Cheetah Mobile Inc. Q2 FY2024 Earnings Call

Good day, and welcome to the Cheetah Mobile Second Quarter 2024 Earnings Conference Call. All participants will be in a listen-only mode. After today's presentation, there will be an opportunity to ask questions. Please note this event is being recorded. I would now like to turn the conference over to Helen Zhu. Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's second quarter 2024 earnings conference call. With us today, our Company's Chairman and CEO, Mr. Fu Sheng, and our Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct a Q&A session. Please note that the CEO 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, we will now let the AI agent speak on behalf of our CEO and Chairman, Fu Sheng.

Thank you for joining us today. In Q2, our total revenue accelerated with a year-over-year growth of 12.3%. AI and others accounted for about 40% of our total revenue. This growth was primarily driven by the sales of our wheeled service robotics in both domestic and international markets, showing our progress in becoming an enterprise-facing company. The acquisition of Beijing OrionStar has made service robotics a key pillar for Cheetah Mobile, contributing to solid revenue growth. Our service robots hold a dominant position in voice-based use cases and are widely used in exhibition centers, museums, corporate receptions, and other areas. Moreover, our delivery service robot ranks among the top three in restaurants and continues to gain market share from competitors. Customers choose us because of our best product experience and after-sales services. Strong core AI capabilities, including far field voice recognition, our robots have benefited from large language models, being able to better understand end-to-end customer inquiries and respond smartly to their requests compared to previously. Customers can easily tailor-make apps within our robots, thanks to our strong software abilities that support customization. To further expand the revenue growth of our wheeled service robotics business, we are focusing on two key strategies. Firstly, we aim to broaden the use cases of our service robots through continuous product innovations, with a focus on our core competencies, including voice interaction capabilities and enabling autonomous deliveries. For instance, in factories and fulfillment centers, we have recently introduced robots designed for autonomous delivery for relatively low payloads. We offer overhead performance and pricing for our customers in both China and overseas with a focus on providing the most reliable robots in the market. We already started shipping robots to South Korea and have received orders from customers in Japan and Southern Europe. In the hotel industry, our service robots are making progress. Hotels are a proven use case for wheeled service robots, and we are gaining market share from existing players. We are also updating our robots for the hotel industry to increase our competitiveness. In supermarkets, our service robots have successfully facilitated the sale of low ticket products by identifying potential buyers, proactively approaching them, providing comprehensive product introductions and responding clearly to consumer inquiries. Following our collaboration to sell hot sausages in supermarkets, we are now expanding into more local stores and supermarkets. Secondly, we are expanding our service robotics business globally with overseas revenue already surpassing domestic revenue. Following our success in South Korea and Japan, we are actively building our presence in Southern Europe, Southeast Asia, North America, and Australia, particularly in use cases such as restaurants, factories, and fulfillment centers. Chinese electronic products have demonstrated their ability to compete globally. With Cheetah Mobile's extensive experience in international operations, we are confident in our potential to succeed in these overseas markets. We believe that the service robotics industry offers one of the largest market opportunities for AI commercialization. Large language models and generative AI serve as brains for robots, enhancing machine intelligence and accelerating their commercial deployment at scale. However, this industry is still early and will take time to unlock its full potential. Cheetah Mobile is working hard to make service robots more affordable for enterprises across a growing number of use cases, enabling them to reduce labor costs through the use of our robots. We focus on wheeled service robots because we believe they offer the best balance between performance and cost at this stage, making us the only robotics company in China to have trained a large language model from scratch, with its LLM approved by local authorities for a larger scale rollout. We use this model to power our robots, focusing on hardware, software imaging through serving customers in various use cases. We have notably enhanced our voice-based capabilities, particularly for reception-related use cases, and we will continue to use data generated by our robots to enhance our models and product experience, creating a positive feedback loop. This approach should allow us to deliver the best products for price and performance in the global market. At the same time, we remain committed to product innovation, driving to balance performance with cost-effectiveness. During the last earnings call, we discussed how we are helping enterprises build LLM-based applications. Applications are key to making large language models useful in enterprises. This is because they need industry-specific or company-specific knowledge to avoid errors and effectively address enterprise challenges. Cheetah Mobile is developing Gen AI applications for enterprises. We are encouraging our employees to create Gen AI tools or apps to improve work efficiency. At the same time, we are working closely with key accounts to help them develop Gen AI applications to streamline their daily operations. Our goal is to identify cases where Gen AI can enhance efficiency and then standardize tools and features that can be scaled to other companies. For example, an LLM-based cloud management app helps enterprises monitor and optimize cloud usage across platforms. After successfully using it at Cheetah to reduce costs, we've begun offering it to enterprise clients. Early customer feedback has been positive. We've also helped a major hotel operator in China develop a Gen AI for employee training programs. Looking ahead, we will continue working closely with key customers behind the Gen AI products we've developed internally to their business operations. This approach will help us further refine our product experience and gradually build a comprehensive product portfolio. Before I turn the call over to Thomas for financial highlights, I want to emphasize the following. First, our enterprise businesses, service robots and LLM-based apps each represent a huge market opportunity and are still in the early stages of development. Second, Cheetah Mobile, with a team that has extensive experience from the PC and mobile areas, along with strong AI capabilities, is investing in development to capture these opportunities; we are focused on achieving high-quality, long-term growth rather than pursuing short-term gains.

Thank you, Fu Sheng. Hello, everyone on the call. Please note that unless stated otherwise, all money amounts are in RMB terms. Cheetah Mobile delivered a solid performance this quarter. In Q2, total revenues grew by 12% year-over-year, reaching RMB 187 million. Non-GAAP gross profit increased by 11%, coming in at RMB 122 million. Our non-GAAP gross margin remains stable at 65% compared to the previous year. Despite our ongoing investments in AI, we successfully reduced our operating losses on a sequential basis. In Q2, our non-GAAP operating loss decreased by about RMB 4 million quarter-over-quarter to RMB 62 million. This improvement reflects our strategic decision to reallocate resources from our legacy internet business to our AI initiatives. Looking at our Internet business, excluding share-based compensation expense, the operating margin increased to 12.4%, up from 7.9% in the previous quarter, and 5.5% in Q2 of the prior year. Revenues from this segment remain relatively flat year-over-year with a 4% increase quarter-over-quarter. As we have indicated before, the year-over-year increase in operating losses was driven by our investments in AI following our acquisition of a controlling stake in Beijing OrionStar. Specifically, these increased losses are attributable to higher headcount in R&D, sales, and G&A, as well as increased hardware-related costs for our service robots. As of June 30, 2024, we had approximately 870 employees compared to around 860 in the previous quarter and 730 year-ago. We are pleased to report that our AI investments are beginning to bear fruit. Our wheeled service robotics business has emerged as a key revenue driver. Additionally, through collaborations with leading companies on their large language model initiatives, we have gained valuable industry insights and are currently testing our LLM-based applications for enterprise use. One of the quarter's standout achievements is our cash generation capability. Despite continuing to incur losses, we generated almost RMB 220 million in cash from operating activities in Q2, highlighting our strong cash management and generation capabilities. Finally, our balance sheet remains robust. As of June 30, 2024, we have approximately US$270 million in cash and cash equivalents, along with short-term investments, and above US$119 million in long-term investments.

Thank you, Thomas. Everyone, for today's call, management will answer questions in Chinese and the AI agent will translate management's comments into English in another line. Please note that the translation is for convenience purposes only. In the case of any discrepancies, our management's statement in Chinese works well. If you are unable to hear the Chinese translation, a transcript in English will be available on our website within seven working days. Operator, we are now ready to take questions in Chinese. Thank you.

One, my question is about the product forms of robots. Nowadays, humanoid robots and embodied intelligence have been discussed a lot. Many people also define 2024 as the first year of the industrialization of humanoid robots. Cheetah focuses more on traditional wheeled robots. How do you think about the similarities, differences and obstacles of the implementation of wheeled robots and humanoid robots in different scenarios? How do you plan the forms of Cheetah's future robot products?

Yes. This wheeled machinery has become traditional human-related machinery. Actually, I think whether it's human-related or humanoid, the essence is to complete a certain type of work, right? Humanoid robots are more like humans and can attract more attention in terms of publicity. But in fact, most of our real robots complete point-to-point deliveries indoors or use robotic arms on the mobile robot bodies to complete some tasks. So I always believe that wheels can meet most of the mobile scenarios because today, whether it's in factories, restaurants, including hotels, the situation of steps is very rare. In many places, there are slopes, and these wheels can be fully realized. Then the cost of the wheels themselves is much lower than that of bipedal ones. Anyway, it has a cost advantage. Today, although embodied intelligence is very popular when it comes to whether bipedal or humanoid robots of embodied intelligence can be practical, it should be said that a particularly clear time point or a specific video cannot be seen; everyone just thinks there is an opportunity, mainly because Tesla made humanoid robots. So this industry has been driven. My view has always been that in most scenarios, there is wheeled movement, but some lifting robotic arms can be added later to complete the same work. So for the form of our robots, we will not pursue humanoid robots at present, especially bipedal ones. But when it comes to delivery and voice interaction of our machines, we'll do something like putting robotic arms into complete some tasks, such as picking things up or doing some simple and small handling. I think this is within our plan. So I think maybe for a long time, we won't consider this bipedal thing. I also think that in the entire industry for a considerable or a very long time, movement must be mainly wheels. It will take at least several years for bipedal ones to be commercialized.

Two, from the historical experience of SaaS, the willingness and ability of Chinese enterprises to pay are relatively low, which may affect the overall charging and profits of the large model industry. Based on practical experience, how do your clients consider the budget for large models? Where does the budget come from? In the current macro environment, do enterprises have any reduction in their investment in large models? How do you compare the monetization models and potential of different enterprise large model applications? A, if it is to help enterprises save cost, how large can the budget of enterprises be? B, if it is to help enterprises increase revenue, is it possible to charge based on effects, commission, etc.? Is the ceiling really very high?

For such software, I think there may be many historical factors. But today, in such an environment in China, enterprise's pursuit of efficiency is increasing. Also, because the large model itself is very new, it’s not something that especially non-tech enterprises can do by recruiting a few people for SaaS. I think personally that it emerged relatively late in China. Before the division of labor was formed, there were already relatively few software talents in China. So many enterprises could do it themselves, or some large companies did it and used it for free for everyone. But today, when it comes to the implementation of large models, as we have seen, it still requires a very deep integration with the actual scenarios of enterprises. It's not that you can take an API and have a few people do the application well. We are currently cooperating with several leading clients, and we have already seen such a trend. So professional teams are still needed to help them. Then to answer the two questions, if it helps enterprises save costs, we are currently discussing and negotiating the quotation plan with a large enterprise, which is to take a part of the saved cost as its budget. This client is very willing; it can really help him save a considerable amount of money. So taking a part of it as the budget is acceptable, including that we have also promoted for some clients. For example, we have developed an AI application for cloud management, which helps analyze the idle rate of your cloud system and how many machines can be saved. We have run the accounts of several enterprises, and they can combine to save maybe 30% to 40% of the cloud cost. This service is now very popular with this client. The charging model we have promoted is about 2.5% of his entire cloud usage as the software fee. Currently, several clients we are negotiating with have entered the deployment period and they are willing to pay. That is to say, it's about the saved budget, which essentially is taking a share from the saved cost. Generally, enterprises are willing to do this. The second one for increasing revenue is, it is possible to charge based on the effects or commission. We think it's also feasible. We have worked with another enterprise to build a large model application for them. Later, their franchisees of chain stores will purchase this service, and we will share the commission with them. This business model seems successful at present; it is also quite okay. As for whether the ceiling is really very high, I think that for such enterprise applications, if you really talk about the huge technical difficulties themselves, I don't think there is such a significant obstacle. We are doing applications ourselves. The real difficulty of applications does not come from the technology itself, but from truly understanding the needs of enterprises deeply and doing a good job of the product points to meet their real needs and achieve a closed loop in the results. This is one of our past advantages. So this is also what I think is the ceiling, because at this stage, how to say, there are not many large model or application companies that are genuinely willing to conduct research with enterprises and spend a lot of time. Everyone is still talking about the parameters of the model and such things. So I think this is an opportunity for us instead. Moreover, through conducting depth research with such leading enterprises, we believe that we can standardize this product, such applications into components and promote them to other enterprises or other industries. AI has a very good opportunity in that the difficulty of crossing industries is much simpler than that of the previous set. Previously, it also relied on a large amount of code alignment. Now, AI has its own understanding and generation; thus, the amount of code will be much smaller. The key is achieving a closed loop in the process and experience. Okay, this is my answer.

Three, the company has sufficient cash reserves on its books and is still generating net cash. How does the management plan to use these funds? Is there a privatization plan? Or are there any plans for share buybacks or dividends?

The question was raised. Indeed, as you said, we currently do have sufficient cash reserves and also generated net cash this quarter. However, we believe that in the current overall economic environment, which is quite uncertain, it is particularly important for the company to maintain sufficient cash reserves. So we will continue to maintain a relatively cautious financial strategy. This can ensure that the company has sufficient flexibility and risk resistance in the face of market fluctuations. Currently, the company has no privatization plan; we believe that maintaining the status of a listed company enhances our transparency and governance level while providing better liquidity for shareholders. Currently, the company also has no specific plans for share buybacks and dividends. If the Board of Directors approves any relevant plans, we will make an announcement to the market as soon as possible.

We have seen robot companies collaborating with large model companies using the most advanced large models to accelerate product implementation. How does Cheetah consider cooperation with large model manufacturers? Or is it more inclined towards the idea of end model integration?

At this stage, it is certain that we will cooperate with large model manufacturers and use the most powerful models to enhance the intelligence level of robots, which is what we need to do now. Currently, in this entire AI industry, the supply of large models is excessive, and the cost of tokens has dropped sharply. This cooperation is very advantageous for our cost and rapid implementation. Our idea is that through a period of exploration, after making the product experience good, we will gradually see if it is necessary to train our own model for our end. In fact, we have already trained one, a RMB 14 billion parameter model. We shall also release a model next month. But I think, basically, at this stage, we still need to enhance the product implementation and product intelligence first. After obtaining sufficient experience, we can come back and better configure the model for our end. Maybe in the long term, we will take the path of end model integration; that is, for our applications or robotic application, we will better train a model that can be implemented on the end. But for now, we are still cooperating deeply with large model manufacturers. Thank you.

Five, we have seen many start-up projects that are very similar to Cheetah's robot product forms. How do you view Cheetah's competitive advantages in making robots? Where are the differences in the robot products?

I think our advantages mainly lie in three aspects. The first aspect is that we still have sufficient technological accumulation. We have accumulated in a field of service robots for six or seven years. Therefore, our intelligence level of the robot, whether it is the ability of interactive dialogue or autonomous navigation indoors, including the secondary development ability of our system, is leading in the industry. Next, we will introduce our large model; we will launch the concept of a large model robot. Today, very few companies in the industry have trained their own models. We are possibly the first one or maybe one of them; our team is not only responsible for the design and manufacturing of the robot hardware but also for the software operating system that now has the ability of large models. So our technological advantages are reflected in the interaction ability, intelligence level, and secondary development ability. The second aspect is that through these years, we have launched a productized robot because the robot is a combination of many technologies such as navigation technology, various electronic circuit technologies, and mechanical structure technologies. It may be relatively fast to make a sample, but to ensure the product quality is stable enough to run in many scenarios and pass quality inspections based on user experience, our team's efficiency in this aspect is much higher than before. Our ability to launch a new product or promote it in a new scenario should not be easy for general small start-ups to compare. The third aspect is, through many years of accumulation, we have explored the ability of commercialization or the establishment of channels. We have hundreds of agents in China and dozens of agents globally, with agents in some countries being significant and cooperating to build this business network, enabling our products to quickly reach users through our channels. Today, we can inversely conduct research and development based on the needs of our users for the products they require, including the lightweight delivery robot for factories we launched recently, which was based on our agents and channel feedback. So I think these several points have formed a closed loop for our Cheetah robots today; that is, a closed loop from research and development to sales to user experience. I think this closed loop is our real differentiating point. It’s not a particular technical point because others can also do that, but the establishment of this closed loop, especially for a channel network, means that the cycle will not be too short, so I'm not too worried about the competition from such start-up projects. Okay. Thanks.

Sixth, in terms of data. After our robot products are exported overseas, how is the data generated attributed? Can we still use this data to continuously iterate the models and products?

In the process of going overseas, our general principle is to handle it based on the legal requirements of different regions and their laws and regulations. For the collection of this data, it is stored on the AWS server overseas and undergoes necessary storage encryption, security reviews, and strict access control measures. We also completed the ISO 27001, the Information Security Management System Certification, 27701, privacy information management system certification, and ISO 42001, Artificial Intelligence Management System Certification with reference to the ISO certification standards. We are the first company in Asia to obtain this artificial intelligence management certification. So the establishment of these systems ensures that our data usage complies with international standard security certifications. Since Cheetah went overseas early, we encountered this problem before. We also dealt with these issues when our apps went overseas earliest, including the GDPR in Europe and other regulations; we attach great importance to compliance and have such experience inheritance. As for the usage principle you asked about, we definitely follow the principle of necessity of use and minimization of data; this ensures the analysis of machine operation problems does not involve non-essential personal information. For example, in some countries, we will turn off certain functions just to achieve basic functionality. Overall, we are very cautious in the usage of this data and comply with local laws and regulations. Regarding whether this data can be used to train the large model, the capabilities of the real large language model like those provided by foreign services are already very good. We do not need the training of this data for the time being. For things like navigation, it does not generally involve AI data training. Today, we attach great importance to overseas data security and are also very compliant. Thank you.

Eight. My question is about organizational capabilities because in the era of large models, starting a business is not only about technology and product entrepreneurship, but also requires organizational innovation. Especially when enterprises are developing products, many variables need to be considered, such as models, data, customers, end users, etc. In your opinion, since Orion was integrated into the listed company, during this period of more than half a year, what significant results have Cheetah Orion achieved in organizational innovation? How does Cheetah recruit the best talent? How much time do you spend on talent recruitment?

Okay. Let me start with this. Frankly speaking, our organizational innovation is not particularly innovative because the building of organizational capabilities is a long-term and routine work, especially, as you mentioned, after we acquired Orion, Cheetah has done many things in organizational reform. Today, Cheetah has gradually transformed into a company with a core focus on the B2B business, whether it's the sales of robots or our international advertising agency businesses. In terms of organizational capabilities, the first thing is to strengthen training. I myself participated in training this year in various sales trainings organized by the company; we have put in a lot of effort and resources. The second aspect is sales management. This was a weakness for Cheetah before, but since 2017, I have started the number one position in sales. In the management of the sales system, we learned from Huawei’s approach and applied it to the management of the B2B sales team. We have made significant changes in our organization. The reason why it's hard to say it is innovative is that 2B itself has a set of mature methodologies. We are learning and implementing advanced models. One obvious change is that today, for some so-called key accounts or CA customers, our ability to form cooperation or conduct business has definitely strengthened compared to before. This year, in different businesses, we have reached certain agreements with many key accounts or industry-leading customers. For example, regarding hotels and some leading mobile phone manufacturers in AI, we have also established cooperation with automobile manufacturers on the cloud. These should all be some changes brought about by organizational reform. Of course, it’s not easy to shift from B2C to B2B. We have explored for a long time, spent considerable effort, and taken some detours. Now it seems that our entire B2B sales management system, including the integration of industry and sales, has made considerable progress. Regarding recruitment, I also spend time on this. Every week, I have some interviews or related recruitment matters. Today, because we have spent several years building this B2B sales system, we don't have a substantial talent gap at this moment. We mainly focus on finding some excellent B2B sales talent and putting in effort. Overall, it also depends on internal training. We place great importance on the cultivation of young talent in addition to recruiting top talent. This year, we also restarted our campus recruitment program, and the entire company invests much time in training campus recruits, and I also give lectures to them. So this is our current situation regarding organizational management and reform. Thank you.

Based on practical experience, which model applications have better implementation effects and meet enterprise demand? At this stage, how far are these applications from being truly useful? Historical experience shows that Chinese companies are better at developing applications. But if the underlying large models in China cannot reach a particularly intelligent level, can the effects of the applications based on them be comparable to those overseas?

The first one is the application effect of today’s B2B large models. In fact, the effect of the first wave of B2B large model usages, like text-to-image and text-to-video, have seen rapid growth. It’s because there’s a significant demand for creating graphics and writing text. This is the simplest application. I think it's entering a stage. For example, what we are doing with the client is an internal training system for them. All new employees need training, assessment, and practical scenarios. Previously, you could only read documents and answer questions; now you can interact with an AI, right? Currently, the effect seems very good and users are quite satisfied. This type is connected to internal training related to training and document content skills, and I think AI seems capable of achieving this. As for whether it’s truly useful, well, large models cannot be used randomly as they are because they have hallucinations and need internal understanding of the enterprise’s knowledge and requirements. It requires building a product system to make it truly useful, which takes time and effort. So I think it will take time to make the experience good enough for this progress. The second example is our data organization types related to buy requirements. Previously, a large amount of code had to be written for each requirement, changed according to the application needs of enterprises, and using large models to understand the intent and write good code has shown effectiveness. I see that some of our clients are also implementing this. Regarding whether the underlying large model in China is at a lower intelligence level, indeed, I think today, the Chinese large models have a gap compared to the top levels of foreign models, but the gap is not too vast. In many scenarios, we use foreign models in the experimental stages first, find them feasible, and then switch to domestic models to provide services because this is the requirement in China. However, after switching to domestic models, there isn't a significant difference in final performance compared to foreign ones. Basically, there is no essential difference as in many enterprise scenarios; the level of intelligence we envision isn’t always necessary; we need to clarify demand. Today, there’s a very popular term called agent. An agent essentially involves writing something for this type of demand. For example, let the large model detect mistakes itself and combine traditional technologies. This integration can actually achieve a high level of effectiveness in real applications. I am confident about the effect of Chinese companies compared to overseas ones because overseas firms definitely have algorithmic advantages, but when it comes to detailing application development and fulfilling user demand, I think Chinese companies have an edge now. Thus, for real application development, I believe the effects will be good and won’t be limited by the so-called underlying large model level.

The last question. From the perspective of research and development, talk about Cheetah's competitive advantages and R&D plans?

Okay. Thank you for your question. Let me repeat it this way: when we started to invest in the AI strategy, we also experienced the first wave of AI, which was quite hot back then. Now, my current thinking is more pragmatic, focusing on technological updates step-by-step instead of overwhelming the market with super technology. I think that robots are a complex system, integrating various technologies such as hardware, software, and artificial intelligence, operating in different scenarios, whether a user takes out a mobile phone or a robot functions in a restaurant or hotel. The advantages in promoting research and development do not lie in a specific technology but rather in whether R&D can efficiently link with the sales chain and end customers. Today, everyone has different ideas about robots, and while some are making humanoid ones, our focus is on implementation. After we implement and run in a scenario, we encounter problems requiring the R&D team to respond quickly. When issues arise, the efficiency of your team is your advantage. In today's business environment, distinguishing oneself based solely on technology is difficult. Competitors are evaluated by the integration from the terminal to the back to the product, enabling the quick push to customers. This is our R&D capability that we've developed over these years. If there is an advantage, this is it. We are also exploring new technologies, including AI and large models, by gradually applying them to our products. For a company of our size, we cannot be too advanced, and there is no need to be excessively so. Sometimes, costly one-time trials are not feasible. We keep tracking new technologies and applying them to ensure that our R&D and sales system can iterate quickly while responding to real-user issues. I believe this is our current advantage.

Thank you all for joining our earnings conference call. This is Helen from the company's IR team. This is the line for translation purposes, and we will have the transcript in English available as soon as we can. Thank you so much for participants. Bye.

The conference has now concluded. Thank you for attending today's presentation; you may now disconnect your lines.