arguably just as capable as frontier models, especially with open weight harnesses, open source harnesses. So, the, does AI work? I think we're at a yes. Can I reproduce this myself on open weight models and my infrastructure GPUs? Yes. That's where Red Hat comes in because if you are now an enterprise and you have to buy your own GPUs and they're going to depreciate quickly.

They're expensive property for you to have. And you need to put on an optimized model or multiple open weight models. You need to make this run effectively, not just on 1 GPU. I mentioned that Kimi K3, that's probably running across dozens of GPUs, just for 1 instance, and do all of that with the same efficiency of a Google or Amazon, that's what we do in the AI side. That's been an earlier market. But at this point, we have had successes of companies doing this in every sector and every geography.

So, that shift to production, that's our world is how do we bring up this stack for you. It could be on infrastructure you rent or infrastructure you buy so that you can actually drive AI yourself, your data, your information. And it is exciting. I'm a techie on it, but I love the opportunity here. I think there'll be a tremendous amount of innovation and value we drive for customers there.

I do want to peel back the onion a little bit further. But before I do, I wanted to talk about Red Hat's halo to the rest of the software business. How does Red Hat in its entirety? And how has it risen or raised the importance and its contextual influence to other parts of IBM's full portfolio and also on the hardware side of things. If you can sort of talk through coincident levels of growth in other areas where IBM and Red Hat together have had a 1 plus 1 equals 3 revenue synergy outcome for customers.

Got you. If you look at the core, let's pick OpenShift. Like what does OpenShift do for IBM and then in turn, IBM's middleware portfolio or Db2. IBM has a tremendous global reach with customers. And there's been this pull to whether it's geopolitics or sovereign use cases to be able to run things yourself. OpenShift's ability is it runs everywhere. It runs in public clouds. It runs on-premise. So, does RHEL, so does Red Hat AI. This is what we do in Red Hat.

IBM's choice to really standardize on Red Hat technologies meant Db2 or MQ, their traditional line can now run anywhere. It can run in Amazon as effectively as it can run on-prem. We drove that integration deeper and deeper into areas like mainframe with this to expand that footprint. That's Red Hat at its core by IBM really standardizing on Red Hat just expands the reach of where IBM can run software. If you get some of the newer areas where I'm excited about this, we are plumbers.

So, starting with a, we help you run a model as a service really effectively, as effectively as Amazon or Google. That is not necessarily enough to get that customer win. IBM adds agents with products like Orchestrate. They help bring data into that equation with watsonx.data. They can help guide developers with Bob. And we have that affinity and integration together to let our strength in infrastructure plumbing combine with the end-to-end use case that IBM brings.

So, those are a couple of examples just on like the very traditional side of helping Db2 run more efficiently in AWS, combined with a help you actually use a model as a service infrastructure to run agents that are going to help you in your; finance use case or your telco use case at the line of business level.

So, in summary, there are a couple of pretty interesting ingredients that you're working on and putting together and brewing to underwrite a path towards better structural growth in the business? I know we've gone from $3 billion to $7 billion in size for the entire Red Hat business since acquisition, as you mentioned. But when we think about the growth trajectory of Red Hat from here, some of the dynamics you were alluding to in terms of potential growth drivers, what are the explicit pathways for you to execute the business to drive an acceleration towards a mid-teens growth ZIP code as you've discussed in recent Investor Days?

Yes. So, if you look at, first, our guide for the year, looking, so we're confident it's at 10%. And then how does that, how do you have the durability to say like is there room above the 10%? A lot of times, we'll talk about like value destroyed or deferred or these concepts. So, I'll go back to the fundamentals. One of the things we've talked about in the RHEL business that has been a headwind is we deploy on servers that are actually on site at a customer. You think of like as a real volume, server pricing and those things are up quite a bit.

Supply and deployments on this are actually lower because supply chains or the deployment for this stretches out quite a bit. But if you go back to the fundamentals of do customers still need the compute capacity? Do they need the GPU capacity? I would argue that, that fundamental has been as strong as it ever has. Like there are an infinite number of ideas that you can do now with this combination of building new apps and AI. If I picked a customer example was using Red Hat AI would be BNP, BNPP and hybrid AI has taken this like on-premise deployment option with it. They address over 1,000 use cases at this point themselves with their own AI have created EUR 600 million of additional revenue tied to this.

We are supply constrained, but the fundamentals of this don't change. So, that is a headwind. But as supply eases up, we're very confident in that extended growth. You go into OpenShift, the core growth driver for OpenShift, it is both AI and virtualization. The techy language of AI, you will run it in containers. That is a given. You might run it in multiple sets of this, but that is sort of the technical underpinning of how everybody is running AI today. That is what OpenShift does incredibly well. Adding to this, you have all the changes in the virtualization market that we are confident that will go on for years. Broadcom's changes to VMware. VMware has always been a strong competitor.

There's a tremendous amount of VMware out there. We think we have a great advantage in being able to serve containers and virtualization more effectively. If you move into, say, Ansible, compare Ansible to the security needs of customers post-Mythos. Customers are having to rethink in total how they address security. A statistic we went through that we've learned with Lightwell, if you go back just a few years, let's say, 3 years, from a security incident being found to how long it took for the first confirmed exploit to come out, it was, let's say, roughly 5 months, 5 to 6 months for that. So, as an IT team, if something popped up, you had 6 months to go figure that out.

A year ago, let's say it was about a month. Today, it's about 10 hours. And so just that velocity of saying, just a couple of years ago, I had half a year's worth of time to figure out what to do here, and I have less than half a day today. Ansible sits at the core of how do you start adopting these things at a machine speed, not a human constraint. So, those fundamentals, there is a tremendous opportunity with just those 3 that I mentioned at the beginning, that is not even talking to our opportunity in Lightwell, where we are patching open source application vulnerabilities or Red Hat AI enterprise as hybrid AI starts to pick up in that production use case. So, those are some of the core growth drivers there, both in the traditional businesses as well as upcoming businesses that we're really excited about.

Matt, I want to spend a little bit more time on OpenShift. It was a relatively embryonic business 7 years ago, now $2.2 billion in ARR. And you did highlight very strong traction there in terms of the first principles of containerization and virtualization are going to see renewed or resurgent importance as we move AI into production. So, how does open source here complement and accentuate the crown jewel RHEL franchise, so Red Hat Enterprise Linux franchise and vice versa? And then maybe to play devil's advocate, how would you counterargue a scenario or concerns or fears that actually the markets, the respective markets for containerization and virtualization are actually relatively mature and pretty saturated. How would you counter argue that?

If we look at the affinity of OpenShift to RHEL, the reason I'm excited about this in this environment, there has always been in the early days of OpenShift to today, this thought of do I go to a vendor for doing this? Or can I do this myself on it. Our goal in an OpenShift or RHEL is to provide this layer of security at the most fundamental tiers in infrastructure. If you look at the OpenAI hack of Hugging Face, those are the things we focus on not letting happen for customers.

If you have a tremendous amount of expertise in that domain, it's open source. You can do that yourself. But we think for most businesses, expertise is probably not in the depth of Kubernetes. What that creates for us is there is this what we would call the free-to-fee opportunity of pre-Mythos, this belief and risk reward balance where I can run open source software myself and the risk is not that high. That was pretty prevalent in enterprises. That is a totally untapped opportunity right now for saying in your pursuit of AI, where do you actually choose to leverage the expertise of a Red Hat at that operating system level, at that OpenShift level so that you can pursue things above that.

You'll have enough challenges in agentic security. That is something we are very strong in. That is an area where we know it's early days. This really, this thought wasn't in enterprises pre-mythos. We are going through a rethink in total of security required. That's certainly a growth driver. When we look at virtualization, virtualization, I still think is a very capable tool, but as I mentioned before, the world of AI will run in containers. They're more dynamic. They're faster. That volatility is required as well as things you'll hear in the security rethink like micro segmentation and being able to tightly control that. That is what we do.

I think that will be directly coupled to AI's growth. And I don't really see a limit or a limiter within that. At the core of OpenShift, we run all of the containers on RHEL. We run that at the tier. We have RHEL hardened images. And I do think the shift to enterprises realizing of you have to have security at every single layer now. It may have been an option pre-March where you could sort of run with the risk of not doing that. I think that capability, whether we think it's a good or bad capability to have like this knowledge of AI's ability to exploit infrastructure, it is, it's generally available at this point. It's not limited to Mythos.

It's in all of the capable models. That risk/reward balance has shifted. And we think we are, this is what we do at its core is protect that infrastructure so you can focus on your business domain. So, I think there's a lot of growth opportunity left there. But I'm biased this is also what we sell.

So, let me ask you this, Matt. You mentioned the word hybrid earlier on in our conversation. One of IBM, one of IBM's core messages is that enterprises want more flexibility across models, across infrastructure or clouds and having multimodal instantiation footprints, right, for running their business, right? So, on-prem/cloud environments, toggling between those 2. How is Red Hat benefiting from the industry's desire to actually shift more towards hybrid?

And this notion of, one of the things that we've characterized this notion of repatriation of workloads and applications in the context of, I think, something else you mentioned in the earlier answer and discussion around data sovereignty and demands around more regional constraints around data stewardship, right? So, how do a lot of those factors meld together to make hybrid more of the de facto standard? And how are you participating in pushing that envelope forward?

Yes. So I think this has been, whether it is regulatory drivers, whether it's privacy of information that this is my intellectual property, and I no longer trust sharing it because someone else may use it to compete with me. This has been a really strong trend. I think the best way, if I go through just some of the public references of the minute that customers believe that this is possible in AI, they quickly move to, can I do this myself with my own models capabilities.

If I pick a, and for us, this is across every sector, every geography at this point, but I'll pick some ones I like. If you look at a SoftBank in telco, they've used Red Hat AI to look at radio access networks, how can they really use AI to rethink how radio access networks and telecommunications are going to operate. Obviously, very critical to sovereign data and being able to deploy that. But that's an example that we could pick within telco. BNPP, I mentioned in banking, you're also looking at Emirates NBD.

Again, banking minus some of the real global drivers in banking, that is also pretty sovereign by design. Regulators will make you keep customer data in local areas. Emirates NBD has been a great example of using Red Hat AI to drive their own AI and capabilities and use cases very similar to BNPP. If you jump into defense, where defense, it is sovereign, but it is sensitive data that they necessarily can't share with AI labs, but they need that capability.

The work we've done with the U.K. Ministry of Defense or Northrop Grumman, it fits that use case for they need to be able to use AI internally to drive their use cases that cannot share that with a start-up no matter how large that is. If you zoom in on the U.S., you could look at another telco player with Verizon or a small start-up like Zero Latency, who is taking their background of energy infrastructure is actually where they came from and are using Red Hat AI to try to change how customers can use inference and route it similar to the energy grid.

So, I think all of these use cases, whether it's in APAC, whether it's in the U.S., whether it's in telco or defense or early start-ups, this desire to be able to run it themselves on models they control and really be able to drive their own differentiator with that has been really strong. So, those are some of those areas where it's not just a theory we talk about, this is like a real driver where we see companies of all sizes starting to be successful at implementing this. Obviously, the nice link to us is IBM's global reach. We know this technology. We are the leading developer in vLLM and llm-d in the technologies that every AI labs use, all the hyperscalers use were the core contributors to this. But IBM adds that global reach and trust to help customers actually deploy it and be successful with.

So, bringing together these favorable market backdrop dynamics, in terms of what we talked about vis-a-vis the growth algorithm for the Red Hat business near term and towards the mid-teens sort of bogey, so to speak, right? Can you help us think about or stratify the most potent product level growth drivers inside Red Hat? So, between RHEL, between OpenShift and between Ansible and some of the kind of the other flagship portfolio SKUs, what are you expecting is going to kind of drive the bus on aggregate acceleration?

So, I think the, if you look at core RHEL and OpenShift and how they deployed today. The minute we have supply chain relief, servers are shipping faster. That becomes a significant growth driver for us. Customers have this demand and desire, but we don't start booking really until servers are in data centers. That's 1 we don't necessarily control, but we still see the fundamentals there. That will be a timing aspect. In OpenShift, virtualization is a significant driver for us.

That is a mature business. We have a technology advantage because we're able to couple containerization. And then Broadcom has changed the commercials quite a bit. It's created that compelling event for customers to decide whether they stay on that path or move to a more consolidated play with OpenShift. Those are major drivers in those businesses. If you pick in Ansible, Ansible is going to be very closely correlated to the security posture and rethink. This is new for enterprises.

They didn't ask for this problem. They didn't budget for it this year, and yet this happened to them come March. But on the Ansible side, we have a great opportunity to being able to drive how they rethink patching those. Those are our 3 largest businesses. I think our largest by the numbers growth will always closely correlate with those. But if you look at the new opportunities, post March and today, we announced the Lightwell opportunity, which is patching application level dependencies that customers have just used for open source today.

They depended on maintainers to keep software secure. But now the vulnerabilities found are well outstripping random open source maintainers ability to support it. This is a new offering from us where we have Lightwell Network, which will distribute secure application patches to customers. We also have our Clearinghouse option, which is limited, but we will allow customers to actually request that we patch patches for them. This really, the demand probably outstrips our ability to deliver.

This is 1 of the hardest problems in the industry today, but it's an exciting 1 for me because our knowledge of AI has let us take on this business and this problem. So, that's an early driver to business, but it is a multibillion-dollar market and opportunity. We're in the early days there, but I think over time, that will be a really significant driver. And then in the middle is Red Hat AI. You could just say like as hybrid AI becomes the default as the security of AI, like how do I keep AI agents, Genie in the model with this. That is what we do. As that maturity builds in the market, you'll see Red Hat with it.

Just sticking to Lightwell, Matt, it seems as though Lightwell and the monetization thereof is essentially a call option on growth for you, right? I mean the SKUs and the sellable opportunities were only introduced a couple of months ago, right? So, is that the right characterization? And then secondarily, can you give us a little bit more granularity about kind of the economic model here? Is this a subscription fee? Is there a seat-based component? Is it pay for access? I mean how are you adjudicating and structuring the pricing between those 2 flavors of the Lightwell, the Clearinghouse versus the Network?

It's perfect. So, to your point, this is new for us. So this is largely upside in terms of the new offering halfway through the year, what builds on that is not something that we are putting into January because this problem didn't even exist in January. So, there's a good, I think, characterization of that. When we look at network and Clearinghouse, I'll use round numbers, they're simpler. But Network is roughly $1 million for an account. It gives you access to all of the application packages that we include network.

Within that, you get roughly a 14-day window embargo for anything that is not known in open source. 14 days feels very short to most enterprises. We remind them that the actual bar is 10 hours on that. S,o, they want 6 months of embargo. We give 14 days. In reality, we have to get dispatching to sub day. But that is Network. It is very simple. In fact, if you go back to like last week, we put 1,000 new packages. So, this in the techy terms, you'll hear like SBOMs and SLSA verification.

All of these things say, I know where I got the software from. I know who checked it, and I have some assurance that Red Hat deli this to me. That is network. You don't get to influence network though. So, if you say, but Matt, I really need this package patch. I'm dependent on it. You do not get that option with Network. In clearinghouse, you do get that option. Clearinghouse gives you a ramped SLA per week of how many requests you can make to us of I have this package, and I need this vulnerability fixed in it.

Clearinghouse is roughly $5 million per account, and it is limited. The reason it's limited, customers have to have a maturity. They have to know how to scan. They have to know how to prioritize. There is no use in sending us 100,000 random things to patch. That really has been our, we started with the G-SIBs on it because they were in glass wing. They were building its maturity early. But that's where we promote someone from Network to Clearinghouse.

At the risk of growth oversimplification, can we think about Lightwell as being an institution like the FDA where you're looking at, you talked about SBOM, so that software bill of materials for those of us uninitiated, verifying the ingredient list, making sure that we don't have the red dye equivalent of something in the software supply chain. Is that thematically kind of a loose analog that you would agree with?

It is. I think if we went back even to last year, that trust proxy wasn't just open source. Like I'm going to, I've never thought of running software off the Internet that I don't understand was a good idea, but I could accept that we did it all over the place. Post March, that is no longer a good idea. And so we are that trust proxy of saying, you can see the ingredient list, we sign it. And then Clearinghouse is the, you don't like that 1 ingredient.

If that ingredient may still carry some risk for your business, Clearinghouse, we will go fix that 1 ingredient as well, and then we will share that back to Network. So, that really is that trust proxy for open source software consumption. I think that will continue and go up exponentially, but we need a better trust model for the FDA type equivalent of do I actually know where I got this from versus Jimmy, the maintainer on the Internet that I have never met. That's probably not okay to be running a world critical banking infrastructure on. So, that's the...

Jimmy sounds reliable. He sounds like.

Right. It does sometimes and sometimes not.

Matt, just my last question for you, and this is a riff off something I asked earlier around portfolio and revenue synergy. There is a lot of interesting conceptual and persona overlap between Ansible inside the Red Hat portfolio specifically and with HashiCorp's products, right? So, Hashi has come into the family, Confluent has come into the family. So, a lot of interplay between Red Hat's core portfolio and some of the offerings there. In very broad strokes, how would you characterize maybe some overlap synergy on that continuum of, again, overlapping versus synergistic and complementary. How would you sort of frame that for us?

I think it's always tough to believe what I'll pitch. So, I'll go back to some data points. The pre-acquisition, we worked with Hashi all the time in this. The clear complementary is Hashi's secrets management in Vault. And we're in a world where you cannot put a secret in a single text file, AI agents will find it on it. Vault has been a phenomenal solution that's purely complementary. If you look at core HashiCorp deployment options, it's like a Terraform, since the dawn of time, there has always been state-based configuration management and procedural management.

It has always existed. I've worked in IT most of my career. Those 2 have always been there. These 2 tools are very complementary. Ansible is procedural, Terraform is state-based on this. Having them all on the same team in IBM means that we no longer have to worry about who might grab whose open source bits and compete with each other. We are 1 single team and doing the best procedural, the best state-based, the best secrets management. Confluent is a bit different in this of every AI use case, if you think of like an OpenClaw that you may run yourself in like a personal world for your e-mails for it to work, it means every new e-mail you get put into that engine or Hermes or any of these Confluent does that streaming data.

That's what it does. The minute you have Red Hat AI running on your models with memory, you need to get every new piece of data into that engine. That is what Confluent is the best in the world at doing. It's on Kafka. We know the technology. So, I view those 2 as just perfect complements to the team because they do other pieces that we don't do in the infrastructure plumbing side. So, hopefully, that helps.

And I think that's a fantastic place to put a pin in our conversation. Thank you so much for the time.

Right. Thanks for having me. Thank you.

Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time.

Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's Second Quarter 2026 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer.

We'll post today's prepared remarks and a replay of today's webcast on the IBM investor website within a couple of hours. The earnings presentation is already available. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website.

Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings.

So with that, I'll turn the call over to Arvind.

Thank you for joining us today. I saw in our preannouncement, our second quarter performance fell short of expectations. Consistent with our commitment to transparency, we shared the results as soon as our financial close process was substantially complete. I won't revisit the details from the preannouncement as Jim will provide additional context.

Instead, I will focus on the positioning of our business, the growth opportunities we see ahead and actions we are taking to address execution in the quarter. Our conviction in the strength of our business and our ability to grow and drive shareholder value remains unchanged. We recognize that the technology spending environment remains dynamic, and we must continue to evolve how we engage clients, bringing the full breadth of IBM's innovation to bear on their most important priorities.

As clients determine how and where to deploy AI, we believe our portfolio is well positioned to help them realize value in a secure, cost-effective and scalable way. Over the last 5 years, we have transformed our business, improved the durability of our revenue growth and strengthen our operating model. Those fundamentals remain intact. Software is nearly 45% of our total revenue and has been repositioned to higher-growth end markets across hybrid cloud, data, automation and mission-critical transaction processing software running on mainframe. Our offerings help clients build and run applications and AI anywhere, unlock the value of their data, orchestrate and govern AI at scale and operate more efficiently, securely and resiliently.

Our second quarter software shortfall was limited to a CapEx sensitive area of the portfolio. The vast majority of our software business, about 80% of that revenue is recurring in nature and delivered healthy growth in the quarter, reflecting the demand for our offerings and giving us confidence in our growth opportunity. Our AI strategy is the right one for IBM and aligns to what we are known for: hybrid, sovereignty and trust.

We have held the view for a while that the unprecedented investment in AI infrastructure and models will increase pressure on enterprises to generate meaningful returns from that spend. Value will increasingly shift towards the orchestration and data layers so that clients can optimize outcomes, cost and governance across multiple models and agents and keep control of their proprietary data.

IBM's differentiation lies in our neutrality and enterprise-grade operational control, the ability to orchestrate agents across models, clouds and on-premises environments, while also providing built-in observability, evaluation, governance, identity management and security. What's the next orchestrate is the control plane that helps clients build, manage and govern agents which combined with Red Hat gives clients a foundation to run inference and applications on any infrastructure.

Bob is our entry point into the developer ecosystem, helping clients build enterprise-ready AI applications and agents while rating a natural pathway to adoption of WatsonX Orchestrate and IBM's broader AI platform. Confluent delivers real-time governed data to models and agents across our control plane. [ Concert ] provides enterprises with a unified view of application health, security, combine and operational performance. Clients remain in the early stages of AI adoption making a combination of consulting expertise and technology, a key differentiator. We are helping move clients to deployment, and that's translating into growing demand across consulting led by generative AI.

In infrastructure, despite challenges this quarter, z17 is having the best refresh cycle in reported history. Transaction volumes, cyber requirements and resilience continue to drive growth for the mainframe. While clients continually evaluate workload placement, we see no evidence of clients moving off the mainframe. z17 remains at nearly 130% program to program, well ahead of z16, which was our strongest on record.

IBM Z runs over 70% of the world's transaction volume in terms of value. To give you a perspective on our reach, there are over 140 million installed MIPS running mission-critical transactions across every industry. Clients representing 85% of these installed MIPS are either maintaining or growing capacity. Earlier this month, we introduced a smaller LinuxONE system that allows clients to address data center space and cost constraints while offering the security, resiliency and real-time inferencing Z platform can deliver.

This innovation expands our addressable market. Distributed infrastructure just delivered its best quarter of revenue growth and record growing 37%. We see this as an increasingly important growth vector for IBM, driven by AI adoption and the rapid growth of enterprise data. We have been investing across power and storage AI infrastructure to position ourselves for this market opportunity. Power continues to gain momentum as the value proposition of Power11, resiliency, performance and Linux modernization resonates with clients. We are gaining share in storage through differentiated offerings across flash, fusion and tape, including AI-enabled capabilities that help clients scale and manage data for AI.

Demand remains strong, driving a robust pipeline and pushing us well for the second half of the year. We are acting decisively to capture new market opportunities as they arise. The MITAS release in early April has accelerated the discovery of security vulnerabilities for clients. This creates a multibillion dollar TAM for IBM and Red Hat to help clients secure their open source software through our new capability, Lightwell. IBM offers a differentiated value proposition as a leader in open source through Red Hat and also recent acquisitions of Confluent and Harshi.

Clients can subscribe to Lightwell for $1 million per year to access open source packages that have been remediated or validated. In the first 2 weeks of availability, we have already made more than 7,500 package versions available. Early adopters of Light well include organizations like Bank of America, BNY, Citi, Goldman Sachs, JPMorgan Chase, Mastercard, Morgan Stanley, Royal Bank of Canada, State Street Visa, Wells Fargo and more.

Finally, quantum computing is no longer decades away. It is upon us, and we are investing aggressively. Recently with the U.S. Department of Commerce we announced a letter of intent to build Enderun, the world's first pure-play quantum foundry, supported by $1 billion in chips incentives provided by the Department of Commerce and $1 billion cash contribution by IBM. Shortly after that, we disclosed plans to invest more than $10 billion in quantum over the next 5 years, spanning already, CapEx, manufacturing scale-up, M&A and ecosystem expansion.

This supports our road map to stalling in 2029, the world's first large-scale fault-tolerant quantum computer. Recently, IDC evaluated 11 quantum computing vendors and ranked IBM first overall, citing our ecosystem readiness for quantum advantage, our track record of delivering the road map on time and our quantum classical integration. With the portfolio we have and the opportunities ahead, it comes down to execution. That is where we fell short in the second quarter.

We have engaged with clients on the transactions that slipped and have a clear understanding of what needs to change. We are adapting to deliver greater business value to clients around our innovation and greater economic value to better align with client priorities. At the same time, we are accelerating productivity actions across the company, spanning both spend reduction initiatives and actions designed to drive growth. These include leveraging AI to improve software development productivity increasing the effectiveness of our sales and marketing organization and accelerating our supply chain.

Together, these actions help enhance our margin and free cash flow commitments, while improving our ability to capture the growth we see ahead. For this growth, we are also accelerating changes to our go-to-market model. The goal is to expand coverage across thousands of additional clients where our portfolio is highly relevant and where we see significant wallet share opportunities.

While IBM has a strong presence within the Fortune 1000, there is a lot of opportunity beyond this. These changes are aligned with the areas of our business demonstrating strong momentum, including Red Hat, HashiCorp, Confluent, WatsonX and Storage. As AI adoption moves from experimentation to enterprise scale deployment, we are also investing in more specialized technical and client-facing talent, including forward deployed engineers.

Our conviction in the strength of our business and our ability to capture the growth opportunities ahead remains unchanged. We now expect revenue growth for the full year in a range of 4% to 5%. We continue to expect to grow free cash flow by about $1 billion this year as we accelerate our productivity initiatives.

With that, let me hand it over to Jim to go through the financials.

Thanks, Arvind. In the second quarter, we delivered 1% revenue growth, 30 basis points of operating pretax margin expansion and 5% diluted operating earnings per share growth. And through the first half, we generated $4.8 billion of free cash flow.

Let me go through what played out in our segments in more detail. Software revenue grew 5% this quarter with flat organic revenue growth. As Arvind discussed in our preannouncement, in the final weeks of June, we saw a shift in client spending priorities. Many clients redirected spending towards servers, storage and memory purchases to secure supply-constrained infrastructure ahead of expected price increases. As a result, tens of large deals failed to close on the time lines we expected, accounting for the majority of the shortfall.

To understand these dynamics, it is important to recognize that many clients purchase mainframe and the associated software stack through our enterprise license agreements which create a strong incumbency moat for IBM and are generally treated as capital investments. These agreements typically contain a high concentration of transaction processing software although they may also include data and automation products.

As clients prioritize other CapEx investments, the timing of these deals shifted resulting in transaction processing revenue declining 9%, while data grew 18% and automation grew 3%. In contrast, we continue to see strong underlying demand in our subscription and consumption-based software. Because these are generally purchased as operating expenditures, they were largely unaffected by the CapEx dynamics that impacted some of our ELA transactions during the quarter.

Let me take a step back and discuss the composition of our software revenue. We offer customers flexible purchasing options that align technology investments with their business needs, making it easier to adopt and scale solutions across their businesses. About 80% of our annual software revenue is recurring and is made up of subscription and consumption-based revenue like our Red Hat, HashiCorp and Confluent products.

It also includes subscription and support revenue that provides customers with ongoing access to software enhancements and support. This revenue grew nicely in the quarter. and our ARR was $24.6 billion, up 8% since last year. The other 20% of software revenue is transactional in nature and is predominantly related to the incumbency with mainframe and the associated enterprise license agreements with transaction processing and other software products in data and automation.

ELAs are highly valuable to IBM because they establish long-term strategic client commitments that expand adoption across our software products, creating a multiplier effect. Transactional revenue was down high single digits in the quarter given the shift in buying patterns we have discussed. On the subscription side of the business, HashiCorp had another record bookings quarter and accelerated revenue growth through the first half of the year. And Confluent is off to a strong start and on track with our expectations after its first full quarter post-close.

Red Hat growth accelerated 1 point sequentially to 11%. And driven by improvement in the subscription piece of the business and stable growth in consumption-based services. OpenShift ARR is now $2.2 billion with strong growth reflecting demand of our containerization and virtualization products, where we now have about $680 million of contracts signed since early 2024. In RHEL, performance continue to be impacted by constrained hardware availability similar to last quarter. Infrastructure revenue declined 7% this quarter. While Z performance was below our expectations through the first 5 quarters of z17 availability, revenue is well ahead of the prior cycles and nearly 130% program-to-program.

As Arvind said, we see no evidence of clients moving off mainframe. Clients continue to invest in IBM Z to modernize mission-critical workloads with a focus on resiliency, security and increasingly enabling AI on the platform. AI is driving incremental capacity growth and new workloads as clients look to run AI closer to their most sensitive data. We are seeing strong early adoption of our AI innovations with nearly 50% of z17 customers investing in AI capabilities with Spire Accelerator and clients deploying WatsonX code assistant for Z our growing MIPS capacity 3x faster than those who are not.

In a world where infrastructure costs are rising and efficiency matters more than ever. IBM Z offers a compelling economic advantage. Depending on the size and complexity of workloads, clients can realize a 2 to 15x total cost of ownership benefit versus moving these workloads off the platform, reinforcing why the platform remains central to their operations and positioning us to capture additional value as AI workloads grow.

And adding to Arvind's comments on the strength in distributed infrastructure, we exited the quarter with approximately $500 million of backlog, our highest on record, supporting continued momentum. In Consulting, signings grew 6%, marking our second consecutive quarter of growth and reflecting continued client investment in business transformation initiatives. Revenue grew 1% and driven by demand for application modernization, data transformation and cybersecurity services as clients balance the needs to increase productivity through AI with the need to strengthen resiliency and manage risk.

Revenue growth was balanced with both strategy and technology and intelligent operations up 1%. Generative AI represented about 50% of our settings in the quarter and now makes up over 30% of our backlog. Underscoring the demand for AI-powered transformations that extend beyond technology modernization into core business operations. As clients move from pilots to enterprise-wide deployment, they are increasingly turning the consulting to reengineer business processes and unlock productivity and new business value through AI, automation and digital labor.

Let me now discuss profitability. Operating gross profit margins were down 70 basis points, largely driven by our revenue shortfall and mix. Despite this shortfall, productivity actions were ahead of plan, providing the flexibility to absorb confluent related dilution, continued investing for growth and expanded adjusted EBITDA and operating pretax margins by 20 and 30 basis points, respectively.

Segment profit margin expanded by 160 basis points in consulting and 110 basis points in software, driven by ongoing productivity actions. Infrastructure segment profit margin declined 150 basis points, reflecting the IBM Z mix headwind mitigated somewhat by strong distributed infrastructure margins. Through the first half of the year, we generated $4.8 billion of free cash flow, flat year-over-year, driven by about $700 million increase in adjusted EBITDA, offset by inventory, higher cash taxes and net interest expense.

Given the constrained infrastructure supply environment, we proactively took actions through the first half to strengthen our supply chain and support anticipated customer demand in the second half, reflecting the strength we see in our infrastructure business. We exited the second quarter with a strong liquidity position and a solid investment-grade balance sheet with cash of $8.2 billion.

We returned $3.2 billion to shareholders in the form of dividends through the first half of the year. Our debt balance ending the quarter was $62 billion including debt of $13 billion for our financing business, with the receivables portfolio that is 80% investment grade. Over the last 5 years, we have repositioned our business to higher growth end markets, improve the durability of our revenue and expanded operating pretax margins and free cash flow through disciplined execution and laser focus on productivity. As Arvind mentioned, we are confident in our portfolio and strategy and the growth opportunities we see ahead.

Let me now address our expectations for the remainder of the year. We now expect revenue growth for 2026 in the range of 4% to 5% and are maintaining our expectation to grow free cash flow by about $1 billion. We believe the low end of the revenue range appropriately reflects the current environment and is our base case. Given the second quarter shortfall, we are revising our software growth expectation to a range of 6% to 8% for the full year. While several of the deals that slipped from the second quarter have already closed in the first few weeks of the third quarter, the low end of the software range assumes that recent spending dynamics persist through the second half. The high end assumes a more typical conversion of our pipeline.

In light of the strong demand environment in Power and Storage, we now expect infrastructure to grow low single digits in 2026, driven by growth in distributed infrastructure and continued strong program-to-program performance in IBM Z through the second half of the year. Demand across Storage and Power remains strong, and we continue to secure inventory and accelerate our supply chain to capitalize on the opportunities we see in front of us.

In consulting, the quality of our backlog and momentum in Gen AI continue to support an acceleration in revenue growth to low to mid-single digits for the year. We continue to see strong fundamentals of our business play out in the second half of the year as we accelerate our productivity initiatives to help enhance our margin and free cash flow commitments while continuing to invest in growth. These actions include deploying AI and automation at greater scale across the company. Reducing third-party spend, improving sales and marketing efficiency, using AI to drive more efficient software development, optimizing our supply chain and enhancing services delivery. While revenue dynamics are creating margin pressure for the year, the pace of our productivity actions have exceeded our expectations.

As a result, we now expect to deliver 100 basis points of operating pretax margin expansion with productivity more than offsetting the revenue-related headwinds. Our operating tax rate for the year should be in the mid-teens, and the timing of discrete items can [ cost ] the rate to vary within a year. For the full year, we continue to expect to grow free cash flow by about $1 billion.

The primary driver of this growth will be adjusted EBITDA, offset by cash tax headwinds, higher CapEx and higher net interest expense, the majority of which is behind us. Looking to the third quarter, we expect constant currency revenue growth consistent with the full year. Given the strengthening of the dollar, we now expect currency to be a 1.5 point headwind to revenue growth in the quarter. For operating pretax margin, we expect similar performance to the second quarter. Our third quarter operating tax rate should be in the mid-teens.

Let me close by saying that our strategy has not changed. We are a leader [ in ] hybrid cloud. We are investing behind an AI opportunity that plays directly to IBM's strengths in data, orchestration and enterprise trust, and we continue to build leadership in Quantum. We remain confident in the growth opportunities ahead of us and our ability to capture them.

Arvind and I are now happy to take your questions. Olympia, let's get started.

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And second, as always, I'd ask you to refrain from asking multipart questions. Operator, let's please open it up.

[Operator Instructions] And our first question comes from Amit Daryanani with Evercore ISI.

I guess, Arvind, your commentary strongly suggests that the headwinds we saw in June quarter were a result of customers just reallocating spend towards AI infrastructure rather than reducing overall IT budgets. And I think the biggest debate everyone's had since the preannouncement has been, is this demand deferred or destroyed for IBM?

So I'd love to understand what gives you the confidence that this is more a timing issue versus a structural shift in enterprise spending? And what milestones should we all be watching for over the next couple of quarters to gauge if demand truly starts to normalize for you folks? That would be really helpful.

And Jim, I have to acknowledge the very impressive free cash flow guide from you folks despite the June quarter blip. Maybe just unpack a little bit on the building blocks behind your ability to maintain the free cash flow target as well.

Amit, thanks for the question. Well, given that you use the word impressive, I'm going to ask Jim to address the impressive part first, and then I will address your question on the priorities.

Thanks, Arvind, and thanks, Amit, for the question. I think it's a great place to start this call here tonight. Free cash flow, as you all know, has been one of the two key leading indicators of our financial investment thesis and our shareholder value creation model inside IBM. And I appreciate the word impressive because the team has worked extremely hard over the last 4 years, put this in perspective.

Over the last 4 years, we have grown our free cash flow over $6.5 billion in this company and grown free cash flow margin 700 basis points. That gives us a lot of confidence here within the company. But when you look at first half, we produced roughly about $5 billion of free cash flow. That was flat year-to-year. The underpinnings really are driven by the fundamentals, the productivity mindset in this company and the ability to generate operating leverage is extremely strong overall.

Adjusted EBITDA was up high single digit. And as I said in the prepared remarks, it was offset by higher inventory, cash taxes and net interest, which, by the way, the latter two, I've been talking about all year long. But let's take a step back. We entered this year with extreme momentum and confidence in what we guided. We guided free cash flow up about $1 billion year-to-year with record free cash flow margins. And we said that was going to be entirely driven by the quality and sustainability of the fundamentals of our business, adjusted EBITDA with some headwinds on cash tax, CapEx to invest in our business and net interest.

Through the first half, there are some unique dynamics that drive that free cash flow flat, which, by the way, attainment wise, we're in a very similar position that we were during the last similar point in time of z16 mainframe cycle. Right about 30% attained overall, albeit our free cash flow margins are up 300 basis points from that period of time.

But the first thing, as I said in prepared remarks, given the infrastructure supply dislocation that's happening in the market, we took very conscious and proactive actions to strengthen our supply chain and address the explosive demand growth we see happening in our business around our distributed infrastructure, Power and Storage, which we exited the quarter with $500 million of backlog. This was done at economically prudent prices with buy aheads. Our inventory exiting the first half is up $600 million year-to-year. Given the confidence we have in our guidance in the second half, we will flush through a major portion of that inventory headwind. So that will become a tailwind in the second half. Second, the headwinds I've been talking about all year long, CapEx and cash taxes, they were front-end loaded.

So they're predominantly behind us. So where does that leave us? It leaves us with a position to have the conviction and confidence to maintain growing free cash flow, at least $1 billion, expand record free cash flow margins with free cash flow realization, important point, free cash flow realization consistent to where we were in January and throughout the year. That is going to be driven by the fundamentals of the adjusted EBITDA, that productivity, that operating leverage in the company.

So we believe with the focused portfolio that we have with a very disciplined capital allocation, the diversity of our business models you see in our guide with a relentless focus on productivity, we drive the durability of that free cash flow engine that by the way, enables that flywheel to invest for growth.

Thanks, Jim. So Amit, let me address the first part of your question. I have to start by first describing the nature of what it was that didn't happen. The majority of what didn't happen in the second quarter was large CapEx deals at large clients. So I had said previously that the volume or the number of those deals was in the low 10s. We have been very pleased to see that about 1/3 of those have already closed.

To give you a perspective, normally, we would not expect all of them to close, but we would expect maybe 2/3 to 3/4 of them to close over the next 6 months. So the fact that a 1/3 already closed in the first 3 weeks gives us an indication not yet full evidence, but a good indication. This was deferral and not destruction.

The second part that is really important. There was a lot of points, as both Jim and I pointed out in prepared remarks, where there was a lot of double-digit growth strength also. A lot of that is in the consumption part of the portfolio, and we are going to be doubling down on the demand there because the pipelines are strong and the signals from our clients are strong about where that is happening.

So when I couple that together, those, I'll call them factoids, together with what we are seeing in our pipelines, together with what we are seeing in terms of signals from clients in terms of what they're purchasing and putting in, then I come to the conclusion that a lot of the demand is deferred, not destroyed, albeit it's sometimes hard to predict exact timing on a month-to-month basis. But the fact that 1/3 of those deals closed is a strong signal. In terms of what are the milestones that we expect of the KPIs, I'm going to ask Jim to comment on that.

Yes. I think it's plain and simple. Arvind has been running this company for the last 6 years around two key components of KPIs. We just talked about one, free cash flow because that's the engine that provides the financial flexibility for growth. The first is revenue growth. We've taken this company from a no-growth company to a low single-digit company to a mid-single-digit company, and our future aspirations have always been to continue to increase.

So the first thing is we just posted 1% revenue growth in 2Q. The first indicator, we have to show acceleration as we get into the second half of the year. Our guidance range, 4% to 5% should be noted right upfront. That low end of the range we put in as an anchor, an anchor so that we can show the investment community, the level of productivity and operating leverage we have in this company that allows us to maintain earnings and maintain free cash flow at that low level.

That low level says we take a business that just produced 1%, up to mid-single digits, give or take, in the second half. That's not our aspiration. But that's an anchor as a range on how we put this in place. The high end, that 5%, that says we accelerate growth into the second half overall. Now two key components, KPIs underneath that, I would venture to say getting right back at the core second quarter.

Number one, the strength and confidence we have in our mainframe platform cycle if we can maintain this at a high 120%-plus program-to-program, that is a tremendous indicator that will lead us to the high end of that guide range. The second is our software. And that software, when you look at it at the high end of the range at 8%, that is the second half that's approaching double digits.

Based on what Arvind said, the strength of our 80% high-value recurring revenue accelerating and a return to some normal level of closure rates of a strong pipeline in the second half. So those are two KPIs.

Operator, let's take the next question.

Your next question comes from Wamsi Mohan with Bank of America.

We'll move on to the next question. The next question comes from Brent Thill with Jefferies.

Jim, I just want to go through the guidance for the rest of the year. So your prior guide was 5-plus percent. You're guiding now 4% to 5%. If you take a 1-point guide to the cut to the guide, I think it's like $650 million. You missed Q1 by approximately $675 million. So that just assumes that some of the slip deals aren't coming back yet. Arvind said that some of those deals have closed. Can you just kind of square up the guide to what this means?

Absolutely, Brent. Thanks for the question. Let me just frame the components about the guide overall. One, we said revenue guide now, 4% to 5%. On the low end, it's about 1 point plus takedown. On the high end, give or take, it's pretty damn close to where we were at. I talked about maintaining free cash flow in either case because we have built this around prudently the low end of the range to show the operating leverage and productivity and by the way, the investment capacity we have in the company to drive that level of profit, that level of cash, which, by the way, both will be in high single-digit level overall with record margins.

And that basically says we're taking up our operating pretax margins by 100 basis points. But let's unpack the 4 to 5%. Number one, underneath it, we see tremendous momentum playing out in infrastructure overall. That infrastructure guide 90 days ago, we said was going to be down low to mid-single digit. coming out of first half, given the mainframe, albeit fell short, the mainframe is still at 129%, 130% of the prior program.

It assumes the confidence that we see in all the demand indicators, and we can get into that later, that we maintain that level and that we continue to see the tremendous growth opportunity that we have prudently protected the supply chain to optimize on distributed infrastructure. So guide to guide, we're up about 2 points there.

In software, 6% to 8% for the year, the lower end was an anchor. That says nothing changes, and I'll get into that in a minute. The high end says that we basically are approaching double digits in the second half. So let me break down those two because we can get into consulting later, there's basically no change there.

When you take a look at the scenarios that we ran on software, one, as I talked about in prepared remarks, we -- and we think this is a competitive differentiation. We have a software portfolio that plays at the heart of infrastructure software that we allow our clients flexibility in how they want to buy. They could buy perpetual license models, they can buy subscription models, they can buy consumption models. And albeit although it's a very small percentage, we offer SaaS-based components, right?

Underneath that, we have -- our business is roughly 80-20, 80% high-value recurring revenue, and that is think of our portfolio, Red Hat subscriptions, our acquisitions that are mostly subscription consumption base, our Gen AI portfolio and WatsonX and parts of our data automation portfolio that operate on consumption models. Our remaining 20%, that's the piece, transactional revenue that leverages perpetual license sales, enterprise license agreements that is our mainframe moat and incumbency provides tremendous value to IBM because it creates that multiplier effect I always talk about with every dollar of hardware revenue we land, we get $3 plus of software with long-term commitments. Now how do we construct the ranges?

On the high end, at 8%, we said that, that 80% revenue, we see continuing accelerate. We accelerated from 7% in the first quarter, 8% in the second quarter, and we see this going to about 10% in the second half. That is good acceleration, and that talks to the value and innovation we bring to clients in that part of the portfolio.

In the transactional piece of the business, make it very clear, the second quarter slip deals we have, by the way, typically, we only close about 75%. Arvind stated already in 3 weeks, we're about 1/3, 40% already closed of those deals. Our high-end range says we close all of them, which, by the way, we haven't really lost maybe 1 of the deals, maybe 2, but we close all those deals.

Then we've taken a look at all of our demand indicators, propensity to buy models, our pipeline closure rates. We entered the second half with a relatively robust net new business pipeline that's growing double digits. We have, Brent, to your question, we've taken a discount off of our traditional historical -- that's why we're sitting at 8%. I think that's prudent. But back to Amit's question, the KPIs will tell how we finish third quarter, how we get in the fourth.

The low end of the range, like I said, I can't stress -- it's the anchor to get profit and cash stable to our last guide and maintain that level of profitability. That basically assumes we get modest acceleration in our high-value recurring revenue and our 20% transactional, we see similar buying behavior in yields, right?

That's not what we're seeing here, but we're basing our framework of our business model so we can drive the right management actions, the right level of productivity to create incremental financial flexibility to go invest to capture that growth opportunity. Hopefully, that helps you.

Operator, we'll take the next question.

Your next question comes from Ben Reitzes with Melius Research.

I first just wanted to see if you guys could clarify -- there's 2 lines that are really bothering folks in the guidance. It's TPP and then the mainframe and how you're going to improve from negative 9% and negative 42%. I think if you could explain that, that will make us feel a lot better about the 4% to 5%. And then, Arvind, this is a question for you is what do you -- are you still looking for double-digit growth in software long term? Do you still think you have this kind of portfolio? Or does the transactional stuff keep you from having like a 10% long-term guide for software and what you saw in the quarter?

Yes. Ben, thanks for the question. Let me actually address your question first. We and I have complete conviction in the double-digit long-term software growth. Let me be clear about that. As you heard Jim describe, 80% of our business is already -- I'll use the word annuity or in some sense, subscription-based. That piece, we have grown from extremely low numbers. I would say it was low single digit 7 years ago to 8% and approaching 10%. We already got that there.

We have a few different paths on how to get to 10%, not just 1. We can keep growing that part faster, and that can approach not just 10%, but above 10%. That means that the other 20% has to grow smaller. By the way, every single year, that mix of 80-20 used to be 60-40. So as we go over time, that mix is going to climb up and more and more is annuity as opposed to transactional. And both organically and inorganically, we are investing in capabilities that are much higher growth than this mix. We are investing in capabilities that grow more at 15% to 20%. So that helps that overall growth rate.

As an example, we brought Harshi in, and we accelerated it we brought Confluent in and we accelerated it. Our organic WatsonX portfolio is also growing at much higher rates than the aggregate. So that is the point that we are going to and we are not going to back off from our commitment of long-term double-digit for software despite the 2Q hiccup.

And I'll let Jim comment on the TP and mainframe growth rates, including the cycles that we see there.

Yes. The questions kind of go together, right? Because we run this as a same-frame stack platform economic multipliers we talk about. Every dollar we land on mainframe, we get $3 plus a software. Mainframe overall, yes, down 42%. It's off a 70% growth last year in the launch quarter overall. If you look at the historical seasonality of mainframe, it's always down in the fifth quarter as we wrap around the launch.

Now with that said, I think we were very open, transparent. It was below our expectations. But with that said, let me put this in perspective. One, we're still at nearly 130%, a record across the history of IBM of a program to program. Put that in dollar figures. From our most successful program P16 to now P17 5 quarters in, -- we're at nearly 130%. That is $1 billion more of mainframe revenue. That is $3-plus billion more of software stack on top of that versus where we were at a similar point in time in the z16 cycle. And I would tell you that's the value that we bring around innovation in mainframe, unmatched in terms of reliability, security, resiliency, scalability, AI on the chip.

That's why the mainframe handles over 70% of the world's transaction volumes in terms of value, 140 million MIPS in the marketplace that we run core mission-critical systems across every industry. top 50 banks, 4 to 5 top airlines, 7 of the top 10 retailers, over 70% of the Fortune 100 run on mainframe.

Now Ben, to your question, how do we get confidence because our guide says we stay at the high 120-plus percent throughout 2026 overall. We have looked at key indicators, installed base, upgrade cycles, propensity models, but what drives demand and purchasing requirement in mainframe? I would boil it down to three areas: one, capacity workload. It's the most important determinant. 85% of the installed MIPS capacity out there in the marketplace today, running all those core mission-critical workloads are either stable or growing. Clients are adding capacity and workload to mainframe, the viability.

And by the way, that's coming in new AI workloads, analytics workloads, Linux-based workloads. And those MIPS are growing program to date over 15% to 20% installed capacity. So that's number one. Two, economic factors. We don't talk a lot about this, but I think it's important for our investors to understand things like TCO total cost of ownership, consolidation, lease propensity.

Depending on the size and complexity of the workload, we have anywhere from a 2 to a 15x TCO advantage running on the mainframe. That's why Arvind said in the prepared remarks, we do not see any evidence of clients migrating off mainframe. And lease propensity, which is a great indicator we're actually in a very nice sweet spot as we enter second half from an upgrade.

And then finally, AI-driven value. When you look at it, applications data security all on the platform. We do $450 billion inferences per day at 1 millisecond with 8 9s availability. We've got clients that have already purchased over 50% of our Spyre inferencing. And those clients that have purchased that are growing MIPS capacity, the way we monetize value by over 3x faster than others.

So then you get to -- the reason I went through that on mainframe, how do you monetize the value of that 3x multiplier in TP. The more capacity that's in the marketplace the more monetization opportunity, the more price opportunity, the more value creation opportunity. That performance, we have all the confidence in the world and Z in the second half. But when we look at in the models that we have done, given how much MIPS capacity is out there, the first thing that we should all remember, that is a great future indicator, a monetization and revenue growth opportunity for us to go capitalize on that.

The key question becomes timing. And when you look at it, unlike the high-value innovation we bring in many of our data, automation, et cetera. The client has capability around their purchases of MLC, they could run on an OpEx model without doing a big purchase for 3 years for quite some time. But eventually, that technology upgrade cycle catches them and it moves in IBM's favor, and the economics, as you can imagine, the price differential moves in IBM's favor.

So when you look at that, we have all the confidence in the world around a growth vector of TP. But when we look at the guidance in the second half, I think prudently, when we're looking at 6% to 8%, 6% being the anchor, 8% approaching double digits in the second half, -- we've got TP down low single digits to mid-single digits overall because we're going to look at that as a 2027 growth vector opportunity for us.

Operator, let's take the next question.

Your next question comes from Fatima Boolani with Citi.

Arvind, this is a question for you, and I want to flip the -- some of the commentary on its head a little bit. So clearly, 11th hour, you had the deal slippage on the 10 of the large deals. It's very encouraging to hear that you've been able to recoup and recapture these deals in such a short amount of time into the third quarter.

So what I wanted to ask you, what exactly changed in the last 3 to 4 weeks that catalyzed pushing some of these large transactions that did slip to a successful finish line? And were there any internal or IBM-specific efforts or initiatives that you maybe took a more assertive stance with to recapture these transactions?

And as a follow-up, Jim just mentioned and you also mentioned this in your prepared remarks, 85% of the installed MIPS capacity is still stable to growing in your installed base. So conversely, what is actually happening to the 15% that is not stable to growing?

Yes. So Fatima, thanks for the question. And let's talk about this a little bit. So first, it's important to understand why this slipped? I actually believe that even our clients had every intention until about a month before that they were going to consummate these deals.

You can imagine, we do have a lot of discipline, and we do a lot of work to make sure it's a good business case, budgets exist, et cetera. I think that our clients themselves had not really thought through that some of the alternate purchase they were doing were increasing 30% in dollar value quarter-to-quarter. When they were faced with that issue, then they decided to move budget to those areas where they were having that extreme price.

Now as we have gone through and done, we have actually held our price so far in the month of July. We have not taken increases. I think that our teams are a bit more reluctant and less aggressive than others in portraying some of those increases in hardware that are coming into the market. As they become more aware that they cannot hold these prices for more than a few weeks, then that itself is going to make an impact when we play into that part as well, albeit we are nowhere near as aggressive as some of the alternate infrastructure providers.

Now on the software side, that becomes coupled onto that hardware purchase because they definitely want to leverage that hardware that they're getting from us, and then they will couple the software purchases into it. But I actually believe that the biggest part is the deferred it and then they realized, but this is mission critical.

All I'm doing is taking on tech debt. I better actually act on this once they kind of got over the compression of time they were in the end of the quarter and said, "I don't really want to get into tech debt on a platform that I'm keeping for the long term. So if I'm not going to do that, then I've got to go find the money and get this done. And so my teams can go get the work done and give us all of those abilities that Jim talked about so eloquently in the last question of the innovation we do both on Z and in other platforms.

Let's take the next question, operator.

Your next question comes from Erik Woodring with Morgan Stanley.

So Arvind, I want to touch on the data segment within software. And specifically, I think if you take your really bullish comments on Confluent accelerating you get to organic data revenue growth that was down year-over-year, maybe mid-single digits in the second quarter. And that just stands out from the rest of the portfolio, the non-TP portfolio. So can you maybe just help us better understand exactly why that kind of organic data part underperformed the rest of non-TPsoftware? And why IBM's data software of all things maybe got deprioritized this specific quarter, this early in the AI build-out.

Erik, thanks for the question. First, let's understand, as we said, in aggregate, about 400 to 500 of the miss was in software overall. And when we think of those large CapEx deals, while it is majority, but I think majority is being 50% to 60% TP software. There is a lot of data also in there. It actually has less of automation because a lot of the automation portfolio has already moved to being subscription base with things like Hrashi and Apptio.

So as we think of that, data takes what I would call maybe the run by shooting. So in the run by shooting, data gets hurt because people are buying less of it, and that impacts that. Now if I take the next 12 months and if I look at what both talked about and where we see the demand signals, I fully expect data to perform well and to give us that tailwind that we are wanting there. Also at the end of the day, clients have a certain number of dollars. So if Confluent looks more attractive than other things, they are going to buy more of what is the thing that gives them the most near-term value and we are fine with that.

It gives us incumbency, it gives us a footprint and we can go back in there in the next quarter and the quarter after to get other things in case that there is an aggregate budget pressure. After all, we do have a large share of wallet at many of these clients. So there is a question of how much they will spend here, but I fully expect that we will do well in the data portfolio.

Operator, let's take one last question.

And our last question comes from Matt Swanson with RBC.

Great. Arvind, you've been really deliberate about the areas of security that you've invested in over the last few years. When we're looking at Project Lightwell. Could you just talk about, I guess, post MFOs, kind of the breakdown, one, how big of an opportunity this is? -- as obviously an area open source, you have a lot of expertise but also how important solutions in this area in general are for you as the largest enterprise provider of open source software but kind of protecting the ecosystem you guys have worked so hard to build out.

Yes. So Matt, thank you. Look, if I look at this first, let's step back and look at open source versus proprietary. If I look at the amount of running code out there, I would pause it to you that open source is now larger than all proprietary code in terms of the volume of software that is there.

At the same time, the number of people who have been able to commercialize it or monetize it is limited. I think also, Matt, we should be careful. We say that, but a lot of the hyperscalers, a lot of the infrastructure out there does actually run on open source, albeit with modifications by those providers. So now the question comes, open source is quite good at remedying the last version, maybe the version minus 1, but those movements sort of 6-month cycles.

Once an enterprise is getting value from an application, that tends to run for 7 to 10 years. We looked at this historically, and we said that is a place that we want to go play. 5 years ago, it would have required an army of people to be able to go do it. You would have required people with expertise in each of those pieces of open source. You would have had to build those build environments. You had to keep those committers engaged and productive. And so you look at that and you say, not clear that there is an economic return.

So the same tools that are being used to attack all code, both proprietary and open source can also be used to effectively be those experts and build those out. So what we did was we built kind of a factory using AI models to be able to patch open source, even when it is not one that we have human expertise in. But as we do that, we're actually not patching it. We are running test harnesses against it. We've been able to evaluate it in the environment that our clients need and want, so we can do all of that.

Your question on what is the addressable opportunity? Look, this is early days. How about if I characterize it, we believe that it's in the multiple billions of dollars whether it turned out to be that the TAM for the open source protection here is 5 or 10 or 15, it's in that range of billions of dollars. And here, I will tell you, we are going to measure ourselves in terms of how many hundreds of clients we can get signed up over the first many months.

That is how aggressive we want to get, and we believe that, that becomes a flywheel that multiplies because as we get more and more clients signed up, we learn a lot more also about what they want as remedies. And as we learn what they want as remedies that makes our network effect on this grow more and more.

Let's remember, we put out 7,500 packages of remediations in the first 3 weeks. We believe, by the way, that's not counting the 30,000 packages we already do with Linux and Harshi and Confluent and Kubernetes and OpenShift, et cetera. So in aggregate, we believe that this is a multiple billion dollar opportunity, which we're going to go after really fast and hard, leveraging expertise in AI and open source both.

So Look, thank you all for these questions. I thought it's very fair and very appropriate questioning. Is the business coming back? Is the cash flow there, how are you going to grow in the various parts, what's your long-term commitment? How strong is the mainframe incumbency. So I do really thank you all on these questions. We remain confident in the growth opportunities ahead of us and the actions we are taking to improve execution. I look forward to sharing our progress with you as we move through the rest of the year.

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

Thank you all for participating on today's call. The conference has now ended. You may disconnect at this time.

[indiscernible] Bank of America Global Tech Conference. Delighted you could all make it. See a lot of familiar faces. Thank you all for joining us again. I'm Wamsi Mohan. I cover IT hardware and supply chain for Bank of America. Delighted to welcome IBM here today. Representing IBM, we have Ric Lewis, who is Senior Vice President of the Infrastructure Group.

So Ric, welcome. Thank you for doing this with us. Very exciting time to be in infrastructure. It's the place to be seems like when every day you hear more and more CapEx dollars are being spent on infrastructure. So I would love to get your thoughts on what you see how IBM's position is in infrastructure to kick it off?

Yes, sure. So I've spent the last 37 years in infrastructure. It's never been more fun or more exciting. There's no doubt about it. And I think the strategic nature of it and how important it is to every boardroom and where the future is going and how it's changing every business, not just IT businesses, et cetera, make it pretty exciting. I think interesting thing is over time -- there was a time when IBM was thought of as an infrastructure -- boxes kind of a company. And IBM has grown to be so much more, consulting and software and lots of organic things going on and lots of acquisitions, et cetera. But at its heart, we still do a lot of infrastructure and our Z, Power and Storage. Now you're hearing about quantum and all the things that go with that or IBM Cloud as well.

So we're seeing similar momentum that you're seeing in some of the infrastructure companies that are all over the airwaves here in the latest few weeks, especially. AI Lift is a real phenomenon going on in the industry. Even if you say, well, it's all about GPUs, it's not all about GPUs. I would argue it's all about data. And that data needs moved. It needs stored, it needs fed, it needs processed. It's also about agents, and those agents need coordinated. They need run, and they're not all on GPUs. So there's just a lot of AI Lift is what I would call it in the industry, which makes it really fun to be in infrastructure and IBM is a big part of that.

Yes. No, that's a great point. So maybe on the AI uplift, right, as you think about some of the places where you're observing that in terms of -- historically, you've seen a lot -- let's start with Z, right? So Z, you've seen many cycles in the past. I know you alluded to at your Investor Day as an example, like different types of MIPS have different value. And AI MIPS is something that you saw coming. Can you give us some sense of like how that's being monetized?

Sure. So first, when we transact the mainframe, the Z business, we sell MIPS. We don't actually just say, here's a box. We're going to charge you X for a box. We sell it by the MIPS. So throughput is what matters. And in the last several generations, 3 plus, we've been building momentum on that. And I would say 3 generations ago, it was more about more capacity needed or technology refresh is what drove a given cycle. And even in the last decade, that first wave, we were growing kind of 110% program to program. Then in the last one we put AI, the initial AI, we had AI and Z before there was a ChatGPT or any of that. Clients were using it for more machine learning type fraud detection, et cetera. We saw Z's growth rate jump to 120% to 125% program to program on a given program. And now with this latest generation where we have Spyre and all the AI capabilities that we've built in from scratch and workloads are moving beyond kind of fraud detection to more multidimensional things from code development to being able to do inferencing on things like insurance predictions and actuary tables, all that kind of stuff, Monte Carlo analysis, whatever, all of those things inside of Z have now accelerated that growth program to program. We're seeing in the latest in z17 at 135% program to program.

So not only has the Z mainframe franchise been growing, its growth is accelerating, not decelerating. And so that's just Z that we're seeing tremendous AI uplift. And I think Part of that is the industry. But quite frankly, I'd like to selfishly say part of that is us. We invested early and often in building those technologies that were really suited well for the AI wave that has now come. And one thing I want to say there is the cool thing about Z is our customers, our clients help codesign it. They tell us what they need. And by telling us they needed to address the $1 billion fraud industry and by telling us they needed to be able to do real-time processing, Swiss Mobiliar, for example, up their throughput of insurance claim approval kind of application things by about 7% to 10% by using the built-in inferencing going on.

So by telling us what they needed to do, it's forced us to kind of develop new technologies like AI and Z and provide a tremendous lift. So we're really excited about Z. It's the biggest part of my portfolio. It's not the only part. There are other significant parts, but that biggest part is just exceeding expectations left and right, including in Q1, we had our biggest Q1 ever of Z at this phase kind of as the last quarter of a given introduction year. So really great momentum.

Yes. So maybe just to touch on this a little bit more, right? So as you think about the growth in infrastructure, it used to be that we used to talk about infrastructure kind of being flattish through cycles. That seems to have changed.

It has definitely changed. Yes. And it's something we're super proud about and excited about. I came into the group 5 years ago, and we had one very simple big hairy audacious goal, and that was, 'let's have this thing grow and let's have it grow across all dimensions,' across Z, Power, Storage, across our cloud and across our support business, which is associated with all that. And we're doing that now. We're low single digits. We have aspirations to continue to accelerate that. I think some of the things we're seeing with the AI Lift that I talked about before, make us feel like I think we can accelerate that. But it's taken making sure that all of those pieces are growing and carrying their weight in the overall portfolio.

I think a lot of people look at IBM and they don't realize the depth of technology stack that exists on something like Z, on something like Power where it goes -- it's pretty vertically integrated. Maybe if you want to spend 2 minutes on that just because I feel like a lot of people don't necessarily put all of those things together as owning those pieces of the stack that are so valuable.

It is a big part of -- the last question was about monetization. That's a little bit of how we monetize. We don't have intermediate pieces of the stack that can say, well, we want to take all the value out of that. It's our stack, it's our chips, it's our silicon, it's our boards, it's our firmware, our operating system on top of that, and it's our application software, whether it be database kind of capabilities that we have on Z or things that we load on our other platforms. And Z is the obvious case. You'd say, well, that's kind of a closed ecosystem and is self-contained. But even with Power, we have MMA, matrix math acceleration, going on inside of our power processor. We put Spyre cards inside of our power machines. So again, a full stack kind of delivery thing.

Now up at the top level, we support multiple OSs. So we don't close that whole ecosystem. We run UNIX capability with our AIX and obviously, SAP. But we also run databases natively on that. And we also integrate our container platforms into that. Yet another area that's really growing well in the AI area is people are doing new applications. They don't want to do them in the old virtual machines. They want to do them on containers. So we have a ready built full stack power system that has containers built in. Similarly, we have storage, AI in a box that has containers built into it. So we really try to provide that differentiation at the full stack and make sure that we can provide a differentiated experience that helps with clients. So it's something we're really good at. It's fun for me because I look and I go, 'Oh, chips are hot right now.' We do chips. 'Oh, boxes are hot right now.' We do boxes. 'Oh, operating system work and containers are hot right now.' We do that. So it's really a good thing for IBM.

Maybe just to think a little bit about AI and the impact that it's having on the business and maybe on this cycle. As you think about monetization, right, it looks like there's a lot of pricing that is driving the results of a lot of these peer companies in infrastructure because of shortages, because of various reasons. How are you thinking about pricing from an IBM context, from an infrastructure context? I know that some of the commodity pricing is obviously being reflected in a lot of the revenues that other infrastructure players are reporting. But how do you think about where commodity pricing is and where your pricing is and maybe there is a disconnect between the 2 also.

Sure. So a few thoughts on that. So first, let's maybe just try -- let's start with supply and then let's work to kind of demand and pricing. So supply-wise, there have definitely been some shocks in the ecosystem, just dramatic demand increase for memory, dramatic increase for disks and things like that. For us, one, we're really good at supply chain management. We've been doing it forever, over 100 years. We even use our own AI technology in our supply chain management. So we're great at it.

Second thing is when you're selling systems that are not, I'll just say, commodity, I mean, we're doing a full stack, we're doing differentiation, et cetera; that means we have more margin in those systems. That means we have more margin to move when we need to apply it to getting supply for those kind of things. So that's really a powerful dynamic for us. And we use that when we need to.

But what's interesting and that I love about this whole situation is it has actually changed demand as well. And what I mean by that is when memory costs have gone up, when CPU costs have gone up on the rest of the market, that means people want to be a lot more efficient, even hard drives, when hard drives go up, my tape business is now growing double digit pretty reliably because people need a place to put bits and disks have gone through the roof and tape looks pretty economical and affordable. So all of this cost and supply chain shock has actually resulted in demand for my power servers because we make better use, frankly, of memory that you put in a box. We're more efficient with how we utilize that. Increased demand for storage, it's increased demand for tape, it's increased demand for the software, the integrated software that is put. So all of that generally has been a good thing for us. Then you add on to the fact of, okay, you're a value supplier, so you can price appropriately. And when the commodity prices for memory and things increase, then we adjust our price appropriately. We make sure that the clients understand we're pricing for our value and here's the part that is the component increase cost, and they're happy to get our systems that make better use of those expensive commodities. So we end up in a win-win. So it actually doesn't hurt us at all. It actually helps us a little bit.

Have you seen enterprises -- I mean, from what you're saying, it seems like enterprises are changing their buying decisions. But are you seeing any shift in demand acceleration because people are worried about not being able to get access to the compute power or the storage or anything else that they need?

I think it is motivating -- it's interesting. There's 2 parts of that. Is it pull forward? I don't know. I think there's a big enough wave here that we and the other infrastructure vendors aren't thinking this is pull forward. This isn't -- well, I'm not going to say I'm buying everything I need for the year right now. We think it's actually true increased demand. And that increased demand in the long term results in what kind of looks like a pull forward, which is really, I better get some now because I'm going to need more rather than I better get some now because I won't be able to get it. And that's a really important distinction. It's more of the former. I better get some now because I think I'm going to need more, and that's what we're seeing as a behavior from clients.

Yes. And it seems very consistent across the board, like I think everyone seems to think that there is just a very strong demand environment because there are use cases to deploy on this infrastructure that if you don't, you get sort of left behind and there's a sense of urgency to go ahead and deploy this.

Absolutely. I mean businesses are being disrupted daily now. AI has moved from experimentation to production. And not in wholesale, we're still early innings. But it is definitely in production now in a lot of companies, and those companies are disrupting other companies. And so it's an imperative to make sure that you're on the curve.

So maybe talking about disruption, I think there were some worries, maybe transitory, but just on the fact that AI potentially can help refactor some of the code base on the mainframe and what does that eventually do to customer stickiness or usage of the mainframe? How do you think investors should think about that?

This is one of the most fascinating topics. What -- I think at times, people are tempted to say, 'Oh, the reason people are on a mainframe is because you can't translate all that COBOL code.' We've been translating COBOL code now for 3 years. I'll give you an interesting factoid, but people who are doing Watson Code Assistant for Z, which will help you comment and transact -- and translate COBOL code are actually growing at faster MIPS utilization than any of the other customer database, kind of like 2 to 3x faster. What we're finding is actually 2 things. One, fit for purpose is what rules kind of the space, just like you want to do AI transactions in the GPU because matrix math is key to AI inferencing and training and all those kind of things. If you want to do high throughput transaction processing at a global level for the most fastest, most secure, most resilient platform, it's a mainframe. So that's what you use it for. It's not about, 'Oh, COBOL keeps it so I can't move or whatever.' We have clients translating COBOL to Java and other things every single day. It's about I can do a lot more transactions. They are a lot more secure, and it would cost me a ton of money to try to do all this in a cloud environment.

So we don't see that. What we actually see is what might have been perceived as a barrier to exit might have been a barrier to entry. And what I mean by that is I'm worried about skills. What if I end up some of these code -- now I'm not saying people are bringing new COBOL a bunch of that stuff. But what they're saying is if this is the world's fastest transaction and most secure and most resilient, stays up all the time platform, my only worry is, 'Well, I don't want to write COBOL.' Well, you don't need to. You can write whatever you want. Our Linux system or what we call a specialty MIPS, the noncore old-school database MIPS has been growing 3 to 4x faster than the core MIPS for a long time, and that's because clients put other workloads on that. And as the cost of Linux-based machines, AKA x86 boxes running Linux has gone through the roof, Cost of Z hasn't gone through the roof. It's similar to what it was. So that Linux value proposition on our Z looks even better than ever, and it's still the fastest transaction processor and secure environment never goes down. Don't have constant patching to try to deal with all that to make sure that you're up on the latest security.

So that's what I mean by this barrier to exit or whatever turned out to be -- another example, our IBM i, we're seeing a massive resurgence. This is the old AS/400 running on our power systems, massive resurgence in new workloads landing on IBM i because people were worried about 'Well, I don't have RPG code expertise.' You don't need RPG code expertise. We now have software tools that can write whatever language it is you want. You just say what application you want on an IBM i box running on Power. And you don't have to worry about the ransomware attacks that you get on x86, you don't have to worry about the machine going down. You don't have to do all that. So we're seeing a significant growth, not just double digit, but higher double digits in IBM i AS/400 business right now because of this barrier to entry being dropped by. Now we have programs, including IBM Bob that do this really, really well and allow people to put new workloads and new applications on top of that infrastructure that's so reliable and secure. So it's actually working out well for us. We're happy about it.

So on that point of like where you -- I think that's super interesting that the people who have run Watson Code Assistant for Z are consuming like MIPS at a higher rate. What applications are they now running that they weren't running before?

It's a variety of things. A lot of container kind of loaded applications, a lot of Java. It turns out that a world-class transaction processor runs Java extremely well, and it runs a gamut. Usually stuff that -- remember, you kind of have this fit-for-purpose ecosystem depends on what your core thing is. Are you processing insurance claims? Are you processing bank transactions, credit card transactions, et cetera. It will be the surrounding stuff that latency gets a good benefit of being co-located to that is typically it. But we have other people -- we have clients. We've referenced publicly some big banks that say, 'Hey, I have a mainframe estate. I'm just going to do server consolidation of generic non-mainframe-related workloads on Z Linux systems because I don't have to manage data centers full of racks of stuff that need patched and updated, et cetera. I get 2 mainframes and I can run all those workloads and I'm home free. And it fits the same profile I have for all the transaction processing that I have going on in the organization.' So it's a little bit of everything surprisingly.

Yes. Yes, that's really impressive. Maybe just to talk about sort of the broader portfolio, right? So when you think about -- you mentioned the strength across Power. You mentioned the strength across Z. How are you seeing this Z cycle maybe different from prior Z cycles, if there's any compare contrast on either length of cycle or on magnitude in terms of from where we are today -- you already had like a year of very strong Z performance from where we are today. And then maybe just touch on storage as well because -- and maybe infrastructure support, just to make sure that...

Sure. Yes. Excellent. So let's start with Z. Momentum has been accelerating, meaning we've had good acceleration each year. It's compounding now, and so extremely strong. Why do I think that is? I think -- we had talked about the core MIPS, the database MIPS that we sell and that specialty MIPS were growing at 3 to 4x. Wamsi, I've talked to you before and other analysts about this new emerging category of AI MIPS, which basically has to do with clients that are actually adding new workloads like a bunch of inferencing on their insurance processing that I talked about before or a bunch of inferencing on bank transactions or actual customer transactions that are going on in the systems. They can do it at near real time. With Spyre, they can do multimodal AI, so not just kind of transaction focused, but let's say, you're trying to figure out if something is fraud and you can use -- the typical pattern is, okay, 2 deposits of something small, 2 withdrawals of something small, then 1 withdrawal of something really big. Well, now you can mix that with multimodal with Spyre cards, et cetera, where you can say, 'Okay, I see this transaction pattern. Does the entity that's running those transactions have a web page. Are they a physical entity? Do they have a real address? Or is it a PO box? Or is it a website in the middle' -- so things that you wouldn't generally -- it's more contextual, I guess, about what's the company that's trying to do these transactions. You can do that.

So all of those means more workload. That means we're selling watsonx. That means we're selling Spyre cards. That means -- and those Spyre cards have subscriptions, et cetera. In fact, one really cool thing is for those specialty MIPS, the Linux workloads that is containers and that kind of thing, we see 3 to 4x multiplier of Z stack revenue. For these AI MIPS, we're seeing greater than the 3 to 4x, higher 5, 6, 7, 8 kind of multipliers on the MIPS that we're selling for AI. So that's just more in the ecosystem, more software sold, more cards sold, more subscription, more services, et cetera. So that kind of tells you the Z story.

On storage, I think that you've seen the industry catch up on, 'Oh, it's not just going to be chips and GPUs. It's going to be boxes, too,' because somebody's got to run the agent, somebody's got to feed the data. I think the industry hasn't caught -- and memory, memory companies exploding, going big right now. I think the industry hasn't caught up on -- it's all data. There's tons of data, training data that you want to keep track of, data that you need to feed, sometimes redundant copies of data. We say you don't have to make redundant copies in our system because you're not moving it to a cloud or whatever you already have that data. But that data needs stored, and we're definitely seeing huge stored and fed, and we're seeing a huge pickup in our storage along those lines. We have our Fusion platform, which is AI in a box that makes it easy. We have Scale, which is the -- IBM Scale, which is the best way to feed NVIDIA GPUs.

We have -- in fact, the National Oceanic and Atmospheric Association does that for worldwide weather, and there's no other platform that could feed these GPUs at a level that we do. And so storage software is big. But as we move that storage software, there's a huge amount of storage hardware that goes with that. On mainframes, it's our DS8000. In the distributed space, it's our flash systems, but that stuff is growing double digits and north. And in fact, the stuff that our software runs on, so Flash and Scale and those things, we see that growing at like 50%, 60%. So storage as an industry is kind of quiet. It hasn't gotten this big pop that chips and boxes, server boxes have got, but I see that coming because I see the demand there.

And then all of that needs supported worldwide in a global kind of way. That's our TLS support business, and we've been really happy with that. TLS, if you watch our results, it will look kind of strange this year where you're like, 'Hey, that seems to be struggling.' A lot of that is mainframe refresh. When people buy their new set of mainframes, which are overachieving, they get a warranty. That warranty, then they're not paying me for service on it. It came bundled with the system when it comes. So it takes a little while for the TLS part of that business to kind of go back up again. So we're watching for that and feel really confident in it.

And then just on transaction processing sort of as we think about -- in mainframe launch years, typically, we've seen a little bit of deceleration in transaction processing. Through cycle, it sort of picks up. So are we kind of at that point in the cycle where we should be expecting transaction processing to pick back up?

Over the last 3, 4 generations, and we see no reason for it to change, flattish in the year of intro, single digit in the next year, which is this next year and then accelerating beyond that in the third year, then we ship again. Oh, and the cycle on Z, I often get that question. We've settled now. I mentioned that we have over 100 clients that help us codesign the next generation of Z. And what we figure out -- we have a client design council and all that. So they basically influence -- they don't even just influence us. They vote on what they want us to do with that platform. We're locked in on a 3-year cycle at this point. It's too disruptive to them to say, 'Well, we're putting this and that in, maybe this one will be 5 and the next will be 2.' They know, 'Okay, you can plan on that, we can plan on that.' So it's kind of -- it's a heartbeat. And we even structure our design cycle that way. It's a release train. You're either on the train or you're not on the train. It's coming out in that second quarter of the third year. And so that's very predictable. So that kind of tells you. We know what's going on with TPS, and it's tracking the way we'd expect it to.

Yes. I know we've only got like 2.5 minutes left, but I want to hit on a big topic that something we didn't touch on. Yes, like what are you excited about the future?

A whopper is quantum. And we've been quietly excited about it inside the company, but we've now been talking a lot more about it and announcing some really big things. So certainly, most of you probably saw our Anderon announcement, our partnership with the government. They're investing. We're investing to create the world's first quantum foundry. You say, what's a quantum foundry? Well, somebody's got to do chips for quantum. And those chips are like silicon chips. They're still silicon, but they're not digital. They're not ones and zeros. It's the analog technology that you have to build to be able to do quantum computing. And it's the first of its kind. I think it's really awesome because it shows that we're ahead of the industry. They wouldn't be betting on a second-placed player. So that's really good.

We've announced that this year, we expect to show Quantum Advantage. For those of you who haven't been following the space, that basically means solving some problems that you can't solve on traditional computing in any kind of reasonable time frame. And we've got various clients that we've talked about publicly and a bunch that we haven't talked about publicly that are on the cusp of that for various science problems in medical research and chemistry and financial, I think advanced Monte Carlo kind of simulation stuff. So that's something we're really excited about. So we've talked about Quantum Advantage this year and then the world's first 2,000 qubit -- qubit is kind of a notion of these quantum state elements and first 2,000 quantum qubit fault-tolerant machine by 2029, which is when we think it's kind of -- that's its moment of, 'Oh, this is a real volume thing and cranking along.' So we're excited about it inside of IBM. Arvind likes to say it's no longer a science problem. It's an engineering problem, meaning we know how to do what we need to do, and we need to go execute it, and we're executing it, and we're on schedule to execute it. We just need to follow the steps. We can see, okay, this is where we'll be in the FET technology, the error correction technology, the cooling technology, and we're on track to go deliver that. So quantum is going to be a real thing this decade. And we already have 90 systems with clients doing quantum development on our Qiskit software, pretty excited about that. If you think about how AI team, kind of with its ChatGPT moment, there was a lot of stuff going on with GPUs and CUDA and that kind of stuff. That's the stuff that's going on right now with Quantum and Qiskit is developing, experimenting universities, medical centers, all those things. So really excited. It's good to...

Well, thank you so much, Ric. I really appreciate you being here. Thanks for bringing all your insights as usual. Always a pleasure.

Thanks, Wamsi. Always nice talking to you.

Thank you so much.

Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time. Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations.

Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's First Quarter 2026 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer.

We'll post today's prepared remarks and a replay of today's webcast on the IBM investor website within a couple of hours. The earnings presentation is already available. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website.

Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings.

So with that, I'll turn the call over to Arvind.

Thank you for joining us today. Let me start with our first quarter results and then provide context on what we are seeing across the business. IBM is off to a strong start to 2026.

Revenue in the first quarter grew 6% and combined with strong margin expansion, drove 13% growth in free cash flow. These results reflect the durability of our portfolio, the mission-critical nature of the work we do for clients and the continued execution of our strategy.

Let me first touch on the macro. While we are operating in a dynamic environment, Middle East developments didn't impact us in the first quarter. Uncertainties remain, but our diversity across businesses, geographies, industries and large enterprise clients position us well. Conversations we are having with clients remain consistent. Enterprises are investing in capabilities that increase resiliency, productivity and accelerate growth.

They are modernizing core systems. They are scaling AI and they're making deliberate choices about where workloads should run and who controls the infrastructure underneath them. These are structural priorities and they align directly with IBM's strengths. This quarter's performance reinforces the strategic choices we have made over the last several years to advance IBM as a software-led hybrid cloud and AI platform company.

Software revenue grew 8%, with Data and Red Hat growing double digits. Infrastructure grew 12% with another record Z quarter, up 48%. We also had strong performance in Distributed Infrastructure as generative AI increases demand for our storage offerings.

Consulting grew 1% with momentum in enterprise data and business application transformations as clients modernize to deploy AI securely and at scale. The durability of our portfolio is a defining feature of IBM today. Let me spend a few minutes on AI. Enterprises are still figuring out where to deploy this technology and where competitive advantage truly sits.

Every major technology wave has followed a pattern. Value begins with infrastructure, moves to enabling platforms and ultimately concentrates in the workflows where businesses operate. Right now, the spotlight is on foundation models. Enterprises are building portfolios, frontier models for some workloads, smaller models running on-premise for others and open source models where control and flexibility matter the most.

Enterprises will want to retain control of the proprietary data. AI will run everywhere across public cloud, private and sovereign clouds and on-premise. The core challenge is making all of this work together. This includes orchestrating across models, agents and workflows, governing enterprise data and securing these systems at scale. And that is exactly where IBM operates. We are building the platform that lets enterprises put AI to work on their terms, wherever it runs, whichever models they choose and under governance they control.

Our portfolio is built around world-class security, support and integration for an enterprise environment. Red Hat provides a common open platform that lets enterprises run applications in AI consistently across any infrastructure. More AI adoption means more demand for open flexible infrastructure. In automation, the logic is similar, agents multiply applications, integrations and execution paths. Managing that sprawl requires a controlled plane to provision infrastructure, integrate applications, secure environments and manage cost. This is what our end-to-end automation portfolio provides.

In an AI-driven world, security risks are rising. IBM Concert identifies vulnerabilities proactively and automates remediation, helping enterprises maintain resilience at scale. Our data business is seeing similar AI tailwinds. AI is only as good as the data it can access. And increasingly, that data is not static. It is generated continuously across transactions, applications and interactions. To deliver real-term AI outcomes, data must be available in motion, governed and delivered securely to models and agents wherever they are running.

Confluent, which we closed this past quarter, solves that directly. It streams live, governed data to models and agents across the hybrid environment. And the orchestration layer ties it together. In a multi-model world, clients need to route between models, manage agent workflows and maintain governance. That is what watsonx Orchestrate and our watsonx platform deliver. We have also created AI additions of critical software products like Db2, Cognos and MQ. These embed agentic AI that can reason, act and automate at scale while preserving IBM grade security and trust.

Infrastructure remains a critical differentiator as AI moves into the core of enterprise operations. IBM Z delivers the lowest unit cost architecture at scale for workloads that require end-to-end encryption, continuous availability and ultra-high throughput. Clients rely on our Z platform to process billions of transactions reliably with 6 to 8 9s of availability.

They run AI inferencing directly in line with those transactions. Our Spyre accelerator lets clients run AI on 100% of the transaction volume without moving data off platform, allowing them to embed AI directly into their transaction flows. Financial services clients are using this for real-time fraud detection, saving tens of millions of dollars. At the same time, AI-assisted modernization, including code understanding, refactoring and API integration makes it easier to evolve applications without compromising the guarantees the platform provides.

Our watsonx Assistant for Z made available over 2 years ago to help clients preserve the architectural strengths that deliver resilience, security and scalability while making the platform more productive. Clients who have deployed watsonx Code Assistant for Z are growing MIPS capacity 3x faster than those who have not. In consulting, AI is both a growth driver and a productivity engine. As agents take on more work, delivery becomes faster, more software driven and more scalable.

IBM is leading into the shift through our consulting advantage platform and unique integrated value sitting side-by-side with software. This helps clients operationalize AI while improving our own efficiency. Demand continues to accelerate as clients move beyond experimentation and focus on transforming applications, data and workflows to embed AI into core operation.

All of this allows us to drive value for clients. ServiceNow is leveraging watsonx for automated data quality and observability to deliver AI-ready data and code generation to refresh legacy applications to modern application run times, including ServiceNow. Visa continues to work with IBM on ongoing software and data modernization efforts supporting the scale, resiliency and performance of VisaNet. With Nestle, we are using NVIDIA accelerated watsonx.data to embed AI directly into core order-to-cash operations, enabling faster real-time insights across Nestle's global supply chain.

This highlights how quickly we can bring research to bear for commercial value. Nestle was ideal for this proof of concept because of a strong digital backbone. In infrastructure, clients such as NatWest and RBC are modernizing their mainframe environments using AI and automation capabilities, including watsonx Assistant and watsonx Code Assistant for Z to improve resiliency, security and developer productivity.

We continue to accelerate organic innovation. IBM Bob, our AI-based software development system, is now generally available. Our entire developer workforce is using Bob with average productivity gains of 45%. Bob automates the full software life cycle from legacy modernization to security using specialized agents and multimodal optimization. It drives developer productivity and predictable costs.

We also introduced Sovereign Core software that lets organizations run AI workloads under their own operational authority within a defined jurisdiction and auditable controls. [ PC ] sovereignty becoming a bigger factor in where and how workloads run. Every enterprise and every nation is waking up to the same reality. They need AI and cloud infrastructure they control, infrastructure no one can turn off or tamper with because of geopolitics.

During the quarter, we also announced strategic collaborations with NVIDIA, expanding our work across GPU native analytics. In addition, we announced a strategic collaboration with ARM to expand how AI workloads run across IBM infrastructure. By enabling the ARM software ecosystem within mission-critical environments like IBM Z, clients can scale AI closer to the data while preserving the security and resilience they require. These partnerships reflect our approach open, flexible and all the infrastructure clients choose.

We continue to make progress in Quantum and remain on track to deliver the first, large-scale fault-tolerant quantum computer by 2029. Here are some signposts of progress. In March, researchers used IBM Quantum hardware to simulate a 300 atom system with the Cleveland Clinic, demonstrating that quantum computers can serve as reliable tools for pharmaceutical discovery. Another team accurately simulated real magnetic materials. Magnetism is central to new forms of energy and electrification. These are significant demonstrations to date that quantum computers can serve as reliable tools for scientific discovery.

We also released a new blueprint for Quantum-centric supercomputing that outlines the architecture for integrating quantum and classical systems at scale. We strongly believe that our partners will achieve the first examples of Quantum Advantage this year leveraging IBM hardware.

In closing, we are executing on our strategy of accelerating revenue growth and delivering higher profitability. Given our strong start to the year, we remain confident in our ability to sustain revenue growth of 5% plus and grow free cash flow by about $1 billion this year. With that, let me hand it over to Jim to go through the financials.

Thanks, Arvind. In the first quarter, we delivered 6% revenue growth, 140 basis points of operating pretax margin expansion, 17% adjusted EBITDA growth, 19% diluted operating earnings per share growth and $2.2 billion of free cash flow, growing 13% year-to-year, representing our highest first quarter free cash flow in a decade and free cash flow margin in reported history.

This performance reflects the work we have done to strengthen our software-led platforms, deliver innovation, value to clients and the durability of our financial model. Now I'll dive deeper into our segment performance.

Software revenue grew 8% marking a strong start to the year. This reflects the diversity of our portfolio, ongoing GenAI innovation, continued shift to higher-growth end markets and flexible consumption model. Our ARR was solid at $24.6 billion, up 10% since last year. Data revenue grew 16%, fueled by demand for our GenAI products, strengthen our strategic partnerships and inorganic contribution from data stack and Confluent, which closed in mid-March.

Red Hat growth accelerated 2 points sequentially to 10%, largely driven by the stabilization of consumption-based services revenue growth that we expected. OpenShift is now $2 billion ARR business with strong growth. And virtualization continues to gain traction with over $600 million of contracts signed since the beginning of 2024.

Automation grew 7%, with February marking the 1-year anniversary of the acquisition of HashiCorp. Over the last year, we have seen record HashiCorp bookings, leveraging IBM's go-to-market scale and achieved adjusted EBITDA accretion ahead of expectations. Transaction processing grew again, up 2% as we monetize on a strong Z17 program. In infrastructure, our revenue grew 12% this quarter, with hybrid infrastructure up 25% and infrastructure support down 6%.

Within hybrid infrastructure, growth was broad-based with strong demand for our offerings across IBM Z, Power and Storage. IBM Z continues to outperform prior programs, growing 48% this quarter. Clients are investing in IBM Z as they modernize mission-critical workloads driven by requirements for resiliency, security and compliance, while enabling new AI capabilities on the platform.

Distributed infrastructure grew double digits with strength in both power and storage. Our growth was driven by demand for [ Power11 ] with its resiliency and performance advantages supporting data-intensive workloads. In storage, growth reflected strong adoption of our new flash offerings introduced in the first quarter, which incorporate industry-leading agentic AI capabilities.

In consulting, our revenue grew 1% this quarter, reflecting momentum in the business as client demand continues to shift towards enterprise-wide transformation. Signings returned to growth, up 6% with strength across our application and data transformation offerings, driven by clients modernizing their environments to support AI adoption and capture value.

Revenue growth was balanced across the portfolio with both strategy and technology and intelligent operations up 1%. Generative AI is now firmly integrated across our consulting engagements, representing about 30% of our backlog. This reflects how generative AI has become embedded in the work we do. Our differentiated asset-led delivery model continues to drive productivity and speed to value, combining deep domain expertise with software automation and reusable assets to help clients deploy AI securely and at scale.

Let me now discuss profitability. Several years ago, we set an ambitious objective to reinvent our enterprise operations for greater speed, lower friction and structurally lower cost. Through disciplined execution, eliminating manual touch points, simplifying processes and applying data, automation and AI at scale, we have built a proven repeatable AI-enabled transformation engine that is accelerating.

Since 2023, this has driven $4.5 billion of productivity savings, with an additional $1 billion expected in 2026. Our success is enabling us to accelerate investments in innovation, strengthen our competitive advantage as [ client zero ] and fuel our growth flywheel while expanding our margins. You can see this in the results this quarter with productivity revenue scale and mix driving expansion of operating gross profit margin by 110 basis points, adjusted EBITDA margin by 170 basis points and operating pretax margin by 140 basis points, all ahead of expectations.

Segment profit margins expanded by 720 basis points in infrastructure and 60 basis points in software. Consulting segment profit margin declined modestly, reflecting currency headwinds from geographic mix of the business and the reinvestment of productivity gains amid an improving demand environment. In the quarter, we generated $2.2 billion of free cash flow, up about $300 million year-over-year.

The primary driver of this growth is adjusted EBITDA, up about $600 million year-over-year, partially offset by higher net interest expense and increased investments in CapEx as we expected coming into 2026. We exited the first quarter with a strong liquidity position and a solid investment-grade balance sheet with cash of $11.8 billion.

We invested $10.5 billion in acquisitions, driven by the closing of Confluent and returned $1.6 billion to shareholders in the form of dividends. Our debt balance ending the quarter was $66.4 billion, including debt of $12.8 billion for our financing business, with the receivables portfolio that is 80% investment grade. Let me now pivot to discuss our expectations going forward. The strong start to the year drives our confidence in delivering constant currency revenue growth of 5-plus percent in 2026 and free cash flow growth of about $1 billion year-over-year.

Given where we are in the year, we believe it is prudent to maintain our guidance even as the underlying performance and execution are off to an encouraging start. The combination of our focused portfolio, investment in innovation and our diversity across businesses drives the durability of our performance.

Our revenue expectations are underpinned by our accelerating software business, which we now expect to grow 10-plus percent this year. In consulting, the quality of our backlog and momentum in GenAI with backlog penetration at about 30%, continue to support an acceleration in revenue growth to low to mid-single digits for the year.

We are off to a great start with z17. And 4 quarters into z17's launch, we prudently continue to expect infrastructure revenue to be down low single digits for the year, representing about a 0.5 point impact to IBM. We remain confident this will be our strongest cycle given the AI innovation value we are delivering to clients. The momentum in our productivity flywheel is fueling margin expansion, while enabling investment in innovation.

Last quarter, we disclosed that we anticipated absorbing about $600 million of dilution from Confluent in 2026, driven largely by stock-based compensation and interest expense. While we are absorbing incremental dilution given the early closing of Confluent, actions we are taking to accelerate our cost synergies enable us to stay on track to expand operating pretax margins by about 1 point this year.

Our operating tax rate for the year should be in the mid-teens and the timing of discrete items can cause the rate to vary within the year. For free cash flow, we continue to expect to grow about $1 billion for the full year, driven primarily by growth in adjusted EBITDA. The headwinds I discussed heading into the year of higher cash taxes, higher CapEx and higher net interest expense remain the same.

Looking to the second quarter, we expect our constant currency revenue growth rate to be similar to the full year. And for operating pretax margin, we expect about 50 basis points of expansion as software mix and productivity are offset by dilution from the early closing of Confluent. Our second quarter operating tax rate should be in the mid-teens. AI is fundamentally reshaping our clients' operating environments, increasing complexity, risk and the need for flexibility.

IBM's flywheel for growth built on trust, security and governance, a portfolio that helps enterprise put AI to work on their terms and sustained productivity that fuels rapid innovation, positions us to deliver value for our clients. We feel confident in our outlook and are excited about what's ahead.

Arvind and I are now happy to take your questions. Olympia, let's get started.

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And then second, as always, I'd ask you to refrain from multipart questions. Operator, let's please open it up for questions.

[Operator Instructions] And our first question comes from Amit Daryanani with Evercore ISI.

Arvind, it's really nice to see the pickup in Red Hat growth and software acceleration broadly, especially as investors are debating software durability right now. When you step back and look at IBM's software portfolio, I want to understand how would you characterize your mix between infrastructure versus applications, consumption versus subscription kind of stuff. And as AI adoption really scales, where in that stack, do you see the most incremental value accruing to IBM versus the ecosystem. If you just frame how you think of software, the puts and takes in the AI-centric world, would be really helpful.

Good to hear from you, Amit. And as you can imagine, that's the question that occupies us, actually occupies our clients, and I know it occupies many investors' minds. So let me first directly frame the answer in the dimensions that you laid out. If I think of infrastructure versus applications, I think if I count right, 4% of our portfolio, if I'm to be generous, could be called an application. Specifically, I think the only part of our portfolio that is applications would be [ Maximo ] and even that, which is looking at maintenance and asset management operations, given that many people would not really call an application because it is the system of record as utilities and other people with very expensive infrastructure, keep their maintenance records, including where a part may have come from 30 years ago.

So why do I say that? So if you look at the Red Hat portfolio, that's operating systems and container-based software and automation and runbook software. I think we would correctly call that all enabling software. The word 30 years ago would have been middleware, but that word has sort of gone away. If I look at our data portfolio, it is data basis, both relational and nonrelational, and then there is data movement and then there is AI enabling. But data movement is Confluent. And by AI, it's the Watson portfolio.

In automation, it's all about helping people take complexity out of how they manage their IT infrastructure be that Turbonomic or Apptio or HashiCorp. And then there is mainframe software, which is largely very similar to the first 3 I mentioned, but for mainframe. If you look at these, that is all I think in your words, I'll call it infrastructure, but I would call it more enabling software.

Second part of your question was on subscription versus consumption. I think our entire portfolio is very tied to consumption. We sometimes use the word capacity because our mainframe, it is capacity, but capacity is equal to consumption. It's literally the MIPs that people use. It's not actually the installed capacity of the machine and off the mainframe in the distributed world, whether on the cloud or on-premise. A lot of it is sometimes tied to amount of compute capacity that the software runs on.

That's consumption in a lot of sense. Nobody is going to put it on processers if you're not actually consuming it. And that's the vast majority of our software portfolio. So it is very much tied to that. But then I think the implicit question you're asking is, why would that get a tailwind from AI as opposed to a headwind? So as people get serious, about AI because when they start experimenting, they may take a little bit of the data, they make a copy of it, they put it on a public cloud, they run it on some public frontier model, they get some results, and that's exciting to them.

As they get to scale, they've got to use the data from their internal systems. If they're using data from the internal systems, many parts of our portfolio, be it Red Hat, be it Confluent, will come to be consumed more and more. As that gets consumed more and more, the automation part of the portfolio gets consumed more and more. As people do more and more fraud protection, not on sampling 1 in 10 transactions in the mainframe, but every single, that causes the mainframe consumption to go up.

And we can see that, by the way, in the mainframe numbers we printed in the first quarter. So as we go through all of this, I think that this is a tailwind because of the model that we picked. And by the way, I'll point out, this is not a model that's an accident of history. We have very consciously over the last 7 years driven the portfolio into this because we remain convinced that there is value in the underlying data layers.

There is value in the business logic and then there's the interaction layer. Value is going to decrease in that interaction layer because as agents replace people for some fraction, we can debate how much of the interactions, then the interaction layer by itself is not sticky. The agents are going to be interacting much more with the underlying data and the business logic. And we sort of saw that coming 6, 7 years ago, and that is why we picked the portfolio we did.

I think that, that hopefully gives you the sense and we can see it in our numbers that AI is structurally increasing the demand for the portfolio, but that is also why both the organic products that we are building, for example, in software development and some of the acquisition targets we have had is to play into the tailwinds of AI demand.

Our next question comes from Wamsi Mohan with Bank of America.

You said greater than 10% growth in software now in 2026. The early close of Confluent itself should add about a point of growth just by itself. So how are you seeing the growth trajectory for the remainder of the software portfolio as we go through 2026. And Arvind, maybe quickly for you, is IBM's appetite for M&A changing now that Confluent closes behind you and given the broader [ derate ] in the software space.

Thanks, Wamsi, for the question. I appreciate it. Let's break down our software portfolio overall. First of all, we exit first quarter feeling very confident. In our software portfolio, the innovation value and the value proposition. And I think that goes to the core of Amit's question and how Arvind answered it about how we're playing central to the thesis of where enterprise software and AI come together that has always been predicated on our innovation strategy, our capital investment strategy and our M&A strategy.

And I think what you see in the first quarter is a reflection of the diversification of our portfolio and the durability of our software model coming out of the first quarter. But if you down back 90 days ago, what did Arvind and I say entering 2026, we felt very confident about the strategic repositioning of software. Why? One, portfolio shift to higher growth end markets; two, strong annuity base that we've been building up both organically and inorganically.

By the way, we exit first quarter approaching $25 billion, growing 10%, [indiscernible] new innovation and GenAI realization, and we could talk about that, M&A growth synergies. And now we're into the second year of a very encouraging TP monetization opportunity. But for the full year, how is it going to play out? We talked about entering the year 10% growth. Now we see it growing 10-plus percent accelerating. Data we started out extremely strong, growing 16%. We are taking data up for the year.

Yes, we closed Confluent early, let's call it a couple of months overall, close it at the end of March. We were assuming somewhere in the mid-May time period in the end of second quarter. We now see data up low 20-plus percent range. That's going to deliver 5 points of software growth. That's representative of new innovation GenAI, the value of our platform-centric model and strategic partnerships and then also M&A contribution from Confluent, which should be about a little bit north of 15 points of that 20% to 25% growth overall for the year.

So it's very strong organic. Hybrid cloud. Red Hat entered the year, we accelerated, delivered as expected, we posted 10% growth. Underneath that, contributed 2.5 points to IBM for the year, and we're well positioned for that. Our subscription business accelerated. We got revenue under contract double digits, Red Hat OpenShift accelerating $2 billion ARR virtualization, now north of [indiscernible] and consumption model returned back to expectation. We are monitoring RHEL. RHEL did decelerate. I think that's a function of the federal lack of signings in the closure of the government in the fourth quarter that played through, but also a very dislocated hardware supply chain market.

Automation on model, delivering over 2 points of growth. Hashi great first year, record signings, we generated over $200 million in new incremental ARR that should position 2026 well, new innovation, M&A growth synergies and [indiscernible], continued growth and we're off to a tremendous start, record start in our new z17. So we actually feel very good and more optimistic than where we were 90 days ago on software.

Let me just address the M&A question, Wamsi, very quickly. Yes, the values that are out there right now are very attractive. That does not always mean that the sellers are willing to accept these values that may take a few months for them to acknowledge that this is a new baseline. So if that's the case, I'm acknowledging that these are very attractive values. Now we have been a very disciplined acquirer one, let us make sure that we fully integrate in and get all the benefits from Confluent. So that is going to take some months to get done.

As we get through that and as the markets are at these values, that does open up our appetite perhaps more than it would in a normal year, but it's going to take a few months before we can go acknowledge whether or not that's going to happen. And that's where I would sort of give you the bit of color. So second half, if things stay where they are and if the values are where they are, maybe we can do something in the second half as we build up our cash balances, and we are 100% sure that Confluent is off to a strong start.

Our next question comes from Ben Reitzes with Melius Research.

Appreciate it. Arvind and Jim, I just want to talk about guidance. You guys rarely raised guidance after first quarter, I get it. I think there's just some concern out there as to -- are you seeing something in Europe that keeps you at bay right now? Are you seeing evidence of something slowing that keeps you from raising guidance? I mean there's so many good things that are going on with regard to infrastructure and the software that you went through.

So just wanted to clarify that -- and then with -- also with regard to guidance, the free cash flow was better than expected in the quarter. Why not raise it? Or do you just need to see more evidence .

Great. Ben, let me start out with just describing a little bit of the macro and what we have seen as evidence and then why we are being a little bit prudent. And then Jim will address all your questions on the specific guidance. Let me start with the Middle East. We had the strongest growth we have seen in decades, not years, decades in the Middle East. .

So that gives you a sense that we are not seeing. There is no signal. I would tell you that I would expect the second quarter will play out similar to the first quarter in the Middle East. Our clients there be it the larger enterprises, be it government, they are clear. They need to use and leverage technology to improve their own business.

In the first quarter, Europe was also strong. You can see that in the supplemental materials that we have provided. So there is nothing in the -- what has already transpired. There has been no slower RN deals have actually progressed at the rate and pace that we would want. If I look at pipeline and demand signals of the second quarter, we are not seeing any of this slow down.

The only macro comment that we make is if the straits stay closed for another few weeks, then we know that there could be energy impacts in Europe, but that is speculative. That is not what we are seeing. And I expect that actually some of that we'll be able to absorb and maintain our acceleration. It's only if it crosses a certain level. So just based on only 3 months of the year have gone by is why we're making the prudent comment?

Jim?

Yes, Ben, thanks for the question. Let's take a step back and put this in perspective because I think you teed it up extremely well. I've been in this role now 9 years, Arvind's been in the role 6, 7 years. I don't think we've ever raised guidance in the first quarter. But let's talk about the mentality. We've done a lot of work about strategically repositioning our portfolio, our business operating model and the structural competitiveness of this business.

And part of that was around how we were going to build discipline around execution in this company. And that execution mentality was around always [ a beat ] mentality at the end of the day. The numbers speak for themselves in the first quarter. Strongest first quarter revenue growth that we've had in over a decade. Arvind talked about the macro environment. Arguably, yes, we're operating in a dynamic world. And there's more uncertainty than there was 90 days ago, as we all know.

But within our lens of what we're looking at, we're executing extremely well across our high-value innovation software, infrastructure and consulting that see signs of progress. Underneath that, look at what's happening to the fundamentals of our business, our operating margins are up 140 basis points, our earnings are up nearly 20%, profits up 23%. This is an extremely strong start to the year. And now you get the free cash flow, which I know is a valuation measure as I spent a lot of time out with our investors talking about our strategic narrative and our financial investment thesis, yes, Free cash flow generation is the multiple that people more and more are valuing IBM, and I would agree with that completely.

We started out with the strongest free cash flow position in over a decade, highest free cash flow margin, up mid-teens. Let's put this in perspective. Less than 15% of what's required for the year. We got a lot of work ahead of us. But let's also put it in perspective, dial back a year ago, same call, same question. Look at how we execute on that mentality that Arvind's been trying to drive in this company.

We had that same discussion. We executed well. We took up free cash flow throughout the year, and then we blew through it in the fourth quarter. And I will tell you coming out of the first quarter, there's no different mentality that we have here today. The underlying fundamentals are adjusted EBITDA, by the way, all of this is high quality, sustainable, high-value realization overall. That is our free cash flow engine flywheel that provides tremendous investment flexibility for us to continue to invest and drive long-term sustainable competitive advantage, and we don't see any difference coming out of first quarter. But again, first quarter in, we're 90 days into an extremely important year. And our view is we should be prudent.

Your next question comes from Fatima Boolani with Citigroup.

Arvind, I wanted to pull on a thread in your prepared remarks with respect to the mainframe potentially being a destination for more emerging use cases, especially around AI inferencing. So call them not your traditional or conventional mainframe use cases, I was hoping you could put some quantitative framing around that. What type of a workload mix are you seeing today that you would consider conventionally mainframe?

And what is that velocity of potential mix shift? And then as a related matter, as we think about the transaction processing and the MIPS growth momentum, how should that transpire and be expressed in the business in terms of the growth cadence for that particular segment of the follow-through. I appreciate there's a little bit of a lag there. But would love your [indiscernible] and Jim's comments on that.

Great. Thanks for the question, Fatima. Let me address the first part of your question, and then I'll actually give it to Jim to address some of the quantification of those workloads. So if we step back and look at it over the last 60 years mainframe has driven 2 great ways to monetize it. One has been what we call the classic MIPS or these are the compute parameters underneath that drive the transactional workloads that are great for the mainframe.

Many people actually don't realize, but there are also, we call them Linux MIPs that are associated with the mainframe that people have been using to great effectiveness. But let me acknowledge it is more sparse Linux workloads as opposed to the highly, highly intense Linux workloads. AI is adding a third kind of compute capacity into the mainframe.

So just to make it very simple. Today, if people are doing a payment authorization, almost all the credit card companies in the world use the mainframe for their credit card authorizations. If they want to do fraud, they can run a few rules in that engine, but then they'll take a sampling of the transactions, let's call it, 10% of the platform because the latency that it introduces to take it off platform, you can't take them all, just slow the whole system down. That's what they do off.

What happens if you could run a 20 billion, 30 billion parameter model right on the mainframe, suddenly because that is only milliseconds of latency, you can do that to every single transaction. So if you can take your fraud rate down from 50 basis points to 40, you can now do the math on what that is. They are all seeing that.

So as I do that, I think we can do that for credit card authorizations. We can do it for retail banking transactions. We can do it for other payment operations. We can do it for claims and billing purposes. So those are the workloads that are now coming on. So it is effectively a new capacity of the mainframe that previously was either very small but outside the mainframe or running on systems that are what we would call distributed infrastructure.

We believe that this is going to play out. We see a large majority of our clients asking for the capacity. And currently, I believe we have a fully populated system we can do about 450 billion inferences a day on the mainframe. So that gives you a sense of that. We monetize that both through the extra hardware that is sold but also by the supporting software for all of the AI inferencing that then runs on that increased capacity. So with that then, hopefully, that gives you some color on what is happening. I'll give it to Jim.

Yes. Thank you for the question overall. I mean, mainframe modernization increases the strategic importance of IBM z, as Arvind talked about. Why? Because the source of value is architectural. It's the platform. It's not the language. It's a tight integration of software, hardware, database, security, run time, resiliency.

And as Arvind talked about, this is a whole new monetization area of opportunity for us on that platform stack. What is the driver of growth? Yes, 450 billion AI inferences at 1 millisecond of response time, 25 billion encryptions, transactions per day, up to eight 9s of availability, quantum-safe encryption and a TCO advantage running it on mainframe, on-prem versus the cloud anywhere from 3 to 15x depending on the size and complexity of that platform.

So that's why mainframe runs 73% of the world's transaction volumes in terms of value, 45 of the top 50 banks, 9 of the top 10 retailers, 4 to 5 top airlines, et cetera. Now you go to your second question about how do we monetize that value. One is the monetization of the platform of hardware Arvind talked about AI MIPS. Second is that stack economic multiplier.

Historically, we've been averaging about 3x to 4x stack multiplier for every hardware dollar we land. Let me give you a stat. We just anniversaried our first full year of z17. That first full year is z17 versus the prior program, z16 first full year, which, by the way, was the best on record at that point in time. We've increased hardware placement value by over $1 billion.

Now you take that $1 billion and you think about the future monetization opportunity that we get. That's that 3 to 4x multiplier that will play out over time. A big chunk of that being our TP software, but it's also our storage attach, it's our maintenance business, it's our financing business.

We monetize that value based on how many MIPS are shipped in the market and for 4 quarters in a row, on Z17, we've shipped over 100% growth of new MIPS in the market, including first quarter. Why does that matter? Higher capacity is higher monetization opportunity, it's higher price opportunity, it's higher value creation opportunity. So we feel pretty good about that future modernization and multiplier effect as we play out 2026 and 2027.

Our next question comes from Brent Thill with Jefferies.

Jim, just on the constant currency for software, not to nitpick, but if you look at last year, 9% growth in Q1, I think it was 8% -- 11% in Q4. So investors are asking, you're seeing a little bit of a downtick. Is that due to seasonality where maybe your contract signings were better in Q1, but maybe are being reflected in the reported numbers? Again, I know it's a modest deceleration, but anything to point out there?

Yes, Brent, thank you very much for the question overall. I fully expected this one because when you just look at the media, print and the press release, fourth quarter, we posted a little over 11% growth. This quarter, we posted 8% growth. What gives -- do you feel still strength about your portfolio, your business, your investments, your new innovation, I think you nailed it right upfront.

One, understanding our business, our software portfolio high-value recurring revenue, about 80% of our annual business, about $30 billion plus trailing 12 months, 80% of that is high-value annuity-based business, is a transactional engine underneath it. It's a big component of our perpetual license model, but it's a component of our subscription model, et cetera. If you look at it. The entire 3-point drop quarter-to-quarter is the fundamentals of the mix of the portfolio.

In fourth quarter, we have about 30% of our business in the fourth quarter is transactional. In the first quarter, that's about 10%. When you look at the underpinnings of the core annuity by itself, we're actually accelerating that fourth quarter to first quarter. I think I said earlier on the call, our annuity ARR exiting first quarter approaching $25 billion, that's up 10%.

Throughout the rest of the year, we'll go from a transactional quarter of about 10% first and will peak probably in the fourth quarter of about 30-plus percent. On average, we'll be in the 20% overall. That will accelerate growth. That, coupled with M&A growth synergies, our GenAI portfolio, which has had a lot of momentum behind it. And our TP monetization and cycle I would tell you, coming out of first quarter, I feel pretty good about 8% growth, and it positions us why we said 90 days ago, confident in 10%. Now we're saying, yes, we closed Confluent earlier, and we're confident now in accelerating that to 10 plus.

Our next question comes from Erik Woodring with Morgan Stanley.

Jim, you briefly alluded to it earlier, but -- can you maybe just detail how IBM is broadly managing and/or mitigating some of these supply chain headwinds, whether that's higher memory costs or supply challenges, meaning how material is memory within the infrastructure base? How are you mitigating. How are customers responding how does it impact your outlook on growth and margins? If you could just maybe dig into this, that would be super helpful. .

Yes, absolutely. Thank you, Erik, for the question overall. You understand our business extremely well. Underneath Arvind's leadership, we have strategically reposition this portfolio. There's been a lot of work around portfolio optimization. By the way, that's both leveraging the strength of our cash flow, our financial flexibility, to buy high-value innovative base companies in category-leading technologies with structural growth profiles to help IBM but it's also around divestitures of portfolio.

But where I'm going with this is today, when you look at IBM's portfolio, we're a human capital asset, IP-based business at 75% on a way to 80% by the way, underneath that, software, 45% on its way to 50-plus percent. Overall. Our hardware business is extremely important as a value creator to IBM -- but top line, it's about 25% of our business, but that's high-value innovation on mainframe platform overall. Now you look underneath it around the supply chain dislocation around, commodity cost increases, in particular around memory, it has a de minimis impact to us overall. Think about mainframe overall.

Will it impact storage and potentially some components of our distributed infrastructure, absolutely. But look underneath it, we're able to -- one, we've been in existence for 115, 116 years overall. We know how to run global supply chains. We drive supplier optimization, supply chain diversification, procurement strategies overall.

And I think we've been able to mitigate this dislocation overall. The area we're watching it is in the software area around RHEL. I mean RHEL's tied to enterprise hardware placements overall, and we'll continue monitoring that. But look at our hardware performance, we accelerated growth at 15%, our distributed infrastructure at actual rates growing 17%, constant currency growing 13%. By the way, I didn't even talk about it. our infrastructure pretax margins are up 720 basis points. So we know how to manage global supply chains and commodity costs inside the company and extract value overall.

Jim, let me just add a couple of sentences to your statement. Erik, Jim mentioned that we have worked with a lot of the suppliers for a number of years and decades. They like working with us, partly because of the relationships we have built up with them over the years, but also because we help them stress test new capabilities and they like the fact that our systems are very high performing because that gives them brand reputation as they go out of the wider market. That does help not completely, but somewhat mitigate some of the supply chain constraints because we are early users of the [ new S memory ] technologies.

Your next question comes from Jim Schneider with Goldman Sachs.

I was wondering if you could maybe comment on the AI bookings, which is a metric you previously given -- you've given, but I think you just commented as a percentage of your total bookings right now. Did that accelerate or decelerate in the quarter? And then maybe just kind of comment on any update you see for the consulting business this year, either given more macro uncertainty. Do you expect any kind of diminution in the growth rate you expect this year? .

Yes. Thanks, Jim. As we talked about, we exited last year with a book of business around AI, which, as you know, we talked about consulting and software within that vernacular. I think it was important over the last couple of years because as the explosion of GenAI hit, we had to give a perspective about whether we were winning and capitalizing and participating in that market. We exited last year what $12.5 billion, over $12.5 billion book of business. But now let's bring it back because in January, we talked about it's embedded across our portfolio. It's embedded in software, Its central thesis to how we run our consulting business right now.

It's embedded across our infrastructure business. And we said coming into 2026, we were going to talk about it more from an outcome-based revenue base and value contribution base overall. But let's talk about software. Software GenAI continues to be a tailwind overall. The positioning of our portfolio with the explosion of AI, with applications agents with us owning the foundational layer of Linux, containerization, you see that play out with the acceleration Red Hat OpenShift business, now $2 billion, growing north, I think, high 20% growth overall. Second, the importance of the data layer.

Arvind talked about Confluent positioning us to be the cross platform as a data connector, automation, the need of resiliency, observability FinOps. Software, let's talk about, one, it's accelerating our growth profile overall. But let me put some numbers behind it versus just an overarching book of business. Our software book from an annualized revenue trailing 12 months, we finished last year at $30 billion, right? 80% of that, as I said earlier, high-value recurring revenue, 20% transactional. We did about $6 billion.

Over the last trailing 12 months on an accelerating basis, our AI platform agents, assistance orchestration is north of $1.5 billion. It's already about 25% penetrated and our software business growing north of 40%. It's contributing 2 points of growth on an annualized basis. And a thing we love about it, it has a multiplier effect over time. So it's an acceleration there. Consulting. Consulting is about 40% of our signings, 30% of our backlog is GenAI now, over 20% of our revenue. And on an ARR revenue perspective, in the first quarter, we eclipsed $4 billion ARR. So it is central to the way we run a services as software model overall.

And in infrastructure, both Arvind and I talked about, it's embedded on the chip of z17, [indiscernible] inferencing. I think Arvind talked about it in prepared remarks, clients that have implemented watson Code assistant for Z, we're seeing 3x differential on growth and capacity, and you see in our distributed infrastructure, we're accelerating growth.

Now your second question around consulting. We are seeing signposts of progress overall. One, our demand profile, our backlog quality, our GenAI, which I just talked about, our strategic partnership headroom opportunity, our portfolio mix composition more to higher growth areas and our services as software model, which we think we have an industry-leading position with our IBM Consulting Advantage platform. But let me put some stats on it. One, signings, we return to growth. Great quarter overall, large transformational deals around GenAI, the health and mix of net new business and expansions up 7 points year-to-year, up 4 points quarter-to-quarter. 400 new clients captured in the first quarter. Our backlog quality overall, our erosion is stable.

Our duration continues to come down. Our backlog realization is actually accelerating throughout the year, and our backlog yields are up 4 points year-over-year talking about the quality and value we're able to deliver. And I talked about GenAI, 80% of our GenAI book of business right now is coming from capture from net new clients overall. And I'll stress that over $4 billion revenue ARR. So that positions our confidence in the year of us accelerating our revenue growth around low single digits and if things go well, can we do better than that? Obviously, yes.

Operator, let's take 1 last question.

Last question comes from Matt Swanson with RBC Capital Markets.

Great. Thank you so much for squeezing me in here. Arvind, it's really interesting going over the software segments, and you showed how [ low ] of an exposure you have to the application space. There's obviously been a ton of debate right now around who's going to kind of win the GenAI workloads of application. We've seen you operate at such a strong kind of [ Switzerland ] foundational player in the hybrid cloud. When we look at AI, like how are you setting IBM up to win kind of regardless what ends up being the winner of the GenAI application layer? And I mean, what kind of investments does that take?

Matt, thanks for the question. So we made the decision about 3 years ago that we were going to be neutral and Switzerland like also on our usage of frontier models. Because I think when we are saying the GenAI applications, I think for many people that is synonymous with the frontier model providers, not just the fronter models, but all the surrounding software [indiscernible] that all of them are giving.

So we are going to play where clients want to be hybrid. The clients want to function across multiple clouds or also because of either sovereignty or brand or privacy or in the end, economics, they might also have a private addition in addition to what they use on public. So as we go across that, we are building, for example, our software development AI product, Project Bob. It is out, we actually chose not to announce it. Nevertheless, 200 people signed up to use it. So that gives us a signal that we have something.

Now why would they use us as opposed to just one of the Codex or equivalent models is if they also have a lot of code that they do not want to actually take out in public. And also, they want to address the entire software development, meaning including testing, including [ patching ], including documentation, including maintenance are the kinds of things that we provide.

Ditto as we look at how they might want to use agents that come inside their enterprise, then we use Confluent to go manage and control how they expose data from inside things. So as we sort of look at that math, I think we're very clear right. There will be people who will be frontier model providers. You can debate are those half a dozen or a dozen? Today, it's somewhere in that range. We actually do not want to even predict which of them will be the eventual winners. We want to work with all of them.

Then we also work with open weight models. And we produce models where we have either domain expertise or people may want much smaller models to be able to run them on-premise or I'll say, euphemistically on a 1 to 4 GPU server node as opposed to a very, very large cluster. So that's the model place. We are going to then help our clients deploy these models to gain value.

As we have unlocked, Jim talked about the $4.5 billion of internal value, how do you reduce your total tax expense? How do you reduce procurement expense? How do you reduce accounts payable, how do you reduce [ quote to cash ] as we walk across these processes, we get a lot of knowledge on how to capture that into agents, but then we are not going to be fixated whichever model you want to use, you can use. And wherever you want to run them, we'll help you run them. And we think that's a good half of the world is interested in that paradigm, and that's how, Matt, we are going to be able to go win in this world as it unfolds going forward.

So just to close, the innovation value we are delivering to our clients and our strong start to the year, reinforce our confidence in our growth trajectory. We look forward to continuing this dialogue as we move through the year.

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

Thank you for participating on today's call. The conference has now ended. You may disconnect at this time.

Hello, everybody, and welcome to the View from the Top CEO Call Series, Q&A with IBM's CEO, Arvind Krishna. My name is Elliot, and I'll be your coordinator today. This call or the replay is not for media representatives. All such individuals are instructed to disconnect now. I will now hand over to Wamsi Mohan. Please go ahead.

Yes. Thank you so much. Good afternoon, everyone. Thank you again for joining us on our View from the Top CEO Call Series. I'm honored to welcome back Chairman, President and CEO of IBM, Arvind Krishna on this View from the Top Series. This is actually Arvind's fourth appearance, so we're really, really grateful to have him here again.

Arvind really doesn't need any introduction. He -- most of you have met him in the past. You know he's among really one of the top thought leaders in our time. He has had a very distinguished career. He's driven innovation. He's driven sort of some key things within AI, quantum, blockchain and so many other areas in his time as Head of IBM Research.

He's also served as SVP of Cloud and Cognitive Software, where he pioneered the company's hybrid cloud business. Arvind also led some of the transformational deals, including Red Hat and the spin-off of what is today Kyndryl. And he's driven so many significant changes, including portfolio shifts, cultural shifts and really focused investments to drive further growth. IBM's stock performance since Arvind took over is up 194% since April 6, 2020, relative to the S&P at 158% in that time frame. So real material outperformance over here.

And frankly, we just witnessed a remarkable volatility in the stock, which has actually retraced some of the maybe unfounded fears at the time from last week.

So Arvind, welcome, truly an honor to have you here today for this call. Thank you so much for doing this.

Well, Wamsi, it's a pleasure to be on with you. You've been most generous in your description. Hopefully, I live up to some of that at least. But also thank you to the audience for wanting to listen.

No, thank you so much. And yes, if you don't know one thing about Arvind, he's extremely modest for a person who knows as much, as he does. And I remember back the time when I probably have had discussions with Arvind all the way about whether or not floating point operations on NVIDIA processors are the key to running like the future of AI or not. So the technological depth and detail that Arvind has is just truly commendable. And I'm always impressed when I learn something from him every single time.

So Arvind, this is such an interesting time. There's just so much news flow, the velocity, the pace of this has been pretty intense. A lot of news focus is around Agentic AI capabilities, including the ability for Anthropic and Claude to translate COBOL code, which has been created like some significant volatility, all of which the stock has recovered. But the question really is in investors' minds, why should or will customers stay on the mainframe, even if they can just click a button today and all their COBOL code can be converted to a modern language.

Yes. So let's, for a moment, presume that you could click a button and translate 1 million lines of code at a time like for the sake of hypothesis. Maybe it's not completely accurate, but let's for the sake argue that it is.

I would argue that programs written in COBOL have very little to do with why people leverage the mainframe and use the mainframe. And we would be the first to tell you that if something is not fit for purpose on the mainframe, let us help you migrate it and let us help you modernize it and let us help you take it to the right place.

So let's come back to why is the mainframe so used, whether it's in payment systems, whether it's in retail banking, whether it's in certain batch processing, whether it's in reservation systems. It has to do with the complete architecture of the system. How do you make sure that something is resilient? We have clients who in aggregate manage to run at 6, 7 and 8, 9s of reliability. That means you're talking about milliseconds to seconds of outage over the whole year.

I think that if you try to replicate that level of resilience on alternate architectures, your price would be 3, 4, 5x of what it would be on a mainframe architecture. That usually works next when there is enough scale in what you're trying to do. Are you trying to do 1 billion transactions a day? Are you trying to do 10 billion transactions a day? Are you trying to do 450 billion inferences, let's say, to avoid fraud and credit cards a day? For those kind of workloads, the mainframe is both cost effective and architecturally the best fit for those kinds of workloads. Which platform do you have where everything can function encrypted, be it in memory or be it the actual transaction without having to go through multiple systems, adding latency, et cetera.

So as I argue that it is that architecture that keeps those workloads in the mainframe. Then the next part now, let's look at the seriousness of could you actually migrate COBOL. This is -- it's not so much that you look at that and say, COBOL is bad. What COBOL has done, it has captured the business logic of people over the last 10 years, 20 years, 30 years or 40 years. So if you're going to replicate it, you've got to make sure that, that business logic is correctly there in an alternate language.

When your issue becomes that a single mistake in 1 million or 1 billion transactions a day gets the regulator breathing down your neck, you're going to have to be really careful that what is translated is 100% accurate, not 90% accurate, not 95% accurate. So that's going to take a lot more work and a lot more checking. I would not tell you it cannot be done.

The question is, are there any economics? And what is your advantage of doing it for those workloads. But on a more serious note, we came out with our own product that does this, watsonx Code Assistant for Z, as Z being code for mainframe. And it actually can translate COBOL to Java, it can translate COBOL to modern. And this is what happens. So clients say, and we are not at all defensive. You want to modernize the platform, let us help you. This is a great tool. It really helps you do that.

The first thing you need to do is what is the business logic to my point of the 50 years. It helps you document that. It takes all kinds of spaghetti code where people have undocumented code and you have no idea, is this something stuffed out or is it real? And it can comment it for you. The moment people see that, they then begin to understand, "oh, this is not a spaghetti ball of mess." I understand now what it is doing. Do I want to take this and move it? Do I want to modernize and rewrite this? Do I want to put another API call on this? This should stay, this should go somewhere else. And that becomes a much more productive conversation that we have been having.

The last part I'll say, it's not about a stateless piece of code. If there's a stateless piece of code, you can move it around. It is also tied in to the underlying data that's on the platform. It is typically tied in to tens, if not hundreds of other applications that touch the same data and the same variables that are on the same platform.

So moving it means you've got to make sure that, that entire ecosystem can go along with it. So sort of really long answer on the why, but we actually believe that we should modernize and we want to help our clients modernize. And if in the process, they find there's some applications sitting there that don't belong, we want to help you take them to the appropriate place, be it a public cloud, be it a Linux server, be it some other platform that is close by.

So Arvind, -- no, that makes a lot of sense. So when you talk about watsonx Code Assistant for Z, when you think about how long that's already been out there, it's been there for a couple of years. So when you see how clients have been using that, what are some of the lessons from that? What are the ways in which AI is changing how customers are actually using the mainframe?

Yes. So before I even come to watsonx Code Assistant for Z, I always like to first focus more on the revenue side where people actually get a bigger business benefit, and then we'll come back to the productivity side and the efficiency side. So in our prior generation mainframe Z60, we began to introduce some AI capabilities. Then in this latest one, we introduced the Spyre processor. That in aggregate, lets you run, let me call it, small language models. I wouldn't call it the mega models, not the frontier models, but 10 billion, 20 billion, 30 billion parameter models can run on the mainframe.

So we have a client, a North American bank looking at credit cards. And previously, they used to maybe be able to pass 10%, 20% of their transactions through an off-platform system, looking for fraud alerts and then you use those to kind of create rules on the rest. Now they can run 100% using the Spyre on the platform. And that means you're now saving all the money. So the bank saves money, tens of millions. More importantly, the 0.5% to 1% of fraud that either the merchant or the customer has to then swallow also goes away. I think that's a tremendous benefit.

I think there will be more and more cases. Pretty much everybody around payments is looking at how can we leverage that capability.

So now let me turn it back to the efficiency side. Our Code Assistant for Z is deployed at about 150 clients, if I remember correctly. So they have actually purchased it. They are beginning to leverage it to both understand the code base, understand what they should modernize and place, understand what does not need modernization at all and also understand, which pieces they want to take off and perhaps run somewhere else. And the tool will then spit out a Java version of that complete application. I think that, that is tremendous.

So both sides, can I leverage AI to get a bigger business benefit? And can I leverage AI to make it much more productive on how I run and manage the platform. And the last piece I got to say I have a lot of pride in -- as you know, there is -- every system has all kinds of knowledge and travel knowledge that the experts know, but then everybody scrambles to find those experts when something happens. We're also writing a lot of AI-based tools that run on the mainframe that help you manage what might otherwise require a lot more expertise and sort of you make the 6-month system admin equal to the 10-year system admin.

Yes. No, that's really, really interesting insight. Arvind, when you think about the future of an era where these workloads are being modernized, there's been the time frame from 2010 to 2020 where public cloud was gaining a lot of traction and some workloads were considered to be deplatforming at that time. What has changed since then? Why is that maybe not the case anymore? And what is the TCO value? I mean you alluded to it a little bit in sort of talking about the 5, 6, 9s of reliability. But what is the TCO truly on a comparison basis that clients are seeing that keep them on the mainframe?

Yes. So look, I think first, if I go back even older than 2010, let's go back -- the decade before that. And if I go now to the late '80s and the early '90s, the mainframe was probably the workhorse of enterprise computing in those days. Then web serving, e-commerce clearly happened on what used to be called, if I remember the LAMP stack, right, Linux, Apache, MySQL, et cetera, because that is fit. It's a stateless application in some sense. People come in, they browse, they buy, but people hadn't really touched that mainframe estate, which was the enterprise workhorse.

They then began to look at it and say, okay, which applications are running here that really as a modernized don't belong here. They should run on a public cloud. That's the ones that moved, but I would put that much more in the 2000 to 2010 time frame. They might have started with thinking about moving it to an internal Linux or UNIX cluster. But then as cloud was coming, that became a likely destination as well.

Then -- I would say 2010 to 2020 was not actually that much migration. There's a lot of new workload that went on to public cloud, but not necessarily so much mainframe migration. Now people begin to look at it. And I think a lot of our clients at least have become a lot more mature. So to your point, if I pick 2010, I'll sort of paraphrase it as maybe, "oh, the answer is public cloud. " What's the question? Now people are saying, wait, what are the true economics of running it where? Can I understand those economics? And if I include what is my cost of having hot backups, what is my cost of having a failover in a few milliseconds.

And once you put all that in, then for some workloads, I'm not going to look at you and say for all. For some workloads, the mainframe is the lowest unit cost as long as you have sufficient volume. So if you're trying to do and move, let's make up a number, 100 million retail banking transactions, and you got to net those out in 30 to 40 minutes at the end of the day, for that workload, I can look you in the eye and say the mainframe is the lowest economic cost. You turn around and look at me and say, "well, can I stream movies from it? " and I'll tell you, it's not the right economic cost for that kind of workload. I'm just sort of painting 2 opposites. And that is the work now that we are doing.

So approaching people with that and saying, just believe me, that's not an appropriate thing. So what we have done is we have taken other tools in our portfolio like Apptio, which does technology business management, and we put the data into that. So then you can look at it yourself and say, okay, I'm transparently seeing what is the best place to do this. And so that is where we want to be able to prove to people that on their cost structures, what is the right answer.

Okay. No, that's very helpful. So Arvind, just talking about Apptio and let's talk about software a little bit, right? So Agentic AI and wive coding, they seem to have really elevated the perceived risk for software companies and in particular, SaaS companies. So maybe you can address like when you look at IBM's portfolio, like what is the moat around the software portfolio and why you feel comfortable with the moat that exists around it?

Yes. So let's first just maybe unpack your first statement, Wamsi, for the audience. I actually am a very strong believer in both what LLMs can do and as well as what Agentic leveraging LLMs can do. We believe it's going to make software development a lot more productive. We actually believe there'll be 1 billion new applications, leveraging agents and LLMs that are going to get created over the next 5 years.

So clearly, if that $1 billion gets created, it's replacing some of what is there now. So if I unpack a typical SaaS and for a moment, let's call it, it's a SaaS business application. For those that are reasonably deep, there is a system of record at the heart of it or the database to call it that. I actually don't think that goes away.

There is often business logic on top. If that business logic is of the nature where you're touching a tax issue, you're touching revenue recognition, you're touching topics where if you make a mistake, you could get in the -- really the bad side of a regulator of some type, be it a financial regulator, a national security regulator, a data regulator, there are all of them that are there. And I'm being generics across all industries. That's a really bad place.

So I think those 2 values of the SaaS applications actually stay. Then you get to the UI and how people interact. Today, as you know, when you deploy these, there's a lot of training done to make people aware of it -- aware of the interfaces. Well, I'm sorry, but an agent, an AI agent could probably take care of a lot of that for you. So I think that this is what is going to happen that the value of those applications is strong, but it is less than if you had the UI also.

The other part is that as I think demographics are going to point to fewer people, then that is another impact on these. As always, when the value decreases a little bit, there is a bit of a race down on pricing and those things. And we are seeing, I think, kind of that play out.

Now the second part of your question, because I think we needed to preface with what is the areas that are much easier for AI and agents to go -- do in an easier way. So the next part is, but if I look at the actual database, there's no AI or AI agent that can replace the actual storage or data in a meaningful way. And then if I look at how do you move data around, that's not what AI agents do. They actually need that in order to function.

So if I -- I don't love the term infrastructure software, but if I kind of look at it as a horizontal software that enables all these things, that is where we had focused by design back in 2020. We had kind of said we're going to focus on automation. We're going to focus on data, in addition to what we do on hybrid cloud. And I think that these actually get tailwinds because as people want to deploy AI, they're going to need more of all 3 of these, unless they're going completely to SaaS. But as we just argued, nobody is going all the way to SaaS.

Actually, to leverage all this, this is a bit of a back to the future. People are going to build a lot more custom agents. That means they're going to need all of what we provide, where it's going to go run in turn. So sort of that's our thesis. And sort of our numbers kind of say that our growth rates have accelerated over the last 5 years, and all these parts of the portfolio are doing reasonably well.

Yes. No, that's a helpful contextualization of where there is disruption versus where there is security and resilience and the ability to continue to provide maybe even accelerated value. So maybe, Arvind, just thinking through your portfolio in particular, right? You've got sort of Hybrid Cloud, you've got transaction processing, data automation. Can you help us think through how AI is either additive or subtractive to the TAM? I mean there's been a lot of news recently about how there could be significant subtractive disruption elements like the [ Security piece ] for example. And I'd love to get your take on sort of IBM's strategy to capture the incremental TAM that you see and areas where you think there could be incremental disruption.

Yes. So let's begin first with the Hybrid Cloud portfolio. To a large extent, we identify this with the Red Hat set of products. So you have Linux, you have OpenShift, you have Ansible, and we have other new products that we'll keep creating and/or buying in that, always these are all open source based. Well, it doesn't matter whether the workload is custom apps or migration of apps or new AI, still needs to run on something. So our view is that, that side of it is pretty resilient to AI.

Now the question is, is it not just resilient, but can it actually get a tailwind? And I think that if there's a lot of custom agents and custom AI deployed as opposed to just using it in the hyperscalers, then actually Red Hat is a beneficiary as well. And the argument I'll make is much as we've seen play out over the last 6 years for non-AI applications, there is a huge amount that will be in SaaS and public clouds. But as people look at it, I think hybrid has become the answer, and that is including because of sovereignty.

So if sovereignty becomes a big play where people say, "Oh, I'm willing to use a big public cloud from 1 of these 2 countries for certain workloads," but I need to make sure that even if a fiber optic cable gets cut or geopolitics comes in the way, I can still function in an autonomous way, then we get a tailwind on that -- for that part of the portfolio.

Next is our Automation portfolio. And let me acknowledge the word automation doesn't really do it justice, but that's the word we have. This part of the portfolio is all about how do you manage your hybrid infrastructure and how do you run it in a way with much less labor and much less complexity? So is it about resource management? Is it about understanding response times? Is it about how do you deploy software? I'm thinking of HashiCorp when I say that. Is it about how do I manage my secrets? Is it all about, hopefully, the regulators and antitrust authorities will soon approve Confluent? How do I do that? Or it's about web methods of how do you knit applications together using API calls and data movement or about transactional way to move data and messages, including I can recover even in the face of a power outage or a natural catastrophe like MQ.

If you look at all of these, these all get a tailwind and get deployed against all the new needs. The only time they would not be in massive use is if your entire estate sits inside one SaaS provider, which is unlikely. So at least if I parse the market down and say about half the spend is in the top 2,000, there's another 30% to 35% of the spend in the next 100,000, and then there's a very long tail.

I think for 85% of the enterprise tech spend, this area gets a tailwind. And our numbers show that. I mean we've been running this at double-digit growth for the last 3 years. Now you have data and AI. So this -- AI is the place where you have higher growth here, meaning how do you do agents? How do you orchestrate across applications? I actually believe the world is going to become a multimodel world. It's not a one model wins all. So people will say, "Hey, I'll use small models inside. I'll use some open source models. I want to be able to leverage these 2 or 3 big models, " then the place where you would do that and help manage all that is through our portfolio.

And so that becomes then a place where you go win in that one. And the last one we touched on is mainframe or what we call TPS or transaction processing software. And that one we just talked about in the beginning, we've been seeing anywhere from 15% to 30% of volume or capacity increases in the mainframe. Some of it we give back to our client as kind of a price optimization. But some of it, we can see that comes through in the volume increases there. And typically, that is a bit of a lag compared to the hardware of the capacity.

So historically, about 6 to 12 months after the hardware capacity goes in, we begin to see the increase there. So there, I would look at you and say that's not going to be double digits. So that's probably in the low to mid-single digits growth. So sort of 3 parts that should be in double digits, maybe very low double digits to mid- to high and one part that is going to be sort of low to mid-single digits is kind of how the portfolio makeup goes across those 4.

And I would tell you, I think these are pretty AI resilient. But there is one scenario in which they're not AI resilient. If the world kind of decides one vendor on one hyperscaler is a winner take all and all the workload gravitates there, then you would turn around and say, well, I don't need a hybrid orchestration. I don't have multiple models. I don't have a multi-infrastructure, it's only one. I think that's very low likelihood. And each time we see most skirmishes in the world or more geopolitics, I think that becomes even less likely.

Well, that's a good point. I mean -- and I think maybe underappreciated in the sense of how long it takes enterprises to actually work through all the friction associated with business disruption potential with the lack of maybe resiliency that exposed risk from compliance and governance standpoint. And all of those things play a role.

And I mean, you've done this longer than I have, and you've seen, I think, some of these transitions where people have projected these transitions to happen during very short windows of time. And frankly, it's not happened in 20 years. So it could take a very long period of time for a winner take all. And frankly, we have not seen a winner take all in almost any technological scenario to think of. Even if there is one very dominant player, it's not been a winner take all.

Maybe, Arvind, just would love to get your perspective on Agentic pricing. I mean you do think that agents have a significant role in the future. How do you think these pricing capabilities will be embedded within core software? Is it going to be on a per seat? Is it per agent, per transaction, per outcome? And it's interesting, like we've heard in instances where people are running some of these Agentic workloads at some scale where the cost starts to mimic one of a human employee. And so how do you prevent sort of Agentics frawl from happening? And I would love to get some insight on your thoughts on this.

Yes. Look, part of the questions have been faced in software for the last 40 years. Do I want to price it per employee? Well, as long as employees were growing and everybody was increasing, that works. What happens if half your employees are not humans, but other agents talking to agents. So I actually think that the per seat pricing model begins to show its fallacies as you kind of go down that road. Then some people say, let's price for outcome. It sounds great. I found very few clients who really want to do that unless it's a lot of human work and so that is actually shared risk of some type.

If you're pretty sure of the outcome, I think it's very hard to get pricing for the outcome because in some sense, if you're living in a capitalist system, you're motivated, you want to keep the outcome for yourself. You don't really want to share the outcome with somebody else. You then get to capacity pricing.

If you look at our portfolio, I would look at you and say 80% of our portfolio is priced for capacity. So if I look at our Hybrid Cloud, it's priced by VPCs or virtual processor cores. And that seems a logical way. That way, if you consume a lot more, so we are motivated to help you to consume more. But if you're not consuming it, you're going to turn the price down. And so a lot of our portfolio is priced by capacity or usage more than either sort of blindly per seat or any other way. I really do think that this is going to turn out to be how it's used.

By the way, I will turn around and tell you, I think the people who do token pricing in some sense, it's a capacity pricing. Now a fixed per person works in the consumer world, but the enterprise probably doesn't really want to see that. Or as I said, if a lot of your employees become "digital employees," what happens to pricing? Or do you want to charge for it? There is also this danger that if you let agents or AI models run a mock and you're consuming millions or tens of millions of tokens a day, I think if you do the pricing on a per year basis, that's way more expensive than a human employee.

Yes. Yes. No, this is going to be a very interesting evolution to monitor. Arvind, where do you think the durable profit pools will be in the Agentic stack? Is it in the models? Is it in orchestration, domain agents, observability? I mean, and where is IBM choosing to play or not play within that?

Look, the -- since at the end of the day, the intelligence comes from a model other than sort of the consuming application, let's call that an agent. The model will always get a share of revenue.

Now this is what we're going to see play out. If very large models are roughly equivalent, that means you can switch between them, then that says that, yes, they certainly derive value, but it's very hard for them to command the premium. So you then go -- they'll certainly get a big share of the revenue, but it's going to be coming down to sort of more of a commodity-like pricing.

You then say is the knowledge of sort of the domain, the application, the actual usage is in what we'll call the agent, but it's really a full-blown application. The agent is just sort of a name for that. And that has got a lot of intelligence now and routing between models, figuring out what to do, when to do, when do I need to do a second iteration or a third or a fourth, then that is certainly going to command the price where the model price is kind of built into that price to some extent.

And if I look at every technology cycle, if I go back to the mainframe in the '60s and '70s, originally, the hardware drove the pricing. Then over time, the software, which made it easy to use became the pricing. Then over time, the applications became. I think the mobile phone revolution is the easiest probably for the audience to understand.

Back in 2007, if I pick that as the era of the smartphone start, for the first couple of years, it was all about whether it was an iPhone or an Android. Then very quickly, the App Store came along, all of the enabling software came along, you would then turn around and say, "Hey, actually, iOS and Android are the lock-in. " The hardware is important, but that's what enables the hardware. And now what are the 6 million applications or more on the iPhone? That ecosystem is much bigger than just the hardware or software. So I think it always goes in order.

The infrastructure or the silicon gets it first, then the enabling software and tools get it second. And then for the value to hold, it has to be the multitude of applications, and that was everything else, but it's because it's also in the millions. And every cycle, I think, has seen the same. The PC is identical to that. The Internet is identical to that. All of these, I think, kind of play out along the same arc. But it takes 10, 15 years to get from beginning to end.

I was just going to ask you like where do you think we are in that cycle for AI and agents?

I used to say till end of last year that if I think of it like a baseball game, we are still in the first innings. Maybe we are just transitioning from the first to the early second innings at this stage. So you kind of know who's on the field. You kind of know who's playing. It's hard to know who's hot today, who's not hot. You kind of need to get to the fourth or fifth innings before you can kind of say, okay, I have some sense of who might win this game today.

Okay. All right. That's a good analogy. Arvind, look, if agents start doing maybe half the work of what people do today, what happens to the consulting business model? What happens to pricing, margin structure? How do you measure productivity in those cases? What happens when like the unit of delivery becomes a digital worker?

So I think, first, I would look at you and say, we can debate is it 2 years, 3 years or 5 years. I think half of all repetitive work will be done by agents. I actually see no reason why that would not be true. And that is assuming AI makes no more strides. That is assuming today's level of AI capability, okay? So now you turn around and say, well, if hardware is done by digital workers, then your revenue gets halved. Well, of course, not because it takes experts and people and technology to deploy those. And the other half, we still need people.

So if that's the case, then I think in some sense, you are going to get 10%, 20% price compression and you are going to need -- if the amount of work stays constant, you're going to need fewer people. But then I turn around and say, if you are the early ones to embrace that, and so your unit pricing comes down, then you become a share winner, so I can't tell you whether the number of people is exactly the same. But in terms of revenue, you can be a share winner and keep better margins and better revenue by being embracing of this trend as opposed to fighting it. And that's where we lean in very heavily with our Consulting advantage platform.

I think over 350, if I go to a course level kind of agents that can be deployed against workflows. And to just make it very real for the audience and tangible. Previously, when we did an SAP project for a client, you would spend 6 months and 30 people kind of coming through requirements, capturing documents, making sure you've captured the client's process correctly. That can now be done with agents and like a half dozen people over a few weeks. So you've shortened 6 months to a few weeks. You've taken 30 people down to 5 or 6, and you can be much more complete -- what's the got you? There's got to be some written documentation on the process side from the client.

If that's not there, you still have to go do the discovery work to go document it. But if you have something written down, that's a great starting point on those. And that's like one extreme example where people don't even think that agents will be useful there. But agents to do software development, agents to -- we see anywhere from 30% to 70% productivity depending on the kind of work for software development across the life cycle. And I think that's going to be a given. If I think about answering customer queries or customer experience anywhere from 50% of the low end to 80% of the high end of the work can be replaced by agents. And in all these cases, these are at a fraction of the cost of a human employee.

Okay. So if I was to paraphrase that, maybe it sounds like there is significant amount of work that will be done by agents. There is going to be some pricing compression, but people who embrace it early will be the share winners of the future. And that's kind of IBM's strategy is to make sure that they are embracing some of this change early.

Correct. With one exception where I think it is sheer disruption. Not yet seen it, but I think it will happen. So if I look at the kind where basically pure clinical work has largely moved offshore, people call it the BPO industry, but that's a very wide term, but more sort of call centers, document processing, transcription, all those things. I think there, it may not be some. It could be pretty massive disruption, but it will take 3 to 5 years to play out.

Okay. Okay. That's helpful. What's the competitive threat that you take most seriously in Agentic AI? I mean you've got hyperscalers who are investing hundreds of millions in their CapEx plans. They're embedding agents everywhere potentially? Or is it going to be more pure-play AI vendors or other consulting firms that are turning themselves into software companies? Where do you see the biggest competitive threat?

Look, we tend to partner with a lot of these players. So if I look at the CapEx spend, we'll be a beneficiary of that. So if Amazon or Azure or Google have more capacity, that's great because we partner with all of them, and we could leverage that capacity for the benefit of our clients, including the capabilities that they have on their clouds.

Now it's always been competitive, like if you want to use a database, but then there's a wide variety of providers. Are you going to use the database that's needed for the cloud? Or are you going to use something from my friends at Mongo? Or are we going to use one of IBM's capabilities? I think it gets down to fit for purpose. You've got to engineer well. You've got to be consumable, you've got to be easier to run.

And we tend to be more at the places where there is more enterprise need, meaning it's more scalable, it's more resilient, it's easier to monitor, becomes the cases where we'll win, but we also leverage all our partner capabilities.

DiTTo was a pure AI model providers. We tend to partner with all of them, and that's very much been our strategy. I actually think that our differentiation is that we want to remain hybrid, meaning we'll not be tying ourselves to one particular public cloud. We certainly -- it's too hard to use all of them, but we certainly will use the 3 majors that I mentioned for sure. But we also want to help you run things in your own data center. So that's your need. I actually think we might be not quite but almost unique on that lens. The only other one I think, who plays into that theme is probably Broadcom, who also takes a hybrid approach.

But I think that if I look at the breadth, we are perhaps unique in that. Now that may not be the whole market, but I would tell you it's probably half the market. Two years ago, I would have told you it's 40% of the market. Today, I'm going to look at you and say it's probably 50% of the market has a need for being able to straddle multiple public and sovereign.

Maybe, Arvind, just since we're talking a lot about Agentic AI and the cost sort of implication of these -- how much these agents will cost is going to be important. But over time, and we've already seen it in the last few years, the cost per token is going down at a pretty rapid pace. And it's kind of Moore's Law in some ways, maybe even stronger than that in some ways where you're seeing this significant decline in cost per token.

As you think about that, from your experience, if you are lowering this cost of the barrier to entry probably in some ways to adoption of incremental agents for workloads. Do you think that spurs up incremental demand? And just sort of how should one think about a future where you're really cutting down this cost of delivery? What happens to productivity and what happens to usage from what you've seen?

Yes. Look, I think it's going to be very hard to make a like a 90% accuracy prediction in this space. Let me just acknowledge it. But I also want to be a little bit provocative maybe, Wamsi, by saying the price per token is coming down dramatically. It's not clear to me that the cost per token is decreasing that dramatically. I think the next few years will tell us whether these things are being run in a way that is profitable for those who run them or not. Right now, it's kind of a land grab where people want to make sure they can get the workload, they can get the clients, they can get the population using it as opposed to is it being run in a way that is economical for the end investor. So I'll just sort of caution with that.

But it's also moving really fast. So it's hard to tell where it's going to end up. But all the capital being spent needs to get a return. So that's my point on the difference between price and cost, okay? Now I do -- there hasn't been a technology evolution ever of any size or scale where unit cost doesn't come down.

Back to your point on Moore's Law, the effective cost for compute on the x86 architecture came down by about 2x, let's say, for a multiplication or whatever you want to call it, every 2 years. So that means you could do more and more. And by the way, if it's every 2 years, that means after 20 years, it's at 100 the cost, 1,000 to be more precise, but it's actually about 100 in practice because Moore's Law had some approximations built into it. That's dramatic. But you've got to now get careful, which is not what is being done today.

If instead of a human doing the work, who says, I'll look at those 3 things and that's what I'm being paid to do. If you let agents say, well, I want to explore the entire universe and go to tokens and do it for $1 million just because I can because I'm running in the background, suddenly your costs will not be that good.

So you've got to then say, what am I asking these agents to do? And is there an actual business benefit for every single thing I'm asking them to do or there isn't. I think we are so immature today, we have no idea. I kind of jokingly say it this way.

If I ask you that if I throw a ball up, does it come down, you know the answer. But if you ask an LLM, it actually has to go back and recompute it based on its internal wakes. That's a lot more work than knowing the answer. So we have to arrive at this mix of what is the value of doing something and do I want to let it go off and ask itself 1 million iterative queries? Or can it get there quickly? And if it's doing 1 million, do I know that the return is worth that cost? No one has built out that infrastructure and that level of sophistication at this point. I'm sorry to give you such a long answer.

I want to understand that these things are nowhere near the point of maturity that you're pointing at. We can intuit the value, but now we have to sit down and prove. And I do think that '26, '27 is going to be the years when people are going to turn around and say, at least the serious clients, okay, I need to know what the ROI is. Experimentation is done. I got it that experiments can work, but is there an actual return now on doing it this way?

Yes. And ROI is going to be a big question to sort of the spend point that you made earlier on the CapEx levels that are being put in. Maybe shifting gears a little bit. You've done so many deals to change the portfolio. You kind of crafted Red Hat to begin with. That's been very instrumental in turning the company's software growth around. You've done a lot of others, Apptio, Turbonomic and Sana, I mean, to mention a few and Hashi and Confluent as well. So what is IBM's broader aspiration in software as you sit here with all this agentic uncertainty in some ways? And what do you think is the best use of capital given where some of the valuations are today?

Yes. So first, let me just -- it's clear to me that we want IBM to grow, and I wanted to grow maximum in software. Just to put some numbers, when I started, software was about 20% of the total revenue. I think as we finished '25, it was about 45%, so that just paints the stock picture of where we are investing, where we are putting our dollar, and we're kind of putting our money where our mouth is. That's where the growth is, that's where we're going to invest, but in a very focused way.

So without even mentioning which capabilities, number one, it's got to lie in our strategic priorities. And that today is the 4 areas we talked about. Is it Hybrid Cloud? Is it Automation? Is it Data & AI? Or is it mainframe? In mainframe, it's very unlikely we'll do a sizable acquisition. So it's really the first 3.

And if you look at the last 4 years, there's been a couple of data and hybrid cloud, but the majority has actually been in Automation. All the examples you reeled off Turbonomic, Sana, Apptio, Hashi are actually in automation.

Now with Confluent and with DataStax, we've done a couple in Data & AI. And we've done a couple in Hybrid Cloud. But because that is very much tied to open source, there haven't been that many there. We did a couple on Neural Magic, which was how to do VLLMs inside Linux really well and a couple around container security. So number one, it's got to fit in my strategic priorities. Number two, we can use the word synergy. That's a very generic word. I'll be more sort of direct. Can we make the thing grow faster than it will grow on its own? Because if all you get is the growth it was doing, that's priced in. I'm not getting any benefit from my investor by using our capital then.

So I look at, all right, is that synergy from the fact that I can leverage our global go-to-market by taking it to countries where they're not? Can I take it to clients where they can't go? Can I get a much more effective go-to-market structure by leveraging the footprint we already have? Can we leverage the partnerships we have to make the acquired entity grow faster? Or is it making something else in IBM grow faster because effectively then that is synergy. In the best cases like Red Hat, you get all of those.

In smaller ones like, let's say, Apptio, the biggest use is the expanded go-to-market where we can take it to places where they would have gone, but -- what they were going to do in 10 years, we can do in 2. So why I mentioned all those is that's really important. And third, we are putting money at risk. So we want it accretive to free cash flow within 2 years. So that's the discipline we follow.

So just to put numbers on one, maybe the largest of these, let's take Red Hat, $3.4 billion of revenue when we closed the deal in 2019. Our run rate is almost $2 billion a quarter now. So that tells you. The OpenShift portfolio was like $130 million a year in 2019. It's now close to $2 billion. So that's a massive growth when it went from a contender to being the leading product in its space for a container and virtualization platform. So that's a great example of what we did. But we can see it play out, whether it's in Turbonomic, whether it's in Hashi. In all of these, we begin to see an expansion of the market, and it's accretive to free cash flow because we do all.

We get the added revenue, but we're also very good at making the entity far more productive by leveraging all of the shared services and the go-to-market structures we already have. We very rarely touch engineering. Engineering tends to actually be increased spend from where it was. So we can give even more innovation to the clients. Hopefully, that gave you both the 3 vectors that is our discipline, but also we actually have a very tight methodology on how we do these.

Yes. Yes. And what about software valuations where they are? Like does that make it like a better sort of more attractive maybe time for IBM to consider public company deals?

Actually, I would say public and maybe even PE. Look, in the end, maybe it will take a few months, maybe a year. But even the PEs have to reconcile to where the valuations are in the public markets because that's hard. That's evidence in front of you. Look, it's all based on -- if the multiples are well over 10 for something of size, by multiple, I mean valuation or EV to revenue, that's very hard for me to make our accretive in 2 years work. It can -- the growth rate is incredibly high, but that's unusual to get that for a sustained period of time.

So the current coming down where now the multiples are often 5, 6, 7, 8, that's a very attractive place compared to what the last 10, 15 years have been. So I'll just sort of smile and say, "hey, it's a great opportunity for those of us " who have the cash flow and the financial flexibility. And maybe -- I mean, it's got to stay in place for some time. But if it stays in place, maybe it gives us a chance to accelerate what we might have done over a few years into a shorter period of time. But it's got to be the right target. I'm not going to get deal heat. I don't want to just run after everything. It's got to be #1 in its category. It's got to have something which we believe is sustainable. It's got to have a great culture, and it's got to have innovation, which has a moat around it.

Yes. Okay. That's helpful, Arvind. We're sitting at a time of, again, unfortunately, a lot of global macro uncertainty. And IBM generally has done quite well in turbulent times. I mean it's actually been a flight to quality from a stock perspective and I think business resiliency, too. Any thoughts on sort of quick takes on that? And I know we don't have a ton more time, but I do want to ask you about Quantum, too. So I would love to get some quick thoughts around this.

Yes. So I'll try to keep it brief. Look, we have faced a bunch of this macro uncertainty based on geopolitics now for the last many years. We often ignore China. China is buying less and less American technology. That's the first one.

The Russia, Ukraine, we only got hurt not so much from the footprint in Ukraine. We did have to give up a business in Russia, which was a few hundred million dollars. So that went out. I wouldn't call it the highest growth business, but an absolute amount went out. I'll have to give a lot of credit to how Israel functions. They've been remarkably resilient despite all of the issues going on there.

In some sense, they have almost made a decision, a willful decision that they want to carry business on as much as possible. The Middle East one is harder to predict just because it's very young. It's like a week old. I think tomorrow. So if -- now my view is that we find that these things don't impact us as much maybe because we are in the more critical parts of the infrastructure and the more critical workloads.

I think a big reason it's partly it's a flight to quality, partly it's also when clients are trying to understand who they can trust and who they can't trust, who will stick with them, who will give them great service, who will give them kind of what they need to run their business, we tend to come high on that list. Like often when I go to people and say, look, we are never going to look at your data. They look at me and say, we don't even have that question where you're concerned.

We actually know this, given your history that this thing, like when a client has an issue, they don't even ask us, "Hey, did you look after our data correctly if I'm giving you a dump and a log that it has all kind of sensitive information. " They said, we know that you will destroy it, and we know that you will not misuse it, and we know.

So that also helps in that perspective that we are a safe harbor when times are uncertain. And I certainly expect -- but we've got to prove it. We've got to prove that our performance is there. We've got to prove that we can produce the revenue and the cash flow that we have committed. And as long as we keep proving it, then we should be able to attract investors even at this time.

Yes. And I have to say that you definitely have surprised to the upside on free cash flows ever since you were doing closer to low double-digit billions to now 50% higher than that has actually been a very significant progress in your cash flows.

Maybe to wrap it up, Arvind, I know we're coming up on the top of the hour. What is the biggest opportunity that you see for IBM in the next 5, 10 years? And I'm sure Quantum is maybe at least part of that, if not it, but would love to get your thoughts around that. How you think about it? When does commercialization of this start? And what -- how disruptive could this be as a technology?

So Quantum is the single largest opportunity for us over the next 5- to 10-year horizon. I look at it's disruptive because it will solve problems, which you actually cannot solve using alternate techniques. How do you make new molecules without doing an actual experiment? How do you do fertilizers? How do you do portfolio management? How maybe do you price in a risk environment. And in all of these, if you can get a few basis points of improvement on one, but a few months of advancement of materials, that's a pretty big advantage for our clients.

The timing I would put it as 2029, if I had to pick a time, you can give it a plus/minus some, but I would actually pick 2029. Given that I said that 1.5 years ago, and I'm saying the same today, as every 6 months goes by, that gives you a lot more certainty on that date. And it will begin as usual. It will be national labs, it will be scientific workloads. It will be people like that who will consume it initially very quickly. Usually, capital markets will follow.

But this time around, I think the industrials will come next, who historically have not been the big beneficiary of new forms of competition. We did work with some third-party consultants. We worked with both BCG and McKinsey, both kind of had the same number. They think the value impact of the disruption is on the order of $0.5 trillion by 2035. You can then back up from there and say, what's the rate and pace, and probably that's the value disruption. Usually, about 30% to 40% will accrue to the vendors in the space, which is not just us. It will be the software providers, the implementers, the consultants. But about 30% to 40% of that $0.5 trillion is going to accrue back to the ecosystem of people who kind of provide that. I'm very excited by our progress.

I'm very pleased with kind of where we stand in this ecosystem right now. And I would turn around and look in eye and I say, of course, we have to keep doing it because you're still a few years away from the price, but I feel very good about our competitive positioning in this space.

No, that's amazing. I know we're at the top of the hour. We're actually 1 minute past. So Arvind, thank you so much for being so generous with your time and your insights. We work back the COBOL disruption on the stock. Now I guess, hopefully, this will give people a way to think about the misunderstood maybe software disruption risk to IBM's portfolio as well. Thank you for all your time. We really appreciate it. As usual, I feel like I'm walking away with a ton more than the start of the call. Arvind Krishna, everyone, thank you so much for joining us today.

Good to be here with you, Wamsi.

Thank you so much. Thanks, Arvind.

This concludes today's webinar. You may now disconnect from the call. Thank you.

Well, let's get started, guys. Thank you very much for joining us. Welcome to day 2 of the flagship TMT Conference. My name is Erik Woodring. I lead the U.S. IT hardware coverage here. I am delighted to be joined by Rob Thomas, IBM's Head of Software and Chief Commercial Officer. But before we get started, I just need to read this disclosure statement. I need to mention that important disclosures can be found at the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative.

So Rob, thank you very much for joining us today.

Thanks for having me. Great to be with you.

So I think the best place to start maybe is just better understand how to think about the key drivers of growth for IBM. So exiting 2025, 3 of your 4 software subsegments were growing double digits. Mainframe had a record year, helping to offset maybe more tepid growth in services. So taking a step back as we think over kind of the medium term and beyond, what are the key growth drivers as we think about the IBM model? And let's start from there.

So maybe go back to 2020 when Arvind Krishna took over. He had the insight that we can be uniquely differentiated and win in hybrid cloud and AI. And that was actually before AI was, I'd say, such the moment in discussion that it is right now. But everything we've done is about repositioning the company for that. You take software, we kind of moved away from applications, divested health care applications, weather. We really said we want to be an infrastructure software company because we think we can be really good at that. For consulting, we built big partnerships like AWS, Microsoft, expanded what we've done with SAP and really became, I'd say, a partner of choice for key skills and consulting helps our software business a lot.

And then for infrastructure, we said we need to continue to innovate, simplified what we're doing in storage. As you said, we've had great growth in mainframe. And a big catalyst has been, I'd say, everything we've done in the go-to-market, and we'll kind of talk about that as we go. But as you look forward, it's not a change in strategy. We have the right strategy. It's about how do we continue to be a high-growth software business. We kind of said we can get to double digit for this year, which we think is a big step. And we have a lot of momentum. So we feel good about that.

Great. So let's first touch on something that obviously was the market responded to, which is AI disruption risk. We've heard it across the software stack. I want you to address IBM disruption risk at IBM because you lead the software business, you lead the sales team, so you perhaps have maybe the broadest purview to the potential impact to IBM. So there's maybe thoughts around disruption risk to mainframe and software more broadly. Let's just start on the software side and say, help us understand how you think about that risk to the portfolio? And could it bring disruption risk, could that bring opportunities as well if we want to maybe flip that question on its head.

Obviously, we think about this a lot. And AI has been a good tailwind for us, which you've seen in the last few years. And it's on us to make sure that AI continues to be a tailwind. And so we think a lot about how are we ensuring that, that is true. I would say now you take a slight step back, when you hear all the discussion of generative AI is -- makes it easy to build new applications. That's kind of the buy/build discussion that has always existed in technology. Yes, clients can build something or they can buy it. We shouldn't forget normally 20% of the effort in deploying software is actually building the application itself. The 80% is how do you do upgrades, how do you bring new features? How do you support it? How do you do security fixes. There's a lot that goes into that. And I'm not sure the buy/build equation will change dramatically for our clients.

Let's look at what's happening in software. So think about maybe a software application, which we're less in that business. You've got a user interface, you've got business logic and then you've got a back-end database. I do think AI will become the new UI. So there's probably a lot of risk if your moat is based around a UI.

Business logic, I would say some of that can be done with AI, but maybe not all of it. And then you have kind of the back end, the plumbing, that's what we do. When we see things like projections of 1 billion new applications being built based on generative AI, to us, that's a catalyst because every one of those applications is going to need containers. It's going to need real-time data. It's going to need infrastructure automation. It's going to need all the things that we do. So I really view this as an opportunity, but we have to stay sharp here and stay focused. To give you an example, one thing that we previewed at our developer conference last November was Db2, but an AI edition. And I think there's a big opportunity for companies like us where we have incumbency to take a product like Db2 that's used everywhere in the world and say, bring AI capabilities that takes 85% of the labor out of operating Db2, I think that creates a growth catalyst.

Yes. Okay. That's extremely insightful. So let's turn that conversation to mainframe, and we're going to kind of ask a 2-sided question as well, which is it's obviously been a very strong start to the z17 cycle despite the kind of perceived AI disruption risks around COBOL modernization. So I'd love for you to add any color on, first, why this cycle has started strong, but then also maybe understand your long-term view on the mainframe. And maybe the simple question is, is it an AI disruptor? Or will it be disrupted by AI?

So we've been working on mainframe modernization. I think it goes back a decade. And why would we do that if we didn't think it was good. So we actually think it's very good, modernization. And if you look at what happened, call it, 2015 to 2020, I think that's the time where we working with clients, we looked at certain applications. We said, we're not sure this belongs on the mainframe. You don't need a CRM app or a mobile app on the mainframe. Mainframe is really good at processing transactions. So anything that's transactionally intensive, that belongs there.

So we did a lot of work on mainframe modernization. And I think we got to the point where clients realized this is the most price-performant way to do transactions. And that's why you've seen the growth that you've seen. I mean, 3x MIPS growth in the last couple of cycles. That's because the right work is happening there and people are willing to lean in and do more. So number one is I think mainframe modernization is good. We will continue to do that.

Secondly is I think what gets lost in some of the COBOL discussion is mainframe is truly a platform. COBOL is machine-level code, meaning it does not run unless it's taking advantage of the hardware architecture of the mainframe, storage subsystems, memory optimization, how you're using the compute. The very simple analogy I would use is you could vibe code a version of iOS. That doesn't mean you're going to deploy it to 1 billion phones because the software works because it's tied to the phone. It's actually the same thing with mainframe. COBOL applications built on mainframe are designed to run there. They don't really run that well anywhere else. And so I think this platform point is really critical, which kind of links to the first one. If you have the right apps there, you should keep going, and that's what most clients do.

Third comment I'd make is there's a lot in the world right now on sovereign technology. I think it's unlikely people are going to use SaaS applications to do modernization of the mainframe. Clients, governments that are using this the fact that it's their environment, they can control it, they can control who's utilizing it. So I think there's a lot of positives to what AI can do for mainframe. You can see it in a lot of the products that we've built as well around this. And so that's where we are at the moment.

Okay. Okay. Really helpful. I want to now maybe move beyond AI disruption and kind of touch on some of the subsegments. And the first one just being Red Hat because the business has compounded around 12% constant currency over the last 2 years. You hear Arvind or Jim speak, and they sound extremely confident that Red Hat can kind of reaccelerate into -- back into kind of a mid-teens growth rate. So help us understand the specific factors or the specific drivers that give IBM the confidence to say, yes, we can get back to that mid-teens growth rate.

Let's start with the original thesis on the Red Hat acquisition. Our view was that we were at the start of what I think will be probably a 20-year cycle of a new application architecture, which is containers. We had a really good play in IBM, if you go back to, call it, 1999 with J2E and Java. And you get about 15 years of growth and then you get a long tail of this is how applications are built and run, and we had a very strong play then. Our view was the same thing was going to happen again, but this time, the architecture would be containers because that gives you the flexibility to run on multi-cloud or hybrid cloud. It makes applications easier to utilize. And so this is why you see, I would say, the success we've seen with OpenShift. So a couple of billion dollars now, growing 30%. I think we're actually just getting started. If you had to put innings on this, we're probably in third inning on containers and what will happen around application modernization. So I think there's a lot of room to run here.

Second is there's been obviously a lot of, I'd say, question marks on virtualization and do people want to stay on VMs or do they want to move to something else? I think we have a great alternative there for companies that are looking to move to containers. We've had really strong bookings there, which has been great. That's a good catalyst, but that takes time to play out. It's a lot of effort to move from VMs even to Red Hat virtualization or Red Hat OpenShift ultimately. And so I think that will continue to go.

Ansible is a great product. There's a lot of free use of Ansible. So part of what we've done kind of connecting to our M&A strategy, when we buy something like HashiCorp, the integration we do between Terraform and Ansible requires that you have premium versions of each. So that's a good way to create monetization momentum around Ansible.

And the last is Linux. I think probably the most underdiscussed point on Linux was a couple of years ago, we announced a partnership with SAP that said they would be moving from SUSE to Red Hat Enterprise Linux. That takes a long time to play out. But as you probably know, SUSE is kind of what it is because of SAP. And so we think that's a big growth driver for us over time.

Yes. Okay. If we move to transaction processing, obviously, very clearly tied to mainframe. You've guided to low to mid-single-digit growth in 2026 for transaction processing. I'm going to ask maybe kind of in an aggressive way, which is why isn't there upside risk to that? If I consider that there's been 100% growth in installed MIPS capacity with the z17, your long-term TP guidance is for mid-single-digit growth. Are there offsets that maybe wouldn't allow TP to grow in line with that midterm model? Or is there conservatism baked into the 2026 outlook? Would just love -- what are the building blocks there to go underlying that mid-single-digit growth?

There's a few drivers of TP growth, I would say 3. So one is MIPS growth that you alluded to. As I said, as people have modernized, we think the workloads are roughly right. And when people like it and they are using it, they're going to continue to grow MIPS. So that's one.

Second is price. We can command price on mainframe. At the same time, we're not going to do that too aggressively. We want to be the gentle in the room that's helping clients be successful. We don't want to push on price. But it's a lever that we can use. And third, and I think this is probably the big swinger is new innovation. If you go back in time, people ask a lot why was there not as much TP growth, call it, 2015 to 2020? We didn't do a lot of innovation then. There wasn't a lot of new product.

And you look at since 2020, let's see kind of off the top of my head, we've delivered Code Assistant for Z, watsonx Assistant for Z, IntelliMagic, which is AIOps. We brought HashiCorp Vault on to Z, Terraform on to Z, we brought a ton of new product on to Z. And what we know with Z clients is they will adopt new capability if they see value. They also will not do that nearly as fast as any of us may want because the mainframe is the heart and the lungs of the business. So you're not going to start to do major surgery without making sure everything is lined up, and it's going to go well.

And so that does take time. There's also a lot of mainframe tools that are not IBM and many of those clients -- many clients come to IBM now saying, "Hey, we'd like to use IBM mainframe tools." We can help them with that. But that also takes 2 to 3 years to do. So I think kind of the upside, downside to your question, it comes down to adoption of the new innovation, and I think that kind of ebbs and flows.

Okay. That's really helpful. Last question on software, at least specifically to kind of how we think about the segmenting is just talking about your overall strategy on the automation portfolio that you've built, how Hashi fits in with that strategy? And then specifically on Hashi, just an update on integration, how cost synergies are progressing as the Chief Commercial Officer. There's a lot of -- you kind of outlined a lot of benefits that Hashi can benefit from under the IBM umbrella. So let's maybe unpack that.

So I'm super pleased with everything we've done in automation. This wasn't even really a business we were in if you go back to 2020. And our thesis at the time was there's AI models, and those will be important, but I would think of this as applied AI. We thought every company that has technology and operations, which is every company is going to want to apply AI to make that way more productive and efficient. And that was the thesis behind automation.

So we started with -- we bought Turbonomic, which is how you optimize your compute and storage. We bought Apptio for FinOps and understanding how you spend money on technology. We organically developed a product called Concert, which is resiliency for your technology and operations. Then we bring in HashiCorp. So we've built the best portfolio for how you apply AI to improve your technology and operations. And I don't think there's a close second at the moment. So that's why you can see the momentum that we've built in the business, which has been pretty consistent.

I actually think we're just getting started here because any client you visit, they still have a lot of complexity in what they're doing. So as we think about things like Concert, we will evolve this to become a platform for all of your need for FinOps and AIOps taking advantage of these other capabilities. So I think there's a lot to do here.

Now linking it to HashiCorp, I'd say probably the biggest driver here is what I would call the new stack of enterprise software, which is companies want something that's open architecture, open source in many cases, easy to utilize. And think of that as Red Hat HashiCorp for automation, Confluent when it comes for real-time data. I think we have a unique play for a new enterprise stack, which will have sovereign deployments, can run on public cloud, can run hybrid. And to your question on kind of the Hashi integration, I think it's gone incredibly well. I think a few people ask questions when we did it, will there be dilution? I think we've really perfected the playbook on M&A, which is why we are confident to then go after something like Confluent and say, we can do this, and we can absorb the dilution as part of our model.

Yes. And on that point, just using HashiCorp as the example, lessons learned for as Confluent closes, integration, again, the ability to drive cost out of the model. What have you learned from Hashi that you are applying as you think about the closing of Confluent? And then what you would do, obviously, beyond that into the future?

One, when we buy companies, we tend to invest more into R&D because we can get innovation going faster. We'll continue to do that. Two is we can integrate kind of back office quickly. What we've learned from both HashiCorp and hopefully Confluent to come is with companies like this, they tend to have a different way of leveraging technology to run their business. We can learn from that. We learned a ton from HashiCorp on that, and I sense there will be some things for Confluent as well. Third is we can get a lot of synergy and go-to-market quickly. We have broad distribution. We've got great coverage across the top 1,000. We've got a big ecosystem. And so as we bring in new capability, we can distribute quite quickly.

Okay. I want to ask one services question, and that is the market is going through its own kind of perceived risk related to AI disruption. You guys have been able to capture, I think it's over $10 billion of cumulative AI bookings in 2 years. I know you're not necessarily going to share that going forward. But how much of a driver will AI be in reaccelerating this market or maybe more specifically, the IBMs specific services business? And how do we think about maybe the risk of cannibalization between AI engagements as you see them today and the traditional old projects within the services business? Is there cannibalization risk? Or how do we think about all of those together?

So I'll decompose the services market, kind of simplistic. But do you think kind of at the bottom end, there's kind of body shop, just work for hire. Kind of the top end is the McKinsey BCG types. We're kind of more in the middle, which is how do you apply technology to make your business better. I think AI coding tools are very good. So body shop type work, we've tried to move away from that because I'm not sure that's where you want to be as there's more and more AI in the world.

We announced Consulting Advantage, which is really our play on services as software. And I think we hit a sweet spot here where clients want assets as part of a consulting engagement. They don't just want labor because, obviously, they want to get more productivity over time. So I think we kind of hit the sweet spot there. We don't really play at the high end. So at this point, we don't really play at the high end. We're not trying to be BCG and McKinsey. We don't really do the body shop work. We're kind of in the middle. I think the partnerships we've built was really all upside for us because we weren't doing much with AWS and Microsoft before. We're doing a good bit with SAP, but there was more we could do. So I think partnerships with Palo Alto, we've built partnerships that kind of catalyze what we're doing. We've got consulting advantage that gives us a unique platform for deploying technology. I think we've found the right balance here.

And I think something that maybe is differentiated for you guys as it relates to this business is, while you do have partnerships and those have been very strong, you also are -- you deliver technologies that can be solutions for what your customers are coming to you on the services side. Just talk about how that integrated portfolio maybe gives you an advantage over peers.

Well, it's certainly a big driver of the AI book of business that you described because any company that we're talking to about AI or applied AI, their next question is, can you help us? And so having an integrated model, we're one of the few companies that does. I think that gives us an advantage. If you think about what we've done, the progress on Red Hat with things like OpenShift and containers, that would not have happened without the role that consulting has played in that.

So we kind of pick the key places. It's not we want our consulting to do every IBM product, but it's make the bigger bets. Red Hat was a big bet. Watsonx was a big bet. I'd say the next one is this kind of this new enterprise stack I talked about, which is as we bring in Confluent, HashiCorp, there's an opportunity to do more there with consulting.

Okay. Cool. So I'm going to circle back to software and then maybe get into things like M&A and cost rationalization. As we think about the totality of the software business that you run, what are some of the maybe potential upside you see to that kind of 10% low double-digit targeted software growth over the next 1 to 3 years? What could be some of the downside risk? Maybe just frame the risk-reward as you think about it relative to how you've communicated growth to the market.

So I think maybe the thing we haven't talked about much yet is the change that we've made in go-to-market, which has been dramatic. And go back to 2020, I would say there's 3 main things we did. One was a change in incentives. Two was really, I'd say, opening up the ecosystem. And three was kind of building a technical go-to-market. So let me describe all 3.

Incentives, I would say we're largely done with that. We saw the opportunity to create a much more aggressive sales culture where people were incented to go out and deploy technology, make clients successful. I'd say we're largely done with that. For the rest of go-to-market, I think we're actually less than halfway through the transformation. So on partnerships, we've built some great ones. We have now over 100 software products that run on AWS, about half that, that run on Azure, about 1/3 of that, that run on GCP. So we've built big hyperscaler partnerships. We're listed in all the marketplaces. That's a big lift for how we're going to market.

We've also built partnerships with SAP. I talked about that a little bit before with -- obviously, with Linux, but they're also using Red Hat OpenShift. They're using HashiCorp. So different dimensions on partnerships. On technical sales, the way I'd describe it is we were too much steak dinner and not enough writing code. And I would just go talk to clients and say, we want you to help us deploy technology. We don't need all the other stuff. And so we've done a pretty dramatic change in the complexion of the sales force to be way more technical.

When we show up to a meeting, it's not PowerPoints. We want to roll up our sleeves, demonstrate technology, get it up and running. But I say, for these 2 for ecosystem and kind of technical go to market, I think we're only maybe midway through that. But we've done a lot on the R&D side. We kind of talked about that. I think we have a great portfolio. Go-to-market is the other piece. The combination of those 2, that gives me a lot of confidence.

Okay. Great. Let's maybe shift to M&A. And maybe the question is, how much internal desire is there to quickly get another acquisition? I know you're still even in the process of closing Confluent, but it is a key part of the model and specifically in software. So how much internal desire is there to get that done? And then second, has the criteria for M&A kind of changed at all as you think about what could come beyond Confluent?

Well, priority #1 is get Confluent done, so we should be clear. Not that I call desire. I'd say we are always open to what's possible, and we're always looking. I think it was Benjamin Graham that talked about Mr. Market gives you opportunities. Clearly, with what's happening in the markets right now, there's a lot of opportunity that was maybe less obvious a year ago. So yes, we keep a close eye to that.

But in every decision, we're always going through buy/build. To some extent, I think we can build anything, but we tend to buy when we can accelerate time to market or we can acquire category-leading assets, that's kind of the criteria, the process that we go through. It has to be at a reasonable price. So there's no real urgency or desire, but I'd say we're always open to looking at this.

And the criteria in terms of -- I think the way that Arvind has communicated it was, I think it was 75% software, 25% services. It has to be additive if you bring it into the IBM distribution channel. There needs to be a path to becoming free cash flow accretive within kind of, I think it's 1 or 2 years. That criteria has not changed. That is still kind of the building blocks for how you approach those targets.

Yes. Criteria has not changed. It's accretive within 2 years. And I think there's -- as you think about our software portfolio, there's certainly more we can do on automation. There's more we can do in data. I'd say most of what we would do in Red Hat or transaction processing tends to be more tuck-ins. Those tend to be smaller. But I think there's still a lot that we can do, but the criteria hasn't changed.

Okay. Cool. I know not necessarily core to your role at IBM, but something that I think of that is extremely impressive and it ties together with M&A, especially sometimes companies that might be dilutive when you purchase them at first is the cost rationalization we've seen at IBM. You are leaning into R&D. That line is clearly growing. You've talked about innovation. But if you look at SG&A, you've really been able to kind of hold that steady. How are you able to basically keep that spend steady while also absorbing, let's call it, at least one pretty sizable transaction, at least one every year?

So maybe to go back to where you started. I actually do view this as core to my job. And I think that's something Arvind kind of has pushed all of the management team on as we all own productivity for IBM. So it's part of all of our jobs. I think it's critical. To your point, the main thing that we looked at was we're spending a lot of money in what I would call back office. That tends to be repetitive tasks.

And our view was AI, specifically watsonx, could do that very well. So we've driven nearly $5 billion of productivity, all coming from back office. And we've taken that -- and a lot of it has been reinvested into R&D to your point. R&D has grown 30%, basically, almost over $2 billion since 2019. And that's the important part of being a technology company is you have to invest more in -- you have to invest at a competitive level in R&D, and I think we're getting to that point now. So that's all been, I'd say, an intentional strategy.

Now where do I think we go from here? As I look at things like our go-to-market, I still think we can be more productive, whether it's through leveraging channels or ecosystem. If you become more technical, then your deal sizes can go up because your consumption grows faster over time. So I think there's still more to do on the productivity and the go-to-market, which is why we can kind of, I'd say, stay where we are. But as we grow revenue, we get more leverage out of the business model, which is what we've tended to do well.

Okay. I want to touch on kind of the combination of everything you've talked about plus leaning into R&D, and that is it's clear you are leaning into innovation, you're expanding your portfolio, perhaps in ways that people don't appreciate or perhaps in ways that you didn't necessarily do, as you mentioned, from 2015 to 2020. How would you characterize what the next phase of innovation at IBM looks like? What should we expect to see more of going forward?

So first of all, it will be rate and pace. Because of what we're doing with AI, the productivity of the engineering team is going up dramatically, which is great. So we built a software development tool called Bob. We now have 40,000 people in IBM using it. And it's not just software developers. We have people in our finance organization using it. And the productivity that this drives is immense. And so one is I just look at rate and pace. Can we improve the amount that we're doing and the speed at which we're doing it.

Two, kind of my example before on creating AI additions of existing products. That used to be very hard. It's not easy now, but it's actually easier. You can get something out there, you can iterate with the client. And so we'll continue to do those types of things.

Next is we made -- we kind of did a technology preview at Davos this year around something called IBM Sovereign Core. And this is our view that technology architectures are going to change again in the next couple of years, which is countries around the world are looking for sovereign deployments of technology. And so again, we went from kind of idea to a technology preview. We'll make it available later this year for what we think is really the only alternative in the market for sovereign AI that can run on-premise that can leverage containers, that can leverage automation. And so we're going into, I'd say, spaces that before we probably didn't have the capacity to do or maybe even the imagination, but I think now we can do that.

Okay. Before we wrap, there's a lot of things to kind of get excited about here. We didn't touch on quantum. I just maybe want to give you the opportunity because this should be a very important year. The goal is to kind of prove quantum advantage. Just your broad thoughts on that before we maybe get to a wrap-up question.

I think quantum is going to be significant, and it will have a decent piece of computing infrastructure going forward. We've kind of said 2029, 2030 roughly is the range. I think probably the question is, is quantum a huge part of computing or a small part? I don't think we know that yet. But I'd say our view is it will be significant because as we demonstrate quantum advantage and as we solve error correction, the use cases are things that cannot be done with classical computing, whether it's going to be drug discovery, material sciences, natural resource discovery, we will go after a set of use cases that economically and in terms of GDP growth are really critical, but are really untapped today.

And I think the HSBC example that was shared last year was kind of the first demonstration of this has relevance to even a use case like bond trading and bond pricing, which I think kind of woke people up to say, maybe there's something really here. We open sourced our development kit, Qiskit, a couple of years ago. The momentum there from students, developers, academics, research labs is enormous. I think it's really the default answer for quantum applications right now. So I think we have a lot of momentum here, but we still have a lot of innovation to go.

Yes. Okay. Fair. Last question before we wrap up is just as we think about everything going on at IBM go-to-market changes, what's happening in software, the momentum you're seeing in mainframe, kind of the changes to the market going on in services, quantum, there's a lot obviously clearly going on. What are you -- maybe the question is what are you most personally excited about as you think about the next 3 to 5 years? What do you want investors to kind of take away from that and this broader conversation?

I go back to this notion of there's going to be a new stack for enterprise software because these kind of run in 10- to 15-year cycles. We're kind of coming up on this next moment. And you look at what we've constructed with Red Hat, HashiCorp, Confluent to come, watsonx Orchestrate, which is our agent fabric. I think we're the only company that truly has what customers want out of this new enterprise stack. So that's number one. I think this is a shift in technology, and I think we're leading this.

Two is a lot of IBM's business today is in call it the top 2,000 customers in the world, 2,000 companies. We've made a shift in our go-to-market to focus on outside of the top 2,000. And I'd say after a year or so of doing this, the results are encouraging, meaning I think we have the portfolio now that we can cater to a new customer base, people that may not know IBM, that want to start to learn about IBM. So I'm pretty optimistic that there's a lot of TAM expansion for us, just getting outside of the top 2,000. By the way, it's half the IT market. So it's not a small prize.

That's perfect. I think it's a great place to end. Thank you very much, Rob.

Thanks, Erik.

Awesome.

Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time. Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's Fourth Quarter 2025 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer. We'll post today's prepared remarks with a replay of today's webcast on the IBM investor website within a couple of hours. The earnings presentation is already available. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of our presentation, which is posted to our investor website. Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings. So with that, I'll turn the call over to Arvind.

Thank you for joining us today. Let me start by reflecting on our strong performance in 2025 and the execution of our Investor Day model, then get into more detail on the quarter. We are excited about the progress we made in 2025, delivering 6% revenue growth, our highest level of revenue growth in many years and $14.7 billion of free cash flow, our highest level of cash generation in over a decade. As we laid out at our Investor Day in February 2025, we are executing on our strategy to advance IBM as a software-led hybrid cloud and AI platform company. We entered 2025 intently focused on investing in innovation and productivity initiatives to accelerate our shift towards durable, higher-growth end markets in software with expanding margins and strong free cash flow. Today, software represents approximately 45% of our business, up from about 25% in 2018. Software grew 9%, our highest annual growth rate in history with 3 of our 4 software subsegments delivering double-digit growth rates. Innovation value can also be seen in our IBM Z performance, up 48% this year, achieving the highest annual revenue for Z in about 20 years. I'm proud of our achievements in 2025 as we exceeded all of our target metrics for revenue growth, profitability and free cash flow that we laid out at our Investor Day. Our flywheel for growth is underpinned by client trust, flexible and open platforms, sustained innovation, deep domain expertise and a broad ecosystem, and that's exactly what played out for the year. Let me now touch on the macro. We continue to operate in a dynamic environment, but one where client demand remains resilient in the categories that matter most to IBM. Enterprises are prioritizing technology investments that drive productivity, resilience and flexibility, particularly in hybrid cloud, AI and mission-critical infrastructure. These technologies are no longer viewed as incremental tools, but as platforms that fundamentally change how businesses scale, compete and operate. As clients modernize core systems, redesign workflows and seek to extract more value from growing volumes of data, expectations for integration, security and performance continue to rise. These trends are structural, and they align closely with IBM's strategy and strengths. Now turning to our execution in the fourth quarter. We delivered total revenue growth of 9%, our highest level in over 3 years. Software growth accelerated to 11% in the fourth quarter, driven by the strength of our diversified portfolio. Both data and automation are gaining strong momentum with clients, growing 19% and 14%, respectively, in the quarter. As AI adoption accelerates, enterprise clients are increasingly focused on how to keep operations running smoothly in a more complex and hybrid environment, fueled by a surge of new applications. Our end-to-end portfolio of leading automation and data solutions help clients manage and optimize operations, automate infrastructure and workflows, build resiliency, secure and govern data and drive cost efficiency. Consulting continued to grow, up 1%, reflecting increased demand for AI services as clients need help designing, deploying and governing AI at scale. And infrastructure delivered another robust quarter, growing 17%, driven by strength in Z17, which has been outpacing z16 performance. A key contributor to this momentum is the innovation value we are delivering with Z17 processing 50% more AI inferencing operations per day than z16 and bringing real-time inferencing capabilities inside IBM Z. The breadth of our AI offerings is another key differentiator, combining an innovative technology stack with consulting at scale and our client zero journey. Our cumulative Gen AI book of business now stands at over $12.5 billion, of which software is more than $2 billion and consulting is more than $10.5 billion, with both seeing the largest quarterly increase to date. As we look at the evolution of AI, our opportunity is to make it easy for clients to build AI that is specific to their data, their processes and their competitive needs, including the effective use of smaller, more efficient models where they make sense. That is why IBM's approach spans consulting, watsonx, our Agentic platform orchestrate and Red Hat AI. Our announced acquisition of Confluent is another pillar in this strategy, helping unify our hybrid cloud and automation solutions through a smart data platform. Confluent has the most capable technology to unlock the real-time value of data across applications, clouds, APIs and as AI agents enter the enterprise, they will need access to that data in real time. Confluent is a great way to deliver that in a controlled, secured and governed manner. Our hybrid approach to models also enables clients to use the best option for each use case, IBM's granite models, third-party models or open models from Hugging Face, Meta and Mistral. In addition to being a demand driver, AI is also a powerful productivity driver for IBM, contributing to our strong financial performance. In 2023, we set out on a goal to achieve $2 billion of productivity savings exiting 2024. And today, we are well ahead of that, exiting 2025 with $4.5 billion of annual run rate savings. We have been accelerating our productivity initiatives to enable investment in innovation and highly strategic acquisitions like HashiCorp and Confluent while continuing to deliver strong margin expansion and free cash flow growth. HashiCorp continues to accelerate within IBM, benefiting from our go-to-market distribution and joint product innovation. We see a similar opportunity with the announced acquisition of Confluent, leveraging IBM's global go-to-market reach to accelerate growth and disciplined G&A structure. Accelerating organic innovation is a core focus for IBM. Project Bob is IBM's next-generation AI-based software development system designed to transform developer productivity. Bob introduces intelligent orchestration between industry-leading frontier models such as Anthropic, Claude and Mistral, small language models, including IBM Granite and custom models, all optimized for cost and performance. We have more than 20,000 IBMers that are using Project Bob, reporting productivity gains averaging 45%, a powerful client zero use case. We are also advancing innovation through deep M&A product synergies. For example, we recently developed Hashi InfraGraph, a real-time graph of infrastructure and application configuration. By fusing InfraGraph's insights with IBM automation products like Concert, we unlock true root cause analysis and proactive prevention for clients. All this leads to real tangible value for our clients. Companies like Morgan Stanley and FedEx are leveraging our technology solutions and infusing our GenAI products into core workflows. And Mastercard is leveraging our technology solutions, including data management platforms, software platforms and Gen AI products and solutions. In infrastructure, clients such as CVS are turning to z17's AI capabilities for enhanced management of mainframe application workloads and increased resiliency. We also announced new or deepened strategic partnerships through the year with AMD, Anthropic, AWS, Microsoft, OpenAI and Oracle. Recently, we announced a partnership between Red Hat and NVIDIA that aligns our hybrid AI solutions and NVIDIA's AI stack. This collaboration allows enterprises to deploy AI-accelerated applications across any environment from the data center to the public cloud using a unified automated infrastructure. It represents a significant step forward in making high-performance AI more accessible and scalable for the hybrid enterprise. Innovation, combined with our strategic partnerships across consulting with key hyperscaler and ISV relationships and software with key data providers drive a multiplier effect that fuels our flywheel for growth. We continue to make steady progress in quantum computing. Over the past quarter, we advanced our development road map, improved error correction capabilities and expanded ecosystem partnerships. Our collaboration with organizations such as Cisco and participation in government initiatives like the U.S. Department of Energy's Genesis Mission and DARPA's Quantum benchmarking initiative reflect growing confidence in IBM's approach to building scalable fault-tolerant quantum systems. Quantum Advantage will require high-performing hardware. And in December, we deployed our first 120-qubit IBM Quantum Nighthawk-based system for use by our clients. Back in 2024, we predicted that we'd see Quantum Advantage by the end of 2026. And with the help of IBM hardware, software and rapid cycles of learning, our partners in the scientific computing community are starting to make the first credible Advantage claims. We remain on track to deliver the first large-scale fault-tolerant quantum computer by 2029. To conclude, we finished the year with strong execution and continued progress against our strategy. IBM has long been known for innovation. What matters most is how that innovation is used to help clients operate better, grow faster and compete more effectively. We made a clear set of strategic choices over the last several years to help our clients do exactly that, and it is playing out in our results today and going forward. We enter 2026 with momentum and confidence in our ability to sustain 5% plus revenue growth and grow free cash flow by about $1 billion. With that, let me hand it over to Jim to go through the financials.

Thanks, Arvind. As we enter 2025, we provided guidance of accelerating 5-plus percent revenue growth, greater than 0.5 point of operating pretax margin expansion, double-digit adjusted EBITDA growth and about $13.5 billion of free cash flow. We exited 2025 beating all of these metrics, delivering 6% revenue growth, 100 basis points of operating pretax margin expansion, 17% adjusted EBITDA growth and $14.7 billion of free cash flow, growing 16% over last year. This represents our highest free cash flow margin in reported history, and we delivered 12% growth in operating diluted earnings per share. This performance reflects strong execution of our flywheel for growth. through client trust, leadership in hybrid cloud and Gen AI, accelerating innovation, deep domain expertise and an ecosystem multiplier effect. In 2025, we were intently focused on strengthening and accelerating our software portfolio, delivering innovation value with our next-generation mainframe launch, expanding our early leadership in Gen AI and quantum and executing M&A growth synergies across IBM. All of our segments accelerated in the second half of 2025 with these drivers playing out and demonstrating momentum across our diversified business. For the full year, software grew 9%, our highest annual growth rate in history with 3 of our 4 subsegments delivering double-digit growth rates. Infrastructure was up 10%, reflecting a record z17 launch, achieving the highest annual revenue for IBM Z in about 20 years and outpacing z16 over the first 3 quarters of the program. And consulting inflected back to growth in the second half, driven by Gen AI momentum with our Gen AI book of business in consulting at more than $10.5 billion inception to date. Let me now dive deeper into our fourth quarter performance. Software revenue growth accelerated to 11% on top of last year's growth of 11.5%, which was the highest in 15 years. Growth was driven by the strength of our recurring revenue base, our shift to higher growth end markets, innovation, including our early leadership in GenAI, M&A growth synergies and monetization of our strong IBM Z placement with an inflection in transaction processing. Our ARR was strong at $23.6 billion, up over $2 billion from the end of 2024. This quarter's performance was broad-based across our synergistic portfolio with organic growth accelerating to over 7%. Data grew 19%, fueled by the demand for our Gen AI products and strong performance with established strategic partners who enable customers to power our AI innovation and mission-critical workloads. These market dynamics underscore the synergy opportunity we see with Confluent. Automation grew 14%, including another record bookings quarter for HashiCorp. Red Hat decelerated to 8%, driven partially by the wrap on last year's elevated consumption-based services that we called out last quarter and also from the in-quarter yield on single-digit bookings growth driven by delays in U.S. federal business deal activity related to the government shutdown. While a longer growth arc, virtualization continues to gain momentum, including over $500 million of contracts signed over the last 2 years. OpenShift is now $1.9 billion ARR business, growing more than 30% -- and as we expected last quarter, given the record z17 placement this year, transaction processing inflected back to growth of 4%. Consulting revenue grew 1% in the fourth quarter, with Intelligent Operations up 3% and strategy and technology remaining stable. Performance was driven by steady demand across key offerings, business application transformation, application migration and modernization, application operations and cybersecurity as clients prioritize cost efficiency while continuing to invest in AI-enabled transformation. Our consulting generative AI book of business surpassed $2 billion in the quarter, our largest quarter of Gen AI, reflecting continued momentum. We are also expanding our impact through client zero, applying our generative AI experience in driving productivity and efficiency to help clients operationalize AI at scale. This practical experience, combined with our domain expertise is resonating with clients. While overall signings were down as we wrapped on record fourth quarter signings last year, the mix continued to improve with a greater share of strategic wins from both new clients and expanded engagements within existing ones. Infrastructure revenue grew 17% this quarter, with hybrid infrastructure up 24% and infrastructure support down 2%. Within hybrid infrastructure, IBM Z had another outstanding quarter, delivering its highest fourth quarter revenue in more than 2 decades, up 61% year-to-year, reflecting the enduring value of the platform and the success of our latest z17 program. Clients are investing in z17 for its differentiated capabilities, real-time AI inferencing, quantum-safe security and AI-driven operational efficiency, which are critical as enterprises modernize mission-critical workloads and scale for data-intensive environments. IBM Z continues to be the backbone of enterprise IT, enabling clients to integrate seamlessly with hybrid cloud while unlocking new levels of resiliency, scalability and performance. Distributed infrastructure revenue was flat with product cycle dynamics impacting storage, offset by growth in power, supported by solid adoption of our newly launched solutions. Now turning to profitability. In 2025, we delivered our highest operating gross profit margin in reported history and highest operating pretax margin in a decade, demonstrating the evolution of our portfolio mix and our laser focus on productivity. For the full year, productivity, mix and revenue scale drove expansion of operating gross profit margin by 170 basis points, adjusted EBITDA margin by 230 basis points and operating pretax margin by 100 basis points. And we achieved this despite absorbing more than $300 million of dilution from HashiCorp. Given the announcement of our intent to acquire Confluent, we accelerated productivity initiatives in the fourth quarter to help mitigate 2026 dilution, similar to our playbook on HashiCorp. Excluding resulting workforce rebalancing charges we took in the fourth quarter, operating pretax margin expanded by 140 basis points for the full year. Segment profit margins expanded by 100 basis points in software, 180 basis points in Consulting, with consulting margins at the highest level in 3 years and 450 basis points in infrastructure. For the full year, we generated $14.7 billion of free cash flow, up $2 billion year-over-year, resulting in the highest free cash flow margin in reported history. The primary driver of this growth is adjusted EBITDA, up $2.8 billion year-over-year, partially offset by increased investments in CapEx, higher cash taxes and higher net interest expense as we expected coming into 2025. Let me talk about our free cash flow evolution in a little more detail. Our repositioning to a software-led business, in addition to our cost discipline and productivity initiatives, drive significant operating leverage in our financial model. Since 2022, we have consistently delivered double-digit growth in free cash flow, well in excess of revenue growth, demonstrating this business model evolution. Our flywheel for growth and disciplined execution of productivity initiatives lead to sustainable and high-quality free cash flow generation. This durable cash flow engine enables us to invest in our business to accelerate growth. This includes increased organic innovation with R&D up about $2.5 billion since 2019. And it allows us to pursue highly strategic M&A transactions like Apptio, Software AG, HashiCorp, Data Stacks and Confluent that drive M&A synergies across IBM. Our diversified and integrated business drives a platform multiplier effect that uniquely allows us to deliver M&A synergies. This includes synergies from our global go-to-market distribution scale, platform synergies that amplify value with IBM's complementary offerings and operational synergies through our G&A discipline. Most recently, this can be seen with HashiCorp, delivering adjusted EBITDA accretion ahead of expectations within the first full year in IBM. Our financial flexibility fuels innovation and our disciplined capital allocation policy, including our commitment to return capital to shareholders. We exited 2025 with a strong liquidity position and a solid investment-grade balance sheet with cash of $14.5 billion. We invested $8.3 billion in acquisitions and returned $6.3 billion to shareholders in the form of dividends. Our debt balance ending the year was $61.3 billion, including $15.1 billion of debt for our financing business with a receivables portfolio that is almost 80% investment grade. Now let me discuss our expectations for 2026. Our strong performance in 2025 reflects the strength of our diversified portfolio and a multiyear execution of our strategic repositioning. Consistent with our Investor Day model, we expect to sustain constant currency revenue growth of 5% plus in 2026 and free cash flow to be up about $1 billion year-over-year, growing high single digits. Our revenue expectations are underpinned by our durable and accelerating software business, which we expect to grow 10% this year. This acceleration is led by organic growth, driven by the strength of our recurring revenue base, our shift to higher growth end markets, GenAI traction, M&A growth synergies and monetization of our record Z placement with an inflection in transaction processing, a tremendous source of profitability and free cash flow for IBM. And we continue to expect Confluent will close by the middle of 2026. In Consulting, our backlog levels and momentum in GenAI with backlog penetration over 25% support an acceleration in revenue growth to low to mid-single digits for the year. The powerful combination of our integrated platforms, services as software model and client zero experience allow us to deliver differentiated value to clients. We enter 2026 3 quarters into the Z17 launch. We expect infrastructure revenue to be down low single digits, about a 0.5 point impact to IBM, with Z growth in the first quarter balanced by product cycle dynamics throughout the rest of the year. The strength of our Z placement fuels our flywheel for growth with its attractive 3 to 4x stack multiplier across IBM. Let me now touch on our GenAI book of business before I turn to profit. We have been reporting our cumulative GenAI book of business since the third quarter of 2023 when it was in the low hundreds of millions of dollars. We exited 2025 with the GenAI book of business greater than $12.5 billion, demonstrating strong momentum in consulting and software. This will be the last quarter in which we report this metric separately. AI is now embedded across our business from how we deliver services to our software portfolio to the capabilities we are adding to our infrastructure platforms and how we drive our own productivity. As a result, a stand-alone Gen AI metric no longer reflects the full scope of how AI is driving value across IBM. For the full year, we expect IBM's operating pretax margin to expand by about 1 point. Our software portfolio mix and ongoing productivity initiatives continue to drive margin expansion and mitigate Z product cycles and the impact of dilution from acquisitions. Our operating tax rate for the year should be in the mid-teens, and the timing of discrete items can cause the rate to vary within the year. Let me give a little bit more color on Confluent dilution dynamics. We anticipate absorbing about $600 million of dilution from Confluent in 2026, driven largely by stock-based compensation and interest expense. We expect Confluent will be accretive to adjusted EBITDA within the first full year and to free cash flow in year 2 post close. We have multiple levers that underpin our confidence in these accretion targets, including revenue synergies, operational spend synergies and ongoing productivity savings. Revenue synergies include both the ability to accelerate revenue leveraging our go-to-market distribution platform as well as drive product synergies, which play out over time. We expect to realize about $500 million of operational spend run rate synergies by the end of 2027. We continue to accelerate our productivity initiatives and now expect an incremental $1 billion of productivity savings this year, driving $5.5 billion of annual run rate savings by the end of 2026. Taking this all into account, we are confident in our ability to expand operating pretax margin by about 1 point in 2026. For free cash flow, we expect to grow about $1 billion in 2026, in line with our Investor Day model of high single-digit growth. Given the strong fundamentals of our business, adjusted EBITDA growth will be the primary driver of our free cash flow, offset by similar factors as last year, including cash tax headwinds, higher CapEx and higher net interest expense. Looking to the first quarter, we expect our constant currency revenue growth rate to be similar to the full year. And for operating pretax margin, we expect about 100 basis points of expansion with workforce rebalancing fairly consistent with the prior year. Our first quarter operating tax rate should be in the mid-teens. We are excited about our prospects in 2026. Our growth accelerators, portfolio mix, integrated value and continued investment in innovation are driving sustainable revenue growth and strong free cash flow. As we shift toward a software-led business and speed our pace of innovation, our growth flywheel continues to strengthen. We enter 2026 with a solid momentum across our business and remain focused on disciplined execution with unwavering focus on productivity, enabling investing for the future and delivering value for our shareholders. Arvind and I are now happy to take your questions. Olympia, let's get started.

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And then second, as always, I'd ask you to refrain from asking multipart questions. Operator, let's please open it up for questions.

[Operator Instructions] Our first question comes from Brent Thill with Jefferies.

Arvind, good to see the comment around software growth accelerating to double-digit growth this year. I was just curious if you could maybe dig into the components and why you're excited for that organic-led initiative? And then anything else that's important to note this year on the software portfolio that maybe we haven't seen in '25?

Give you a bit of a perspective on the dynamics of the different parts of the software business. And then for quantification, I'll turn it over to Jim. So first, we are incredibly pleased with how we got to the end of the year on software. We are pleased with the organic growth in software, and we are pleased with the inorganic contribution. If I sort of peel it first from the subsegments, automation, I think, is on a secular demand increase. The reason for that is as people have more and more infrastructure, they put more and more AI, they put more and more compute, they need that software to help them manage all of it, and we're seeing that play through in the demand for HashiCorp, which helps you deploy hardware and software with the demand for Apptio, which helps you manage the costs and gives you a perspective of what is happening as well as all of the pieces around how do you integrate applications, how do you integrate data and all of those components. So I think that expecting well into double-digit growth for that part is appropriate, and we see that in our early demand signals. If I look at data, data benefits both from our data products that we provide, the organic innovation we have done with Watson X, both the AI pieces and the orchestrate piece for agents. And we expect that, that demand will keep pulling through and going forward as people are deploying AI for enterprise productivity and inside the enterprise. Then we also have a lot of partnerships in the data space that we see strong demand for and that we expect are going to continue. Mainframe, given the very strong cycle we had, somewhere between low to mid-single-digit growth is reasonable, and we have seen that dynamic play through, but it kind of follows the hardware placement by a few quarters. So all that hardware as people consume it, is going to cause that to grow. And then we get to Red Hat. First, we are very pleased with the doubling almost more than doubling of the Red Hat business since we acquired it. It was 3. early $3 billion, $3.2 billion when we made the acquisition, finishing the year at $7.5 billion on a run rate, just extrapolating from the fourth quarter towards $8 billion. We see strong demand in many of the pieces there, including on Red Hat Linux, but that probably in the mid-single digits, more than in the high to double, which is in line with server growth. You're still taking share. And the piece there that continues to be very, very attractive to growth is OpenShift, which is almost at $2 billion and running at a 30% growth rate, and we expect that to continue both for containers, for hybrid applications and for virtualization. So if you put all of that together, then that gives me confidence on the growth that Jim laid out about 10% for the year. This all assumes a midyear closing for Confluent, which is sort of baked into our expectations right now. Now of course, we're going to strive to do even better, but I think that this is a prudent set of numbers to put out.

Yes. The only thing I would add just to wrap it up, and I'll say it on behalf of Arvind's given how humble he is. When you look at 2025 and our software execution, I think what you see, one, is the strength of our portfolio and the diversification of that portfolio. And that's a reflection of a multiyear strategic repositioning of the IBM company to a software-led platform-centric company. We finished '25 with one of the highest growths we've ever had in software overall. but it's pervasive with 3 of our 4 software categories growing double digits. You dial back only about 3 years ago, we only had one growth vector, and that was Red Hat. That is the foundation of our hybrid cloud and AI strategy. The work we've done around repositioning the portfolio, a disciplined capital allocation to build out an automation portfolio, a data portfolio and always capitalizing on that high profit margin transaction processing portfolio has just been phenomenal, and it's leading to a sustainable, durable growth engine that gives us that conviction of double-digit growth here in 2026. But when you cut to the underpinnings, -- it reflects that diversity. ARR, roughly $24 billion growing high single digits. Gen AI, over $2 billion book of business, up 2x in the fourth quarter. And that M&A growth synergies, you're just seeing the beginnings of that play out with Hashi with another record quarter overall. So we are excited about the opportunity ahead, and we feel confident about software now at double digits, not approaching double digits.

Your next question comes from Amit Daryanani with Evercore...

On some nice numbers here. I just want to focus on free cash flow a bit. And if I go back to the start of the year in '25, you folks talked about $13.5 billion free cash flow guide. You sort of ended up being $14.7 billion, I think, when it's all said and done. And I think revenue growth helped you somewhat, but it was really good free cash flow margin conversion. So I'd love to know from your perspective, what drove the strength, the better performance in free cash flow in '25? And where I really want to go with this is I want to understand the $15.7 billion free cash flow guide for this year, which is impressive, but it implies high single-digit free cash flow growth versus the 16% that you saw last year. So maybe just help me appreciate, was there something unique that you saw in '25 that led to the 16%? And any puts and takes around the 15.7% number for this year?

Thanks, Amit. I appreciate the question. As we've been talking about for 5 years, 6 years now going on, as Arvind has taken over this company, we've got 2 key measures that we drive this company on, revenue growth and free cash flow generation. And by the way, they're synergistically aligned because it provides that flywheel of investment flexibility. But you're exactly right. We entered the year, and I remember a year ago sitting in this chair, I got asked the question, I think it was either you or Ben about are we -- my words, are we sandbagging free cash flow? And at that time, we said $13.5 billion. And the underpinnings behind that was double-digit growth in adjusted EBITDA and that we would have partially mitigating that some headwinds on -- or I would talk about tailwinds, higher cash tax because we got a higher profit profile, higher CapEx because we're going to invest in this business for long-term future growth. And we were going to have higher net interest and acquisition-related charges. Now you fast forward a year later, we posted $14.7 billion. By the way, up $2 billion, up 16%, highest free cash flow we've seen in well over a decade, highest free cash flow margin on record of 114-year history of our great company. And the underpinnings behind that cash flow were entirely driven by the fundamentals of our business, the acceleration we saw in our revenue growth throughout the year and the strong operating leverage we continue to get out of this business. So where we started with adjusted EBITDA at double-digit growth, we finished at 17%. That's an incremental $1 billion of adjusted EBITDA from where we were a year ago. And by the way, that's a flywheel engine. Revenue acceleration, operating leverage and driving an efficient balance sheet where we're very proud about a solid investment-grade balance sheet, generates significant substantial free cash flow. So now you fast forward to this year. We enter 2026 with a lot of momentum, confidence. By the way, I should have said over the last 3 years, we've grown free cash flow $5.5 billion. So that growth vector of what's been happening. But you look at 2026, our investor model is high single digit. We got a lot of work to do in 2026. We said we'd guide confidently up $1 billion at $15.7 billion. That's on a high single-digit growth. It's early in the year, high single-digit growth of adjusted EBITDA. And we are sitting here with a tremendous amount of leverage and opportunity just like we were a year ago. And our goal is to continue driving the durable, sustainable performance of this company as we move forward. So why do we feel excited? One, we have a focused portfolio, disciplined capital allocation, diversification of our business model and a relentless focus on profitability that drives the durability of free cash flow engine in this company that gives us the financial flexibility to continue to invest for long-term sustainable advantage. So we feel confident about that 15.7%, and our job is to beat it this year.

Our next question comes from Ben Reitzes with Melius Research.

At the risk of getting in trouble with Olympia here, on Amit's question, I was surprised you didn't say there's a multi-hundred million hit due to Confluent in the cash flow guide, which without it, it would be higher. But my real question is with regard to Red Hat. And I wanted to just clarify, I appreciate the double-digit guidance for software. I wanted to see what -- how we're going to bridge Red Hat. What -- how do we get the 8% to the mid-teens? Or is mid-teens no longer the growth rate there? Obviously, everything else was great and better than our model, but I'd like to just focus -- laser focus on Red Hat and how we bridge it to within your forecast and towards the prior goals.

Yes. Thanks, Ben. And I didn't say the dilution effect because, to be honest with you, dilution is part of our model. Our investors expect us to be disciplined capital allocators. I think we've earned the credibility about that. And we have to take that into account. That's why we drive portfolio mix. We drive revenue scale. We drive productivity, which, by the way, I also didn't say we exited last year $4.5 billion over the last 2 years. And we see that going up by another $1 billion this year. So we got a lot of levers in this business. And we understand that dilution. But you know what, the strategic value of Confluent and the synergistic value of what it does to our portfolio, we definitely could take into account that dilution. So I know you were trying to help on 15.7 is higher. Yes, it is. But all in, which is how we operate and report, we got to deliver or beat that number. So -- but let's get back to software. We talked a lot about the first question. We enter '26 with confidence around the momentum, the diversification of our portfolio, about the strategic repositioning, about the flywheel of growth. And we expect now double-digit growth. Underneath that, one, that's going to deliver over 4.5 points to IBM's growth in '26, above our model. And as Arvind said, an acceleration of organic growth. Organic will be north of 7 points this year and acquisitions about 3 points. How are we going to do that? We're going to do that. One, we have to acknowledge we're operating in an attractive TAM with a market backdrop for tech that we think is pretty exciting, and Arvind has been on that point about opportunistic. Two, we continue -- the thing I think is underappreciated in our disciplined capital allocator. We continue to shift this portfolio mix to higher growth end markets. with companies that we buy, which we always say category leaders in structurally growing markets that we can provide unique value, integrate and deliver differentiated synergies to accelerate growth. That is shifting our underlying portfolio that's also advancing our organic growth profile. Annuity strength, $24 billion exiting the year, growing high single digits, 8-plus percent. New innovation, GenAI. GenAI in the fourth quarter, up 2x, and we see that continuing as we move forward. M&A growth synergies kicking in off of a record Hashi year, and Arvind talked about TP cycle monetization. By the way, one point on TP that I think is underappreciated. We talk about very similar to z16 cycle. z16 first year of that cycle, TP was flat. We finished this year flat. One big difference. That flat on z16 was off a down 9%. The flat this year in 2025 was off a plus 10%. So you could see the compounding effect of what's happening to TP, and we see that inflecting the growth. So underpinning, and Arvind gave you some of the math. one, data, high teens, contributing about 4 points of that software growth, well above model. That's going to be driven by new innovation, Gen AI capability, platform economics. Two, hybrid cloud. We think that's double digit. Back to where I started my first answer. 3, 4 years ago, we only had one growth vector. Now we got 3 growth vectors that are growing double digit. Underpinning that, we got a subscription revenue under contract that's growing mid-teens. That's not at model. We only delivered single-digit ACV bookings in the fourth quarter because as we stated in prepared remarks, we were disrupted by the shutdown in the federal government. We got to get through that. We're monitoring it. And I would say the word choices, we're being prudent on Red Hat's guidance right now because we only need that at double digit to get software over the line of double digit. Upside will deliver upside to software overall. Automation, great portfolio, low double-digit, 2.5 point contribution to software on or better than the model. And that's leveraging M&A growth synergies of Hashi's success. And then TP, we're pretty much back to model, low single, mid-single-digit growth, capitalizing on that economic multiplier. So it gives you a little of the underpinnings behind why we're confident in that 10-plus percent growth of software.

Let's take the next question.

Your next question comes from Wamsi Mohan with Bank of America.

Just a follow-up, a clarification quickly on the last couple of questions around hybrid cloud growth. Are you anticipating any potential pressure on the server refresh cycle from higher memory pricing? And could that sort of have any adverse effect on the Red Hat Enterprise Linux side of things? And my question really is, Jim, on the cadence of PTI improvement, clearly, you're driving a lot of productivity improvement in being able to absorb entirely the dilution from Confluent and then some. So how should we think about the cadence of the progress of that given that Confluent is going to hit midyear, but you've already taken some productivity actions here in 4Q of last year. So should we be seeing more outsized PTI improvements in maybe second quarter? I think you already said what it is in the first quarter, but like in the second quarter. Is that the right way to think about it?

Okay. So Wamsi, let me take the first part of that question, and I'll ask Jim to address the second part of your question. Look, the server dynamics are volatile. And you're right. If I remember correctly, spot memory DRAM prices are 6x that of last year. A big reason for that, for those who are interested is because a lot of the capacity is moving over to HBM or high-bandwidth memory, which is required for AI servers. And if I remember correctly, it takes about 4, maybe 8x the capacity of DRAM to do HBM and the pricing and the demand for HBM is driving all the vendors into that. I personally believe as long as that dynamic is there, those pricing issues are going to be there through the year. Now the demand side, there is no AI server without a bunch of CPUs right next to it. So the reality becomes that the AI demand also drives demand for normal servers that in turn feed and load up those servers. So I don't expect that the overall server dynamic on which there may be a little bit of an issue is actually going to be any headwind to us on the hybrid cloud or the Linux side. And the reason for that is that there is enough growth going on. There is also a market share movement towards Red Hat as opposed to alternate answers in the marketplace. So that mix overall, I think, keeps us growing well. I also believe that both Red Hat AI and OpenShift AI will feed into the demand from the AI servers, which was going to help that demand. So let's acknowledge there is going to be memory pressure, and that probably lasts at least a couple of years. But as we look at that, it also gives opportunity for the AI portions of the portfolio to get a lot of tailwinds. So with that, I'll give it to Jim for, I think, another free cash flow question.

Yes. The skew of profitability, Wamsi, I think, was your question. So thanks very much, and I appreciate that, just given we do still expect Confluent to close by midyear this year, obviously, very excited about the strategic capabilities that's going to add. But I think I was very transparent in the prepared remarks about the level of dilution, et cetera. But let me bring it up a level first around how do we run this company and how do we drive the operating leverage that's been going up well above our model over the last 3 to 4 years. Portfolio -- we drive this through portfolio mix. That's why software being the underpinning of IBM's leverage around 45% of IBM's revenue, but about 2/3 of our profit. Also the high-value recurring revenue, our ARR book of business, high marginal profit dollars. So portfolio mix being one lever, productivity, every dollar we invest in this company around go-to-market, around R&D, around innovation and around our consulting services, we look and we drive a very maniacal ROI concept around that profile, just as you would expect us to as an investor like an inorganic M&A acquisition. And three, we drive to bring this company the most productive company in the world and getting G&A scale leverage overall. Fundamentally, you manage those 3 separate buckets differently. When you look at 2026, we said about 1 point growth in margin. We're going to get about 0.5 point on a revenue scale just given the leverage of the acceleration of revenue. We will lose 0.5 point on portfolio mix because we are going to wrap on an unprecedented launch momentum on mainframe, which carries higher profit and higher mix. The remaining full point basically is going to be driven out of productivity. And our model, as you know quite well, we've been driving north of 300 basis points per year of productivity, and we've been reinvesting about 200 basis points of that. That's why you've seen over the last handful of years, our R&D up double-digit growth year-over-year. And we've taken over the last, what, 5 years, we've taken $2.5 billion of R&D up overall. But when you look at 2026, productivity is going to drive the day. We feel confident we took up our productivity target to $5.5 billion. We will get revenue scale leverage on G&A, which will mitigate dilution and mitigate product cycles overall. To your last question, we think the skew of that profitability is going to follow our normal historical attainment. First quarter will be pretty much on historical attainment. By the way, you do the math, that's double-digit profit growth, double-digit EPS growth. So pretty consistent building off the momentum in fourth quarter.

And your next question comes from Jim Schneider with Goldman Sachs.

I was wondering if you could maybe outline the path or trajectory you're expecting for the consulting business throughout the year. Signings were a little bit weaker in this quarter, but you noted the strong backlog in AI that you're seeing. So maybe talk about how you expect that to convert into revenue over the course of the year and whether you see any kind of further improvement in discretionary or short-cycle spending and projects as you head throughout 2026.

Great, Jim. It's good to hear from you, and I appreciate the question. First of all, we're encouraged by the inflection shift that we see in consulting business exiting '25. We returned the business back to durable, sustainable growth in the second half, a little bit over 1%. But more importantly, and I think underappreciated, we got a lot of headroom to go, and the team is working diligently around improving the fundamentals of this business, and our operating pretax margins were up by almost 200 basis points in 2025. When you look at the market, market definitely remains dynamic, as we talked about earlier. We continue to see opportunity for growth as clients accelerate their investments in AI-driven transformation to unlock operational efficiency to unlock business model innovation and unlock growth. I think it's a very different mindset and client buying behavior than we saw 18 months ago when it was pure disruption and discretionary spend that's moving out. So as we look at '26, this is what gives us the confidence that we guided low single to mid-single, which we think is going to be pretty much where the market is at overall. And we still believe we've got continued headroom on operating margins. We guide about another 1.5 points improvement year-over-year. Both of them are going to be accretive in contribution to IBM overall. How is that going to happen? One, I would put it in a handful of buckets. One, backlog realization and what we see; two, our Gen AI momentum and the rate and pace and growth and acceleration we're seeing; three, strategic partnership headroom that we still have; four, our portfolio composition we've been talking about is aligned to much more growth end markets, higher growth end markets. Our portfolio is less around pure BPO. It's more around digital transformation, application modernization, AI, around data transformation, cybersecurity, governance. And then five, Mohammed and team, we've been doing a ton of work around reshaping our model to an asset-based services as software model with our industry-leading IBM Consulting advantage. So let me talk about just a couple of them to give you some math and statistics on why we feel confident. One, backlog, $32 billion, up 2% overall, but underpinning record low erosion, duration that has continued to come down. And our realization to your question, we see a realization out of that backlog that fully supports that low to mid-single-digit growth, and we see it pretty much modestly growing throughout the year. So low single-digit first quarter and then growing throughout the year. Two, we added over 400 new clients this year, and that has improved our net new business contribution, as we've been talking about, by 8 points, which is having a higher revenue realization by 4 points year-over-year. So backlog composition and the acceleration is one aspect. GenAI is the second. GenAI now represents over 1/3 of our bookings. over 25% of our backlog right now, $32 billion backlog and over 15% of our revenue on an exit run rate. We have a $3.6 billion ARR Gen AI revenue run rate in consulting, and we see that continuing to accelerate. And then finally, the repositioning. We see more headroom on margin leverage, about 1.5 points as we go forward, and that's through productivity, portfolio mix. But we have to manage -- we are operating in a very aggressive pricing environment, and we'll continue monitoring that.

And your next question comes from Eric Woodring with Morgan Stanley.

Arvind, a really strong infrastructure year, obviously, outstanding performance in Z. I think there's an argument to be made, and I hear from you that with the Tlem 2 processor, given the amount of data and transactions on the mainframe, the mainframe can be an AI workhorse. You had really strong outperformance relative to your historical model in Z. You're guiding to a bit of infrastructure decline in 2026. And so I'm just trying to understand, was there just some kind of different buying trends in 2025 and perhaps a bit of pull forward that was different than maybe past launches? Or could you just be a bit conservative as you look out into 2026? I know you cite cycle dynamics, but could you see some more sustainability in the infrastructure business in the z cycle that maybe isn't fully accounted for in the infrastructure guidance that you gave?

Yes. So Eric, as Jim actually explained in the answers to a few questions, we want to give guidance, and we want to be where we have incredibly high confidence that we can hit or beat those numbers. So let me just caution that all guidance we give is with that in mind. Let me just actually go back to a few of the dynamics that are driving even stronger adoption of the mainframe. Let's point out, Z17 has been the strongest start 3 quarters in. Before that, z16 was the strongest in about 20 years. Before that, z15 was better than '14, '13 and '12. So we have been on this continuing improvement. Let me point to, I think, at least 3 secular factors under it. Number one, there is a lot more demand for people to have -- we can use the word sovereignty or we can use the word on-premise control, which goes along with the economics of the mainframe platform. I think more and more clients have woken up to that for certain workloads, the mainframe is actually the lowest unit cost economics platform, and that is really important. Number two, the ding often is, well, but it's a very hard platform for our developers to use and for our operators to leverage. the Gen AI tools we have provided with the Watson Code Assistant for Z really takes that onus away. It can refactor COBOL into Java. It can help people understand a code that is already running on the platform. It can help you refactor that code if you want to keep it exactly as it is. It can help you take things out. So that headwind of people saying this is a hard platform to modernize has gone away. That then comes to your AI capabilities. You're right. I'm incredibly excited by our ability to do AI right in line. If you can do it right in line with the transactions, that's a milliseconds delay as opposed to multiple seconds if you take it off platform, which is how people have been doing it so far. That capability, while we introduced it in the beginning of 2025, really came to market only in the fourth quarter of 2025. So I do expect, and we look forward to helping our clients install that. And a very large number of our clients actually told us they're interested and they kept space in the machines to put in those cards. We call them the Spire cards or you can think of it as the Gen AI card, which gives you all that capability. As those cards go in, then the software stack to support that goes in. But we also have to give help to our clients to let the models run on the platform and to get their use cases up and going. I expect that will take some months to happen, but that does provide possible tailwinds against some of the dynamics that both Jim and I have explained.

Great. Operator, let's take one last question.

And this question comes from Matt Swanson with RBC...

Great. Arvind, if I can maybe take a more holistic view of part of your answer you just said. So I mean, as you were talking about, there's been a lot more conversations right now about where enterprise Gen AI workload should reside. And it really feels like that conversation is the intersection of your dual pillars of AI and hybrid cloud. So when looking at the strength of the refresh cycle, as you mentioned, but also the strength in demand for the data segment, the automation segment or conversations in consulting, what do you think combining all these things says about the current state of enterprise transformation for Gen AI maybe today and heading into '26?

Matt, thanks. And thank you for that question because it is one, as you can imagine, that we both think about a lot and then play out the various scenarios. So let's look at Gen AI so far. Gen AI so far has largely been a consumer topic. People use chatbots, people use it to improve their e-mails. People are using it to improve document writing. Half the videos, if I believe the statistic I read are now generated with the help of AI. I'm not saying only AI, but with the help of AI. All of that tends to be AI that is running on a hyperscaler public cloud somewhere and that people go leverage. As we look forward now to the enterprise, I am convinced this is an and world. I'm not saying that the hyperscaler public model usage is going to decline, but the enterprise is going to get concerned with how much of -- not the data. I don't think people are going to steal data, though that has happened a few times. But I do think that there is going to be a lot of concern around the nature of what are the models learning from answering these questions and do we really want to share that with everybody else or not? There is going to be issues around sovereignty on the usage of these models, and there is going to be questions around just basic privacy. Hey, this is not data that we want to take anywhere else. So I take that into -- I believe if I look out 3 to 5 years, 50% of the enterprise usage of AI is going to be in either a private cloud or it's going to be in their own data centers. And the other 50% is going to be usage of public models. Now there's also an efficiency question. So if what's being used on-premise is smaller models, then actually it could be that 80% to 90% of all the inferencing is really in a private/on-premise and 10% of the inferencing is on a public, but that 10% could be at 5 to 10x the price and hence, the dollars sort of even out. We see this playing out really importantly over the next 3 to 5 years. And that is why we've been positioning very strongly. We use the word sovereign or sovereignty for how people want to manage their technology. We announced a sovereign core offering last week to help get this done. But we do believe that this is going to play out. The mainframe is an important element, but not the only element in that story. So it's an and. Matt sorry, a really long answer, but this is what is going to play out over the next 3 to 5 years. So thank you all for all of these questions. As you can see, we have been excited about our year and the changes we have made to our business over the last few years and our performance in 2025 reinforce our confidence on the next chapter of our growth. We look forward to continuing this dialogue through the year.

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

Thank you for participating on today's call. The conference has now ended. You may disconnect at this time.

Back day 2 of the RBC TIMT Conference, we are super excited to be joined by IBM, Ric Lewis, SVP of IBM Infrastructure. Thank you for being here.

Thanks for having me.

I guess we'll start at the beginning. Could you share a bit more about your career and then the journey leading up to your current role as the SVP of IBM's Infrastructure business?

Sure. Happy to do that. So I've been in the industry, the IT industry now for going on 40 years, not quite. I spent 32 years at Hewlett Packard Enterprise, worked there, did a lot of -- I was always on -- I grew up as a chip designer and then started to own businesses, small ones, bigger, bigger then big chunks of the business. But I was always spending my time on the entrepreneurial innovation side of things. So we did a lot of work with as-a-service delivery. We did a lot of work with different types of hyperconverged and composable infrastructure. It was a great ride. I had a great time.

Then I retired at 55 on the early retirement, planning to fully retire and stay retired, and then I failed at that and ended up in a conversation with Arvind at IBM about the transformation he wanted to do with the company. And I thought I don't want to miss that. And I've been doing that now 4 years and almost half, and it's been a fantastic ride, and we've made a lot of progress in IBM. And so it's been really fun. And the industry has changed pretty significantly in that time. It's never been more fun to be in the IT industry because it's so critical to what's going on in every company.

Yes. Sorry, I think that's exactly where we want to start off is under Arvind's leadership, we have seen a transformation that you've been a part of these last few years. At Investor Day that you highlighted the turnaround in infrastructure from declining revenue to growth and profitability. And recently, I think what we've heard more and more is about this being a secular, not cyclical trend. So I think for a lot of investors, when they think hardware refresh, they think replacing boxes with boxes, right, a cyclical dynamic. Can you kind of talk about what are the key actions that you've taken on the strategy to really like lean into this opportunity?

Sure. Yes. I'm sure we'll talk later about Z and the cycles and all of that. But really, it's interesting, my job to kind of help the group go from what had been a general flat to declining business with big cycle swings. From the beginning, the goal was let's shift that to generally growing with dampened cycle swings so that it's still going to -- you're always going to have a big bump when you ship new hardware, but the idea would be less dramatic and more importantly, generally growing.

And we've been able to do that in those 4.5 years. At the core of it was really getting great at where we're investing and focusing on the innovation areas and making sure that, that's tied exactly to client needs. That would be the first thing that we did. The second thing was really all about the business model and product strategy, getting really tight about what's the segment we're going after, what do they really value? Is it hardware refreshes that they value? Or is it software stack performance or is it AI, all of those things. So getting really good at that.

The third thing was if you're going to invest in innovation and ramp up to be able to grow, you got to fuel and fund that. And so we completely changed our operating model, how we define, design, develop, build, ship, how -- even down to the freight that we use to ship products and again, by segment and product strategy, and we saved over $1 billion run rate out of the business that we could then mostly invest.

Some went to IBM and cash flow and things like that. A lot went back into investing in things like AI capabilities in Z and software, AI capabilities for storage and all of those great things. So it's been a huge transformation project. I mentioned all those mechanical kind of things, but the biggest thing that we did was work on culture inside the group. And that was if you came at this business with a mindset of just protect, just don't lose clients and kind of stay where you're at, there's a lot of corporate research that kind of talks about you have to have a growth mindset.

You have to be taking risks. You have to be collaborating. You have to be getting out of your comfort zone and trying new things.

And so things like putting AI in Z before there ever was a ChatGPT moment before anyone was talking about AI was a big brilliant move by IBM. Things like shifting to software value-add and hardware, all of those were experiments. They're about collaborating, being willing to take some risk and things like that, and it's paid off. We've been really lucky about the places we've invested. And we have a lot more engaged workforce because they feel like, hey, we're part of the growth of IBM at this point rather than just providing some cash flow to do other stuff. So it's been a really fantastic journey.

Yes. And so what you're seeing now, it sounds like is a refresh cycle conversation doesn't start off with capacity. It starts off with security enablement, AI, latency. And I can see that that's just a completely different conversation for you.

Absolutely.

I mean z17 is all about what are the new capabilities I'm getting, the new AI capabilities that we have built into the chip, but also the AI capabilities that we're adding in both an external chip that's on a card that you add to the system that we just started shipping and the software associated with that dramatically changes the use cases for clients about what they can use. So you're talking about new workloads for a platform that's been around for 60 years and very exciting for everybody, clients and us.

Yes. I mean getting into the numbers of z17, we've talked about the secular growth and looking for a 1% to 3% range, really strong momentum to start off this refresh cycle. Do you still think that range is the right way to think about it as achievable? And then I guess, what are kind of the key levers to sustained growth? And if I can just make this a super long question. The secular drivers, will we see less seasonality in infrastructure over time? We came off a long tail from z16.

Yes. I mean I think you're really asking a pretty simple question, are you going to do what the whole infrastructure transformation was set to do, which is grow and dampen those cycles and...

I like this question better. Let's answer that one.

The answer is yes. And we're very confident for a few reasons. One, first and foremost, Z is the dominant product in the infrastructure. It's our biggest revenue source, and it's got fantastic momentum. z15 showed 115% program to program versus z14. z16 was 120% program to program over z15. So far in z17, we're 2 quarters in, and we're running above 120% program to program. So when your core platform is growing cycle to cycle, that gives you growth, right?

But it's pretty cool because Z already had pretty good momentum before a lot of our transformation exercises, but storage didn't and power didn't and infrastructure support had been declining. And now infrastructure support, we've stabilized. It's a flat to plus/minus kind of business. Power, we've stabilized. It grew in all 3 years of its program cycle in this last time. So you're no longer seeing any decline in our UNIX and Linux business on power. And storage has been growing at roughly 1.5x the market rate in the software space and faster than the market rate in the hardware space.

So it's almost like Z's momentum plus these other things that we've got momentum behind that are the distributed infrastructure space and the support space, you look at that and you go, I think the business grows, and that's really what we're doing. So we're really excited about that. To the stream -- to the question of the cycles, you say, well, you still have hardware that you ship every 3 years usually for Z and power. You're still going to have cycles. Yes, but we're working really hard on stream revenue that kind of fills in some of that gap.

So an example would be Spyre that we're shipping in Z. That's our AI processor card. It's not shipping on the same exact cycle as the original Z. In fact, it just came out. So we're selling that card and subscriptions of software that go on it. So that's a new revenue source that's off the cycle. And that can be on a different cadence than the Z thing. We're also doing subscriptions on our software now for IBM i, which is one of the key operating systems that runs on power. We're doing PowerVS in our cloud, which is our power systems in a cloud offered as a service, which is a subscription, it's paid as you go as a service.

So all of these revenue sources that aren't just sell new boxes every 3 years, helped you with -- now we're selling other stuff in all of the years, delivered as a service, that's making a big difference in the business and helping us achieve the second part of the goal, first grow, second, make those cycles stamp out. So that's why we're confident in our prediction of single-digit revenue growth.

I think when I try to explain to people some of the changes that IBM has gone under Arvind's leadership, I consolidate the infrastructure consulting and software strategy into digital transformation enablement is like the term that I like to use for it. assuming you somewhat agree with that term.

I love it.

How do you see the IBM platform, Z Power and Storage, bringing differentiated value to like unlocking hybrid cloud and AI workloads for your customers. And when they have options of how they're going to do that digital transformation enablement and maybe it's hyperscalers, maybe it's other enterprise hardware players, how do you differentiate yourself as IBM?

Yes. So this is a cool question because it really gets at the heart of what Arvind was trying to do. From the first day that he became the CEO, he said, our strategy is super simple and very clear, hybrid cloud and AI. And again, this was before there's ChatGPT and all these people talking about AI and this is our strategy. So we've been differentiating in that area primarily across all of IBM. And you brought it, you said consulting, software and infrastructure. That's really the first thing I would highlight about the IBM value prop because it's all 3.

And that means you have full stack differentiation in the space of AI and hybrid cloud. And so we're providing hardware that helps clients do AI through inferencing and speeding up that capability. We're providing software stacks and models to be able to do the work on enterprise data. And then we have consulting services so that if you don't know how to do that, if you're a client and you say, my Board is all over me and saying, get AI in here and add some value to all this data and do what everybody else is doing, then you can -- then we can help you do that because our consulting team knows exactly, okay, here's what we would do. It may involve our stack of stuff. It may involve others.

But whatever it is, we're going to know what's the most optimal thing. So full stack is the first thing I would say. The second thing I would say is just the notion of hybrid. I think what's cool about centering on hybrid cloud. So there's lots of AI players that are in clouds. There aren't very many AI players who are doing much on-prem. We do both, and we do it seamlessly together. And if you think about these enterprises that we serve, a lot of them, this data is the lifeblood of their company. They don't want it out in the cloud. They don't want it anywhere. They want it on their prem. They want it backed up. They want to secure wall around it.

And so being able to do hybrid, meaning do this work wherever that data is, is a big advantage for us. And we have a lot of use cases for very specific AI applied to enterprise data on and off-prem, the key there being on, lots of people can do off. We can do both, and we do it together. It looks the same and it's seamless. I think that's what really resonates about our value proposition. The last thing I would say is a true laser focus on the enterprise. There are a lot of AI players that are doing a lot of great things and changing our lives, right?

I'm using all kinds of new things, ChatGPT to help plan my vacation and that kind of stuff. But I'm a consumer person kind of thing. IBM, we're not doing that. We're doing enterprise. We happen to be close to these clients' most important data, the transactions that you all do, 45 out of 50 of the top banks run on our Z systems, 4 out of 5 of the top airlines, 9 out of 10 of the top retailers. All those transactions are going through our systems. That data is a gold mine for those clients if they can get their head around, okay, what does this tell me about my clients, their transaction patterns, all of that stuff is where the action is in IT, and we're poised focused, laser-focused on that, not laser-focused on how do I help you plan your vacation or that kind of stuff. So I think we're just in a really good spot with a really focused strategy, and it works really well.

Yes. And I think we've heard before on -- maybe from your consulting arm that some of the early days of these Gen AI projects are helping customers get their data in order, right, set the foundation to be able to build upon that. As enterprise data grows, the volume and complexity, it's going to need different infrastructure strategies. Could you just talk about what role this plays in your product road map and how you're -- again, maybe going back to the idea of enablement, how are you enabling companies with your infrastructure to be ready for Gen AI?

So I like where you went because I think that a lot of people who aren't doing this every day don't think about it this way. But AI, I will tell you, is mostly about the data, right? Whatever you feed it is kind of going to help with the value that you get out of it. So for us, being able to -- yes, 10 years ago, if someone was trying to do an AI project, you had to structure and label your data, and it was a massive data science exercise to even get things to where you could ingest that data.

We have products now, our Fusion product in storage that you can just feed it random stuff. You can just give it a bunch of disks, and we have this thing called content aware storage technology that will look on all those disks and say, here's generally what's on those disks, here's what it's associated with, here's what it's used for all of that, completely unstructured random data. So that's a good example of in this direction, the prework necessary to kind of get things in order to get value out of your data has changed exponentially, which is a really good thing.

Our job is to make that -- continue on that journey, make it easier to ingest that data, get value out of it, tune models, be able to do inferencing on your key enterprise data. And I think the sky is the limit. So we have products in storage that are doing that capability. We have products in Z that I talked about already. Power is being used in -- one example is Julich in Europe, the exascale computer, a 21 petabyte ingest of data and a 700 petabyte backup needed so that when you do your model training and inferencing, you can go back and say, well, what data did I use and how did I use that? All of these are examples of us making it easier for you to be able to do AI in a hybrid environment, and it's really resonating with clients.

And then if we're looking at z17 cycle compared to z16, how would you compare like the current cadence in terms of how those started off? And then if we're looking at maybe sources for upside, how viable is AI inferencing on z17 today? And are you seeing people day 1 using it for this capability? Or is it more future-proofing saying, well, I want to have it, but I don't actually need it currently.

So we are off to a great start with z17. And typically, you would expect me to be saying, that's a tough compare because we were off to a fantastic start with z16. But comparing the starts, z17 is as good or better than z16. So tremendous momentum. To your second question, I think that's part of why is, yes, clients are using the AI. It's not a, hey, I'll just buy this and hopefully, I'll figure out a way to use it kind of thing.

We have 250 use cases that clients are either in production or developing or exploring with us in great detail, but a lot of them are in production. The primary use cases, I would say, in z17 for AI, they're kind of in 3 buckets. One is the obvious and one that we've talked about a lot because we did it on z16. That's fraud detection. And that's a huge value for our clients in this space being able to detect that. But even fraud detection has improved in this latest generation. Old fraud detection looked kind of mechanical in nature.

The transaction pattern looked like this. It was structured data, and we could kind of say that one looks weird, stop. Now we can combine structured data and unstructured data, large language models. So you can look and say, okay, this transaction pattern happened and what's the company rating that's associated with this transaction? And are there bad reviews on websites about this company? Or do they have a better business bureau. So you can kind of take the 2 worlds of mechanical fraud detection and unstructured gut feel fraud detection and make that mathematical so that you can do that. So that's a use case that's kind of a bucket of a lot of what people are doing.

People are also doing code modernization, which means commenting and kind of reorganizing and supporting their code to help with the skills on Z, and then the third thing that they're doing is just managing the Z. We have something called the Z Assistant that's based on AI capabilities. And what we've learned from clients is they just wanted to run the fastest. They'd like to be able to chat with the box and say, configure yourself, this is my typical workloads, configure yourself to do that rather than you have to be a geek who's been doing this for 20 years to figure out, okay, I need to turn on this option, turn on this buffering, turn on this queuing, all that kind of thing.

So that's the third big use case. All of those are within the same, we call it the RAS domain, Reliability, Availability, Serviceability. It means basically you don't have to take any of this off your Z to kind of optimize it. It all happens in there. The inferencing is done in the Z ecosystem. So it's secure. You know it's rock solid. You know it's backed up. You know it's quantum safe. You know all the great things about Z, except even in how you operate it and what you're doing with it. So it's a fantastic platform for the future and resonating more than ever, fantastic momentum.

This may be an impossible question, but it's one that we've all been asking for the last couple of years about when the application layer of Gen AI starts to come in, you have a really unique visibility, right, into infrastructure spend and these consulting projects. Do you have an opinion on the enterprise timeline to see broader production applications?

It's interesting. Your question, I think we're still waiting a little bit in consumer land, meaning AI is kind of this separate thing. You go look, okay, tell me the best place to buy, whatever it is. But then you still kind of end up then jumping over to a web search after -- or give me a link, that link, now I go and get on that link and I do stuff kind of the old way. Usually, consumer leads enterprise, like the for example, the advent of mobile -- we all had mobile phones before work was kind of enabling our mobile phones and things like that.

I would say in what you just talked about, the application space, enterprise might be leading consumer because -- and I think the reason is because the space is smaller. You're not trying to do everything in every language, including with pirate slang and all these kind of things that you're doing in your consumer environment. What you're really trying to do is take, I have this set of transactions, and I want to know what's the customer behavior or we have insurance companies. I have this set of clients that my actuary tables say there's this much risk, but I want to combine that with data about weather and what the season looks like.

And I want to do that real time. If I can get that right, my margin goes way up as an insurance company. So those things are their direct huge value to these enterprises. And so they're motivated to get a competitive advantage with that. And so they're investing to be able to do that. So in terms of simple apps, no, but in terms of deep rich analysis, enterprises are going crazy because they know, they absolutely know disrupt or be disrupted. And I would say more than the cloud era, more than client server, more than any other era in IT, there's the potential for disruption right now more than ever. And so you're either on it or you're going to get it from somebody else. So anyway.

Speaking of disruption, Quantum, everyone's favorite topic of 2025. I guess you've taken what I would consider compared to some of the peers in the space, like a more measured content-driven approach to Quantum Computing. And I think emphasizing maybe like the real-world utility over hype a little bit. 75 systems already deployed, I think 13 production. Is that the most updated number?

Yes, I think we might be a little above that, yes.

Close. 13-ish. Talk about the evolving commercial opportunity for IBM. And then what -- how this kind of fits into, you've said, those 2-pillar strategy, right, of hybrid cloud and AI. And then we were talking about this in the back. For any of us who are looking at maybe competitive press releases or anything else, like what is -- what are the things we should look for, for what's real versus hype?

Yes. So first, let's talk about market opportunity because I think we just got a study or we just saw a study from BCG that estimates that the Quantum opportunity in kind of the early 2030 time frame is $500 billion, which is a big old TAM. Not all of that's for hardware. Probably somewhere kind of IT vendors might expect to see 20% to 40% of that, still a big old TAM. For a technology that's not really mainstream today at all, we wouldn't -- we don't think it's mainstream.

But I think the cool thing that we love about IBM, we are taking a very practical, rational approach to it. And I think Arvind likes to say we're not expecting some scientific breakthrough at this point. It's a matter of engineering and execution to get to where we need to go. And that's a big milestone in and of itself. So when I watch other Quantum people and what they're saying, which, by the way, I know we're ahead in this industry, we're the lead player at this point. I try to watch for a few key things. One, do they have a believable road map, not just a road map, not just an aspiration, but like, okay, you've shown kind of through your progress through the last 5 years that you're on a certain trajectory.

And do you have a believable road map for the next several steps? And our next steps, we're driving in our system development, et cetera, to be able to deliver Quantum Advantage here in the next year. We've got error correction and all of those capabilities in the pipe for coming later in this decade, so kind of '28, '29, and we actually believe we'll be transacting in that timeframe by the end of the decade on a system kind of level. We're already transacting. We already have clients that are buying cycles of Quantum and exploring and using that capability now.

So it's a predictable road map. The other thing I would say is do you have an ecosystem of partners that are helping you develop, helping you figure out a fair value. And there have been some key industry announcements that you can look up, one around HSBC and there are a couple of others that kind of ship the industry a little bit of, no, there's a use case, and this could radically change how we do things like Monte Carlo analysis or Chemical analysis or those kind of things. We have an ecosystem. We have a large number of partners that we've talked about that are working with us on developing this thing.

The third thing I would look for in those announcements is do you really have a software stack that kind of goes with it because you saw that same development in AI land. Before there was kind of the ChatGPT moment, we'll call it, there were stacks and capability that kind of grew out of the graphics space, but that we were helping do AI, mostly in self-driving cars and things like that. We look at our Qiskit, which is kind of our base software platform with that is showing great traction in the industry, both in academia and in business and clients adopting it.

And we kind of look at that and say, hey, we're in a really good place here for Quantum when it comes. And then the final thing is Quantum is -- for lack of a better way of saying it, it's difficult to get right. It's more fragile than our digital. I grew up as a digital designer and things like that. And it's either 1 or 0, and we figured out ways to make it rock solid. Quantum is a little more fragile. There were things we had to do in digital when things got fragile in memories and things like that like check sums and error correction codes and those capabilities. In Quantum, they're even harder to do those things, but do the announcements kind of reference, hey, we know how to do error correction in the Quantum space.

And that's something that IBM, I think, is very far ahead of is being able to combine classic and quantum techniques to do error correction in the Quantum space, and you're going to need it in order to be able to do it at the scale where it's really practical for the kind of problems that you're going to want to solve. So I look for those things. I also look for a philosophy that says Quantum is not a replacement to classical. And I don't -- I believe it's not. I don't believe AI is a replacement to classical computing. It's just another category.

When you combine them together, you end up with something very strong. And since we play strong in classical, we play strong in AI, and I think we're the leader in Quantum, we're really well positioned for as the industry gets to this kind of 2030 time frame and all that TAM. So we're pretty bullish and excited about it, though cautious and practical, like just keep executing the road map, we make our steps, we're going to be in a really good spot. So that's what we're doing.

This time has flown by. Are there any questions from the audience here? All right. So one thing that Jim references a lot is the integrated value prop with -- cycles. Sorry, I didn't realize I was scratching my microphone. Can we talk about the IBM Z stack multiplier and how $1 of Z hardware gets that 3 to 4x of related revenue?

It definitely does. It's a huge value to our company. And if you just kind of look at our stack and the associated things around it, things you would think of, okay, we obviously sell mainframe storage when we sell a mainframe. We sell mainframe transaction processing per second, all the software that goes with it. The things you might not think about is all the infrastructure support, my TLS business is associated with it. You might not think about financing. We do financing on Z. We do leasing on Z. A big chunk of that is Z, and that's revenue and margin for us as we do the financing and leasing for those platforms. So it contributes in a whole lot of way. And then just associated, right? If you're helping the client with their most important data, you can probably help them with other things.

So it drives a big bunch of consulting revenue on getting value from that data, modernizing your mainframe platform, all of those capabilities. In our software space, it gives you revenue on putting containers on there through Red Hat on optimizing the workload distribution through things like Apptio and Turbonomics and all those capabilities are incremental to the Z value proposition, but are associated with it because you're doing something extremely important to that client set.

Typically, our last question is what you're most excited for the next 3 to 5 years, and we've got 3 seconds.

Data for AI is a big one. I've mentioned some of our storage offerings. And focusing on that data and really AI in general and how it will transform how you interact with our systems, what our systems are used for and the value that clients are getting from it. It's never been a more fun time to be in the IT industry. And it's because what 15 years ago was a conversation of just make sure the thing always runs and lower the cost as much as possible. That would be the definition of the job. Now the definition of the job is a meeting with the Board in a week and they want a story on AI, and they want to know how we're going to disrupt rather than be disrupted. And you guys are the key to that in IT. That's a lot more fun conversation than cost reduction. So I think that's what I'm excited about is it's just a really fun time to be doing this.

Yes. And this is also a very fun conversation. Ric, thank you so much for joining us this morning.

Thanks, Matt. Appreciate it.

Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time.

Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's Third Quarter 2025 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer. We'll post today's prepared remarks on the IBM investor website within a couple of hours, and a replay will be available by this time tomorrow.

To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website.

Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings.

So with that, I'll turn the call over to Arvind.

Thank you for joining us today. In the third quarter, IBM delivered strong results across revenue, profit and free cash flow exceeding our expectations. Revenue growth accelerated to 7%, our highest growth in several years, with all our segments accelerating sequentially. These results underscore the strength of our business model and portfolio and the innovation we are delivering to clients. Clients continue to turn to IBM as a trusted partner to help them modernize, embed AI and build resilient infrastructure.

Let me touch on the economy before I turn to our execution. Last quarter, I said we had moved from being cautiously optimistic to optimistic. Technology remains a key driver of growth and competitive advantage. AI adoption is accelerating, and Hybrid Cloud remains the foundation of enterprise IT. Clients are leaning on enterprise technologies to scale, innovate and drive productivity. There are always macro uncertainties, but overall, we continue to see broad-based demand from clients and remain optimistic.

Now turning to our execution this quarter. Our strategy remains focused Hybrid Cloud and artificial intelligence. Our products and services fuel growth and productivity for our clients. You can see this in our results for the quarter. Software growth accelerated to 9%, led by strength in Automation. Automation was up 22%, highlighting our end-to-end portfolio of leading solutions that optimize operations, automate infrastructure and workflows, build resiliency and drive cost efficiency for clients. Many of our Automation products are infused with AI enhancing their capabilities. HashiCorp also continues to accelerate within IBM, benefiting from our go-to-market distribution and joint product innovation, highlighting our synergy potential.

Consulting accelerated reflecting growing demand for AI services as clients need help designing, deploying and governing AI at scale. And Infrastructure delivered robust performance, growing 15% and driven by continued strength in z17, our strongest 2 quarter launch in history. The Spyre Accelerator, which will be available in Q4 will bring advanced generative AI and real-time inferencing capabilities inside IBM Z, redefining our enterprises capture AI value within their most mission-critical environments.

In addition to being a demand driver, AI is also a powerful productivity driver for IBM, contributing to our strong financial performance. In 2023, we set out on a goal to achieve $2 billion of productivity savings. And today, we are well ahead of that with an expectation of $4.5 billion of annual run rate savings exiting this year. I believe we have significant opportunity ahead of us to continue to become even leaner and more nimble.

Our Client Zero approach sets us apart as we have internally identified and addressed pain point on data readiness, siloed and vertical workflows, application and IT sprawl, using our own technology and domain expertise. Clients see these results and look to us to help them on their own transformations, driving over 1,000 Client Zero engagement this year. The breadth of our AI offerings is a key differentiator, combining an innovative technology stack with consulting at scale and our Client Zero journey.

Our AI book of business continues to show momentum at over $9.5 billion inception to date. In consulting, we are embracing disruption and leading the way with our digital asset and services as software strategy. While we are early in this journey, we have over 200 Consulting projects using digital workers at scale. In software, demand for watsonx and Red Hat AI remains strong, with early momentum in our Agentic platform, watsonx Orchestrate. watsonx Orchestrate helps enterprises deploy AI by connecting agents, models and workflows with governance and security.

Orchestration will be critical as enterprises run a variety of models to optimize cost and performance. Our hybrid approach to models enables clients to use the best option for each use case, IBM's Granite models, third-party models or open models from Hugging Face, Meta and Mistral. We recently launched Granite 4.0, our next-generation family of open small language models, Granite 4.0, a delivers high performance and cost efficiency using 70% less memory and offering twice the inferencing speed of conventional models.

We also partnered with Anthropic to infuse cloud into IBM products to unlock new GenAI features and capabilities. This week, we announced a partnership to run watsonx and Grok, giving clients access to their inferencing technology, which provides ultra-high-speed low latency AI capabilities at lower costs. All this leads to real tangible value for clients. Companies like Deutsche Telekom and S&P Global are embedding watsonx into core workflows.

In Infrastructure, clients such as Nationwide, State Street and Credit Agricole are turning to AI to manage increased workloads and use Z 17 for its advanced AI inferencing capabilities and enhanced resiliency.

Accelerating innovation remains a core focus for IBM. At our recent IBM Tech Exchange Developer and Builder Conference, we showcased how we are helping clients and partners with innovation that blends enterprise strength and AI speed. We had almost twice the number of participants as last year with speakers including United Airlines, T-Mobile, Prudential, UPS, Morgan Stanley, Verizon and Cigna.

We announced Project Bob facilitating AI-powered software development, helping teams ship higher-quality code faster. We have more than 8,000 developers within IBM that are using Project Bob reporting productivity gains averaging 45% another powerful Client Zero use case.

We also announced new automation capabilities, including a real-time infrastructure graph connecting applications services and ownership through HashiCorp Terraform.

As outlined at our Investor Day, we are on a path to demonstrate the first error-corrected Quantum computer by 2028, and continue to deliver key milestones in our Quantum road map. As we collaborate with our ecosystem of over 280 partners, we are making tangible progress on near-term use cases. For example, HSBC achieved a notable improvement in bond trading predictions using IBM's Heron Quantum Processor. Vanguard announced a breakthrough in optimizing portfolio construction using IBM's Quantum computing as a service.

We recently announced a partnership with AMD to build Quantum-centric supercomputing architectures, leveraging IBM's Quantum expertise and AMD CPUs, GPUs and other accelerated technologies.

Just last week, IBM as the BaaS government unveiled Europe's first IBM Quantum system to -- this marked the second installation outside the United States and underscores our commitment to global leadership in Quantum computing.

In closing, we are executing on our strategy of accelerating revenue growth and delivering higher profitability. Given these results and the momentum in our portfolio, we are raising expectations for revenue growth to more than 5% and free cash flow to about $14 billion for the year.

With that, let me hand it over to Jim to go through the financials.

Thanks, Arvind. In the third quarter, our revenue growth accelerated to 7% and our highest growth in several years, with all of our segments accelerating sequentially. Revenue scale, mix and productivity drove 290 basis points of adjusted EBITDA margin expansion, 22% adjusted EBITDA growth and 15% operating earnings per share growth, highlighting the significant operating leverage in our business model. And through the first 9 months, we generated $7.2 billion of free cash flow, our highest 9-month free cash flow margin in reported history. We exceeded our expectations on revenue profitability, adjusted EBITDA, earnings per share and free cash flow, reflecting the strength of our portfolio and the disciplined execution across our business.

Software revenue grew 9%, fueled by accelerating organic growth, up a few points since last quarter and continued contribution from our high-value annual recurring revenue base which grew to $23.2 billion, up 9% since last year. Growth in Automation accelerated to 22%, driven by strength in the organic portfolio and early synergies with HashiCorp, which maintained momentum and delivered its highest bookings quarter in history. Red Hat bookings growth accelerated to about 20% and revenue grew 12%. This performance was driven by a softening in consumption-based services and RHEL trending back towards single-digit growth as we wrap on last year's exceptional double-digit performance.

Demand for our Hybrid Cloud products remain strong, and all 3 of our major subscription offerings gained market share again this quarter. With growth accelerating for both OpenShift and Ansible. OpenShift ARR is now $1.8 billion, growing over 30%. Data was up 7%, and driven by continued strength in our AI portfolio. And Transaction Processing revenue declined by 3%, reflecting another quarter of Z 17 outperformance as clients continue to prioritize hardware spend on our latest IBM Z system. While this dynamic impacts near-term revenue, we're encouraged by a healthy pipeline that positions us well for future demand.

Infrastructure delivered another strong quarter, growing 15%. Hybrid Infrastructure grew 26% and Infrastructure support was flat. Within Hybrid Infrastructure, IBM Z delivered its highest third quarter revenue in nearly 2 decades, up 59% year-to-year fueled by the early success of our Z 17 platform, purpose built for AI and Hybrid Cloud with breakthrough capabilities in real-time inferencing, quantum safe security and AI-driven operational efficiency. Clients are investing in Z 17 not only for its reliability and scalability, but because it enables secure high-performance computing at the core of their digital transformation strategies. Distributed infrastructure up 8% reflects broad-based growth across our storage portfolio. as clients scale capacity to meet rising Data and AI demands.

Consulting returned to growth in the third quarter with revenue up 2% and improving sequentially and marking a positive inflection point in performance. Intelligent Operations was up 4%, while Strategy and Technology revenue stabilized. With both lines of business showing quarter-over-quarter momentum. This growth reflects solid demand for our strategic offerings, business application transformation, application modernization and migration, and application operations as clients focus investments on solutions that accelerate AI transformation and maximize return.

As Arvind mentioned, we are embracing AI disruption and leading with a software-driven services delivery model. We are transforming into a hybrid model of people plus software that delivers efficiency and scale. This approach is already driving internal productivity reflected in the 220 basis points of segment profit margin expansion year-to-date and resonating with clients seeking to operationalize AI strategies.

By combining domain expertise, with scalable technology platforms, we reinforce our role as a strategic provider of choice in this evolving landscape. Our Consulting generative AI book of business accelerated to over $1.5 billion in the quarter, with the number of projects more than doubling year-to-year, underscoring our momentum. While total signings declined this quarter, the quality of signings continue to strengthen with more strategic wins from new clients and expanded engagements within existing ones.

Turning to profitability. We have delivered 9 consecutive quarters of operating pretax margin expansion, highlighting the evolution of our portfolio mix and our laser focus on productivity, which again played out this quarter. Revenue scale, mix and productivity drove expansion of operating gross profit margin by 120 basis points, adjusted EBITDA margin by 290 basis points and operating pretax margin by 200 basis points, ahead of our expectations and well above our model.

Segment profit margins expanded by 420 basis points in Infrastructure, 270 basis points in Software and 200 basis points in Consulting. With Consulting margins at the highest level in 3 years. Revenue scale and mix contribution from IBM Z is a significant source of profitability and free cash flow. And combined with the 3 to 4x stack multiplier helps fuel our investment in innovation and drive growth.

Productivity is also a key driver of profit margin expansion. As we deploy AI at scale across IBM in areas including finance, supply chain, sales, HR, service delivery and customer support to improve efficiency and reduce costs. While we have made progress on this journey and expect $4.5 billion of run rate savings exiting this year, there is still significant opportunity ahead for us to drive even more efficiency and cost savings.

Through the third quarter, we generated $7.2 billion of free cash flow, up about $600 million year-over-year, resulting in our highest year-to-date free cash flow margin in reported history. The largest driver of this growth is adjusted EBITDA, up $1.8 billion year-over-year, partially offset by proceeds from the Palo Alto QRadar transaction, which resulted in a reduction in CapEx in the third quarter of last year and working capital dynamics.

Our strong liquidity position, solid investment-grade balance sheet and disciplined capital allocation policy remain a focus for us. We ended the quarter with cash of $14.9 billion. Our debt balance ending the quarter was $63.1 billion, including $11.3 billion of debt for our financing business. With the receivables portfolio that is over 75% investment grade. In addition, year-to-date, we returned $4.7 billion to shareholders in the form of dividends.

Now let me talk about what we see going forward. Through the first 9 months of the year, we delivered 5% revenue growth, 17% adjusted EBITDA growth, 10% operating earnings per share growth, and 9% free cash flow growth. The strength and diversity of our portfolio, disciplined capital allocation and relentless focus on productivity, continue to drive the durability of our revenue and free cash flow performance. Given the strength of this performance, we are raising our expectations for revenue, adjusted EBITDA and free cash flow. We now expect to deliver revenue growth of more than 5%, adjusted EBITDA growth of mid-teens and free cash flow of about $14 billion for 2025.

Let me focus on full year growth for the segments. We continue to expect Software revenue growth of approaching double digits for the full year. Through the first 9 months, we delivered growth above our model of 17% in Automation, an in-line model growth of 7% in Data, and these trends should continue. And we continue to expect mid-teens growth for Red Hat, albeit at the low end. This is underpinned by strong bookings growth in the third quarter of about 20%, and our revenue under contract, which is growing in the mid-teens.

As we wrap on elevated growth in consumption-based services last year, we expect double-digit revenue growth in the fourth quarter with an accelerated growth profile heading into 2026. While Transaction Processing was down 1% year-to-date as clients prioritize spend on our high-value innovation z17. The strength of the new cycle provides future monetization value across the Z stack. We are seeing the strength in our pipeline as we enter the fourth quarter, which we expect will return to growth. With continued strength in z17, we now expect Infrastructure to contribute over 1.5 points to IBM's revenue growth this year.

In consulting, we are encouraged by our return to growth this quarter. and continued progress in our GenAI book of business, and now we see an inflection in growth going forward with fourth quarter revenue performance similar to our third quarter growth.

Now turning to profitability. We started this year expecting over 50 basis points of operating pretax margin expansion. And through the first 9 months of this year, we delivered 130 basis points of expansion, well ahead of our expectations. This performance is driven by our revenue scale, portfolio mix and progress with productivity initiatives, enabling operating leverage while providing investment flexibility. We are raising IBM's full year operating pretax margin expansion to over 1 point. And our operating tax rate expectation for the year remains in the mid-teens. For the fourth quarter, we are comfortable with consensus estimates for constant currency revenue growth and profitability.

Let me conclude by saying we are pleased with our continued disciplined execution and look forward to capturing growth opportunities ahead of us. Arvind and I are now happy to take your questions. Olympia, let's get started.

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And then second, as always, I'd ask you to refrain from multipart questions. Operator, let's please open it up for questions.

[Operator Instructions] And our first question comes from Amit Daryanani with Evercore ISI.

Yes. I guess maybe just -- I want to focus on free cash flow. So I really appreciate the quantification of free cash flow at $14 billion for the year. And if I get my math right, this sort of implies free cash flow is up double digits in '25 and your conversion rates around 125% give or take. Can you just touch on if there's any one-off dynamics that we should be aware of that are help you in free cash flow in '25? I'm really just trying to think that as we get into '26 and if your growth is in line to your longer-term models, is there anything that could preclude free cash flow growing a few points higher than sales growth, some of the way you folks have talked about it? I'd love to just kind of spend a little bit of time on free cash flow. And if anything, all does on the capital allocation as well.

Thanks, Amit. I appreciate the question. And it's right at the heart of how Arvind has repositioned this company around the two key measures: one, accelerating revenue growth and two, is driving that free cash flow engine that's going to fuel the investments for us to continue to make to drive long-term sustainable competitive advantage. But if you take a step back first, as we said in prepared remarks, we're very pleased with our free cash flow engine, starting out the next evolution of our journey coming off the midterm model. Year-to-date, $7.2 billion, up $600 million year-over-year, highest free cash flow margin reported history through 3 quarters for our company.

And I'll just state that underneath it, we overcame in the third quarter, a $500 million headwind from last year as a result of the Palo Alto QRadar transaction that was recorded as an asset sale reduction in CapEx. So we got through 2025 headwind around that.

What's driving that free cash flow? Probably the most important thing is the underlying fundamentals of our business. and accelerating top line revenue growth profile and an operating leverage engine that is driving productivities like we haven't seen in a long period of time. I think we're quarters in a row of driving operating leverage and significant margin productivity. So I would tell you, high-quality, high sustainable free cash flow.

And that's what gave us the confidence for the second quarter in a row to take up our free cash flow estimate for the year, now about $14 billion. Why did we do that? We took up revenue. We took up operating margin. We took up adjusted EBITDA, we took up our profitability, and all that leads to free cash flow. When you take a look at what's driving that $14 billion, $2.5 billion, give or take, year-to-year growth in adjusted EBITDA. Mid-teens growth, well above our model.

And underneath that, you take a look at some of the dynamics we've been talking about since back in January. Yes, higher profitable based engine will pay a higher cash tax. Yes, we're investing long term for this business. We are going to have higher CapEx outside of the QRadar transaction. And yes, we made a significant strategic acquisition. We've got acquisition-related charges and foregone interest. All of that is embedded in 2025's guidance.

Now you take a step back to the heart of your question of 2026. 2026, I would tell you, what is our free cash flow generation engine flywheel? It's accelerated revenue growth the 5-plus percent in this company is driving operating leverage and it's a leveraging an efficient balance sheet. We see all that continuing to play out in 2026. And those underlying fundamentals, yes, they deliver a sustainable realization number, by the way, in the mid- to high 120s, kind of to your question. By the way, we've been there for 4 years in a row already. So we can handle that.

So you bring that all together, I think it talks to the statement and the confidence of our focused portfolio, our disciplined capital allocation, the diversity of our business model and the relentless focus of us driving productivity and operating leverage that gives us the investment flexibility to continue driving long-term sustainable competitive advantage. So thank you very much for the question.

And your next question comes from Wamsi Mohan with Bank of America.

Arvind, you said AI adoption is accelerating right at the top of the call. And I'm wondering if you can maybe help us think through the financial impact in maybe revenue terms for IBM, how we should think about the progression for that? Are we hitting some kind of inflection that we should see meaningful upside into '26 on the AI front.

And maybe quickly, your quick thoughts on maybe the impact of the federal government shutdown if there's any materiality to that to IBM here in the fourth quarter.

And if I could, Jim, if you could just clarify the organic growth in Software in third quarter and expectations for transaction processing going into the end of the year?

Wamsi, thanks for those questions. Let me try and unpack it. Let me go with the easiest one first. The easiest one is on the current government shutdown I would tell you that we see a de minimis impact to IBM. It's always hard to say 0, because something could happen. We still got 2 months to go in the quarter. But so far, we have not seen any impact from the shutdown.

And the reason for that is the makeup of our business. Our technology business is largely comprised of hardware as well as software, software mostly on a subscription basis. These are running critical systems, payments for social security benefits for the [indiscernible], all of these are considered essential, so I don't really see that at risk.

A little bit over half the business is consulting projects, but the consulting we do is of a similar nature ERP, benefits, helping people reduce paper reduce errors back to payments. These are all considered essential, and that is the reason that we maybe in the minority of not seeing any direct impact so far.

Now I'll just leave it at that because so far, we have not. Nobody has come to us about any of these projects. And so that's the first question. That is straightforward.

Next, you asked about AI. Look, our book of business, we talked about it would be over $9.5 billion. Just the Consulting piece was $1.5 billion in the quarter itself. These are very real numbers. So as those Consulting projects start to get executed and as that backlog builds up, certainly the contribution to Consulting is going to be very real. We talked about another number tied there, which is not a different number, is the 200 projects in Consulting, which are already using digital workers, which effectively are the AI agents that we have built that get deployed by our consultants on behalf of our clients.

About -- not quite, but close to 20% of our overall book of business is Technology and Software. And there, that is mostly subscription revenue as well as products that people are purchasing from us. So those numbers certainly begin to add up. And I would tell you that a big fuel behind both our OpenShift growth as well as our Automation growth is due to the AI capabilities that are infused inside those products.

So if I sort of put the first 2 pieces together, de minimis on the government shutdown and definitely, the AIP is a strong contributor to the software growth and I believe it's a big piece of why Consulting is big need to return to growth because we call the play to move towards AI almost 2 years ago. So as that book of business is built up, it is overcoming the headwinds from staff augmentation projects going away and people getting rid of discretionary spending and consulting.

Jim, I'll let you take the third piece.

Yes. Just to amplify the last piece, and then I'll get into your question about software organic and TP. To Arvind's point, year-to-date, from a software perspective, we're growing 8.5% overall, approaching 9% right now. About 2 points of that growth is coming out from our GenAI book of business. So we're getting very good realization and penetration.

To Arvind's point, or Consulting, north of a $7.5 billion book of business I put that up against any consulting company right now. We called that play to Arvind's point, a few years ago. We do think we have a differentiated competitive value proposition of a company with an integrated tech stack plus strategic partnership AI plus a consulting business at scale with an integral part of IBM Clients Zero that drives distinctive use cases and references. We've already had over 1,000 client engagements year-to-date around GenAI from an enterprise, Software and Consulting perspective overall.

In Consulting, it's already north of 22% of our $31 billion backlog. And in this quarter, we eclipsed double-digit composition of our revenue, 12% of our revenue, growing very nicely at still a 2- to 3-point competitive advantage in terms of margin overall. And by the way, you see that play out in our consulting margins year-to-date, up 220 basis points, the highest margins we've had in a long time.

Now to your point about Software. Software, we're very pleased, growing 9% in the quarter. We accelerated about 3 points organically quarter-to-quarter. This wasn't an inorganic contribution. In fact, our inorganic contribution came down as we wrapped on some of these. It's being driven by the strong contribution of our high-value recurring revenue now a book of business, $23 billion, up 9%. And when you look underneath it, TP. TP right now just given the strength of the mainframe cycle, driving cycle dynamics. We're very encouraged around the future monetization value opportunity. And as you heard in our prepared remarks, we're calling a return to growth in TP in the fourth quarter with the strong pipeline we got.

By the way, if you map it back to the z16 cycle, what happened? In '22, our TP revenue was flat. In '23, we grew high single digit. In '24, we grew double digit. You look at '25 right now, we're calling back to growth, probably a quarter early compared to a historical cycle. We feel very good about that growth profile. And given the strong z17 where we've shipped over 100% more MIPS than the p16 -- or the z16 cycle, we're actually feeling pretty good about that valuation opportunity moving forward.

Your next question comes from Ben Reitzes with Melius Research.

Arvind, I appreciate that fourth quarter reported software growth is set to accelerate in your guidance. It sounds like above 10%. And I was just wondering about next year, you do wrap the Hashi acquisition in the spring, I think March. Do you -- are there signs that it can accelerate from here? Obviously, with Red Hat decelerating a little, I just think folks would like to know broadly if you can keep double-digit next year or even accelerate based on the portfolio, realizing that you're wrapping the acquisitions in that time frame.

Yes, Ben, great question. So let me decompose it into the 4 parts of software that we talk about, and then we'll touch on acquisitions and their contribution. And then I'll ask Jim to try to put it all together back into the financial model for you.

So let's take Red Hat. We talked about 20% signings growth this quarter. We had similar numbers in the previous quarter. As that becomes the bulk of the Red Hat book of business entering 2026, we do expect to see Red Hat returning to mid-teens or close to mid-teens growth. So that would be an acceleration from where we are this quarter.

Then next, we talked about and in the last question, Jim touched on Transaction Processing or mainframe software. We have seen this happen multiple times. In the first couple of quarters of a new cycle, tends to come down because people are very much focused on getting their hardware capacity. As that hardware capacity gets deployed, then the TP revenue begins to come up, along with some of the ELA cycle dynamics that are there, and we begin to see that. So I expect to see TP growth, not quite in double digits to be clear, but let's call it low single digits, for sure, into next year.

Automation has been growing in this last quarter at 22%. Yes, the HashiCorp acquired revenue was a piece of it. And as you point out, that will go away in the second quarter. However, the acquired properties we have tend to provide continued growth for quite a while. Because of the Hashi bookings, which are significantly ahead of where we had planned them to be, I expect we'll continue to see growth out of Hashi through 2026 as well. Now not quite as much as and acquired growth, but I do expect that we'll continue to see automation in the double digits, for sure, if -- but maybe not north of 20. And we've continued to see the data and AI portfolio grow in the mid- to high single digits.

Now that does put aside what other acquisitions we will do, part of our model for software is that we'll get a couple of points of growth from acquired revenue, and we see a good market for targets. Yes, that is yet to play out. But in the current regulatory environment, combined with what we can see out there, we expect that we should be able to do that as well. So that was sort of giving you color on the portfolio and the different pieces. I'll ask Jim to get into them closing it back up in terms of sort of what is the organic and inorganic and overall Software numbers.

Yes. Thanks, Ben, for the question overall. I mean, obviously, Software, we are a software-centric platform company overall. So it's at the heart of both our top line growth vector profile and also, more importantly, from a free cash flow generation engine overall because it delivers about 3/4 of our profit.

If you take a look at '25, I think we positioned extremely well with regards to accelerating revenue growth throughout the year off of tougher comps at the end of '24. We got to remember that. And I think that's a reflection of the strength of our portfolio, the diversity of our portfolio across the board and to the disciplined execution. Now when you look at '26, early indicators, I'll put them in some big buckets, Arvind went into some of the detail. First, I think we shouldn't forget, and Arvind called this out 90 days ago, which I think surprised many of you. We're operating in an attractive TAM and a positive backdrop from a technology perspective overall. We feel very good about technology being a source of competitive advantage. And you're seeing that play out in areas around Hybrid Cloud modernization around AI, around Automation in many areas, we see that continue. So the market backdrop, we couldn't be more optimistic around '26.

Two, the strength and diversity of our portfolio, not only has it been repositioned over the last 3 or 4 years to accelerate growth. What is happening? More and more of our composition of software is now aligned to higher growth end markets, which gives us a better vector of growth even as we go into '26.

Three, our annuity portfolio. And I don't think we get a lot of value for this, and we keep bringing it up. over a $23 billion ARR book of business, we feel we're going to exit the fourth quarter at double digits. That's a great indicator for 2026, because that is 80% of our software portfolio overall.

Four, new innovation, GenAI, I already talked about GenAI, the book of business and the acceleration we got, and all of the capital investment that's going into the infrastructure providers, I think, is just going to accelerate the innovation curve for enterprise AI overall. And we are a leader in enterprise AI just given our tech stack, our Software portfolio and Consulting, and that should deliver a few points.

Five, Red Hat, our bookings are 3 months, or 6 months, our 9-month, our 12-month RPO shows accelerating growth coming off a 20% bookings overall. By the way, we actually had more opportunity to do even better than that 20%, and that should fuel an inflected growth.

Next, M&A. The point I would bring up on M&A, Arvind already talked about, it's embedded in our model. We've said that all along. But again, I think we've got to continue selling the investor narrative because that M&A drives a much higher organic growth engine because those synergies play to those acquisitions. That's how we pay for control premiums. That's how we get an accelerated top line growth. That's how we get an accelerated bottom line growth, and we get accretive value in free cash flow in 2 years. So our organic engine continues to grow.

And then finally, TP modernization, Arvind wrapped up on it, let's just remind all the investors, TP gets monetized based on hardware installed MIP usage. I already said 2 quarters in, albeit early, we're 130% program to program on z17. Off of a z16 that was the most successful program in the history of IBM. We're at 130%. So I do my math in calculation, higher capacity opportunity creates higher monetization opportunity creates higher price opportunity creates higher value creation opportunities. So I think when you look at it, we feel pretty good about delivering our model and software.

Your next question comes from Erik Woodring with Morgan Stanley.

Just one quick clarification question there, Arvind. The growth rates you just provided in the response to that question for 2026 or into 2026. I just wanted to confirm those were all organic growth rates or whether they included M&A embedded in them?

And then my question, just taking a step back, Arvind is we've seen cloud providers experienced exceptional growth recently, particularly in infrastructure services and large-scale AI workloads, how does IBM view that trend? And do you see a similar opportunity for IBM Cloud to capture long-term infrastructure-driven demand?

The growth rates that we talked about, we tend not to do much M&A or any in both our mainframe or TPS as well as in some of the other areas. The growth rates I mentioned, I would call it, are largely organic without having any significant M&A. But tuck-ins, small M&A are probably all included in there, Erik. But if we do anything substantial, it would help accelerate those growth rates. Let's just put it that way. I'll also let Jim comment on it after I talk about the cloud opportunity.

We actually partner deeply with all the hyperscalers. A thing that we haven't talked about, but it's certainly no secret, for example, we are one of CoreWeave large clients. We also tend to use a lot of infrastructure at AWS, at Azure as well as at GCP. So as opposed to that it's an opportunity for us, Erik, it's the flip. We got a huge opportunity to do both consulting projects as well as deploy our software on those infrastructures for our clients.

As an example, if I take one of our very large health insurance clients, as they think through where they're going to deploy their AI models, they do not like deploying in a public instance but they are over defined getting a private instance in a cloud and deploying models there, deploying our software stack there and getting growth. So we tend to do that.

We also tend to, in some cases, for example, with Grok, we are deploying Grok in people's own data centers. So that's a big opportunity that comes there. That will show up in revenue for us, both in Consulting as well as in Software because on top of the Grok infrastructure, we tend to put our software stacks in some instances.

So it's less about us getting an opportunity in our cloud only, but much more that that's a growth vector that we are able to ride and that helps increase overall growth rate in both Software as well as in Consulting.

And lastly, let's not forget, our biggest beneficiary of AI infrastructure is our mainframe and our Storage portfolio at this time. The latest generation mainframe, we will surprise you with some of the numbers. This quarter, a fully populated single system is capable of doing 450 billion inferences per day. As clients purchase that capability, that will be both a further accelerate to mainframe infrastructure growth, but it also comes with a software stack that helps them do all of that inferencing.

If I look at our Storage portfolio, as many people have realized, you need a lot of storage to be able to do AI training, and we are going to be beneficiaries of that, inside our Storage portfolio as people deploy that. So I would much more say, we are actually the direct beneficiary of the hyperscale or growth of AI capability and capacity as enterprises use this capability and two, we will be a beneficiary in our mainframe and Storage stock in a direct way.

I think that, that would -- I hope, Erik addressed that part of the question.

Erik, and just to the numbers piece. I mean, first of all, we are all focused here to execute a very important fourth quarter to finish a very successful 2025 for IBM. But we -- both Arvind and I are given some color about '26 about the confidence we have in our portfolio. But let me just take a step back and remind you on Software.

Our Software model shared at Investor Day approaching double digits. That is all in. That has 2 to 3 points, give or take each year, maybe 1 point more and maybe 1 point less depending on our disciplined capital allocation around M&A of inorganic contribution and 6 to 7 to 8 points of organic. When you look at 2025, you go do the math, we're probably going to be approaching 6-plus percent organic growth overall. And we're going to have somewhere 3 to 4 points this year because we took advantage of a very strategic opportunity with HashiCorp this year.

But when you take a look at 2026, the TP growth monetization value that I talked about, the Red Hat accelerated growth profile that's on our revenue under contract, the annuity growth profile that is approaching or now going to be double digits at the exiting the year. Each of those are going to fuel that organic growth engine overall. So I think big picture, the model is pretty much what we kind of look at right now for 2026.

Your next question comes from Jim Schneider with Goldman Sachs.

Arvind, I was wondering if you could maybe elaborate a little bit on how you're thinking about M&A from a target perspective. You've previously stated you're looking to accelerate growth and you're looking for things that fit strategically with the portfolio. But on the margin, anything -- in any way you're thinking about -- differently about either the portfolio or the product piece of it or the potential size of transaction you might like to undertake? And specifically, would you consider undertaking a somewhat larger, more transformative transaction maybe not quite as big as you did with Red Hat, but a sort of similar scale relative to your overall portfolio?

Yes. Jim, thanks for the question. Look, M&A is an extremely important part of our strategy. So I want to just perhaps reiterate because this has come up on prior calls as well. We look at it always in a multiyear window. So we got to look at what is our excess cash flow over a few years. And once we have that window, that means we can sort of buy ahead, which means we can sort of lean in. Or if we don't find a good target, like we didn't, for example, I think, in 2023, then we actually spent much less than we could have.

So that's just a backdrop to the amount of financial flexibility that we have, which if I remember at our Investor Day earlier this year, we laid out that we have somewhere in the mid-20s, perhaps a bit more flexibility over a 3-year window. That's kind of a way to sort of look at it.

Next, we are very focused on the areas that we have already explained as our strategy, very top level, we say Hybrid Cloud and artificial intelligence. That translates into our hybrid portfolio, our Automation portfolio and our Data and AI portfolio. And you've seen us do acquisitions in there. For example, we bought an AI company that does B LLM, but it fit into our hybrid portfolio because it's a direct part of the Red Hat and OpenShift portfolios. We did HashiCorp, which fits directly into Automation. We did data stacks, which fits into data.

When I look at the target list, there is, I think, a pretty rich list of opportunities that are out there in the private markets, in the PE world and the public markets across those opportunities that we think will -- some of them will be actionable. It's hard to predict upfront, which are and which are not. So I think that if I put it that way.

And just to be clear, anything that is of size has to fit three criteria. It has to fit with the strategy we just laid out. There has to be synergy, AKA, the growth rate inside IBM will be above what it was as a stand-alone entity. Some of that comes from our geography spread. We have a sales team in most countries in the world. Some of it comes by the ability to bundle more attractive offerings together. And it comes from faster deployment, for example, leveraging our consulting team or the rest of our sales team. All 3 will be able to add to more -- to a faster growth rate.

Third, if it is of size, then we are very disciplined also that we like it to become accretive to cash by the end of the second year. So those are the criteria, but as you have seen, we have found plenty in the last few years that do fit that whole criteria. So I hope that, that gives you a sense.

Now your question on larger. I'll just use that word larger. We will never rule anything out, but it has to meet all the criteria that we just laid out. It is not for size alone. Red Hat allowed us to enter the new space. helped accelerate IBM's overall growth rate. By the way, both in Software and in Consulting. So that was the sort of the synergy piece that was there, and you -- many people forget, Red Hat also had a very attractive free cash flow profile that we've been able to leverage since the acquisition.

And that question comes from Brian Essex with JPMorgan.

Arvind, maybe as a follow-up as to part of Erik's question, and I appreciate your hybrid exposure here. But could you generalize what you're seeing with regard to mix as enterprises focus on AI readiness? Are cloud-native ISV-based agentic applications may be targeted at task and point solution automation, are those low-hanging fruit to prove ROI before pursuing self-hosted projects? And then maybe within the IT budgets, where is the spending coming from? What's at risk of getting trimmed as companies focused on adopting AI-based technology.

So Brian, let me address the first part of your question with a bit of depth. I actually think that these are an [indiscernible] are people going to leverage ISV, Otherwise, I'll call it, SaaS applications for getting exposure to AI and agents, either as part of those entities or as added value on to them. And there are hundreds, if not thousands, of little boutique companies that provide some of those agents that are out there. I think they will absolutely do that. They'll kick the tires on it, they'll get some value.

But at the end of the day, to get real value from AI, people have to be able to integrate their existing applications. How do they [indiscernible] what is happening in their payroll system and HR system, to perhaps something that is happening in the CRM system, to perhaps something that is happening in their ERP system, as people begin to want to build much more profound agents that is where a lot of the action that we see is happening. As people try to build those agents out, then they get deeply concerned about what is that data, where is the data going and they're going to deploy those either in their own data centers or in a private instance.

Note, a private instance of the cloud is quite protected and people do deploy a lot of critical applications that are there. But if I think about our clients in the regulated industries, banking and insurance still is very much a data center as well as a cloud picture. If I look at health care, health care data tends not to go out very much from their own data centers. If I look at telecom, most people build their own backbones and their data and applications reside there, but certain other things like marketing may well reside in the public cloud.

So as we begin to look upon all that, I believe that we are at the very big ring. I would actually characterize this, Brian, that if I was to use the baseball analogy, we're still in the first innings of enterprise AI rollout. And I expect that we will be seeing and, we will see more SaaS AI usage. We will see more public cloud AI usage and we will begin to see a lot more private AI usage as people begin to get into more critical applications and agents.

On the IT budgets. Look, IT budgets have been growing ahead of GDP. That's simply an observation. I think this began 4 or 5 years ago, but IT budgets are growing typically 2 to 3 points ahead of GDP growth. You combine that then with inflation because GDP after all, is real, not nominal. I see IT budgets stay healthy. So a lot of the growth comes from the fact that the IT budgets are growing as opposed to a cost or something else.

That said, I think people are getting very, very effective at trying to run and maintain with lower costs and putting more money towards newer projects. 5, 10 years ago, that ratio used to be 70:30, 70 are running what is 30 on new. I think that is shifting more towards a 60:40 spread and where it will go, is where we'll get the benefit. That is why our Automation portfolio and our hybrid portfolio get a lot more growth because people are using that. So it's sort of substituting for labor and in some cases, for services, by letting those capabilities move into software.

And the next question comes from Mark Newman with Bernstein.

Very good to see the growing AI book of business, and thanks for those comments just now Arvind. I don't think you've given any specific breakdown yet on the breakdown between software and consulting of the AI book of business. Is there any clarity on that? I think it used to be 80:20. Just want to clarify if there's any clarity you've given on that today.

And then a follow-up on Consulting. I think there's 2 quarters in a row now where we're seeing the book-to-bill ratio a touch below 1. I know you pointed in the earnings to a book-to-bill ratio greater than 1 if you're looking at the trailing 12 months, but I would just like to understand more on a shorter-term basis last 6 months, it seems like the book-to-bill ratio is below 1.

And if you could explain kind of why -- maybe why that's the case, why we shouldn't be worried, especially considering, I think, around 30% of signings, you mentioned are AI, which I believe are longer durations. So just a little bit of clarity around consulting and how AI plays into the to the book bill ratio and that recent number being below 1, would be appreciated.

Okay. Mark, this is Jim. I'll take both of those. Let's start with the second one first, and then I'll come back to your clarification question on GenAI, in particular, around the Software portfolio overall. And I want to dive a little bit deeper than that.

But when we take a look at Consulting, let's dial back 90 days ago. I think we surprised many, just compared to what many other consulting companies have been talking about publicly. We talked about green shoots that we saw entering second half. But I think at that time, we were prudently cautious about how we were going to monitor client buying behaviors, and we didn't expect growth in the second half, although we saw many green shoots overall.

Now we posted, I think, a marked inflection point of Consulting back to growth, up 2%. And it's been driven by what we're seeing as continued opportunity for growth as clients accelerate investment an AI-driven transformation, what we've been talking about on many of these questions here. Why? Companies are looking to unlock efficiency, business model innovation and growth, growth, growth, and AI is accelerating overall.

When we take a look at right now fourth quarter and more importantly, early parts of '26, we again see momentum around those key metrics, our backlog position, our GenAI book, strategic partnerships, and around productivity. Backlog, $31 billion, healthy growing 4% right now. Our best ever erosion, and I think multiple years. What does that see? Clients' commitment to IBM Consulting, the quality of our delivery and the value of our differentiated offerings are doing extremely well.

Now to your point about signings. Signings were down 5%. By the way, signings has been down 5 of the last 6 quarters, something like that. But as I've said many times before, why don't I start with backlog. That, to me, is the most critical component that's closest to the outcome measure. The outcome measure is revenue. Revenue growth, revenue growth as Arvind always likes to say in this conference room with our operating team. Indicators are backlog, indicators are signings. But signings are not all equal. And those signings numbers have been driven down. I think we posted down 5% based on lower large deal renewal volume.

By the way, I would argue that's a best no revenue realization, and probably worst dilutive revenue because renewals typically drive more price and more productivity. But underneath it, what are we seeing? We're seeing a tremendous improvement in the quality of our signings. Our net new business penetration, again, another quarter in a row, up double digits year-to-year on a penetration. Over 300 new clients year-to-date fueling our backlog. Our backlog realization is up over 4 points year-over-year. And when we take a look at our backlog runout, it's pretty healthy growing at market level growth rates here over the next 3, 6, 9, 12 months. A lot of work still to do to sell and bill within quarter, but a lot of good indicators.

And that GenAI, to your point, over 22% of our backlog, 30% of our signings, 12% of our revenue, that's what's inflecting the growth overall. So we feel pretty good. And that's why we called the mark inflection, and we said we're going to grow consulting here in the fourth quarter, and we feel pretty good about getting back to market growth levels in 2026.

Now GenAI, over $9.5 billion book of business, Arvind already talked about over $7.5 billion in Consulting, well over $1.5 billion, almost approaching $2 billion in Software. We're what, 7, 8 quarters in here. That number might vary quarter-by-quarter as far as the composition. But we're still pretty damn close to that 20:80 overall.

But the underpinnings behind that of the Software book in the generation. You see that play out and how it's accelerating Automation growth, but also Red Hat. I know there's been a lot of questions around Red Hat. Let me just spend a minute just to close the call on Red Hat.

Red Hat, we delivered 12% growth. We were down a couple of points quarter-to-quarter. And year-to-date, we're at 13% low teens, right? Let me break down some of the performance. One from a physician of strength and Arvind talked about a few points. OpenShift, up nearly 40% bookings. Our ARR, $1.8 billion, up mid-30s year-over-year, accelerating profile, Virtualization, now we've closed total contract value of bookings over $400 million. We got a $700 million pipeline over the next 5-plus quarters. And Ansible, 20% bookings in the quarter, accelerating the high teens.

So what happened on the sequential decline. One, as we knew we were facing tougher comparison on the consumption-based services. That impacted us by about 1 point. And RHEL about 50% of our portfolio, we've been talking about -- we've been well growing RHEL abnormally in the mid-teens. We reverted back to our model, growing 6% and at about 1 point.

Now taking a step back, Red Hat model is mid-teens. And when you look at it, our 80% subs business, we got to grow low end of that high teens. The consumption base, we got to grow high single digit. When you look at our year-to-date, and Arvind talked about our bookings year-to-date, we're well positioned on that subscription-based business growing high teens already on bookings.

And when you look at that 6-, 9-, 12-month revenue under contract, we're accelerating that growth as we go into that gives us confidence in that acceleration comment that Arvind talked about and I talked about as qualitative statements about confidence in '26.

And when you look at fourth quarter, let's put this in perspective. We're going to accelerate Red Hat growth in '25. It's going to be a nice acceleration on the subs. And we got about a 2-point headwind on consumption-based services. We knew about that because last year, we grew consumption-based services high teens. And when you look at fourth quarter, we're going to wrap on that. We've known about that all year long. So when we look at fourth quarter, double-digit solid double-digit growth in Red Hat, low teen growth for the year, nice composition of where that acceleration is. But the most important thing, we're well positioned for 2026.

So with that, I'll turn it back over to Arvind to close out the call.

Thanks, Jim. Look, to close out, we are pleased with our performance this quarter. All of our segments accelerated sequentially. Our portfolio strength, business model and relentless focus on productivity, reinforce our confidence in the trajectory I look forward to sharing our progress as we close out the year.

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

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