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spk20: 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, Jo Lynn, Beidou's Director of Investor Relations.
spk07: 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, Gong 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 the IR website. I will now turn the call to our CEO, Robin.
spk03: 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 need 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 earning. 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 model 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, 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 continued 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 early. 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 lives 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. Thank you, Robin.
spk16: Now, let me walk through the details of our first quarter financial results. Total revenues were RMB 31.5 billion, increasing 1% year-over-year. Revenue from Baidu Core was RMB 23.8 billion, increasing 4% year-over-year. Vital cost online marketing revenue was RMB 17.17 billion, increasing 3% year-over-year. Vital cost non-online marketing revenue was RMB 6.8 billion, up 6% year-over-year, mainly driven by the AI cloud business. Revenue for IT was RMB 7.9 billion, decreasing 5% year-over-year. Cost of revenue was RMB 15.3 billion, increasing 1% year-over-year, primarily due to an increase in traffic acquisition costs and the causes related to AI cloud-based, partially offset by the decrease in content causes. Operating expenses were RMB 10.7 billion, decreasing 2% year-over-year, primarily due to a decrease in personnel-related expenses and other R&D expenditures, partially offset by the increase in server depreciation expenses and server custody fees, which support general AI research and development tools. Vital cost operating expenses were RMB 9.4 billion, decreasing 1% year-over-year. Vital cost SG&A expenses were RMB 4.5 billion, decreasing 1% year-over-year. SG&A accounting for 19.19% of vital cost revenue in this quarter, compared to 20% in the same period last year. Vital cost R&D expenses were RMB 4.9 billion, decreasing 1% year-over-year. IMD accounting for 21% of Baidu Core's revenue in this quarter, compared to 22% in the same period last year. Operating income was RMB 5.5 billion. Baidu Core's operating income was RMB 4.5 billion. And Baidu Core's operating margin was 19%, one nine. And Nungat operating income was RMB 6.7 billion. Nungat Baidu Core operating income was RMB 5.6 billion. And Nungat Baidu Core operating margin was 23.5%. Total other income net was RMB 2 billion, decreasing 52% year-over-year, primarily due to a decrease in fair value gain from long-term investments, partially offset by the increase in net foreign exchange gain. Income tax expenses was RMB 883 million, compared to RMB 1.2 billion in the same period last year. Net income attributable to Baidu was RMB 5.4 billion, and dilutive earnings per ADS were RMB 14.9. Net income attributable to Baidu Core was RMB 5.2 billion, and net margin for Baidu Core was 2%. Now again, net income attributable to Baidu was RMB 7 billion. Now again, dilutive earnings per ADS were RMB 19.91. Now again, net income attributable to Baidu Core was RMB 6.6 billion. and non-GAAP net margin for Baidu Core was 28%. As of March 31, 2024, cash, cash equivalents, restricted cash, and shorting investments were RMB 191.8 billion, and cash, cash equivalents, restricted cash, and shorting investments, excluding ITE, were RMB 185.8 billion. Free cash flow was RMB 4.2 billion. Free cash flow excluding IT was RMB 3.3 billion. Finally, Baidu Call had approximately 34,000 employees as of 31st, 2024. With that, operator, let's now open the call to questions.
spk20: Ladies and gentlemen, we will now begin the question and answer session. If you wish to ask a question, please press star 1 on your telephone and wait for your name to be announced. If you wish to cancel your request, please press star two. Your first question comes from Alicia Yap with Citigroup.
spk10: Hi, thank you. Good evening, Robin, Rong, and also, sorry. So thank you for taking my questions. I wanted to ask, would you be able to quantify, has the AI technology been helping Baidu to improve ad monetization rate? Can management share some feedback on those advertisers who have adopted the system? In what kind of areas do they see the largest improvement, and are there any areas that can be further enhanced?
spk09: Thank you.
spk03: Hi, Alicia. This is Robin. As you know, our monetization system was the first to benefit from GenAI, generating several hundred million RMB per quarter in incremental revenue. Since the second half of last year, we have been utilizing Ernie to upgrade our monetization system, enhancing various aspects of ad technology. This includes improving ad capabilities, refining the auction system, automating creative generation and ad strategy formation for advertisers. Advertisers have seen better conversion and more sales leads. This improvement has motivated advertisers to increase their spending with Baidu. So in the first quarter, AI-related incremental advertising revenue grow on a quarter-over-quarter basis, and we expect this trend to continue. The incremental revenue has helped us mitigate the broader macro weakness and also bought us some time to reconstruct our user product with earnings. As I emphasized in my opening remarks, We believe Ernie offers a significant long-term opportunity for our online marketing business. Agents function as virtual or domains. Advertisers can establish their online presence and interact with potential customers using natural language through multi-bound conversations. With the introduction of Agent Builder, advertisers can easily create customized earning agents. When advertisers express their intentions to this agent, they can more effectively achieve their goals, whether it's helping potential customers understand their products or improving customer service quality. can also help enrich our content offering and improve user experience on Baidu. While still in its early stage, early agents have helped some advertisers achieve better ROIs. For example, we have a customer in online education. They used Agent Builder to create its AI agent. injecting it with key insights like product introduction and subject matter expertise while continually providing feedback for refinement. This agent has significantly enhanced the company's online customer service by offering round-the-clock, high-quality consultations. The adoption of Earning Agent lead to a 20% increase in ad conversion rate for this online education company. I think this is just the beginning. We believe agent will be a major form of content and services in the new AI era. We will continue to improve the capabilities of Earning Agent. Agents will not only elevate user experience, boost conversion and ROI for advertisers, but also over time foster increase in transactions directly generated on our platform. This shift should help us to transform our traditional CPC model into a more efficient CPS model.
spk20: Your next question comes from Gary Yu with Morgan Stanley.
spk23: Hi. Thank you, management, for the opportunity to ask a question. I have a question regarding AI cloud business. How has the price cut initiative by some of your peer companies affected by AI cloud business? And how should we think about the cloud revenue profitability as competition heats up? And also, how should we... what should be the sustainable level of cloud growth outlook going forward. Thank you.
spk15: Yeah, Gary. This is Do. As we have already mentioned, GeneAI and its foundation models are transforming the cloud industry from general-purpose computing to AI computing. So this shift is reshaping the competitive landscape within the cloud industry. presenting us with the valuable opportunity to establish ourselves as a leader in the AI cloud. So we believe we offer China's most efficient AI infrastructure and the most advanced mass platform for model training and inference. As a result, more and more enterprises are choosing us for model training, fine tuning, and AI native app development on our public cloud. This increasing demand has significantly boosted our AI cloud revenue. Actually, since the second quarter, the second half of last year, so our AI cloud revenue growth has started to accelerate. from year-over-year decline in the third quarter to an 11% increase in the fourth quarter last year, and then further accelerated to 12% in the first quarter of this year. The revenue acceleration was supported by two main factors. The incremental revenue directly generated by Gene AI and foundation models, and also the new opportunities they brought to our legacy cloud business. So as Robin just mentioned, in the first quarter of this year, revenue from Gene AI and foundation models accounts for 6.9 of our AI cloud revenue. And then our traditional CPU cloud businesses are capitalizing on the opportunities presented by Gene AI and foundation models. Both factors are important growth drivers of our AI cloud. On a separate note, while smart transportation business remained subdued, its impact on overall AI cloud business in Q1 was substantially smaller than the previous quarters. So overall, we expect our AI Cloud to continue benefiting from the Mactroint, maintaining strong revenue growth momentum in the upcoming quarters. On the profit side, Baidu AI Cloud continues to generate non-GAAP operating profit, as we have seen in previous quarters. We are committed to sustainable and healthy revenue growth. During the quarter, we maintain our focus on achieving high-quality revenue growth by scaling down low-margin business. Regarding the business of GeneAI and foundation models, the market is still at a very early stage, so our focus remains on educating the market and broadening our footprint across more enterprises. Looking into the long run, So we expect the normalized margin for GNI and foundation models related to business to improve further and to be higher than our traditional cloud business. Regarding the change of pricing policies of some competitors you just mentioned, it is actually pretty common for cloud vendors to adjust their pricing for certain products. This is a trend we have observed multiple times in the past. Given that our cloud offerings has expanded beyond the traditional CPU cloud to a high value AI products and services, the industry changes on CPU cloud pricing has a minimum impact on the development of our AI cloud. Actually, for cloud platform services, by leveraging our unique Proprietary four-layer AI architecture and our strong ability in end-to-end optimization, we have lowered Ernie's inference cost to only 1% after the variant in March last year. This May, Ernie handles about 200 million API calls, or approximately 250 billion tokens daily. We are confident that the earnings expanding adoption will continue to enhance its performance, boosting efficiency, and then further reduce costs. It's also important to evaluate price and performance ratio for different models at different workloads, rather than just focusing solely on some superficial prices. So we believe our state-of-the-art AI infrastructure and advanced mass platform delivers the best value to our customers. Looking forward, leveraging our strong AI capabilities, we aim to continuously attract new customers and encouraging existing ones to increase their spending on Baidu AI Cloud. At the same time, we aim to consistently generate positive non-GAAP operating profit. Thank you, Gary.
spk20: Your next question comes from Alex Yao with JP Morgan.
spk04: Hi, good evening, management, and thank you for taking my question. Given the cheap shortage in China, how does Baidu maintain its commitment to deliberately differentiate value while enhancing the leading advantage in LLM technology in China? Thank you.
spk03: Hi, Alex. We do think very differently in this aspect. We're taking an application-driven approach for our AI push. For example, solving all the high school math problems using the most advanced LLM may not be the highest priority, at this time, while generating convincing reasons for users to buy the right products is more important in a lot of applications. So with that in mind, we are taking advantage of our unique four-layer AI tech stack to optimize the cost and performance of early models. And we make sure customers and developers can easily build applications using tools like Agent Builder, App Builder, and Model Builder. For the AI infrastructure layer, a key factor contributing to our high efficiency in model training and inference is our superior capability in GPU cluster management. We have recently made a breakthrough by integrating GPUs from different vendors into one large-scale unified computing cluster, allowing us to use less advanced chips for highly effective model training and inference. Our deep learning framework, PedoPedo, has, through continuous innovation and enhancement, helped reduce the cost of model training and inferencing on a constant basis. PedoPedo is compatible with over 50 different chips, many domestically designed, and its developer community has grown to 13 million. With early 3.5 and early 4.0, we aim to be the flagship models for sophisticated tasks, we're making earning more accessible and affordable by launching lightweight LLMs, introducing toolkits for model development and app development, applying MOE approach for model inference for better performance and lower cost. With our application-driven mindset, We have used Ernie extensively to renovate our own products. And we have gained experience and insights in training and using Ernie, as well as developing AI-native applications on it. We're making all these capabilities available to our customers and developers. With all these efforts, we are fostering a vibrant and healthy ecosystem around Ernie. You can see that we are actually taking a holistic approach to developing GenAI and LLON, which is very different from some of our competitors. Our reserve and access to the chips on the market should be sufficient for us to support millions of AI applications in the future. And in the long run, I think China will form an ecosystem of its own. probably with less powerful chips, but most efficient homegrown software stack. There's the ample room for innovation in the application layer, model layer, and framework layer. With our self-developed four-layer AI infrastructure, as well as our strong R&D team, our dedication to AI, and our application-driven approach, in building an ecosystem around Ernie, I'm quite confident that Baidu will stand as a leader in China's AI ecosystem in the long term. Thank you.
spk20: Your next question comes from Lincoln Kong with Goldman Sachs.
spk05: Thank you, management, for taking my question. So my question is about the advertising business. So what's the major drag for the advertising growth if we compare it with our peers? So what are the trends we have seen for advertising budgets and advertiser sentiment, especially into second quarter, April and May?
spk06: So how should we think about our normalized advertising growth for 2024? Thank you.
spk03: Yeah, you know, our online marketing revenue grew by 3% year-over-year in the first quarter. Well, traditional search is maturing. We are working hard to renovate the user experience with Change.io. Right now, about 11% of our search results pages are filled with generated results. These results provide more accurate, better organized, and direct answers to users' questions, and in some cases, enable users to do things they could not do before. We have not started the monetization of those Gen AI results, so it will take some time for revenue to catch up. And the weak macro also contributed to the softness of our ad business. Our advertisers come from a wide range of industries, with the majority being SMEs. This takes our advertising revenue highly sensitive to the macro environment, particularly to the offline economy. In the first quarter, advertiser sentiment in some verticals, such as real estate and franchising, remained weak. Specifically in the real estate industry, not only was ad spending from developers and agencies muted. But the impact also extended to both upstream and downstream sectors. For example, energy, chemicals, machinery, building materials for the upstream and home renovation, furniture, this kind of downstream also constrained at spending on our platform. Also, many SMEs in offline sectors need more time to recover as they have been working hard in the past few years. As we enter the second quarter, we have not seen improvements in advertiser sentiment. Given the limited visibility for sentiment improvement, and paired with Tough Comp in Q2, our online marketing revenue should remain fundamentally solid, but from a growth perspective, soft over the next few quarters. Beyond the near-term challenges, we expect online marketing to remain a bread and butter business for Baidu for the foreseeable future. Search is one of the most popular apps in the internet age, and Baidu remains the largest search engine in China with close to 700 million MAUs. Search will likely be one of the killer apps in the age of journey. Technology innovation will enable us to better engage users with developers and merchants, directly connect users' intentions with the most relevant products and service offerings, in more natural ways. Yeah, thank you.
spk20: Your next question comes from Thomas Chong with Jefferies.
spk22: Hi, good evening. Thanks, management, for taking my question. My question is about how much more room do we see in cost-cutting? Previously, we mentioned there will be a lag between investment and AI revenue contributions. How should we look at margin trend in 2024 if you intend to expand your AI offerings? Thank you.
spk16: Hi, Thomas. Thank you so much for your question. I think macro challenges are still reigning on our marketing businesses. But we are confident that there still will be some ways for us to continue optimizing the operational efficiencies. We will stringently manage our costs for each business, and we will take some further steps as necessary, including we try to streamline the organization structures to enhance the agility and support strategy flexibility. We also will relocate the resources to prioritize the key strategy areas. If we take a look into our businesses, for mobile ecosystem, I think we can still manage the cost and expenses. So the multiple ecosystem group continue to remain as strongly profitable and cash flow positive. For AI Cloud, as I just mentioned, we continue to face all the low margin businesses and the products. So we can continue generating the operating profits and margins on a non-GAAP basis. If we look into the longer term, the normalized margin for the general AI-related cloud businesses should be higher than the legacy cloud businesses. For other businesses, we aim to reduce our loss, same as what we talked about in the past report. Particularly, our intelligent driving businesses with more operational efficiency gains and the UI improvements for Robotaxi. And many investors ask me how our investments in early will impact my margins. In fact, our investments are mainly related to CapEx for modern training and inference. In 2023, we have made the large purchase which has arrived in batch time at different times and the different depreciation start dates. While the annualized depreciation expenses will be available in the whole year 2024, the impact on our overall cost expenses and quarterly earnings is quite predictable and manageable. The depreciation expenses are booked under the IND expenses for computing power use training, and the cost of revenue for computing power will be used for modern inference and business expansion. In the first quarter we can see that by the cost of non-GAAP IND and the cost of revenue both increased very slightly. While non-GAAP operating margin actually went expanding to 23.5% due to our discipline spending on other items such as GMA. We believe that the chips we have in hand are sufficient to support the trainings of earning for the next one or two years. And because of the limited Availability of high-performance chips in China in this year, 2024, we expect that our CapEx to be smaller versus last year. In conclusion, the investments in GenAI and large-language models will have a manageable impact on the near-term margins. And with our monetization of earnings already taking off, we expect that more and more revenue and profit will be generated from that kind of businesses. for both mobile ecosystem and AI cloud. Overall, we believe that high-quality growth and investment should be well-balanced. For many years now, we have delivered a solid track record of the top-line growth with the very resolute cost disciplines, and we intend to continue building our futures on this kind of basis. Thank you so much.
spk20: Your next question comes from Zoe Wang with UBS.
spk08: Good evening. Thanks, Benjamin, for taking that question. My question is about shareholder return. So how about we think about the execution pace of current $5 billion buyback program. In addition to current buyback program, should we expect other diversified approach to improve shareholder return? Thank you.
spk16: Yeah, thanks so much for questions. And I think we highly value our shareholders, and we have been making efforts to increase the shareholder returns. We have consistently repurchased our shares from the market over the past four years, averaging around $1 billion annually. In total, we have allocated around 37% of our free cash flow towards the share buyback programs. I think during these periods and going forward, we will continue to buy back more shares from the market as we believe in our long-term growth opportunities. and we are very committed to the shareholder returns. In addition, we have utilized our current share repurchase program to prevent any significant increase in the total outstanding shares count, aiming to reduce the potential dilution of our shareholders' economies stake in Baidu. In the year 2023, we can see that our total number of shares outstanding was flat year over year, compared to 1.2% increase in the year 2022 and a 3.2% increase in the year 2021. In this quarter, the total shares outstanding began to decrease. We can see that it has been declining by 0.5% as compared to the prior quarters. We are adopting a strategy of sustainable and recurrent share buyback programs from open market. And at the same time, we take into consideration the opportunities ahead of us. Now we are facing a huge opportunity in general AI foundation models, and we have structured a concrete plan to capitalize it. So we want to have the flexibility to invest as we consider necessary and in the best interest of long-term value to the shareholders. Furthermore, we believe that the most effective way to create values for shareholders is by building the strong business fundamentals. Our core marketing business remains steady, and we believe that AI will help us to build another growth engine. Thank you so much for your question.
spk20: Your next question comes from Miranda Zong with B of A Securities.
spk11: Thank you. Good evening, management. Thanks for taking my question.
spk13: My question is about RoboTaxi. So can management share more updates on the RoboTaxi initiatives and the geographical coverage for this year? And I think previously you mentioned that ApolloGo will achieve operating UE breakeven in Wuhan in near future. Want to understand what's the logic behind the efforts to continue to improve the UE What's the projected size of the vehicle fleet for this year? And how will we potentially impact the cost? Thanks.
spk03: Sure, Miranda. Let me give you some more color on the robot taxi business. In 2023, ApolloGo made significant progress in improving the regional unit economics in key cities. Let me use Wuhan, ApolloGo's largest operation, to explain how we achieved that. We kicked off ApolloGo's commercial operations in Wuhan back in 2022. Since then, we've witnessed a consistent enhancement in operational UE, which can be attributed to the expanding scale of driverless operations and decreasing costs per vehicle. Regarding scale expansion, our vehicle fleet has been steadily growing. Compared to one year ago, our fully driverless fleet in Wuhan has grown threefold, reaching about 300 vehicles today. Meanwhile, both the operational area and service hours for fully driverless operation have been consistently expanding. thanks to the increasing recognition of our autonomous driving technology by the local government. The operational coverage area for fully driverless ride-hailing service increased by eight-fold from a year ago, now covering over 7 million people in Wuhan. ApolloGo's operational hours also gradually expanded from only the off-peak hours in the beginning to adding peak hours to its operation, and eventually extending to 24 by 7 in March of this year. The scale expansion resulted in a consistent improvement of UE in terms of revenue. Both daily rides per vehicle and distance per ride have been growing. When it comes to cost, the majority is labor cost and hardware expenditures. We have been demonstrating consistent track record of safe operations, which help us to increase the deployment of fully driverless ride-hailing operation. In April, the proportion of fully driverless orders rose to 70% That's up from only 10% in August 2022 and 45% Q4 of last year. We expect this figure to reach 100% in the coming quarters, thereby enabling us to minimize the cost related to safety officers. In addition to lowering the labor cost, we are steadfast in driving down hardware costs. The mass production timeline of RT6, our sixth generation robot taxi, remains on track. Adopting a battery swapping solution, the mass production price for RT6, excluding battery, is below 30,000 US dollars. We will use RT6 as the primary vehicle in the future fleet expansion. and it should help to significantly reduce the hardware depreciation cost for each vehicle and further improving our UE and bringing us closer to profitability. Looking into this quarter, we plan to expand the fully driverless fleet of our Wuhan operation to 1,000 vehicles by the end of the year. more than tripling from the end of last year. Our focus remains on improving regional UEE and narrow the losses for ApolloGo bins. With continued improvement in operational efficiency and cost reduction, we believe ApolloGo will achieve operational UEE breakeven in Wuhan first. And once that's achieved, we can scale up operation quickly. Thank you.
spk20: Ladies and gentlemen, that does conclude our conference for today. Thank you for participating. You may now disconnect. you Thank you. you Thank you. 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, Jo Lynn, Beidou's Director of Investor Relations.
spk07: 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, Gong Luo, our CFO, and Dosheng, our EVP, in charge of Baidu AI Cloud Group, 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 the IR website. I will now turn the call to our CEO, Robin.
spk03: 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 earning, 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. NIO, 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, Sawtooth, Zhaoping, Soyebank, 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 GenAI 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 GenAI and foundation will continue to rise as customer adoption improves. For instance, within our internal cloud revenue, Baidu cores other business groups such as mobile ecosystem groups and intelligent driving groups are increasingly leveraging the power of earning. 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, 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 continued 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. These efforts 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 this early. 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 GenVI 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 slides surpassed 6 million. We are continuing to move forward, move towards achieving unit economics breakeven for APLOGO. To make this happen, our strategy is to reach UE breakeven in key regions and then replicate the success in other regions. To reach 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 goals reached 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 past 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. Thank you, Robin.
spk16: Now let me walk through the details of our first quarter financial results. Total revenues were RMB 31.5 billion, increasing 1% year-over-year. Revenue from Baidu Core was RMB 23.8 billion, increasing 4% year-over-year. Vital cost online marketing revenue was RMB 17.17 billion, increasing 3% year-over-year. Vital cost non-online marketing revenue was RMB 6.8 billion, up 6% year-over-year, mainly driven by the AI cloud business. Revenue for IT was RMB 7.9 billion, decreasing 5% year-over-year. Cost of revenue was RMB 15.3 billion, increasing 1% year-over-year, primarily due to an increase in traffic acquisition costs and the causes related to AI cloud-based, partially offset by the decrease in content causes. Operating expenses were RMB 10.7 billion, decreasing 2% year-over-year, primarily due to a decrease in personnel-related expenses and other R&D expenditures, partially offset by the increase in server depreciation expenses and server custody fees, which support the general AI research and development inputs. Vital cost operating expenses were RMB 9.4 billion, decreasing 1% year over year. Vital cost SG&A expenses were RMB 4.5 billion, decreasing 1% year over year. SG&A accounting for 19% one night of vital cost revenue in this quarter, compared to 20% in the same period last year. Vital cost R&D expenses were RMB 4.9 billion, decreasing 1% year over year. IMD accounting for 21% of Baidu Core's revenue in this quarter, compared to 22% in the same period last year. Operating income was RMB 5.5 billion. Baidu Core's operating income was RMB 4.5 billion. And Baidu Core's operating margin was 19%, one nine. And Nungat operating income was RMB 6.7 billion. Nungat Baidu Core operating income was RMB 5.6 billion. And Nungat Baidu Core operating margin was 23.5%. Total other income net was RMB 2 billion, decreasing 52% year over year, primarily due to a decrease in fair value gain from long-term investments, partially offset by the increase in net foreign exchange gain. Income tax expenses was RMB 883 million, compared to RMB 1.2 billion in the same period last year. Net income attributable to Baidu was RMB 5.4 billion, And diluted earnings per ADS were RMB 14.9. Net income attributable to Baidu Core was RMB 5.2 billion. And net margin for Baidu Core was 2%. Now again, net income attributable to Baidu was RMB 7 billion. Now again, diluted earnings per ADS were RMB 19.91. Now again, net income attributable to Baidu Core was RMB 6.6 billion. and non-GAAP net margin for Baidu Core was 28%. As of March 31, 2024, cash, cash equivalents, restricted cash, and shorting investments were RMB $191.8 billion, and cash, cash equivalents, restricted cash, and shorting investments, excluding ITE, were RMB $185.8 billion. Free cash flow was RMB 4.2 billion. Free cash flow excluding IT was RMB 3.3 billion. Finally, Baidu Call had approximately 34,000 employees as of 31st, 2024. With that, operator, let's now open the call to questions.
spk20: Ladies and gentlemen, we will now begin the question and answer session. If you wish to ask a question, please press star 1 on your telephone and wait for your name to be announced. If you wish to cancel your request, please press star 2. Your first question comes from Alicia Yap with Citigroup.
spk10: Hi, thank you. Good evening, Robin, Rong, and also, sorry. So thank you for taking my questions. I wanted to ask, would you be able to quantify the AI technology you've been helping Baidu with? to improve ad monetization rate? Can management share some feedback on those advertisers who have adopted the system? In what kind of areas do they see the largest improvement and are there any areas that can be further enhanced?
spk09: Thank you.
spk03: Hi Alicia, this is Robin. As you know, our monetization system was the first to benefit from GenAI, generating several hundred million RMB per quarter in incremental revenue. Since the second half of last year, we have been utilizing Ernie to upgrade our monetization system, enhancing various aspects of ad technology. This includes improving adding capabilities, refining the auction system, automating creative generation and ad strategy formation for advertisers. Advertisers have been saying better conversion and more sales leads. This improvement has motivated advertisers to increase their spending with Baidu. So in the first quarter, AI-related incremental advertising revenue grow on a quarter-over-quarter basis and we expect this trend to continue. The incremental revenue has helped us mitigate the broader macro weakness and also bought us some time to reconstruct our user product with earnings. As I emphasized in my opening remarks, We believe Ernie offers a significant long-term opportunity for our online marketing business. Agents function as virtual or domains. Advertisers can establish their online presence and interact with potential customers using natural language through multi-bound conversations. With the introduction of Agent Builder, advertisers can easily create customized earning agents. When advertisers express their intentions to this agent, they can more effectively achieve their goals, whether it's helping potential customers understand their products or improving customer service quality. can also help enrich our content offering and improve user experience on Baidu. While still in its early stage, early agents have helped some advertisers achieve better ROIs. For example, we have a customer in online education. They used Agent Builder to create its AI agent. injecting it with key insights like product introduction and subject matter expertise while continually providing feedback for refinement. This agent has significantly enhanced the company's online customer service by offering round-the-clock, high-quality consultations. The adoption of Earning Agent lead to a 20% increase in ad conversion rate for this online education company. I think this is just the beginning. We believe agent will be a major form of content and services in the new AI era. We will continue to improve the capabilities of Earning Agent. Agents will not only elevate user experience, boost conversion and ROI for advertisers, but also over time foster increase in transactions directly generated on our platform. This shift should help us to transform our traditional CPC model into a more efficient CPS model.
spk20: Your next question comes from Gary Yu with Morgan Stanley.
spk23: Hi. Thank you, management, for the opportunity to ask a question. I have a question regarding AI cloud business. How has the price cut initiative by some of your peer companies affected by AI cloud business? And how should we think about the cloud revenue profitability as competition heats up? And also, how should we... what should be a sustainable level of cloud growth outlook going forward. Thank you.
spk15: Gary, this is Do. As we have already mentioned, Genie AI and its foundation models are transforming the cloud industry from general-purpose computing to AI computing. So this shift is reshaping the competitive landscape within the cloud industry. presenting us with the valuable opportunity to establish ourselves as a leader in the AI cloud. So we believe we offer China's most efficient AI infrastructure and the most advanced mass platform for model training and inference. As a result, more and more enterprises are choosing us for model training, fine tuning, and AI native app development on our public cloud. This increasing demand has significantly boosted our AI cloud revenue. Actually, since the second quarter, the second half of last year, so our AI cloud revenue growth has started to accelerate. from year-over-year decline in the third quarter to an 11% increase in the fourth quarter last year, and then further accelerated to 12% in the first quarter of this year. The revenue acceleration was supported by two main factors. The incremental revenue directly generated by Gene AI and foundation models, and also the new opportunities they brought to our legacy cloud business. So as Robin just mentioned, in the first quarter of this year, revenue from Gene AI and foundation models accounts for 6.9 of our AI cloud revenue. And then our traditional CPU cloud businesses are capitalizing on the opportunities presented by Gene AI and Foundation Models. Both factors are important growth drivers of our AI cloud. On a separate note, while smart transportation business remained subdued, its impact on overall AI cloud business in Q1 was substantially smaller than the previous quarters. So overall, we expect our AI Cloud to continue benefiting from the MacTorrent, maintaining strong revenue growth momentum in the upcoming quarters. On the profit side, Baidu AI Cloud continues to generate non-GAAP operating profit, as we have seen in previous quarters. We are committed to sustainable and health revenue growth. During the quarter, we maintain our focus on achieving high-quality revenue growth by scaling down low-margin business. Regarding the business of GeneAI and foundation models, the market is still at a very early stage, so our focus remains on educating the market and broadening our footprint across more enterprises. Looking into the long run, So we expect the normalized margin for GNI and foundation models related to business to improve further and to be higher than our traditional cloud business. Regarding the change of pricing policies of some competitors you just mentioned, it is actually pretty common for cloud vendors to adjust their pricing for certain products. This is a trend we have observed multiple times in the past. Given that our cloud offerings has expanded beyond the traditional CPU cloud to a high value AI products and services, the industry changes on CPU cloud pricing has a minimum impact on the development of our AI cloud. Actually, for cloud platform services, by leveraging our unique Proprietary four-layer AI architecture and our strong ability in the end-to-end optimization, we have lowered Ernie's inference cost to only 1% after the variant in March last year. This May, Ernie handles about 200 million API calls, or approximately 250 billion tokens daily. We are confident that the earnings expanding adoption will continue to enhance its performance, boosting efficiency, and then further reduce costs. It's also important to evaluate price and performance ratio for different models at different workloads, rather than just focusing solely on some superficial prices. So we believe our state-of-the-art AI infrastructure and advanced mass platform delivers the best value to our customers. Looking forward, leveraging our strong AI capabilities, we aim to continuously attract new customers and encouraging existing ones to increase their spending on Baidu AI Cloud. At the same time, we aim to consistently generate positive non-GAAP operating profit. Thank you, Gary.
spk20: Your next question comes from Alex Yao with JP Morgan.
spk04: Hi, good evening, management, and thank you for taking my question. Given the chief shortage in China, how does Baidu maintain its commitment to deliberately differentiate while enhancing the leading advantage in LLM technology in China? Thank you.
spk03: Hi, Alex. We do think very differently in this aspect. We're taking an application-driven approach for our AI push. For example, solving all the high school math problems using the most advanced LLM may not be the highest priority, at this time, while generating convincing reasons for users to buy the right products is more important in a lot of applications. So with that in mind, we are taking advantage of our unique four-layer AI tech stack to optimize the cost and performance of early models. And we make sure customers and developers can easily build applications using tools like Agent Builder, App Builder, and Model Builder. For the AI infrastructure layer, a key factor contributing to our high efficiency in model training and inference is our superior capability in GPU cluster management. We have recently made a breakthrough by integrating GPUs from different vendors into one large-scale unified computing cluster, allowing us to use less advanced chips for highly effective model training and inference. Our deep learning framework, PedoPedo, has, through continuous innovation and enhancement, helped reduce the cost of model training and inferencing on a constant basis. PedoPedo is compatible with over 50 different chips, many domestically designed, and its developer community has grown to 13 million. With Ernie 3.5 and Ernie 4.0, we aim to be the flagship models for sophisticated tasks, we're making earning more accessible and affordable by launching lightweight LLMs, introducing toolkits for model development and app development, applying MOE approach for model inference for better performance and lower cost. With our application-driven mindset, We have used Ernie extensively to renovate our own products. And we have gained experience and insights in training and using Ernie, as well as developing AI-native applications on it. We're making all these capabilities available to our customers and developers. With all these efforts, we are fostering a vibrant and healthy ecosystem around Ernie. You can see that we are actually taking a holistic approach to developing Gen AI and LLM, which is very different from some of our competitors. Our reserve and access to the chips on the market should be sufficient for us to support millions of AI applications in the future. And in the long run, I think China will form an ecosystem of its own. probably with less powerful chips, but most efficient homegrown software stack. There's the ample room for innovation in the application layer, model layer, and framework layer. With our self-developed four-layer AI infrastructure, as well as our strong R&D team, our dedication to AI, and our application-driven approach, in building an ecosystem around Ernie, I'm quite confident that Baidu will stand as a leader in China's AI ecosystem in the long term. Thank you.
spk20: Our next question comes from Lincoln Kong with Goldman Sachs.
spk05: Thank you, management, for taking my question. So my question is about the advertising business. So what's the major drag for the advertising growth if we compare it with our peers? So what are the trends we have seen for advertising budgets and advertiser sentiment, especially into second quarter, April and May?
spk06: So how should we think about our normalized advertising growth for 2024? Thank you.
spk03: Yeah, our online marketing revenue grew by 3% year-over-year in the first quarter. While traditional search is maturing, we are working hard to renovate the user experience with Change.io. Right now, about 11% of our search results pages are filled with generated results. These results provide more accurate, better organized, and direct answers to users' questions, and in some cases, enable users to do things they could not do before. We have not started the monetization of those Gen AI results, so it will take some time for revenue to catch up. And the weak macro also contributed to the softness of our ad business. Our advertisers come from a wide range of industries, with the majority being SMEs. This takes our advertising revenue highly sensitive to the macro environment, particularly to the offline economy. In the first quarter, advertisers sentiment in some verticals such as real estate and franchising remained weak. Specifically in the real estate industry, not only was ad spending from developers and agencies muted. But the impact also extended to both upstream and downstream sectors. For example, energy, chemicals, machinery, building materials for the upstream and home renovation, furniture, this kind of downstream also constrained at spending on our platform. Also, many SMEs in offline sectors need more time to recover as they have been working hard in the past few years. As we enter the second quarter, we have not seen improvements in advertiser sentiment. Given the limited visibility for sentiment improvement, and paired with Tough Comp in Q2, our online marketing revenue should remain fundamentally solid, but from a growth perspective, soft, over the next few quarters. Beyond the near-term challenges, we expect online marketing to remain a bread-butter for Baidu for the foreseeable future. Search is one of the most popular apps in the internet age, and Baidu remains the largest search engine in China with close to 700 million MAUs. Search will likely be one of the killer apps in the age of journey. Technology innovation will enable us to better engage users with developers and merchants, directly connect users' intentions with the most relevant product and service offering, in more natural ways. Yeah, thank you.
spk20: Your next question comes from Thomas Chong with Jefferies.
spk22: Hi, good evening. Thanks, management, for taking my question. My question is about how much more room do we see in cost-cutting? Previously, we mentioned there will be a lag between investment and AI revenue contributions. How should we look at margin trend in 2024 if you intend to expand your AI offerings? Thank you.
spk16: Hi, Thomas. Thank you so much for your question. I think macro challenges are still reigning on our marketing businesses. We are confident that there still will be some ways for us to continue optimizing the operational efficiencies. We will stringently manage our cost expenses for each business, and we will take some further steps as necessary, including we try to streamline the organization structures to enhance the agility and support strategy flexibility. We also will relocate the resources to prioritize the key strategy areas. If we take a look into our businesses, for mobile ecosystem, I think we can still manage the cost and expenses. So the multiple ecosystem group continue to remain as strongly profitable and cash flow positive. For AI Cloud, as I just mentioned, we continue to face all the low margin businesses and the products. So we can continue generating the operating profits and margins on a non-GAAP basis. If we look into the longer term, the normalized margin for the general AI-related cloud businesses should be higher than the legacy cloud businesses. For other businesses, we aim to reduce our loss, same as what we talked about in the past few quarters, particularly our intelligent driving businesses with more operational efficiency gains and the UI improvements for Robotaxi. And many investors ask me how our investments in earnings will impact my margins. In fact, our investments are mainly related to CapEx for modern training and inference. In 2023, we have made the large purchase, which has arrived in batches at different times and the different depreciation start dates. While the annualized depreciation expenses will be available in the whole year 2024, the impact on our overall cost expenses and quarterly earnings is quite predictable and manageable. The depreciation expenses are booked under the IND expenses for computing power use training, and the cost of revenue for computing power will be used for modeling inference and business expansion. In the first quarter we can see that by the cost of non-GAAP IND and the cost of revenue both increased very slightly. While non-GAAP operating margin actually went expanding to 23.5% due to our discipline spending on other items such as GMA. We believe that the chips we have in hand are sufficient to support the trainings of earning for the next one or two years. And because of the limited Availability of high-performance chips in China in this year, 2024, we expect that our CapEx to be smaller versus last year. In conclusion, the investments in GenAI and large-leverage models will have a manageable impact on the near-term margins. And with our monetization of earnings already taking off, we expect that more and more revenue and profit will be generated from that kind of businesses. for both mobile ecosystem and AI cloud. Overall, we believe that high-quality growth and investment should be well-balanced. For many years now, we have delivered a solid track record of the top-line growth with the very resolute cost disciplines, and we intend to continue building our futures on this kind of basis. Thank you so much.
spk20: Your next question comes from Zoe Wang with UBS.
spk08: Good evening. Thanks, Benjamin, for taking that question. My question is about shareholder return. So how about we think about the execution pace of current $5 billion buyback program. In addition to current buyback program, should we expect other diversified approach to improve shareholder return? Thank you.
spk16: Yeah, thanks so much for questions. And I think we highly value our shareholders, and we have been making efforts to increase the shareholder returns. We have consistently repurchased our shares from the market over the past four years, averaging around $1 billion annually. In total, we have allocated around 37% of our free cash flow towards the share buyback programs. I think during these periods and going forward, we will continue to buy back more shares from the market as we believe in our long-term growth opportunities. and we are very committed to the shareholder returns. In addition, we have utilized our current share repurchase program to prevent any significant increase in the total outstanding shares count, aiming to reduce the potential dilution of our shareholders' economies stake in Baidu. In the year 2023, you can see that our total number of shares outstanding was flat year over year, compared to 1.2% increase in the year 2022 and a 3.2% increase in the year 2021. In this quarter, the total shares outstanding began to decrease. We can see that it has been declining by 0.5% as compared to the prior quarters. We are adopting a strategy of sustainable and recurrent share buyback programs from open market. And at the same time, we take into consideration the opportunities ahead of us. Now we are facing a huge opportunity in AI foundation models, and we have structured a concrete plan to capitalize it. So we want to have the flexibility to invest as we consider necessary and in the best interest of long-term value to the shareholders. Furthermore, we believe that the most effective way to create value for shareholders is by building strong business fundamentals. Our core marketing business remains steady, and we believe that AI will help us to build another growth engine. Thank you so much for your question.
spk20: Your next question comes from Miranda Zong with B of A Securities.
spk11: Thank you. Good evening, management. Thanks for taking my question.
spk13: My question is about RoboTaxi. So can management share more updates on the RoboTaxi initiatives and the geographical coverage for this year? And I think previously you mentioned that ApolloGo will achieve operating UE breakeven in Wuhan in near future. Want to understand what's the logic behind the efforts to continue to improve the UE What's the projected size of the vehicle fleet for this year? And how will we potentially impact the cost? Thanks.
spk03: Sure, Miranda. Let me give you some more color on the robot taxi business. In 2023, ApolloGo made significant progress in improving the regional unit economics in key cities. Let me use Wuhan, ApolloGo's largest operation, to explain how we achieved that. We kicked off ApolloGo's commercial operations in Wuhan back in 2022. Since then, we've witnessed a consistent enhancement in operational UE, which can be attributed to the expanding scale of driverless operation and decreasing costs per vehicle. Regarding scale expansion, our vehicle fleet has been steadily growing. Compared to one year ago, our fully driverless fleet in Wuhan has grown threefold, reaching about 300 vehicles today. Meanwhile, both the operational area and service hours for fully driverless operation have been consistently expanding. thanks to the increasing recognition of our autonomous driving technology by the local government. The operational coverage area for fully driverless ride-hailing service increased by 8-fold from a year ago, now covering over 7 million people in Wuhan. ApolloGo's operational hours also gradually expanded from only the off-peak hours in the beginning to adding peak hours to its operation, and eventually extending to 24 by 7 in March of this year. The scale expansion resulted in a consistent improvement of UE in terms of revenue. Both daily rides per vehicle and distance per ride have been growing. When it comes to cost, the majority is labor cost and hardware expenditures. We have been demonstrating consistent track record of safe operations, which help us to increase the deployment of fully driverless ride-hailing operation. In April, the proportion of fully driverless orders rose to 70% That's up from only 10% in August 2022 and 45% Q4 of last year. We expect this figure to reach 100% in the coming quarters, thereby enabling us to minimize the cost related to safety officers. In addition to lowering the labor cost, we are steadfast in driving down hardware costs. The mass production timeline of RT6, our sixth generation robot taxi, remains on track. Adopting a battery swapping solution, the mass production price for RT6, excluding battery, is below 30,000 US dollars. We will use RT6 as the primary vehicle in the future fleet expansion. and it should help to significantly reduce the hardware depreciation cost for each vehicle and further improving our UE and bring us closer to profitability. Looking into this quarter, we plan to expand the fleet of our Wuhan operation to 1,000 vehicles by the end of the year. more than tripling from the end of last year. Our focus remains on improving regional UEE and narrow the losses for ApolloGo bins. With continued improvement in operational efficiency and cost reduction, we believe ApolloGo will achieve operational UEE breakeven in Wuhan first. And once that's achieved, we can scale up the operation quickly. Thank you.
spk20: Ladies and gentlemen, that does conclude our conference for today. Thank you for participating. You may now disconnect.
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