Cheetah Mobile, Inc. ADR Class A Aktie

Good day, and welcome to the Cheetah Mobile First Quarter 2026 Earnings Call. [Operator Instructions] Please note this event is being recorded.

I would now like to turn the conference over to Cheetah Mobile, Investor Relations. Helen, Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's First Quarter 2026 Earnings Conference Call. With us today are our company's Chairman and CEO, Mr. Fu Sheng, and our company's Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct the Q&A section. Please note that the management of [Indiscernible] will be presented by AI agent.

Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our conference call today as we will make forward-looking statements. At this time, I would now like to turn the conference call over to our Chairman and CEO, Mr. Fu Sheng. Please go ahead.

2026 remains an important transition year for Cheetah Mobile. We are continuing to evolve from a traditional Internet company into a company focused on the AI-enabled applications for AI agents and robotics. More importantly, we believe we are gradually moving from capability building into early-stage commercial validation. Our focus is not only on developing AI capabilities but on turning these capabilities into practical products for real business scenarios helping customers deliver better ROI. Starting from this quarter, we are separating our robotics and others business into an independent reportable segment.

In the first quarter, revenue from robotics and others business increased 176% year-over-year to RMB 51 million approaching 20% of total revenue. And at the same time, adjusted operating loss from this segment narrowed by 57% year-over-year. Customer demand remained strong, and we expect robotics and others revenue to grow strongly in 2026. In Q2, our robotics and other revenue will continue growing both year-over-year and quarter-over-quarter basis.

Today, our robotics business mainly focuses on commercial scenarios with real customer demand and clear long-term value, including reception, guided tours and intelligent service applications. Our smart personal mobility is another important step for us. This product extends our robotics and AI capabilities into personal mobility and health care-related scenarios. More importantly, it further validates that our robotic platform can expand beyond commercial service robots into broader consumer applications, we are encouraged to see recognition from leading industry partners.

During the second quarter, we started initial product shipments to a top global designer and manufacturer of mobility products as well as the leading elderly mobility scooter manufacturer in China. We are seeing encouraging early market feedback and initial commercial traction.

Moving to our agents. We're seeing strong customer adoption. We worked closely with Google Cloud and AWS, helping enterprises serving international markets access AI models and use multi-cloud environments more efficiently. In 1Q '26 revenue from our cloud and AI infrastructure services as part of global enterprise services revenue increased 68% year-over-year contributing 18% of total revenue.

Daily token usage has increased more than 20x since January 2026 exceeding RMB 400 million in May. We expect this revenue growth to continue. We also kept building EasyClaw -- so early, but we believe it will help customers deploy AI agents and boost productivity. The two fast-growing businesses, namely robotics and others as well as cloud and AI infrastructure already accounted for 38% of our first quarter revenue, and we expect their revenue growth and revenue contribution to continue growing in the coming quarter and to exceed more than 50% of our total revenue in the second half of this year.

During the quarter, revenue from our advertising agency business within the Global Enterprise Services segment was affected by policy changes from certain overseas advertising platforms. We believe this revenue decline was primarily driven by external factors rather than changes in customer demand. This was the primary reason for the company's widening year-over-year operating loss in the first quarter. Our Internet services business continues to provide important profit and cash flow support for the company. In the first quarter of 2026, our Internet service business generated approximately RMB 15 million in adjusted operating profit in 2026.

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Excuse me, there has been an interruption. Just one moment, please.

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profit and cash, while agency revenue was hit by policy changes, which impacts our financial results in the near term.

Due to a stronger base for growth. Moreover, our USD 186 million that also supports our AI agents and robotics. Thank you.

Thank you, Fu Sheng. Hello, everyone, and thank you for joining us. Unless otherwise stated, all financial figures are presented in RMB. During the first quarter of 2026, we continue focusing on operating discipline, improving revenue quality and maintaining financial flexibility as we invest in AI and robotics initiatives, total revenue remained relatively stable year-over-year at RMB 259 million during the quarter, while Internet service revenue declined due to continued weakness in online advertising. The quality of our revenue mix continued improving.

Within the Internet Services segment, revenue from Internet value-added services continue to grow steadily and 8.2% year-over-year, contributing 72.8% of segment revenue given a larger portion of internet value-added services. Our Internet service revenue is becoming increasingly predictable. More importantly, the Internet service business remained profitable and continue generating stable cash, which provides an important financial foundation for our long-term AI and robotics investment. Turning to our robotics and other segments.

Starting from this quarter, we began recording the robotics and others business as a separate segment to present the operating progress of this business. Historical results previously reported on AI and others are now presented as robotics and others as well as global enterprise services. During the first quarter, revenue from robotics and others increased significantly year-over-year with revenue increasing 175.9% year-over-year to RMB 51.2 million, accounting for 19.8% of total revenue, adjusted operating loss from this segment narrowed by 57.1% year-over-year, reflecting continued improvement in operating efficiency and commercial execution.

Turning to Global Enterprise Services. This business remains strategically important to the company in addition to profitability contribution, it provides us with valuable enterprise customer relationships, overseas operating experience and real-world deployment scenarios for AI-related services.

During the quarter, Revenue from the advertising agency business was affected by policy changes from overseas advertising platforms, which impacted year-over-year segment revenue performance. However, revenue from our cloud and AI infrastructure services business increased by 68.3%, supported by increasing advertise demand for AI-related cloud and token management services. Moving to profitability. Operating loss was RMB 28.3 million during the quarter compared with RMB 26.5 million in the same period last year. The increase mainly reflected lower profitability from Internet and global enterprise services business following revenue declines in online advertising and advertising agency services as well as our continued investments in AI and robotic initiatives.

More importantly, the Internet service and Global Enterprise Services business remained profitable during the quarter. The Internet service business generated approximately RMB 15.2 million in adjusted operating profit, while our Global Enterprise Services generated approximately RMB 13.8 million in adjusted operating profit. We also maintained a strong balance sheet. As of March 31, 2026, we had approximately $186 million in cash and cash equivalents as well as over $100 million in long-term investments.

We believe our financial position provides sufficient flexibility to continue investing in the area and robotics with a disciplined and sustainable approach. Looking ahead, our financial priorities remain consistent: a, maintaining operating discipline; b, improving revenue quality and operating efficiency; c,supporting long-term investments while preserving financial flexibility. Overall, we believe the company continues moving toward a more sustainable and balanced operating structure as our AI and robotics businesses gradually scale. Thank you. We are now ready to take your questions.

[Operator Instructions] We will The first question comes from Thomas Chong with Jefferies. Please go ahead.

Thanks for management to accept my question. Recently, we can see that the market is attracting more and more attention to robot [Indiscernible] that the real value of robots is not only [Indiscernible] I would like to ask in the past few years, Cheetah has been in multiple commercial and operating robots for a long time. From your perspective. During this operation, do you have to simulate the dynamic data? Thank you for taking over so that we can move to robot. This is the most important foundation capability to develop.

Okay. Let me answer. Thanks, Thomas, for your question. I think you also pointed out a very important issue in the robotics industry, which is the issue of insufficient training data today. The rapid development of AI has given us very high expectations for the robotics industry. Believing that today's AI capabilities have improved. And robots should soon be able to achieve various behavioral capabilities. But in fact, I don't think so because the development of AI agent including the development of large language models is actually built on the development of the Internet for 2 or 3 decades.

The Internet essentially forms the basic training data of large language model. It is a very high-quality data set and the various problem in the robotics industry today is the lack of data. And many ways are being tried today with many manufacturers trying to use training data, including data migration, simulation training and so on. However, there is a very serious problem. The physical world is much more complex than the laboratory environment and the simulator environment. So today, whether it's data migration, collection or truly migrating to different ontologies, this adaptability will be a huge challenge.

Let me give you an example. Today's Tesla's FSD is already very good. But in fact, some older versions of Tesla's own cars cannot install the latest FSD. So indeed, data is a very big problem. I also very much agree with what he said. The data continuously generated in the real deployment environment is actually very important for the robotics industry from our own experience. Let me give you two examples.

One aspect is our voice interaction capability in different environments, which is actually closely related to our long-term exploration in various scenarios. Different noises, different environments, multiple people and so on, we have made some optimizations and training on the data. Therefore, the interaction effect of our interaction robots, including reception are leading in the industry today.

We have a reputation of our own in the industry. Another example is the mechanical mobility, a very simple robot can navigate indoors from point A to point B. It is similar to a small low-speed driverless vehicle, how to use cheap chips and sensors to achieve automatic obstacle avoidance in different environments. In fact, all of these can only be achieved based on massive amounts of data. We recently launched a smart wheelchair, which we just mentioned, we started mass production in May. And now it seems that in overseas markets, especially in Europe, the sales momentum is quite good.

In fact, for a traditional wheelchair product like this to achieve obstacle avoidance and assisted driving, many manufacturers, including some start-up manufacturers, want to achieve this kind of assisted driving capability, but to create a prototype and truly achieve good passing ability in many environments, it actually requires quite a lot of effort. This is related to the fact that we have deployed many robots in many environments over the years, regardless of the surface conditions such as carpets or floors.

We have also enhanced the reflection of walls, all of which have accumulated over time, there is also continuous algorithm optimization based on actual scenarios. Therefore, our wheelchair can truly achieve lower cost, highly assisted driving capability. It has also received...

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So at this stage, the value chain is definitely in this regard. But I want to say the first is why I think it is not the model layer because although the model, there is very fierce competition. But what we see now is that the gap between models is not too wide, and it is not easy to widen. Today, for example, the models of China and the United States, we think there is probably a gap of about half a year. And this gap is probably such a process. And there is no sign that pulls the other side away.

And among large manufacturers, I think the gap is a bit like ebb and flow. Of course, today's models are also in the early stage. And in the future, I think with the continuous increase in production of inference chips and training chips, the training costs will gradually decrease. So I think the model layer will be an infrastructure, but in the long run, it will not be monopolized. And with the continuous improvement of the model's capabilities, now we can see that many models, even if they are not top models but adapted to some daily tasks, have actually achieved very good results.

For example, some open source models in China this year have seen a significant increase in the amount of calls. And I think the core reason is that they offer great cost effectiveness. -- they have achieved high completion rates in some tasks. Therefore, I even think that in the future, various specialized models will continue to emerge. Of course, this will take some time. The second infrastructure layer, we do not fully participate in, but we also see that because we have our own cloud business and we have tokens clients consuming here, the growth is also very fast.

So I think this is a state of mismatch between supply and demand at this stage. But eventually, the infrastructure will also enter an economy of sale. And for applications, today, AI can actually reshape almost all applications. So there are huge opportunities in the application layer today, whether it is the industry we are doing like robots, we have been doing it for a long time, but we are still very firmly optimistic and the capabilities of the models continue to improve and the application of robots is wider, there are many things that it may be a bigger industry than the automotive industry.

There are also many opportunities at the software level, which I will not expand on here. Even today, when we look at some large model companies, their valuations are very high or excellent. In fact, they have truly delved deep into a certain application such as the programming of Stable Diffusion and the rise of Claude is actually an application. Its application is a coding application. It has made the coding application good enough rather than just providing an API for you to consult, but its agent has been well developed including OpenCloud that emerged at the beginning of this year, we have also developed products like EasyClaw.

So I think there is still a large room and opportunities in the application layer. Well, thank you.

The next question comes from the [Indiscernible] please go ahead.

This is my question. I also want to ask you about robotics industry. Has a lot of discussions about the future of technology, for example, can you tell [indiscernible] the people think it's product operation, or the product deployment. What do you think is the core competitive barrier of robots in the future? Which capabilities are the most difficult to replicate?

From my understanding of the robotics industry today, I believe that in the short term or within the next 2 to 5 years, the possibility of a particularly versatile robot appearing is very low. This is limited by both the so-called model capabilities and the entire hardware industry chain. The update on the hardware industry chain is actually relatively slow, and it involves some of the most basic physics and materials as well as the underlying logic of physical laws and materials.

So I believe today that the core skill barriers in the integrated industry in the future still lie in the true scenario operation capabilities. And in terms of client network, if today, we can have enough scenarios and have a good client network so that our products can really be used in these scenarios. We can accumulate our own unique experience or data. The first question has been answered, which is that we can optimize it.

And this optimization enables the product to provide better cost effectiveness to truly meet users' needs. The machinery industry is very high. But when it comes to business implementation, clients don't care whether you are a robot, a machine or a human. What they care more about is cost effectiveness, ROI, input and output. This has been very significantly reflected in our operations in recent years. So whether it is in the media, you've seen a lot of amazing things before, but you will find that it in a really an actual scenario, very few. Without going through actual scenarios. Let me reiterate this.

The operation of robots in the physical environment whether it is actions or work, its complexity is actually much higher than that of autonomous driving of cars. So in this case, a very high complexity, I think in practical application scenarios today, in the operational scenarios and customer networks, a vertical and penetrating points can be formed. It is much more important than a generalized machine and model because today, I don't think the generalized models and machines can quickly complete the ROI required in these vertical scenarios. Okay. Thank you.

Operator

The next question comes from Nancy Lu with JPMorgan. Please go ahead.

We see that recently basic model capabilities converge and API cost continue to decline are driving the acceleration of the commoditization of the underlying model, but enterprises generally adopt a multimodal strategy and no longer rely on a single model supplier has shifted from model performance to model application. I would like to ask in the future enterprise AI market. Where is the irreplaceable scarce capability and for future enterprise level AI products, where is the ultimate moat?

Thank you, Lu. I think this is a very broad question. I think the ultimate moat of enterprise level AI products should come from a deep understanding of user needs and a deep understanding of the industry. and then form an extremely high level organizational capability because the points you mentioned today are also realistic in terms of the capabilities of the model itself, it seems that one thing rises and the other falls. Then cost effectiveness is also increasingly being brought up. So what is the essence today?

It actually allows enterprises to save a lot of money that used to be spent on noncommercial insights, user insights and truly focus on understanding user needs. So the real moat comes from keen insight into user needs and quickly launching new products and services and improving your products and services. So we often talk about the AI-AT5 organization. Its essence is to use AI to reconstruct the internal organizational processes of the enterprise and to quickly and efficiently achieve the operation of the enterprise and to launch its own products and services more efficiently and quickly.

For example, if you pay attention, we have launched various product services in the past year, much more than in the past. But our investment in R&D has decreased a lot from the perspective of cost, although there is still room for improvement, this is an example. So when you launch products and services so quickly, where is your real moat? it comes from users' demand. You can really find users' demand and quickly launch -- and quickly respond to users' demand.

By the way, we have also launched some corresponding services and courses for the organizational construction of AI for the enterprise version and shared some of our experiences with our clients. Now some big clients have started to sign contracts. Operations have also begun. The essence of business competition lies in efficiency and insight into user demand. And I believe AI products can accelerate the arrival of these two points.

The next question comes from Qiong Yang with Guoyuan Securities. Please go ahead.

Hello, you just mentioned our company is investing in enterprise AI projects. We would like to know currently a large number of enterprise projects still rely on customized development and manual services compared to the standardized interaction model of traditional large products. The LLM moat will remain a mixed model of software and services for a long time. What's a key change in this process?

I think the core reason why there is still such a large amount of customization and manual services today is that AI is still in its early stage although we are seeing the moments of various media that most people's understanding of AI and its use is still insufficient. I think only a few people today can really make good use of AI. So this is a generation gap. Today's AI projects in a historical enterprise need to do customized development and manual services for the traditional SaaS has been developing for many years, and it has condensed many things in the code. So it seems that in many cases, it belongs to standardized delivery.

I think as everyone actually understand the AI, the entire staff are getting more and more proficient in AI application. The proportion of this service model will continue to decline. Our company has already achieved a model where all employees are using AI to write code and some of our internal systems are also starting to use AI to be written directly by the business department rather than relying on SaaS software and the service department. So the most critical change in this process is, on the one hand, I think the model capabilities are constantly increasing.

And today, for example, a very important feeling we have this year is that today, the business department is writing some internal software and services. And when using the model, we feel that the model capabilities have been improved a lot compared to last year, and many of them may have been more of a demo before or a demo level products that can already be used internally. The model capabilities will continue to increase. Another thing is that our organizational structure today is still based on the traditional one based on industrial software.

I think with the continuous emergence of emerging companies, new AI native organizations are emerging. And the traditional standardized SaaS model will be broken. So what we provide to our customers today is no longer the traditional type of service, but more of training, training for our clients' employees and assessment of AI capabilities to help them transform their AI organization. I think this change is the most critical, which means that companies need to change their organizational structure and demand for employees based on AI.

I'm Qiong Yang from Guoyuan Securities. I understand the question.

[indiscernible] Please go ahead.

I would like to ask -- in terms of the commercialization, the wheeled robots and robotic arms are still the most widely deployed and the most mature functionality. I'd like to ask Mr. Fu, what's your opinion? What will be the development structure of robots in the coming years?

I believe I've made my view on humanoid robots quite clear in media. I think humanoid robots will not be able to replace humans. In commercial applications, beyond performances in the next 3 to 5 years, no matter in factories or service industry or even in households. The difficulty of developing humanoid robots is extremely high. We have wheeled robots and robotic arms, like xArm in UFACTORY. Those robotic arm products have been steadily growing in recent years and has shown good growth in Q1 this year.

The wheeled robots are also doing well with practicality, cost effectiveness and indoor navigation technology already in a mature stage. Therefore, I believe we will also see rapid growth. This is in my view. I believe robots should evolve from specialized vertical model that continuously grow and gather data until they are advanced now. And then maybe integrate to gradually take a more general form. As for bipedal robots, I don't think they are needed in most scenarios. There's no need to add such cost and complexity, including its reliability. So this is my opinion, and we have reiterated it many times that what we care most about in making robots is the commercial landing that can really be accepted by the market and is really paid by the market entity, not just on project lending or some integrated projects.

So I think, the wheeled robots will gradually be matched with product fee in the future and for a long time, it will be the main form of humanoid robot development.

The next question comes from [ Guang Tao Jang ] from Bohai Securities.

I want to ask the domestic service worldwide is considered to be the largest market for robot in the long term, but at the same time, it is also the most complex in demand, and it is also the same with the highest challenges. In the past quarter, you also launched your own intelligent wheelchair [indiscernible] in the next 2 to 3 years?

Yes. Actually, home robots are a broad concept. If you really talk about home robots, the only breakthrough is a sweeping robot.It's also called a robot, right? But if you consider the robot that can do more household tasks like adults. I think the first reason why we make intelligent wheelchair is that in our view, intelligent wheelchair is a robot. But previously, our robots were used for delivery and intelligent wheelchairs can also be understood as delivering people. So I think the first type of family application is mobility, the ability to move from A to B.

The second is to add some functions on this mobility such as adding the ability to sweep the floor for a sweeping robot. What we see now is companionship, helping you achieve some family control, controlling a voice and integrating it into robots, helping you make some friend, and being a good companion. These are all part of the same direction. Actually, it can also be said that our wheelchair products have such functions, including the companionship function or the elderly, which will soon be launched on our HTP. I think it is like what everyone imagine such as the ability to do housework.

I don't think it's possible to achieve it within 2 to 3 years because we have our own robotic arm company. And our robotic arms are used in many scenarios, whether in industrial or commercial settings. I think in the scenarios like commercial dishwashing, we have seen such cases. Today, it's important to note that interacting with the physical world is extremely complex for robots, not because they can perform certain actions, but because of the stability that follows and the success rate, even the success rate of picking up a cup today is not 100% for any company, even in a kitchen environment or the home use.

If the success rate is 99%, we'll still accumulate broken cups. This negative impact is quite significant, not to mention if it enters a household, there will be issues like falling, bumping into things or hitting people. There's also the reliability of its quality. We expect the home appliance to work fine for several years after purchase. But for a complex robot, ensuring quality over a long period of time without malfunctioning is a tough challenge for many robotics companies today. Therefore, I believe that when it comes to home robots, we should be more pragmatic.

Our view is that robots should be able to truly provide companionship for the family and assist the elderly and people with disabilities in moving around. I think this is a great breakthrough direction. Thank you.

Operator. please check if there are any further questions. And if not, we can conclude the meeting.

Thank you. Seeing there are no further questions, this concludes both our question-and-answer session and today's conference. Thank you for attending today's presentation. You may now disconnect.

Thank you. Bye-bye.

Thank you.

And the conference has now concluded. We thank you for attending today's presentation. And you may now disconnect your lines.

Good day, and welcome to the Cheetah Mobile Fourth Quarter 2025 Earnings Conference Call. [Operator Instructions] Please note, this event is being recorded.

I would now like to turn the conference over to Helen Jing Zhu, Investor Relations of Cheetah Mobile. Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's Fourth Quarter 2025 Earnings Conference Call. With us today are our company's Chairman and CEO, Mr. Fu Sheng; our company's Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct the Q&A section. Please note that the management's prepared remarks are presented by AI agent. Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our conference call today as we will make forward-looking statements.

At this time, I would now like to turn the conference call over to our Chairman and CEO, Mr. Fu Sheng. Please go ahead, Fu Sheng.

Good evening, everyone. Thank you for joining us. In 2025, we finished stabilizing the business and build a stronger foundation for Cheetah Mobile. During the year, our total revenue grew 43% year-over-year, driven by continued growth in both our Internet business and AI and other segments. In the fourth quarter, AI and others already accounted for half of total revenues, reflecting the increasing contribution of our new growth initiatives. More importantly, we achieved full year non-GAAP operating profitability, our first time in 6 years.

Our Internet business remained resilient in 2025, generating approximately RMB 460,000 in adjusted operating profit every working day. This consistent operating cash flow forms the financial backbone of the company and allows us to invest in robotics and AI in a disciplined and sustainable way. Our second highlight is robotics, which is emerging as a key structural driver. For full year, robotics revenue grew approximately 31% in the fourth quarter alone, robotics revenue reached about RMB 60 million, up 94% year-over-year and 43% quarter-over-quarter.

A voice robot in China achieved 100% year-over-year growth for 3 consecutive quarters, accounting for high single digits of the fourth quarter's total revenues. This progress is driven by our strategic focus on core strength in voice robotics and the integration of AI technology to enhance product experience. We are now seeing our voice robot become a must-have solution in resection, guided tours, retail environment, hospitals and service costs as they deliver proven measurable value. We recently introduced a new version of our voice robots, which comes with building skills like guiding, patrolling and advertising, enabling end customers to start using them right away, our robotic business mainly in serving overseas markets is making up high single digit of the first quarter's total revenues.

We focused on long-term demand from research institutions and the R&D teams that value openness and the customization. This customer base is fitting and repeatable, supporting long-term demand, building on our proven indoor autonomous mobility technologies. We are introducing a smart wheelchair, targeting developed regions such as Western Europe and North America. This product is positioned as a premium solution for users who value safety, independence and confidence in daily mobility. We are seeing a clear shift in demand as users increasingly value safety, assistance, and intelligent features in mobility products, while scalable solutions in the market remain limited. By applying our experience in service robots we are able to meaningfully improve the user experience.

During my own recent recovery, I personally use our smart wheelchair and saw a clear improvement in safety and convenience. Importantly, we can deliver these benefits without significant increasing the costs compared to traditional high-end electric wheelchairs making this a more practical and accessible product for users. We have entered into framework agreements with established mobility brands who will manage branding, distribution and aftersales services. Initial shipments are expected to begin in the second quarter of 2026, representing an early-stage commercial validation of this product category.

Across the industry, more companies are starting to test and deploy service robots. We believe the next 1 to 2 years will be a validation phase, where ROI and reliability will matter most. You don't need a robot that looks like a human. You need a robot that works every day, delivers measurable value and it's easy to operate at scale. This is exactly where our current products are positioned.

Our Internet business remains strong, generating steady cash flow, which allows us to invest in AI in a disciplined and sustainable way. For more than a decade, we have built utility applications serving hundreds of millions of users, described DNA, how we approach AI rather than competing in more development we focused on turning AI capabilities into practical tools that help users complete real task.

During the Chinese New Year, I spend a lot of efforts experimenting with an AI agent system built on the open cloud framework starting from a single agent that could barely complete basic test, the system evolved into a multi-agent team capable of running tests continuously. In one scenario, the system generated personalized New Year messages for more than 600 colleagues and manage the entire sending workflow automatically.

What we see emerging is not simply a new AI tool but a new way to organize digital work. AI agents can automate entire workflows from information gathering to processing and distribution, significantly improving productivity. Giving our new learnings, we introduced EasyClaw based on open cloud and open source agent framework for both domestic and overseas markets. EasyClaw is our AI coworker platform that helps users create and deploy task-oriented AI agents capable of executing real-world tests autonomously.

At this stage, we focus on execution capability rather than scale. We are already seeing a continued increase in user engagement as reflected in the rapid growth of our total token usage. We are building EasyClaw into an agentic operating system that changes how users interact with software and teams. By integrating EasyClaw into our PC products, we are improving user experience and driving higher conversion and ARPU.

In robotics, EasyClaw allows users to program and customize robots using natural language, including customization barriers. This helps us deploy faster, reduce cost and scale more easily, making our products more competitive. Some investors may ask how we compete with our training foundation models. It is the real advantage in the agent era, that is not in the model itself, but in the systems built on top of it, including top orchestration, tool usage and cost management. By leveraging open ecosystems and leading APIs, our product can evolve as models continue to improve.

Finally, our global DNA remains a core competitive advantage. We continue to expand both our AI tools and robotics businesses internationally with a disciplined approach. Looking ahead to 2026, we do not provide specific financial guidance, but we see continued structural improvements. We believe our robotics business will maintain strong growth momentum as commercial validation even and become a more important part of our revenue mix. At the same time, AI-enabled products will gradually enhance engagement and monetization efficiency across our software ecosystem.

We will increasingly apply internally to accelerate the development aiming to further improve operational efficiency. As we grow, we will continue improving transparency and disclosure, credibilities to data and our focus remains clear. Execute discipline and those results compound over time. Cheetah is entering its next phase of development combining digital coworkers through AI agents and physical coworkers through service robots supported by real operating cash flow and disciplined financial management. We are building the foundation for our next stage of growth. Thank you.

Thank you, Fu Sheng. Hello, everyone, and thank you for joining us. Unless otherwise stated, all financial figures are presented in RMB. 2025 marked a year of meaningful operational recovery and improved financial discipline for Cheetah Mobile. During the year, we continued improving operating discipline and cost structure across the company. We also created resources on commercially validated use cases in robotic products and practical AI applications, while leveraging open source ecosystem and third-party models to improve want efficiency and optimize infrastructure costs. This approach allows us to accelerate its operation without significantly increasing fixed costs. For the full year 2025, total revenue grew approximately 43% year-over-year to RMB 1,150 million.

Although we reported a GAAP operating loss of RMB 179 million for the year, this represented a substantial improvement compared with operating loss of RMB 437 million in 2024. On a non-GAAP basis, operating profit reached RMB 14 million compared with a non-GAAP operating loss of RMB 232 million, in the prior year, reflecting improved operating leverage. We ended the year with USD 215 million cash and cash equivalents.

Turning to our segment performance. Our Internet business continued to serve as a stable cash generating platform for the company in 2025. Revenue from Internet business increased 19% year-over-year to RMB 615 million with Internet revenue, Internet value-added services revenue increased 21% year-over-year in 2025, contributing 65% of segment revenue, supported by both paying user growth and ARPU expansion.

In addition, we observed that many users subscribe for periods longer than 12 months, reflecting the recurring nature of our utility locations and strengthening revenue visibility. In terms of profitability, the Internet business generated approximately RMB 115 million in adjusted operating profit in 2025, maintaining healthy margins and strong operating cash flow.

As Fu Sheng mentioned earlier, the Internet of business generates roughly RMB 460,000 in adjusted operating portfolio per working day which provides predictable cash flow to support strategic investments in new initiatives. Looking ahead, we expect the Internet business to remain stable and profitable while continuing to provide financial flexibility for the company to invest in long-term growth opportunities.

Turning to our AI and Others segment. Revenue from this segment increased 85% year-over-year to RMB 535 million in 2025, as a result, this segment accounted for 46.5% of our total revenue compared with 35.9% in 2024, reflecting the growing contribution from our emerging businesses. Within the segment, the robotics business continued to scale since the second half of 2025, making up 27% of the segment's revenue and 13% of total revenue in 2025. Robotics revenue increased 31% in 2025 driven by deployment of voice robot in China and continued demand for robotic arms in overseas markets, other businesses, overseas advertising agencies, service and multi-cloud management platform within this segment also contributed significantly to revenue growth, benefiting from increasing overseas expansion by Chinese enterprises.

At the same time, we continued to improve operating efficiency to more selective investment and disciplined cost control. For the full year, adjusted operating loss from the AI and Other segments reduced by 42% year-over-year to RMB 274 million as we continue scaling the business while maintaining disciplined investments.

Turning really to the first quarter performance. Total revenue reached RMB 309 million representing a 30% year-over-year increase and a 7% quarter-over-quarter increase, while Internet revenue declined slightly year-over-year, in the fourth quarter it increased quarter-over-quarter as we continue shifting toward a subscription-driven business model.

In addition, our subscription revenue within the Internet segment increased 32% year-over-year and 16% quarter-over-quarter as we chose to focus on subscription business model, which supports a healthier product and user experience. Revenue from the AI and Other segment reached RMB 153 million, accounting for nearly half of total revenue in the quarter. With this segment, robotic revenues increased by 94% year-over-year and 43% quarter-over-quarter to about 19% of the fourth quarter's total revenue.

Other than that, our revenues from overseas advertising agency service and multi cloud management platform also contributed to this segment's year-over-year growth. On a non-GAAP basis, the company generated operating profit of RMB 15 million in the fourth quarter compared to RMB 42 million operating losses in the same period last year. We believe the improvement we achieved in 2025 reflected structural improvements in both our cost structure and revenue mix.

Looking ahead, our priorities remain clear: disciplined growth, continued improvement in operating efficiency and disciplined capital allocation with stronger financial discipline, clearer strategic focus and increasing contribution from our emerging businesses, we believe the company is entering a more stable and predictable operating phase.

Thank you. We are now ready to take your questions.

[Operator Instructions] The first question today comes from Thomas Chong with Jefferies.

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Operator, can we move to the next question?

The next question comes from [ Nancy Lu ] with JPMorgan.

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Operator, please move to the next question. Thank you.

The next question comes from [indiscernible] Securities.

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Operator, please move to the next question. Thank you.

The next question comes from [ Yongping Diao ] with Guotai Haitong.

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Thank you, operator. Please move to the next question.

The next question comes from [ Jiji Zhu ] with GF Securities.

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Operator, please move to the next question.

The next question comes from [ Wei Feng ] with Mizuho Securities.

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Operator, please move to the next question.

The next question comes from [ Lydia Lin ] at Morgan Stanley.

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Operator, please move to the next question.

The next question comes from Vicky Wei with Citi.

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Operator, please move to the next question.

The next question comes from Zeping Zhao with ICBC.

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Yes. Thank you. Operator, please check if we have any further questions.

We have no further questions at this time, which concludes our question-and-answer session. I would like to turn the conference back over to management for any closing remarks.

Thank you so much for joining our conference call today. And if you have any further questions, please do not hesitate to let us know. Thank you so much.

Bye-bye.

The conference has now concluded, and we thank you for attending today's presentation, and you may now disconnect your lines.

Good day, and welcome to the Cheetah Mobile Third Quarter 2025 Earnings Conference Call. [Operator Instructions] Please note today's event is being recorded.

I would now like to turn the conference over to Helen, Investor Relations for Cheetah Mobile. Please go ahead, Helen.

Thank you, operator. Welcome to Cheetah Mobile's Third Quarter 2025 Earnings Conference Call. With us today are our company's Chairman and CEO, Mr. Fu Sheng; and our company's Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct a Q&A section. Please note the management's script will be presented by an AI agent.

Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our conference call today. Management will make forward-looking statements.

At this time, I will now turn the conference call over to our Chairman and CEO, Mr. Fu Sheng. Fu Sheng, please go ahead.

Good day, everyone, and thank you for joining Cheetah Mobile's Third Quarter 2025 Earnings Call. I'm Fu Sheng, the CEO of Cheetah Mobile. I'm very happy to report that our turnaround efforts are paying off. We hit quarterly breakeven ahead of expectations. In Q3, we made an operating profit -- first time in 6 years. We believe we are well positioned to approach breakeven for the full year 2025. At the same time, our growth stayed strong in Q3, building on the momentum from the first half of the year.

Q3 revenue rose 50% year-over-year, driven by both our Internet business and our AI and other businesses. Our AI and other segment grew even fast, up 151% year-over-year and 6% quarter-over-quarter and now presenting 50% of total revenues.

So far, 2025 has been a solid year for Cheetah. Revenue in the first 9 months rose around 48%. We became profitable in Q3 and took important steps in our two AI focus areas, AI robots and AI tools. We believe this progress shows our investors were right to trust our vision and work. I want to thank our shareholders for their support. I know many of you invested in Cheetah because you believed in our ability to deliver a comeback. We are working hard every day to make that happen. I remain fully committed to leading the company forward and our results this year show that the turnaround is real.

Looking ahead, we will focus on driving growth by building new growth engines through our AI initiatives, AI robots and AI tools. Today, I will talk about our vision and progress in these areas. Thomas will follow with more on how we are building a lean cost structure to support long-term profitability.

Both AI robots and AI tools have enormous market potential, and Cheetah Mobile has strong advantages to build new growth engines in these areas. Additionally, we hold minority investments in several companies in this space, which could extend our organic growth in the future. In Q3, our AI robotic business contributed about 15% of total revenue, growing about 100% year-over-year and 40% quarter-over-quarter.

We see two drivers of this growth. First, strong demand for our voice-enabled wheel robots in China. In Q3, for the second quarter in a row, revenue from these robots doubled year-over-year. They now make up around 5% of our total revenues, supported by both repeat orders and new wins. As of September 30, 2025, the contract backlog for these robots in China was up 32% from the previous quarter. Since then, the backlog has doubled again, reflecting sustained demand. These trends make us comfortable for a continued strong [ UA ] growth in our revenue from these robots in the fourth quarter.

Why is demand rising? First, more customers are open to using robots. And today, wheel robots are the most reliable and cost-effective option for large-scale deployment. But more importantly, product experience is getting better. AgentOS, our voice system powered by AI agents, gives our robots a smarter brain to understand and respond more naturally to people. That improved experience allows us to charge a premium even in a competitive market, but most of our revenue growth came from higher shipment volume. We believe AgentOS not only enhances user experience but also strengthens our leadership in voice-enabled robots.

Our voice-enabled wheel robot, which integrated with Google's Gemini 2.5 Flash was recently featured by Google Cloud at its AI Asia Conference. We believe this is a strong sign of endorsement. We are continuously upgrading our AI agent capability and applying it to our products.

Looking forward, we think these robots can do even better overseas as we combine third-party genAI and multimodal models with our strength in voice AI and autonomous mobility to drive real-world applications. Second, our robotic...

[Technical Difficulty]

Sorry for the interruption, everybody. This is the conference operator. Looks like we've lost the main speaker connection. I will place hold music in the call, and we will get them connected again. Please standby.

Thank you for holding, everyone. This is the operator. I've reconnected the main speaker line. Please proceed with your call.

Hello, everybody, this is Helen from Cheetah Mobile. I think there are some tech issues then our call disconnected. I will just replay our CEO's prepared remarks. Very sorry for the inconvenience.

Good day, everyone. And of this growth, first, strong demand for our voice-enabled wheel robots in China. In Q3, for the second quarter in a row, revenue from these robots doubled year-over-year. They now make up around 5% of our total revenues, supported by both repeat orders and new wins. As of September 30, 2025, the contract backlog for these robots in China was up 32% from the previous quarter. Since then, the backlog has doubled again, reflecting sustained demand. These trends make us comfortable for a continued strong [ UA ] growth in our revenue from these robots in the fourth quarter.

Why is demand rising? First, more customers are open to using robots. And today, wheel robots are the most reliable and cost-effective option for large-scale deployment. But more importantly, product experience is getting better. AgentOS, our voice system powered by AI agents, gives our robots a smarter brain to understand and respond more naturally to people. That improved experience allows us to charge a premium even in a competitive market, but most of our revenue growth came from higher shipment volume. We believe AgentOS not only enhances user experience but also strengthens our leadership in voice-enabled robots.

Our voice-enabled wheel robot, which integrated with Google's Gemini 2.5 Flash was recently featured by Google Cloud at its AI Asia Conference. We believe this is a strong sign of endorsement. We are continuously upgrading our AI agent capability and applying it to our products.

Looking forward, we think these robots can do even better overseas as we combine third-party genAI and multimodal models with our strength in voice AI and autonomous mobility to drive real-world applications. Second, our robotic arm business is growing steadily supported by three key industry trends.

Number one, in manufacturing, collaborative robotic arms are becoming more and more popular because they're smaller, easier to install and more affordable, they're also safer to work with. So they help fill many unmet needs in factories like doing tasks that need flexible movement, careful and precise work or real-time feedback. These tasks now rely on human workers today.

Number two, in commercial spaces like coffee shops and smart retail, because of advances in lightweight design and easy programming and building vision of feedback, we are unlocking new use cases. Our team's ability to understand real-world needs and build practical products gives us an edge.

Number three, robotic arms are a core part of embodied AI. As global demand for physical AI grows, we believe robotic arms will play a key role in bringing AI into the real world. We strengthened our robotic arm business through an acquisition, demonstrating our strategy of combining organic growth with M&A. This business is a great fit for us. It is already profitable with tens of millions of RMB in annual revenue, most of it from overseas customers.

By bringing this company into our group, we've expanded our product line and strengthened our presence in global markets. More importantly, we started testing how to combine our wheel robots with robotic arms to create embodied AI that can handle more complex real-world tasks. It's still in early days, but our solid foundation in both technology and product development puts us in a strong position to grow in this space in the long run.

Moving on to AI tools. This is another area where we see long-term potential. We're using AI agents to quickly build a variety of new tools for both PC and mobile, and we're also upgrading some of our existing products with AI features. For example, in one of our legacy products, Duba Antivirus users can now interact with their PCs through natural language to complete tasks like system settings. No need for complex manual steps. And small-scale testing of other tools like meeting summarizers, we've also seen strong user engagement and good willingness to pay.

What makes this space exciting is that AI coding apps have greatly reduced the time and cost it takes to build and launch new products. This gives us the flexibility to test many ideas quickly and focus on what works. While we're still in the early stages, we believe our strength in building user-friendly tool-based apps, especially with the help of AI agents, puts us in a good position. And since subscriptions already make up more than 60% of our Internet revenue, we're confident in our ability to monetize future products through the same model.

To close, I believe Cheetah has moved beyond the turnaround phase. Looking ahead, our focus is on building long-term value by scaling our AI robot business and capturing the upside of AI-native tools. While we're still early, both segments have real momentum and strong potential to drive growth in the years to come.

Thank you, Fu Sheng. Hello, everyone, and thank you for joining the call. Unless otherwise stated, all financial figures are presented in RMB.

In the third quarter of 2025, we are pleased to reach an important milestone. We reported our first quarterly operating profit in the past 6 years. This achievement reflects the disciplined execution of our teams and the continued improvement in our operational efficiency. Operating profit was RMB 4 million in the quarter. On a non-GAAP basis, operating profit reached RMB 15 million compared with an operating loss of RMB 60 million in the same period last year and an operating loss of RMB 2 million in the previous quarter.

Let me walk you through the key financial results in the quarter. Total revenue reached RMB 287 million, up 50% year-over-year, driven by 151% growth in our AI and other segment. This segment accounted for 50% of total revenue compared with 30% in the same period last year. Our Internet business remained stable with revenue increasing 6% year-over-year in Q3.

Gross profit increased by 64% year-over-year and gross margin improved to 75%, up from 68% in the year-ago quarter. Operating profit improved to RMB 4 million compared with an operating loss of RMB 72 million a year ago. On a non-GAAP basis, operating profit was RMB 15 million compared with an operating loss of RMB 60 million last year.

By segment, our Internet business delivered approximately RMB 21 million in adjusted operating profit in this quarter, up 55% year-over-year. Adjusted operating loss for our AI and other segment narrowed by 82% year-over-year and 53% quarter-over-quarter to [ RMB 15 million ] in this quarter.

On the balance sheet side. Our financial position remains strong. As of the 30th of September 2025, the company has cash and cash equivalents of about USD 224 million and long-term investments of USD 107 million. We continue to maintain discipline in cash flow management and capital allocation.

Looking ahead for our Internet business, we will continue to deliver robust operating profits. We want to be clear that we prioritize operating profit growth over revenue growth.

For our AI and other business, we also aim to further manage our cost and expenses to a more focused and efficient approach. First, we are focusing on high potential use cases for our robotics business, that is the only way to build sustainable and profitable business models. We concentrate on AI-powered, voice-enabled wheel robots, products that have proved to deliver a highly competitive ROI, [ a cheaper ] alternative for reception, museum and exhibition scenarios.

Second, we leverage third-party and open source models and tools to enhance our robotic experience. This approach allows us to accelerate product updates, thereby increasing our overall efficiency. Third, for our advertising agency service and multi-cloud management services, we are taking a more disciplined approach, strengthening contract control [indiscernible] and customer value to better manage our costs and expenses.

Overall, at the corporate level, we will continue to invest in AI robots and AI tools as we believe these two areas will drive our long-term revenue growth. However, we will stay disciplined and ROI focused in every decision. I believe Cheetah has entered a much better phase compared with a year ago. In product development, as we shared in the previous calls, we encourage our employees to use AI tools such as [ coding ] apps to build their own AI, not only to improve productivity, but also to enhance decision-making. Leveraging AI allow us to develop products faster and operate them with fewer people than before.

Most importantly, with the AI opportunity, the business improvements we have achieved over the past year and growing recognition from the capital market, we are seeing renewed confidence and momentum across our teams. I personally believe these changes, stronger execution, disciplined investments, improved efficiency and an inspired team form the foundation for Cheetah to rebuild its success in this new chapter.

Thank you. We are now happy to take your questions.

Operator, please open the call for...

[Operator Instructions] And our first question today comes from Thomas Chong from Jefferies.

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Our next question comes from Vicky Wei at Citi.

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Our next question comes from [ Lydia Lin ] at Morgan Stanley.

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Our next question comes from [ Zhang Heng ] with Everbright Securities.

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Our next question today comes from [ Alicia Soh ] with JPMorgan.

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Our next question comes from Zeping Zhao with ICBC International.

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Our next question today comes from Joanna Ma with CMBI.

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Our next question comes from [ Jing Wan ] with CICC.

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Our next question comes from [indiscernible] from [ CYN ] Securities.

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Our next question comes from [ Yongping Diao ] with Guotai Haitong.

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And our final question today comes from [indiscernible] Securities.

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Okay. Operator, please check if we have any further questions. If not, we will end the call.

Yes, ma'am. We have no further questions in queue at this time. [Operator Instructions] I'm showing no questions, ma'am. You may proceed with any closing remarks.

Okay. And then we can end the call. Thank you so much for joining our earnings conference call today. Thank you so much.

Thank you, and thanks, everyone, for connecting to today's call. You may now disconnect your lines, and have a wonderful day.

Good day, and welcome to the Cheetah Mobile Second Quarter 2025 Earnings Conference Call. [Operator Instructions] Please note this event is being recorded. I would now like to turn the conference over to Ms. Helen Zhu, Investor Relations of Cheetah Mobile. Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's Second Quarter 2025 Earnings Conference Call. With us today are our Chairman and CEO, Mr. Fu Sheng; and our Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct the Q&A section. Please note that the management's script will be presented by an AI agent.

Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our conference call today as we will make forward-looking statements.

At this time, I would now like to turn the conference call over to our Chairman and CEO, Mr. Fu Sheng. Please go ahead.

Thank you, everyone, for joining us today. In the second quarter, we delivered our best results since Q1 2021. Revenue grew 58% year-over-year, driven by a 39% year-over-year increase in Internet business and an 86% year-over-year increase in AI and other segments.

Our operating loss decreased 86% year-over-year, while non-GAAP operating loss was down 97% from last year, almost breakeven.

In the first half of 2025, our revenue grew by 47% year-over-year. We believe we can maintain fast growth in the second half of 2025, driven by about 100% year-over-year revenue growth in our AI and other segments, along with a stable Internet business. This shows our turnaround is working and gaining momentum.

What is even more important is how we work today. We have made AI a core part of our process working in an AI native way. Our R&D teams are small and flexible using AI every day to design, test and build products, much like open source developers. This helps us move faster and use fewer resources, ensures AI allows one person to do what once took a whole team. We have been investing in AI since 2016. And at the intersection of AI and robotics today, we now have advantages and experience that are hard to replicate. For example, GreetBot, our AI tool that turns video, audio and documents into summaries and apps runs with only 3 full-time employees.

Our core Internet business remains solid, thanks to our shift from advertising to a subscription model, which has improved user engagement and retention. Today, subscriptions make up about 60% of our Internet revenues. This healthy base gives us the room to invest in new AI products out staying financially disciplined. We are enhancing existing apps like Duba Anti-virus Wallpaper apps and PDF tools with AI agents. For example, in Duba Anti-virus, we are testing a new AI feature that helps users fix PC issues, especially long tail problems, it couldn't solve before and early feedback is encouraging while we are still in the launch and improvement phase for most AI utilities. We believe Cheetah has a natural advantage utility applications.

At the end of the day, the core value of AI utilities is to help people work more efficiently and productively. If we can deliver on that, we believe users will be willing to use our products. On the service robotics side, we made solid progress. Revenues from service robots continue to contribute to growth in the AI and other segment. In late July, we completed the acquisition of UFACTORY, one of the few robotic arm companies, that is already profitable and earns most of its revenue overseas, combining UFACTORY strengths with Cheetah's distribution network and 100-plus global partners give us a clear advantage to scale globally. UFACTORY arms are already being used at scale in real-world scenarios. From assembly picking, fixing and expensing tasks in factories, to grabbing beverages, making coffee and beers and commercial applications, strawberry harvesting in agricultural setting and even in universities for robotic research.

We now have a broad range of robots and our piloting wheel robots with arms that can handle more physical tasks in more places. We believe the true breakthrough in robotics is not just in using the most advanced lab technology, about finding technologies that match real-world use cases, which can scale and generate earnings for the company. While the future of robotics is exciting, our years of experience tell us that real commercial adoption depends on delivering sustainable ROI that customers can clearly see. Our strategy is to stay optimistic, yet patient, moving forward steadily. We will continue to identify scalable use cases and grow the business gradually.

That said, we want to caution investors that it is not something that will reach mass deployment in the coming quarters. The service robotics market is still developing, but AI agents are making robots smarter and easier to use since adding agent OS, our next-generation voice system powered by AI agents. Earlier this year, our voice enabled robot revenue in China grew by about 100% in Q2, both driven by recurring demand from our existing channel partners alongside expansion into new high-quality customers in health care, education, elder care and cultural institutions, such as the national center for the performing arts.

In addition, this growth does not rely on 1 of large orders, but comes from steady and repeat demand, especially in use cases like poor guiding and reception, which shows it is sustainable. Few companies have both our global experience in consumer Internet products and use of real-world robotics operations. This unique combination allows us to apply AI agent technology across both software and hardware, creating synergies that are hard to replicate, supporting our goal to become a leading service robot company in the coming years.

Looking ahead, our core Internet business remains healthy and profitable. We will keep investing in AI tools and robotics with discipline, and we are on track to reach profitability in the near term. Our strong cash position and zero debt give us the flexibility to grow while keeping our finances strong. The transformation is just getting started, but it is already producing results.

We are building 2 growth engines, AI-powered utility apps and AI robots that work together as synergistic forces, combining software and hardware to create a stronger moat, expand our market reach and open new growth opportunities. At the same time, our solid Internet business and strong cash resource provides a stable base with over 7 years of R&D in AI focused strategy and a culture of innovation, we are confident about the road ahead.

Thank you, Fu Sheng. Hello, everyone, and thank you for joining the call. Unless otherwise stated, all financial figures are presented in RMB.

In the second quarter of 2025, we continue to make meaningful progress narrowing our losses and improving probability as we remain focused on execution, efficiency and financial discipline. In fact, on a non-GAAP basis, we almost reached a breakeven point on the operating level in Q2.

Let me walk you through the key financial results in the quarter. Total revenue reached RMB 295 million, up 58% year-over-year and 14% quarter-over-quarter, marking a strong acceleration. Gross profit increased by 85% year-over-year and 19% quarter-over-quarter to RMB 225 million. Gross margin improved to 76%, up from 65% in the year ago quarter and 73% in the previous quarter. Operating loss narrowed to RMB 11 million, an 86% year-over-year decreased and 58% quarter-over-quarter decrease. On a non-GAAP basis, our operating loss declined RMB 2 million, down 97% year-over-year and 86% quarter-over-quarter.

Net loss attributable to Cheetah Mobile shareholders decreased by 82% year-over-year and 32% quarter-over-quarter to RMB 23 million. Non-GAAP net loss attributable to Cheetah Mobile Shareholders now by 87% year-over-year and 35% quarter-over-quarter to RMB 14 billion. These probability improvements reflect our ongoing efforts to sharpen the focus, improve the efficiency and optimize our cost structure, particularly as we attribute from early-stage experimentation to ROI, focused execution in our AI initiatives, in our AI robotics business, we have exited certain compute-intensive directions, such as creating our own foundation models, a strategic shift that significantly reduced infrastructure spend.

At the same time, we have streamlined our R&D process by levering AI tools and refocused resources on AI utility applications that generate user value. For example, R&D expenses accounted for 24% of our AI and other segment revenue in the quarter, down from 39% in the year ago quarter and 28% in the previous quarter. These efforts have materially improved the operating profit of our AI and other segments where adjusted operating losses decreased 63% year-over-year and 32% quarter-over-quarter.

Looking ahead, we remain confident in our ability to achieve profitability with a clear and disciplined strategy. We see 2 key drivers for this path. First, our Internet business continues to deliver steady profits and serves as a solid financial foundation in Q2. Adjusted operating margin for this segment was 14%, up from 12% in the year ago quarter. Our transition from an app-centric model for user subscription-driven model is showing good momentum. We believe this momentum is sustainable, supported by loyal user cohorts and diversified distribution channels, particularly in RA and Other segment.

We are building for long-term probability by growth in both our consumer-facing AI tools and enterprise-facing robotics. For our robotics business, we are prioritizing salable use cases with clear user demand and engagement, emphasizing our core competence in AI powered voice interaction, including natural conversation capabilities similar to our LLM-based agents and AI-based indoor mobility, which we believe offers the most reliable and cost-effective solution for scalable robot deployment, continuously improving our robot intelligence and product experience through AI agents maintaining a lean and agile team structure.

A recent milestone was our acquisition of UFACTORY, one of the few profitable robotic arm companies globally. UFACTORY brings a proven track record of profitable growth, clear market position and consistent value creation, fully aligned with our vision to scale differentiated robotic solutions over time. On the AI tools front, GreetBot, an AI tool that summarizes video, audio, PDF and other documents into concise takeaways and mind maps, has shown encouraging early user adoption, validating product market fit.

Our balance sheet remains strong. As of the 30th of June 2025, we have USD 282 million in cash and cash equivalents and USD 110 million in long-term investments. We generated RMB 362 million in operating cash flow during the quarter. This financial strength gives us the flexibility to continue investing in high potential AI growth opportunities while maintaining capital discipline. We will also remain open to strategic M&A that can accelerate capability building in targeted verticals.

To summarize, this was another quarter of measurable progress on our path to breakeven. We are encouraged by early signs of sustainable profitability supported by: one, our profitable and resilient Internet business; two, a disciplined ROI focused AI strategy; and three, strong capital flexibility to invest in long-term growth.

Thank you. We are now happy to take your questions.

[Operator Instructions] The first question today will come from Thomas Chong of Jefferies.

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Thank you. Operator, please move to the next question.

Our next question will come from Vicky Wei of Citi.

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Thank you, operator. Please move to the next question.

Our next question is from Nancy Liu of JPMorgan.

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Operator, please move to the next question. Thank you.

Our next question today will come from Brenda Zhao of CICC.

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Our next question today will come from [indiscernible] Securities.

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Operator, please move to the next question. Thank you.

Our next question today will come from [indiscernible] Securities.

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The next question today will come from [indiscernible] Huang of Everbright Securities.

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Our next question today will come from [indiscernible].

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Our next question today will come from [indiscernible] Haitong Securities.

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Our next question today will come from Joanna Ma of CMBI.

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Our next question will come from Jack Yang of Mizuho.

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Operator, can you please check if we have any further questions?

Certainly. [Operator Instructions] At this time, I am not showing any additional questions in the question queue.

Okay. And then we can just end up the call.

Thank you.

Thank you so much for joining our conference call today. So if you have any further questions, please just let us know. You can send us email or just give a call. Thank you so much.

The conference has now concluded. We do thank you for attending today's presentation. And you may now disconnect your lines, and have a nice day.

Good day, and welcome to the Cheetah Mobile First Quarter 2025 Earnings Conference Call. [Operator Instructions] Please note, this event is being recorded.

I would now like to turn the conference over to Ms. Helen Jing Zhu, Investor Relations of Cheetah Mobile. Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's fourth (sic) [ first ] quarter 2025 earnings conference call. With us today are our company's Chairman and CEO, Mr. Fu Sheng; and our company's Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct a Q&A session. Please note that the management's prepared remarks will be presented by an AI agent.

Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our conference call today as we will make forward-looking statements.

At this time, I would now like to turn the conference call over to our Chairman and CEO, Mr. Fu Sheng. Please go ahead, Fu Sheng.

Good day, everyone. Thank you for joining Cheetah's Q1 2025 Earnings Call. I am Fu Sheng, the CEO of Cheetah. We started 2025 with a clear plan to strengthen our position in both our long-standing and new business areas. Q1 2025 marked a strong start to the year, and I'm happy to share some great news about how we are doing.

First, our revenue grew significantly, and we made solid progress in cutting losses. In Q1, our total revenue went up 36%, compared to last year and 9% compared to last quarter. Our Internet business did especially well with a 46% increase in revenue year-over-year. Our AI and Recovery segment grew 23% year-over-year and accelerated to 30% quarter-over-quarter. Just as important, our loss dropped sharply while still investing in AI and robotics and we believe this positive momentum will continue.

Second, AI Agents are becoming a real game changer that smarter AI models keep improving. They can now go beyond chatting. They can handle real tasks and solve real problems with our strong background in building and launching new products. We believe Cheetah is well positioned to take advantage of this big shift. We are actively applying agent technology to upgrade our consumer products and power our innovation pipeline. These smart enhancements are making our products more efficient, user-friendly and align with the expectations of the new AI era.

For example, we launched [ M AI ] and AI tool app that turns videos, audio, PDF and other documents into concise summaries and mind maps, making knowledge easier to digest and act on. [ Duba ], a strong example of how we are planning AI agents to create practical data use tools that improve productivity.

Third, AI has always been at the center of our AI strategy. We are investing even more in R&D and using AI agents to upgrade our consumer products and robotics. One of our biggest step forward is AgentOS, our next-generation voice system for service robots. AgentOS is designed to be a successful digital purpose, AI brain that can handle everyday tasks and further strengthen our leadership in voice-enabled robots.

Since the transaction, we have been working with our distributors to test AgentOS. Customers, say AgentOS makes the interaction with the robots feel much smarter. They understand the conversation, notices what people are wearing and can use tools like maps. They also like that it doesn't get confused. If you pause, say something wrong or switch between languages. We are already working with schools, therapy centers, libraries and museums to bring AgentOS into their daily routines.

Our goal is to create industry-specific apps on top of AgentOS that's smart, helpful and personalized. These apps can greet people, give presentation, help care the elderly and offer companionship. They will use tools and keep running over time, which will help us grow our market share and move closer to general AI that can handle many tasks. While we plan to offer AgentOS for 3, first, to enhance our robot performance. We see strong potential for future subscription-based business model. In the coming months, we will add 8 agents to our existing apps, including our flagship, Duba Anti-virus, and introduce new AI tools to help users work more efficiently in the LLM era.

At the same time, our legacy Internet business remained strong. It continues to deliver steady revenue and profit and gives us a natural entry point for our new AI experiences.

Overall, the strength of our legacy business gives us the resources we need to push forward with our AI plan, while being financially responsible to wrap up Q1 2025 with a strong quarter. We grew our revenue, reduced our losses and took steps in our AI journey. We believe agentic AI is driving the Chinese LLM industry into a new phase, shifting from infrastructure development to application-driven innovation.

This change benefit companies like us both with a proven track record of turning cutting-edge technologies into real world products across the PC, mobile and now AI eras. Our ability to productize innovation is what truly sets Cheetah apart in this new phase of AI applications. We remain focused on building AI especially utility-focused AI tools and robotics that not only understand people, but also help them get things done. Thank you.

Thank you, Fu Sheng. Hello, everyone, on the call. Unless otherwise stated, all financial figures are presented in RMB. Q1 2025 marked another quarter of meaningful loss reduction and improved efficiency. Building on the momentum from 2024, our Q1 results reflect our key us focus on disciplined execution, operational efficiency and strategic investments in AI.

Let me walk you through the key numbers. In Q1, total revenue reached RMB 259 million, up 36% year-over-year and 9% quarter-over-quarter. Gross profit increased by 67% year-over-year and 10% quarter-over-quarter to RMB 190 million. Gross margin was 73.2%, up from 59.2% a year ago and 72.9% in the previous quarter.

Non-GAAP gross profit was RMB 190 million, an increase of 67% year-over-year and 10% sequentially. Non-GAAP gross margin improved to 73.2%, up from 59.6% a year ago, and [ 72.7% ] in the prior quarter. We also made meaningful progress in reducing loss. Operating loss was RMB 27 million, reduced from RMB 81 million in the year ago quarter and RMB 207 million in previous quarter. Non-GAAP operating loss narrowed to RMB 14 million, down from RMB 66 million in the year ago quarter and RMB 42 million in the previous quarter.

Net loss attributable to Cheetah Mobile's shareholders of RMB 33 million, reduced from RMB 80 million in the year ago quarter and RMB 367 million in the previous quarter. Non-GAAP net loss attributable to Cheetah Mobile's shareholders [ increased ] RMB 21 million, a significant improvement from RMB 66 million in Q1 2024 of RMB 202 million in Q4 last year.

By segment, our Internet business continues to provide solid cash flow and profitability. Operating margin nearly doubled year-over-year to 15.5%, driven by improved mitigation and a leaner cost structure. Losses from our AI and other segment narrowed to RMB 46 million, compared to RMB 82 million a year ago and RMB 228 million in the previous quarter. This reflects ongoing efforts to strike the right balance between investment and efficiency.

We remain focused on scalable, monetizable use cases. We also see real improvements in operational efficiency. AI [indiscernible] coding is now part of our daily workflow, improving efficiency and helping our team to scale faster.

On the robotics side, we have prioritized use cases that can be deployed at scale and address real customer needs. We also leverage open source models like [ VIE ] models to enhance hardware performance, including robotic arms. Following the OrionStar acquisition in late 2023, we have continued to consolidate teams and optimize operations.

As of March 31, 2025, our total head count was approximately 815, down from 860 to a year ago. Despite continued cost and expense control. We also launched new products and made our service robots agenetic. Looking ahead, we expect further margin expansion and continue loss reduction. At the same time, we will continue to invest in AI, but in a disciplined and focused way.

Our balance sheet remains strong. As of March 31, 2025, we have cash and cash equivalents of approximate USD 234 million, long-term investments of about USD 112 million. Looking ahead, our goal is clear, reach breakeven while maintaining a healthy cash position. We will continue to invest in AI, but in a disciplined and focused way, ensuring every dollar spent support sustainable long-term value creation.

Thank you. We are now happy to take your questions.

[Operator Instructions]

We noticed that you mentioned 2 directions of AI in this financial report. On the one hand, tool-based AI products. On the other hand, service robots. From the perspective of strategic resource investment and revenue contribution in the next 3 years, will Cheetah's future development focus more on building an AI tool matrix and focusing on improving AI efficiency on the C-side, or will more resources be invested in robots? How do you balance the differences between these 2 directions in terms of technical challenges, commercialization rhythm and long-term moat?

Well, I think this is a very good question. In fact, after all these years, Cheetah Mobile has been focusing on 2 major businesses, AI tool software on the C-side and robots. Regarding the commercialization efficiency and risks on the B-side, I actually don't think these 2 are contradictory because essentially, for all products, software capabilities are what matter in the end. Take Apple for example. Apple is known for its strong software and its hardware manufacturing is also very good. But ultimately, what users care about is the software experience.

So I think the AI tool matrix and robots today have a short-term and long-term relationship, respectively. That is to say, with the Internet business, we can achieve rapid development. Especially now that programming technology has matured, we believe that the AI tool matrix will develop rapidly. This includes the transformation of some traditional software from the past such as Kingsoft Antivirus and others, which can rejuvenate them.

So I think in the short term this year, the area where we can see rapid development is definitely the AI tool matrix. However, the robot itself is a hardware entity that carries AI, or you can think of it as a hardware entity that carries AI tool. So in the long run, I think the robot is, after all, a long-term development direction.

Regarding the technical challenges you mentioned earlier, I think the cutting-edge technologies of these 2 are actually quite similar, which is the final productization of AI technology in enterprises. Of course, robots are more inclined to the long chain of hardware, while AI tools tend to be more short, flat and fast.

In terms of the commercialization rhythm. I think the efficiency improvement of AI tools will be faster, which is obvious in the industry. And the development of robots is a long-term task that requires continuous improvement. Surely, the moat of robots is deeper because it involves hardware and business models. As for this wave of the AI tool matrix, it depends on whether we can really change users' minds in some vertical fields. But overall, to put it simply, this year, the AI tool matrix is the area where we can generate benefits quickly.

We've noticed that the Robotics division is making the construction of a data factory, a key strategic investment aiming to accumulate a vast amount of high-quality data from the physical world for model training. However, Cheetah has already amassed a large amount of scenario data during actual deployment. Could you share the company's thoughts on data asset construction and self evolution? Do you consider providing data externally or forming a B2B service in the future?

This is a very comprehensive question. In the robotics industry, especially when it comes to service robots or the currently popular humanoid robot, there are numerous challenges. A crucial point is that it's difficult for us to convert the data related to human labor into robotic data. This is quite different from autonomous driving where the data from human driving is already machine accessible data. Indeed, we've seen many in the industry, including some start-ups working on the construction of data factories.

However, as of today, in the robotics industry, the conversion of data factories into actual productization and commercialization is still in a very early stage. In the foreseeable 3 years, I won't say 5 years because AI is evolving so rapidly, it won't be possible to turn it into a truly commercial product.

So regarding the data we've accumulated up to now, we can't claim that it has significantly contributed to the company's productization, but we are conducting some exploratory research at the forefront. On one hand, I'm very optimistic about the long-term prospects of the robotics industry. On the other hand, I'm extremely cautious at the moment.

We've been investing in this industry for 7 or 8 years and have poured over RMB 1 billion into R&D. We started large-scale R&D in this area very early. From a technical paper to certain technical direction, and finally, to actual scene-based applications, there's still a long way to go.

Moreover, there will be various changes in the industry landscape, including the impact of open source technologies. In short, for Cheetah, the construction of a robotics data factory is not our priority at present. We'll keep an eye on it, but won't invest blindly.

As for whether we'll provide data externally or offer B2B services in the future, we have no such plans for now. Because in my opinion, it's practical application is still a long way off. I've been in Silicon Valley recently and talked to many people there, including those from relevant startups. There's basically a consensus that this matter is still in a very early stage.

Currently, everyone is still exploring how to build this data factory, whether it's through human remote control or using some videos for data collection. It's not like the situation with ChatGPT, where a clear path has emerged, and we just need to follow it to turn it into a product. I don't think we've reached that stage yet. So this question is too premature for us and we haven't considered providing external data services.

Just now, the management mentioned that the company is leveraging open-source BLA models to drive the intelligent evolution of robots. Given the increasingly mature open source ecosystem, how does the company balance the use of open source models and the self-developed approach in actual deployments, especially in terms of inference efficiency, security and controllability and cost structure? How does the company allocate technologies and resources?

In addition, from a medium- to long-term perspective, does the company believe that Cheetah's moat in the robot business should be built on model capabilities or scenario data assets? Is it possible to consider building a long tail advantage through a data loop?

These are really professional questions. Regarding your first question on how to balance the use of open source model and the self-developed approach, I think most companies are already quite clear about this. For the vast majority of companies, they don't make a strict distinction. If open source models were better, of course, they'll use open source ones because for private deployment, open source models are no different from self-developed ones and they can save a lot of resources and cost. There's no need to reinvent the wheel. Even Tencent [indiscernible] and DeepSeek and Baidu also uses relevant open source resources. So there aren't many companies that are so insistent on self-development. Maybe companies like Google, OpenAI, et cetera, might be, but for a company like ours, we definitely use open source models.

As long as there are suitable open source models, we won't self-develop. There's no need to repeat the work. I've repeatedly emphasized the power of the open source community in my short video programs over the past 2 or 3 years. In the AI industry, open source is extremely powerful. It's very difficult for a single company to compete with the combined efforts of so many peaks worldwide. We've been clear about this for a long time and have been acting accordingly.

For example, in our AI-based operating [indiscernible] Regarding the 3 aspects of inference efficiency, security and controllability and cost structure, many people today only consider the model [ sequencing ] when talking about various VLA models or other models, but rarely mention efficiency or application scenarios.

For example, if you ask it a question and it takes a long time to respond, you can tolerate it when you're sitting in front of a computer, especially when it's writing an article. But if we are using it for robot interaction and it takes 5 seconds to reply after you say something, the person might have already left.

So among these 3 dimensions of inference efficiency, security and controllability and cost structure, we definitely prioritize inference efficiencies. When used in commercial scenarios, the response speed is of utmost importance. There's a lot of fine-tuning to be done here. Of course, security and controllability are also crucial and are a basic requirement, but I believe inference efficiency comes first.

Cost structure is relatively easier to deal with as it's constantly decreasing. If you look at any AI facing the market company, if it's a real market-oriented company, it will definitely attach great importance to efficiency combined with the application scenarios. In our case, efficiency is definitely the top priority. So in terms of technology and resource allocation, we invest a lot in improving the reaction speed of our robots. If you actually run some models on a robot, the latency might be unacceptable to ordinary users.

Regarding the medium- to long-term perspective on whether Cheetah's moat in the robot business should be built on model capabilities or scenario data assets, I think there's no doubt that it should be built on scenario data assets. When it comes to so-called model capabilities, I have some reservations. What exactly does model training ability needs? As I said before, in the context of China and the U.S., model training ability is not a problem for many companies. There are a lot of open source papers and models available. Buying a GPU and setting up the training environment is not an extremely difficult task, but very few companies in the world can actually modify the underlying logical structure of a model. So the current industry trend is becoming increasingly clear.

Most companies' advantages lie in scenario data assets rather than model capabilities. This is also why Tencent strongly promotes Yuanbao, which has DeepSeek integrated because only scenario data can feedback into the model. When the time is right, developing a model that suits your own scenario data is the way for most companies to survive.

Take the autonomous driving field as an example. Initially, Google's model capabilities were very strong. But today, Tesla's autopilot experience is definitely better than Google's self-driving system. That's because Tesla built its advantage on a large number of deployed terminals and real-world scenarios.

So our strategy is very clear. We won't invest blindly in self-developed model. Instead, we'll focus on implementation, pay more attention to interacting with our users and thus build our own scenario data. Then this data can, in turn, enhance the capabilities of even an open source model. In terms of computing power, algorithms and data, without a doubt, we believe data is the core determinant. This is what we truly value.

What considerations does the company have regarding the commercialization path of AI tool applications. Will it consider the user subscription system? Or will it launch enterprise SaaS product or explore directions such as QC licensing? Against the backdrop of the current shift of AI applications from proof-of-concept to actual commercial value, how does Cheetah plan its commercialization path?

I think a very obvious characteristic of AI tools today is that there is an inevitable question whether users are willing to pay for these AI tools because essentially, this wave of AI tools are productivity tools. So basically, the business models that have emerged globally for this wave are all about subscriptions, whether it's the model of OpenAI or the model for PPT-related software or the model for programming software like GitHub Copilot, they are all subscription-based.

And the subscription model is constantly evolving into different tiers. If you use more, you pay at a higher level. Essentially, when it helps users improve their efficiency, users are willing to pay. I think this is also where AI is different from the previous wave of the Internet. This time, the business model is very simple, clear, and has a high user acceptance.

For example, we developed a small product, [ BBL. ] Now users are actively asking how to pay for it, and some have already paid. So for us, the user subscription model is not a consideration. It's a clear-cut choice. Maybe because Cheetah Mobile faced some setbacks in the globalization of tools in the past, we've converted many of our tools to the subscription model in the past few years. Even Kingsoft Antivirus is like this.

Today, user payment is the mainstream, not advertising. In the past few years, in the Chinese software market, although many people may not be fully aware for our own experience, subscription-based payment has become the mainstream for Chinese tool software. Paying for the effect makes us focus more on polishing the user experience rather than on negotiating advertising deals. I think this is a very important reason why our Internet business has been growing continuously in recent quarters. We've made user-centered payment our core business model.

As to whether to launch enterprise-based products, we've actually been trying. Regarding enterprise-oriented products, in response to your question about how we plan our commercial path during the transformation of AI applications from concept to actual commercial value, I think our commercial path is becoming relatively clear.

In the Internet business, we have products like Kingsoft Antivirus, some of our businesses in Japan and other software businesses. What we're doing is using agent technology to transform software like Kingsoft Antivirus and our overseas software from simply delivering functions to delivering results. This is very important for us.

The second thing is that because we've been developing tools for so many years, and I believe the concept of agents is similar to that of tools. In the past, due to immature technology, we could only provide users with a list of functions. But now with agents, we can provide users with results. This allows us to utilize our entire R&D efficiency.

So the second part is that we'll have a lot of new launches in the innovative content within the Internet sector. And because of the efficiency improvement brought by AI tools, our R&D costs for these innovative applications will be much lower than in the past. So one aspect is to use AI agents to transform traditional Internet businesses.

The second aspect is that during this era of the big explosion of agents, we'll also make many innovative attempts. These attempts won't be made with large-scale investments, but rather in a start-up like small team-based way.

In the robotics area, our main focus is on our agent-based OS. This is what differentiates us from our competitors. We won't focus on humanoid robots or robots with overly complex mechanical structures, such as lawn mowing robots. What we're good at is robot interaction, companionship and task planning-based logic with software as the core and hardware as the foundation. I think in 1 or 2 more quarters, this path will become even clearer.

What progress has the company's robots made this quarter? Could you please share some specific cases of actual implementation. From an industry perspective, what significant changes have taken place in the robot industry this quarter? And how has Cheetah Mobile perceived and responded to these changes?

Let me talk about the industry first. I've been not only in China, but also recently traveled a lot in the U.S., meeting many entrepreneurs. Here are my views on the industry. We've always believed that humanoid robots are still a long way from commercialization.

By commercialization, I mean the kind that can form repeat purchases and become a productive force, not the commercialization in the form of exhibitions, rentals or for educational purposes. Although these forms exist and are currently at a certain scale, the idea of humanoid robots being used on production lines, I think, is still a long way off. In my opinion, it will take more than 5 years to achieve real commercial implementation. That's my view at the industry level.

Besides the hype around humanoid robots, I've noticed that there's a rise of robots for various specialized scenarios, including those from startups. These robots are designed for very specific tasks and don't necessarily look human like. This is a clear change in the industry.

Now let me talk about our own progress. I think our progress can be summarized in the following aspects. First, we've clearly sorted out our development ideas for robots. As I mentioned just now in the field of robots, what we're best at is not complex hardware mechanical structures.

Companies like Yaskawa are indeed very strong in that aspect, and I admit it. What we focus on is the integrated interaction experience of perception and action. That is we ensure that the wheel movement of the robot from point A to point B is stable and reliable. This has been verified by our customers in Japan, South Korea and Europe over the past 2 years.

Also, we aim to give full play to the real-time interaction ability such as in scenarios like reception, explanation, promotion and delivery. With the support of large language models, I believe this kind of scenario can fully thrive.

Regarding specific cases of actual implementation. In many corporate exhibition halls and urban service centers in Beijing, for example, our robots have started to be used as tour guides. I even shot a short video yesterday.

With the support of large language models, the robot interactionability, their understanding of what you say, and their task planning ability have been significantly improved compared to before. I think the era when the robot can become an excellent tour guide has arrived.

Moreover, with large language models, it naturally becomes a translator. In the past, we had a lot of headache dealing with multilingual capabilities like English, Chinese and Japanese. Before, for [indiscernible] robot, we didn't even dare to turn on the voice function because we had to go through a series of complex operations by connecting to Google's services to enable a foreign language ability, and the effect was not good. But now, we started to launch multilingual interactive robots overseas.

If you really ask me what our advantages are in making robots. I think our biggest advantage doesn't come from our so-called technological documentation or the years of technological accumulation. Instead, it's that we now have hundreds of domestic and overseas agents. Through these commercial channels, we can quickly obtain users' opinions on things, and we know how to quickly find our customers to actually test run our products and really bring them to the market.

Due to these advantages, we can perceive that, for example, starting this year, with the improvement of large language model capabilities, the market for explanation related services is clearly changing. This year, we're focusing on language version first and the multilingual capabilities for overseas markets are some progress and may be a bit slower.

But in Q2 this year, the repurchase rate and the degree of completion of our indicators for domestic interaction-based scenarios are the best in recent years. I can clearly feel that the voice interaction-based market is emerging. What's our next step? Yes, we may launch some special robot products related to companionship, targeting scenarios like elderly care. You can wait and see.

Regarding [indiscernible] share further customer feedback, this includes user stickiness, customer satisfaction and whether there have been customized deployment or active inquiries. Additionally, how does the company internally evaluate the commercialization rhythm of AgentOS?

These are very detailed and crucial points. No matter how grand the concept is, ultimately, it comes down to whether users are willing to pay for it. So far, we've conducted some user satisfaction surveys. Generally, users have reported that when it comes to real-life conversations, especially in noisy and crowded environment, the responsiveness has significantly improved compared to the previous generation. I don't have the specific satisfaction data at hand. Maybe we'll release some articles about it in the future.

Regarding customized deployments, it's like what Henry Ford said. If you ask customers what they want, they won't ask for a car, but a faster horse. In the past, due to the limitations of previous ASR, automatic speech recognition technology, which involves converting speech-to-text, and then processing it with NLP, natural language processing, it couldn't meet users' requirements.

As a result, users thought these products were useless. We all know that when people bought smart speakers in the past, they could only use them to play songs, and the speakers would become unresponsive with a bit more complex instructions. However, after the emergence of GPT, people realized that it could understand such complex text, which triggered the development of various applications.

So with our AgentOS, through multiple sensors such as vision sensors, microphones and even some radars, its ability to understand user intentions has improved significantly. I believe this is what can truly open up the market for users. We've already received some requests for customized deployments and inquiries, but we won't disclose the specific details for now. We started with domestic operations.

First, we're training our agents and providing authorization and training for the secondary development platform, so that they can develop their own applications on it. As for evaluating the commercialization rhythm of AgentOS, we mainly focus on the sales progress of our voice interaction-based robot. It's currently Q2, and we're looking at whether we can achieve our goals in Q3. This is a very critical point in evaluating the commercialization rhythm.

Overall, at this stage, the key is whether we can integrate user needs with our products more efficiently, enabling our products like our robot tour guides to be ready for service at any time, and our robot salespersons to perform well. If we can achieve this, I think it will mark the beginning of rapid commercial development. I'm quite confident about this. I believe the basic framework has been established.

Cheetah currently holds over $200 million in cash. I'm wondering if the company is considering making acquisitions to further address the shortcomings in the AI application chain.

Thank you, Mr. [indiscernible] Chang Lu, for your question. We appreciate your attention to our cash reserve scale and the focus on our strategic investment directions.

Indeed, having over $200 million in cash provides us with considerable strategic flexibility. In recent years, Cheetah's investment department has been closely monitoring and actively evaluating areas related to artificial intelligence, including AI large model, vertical AI applications and the upstream and downstream of the robot technology industry.

We believe that external cooperation or integration is an important way to accelerate the construction of our capabilities and popularize key chains, and it is also crucial for promoting Cheetah's long-term competitiveness in the AI field.

Regarding the acquisition strategy specifically, our core considerations mainly lie in 2 aspects. One is the alignment with Cheetah's strategy, and the other is the potential to enhance the overall value creation for Cheetah's shareholders.

For potential target companies, we generally conduct a systematic evaluation from the following aspects. Firstly, the synergy between their technology and business and our company. Secondly, the strategic value they can bring to us. Thirdly, the compatibility of their team with Cheetah's culture and values. And finally, the fourth aspect is the financial valuation and its rationality.

If a potential target fully meets our standards and both parties can highly agree on strategic operations, we will consider acquisition as a major strategic option to accelerate the construction of our capabilities in key links of the AI or robot industry chain. Of course, during the evaluation process, they're quite flexible and maintain an open attitude. Depending on different targets, development stages and cooperation depth requirements, we may also participate in the construction of the entire ecosystem through forms such as minority equity investments, strategic partnerships or joint ventures.

In summary, the core principle of how we use our cash reserve is to maximize the long-term value for our shareholders. In strategic key areas such as AI and robots, we will continue to actively seek and rigorously evaluate opportunities that can bring competitive advantages and value enhancement, including strategic acquisitions that meet our standards.

Will the company achieve overall breakeven in the second half of 2025? I would like to know further that on the revenue side, will future profitability rely more on the restorative growth of the Internet business or the new driving force of the AI business?

At the same time, we've noticed that the Internet business revenue has grown well in the past few quarters. What are the main driving factors behind this? Do these factors have sustainability? For the next few quarters, can the management give some directional judgments regarding the revenue growth rate and profit margin level of the Internet segment? In addition, since the AI business is currently in the investment stage, does the company have an internal plan for achieving breakeven at certain stages?

Okay. Let [ Thomas ] answer this question. Your questions focus on several aspects, including our breakeven situation, growth drivers and business outlook. I'll answer them separately. Regarding the company's outlook for overall breakeven. In the second half of the year, achieving profitability in the second half is a major internal goal for us, but we do face some challenges.

Whether we can reach this goal largely depends on the progress of our core businesses, especially the speed of business development as well as the overall market environment. Of course, our management and team will go all out. We will also update our expectations to the market in a timely manner according to the progress.

Regarding the future drivers of profitability and the analysis of the Internet business, I think the main drivers for the company's future profitability will surely come from the driving force of our AI and other businesses.

The Internet business is an important foundation for us, and it is expected to maintain stable growth. Some of the driving factors for the growth of the Internet business in recent years, as the Vice President mentioned earlier, is that we have completely transformed from the traditional advertising model to the user payment model over the past few years.

By returning to the value of the product after years of refinement, by adhering to the user-first concept, we have enhanced our product strength, which has brought stable user growth as well as stable long-term partners and customer acquisition channels. I believe these driving factors are sustainable.

The subsequent growth of the Internet segment mainly depends on whether we can expand more partners based on the existing channels and partnerships. Also, as the Vice President mentioned, we will use [ AI ] technology to upgrade our traditional PC and mobile end tool products to enhance the competitiveness of our products.

Regarding the growth rate and profit margin level of the Internet segment, generally, we don't usually make specific forecasts in the short-term. But in the short-term, the growth rate and profit margin improvement mainly depends on what I mentioned earlier, that is the expansion of new partners or channels in the next few quarters as well as the development and implementation effect of new AI-related features that empower our traditional tools. We will actively promote these aspects.

Finally, regarding the investment and planning of AI, as mentioned before, our business focus is on how to refine our products for scenarios that are more in demand by users and have more commercial prospects. The AI business, especially the robot business is the core growth engine for our future. Currently, it is still in a crucial strategic investment stage. We have set clear phase goals internally. Our key task is to concentrate our R&D resources and strive to roll out products suitable for various user usage scenarios aiming to achieve the goals as soon as possible.

The losses of the AI and other business segments significantly narrowed in Q1. What were the main areas where investment was scaled back? Does this mean that early exploratory projects have been phased out and their transition towards an ROI-oriented approach has begun? Against the backdrop of the current shift of AI investment from proof-of-concept to actual commercial value, how has Cheetah adjusted its investment strategy?

Let me explain a bit. The significant narrowing of losses in the AI and other business segments isn't just due to one factor. On one hand, some of our explorations have indeed reached a certain stage. For example, we've realized that large-scale model training doesn't hold much significance for a company of our size. So we saved a significant amount of computing costs by no longer starting from pretraining.

Although we're still doing things like fine-tuning after pretraining, we've stopped the pretraining process for 2 models, one with 141 parameters and a medium large mode model. Once our team had grasped the entire technical chain and its capabilities, we ceased this training. We believe that in the future, there won't be many model providers.

Only a very few companies will succeed with models. Maybe OpenAI is one of them. But in the future, most companies will be application-based rather than model focus. The key is to do a good job in applications. Whoever can excel in applications has the potential to become a giant. And perhaps in the future, after having successful applications, one can then consider modifying the model. For now, the focus is on applications.

On the other hand, our R&D has become more efficient. This is an important reason for the significant reduction in losses. As for what you mentioned about some exploratory projects being phased out, for instance, we had some projects related to large-scale systems for the geospatial domain. But later, we found they weren't suitable for us, so we made quick and decisive adjustments. In fact, almost the entire company is now transitioning towards an ROI-oriented approach, whether it's the Internet business, the advertising business or some new products we're developing for robots.

As I mentioned in previous answers regarding robots, simply emphasizing technological advancement doesn't carry much meaning. The ones who can find a scenario and scale up first has the opportunity to win in this round of competition rather than relying solely on a few advanced technical points. Technical points don't confer a first-mover advantage. The real advantage lies in the scenarios, the users attracted and the resulting growth. Technical points themselves don't have an ecological edge. So we have indeed shifted towards an ROI-oriented model. However, it doesn't mean that all exploratory projects will be eliminated. We still need many of them.

We believe that this wave of AI agents represent a major upheaval in rewriting applications. No company can clearly predict how it will unfold. At this time, we need to not only transform our existing businesses like the Internet and robot businesses with AI technology, but also carry out exploratory projects. Nevertheless, the ROI orientation for these exploratory projects is very clear. We'll strongly emphasize it.

As I also mentioned, since the user subscription payment model is a very clear-cut business model, it's easy to understand. In short, this is the overall operating concept of Cheetah. That is use AI to transform old products and improve the energy efficiency ratio. For robots, also use AI to enhance capabilities such as interaction and task planning rather than competing in terms of mechanical structure complexity. We'll enhance the productization of the overall AI experience across all lines. For AI agents, we'll continue to adopt a startup-like approach with small teams making rapid attempts, but with ROI as the assessment criteria.

So in the context of the transformation from concept validation to commercial value, our investment strategy, both for internal and external investments, highly valued practical implementation. Whether it's an internally incubated project or an externally eyed project, the key point for us is whether it can generate real-world commercial returns from the market. Thank you for your questions.

In the current context, with the capabilities of large models at home and abroad are gradually converging, what are Cheetah's competitive advantages in the AI application layer? And how does it guard against the risks of being replicated or marginalized by platform-based products in the future?

Yes. Actually, this is something I've been thinking about a lot recently and have also witnessed in practice. Given the driving force of large model capabilities, an important point is that if you can create a product, you won't be easily marginalized by platform. First, many of today's so-called platforms are built on past experience points. At this time, the products made with agents bring a brand-new experience to users.

Just to answer your question, I won't go into details about what an agent is and the specific differences it brings to enterprises. But it's very clear. Let me give you some examples. Take the search field. Google has been in this business for so many years. Today, products like ChatGPT, you can also consider OpenAI's ChatGPT as a kind of trend, have led to a gradual decline in some of Google's vertical search traffic. It's the same in China.

I even made a special video called, The Death of Search Engines. Besides search in the programming field, products like GitHub Copilot are emerging. When big programming tools like Visual Studio were very powerful, they were all in one product. But GitHub Copilot has developed extremely rapidly and offers a completely different user experience. Today, I know many companies are using such products across the board.

What I'm trying to say is that if we start from the user's perspective instead of the competitive perspective, we'll find that the experience by building products with agents is difficult to achieve with traditional software technology. At this time, you can create a new perception for users, which has nothing to do with the past. So today, it's actually the platform that should be worried because various agents-based products might, in some aspects, really have the potential to disrupt the platform. This is the first point.

Second, whether it's due to the convergence of large model capabilities or the rise of open source model capabilities isn't a decisive trump card in large model operations currently. For example, when building your product experience, you can choose to buy services, use models like GPT through API. Your enterprise can stand on the comprehensive capabilities of all models. So the user experience can be very good, and it won't be affected by the fact that, for example, the current financial situation leads to all major platforms opening up various APs.

Take the example of our product names in the specific area. You can try it. Its ability to summarize meetings is, I can say with full responsibility, far better than [indiscernible]. This is the result of a [indiscernible] by a small team of ours. The user experience is so good that users are willing to pay for it. It's much better than some companies that have raised a lot of money. If you give them APDS and ask them to summarize it, using our product name will definitely give you a better result.

Moreover, the capabilities of large models are constantly improving, which is a great advantage. It's not like traditional coding with fixed logic. Once someone comes up with better code, you'll be surpassed. But for agents, a true agent relies less on a specific model and more on the capabilities of large models themselves to make judgments. As long as the large model capabilities improve, the capabilities of your agent-based products will also improve, and users can feel this improvement. They won't be easily replaced by a new model because the product can simply choose a better model.

Just as I mentioned, this is why agents are so promising. First, they allow users to form a new perception. Second, since the underlying capabilities are provided by large models, it's like using electricity provided by a power plant. The more stable the power supply, the better your electrical appliances will perform. When we were kids, the power supply was unstable, and we felt that electrical appliances were not easy to use. But when the power supply is stable, electrical appliances can be sold better.

So the second point is that the new product model based on agents actually benefits from the advancement of large model capabilities. For a company like Tencent, although they've developed their own large model, recently, I saw they're still focusing on integrating data with the platform. But as platforms manage to do all these things, small companies might really be in trouble.

However, I want to say that during this era of rapid technological change, platforms usually move relatively slowly. At this time, if you can quickly seize the opportunity brought by this technological change and acquire enough users, you can gradually form a growth flywheel that platforms can't replace, especially platforms from the previous era, and it won't be easily marginalized.

I can give another example. Although it has nothing to do with the agent platform for now, but I think it will soon, a product like Kingsoft Antivirus has a history of 20 years. By seizing one opportunity after another in the Internet era from the initial feature to the subsequent intermediate feature and now to current feature, it has continuously grown. Many of its contemporaries like competitor names have long since disappeared, but it still thrived today. I think in the era of agents, we might be able to seize this opportunity to provide better services in terms of users' computer using convenience, and this is not something that a platform can simply replace.

Ladies and gentlemen, the conference has now concluded. Thank you for attending today's presentation. You may now disconnect your lines.