Cheetah Mobile Inc. (CMCM) Q1 2026 Earnings Report, Transcript and Summary

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. [Audio Gap]

Excuse me, there has been an interruption. Just one moment, please. [Audio Gap]

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... [Audio Gap] 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. [Statements in English on this transcript were spoken by an interpreter present on the live call.]