Baidu, Inc. Q3 FY2023 Earnings Call

Hello and thank you for standing by for Baidu’s Third Quarter 2023 Earnings Conference Call. 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, Juan Lin, Baidu’s Director of Investor Relations.

Hello, everyone and welcome to Baidu’s third quarter 2023 earnings conference call. Baidu’s earnings release was distributed earlier today and you can find a copy on our website as well as on newswire services. On the call today, we have Robin Li, our Co-Founder and CEO; Rong Luo, our CFO; and Dou Shen, 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 provision of the U.S. Private Securities 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 SEC and Hong Kong Stock Exchange. Baidu does not undertake any obligation to update any forward-looking statements, except as required under auditable law. Our earnings press release and this call include discussions of certain unaudited non-GAAP financial measures. Our press release contains a reconciliation of unaudited non-GAAP measures to the unaudited most directly comparable GAAP measures 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 Baidu’s IR website. I will now turn the call over to our CEO, Robin.

Hello, everyone. Baidu Core delivered solid revenues, profit and cash flow in Q3 despite navigating a challenging macro climate for both our online marketing business and AI cloud business. I am proud that our team managed to strengthen operational efficiency and maintain stable margins through a full-scale reinvention of our product portfolio with ERNIE and ERNIE Bot. Today, I would like to share an update on the new opportunities that ERNIE and ERNIE Bot have opened up for us. After that, I will discuss some key highlights of each of our businesses. Presently, we are in the midst of a broad-based platform shift driven by generative AI and foundation models that is set to revolutionize every industry. On August 31, we received the approval to deploy ERNIE Bot on a large scale and open ERNIE API to enterprise customers. Since then, we have witnessed a significant increase in queries handled by ERNIE Bot and through ERNIE API. Moreover, we have received valuable feedback from these users and customers enabling us to further refine our model’s performance. At Baidu World in October, we showcased our progress in ERNIE Bot and AI native products. During that event, we introduced ERNIE 4.0 or EB4, our most advanced foundation model. We believe that EB4 is a GPT-4 level model, displaying human-level performance in understanding content generation, complex reasoning and memory retention. These capabilities are crucial for developing AI native applications and solutions. We are pleased to launch EB4 earlier than our expectations. It resulted from our unique end-to-end 4-layer AI infrastructure, which helped us achieve efficiencies in model training. The input and feedback from our users and customers also played a significant role. In products, as I said in the past, we continue to use ERNIE to reinvent our entire portfolio and introduce an AI-native experience. On the customer front, the ERNIE Bot enables Baidu search by generating direct answers to search queries, complementing traditional search. In the quarter, we initiated tests on our new features that recommend news feed-like information together with the generated search results and enable multi-round conversations to encourage further user expression. Our initial tests have received promising feedback. We believe these features will help deepen user engagement and prolong time spent, unleashing new monetization opportunities. In particular, they benefit sectors such as healthcare, education, travel, legal and auto, where advertisers are willing to invest heavily in customer acquisition and reengagement. ERNIE Bot, our new AI native product serves as a versatile multi-brand conversational AI assistant on both desktop and mobile. Given the exceptional performance of our large language model, we are confident in monetizing our services. Starting from November 1, EB4 was open to the public through ERNIE Bot at a subscription fee of about $8 per month. This marks us as the first company in China to implement user charges, distinguishing us from other models in the market. Our primary focus is to encourage seamless collaboration between users and AI co-pilots, which we believe is a key trend in the new era. For example, with an AI co-pilot, Baidu Wenku has transformed into a one-stop shop for various document creation needs. We have already seen an increase in paying user accounts, a trend that we expect to continue in the coming quarters. On the enterprise-facing product front, we recently introduced GBI, generated business intelligence with ERNIE Bot. GBI simplifies data analysis using natural language interaction, facilitating faster decision-making for business operations. The introduction of GBI was prompted by the recognition that customers across various industries need AI co-pilots to help them analyze data more efficiently. During our last earnings call, we also discussed how we use ERNIE Bot to create Baidu Comate, our AI coding assistant, and Infoflow, our enterprise communication and collaboration platform. These products focus on boosting productivity and efficiency gains, presenting upsell opportunities for our cloud customers. An increasing number of our cloud customers in China's traditional industry and public sector have used the trial version and shown interest in these products and features. Additionally, these products and features allow us to acquire new customers across even wider industries. In terms of ecosystem, we empower enterprises to leverage ERNIE through API to create their own AI native applications and solutions that will drive the development of generative AI and LLM. As more successful AI native applications built on top of ERNIE emerge, whether developed by us or by our customers, ERNIE will likewise be successful. Now, over 10,000 enterprises are actively using ERNIE through API on a monthly basis, a number that has been growing quickly since we received regulatory approval at the end of August. Currently, ERNIE is handling tens of millions of queries every day. Right now, a large and rising number of these queries come from the Baidu family of products as we have been pioneers in building AI native products and have invested significantly in reinventing our offerings. In the first half of November, the number of daily external queries increased by over 50% compared to the same period in October. As we assist our cloud customers in creating AI native applications, we believe there will be continuous and significant growth in external credits in the future. We are also actively attracting developers to connect their information and services to ERNIE Bot through plug-ins. With plug-ins, ERNIE can help users with more tasks, unlocking a wide range of possible use cases. As of today, various plug-ins have already been accessible to ERNIE. The initial batch of third-party plug-ins includes ctrip.com, CITIC Press Group, China Justice Big Data Institute, New Oriental, Autohome, and Tree Mind. In summary, during the quarter, we made significant progress in using Generative AI to revolutionize product usage and transform business operations for our users and customers. We believe that this is just the beginning. In the future, we will realign resources to invest in this growth opportunity and shift away from lower priority efforts to improve efficiency for existing businesses, thus balancing investment and margins. We are excited about the possibilities for Baidu for our users, customers, partners, and the entire ecosystem. Now let me recap the key highlights of each of our businesses. The mobile ecosystem continued to exhibit steady growth for both user metrics and financial performance in the quarter, Baidu Apps MAUs increased by 5% year-over-year to RMB663 million in September. Search queries and content distributed by Baidu App remained resilient. In particular, videos distributed by search and feed within Baidu App both experienced double-digit growth in the third quarter. Baidu Core’s online marketing revenue increased by 5% year-over-year in the third quarter, consistently generating strong profit and cash flow for the group. This growth was driven by the continuous recovery in verticals such as healthcare and travel, among others. In the quarter, we continued to use Generative AI to help advertisers increase ROI and conversion on our platform. Starting from September, advertisers can engage with our new marketing platform. This platform supports natural language input and multi-round conversations, which help advertisers articulate their requirements more comprehensively, enabling us to formulate more effective campaign strategies for investment. Moreover, we made ongoing enhancements to our monetization system, focusing on improving targeting capabilities and the auction system. For example, Tarena Education and IT Professional Education Company achieved an increase of 23.3% in conversion rate and a 22.7% boost in ROI after using this enhanced platform and capabilities. We are still in the early stages of using Generative AI to help advertisers achieve higher conversions and ROIs on our platform. Our efforts will ultimately lead to significant improvement in marketing capabilities and contribute to future revenue growth. In the quarter, we also used our AI capabilities to help merchants grow their sales on Baidu. One example is how we help SMEs with live stream shopping. With ERNIE Bot, we introduced a tool that allows them to easily create their own digital human, generate live scripts and significantly lower the barrier and cost for live streamers to sell merchandise on Baidu. Looking forward, we are optimistic that the growth of our online marketing revenue will continue to exceed China’s GDP growth. At the same time, we will continue to test AI native marketing products that could potentially open up more opportunities than traditional general search ads. This gives us confidence in Baidu’s long-term online marketing growth prospects. Turning to AI cloud, we continue to generate positive operating profit on a non-GAAP basis in the quarter as we remained focused on the health of our business. Generative AI and LLM have brought us a lot of opportunities, strengthening our competitive advantages in the cloud and increasing our total addressable market. A growing number of enterprises are using ERNIE API to develop their own AI native applications and solutions. We are also helping customers build their own models efficiently by leveraging our unique 4-layer AI infrastructure and our years of experience in building and using foundation models. LLM training is very complicated. It requires a large number of GPUs working simultaneously. ERNIE GPU failures can impact the entire process. We have developed ways to identify and address GPU failures quickly, leading to a significant reduction in training costs. Now about 98% of the training time on our platform is valid, setting an industry benchmark. We also have a set of different resources, including toolkits and data sets for enterprise customers to easily fine-tune their customized models. Generative AI has helped us grow our cloud customer base. A large number of cloud customers using ERNIE API are new customers. At the same time, some of our existing cloud customers have increased their spending with us because of generative AI. AI cloud revenues declined by 2% year-over-year in the third quarter, mainly due to weak demand in smart transportation projects. We believe AI cloud revenue should rebound to positive growth in the fourth quarter, driven by the increasing momentum in generative AI-related businesses. Additionally, since smart transportation revenue started to slow down in Q4 of last year, we will have an easier year-over-year comp base in Q4 this year. Moving on to intelligent driving, our target remains unchanged, which is to achieve breakeven on the regional unit economics for robotaxi operation in a couple of years before turning operationally profitable. To this end, we are strategically concentrating our resources on pivotal regions. Wuhan remains our largest operational area and we believe it is also the largest region globally providing autonomous driving services, currently covering a population of about 2.7 million. In the third quarter, the portion of fully driverless orders within the overall order portfolio in Wuhan exceeded 40%, up from 35% in Q2. We are also particularly pleased to highlight that Apollo Go’s operation in Wuhan continues to expand. In late August, Apollo Go was the first company in China to provide autonomous ride-hailing services to the general public at Wuhan Kinko International Airport, one of the busiest airports in Central China. The extended reach into the airport transfer involves longer travel distances, presenting an excellent opportunity for future improvement of unit economics. All of these developments contributed to the improvement in unit economics, and we aim to reach regional breakeven in a couple of years. In Q3, Apollo Go provided 821,000 rides to the public, marking a 73% increase year-over-year and the cumulative order volume has surpassed 4.1 million by the end of Q3. As part of our executive reshuffle program, we have recently named Dr. Wang Yunpeng as Corporate VP of Baidu, who will lead the Intelligent Driving Group. Yunpeng has been with us since 2012 and has been responsible for autonomous driving business since 2018. I take great pride in seeing another business leader developed within Baidu. Zhenyu has taken a rotational position as CEO Assistant and the Chairman of the Technology Ethics Committee. Now let’s proceed with Rong’s financial performance review.

Thank you, Robin. Now let me walk through the details of our third quarter financial results. Total revenue was RMB34.4 billion, increasing 6% year-over-year. Revenue from Baidu Core was RMB26.6 billion, increasing 5% year-over-year. Baidu Core’s online marketing revenue was RMB19.7 billion, increasing 5% year-over-year. Baidu Core’s revenue from IT was RMB8 billion, increasing 7% year-over-year. Cost of revenue was RMB16.3 billion, which remained essentially unchanged compared to the same period last year. Operating expenses were RMB11.9 billion, increasing 8% year-over-year primarily due to an increase in promotional marketing expenses, server depreciation expenses, and the server custody fee, which support ERNIE Bot repurchasing costs. Baidu’s operating expenses were RMB10.5 billion, increasing 10% year-over-year. Baidu Core SG&A expenses were RMB4.8 billion, increasing 14% year-over-year, accounting for 18% of Baidu’s Core revenue in the quarter compared to 17% in the same period last year. Baidu Core R&D expenses were RMB5.6 billion, increasing 7% year-over-year, accounting for 21% of Baidu Core revenue in the quarter, unchanged from the same period last year. Operating income was RMB6.3 billion. Baidu Core operating income was RMB5.5 billion, and Baidu Core operating margin was 21%. Non-GAAP operating income was RMB7.6 billion, non-GAAP Baidu Core operating income was RMB6.7 billion, and non-GAAP Baidu Core operating margin was 25%. Total other income net was RMB1.9 billion compared to the total other loss net of RMB4.8 billion for the same period last year, mainly due to the first recognition of RMB338 million gain versus RMB3.1 billion loss for the same period last year from a fair value change in loan investments, and a decrease in impairment of long-term investments by RMB1.4 billion. Income tax expenses were RMB1.3 billion, increasing 41% year-over-year, primarily due to an increase in profit before tax. Net income attributable to Baidu was RMB6.7 billion, and diluted earnings per ADS were RMB18.22. Net income attributable to Baidu Core was RMB6.4 billion, and net margin for Baidu Core was 24%. Non-GAAP net income attributable to Baidu was RMB7.3 billion, and non-GAAP diluted earnings per ADS were RMB20.40. Non-GAAP net income attributable to Baidu Core was RMB7 billion with a non-GAAP net margin for Baidu Core of 26%. As of September 30, 2023, cash, cash equivalents, restricted cash, and short-term investments were RMB202.7 billion, and cash, cash equivalents, restricted cash and short-term investments excluding IP were RMB197.4 billion. Free cash flow was RMB6 billion, and free cash flow excluding IP was RMB5.2 billion, with Baidu Core having approximately 35,000 employees as of September 30, 2023. With that, operator, let’s now open the call to questions.

Your first question comes from Alicia with Citi. Please go ahead.

Hello. Thank you. Good evening. Robin, Julius and management team. Thanks for taking my questions. My question is on advertising. It seems like Baidu ad revenue growth is tracking slower than some of the Internet peers, so besides macro issues, can management elaborate on any other reasons that contributed to the softer ad revenue growth? Looking into the fourth quarter, have you seen any demand picking up? What is the e-commerce sector contribution, and how will AI change the advertising outlook? Thank you.

Hi, Alicia, this is Robin. In Q3, apart from the macro weakness, online marketing revenue from e-commerce platforms was also relatively weak. Revenue from e-commerce platforms is one of our top revenue contributors, accounting for about 10% of our total online marketing revenue. Like many other Internet platform companies, we are building our own native e-commerce business. Revenue growth from our native e-commerce business is tracking very strong as we continue to improve the shopping experience on Baidu. I would like to highlight the strides we have made in our ad business through Generative AI. We are essentially restructuring the overall ad platform, including creative construction, ad targeting and bidding investments. These efforts have started to pay off, and the incremental revenue from these initiatives is expected to reach hundreds of millions RMB in the current quarter, which is Q4 of this year. Looking forward, we are optimistic that the growth of our online marketing revenue will continue to exceed China’s GDP growth. Thank you.

The next question comes from Alex Yao with JPMorgan. Please go ahead.

Thank you, management for taking my question. I have a few questions on cloud revenue. I believe Robin mentioned despite moderate revenue decline in Q4, the cloud revenue will return to positive territory in Q4. Should we expect the cloud revenue to further accelerate into the first half of 2024? With regard to the Smart City projects, are there any more projects that are still at risk? And more importantly, as you guys start to monetize the AI capability, when will AI contribute to the cloud revenue meaningfully? Lastly, any preliminary view on cloud revenue growth outlook for 2024? Thank you.

Hi, Alex, this is Doug. Thank you for your question. As we mentioned before, we have been focusing on improving the health of our business for sustainable development. As a result, we have achieved non-GAAP operating profits in the past few quarters. As Robin already mentioned, due to weak demand for intelligent transportation, the cloud revenue experienced a slight decline in Q3. While excluding small transportation, the rest of our AI cloud business showed pretty solid growth, and we believe AI cloud revenue will return to positive growth in the fourth quarter, with the trend continuing down the road. What is even more exciting is seeing new opportunities brought by Generative AI and large language models. Last quarter, we mentioned that more customers across various sectors came to us for model training, application development, and solution enhancement. Although the current revenue from Generative AI and LLM-related business is still very small, it’s growing very fast. We have seen more enterprises proactively adopting these new technologies for productivity and efficiency gains. Some of these customers, especially in the Internet, education, and tech sectors, have started to see efficiency gains through working with us. In Q4, we aim to leverage our leadership in generative AI and large language models to continuously attract new customers and encourage existing customers to increase their spending on our Baidu AI cloud. We believe this should lead to long-term revenue growth and continuous margin improvement. Thank you, Alex.

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

Thank you, good evening. Thanks, management for taking my question and congratulations on the results. My question is about ERNIE. Can management share with us the recent feedback for ERNIE 4.0 since the rollout last month? Any insights on the customer adoption of ERNIE 4? Also, how has feedback been after ERNIE Bot started to charge end-user subscription fees? Lastly, among the various opportunities you mentioned, which do you see becoming the biggest revenue driver? Thank you.

Hi, Miranda, let me answer your questions. Since the release of EB4 in mid-October, we have been receiving positive feedback from both users and customers. Many enterprises have reached out to test EB4 and have been impressed with its capabilities. EB4 has gained a reputation for advanced understanding and complex reasoning abilities. Compared to EB3 and other LMs in the market, we have noted that EB4 generates more structured and clearer responses and excels in coding. From November 1, we started charging enterprises and users for using EB4. We’ve seen a growing number of customers and users willing to pay for its use. We’re proud to be the first company to introduce a GPT-4 level model in China. EB4 further widens our lead over other LMs in the market, and we are the first LM to charge end-user fees that sets us apart from other peers. Regarding monetization opportunities, we see significant potential in AI native applications, either developed by Baidu or by our customers who leverage our AI capabilities. If you look at our own products, we see significant opportunities in the new search and the revamped ad platform. The new search complements traditional search. It can address complex questions that were previously unanswerable and enables users to conduct more personalized and in-depth research on various topics and projects. We will soon enable users to have multi-round conversations with us, which will create more potential on the commercial side as well. We are experimenting with a chatbot-type ad product for SMEs and brands. We believe this will not only help drive effective conversion but also allow us to eventually transform from a CPC model to a CPS model. Our ongoing efforts to revamp the ad platform have already shown positive results, and we will continue leveraging generative AI to assist our advertiser team in achieving durable ROI growth on Baidu. In terms of empowering our customers with Generative AI, as Doug just mentioned, customer needs are different now. Some customers still prefer to train their own model, but the GPU export restrictions will put a brake on that. It will become clear that training LLMs from scratch is difficult, especially when trying to achieve emergent abilities. We will encourage advanced customers to use ERNIE for application development. As customers become more adept at using LLMs for applications and more AI native applications powered by ERNIE become widely used, we should see continuous revenue growth through model inferencing. Over the long-term, inferencing should become a major source of revenue. Meanwhile, we will also help customers fine-tune our existing model offerings to suit their customized needs in each scenario because our models are better, faster, and more cost-effective. In summary, Generative AI and LLM will bring us massive business opportunities. We have already made good progress in commercialization thus far, and this is just the beginning of a promising future. Thank you.

The next question comes from Gary Yu with Morgan Stanley. Please go ahead.

Hi, thank you, management for the opportunity to ask questions. Can management share the latest advertiser feedback on the AI-powered ad system upgrade, and how do you think about the level of revenue boost to the core ads in 2024 as it gets rolled out to more and even all advertisers? Thank you.

Yes, we are very happy with the rapid AI transformation of our ad system and thrilled by the positive feedback from our advertisers. Overall, I think advertisers appreciate our efforts to help improve their ROIs on our platform. They are fond of our new features, which help them to be more productive. As I mentioned in the prepared remarks, we’ve put a lot of effort into using Generative AI to reinvent our ad system over the past few quarters. Now we have an integrated advertiser-facing marketing platform. Advertisers can use it to generate creative advertising materials that deliver higher conversion rates than materials created by humans. Our platform allows advertisers to engage in natural language interactions, enabling multi-round conversations, which enhances the understanding of their intentions. This allows us to create campaign strategies that deliver higher ROI and significantly reduces the time that ad managers need to spend creating campaigns. While we have a few thousand advertisers already migrated to our new platform, which is relatively small compared to our 0.5 million advertiser base, it is certainly growing fast. We continue to use AI to improve our bidding system and ad targeting capabilities. Such improvements may not be directly perceptible to advertisers, but they have observed improvements in their ad conversion and ROI. Advertisers using these capabilities probably achieved a high-single digit increase in conversions in Q3. Our efforts should attract advertisers to allocate more of their budgets to Baidu. As previously mentioned, online marketing revenue related to our upgraded ad platform is rapidly growing and has already become meaningful. This is just the beginning, and as we experiment with AI chatbots, it could serve as a replacement for landing pages in the future. This is particularly useful in verticals where users typically research and engage in a long decision process before purchasing. We believe this AI chatbot, combined with Generative AI-powered new search, will present more opportunities. The incremental revenue from these initiatives is expected to reach hundreds of millions RMB in the current quarter, Q4, and this trend is expected to continue strengthening in 2024. Thank you.

The next question comes from Wei Xiong with UBS. Please go ahead.

Good evening management and thank you for taking my question. I wanted to follow-up on your cloud business. So, could management share more color on how to think about the industry competitive landscape in the China cloud market, especially among the Internet cloud vendors as well as when competing with the telcos? With the development in generative AI and the large language model, how do we assess our competitive advantage against peers? Do we expect the competition to intensify next year as other companies try to catch up with us? Thank you.

Great question Wei Xiong. As you noticed, the traditional cloud business is slowing down, while generative AI and large language models are reshaping the competitive landscape of the cloud business industry. In the past, the focus in the cloud market was on basic services, and people were competing on pricing. But now, with the rise of generative AI and large language models, things are changing. There is growing interest among cloud customers coming to Baidu to utilize these sophisticated technologies to increase productivity and efficiency. They choose us not only for our advanced AI technology but also for our experience and track record in applying AI to help enterprises solve problems. Many customers are still in experimental stages, but we have firm belief in the capacity of new technology to rebuild their products and services, as seen in successful stories overseas. That is why new technology is increasing our market share and expanding our competitive edge. As Robin mentioned, EB4 is China’s first GPT-4 style model, and we shared the positive initial feedback we received for EB4. Our team is engaged in dialogues with clients, assisting them in understanding the technology and utilizing ERNIE to redevelop their existing products and create new ones. ERNIE has already helped us attract new customers and additional IT spending from existing customers. Regarding our advantage compared to other players in the market, our unique four-layer AI infrastructure provides flexibility to make adjustments or innovations at every layer to keep driving efficiency in both model training and inference. Additionally, our capability to develop GPU clusters for large language model training sets us apart. Our customers, including several leading Internet and tech companies, have increased their investments in our services. We will continue leveraging our unique AI architecture to drive efficiency gains, reducing costs in model training and inference on our cloud, giving us the flexibility to offer compelling prices and strengthen our market position. In terms of competition with telecom operators, our focus is on different market segments as we differentiate ourselves through our AI capabilities. Our strong AI capabilities and ERNIE are well-recognized and will set us apart from our peers. To sum up, we believe our strong AI capabilities, particularly in generative AI and large language models, will solidify our position as a market leader in cloud services.

The next question comes from Lincoln Kong with Goldman Sachs. Please go ahead.

Thank you, management for taking my question. So, my question is also about ERNIE. Given the successful upgrade of ERNIE 4.0, what would be the future strategy for model iteration to certify our tech leadership? Do we foresee any competition in the foundation model in the industry, either stabilizing or intensifying in the future? Thank you.

Hi Lincoln, the AI chips we have in hand allow us to launch EB4. We are ahead of the competition. To take our lead in LLMs to the next level, we will take an application-driven approach. We will allow AI native applications to dictate improvements in ERNIE Bot capabilities. Given the limited number of AI native apps in the market now, the majority of ERNIE API usage comes from internal apps, our internal apps like search apps, Wenku, etc. The rebuilding and restructuring of our existing products drive ERNIE innovation in the right direction. Additionally, we are assisting enterprises in using ERNIE to build their offerings. Over 10,000 enterprises currently use ERNIE through API on a monthly basis, which propels ERNIE’s improvement as well. We continue to improve our models for efficiency; for instance, compared to the version in March, inference costs of the current version have been reduced by 98%, resulting in a 50x increase in queries per second for the same amount of hardware activated. Continuous inference cost reduction has strengthened our model’s competitive advantage, giving us the flexibility to offer more compelling pricing. Looking at the long term, factors such as the scarcity of high-performance chips, high demand for data, AI talent, and the substantial upfront investments signal an upcoming consolidation stage in the industry. We anticipate fewer foundation models available in the market, and Baidu will certainly be one of them. As this phase of industry development progresses, more enterprises will begin to leverage advanced foundation models like ERNIE to create AI products rather than investing resources into building their large language models. We expect the number of native apps based on ERNIE to reach millions in the future.

The next question comes from James Lee with Mizuho. Please go ahead.

Great. Thanks for taking my questions. Can you quantify the investments related to AI and how that affects various cost items in your P&L? Should we expect these investments to accelerate over the next few quarters, especially with the launch of ERNIE 4.0, and potentially higher inference costs as more users utilize it? If we extrapolate that over the longer term, how should we consider Baidu core operating profit margin over the next few years, given all the shifting revenue dynamics, AI investments, and continued improvement in cloud profitability? Thanks.

Hi James. Let me take your questions. Currently, the primary investments for generative AI and large language models are concentrated around computing power, recorded as part of CapEx. In the past few quarters, we have allocated significant resources toward training our new ERNIE models. As more AI native applications powered by ERNIE are widely used, we will allocate more resources for those applications. However, all expenses related to AI investments are manageable since hardware depreciation is spread over a few years. For example, expenses linked to the computing power used in training ERNIE are recorded through depreciation expenses. Model inference costs are highly related to model usage, either internally or externally, and should support funding through future developments. We are pleased to see that our investments in generative AI and large language models are beginning to yield results, with growing revenues from earnings powered by both consumer and business services. While we are using generative AI and large language models to renovate our businesses, we are also paying attention to ensuring the solid performance of Baidu Core. In Q3, we saw that the mobile ecosystem continues generating high margins, ensuring strong cash flow. The AI cloud business maintained healthy growth, achieving profitability again. Looking ahead, we expect the traditional cloud business to remain profitable, while new opportunities arising from generative AI and large language models are expected to offer favorable margins over the long-term. For intelligent driving businesses, we will continue to invest at a measured pace. Overall, we will concentrate our resources by reallocating them from non-core businesses to AI-related businesses, which will be beneficial for long-term growth. Thank you, James.

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

Hi. Good evening. Thanks management for taking my question and congratulations on a solid set of results. My question is about the chips side. Can management comment on the impact on AI development after further restrictions on chip exports from the U.S.? How does that affect our AI product offerings and user experience, if at all? Thank you.

Yes, the restrictions on chip export to China actually have a limited impact on Baidu in the near term. We have successfully launched EB4 in mid-October, our most advanced foundation model in China. This is a significant milestone for us. As I mentioned earlier, we have a substantial reserve of AI chips that can help us keep improving ERNIE Bot for the next one to two years. Inference requires less powerful chips, and we believe our chip reserves, along with other alternatives, will sufficiently support many AI native applications for the end users. In the long run, difficulties in acquiring the most advanced chips may slow down AI development in China. We are proactively seeking alternatives; while not as advanced as the best chips in the U.S., our unique four-layer AI architecture and algorithm strengths will enable us to improve efficiency and mitigate some of these challenges. For example, we have made several innovations in our deep learning framework and ERNIE Bot foundation model to ensure compatibility with various AI chip types for both training and inference tasks. Given that all other Chinese companies are facing similar challenges, we believe we are best positioned in the market. Unlike some of our peers, who attempted to ride the generative AI wave by investing in startups to train foundation models, we focused on optimizing everything from the infrastructure layer to the application layer. Our end-to-end optimization approach means we can do training more efficiently and cost-effectively, and we can execute inferring faster and cheaper. Over time, more companies will realize that they don’t need to develop their own foundation models; they just need to create AI native applications based on Baidu’s foundation model, which is the most competitive in the market. Therefore, we are enthusiastic about the results of our investments in end-to-end optimization over the years. Thank you.

Ladies and gentlemen, that does conclude our conference for today. Thank you for participating. You may all disconnect.