Good day, and welcome to the Cheetah Mobile First 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, Investor Relations for Cheetah Mobile.

Thank you, operator. Welcome to Cheetah Mobile's fourth quarter 2024 earnings conference call. With us today, our company's Chairman and CEO, Mr. Fu Sheng, and Director and CFO, Mr. Thomas Ren. Following management's prepared remarks, we will conduct a Q&A session. 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 Chairman and CEO, Mr. Fu Sheng. Please go ahead, Fu Sheng.

Hello, everyone. Thank you for joining us today. This is our first earnings call since November 2021, and we are excited to share our progress as we resume our quarterly updates. Cheetah Mobile is making changes. We are moving from a focus on 2C to 2B. In Q1, our revenue from AI and others, or enterprise-focused, increased by 62% compared to last year and 36% from the previous quarter. Now, these revenues make up 43% of our total revenue. We expect this to grow to about 50% by the end of the year, making significant steps in our transformation. Our recent acquisition of Beijing OrionStar, an AI service provider, was an important move. It gave us a skilled sales team, strong tie-ups with business customers, and end-to-end capabilities for LLMs, including model training, fine-tuning, developing LLM-based apps, and enhancing service robots—a new attachment for interacting with end users and customers in the AI era. With OrionStar, we are now focusing on making customer enterprise apps with LLMs and introducing LLM-powered robots for specific business needs. We see two main reasons for this focus. First, market opportunity. Unlike the competitive 2C market, enterprises are increasingly choosing LLM-based apps on private cloud due to data security concerns. However, they face challenges in developing tailored apps and presenting substantial opportunities in China's enterprise sector. Second, the combination of Cheetah and OrionStar allows us to combine our app enterprise with AI skills, better converting market opportunities. By selling robots to businesses, we can even find new ways to use LLMs to improve efficiency. We are using a product-driven approach to enhance our LLM capabilities. This is why we focus on the 10B parameter LLM segments and avoid large upfront investments in GPUs. We believe that a trillion parameter LLM is necessary, and enterprises can deploy and use 10B LLMs on private clouds at a lower cost. Over the past few months, we changed our 14B parameters foundation models from scratch, which has been approved by authorities for large-scale rollout and ranks among the top of various lists. Additionally, we are incorporating nearly all leading open-source foundation models to offer more options for our customers, all without significantly increasing costs. Furthermore, we have seen positive developments by integrating LLM-based apps into our service robots. In particular, our delivery robot can now interact better with users, leading to increased demand, especially in Japan and South Korea. Currently, our overseas revenue has surpassed domestic revenues and continues to grow steadily. With LLMs, we believe the features of our service robot will expand even further. I would also like to highlight how we assist our customers in using LLM-based apps efficiently. For example, we helped Chengdu University develop an LLM-based QA feature for its apps, improving user experience. We also developed LLM-powered customer service features for another customer's product, including WeChat mini programs, apps, and our service robot. This service is now available in certain localities, helping residents apply for housing funds. We are also working with enterprises in China's franchise industry to improve management efficiency with LLM-based apps. In the early stages of LLM-based app development, we closely work with our customers to understand their needs and identify areas for improvement with LLMs to find the most appropriate LLM solutions for developing customer apps. This process helps us standardize some LLM-based apps and capabilities, particularly in customer service, enterprise management, and training, which we can replicate for more customers. As a result, we are monitoring customer feedback and certification. Additionally, all the applications can be incorporated into our service robots. Our long-term business model in the LLM era will involve selling robots and offering value-added services. As we focus on building LLM-based apps for enterprises, we will shift our resources from our agency internet business to the AI business. This will improve the operating margin of our internet business, which we use as a financial performance metric. In summary, LLMs present a once-in-a-generation opportunity. With OrionStar and our clear strategy, we are now confident in our direction. We would like to emphasize that we don't want to set short-term revenue growth targets, but we are aggressively prioritizing customer satisfaction and building lighthouse projects. By doing so, we believe we will establish a new growth engine to drive sustainable long-term growth in both revenue and margins over time. All we need is a bit of patience. We thank all our dedicated employees for their hard work in making this happen. Thank you. And Thomas?

Thank you, Fu Sheng. Hello, everyone on the call. Please note that unless stated otherwise, all monetary amounts are in RMB terms. Today, I am going to talk about two topics. First, our continued investment in large language models, or LLMs, resulting in a widened operating loss for the quarter, while total revenue has resumed its increase. Second, our healthy balance sheet. First, we are investing in LLMs. We aim to help enterprises quickly develop LLM-based new apps. As Fu Sheng mentioned in his speech, our acquisition of OrionStar has allowed service robots to become a key revenue contributor to the segment of AI and others. In Q1 of 2024, revenues from AI and others increased by 62% year-over-year and 36% quarter-over-quarter to RMB81 million, accounting for 43% of total revenue in the same period. Driven by contributions from Beijing OrionStar, our total revenue increased by 12% year-over-year and 14% quarter-over-quarter to RMB190 million. This acquisition also enables the two teams from Cheetah and OrionStar to work more efficiently together to better capture the opportunity in LLMs as we help Chinese enterprises develop apps on LLMs to boost productivity. We expect this will lead to substantial growth in revenue over time. In addition, LLMs are enabling us to improve the product experience provided by our service robots, which are now more capable of answering users' different inquiries. This enhancement has strengthened our competitiveness and should drive the sale of our service robots over time. In Q1 of 2024, our total non-GAAP costs and expenses increased 21% year-over-year and 19% quarter-over-quarter. Our non-GAAP operating loss was RMB66 million in the quarter, up from RMB42 million in the same period last year and RMB49 million in the previous quarter. This is primarily due to the investments in LLMs as mentioned earlier. Through Beijing OrionStar, we acquired many R&D talents and 2B sales personnel, which are very important for us to capitalize on the opportunity in this sector. As of March 31, 2024, we had about 860 employees, up from about 720 a year ago. We are also acquiring GPUs for model training and other needs. Excluding the impact of the aforementioned investment in LLMs, our costs and expenses as well as our margins remain stable. For example, excluding stock-based compensation, our operating profit for the internet business was 7.9% in the quarter, up from 3.1% in the same quarter last year. As we continue to refine our product portfolio and remove products that did not address user pain points, we will continue this approach moving forward. At the same time, we will continue to invest in talent, both in R&D specializing in LLMs and in sales personnel to help us seize the LLM opportunity to build a new growth engine for Cheetah. Our investments will be backed by our strong cash reserves. At the same time, we will continue to increase our operating profit for the internet business. Secondly, Cheetah Mobile has a healthy balance sheet. As of March 31, 2024, we had cash and cash equivalents and short-term investments of about US$250 million. In addition, we had about US$130 million of long-term investments, which include several holdings in well-known entities. Lastly, we have decided not to provide revenue guidance going forward. Thank you.

Everyone, for today's call, management will answer questions in Chinese. An AI agent will translate management comments into English in a separate line. Please note the translation is for convenience purposes only. In the case of any discrepancies, management's statement in Chinese takes precedence. If you are unable to hear the English translation, a transcript in English will be available on our IR website within seven working days. Thank you so much. Operator, please now take questions. Thank you.

We will now start the question-and-answer session. The first question today comes from Nancy Lu with JP Morgan. Please proceed.

Thank you so much. Operator, please now take questions. Thank you. We will now begin the question-and-answer session. The first question today comes from Nancy Lu with JP Morgan. Please go ahead.

Thank you so much. Operator, please now take questions. We will now begin the question-and-answer session. The first question today comes from Nancy Lu with JP Morgan. Please go ahead.

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

The next question comes from Thomas Chong with Jefferies. Please go ahead.

Thank you so much. Operator, please now take questions.

We will now begin the question-and-answer session. The first question today comes from Nancy Lu with JP Morgan. Please go ahead. Sheng Fu, Chairman and CEO, will address the next question. Thank you. The next question comes from Thomas Chong with Jefferies. Please go ahead.

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

The next question comes from an unidentified analyst with Citi. Please go ahead.

Operator, please move to the next question. Thank you. The next question comes from an unidentified analyst with Citi. Please go ahead.

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

Operator, please move to the next question.

The next question comes from Miranda Zhuang with Bank of America. Please go ahead. Miranda, your line is open; you may ask your question.

Please move to the next question. Thank you. The next question comes from an unidentified analyst with Citi. Please go ahead. The next question comes from Miranda Zhuang with Bank of America. Please go ahead. Miranda, your line is open; you may ask your question.

Please move to the next question. Thank you. The next question comes from an unidentified analyst with Citi. Please go ahead. The next question comes from Miranda Zhuang with Bank of America. Please go ahead. Miranda, your line is open; you may ask your question.

Please move to the next question. Thank you.

The next question comes from Karen Kong with TS Securities. Please go ahead.

Please move to the next question. Thank you.

Please move to the next question. Thank you.

Okay. Operator, please move to the next question.

The next question comes from Miranda Zhuang with Bank of America. Please go ahead.

Please move to the next question. Thank you.

Please move to the next question. Thank you.

Please move to the next question. Thank you.

The next question comes from Zhai Lulu with an unidentified company. Please go ahead.

Okay. Operator, please move to the next question. The next question comes from Miranda Zhuang with Bank of America. Please go ahead. The next question comes from Zhai Lulu with an unidentified company. Please go ahead.

Helen Zhu, from Investor Relations, requested to move on to the next question. The operator indicated that the next question was from Miranda Zhuang with Bank of America. Sheng Fu, the Chairman and CEO, spoke in a foreign language. Helen Zhu again requested to move to the next question. The operator then announced that the following question was from Zhai Lulu, representing an unidentified company.

Next question, please, operator.

The next question comes from an unidentified analyst with Bernstein. Please go ahead.

Please move to the next question. Thank you.

Please move to the next question. Thank you.

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

The next question comes from Richie Sun with HSBC. Please go ahead.

Next question, please, operator. The next question comes from an unidentified analyst with Bernstein. Please go ahead. Operator, please move to the next question. Thank you. The next question comes from Richie Sun with HSBC. Please go ahead.

Helen Zhu, please proceed with the next question. Thank you.

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

The next question comes from Wei Fang with Mizuho. Please go ahead.

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

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

Okay. Thank you.

Operator, we have no further questions. Now we will end the call.

There are no further questions at this time. I'd now like to hand the call back over for closing remarks.

Thank you, operator, and thank you so much for joining our conference call.

Thank you.

Thank you, everybody.

The conference is now concluded. Thank you for attending today's presentation. You may now disconnect.