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…

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,…

[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…

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…

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…

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…

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…

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