管理層發言
Good day, and welcome to the Cheetah Mobile First Quarter 2025 Earnings Conference Call. 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 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 as 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 an AI tool app that turns videos, audio, PDFs, and other documents into concise summaries and mind maps, making knowledge easier to digest and act on. This is 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 steps 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 conversations, notice 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 are smart, helpful, and personalized. These apps can greet people, give presentations, help care for 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 to enhance our robot performance, we see strong potential for a future subscription-based business model. In the coming months, we will add agents to our existing apps, including our flagship 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 remains 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 has been 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 benefits companies like us that have 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 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. We also made meaningful progress in reducing losses. Operating loss was RMB 27 million, reduced from RMB 81 million in the year-ago quarter and RMB 207 million in the 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 was RMB 33 million, reduced from RMB 80 million in the year-ago quarter and RMB 367 million in the previous quarter. 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 segments 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 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. Following the OrionStar acquisition in late 2023, we have continued to consolidate teams and optimize operations. As of March 31, 2025, our total headcount was approximately 815, down from 860 a year ago. Despite continued cost and expense control, we also launched new products and made our service robots smarter. Looking ahead, we expect further margin expansion and continued loss reduction. At the same time, we will continue to invest in AI in a disciplined and focused way. Our balance sheet remains strong. As of March 31, 2025, we have cash and cash equivalents of approximately USD 234 million and 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, ensuring every dollar spent supports sustainable long-term value creation. Thank you. We are now happy to take your questions.
分析師問答
Please proceed with your questions.
We noticed that you mentioned two 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 three years, will Cheetah's future development focus more on building an AI tool matrix and focusing on improving AI efficiency on the consumer side, or will more resources be invested in robots? How do you balance the differences between these two 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 two major businesses: AI tool software on the consumer side and robots. Regarding the commercialization efficiency and risks on the business side, I actually don't think these two 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 transforming some traditional software from the past, such as Kingsoft Antivirus, 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, robots are a hardware entity that carries AI, or you can think of it as a hardware entity that carries AI tools. So in the long run, I think robots are, after all, a long-term development direction. Regarding the technical challenges you mentioned earlier, I think the cutting-edge technologies of these two areas 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 shorter, flatter, and faster. 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 robots, 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. Indeed, we've seen many in the industry, including some startups, 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 three years, I won't say five 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 seven or eight 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, its 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 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 models 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 are better, of course, they'll use open-source ones because for private deployment, open-source models are no different from self-developed ones and can save a lot of resources and cost. There's no need to reinvent the wheel. Even major companies use relevant open-source resources. So there aren't many companies that are so insistent on self-development. Maybe companies like Google, OpenAI, etc., 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, as 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 two or three 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 many experts worldwide. We’ve been clear about this for a long time and have been acting accordingly. For example, in our AI-based operations, regarding inference efficiency, security, controllability, and cost structure, many today only consider model attributes, but rarely mention efficiency or application scenarios. If it takes a long time to respond, you can tolerate it when you're sitting in front of a computer. However, if we're using it for robot interaction and it takes too long to respond, users may leave. So, inference efficiency is our priority. While security and controllability are crucial, they are basic requirements. In conclusion, we prioritize improving reaction speed in our robots. A lot of fine-tuning is necessary. We believe that scenario data assets should be built rather than solely focusing on model capabilities since real advantages lie in scenario data rather than just having strong model capabilities.
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 products or explore directions such as 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 the inevitable question of 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 OpenAI's model or models for various software. The subscription model is constantly evolving into different tiers. When it helps users improve their efficiency, they are willing to pay. I think this is where AI differs from the previous wave of the Internet. This time, the business model is simple, clear, and has high user acceptance. For example, we developed a small product, and users actively ask how to pay for it, and some have already paid. So for us, the user subscription model is a clear-cut choice. 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.
User payment is mainstream, not advertising. Over the past few years in the Chinese software market, subscription-based payment has become the mainstream for tool software. Paying for the effect makes us focus more on user experience than negotiating advertising deals. This is a crucial reason our business has been growing continually in recent quarters. We've made user-centered payment our core business model. As for whether to launch enterprise-based products, we've actually been trying. We plan to transform software from simply delivering functions to delivering results.
What progress have 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 productive, not the commercialization in the form of exhibitions or 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 five years to achieve real commercial implementation. That's my view at the industry level. Besides the hype around humanoid robots, I've noticed there's a rise of robots for various specialized scenarios, including those from startups. These robots are designed for 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, what we're best at is not complex mechanical structures. What we focus on is the integrated interaction experience of perception and action. With the support of large language models, I believe this scenario can thrive fully. Regarding specific cases of actual implementation, in many corporate exhibition halls and urban service centers in Beijing, our robots have started to be used as tour guides. With the support of large language models, the interaction ability of the robots has been significantly improved. I think the era when the robot can become an excellent tour guide has arrived. Moreover, with large language models, robots can now effectively interact in multiple languages. We may launch special robot products targeting scenarios like elderly care. You can wait and see.
Could you share further customer feedback, including user stickiness, customer satisfaction, and whether there have been customized deployments 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 environments, responsiveness has significantly improved compared to the previous generation. I don't have specific satisfaction data at hand. We also received requests for customized deployments and inquiries but won't disclose specific details for now. As for evaluating the commercialization rhythm of AgentOS, our main focus is on the sales progress of our voice interaction-based robots. We are looking at whether we can achieve our goals in Q3. Overall, at this stage, the key is whether we can integrate user needs with products efficiently. If we can achieve this, I believe it will mark the beginning of rapid commercial development.
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 for your question. We appreciate your attention to our cash reserve size and 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 models and vertical AI applications. We believe that external cooperation or integration is an important way to accelerate the construction of our capabilities and enhance competitiveness in the AI field. Regarding the acquisition strategy, our core considerations lie in two aspects: alignment with Cheetah's strategy and potential to enhance overall value creation for shareholders. For potential target companies, we conduct a systematic evaluation of the synergy between their technology and our business, the strategic value they can bring, team compatibility, and financial valuation. If a target meets our standards, we will consider acquisition as a major strategic option to accelerate our capabilities in AI and robotics.
Will the company achieve overall breakeven in the second half of 2025? What are the main driving factors behind the recent growth of the Internet business, and are they sustainable?
Achieving profitability in the second half is a major internal goal for us, but we do face challenges. Whether we can reach this goal largely depends on the progress of our core businesses and the overall market environment. Regarding the future drivers of profitability, I think they will come from our AI and other businesses. The Internet business is an important foundation for us and is expected to maintain stable growth. Some of the drivers for the growth of the Internet business in recent years have been the complete transformation from traditional advertising to the user payment model. By adhering to the user-first concept, we enhanced our product strength, bringing stable long-term partners, and customer acquisition channels. These driving factors are sustainable. The subsequent growth of the Internet segment mainly depends on our ability to expand more partners based on existing channels. We will actively promote these aspects.
The losses of the AI and other business segments significantly narrowed in Q1. What areas were investment scaled back? Does this mean that early exploratory projects have been phased out and an ROI-oriented approach has begun?
The significant narrowing of losses in AI and other segments isn't just due to one factor. On one hand, some explorations have reached a certain stage, and we've realized that large-scale model training doesn't hold much significance for us. We ceased the pretraining process for two models to save computing costs; our R&D has also become more efficient. While we won't phase out all exploratory projects, we have shifted our focus towards an ROI-oriented approach across all divisions. It’s essential to develop applications and scale up promptly. Our overall operating concept is clear: use AI to transform old products, enhance energy efficiency, and ensure that exploratory projects are grounded in practical benefits.
In the context of the convergence of large models, what are Cheetah's competitive advantages in the AI application layer? How do you guard against the risks of being replicated or marginalized by platform-based products?
If you can create a product, you won't be easily marginalized by platforms. Today's platforms are built on past experiences, but the products with agents bring a new experience to users. From the user's perspective, products built with agents are difficult to replicate with traditional technology. This allows for a distinct perception that platforms should be wary of. Furthermore, companies are increasingly focusing on applications rather than entire models. Our strategy focuses on quickly seizing opportunities brought by technological changes and acquiring users. This position allows us to grow in ways that platforms can't easily replace.
Ladies and gentlemen, the conference has now concluded. Thank you for attending today's presentation. You may now disconnect your lines.