Baidu, Inc.

Hello, and thank you for standing by for Beidou's first quarter 2024 earnings conference call. At this time, all participants are in listen-only mode. After management's prepared remarks, there will be a question and answer session. Today's conference is being recorded. If you have any objections, you may disconnect at this time. I would now like to turn the meeting over to your host for today's conference, JoLynn, Beidou's Director of Investor Relations.

Hello, everyone, and welcome to Baidu's first quarter 2024 earnings conference call. Baidu's earnings release was distributed earlier today, and you can find a copy on our IR website, as well as on Newswire services. On the call today, we have Robin Li, our co-founder and CEO, Zhong Luo, our CFO, and Dosheng, our EVP, in charge of Baidu AI Cloud Group's ACG. After our prepared remarks, we will hold a Q&A session. Please note that the discussion today will contain forward-looking statements made under the safe harbor provisions of the U.S. Credit Security Litigation Reform Act of 1995. Forward-looking statements are subject to risks and uncertainties that may cause actual results to differ materially from our current expectations. For detailed discussions of these risks and uncertainties, please refer to our latest annual report and other documents filed with FEC and Hong Kong Stock Exchange. Baidu does not undertake any obligation to update any forward-looking statements except as required under applicable law. Our earnings press release and this call include discussions of certain annotated non-GAAP financial measures. Our press release contains a reconciliation of the annotated non-GAAP measures to the annotated most directly comparable GAAP measures, and it's available on our IR website at ir.baidu.com. As a reminder, this conference is being recorded. In addition, a webcast of this conference call will be available on our website. I will now turn the call to our CEO, Robin.

Hello, everyone. Our business continued to grow in the first quarter. Baidu Core's total revenue increased by 4% year-over-year to RMB 23.8 billion, and the non-GAAP operating margin reached 23.5%, an improvement from a year ago. In particular, revenue growth from Baidu AI Cloud accelerated to 12% year-over-year order, while continuing to deliver operating profit on a non-GAAP basis. 2024 is the second year of our march on the GenAI path. As we solidify our leadership position in foundation models, we are transforming the company from an internet-centric business to an AI-first business. Given that Ernie is the most powerful LLM in China, we are aggressively pushing the envelope for both our 2C bins and 2B bins to adopt Ernie to provide better user experience, to increase advertiser ROI, to enable developers to write agents and applications, to let customers enjoy more effective and more efficient models. While we operate our legacy bins in a challenging environment and experience lower revenue growth in the near term, we remain confident that AI will bring us sustained growth in revenue and profit in the long run. We expect our cloud bins to accelerate and the loss of our robot taxi bins to narrow for the rest of the year. We expect mobile bins to be soft in the near term and start to recover when GenAI becomes the new core of our existing products next year. Looking beyond the near term, GenAI and foundation models will bring us tremendous opportunities, ushering in a new innovation cycle. Enterprise and individual developers have swiftly transitioned from the fear of missing out on this opportunity to leveraging foundation models like Ernie to build AI applications. Baidu is well-prepared to benefit greatly from this technology transformation. We believe one of the most important long-term opportunities is model inferencing, which will be a key growth driver for our AI cloud revenue in the future. In April, Ernie handled about 200 million API calls daily, a significant jump from around 50 million in December last year. This considerable growth indicates the increasing adoption of Ernie and points to strong future revenue potential from model inferencing. To accelerate the adoption of Ernie, we are building a vibrant and healthy ecosystem around it. We believe Ernie ecosystem will, over time, contain millions of applications, especially agents, developed by a diverse community of enterprise and individual developers across various industries, meeting a wide range of needs in people's everyday life and work. Our large user base in mobile and desktop will enable us to distribute these agents and apps to whoever needs it whenever appropriate. The anchor of this ecosystem is the Ernie family of models, including our flagship models, Ernie 3.5 and Ernie 4.0, as well as the lightweight models we introduced in Q1. Throughout the quarter, we continued improving Ernie's efficiency leveraging our unique proprietary four-layer AI architecture and our strong ability in end-to-end optimization. For example, Ernie has increased its training efficiency to 5.1 times, and its inference cost is only about 1% compared to the March 2023 version. To make earning increasingly accessible and affordable, we now offer three sets of tools on our mass platform. Last quarter, we introduced App Builder and Model Builder for enterprise and individual developers to develop apps and build models. In April, we took a step further by introducing Agent Builder, a platform encompassing tools for easily creating AI agents. This is because we envision AI agent will become one of the most important forms of applications powered by GenAI and foundation models. With the ability to use natural language as the programming language, developers will be able to build AI agents without the need to write a single line of code. Currently, New earning agents are created on our platform every day, and together they are distributed millions of times per day, serving a wide range of verticals, including education, legal, B2B, travel, and more. All these initiatives derive from our extensive experience and insights in building and running earnings, as well as developing AI-native applications. We believe that Ernie's true value will only be realized when numerous applications built on top of it are widely used by users and customers. I'm pleased to note that Ernie is extending its influence across smart devices through API. Last quarter, we proudly announced partnerships with renowned smartphone brands such as Samsung China and Honor. assisting them in enhancing their native app experiences using Ernie. This quarter, we are excited to extend our collaboration with more leading smartphone makers, such as OPPO, Vivo, and Xiaomi, who leveraged Ernie APIs to elevate user experiences. Moreover, our reach now extends beyond smartphones to include PCs and electric vehicles. Earning APIs are now utilized by Lenovo, a top PC brand, to empower its AI assistant in the default browser of its PCs. NEO, China's leading smart EV manufacturer, began using the Earning API to enhance the in-camping experiences for its vehicles. This broadening of our partnerships into various smart devices opens up ample opportunities for large-scale user adoption, paving the way for early-enabled applications to become the entry point into the world of generative AI. In addition to the brands I just mentioned, we have also acquired many notable new customers, such as Trip.com, Sao Tu, Zhaoping, Soebang, and Singapore Tourism Board. Another long-term opportunity is in our consumer-facing business. We have been reconstructing all of our consumer-facing products. Our goal is to build our proprietary AI native applications, potentially killer apps, for the early ecosystem. By doing so, we should be able to generate new growth opportunities. For example, after rebuilding with GenAI and LLMs, Baidu Wenku, our one-stop shop for document creation, experienced double-digit year-over-year increase in paying users in the first quarter. Penetration of earnings for Baidu's search and feed took longer than expected because the user base is in the order of hundreds of millions, and use cases are generally very sensitive to cost and response time. We needed a wide range of early models in different sizes, optimized for different scenarios for best price performance ratio. After trial and error for a few quarters, we are firming up our strategy. Going forward, we plan to accelerate the launch and adoption of new product features such as multi-model generative search result, multi-round interaction in search, and more recently, distribution of earning agents. We are at the forefront globally of this unique technological change, and we are confident in our abilities to innovate. By definition, we are operating in uncharted territory. As always, we want to be flexible to make timely adjustments with evolving consumer and how users incorporate new product features in their day-to-day life. Now, let me review the key highlights for each business for the first quarter. In the first quarter, AI cloud revenue reached RMB 4.7 billion, up 12% year-over-year, and continued to generate operating profit on a non-GAAP basis. The revenue growth was mainly driven by Gen AI and foundation models. In the first quarter, such revenue accounted for 6.9% of total AI cloud revenue. Currently, the majority of this revenue is from model training, but revenue from model inferencing has been growing quickly. We believe revenue from Gen AI and foundation will continue to rise as customer adoption improves. For instance, within our internal cloud revenue Baidu cores, other BINs groups, such as mobile ecosystem groups and intelligent driving groups, are increasingly leveraging the power of earn. As a result, 15% of their payments to the AI cloud group are allocated to GenAI and foundation models. Enterprises choose Baidu AI Cloud to train and host their models because they believe we have the most powerful and efficient AI infrastructure for model training and inferencing in China. Compared to our peers, we help enterprises to train models at all sizes on our AI cloud while also reducing model inferencing cost. This is primarily attributable to two reasons. Number one, our self-developed four-layer four-layer AI architecture has allowed us to animate and optimize at each layer, enabling continuous efficiency gain. And number two, we have superior capabilities and insights in GPU cluster management. Leveraging our technical expertise, we can now integrate GPUs from various vendors into a unified computing cluster to train in LLM. Our platform has demonstrated very high efficiency with this setup on a GPU cluster that is composed of hundreds, even thousands of GPUs. This is an important breakthrough because of the limited availability of imported GPUs. Another growth driver for AI Cloud is cross-selling of our CPU Cloud services to our GPU Cloud customers. With the high recognition of our GPU Cloud among existing and new customers, we have seen customers increasingly switch more and more of their CPU Cloud usage to Baidu. As mentioned earlier, on the math side, we took many initiatives to make the early family of models increasingly affordable and efficient. than open-sourced models. Here are some highlights for this quarter. We have expanded and enhanced our early model portfolio, offering a total of three lightweight LLMs and two task-specific LLMs on ModelBuilder. This model helps enterprises and professional developers balance model performance with cost early to reach a broader audience for model development. In addition, our mixture of expert or MOE approach can partition a user query into distinct tasks, assigning the most suitable models to handle each task, and use only 3.5 or 4.0 only for the most complex tasks. This approach allows for faster responses and lower inferencing cost, while maintaining similar performance level to using more models. Last quarter, we introduced App Builder to developers. Throughout the quarter, we continued enriching and refining the tools for App Builder, enabling developers to easily create AI native apps in just three steps on our platform. With the launch of Agent Builder in April, anyone can create an AI agent with just a few sentences on Baidu. Overall, we remain confident in the strong for our AI cloud revenue, and we aim to continue generating operating profit on a non-GAAP basis. Mobile ecosystem has continued to deliver healthy margins and strong free cash flows. In the first quarter, our online marketing revenue grew by 3% year-over-year. Revenue growth was impacted by a challenging macro environment. At the same time, we have been pushing hard to transform the user experience from a traditional mobile product to a generative AI experience. This transition is ongoing, and monetization has not yet started. We also needed to leverage earnings to reconstruct our monetization system for better conversion and efficiency gain. During the quarter, we further enhanced our ad targeting capabilities and scaled up real-time ad generation. This effort resulted in an improvement in conversion and generated incremental revenue. Earning agents stand for a long-term opportunity for marketization upgrade too. Recently, we have seen not only brand advertisers, but also SMEs gradually adopting earning agents. We have designed this agent for SMEs as virtual storefront and service desk, serving consumers around the clock. We believe that the use of agents can improve SMEs sell-through rate, enhance their productivity, and expand their reach among users. This will be an important step for us to transform our traditional CPC model to a significantly more efficient CPS model, and meanwhile enhancing user experience on Baidu. Going forward, I believe greater opportunities will arise from AI-native apps. particularly for GenAI-enabled search. GenAI complements traditional search, expanding the total addressable market. Since Q2 last year, we have been reconstructing Baidu search with Ernie. Now, more and more search results are generated by Ernie in a growing variety of formats like text, image, third-party links, point of interest, and citation. These results are usually produced in real time to directly address users' questions and problems. By doing so, we have improved and will continue to enhance the search experience, which is crucial for increasing the usage of Baidu Search. While user feedback on this product and feature renovations has been encouraging, it is important to note that we are still in the early stages of reconstructing Baidu Search with Earth. This process will likely take time, given that Baidu Search has a history spanning over 20 years, and user behavior will evolve gradually. Overall, I believe that Search will be one most likely killer app in the Gen-AI era, and we are on the right trajectory to capitalize on this potential. I mentioned early agents as an important opportunity for monetization. With newly introduced agent builder, creators, publishers, and service providers will find it increasingly easy to build on Baidu. It is key to enhancing Baidu's content offerings and ultimately provide an AI-native user experience on our platform. Moving on to intelligent driving. We believe ApolloGo stands as the largest autonomous ride-hailing service provider globally, measured by the rides provided to the public. In the first quarter, ApolloGo offered about 826,000 rides to the public, marking a 25% year-over-year increase. In April, the total number of flights surpassed 6 million. We are continuing to move towards achieving unit economics break-even for APLOGO. To make this happen, our strategy is to reach UE break-even in key regions and then replicate the success in other regions. The regional break-even point, we are focusing on scaling up the operation of fully driverless ride-hailing service and enhance the utilization of each vehicle. Wuhan, ApolloGo's largest regional operation, is progressing toward this goal. In Wuhan, ApolloGo is gradually becoming an integral part of the city's transportation network. ApolloGo more than doubled its operational area from a quarter ago, serving a population of over 7 million and achieving the remarkable milestone of crossing Yangtze River with fully driverless vehicles as part of its expansion. Moreover, our vehicles started to operate 24 by 7 in Wuhan in early March. further expanding Apollo Go's reach and improving the vehicle utilization. All these progresses have led to the rapid growth of fully driverless rides. In Q1, the rides provided by fully driverless vehicles accounted for over 55% of the total rides in Wuhan, which is up from 45% in the fourth quarter last year. This figure continues to rise. exceeding 70% in April, with expectations of sustained rapid growth ahead and reaching 100% in the coming quarters. Looking ahead, we plan to deploy RT6, our generation robot taxi, in our Wuhan Apollo Go operation this year, which will significantly reduce hardware depreciation costs. With the scaling of driverless operations and continuous improvement of cost structure, we believe ApolloGo will achieve operational UE break-even in Wuhan in the near future. As ApolloGo continues to progress, we will closely monitor efficiency and persist in optimizing the operation of our overall intelligent driving business. On AutoSolution, our Apollo self-driving for ASD technology continues to evolve. I mentioned in our last earnings call that Apollo is a global pioneer in the use of visual foundation models in autonomous driving. Now, our state-of-the-art autonomous driving solution solely reliant on vision is made available to OEMs. AFD can effectively navigate complex urban environments across over 100 cities in China, with plans to expanding to hundreds of cities in the coming months. This allows us to make advanced autonomous driving attainable across a broad spectrum of passenger vehicles. From high-end to economy models priced as low as 150K RMB, and it serves as another proof of our technology leadership. With that, let me turn the call over to Rong to go through the financial results.