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Operator
Hello, and thank you for standing by for BIDU's third quarter 2023 earnings conference call. At this time, all participants are in a 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, Joanne Lin. Baidu's Director of Investor Relations.
BIDU
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 Doshan, 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 session 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 and other documents filed with ICC 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 annotated non-GAAP measures to the annotated directly comparable gap measures is 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.
Robin Li
Hello, everyone. Baidu Core delivered solid revenues, profits, and cash flow in Q3. despite navigating in a challenging macro climate for both our online marketing business and AI cloud business. I'm proud that our team managed to strengthen operational efficiency and maintain stable margins amid a full-scale reinvention of our product portfolio with Ernie and Erniebot. Today, I would like to share an update on the new opportunities that Ernie and Erniebot 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. 31st, we received the approval to deploy EarningBot on a large scale and open Earning API to enterprise customers. Since then, we've witnessed a significant increase in queries handled by EarningBot and through Earning API. Moreover, we've 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 ErnieBot 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 four-layer AI infrastructure, which helped enhance efficiency in model training. The input and feedback from our users and customers also played a big role. In products, as I said in the past, we continue to use Earning to reinvent our entire portfolio and introduce an AI-native experience. On the customer front, the Earning Bot enables by-do search by generating direct answers to search queries, complementing traditional search In the folder, we initiated tests on our new features that recommend newsfeed-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 verticals like health care, education, travel, legal, and auto, in which advertisers are willing to invest heavily in customer acquisition and re-engagement. Ernie Bot, our new AI native product, serves as a versatile multi-run 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 1st, EB4 was opened to the public through Ernie Bot at a subscription fee of about eight US dollars per month. This marks us as the first company in China to implement user charges, distinguish 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 the paying user count, a trend that we expect to continue in the coming quarters. On the enterprise-facing product front, we recently introduced GBI, Generative Business Intelligence with EarnBot. 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 have the need for AI co-pilots to help them analyze data more efficiently. During our last earnings call, we also discussed how we use ErnieBot to create Baidu Co-Mate, our AI coding assistant, and InfoFlow, our enterprise communication and collaboration platform. These products focus on boosting productivity and efficiency gains, and each of them presents up-sell opportunities for our cloud customers. In fact, an increasing number of our cloud customers in China's traditional industries and public sector have used the trial version and shown interest in these products and features. Additionally, these products and features also allow us to acquire new customers across an even wider array of industries. In terms of ecosystem, we empower enterprises to leverage learning through API to create their own AI-native applications and solutions that will drive the development of generative AI and LLM. As more and more AI-native applications built on top of Ernie become successful, 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. This number has been growing quickly since we received the regulatory green light 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 put a lot of effort into reinventing our offerings. In the first half of November, the number of daily external queries has increased by over 50%, compared to the same period in October. As we are actively assisting our cloud customers in creating AI native applications, we believe there will be a continuous and significant rise in external queries in the future. We're also actively attracting developers to connect their information and services to Ernie Bot through plugins. With plugins, EARNY can help people with more and more tasks, unlocking a wide range of possible use cases. As of today, hundreds of plugins have already been accessible through EARNY. The initial batch of third-party plugins include ctrip.com, Civic Press Group, China Justice Big Data Institute, New Oriental, Auto Home, and TreeMind. In summary, during the quarter, we made significant progress in using GenAI 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 and 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. Mobile ecosystem continue to exhibit steady growth for both user metrics and financial performance in the quarter. Baidu apps MAUs increased by 5% year-over-year to 663 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 a double-digit growth in 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 continue to use GenAI 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 them. Moreover, we made ongoing enhancements to our monetization system, focusing on improving targeting capabilities and the auction system. For example, Carina Education, an IT professional education company, achieved an increase of 23.3% in conversion rate and 22.7% boost in ROI after using this enhanced platform and capabilities. We are still in the early stages of using GNI to help advertisers achieve higher conversions and RIs on our platform. Our efforts will ultimately lead to significant improvement in monetization capabilities and contribute to future revenue growth. In the corner, 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 EarlyBot, we introduced a tool that allows them to easily create their own digital human, generate live scripts, and more, significantly lowering 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-gas basis in the quarter, as we remained focused on the healthiness of our business. GenAI and LLM have brought us a lot of opportunities which have strengthened our competitive advantages in cloud and increased our time. A growing number of enterprises are using Ernie API to develop their own AI native applications and solutions. We're also helping customers build their own models efficiently by leveraging our unique four-layer AI infrastructure and our years of experience in building and using foundation models. Our training is very complicated. It requires a large number of GPUs working simultaneously. Any 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, data sets for enterprise customers to easily fine-tune their customized models. General 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 the 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 benefits. Also, since smart transportation revenue started this low down in Q4 of last year, we will have an easier year-over-year compass in Q4 this year. Moving on to intelligent driving, our target remains unchanged. which is to achieve break-even on the regional unit economics for robot taxi 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 ride-hailing 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%, that's up from 35% in Q2. We are also particularly pleased to highlight that ApolloGo's operations in Wuhan continue to expand. In late August, Apollo Gold remains the first company in China to provide autonomous blind-hailing services to the general public at Wuhan Tianhe International Airport, one of the busiest airports in central China. The extended wage into the airport transfer involves longer travel distances, presenting an excellent opportunity for the future improvement of unit economics. All of this development contributed to the UE improvement, and we aim to reach regional UE break even in a couple of years. In Q3, ApolloGo 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 Chairman of the Technology Ethics Committee. Now, let's proceed with Rong's financial performance review.
GPT-4
Thank you, Robin. Now, let me walk you through the details of our third quarter financial results. Total revenue was RMB 34.4 billion, increasing 6% year-over-year. Revenue from Baidu Core was RMB 26.6 billion, increasing 5% year-over-year. Baidu Core's online marketing revenue was RMB 19.7 billion, increasing 5% year-over-year. Baidu Core's non-online marketing revenue was RMB 6.9 billion, up 6% year-over-year. Revenue from ITE was RMB 8 billion, increasing 7% year-over-year. Cost of revenue was RMB 16.3 billion, which remained essentially unchanged compared to the same period last year. Operating expenses were RMB 11.9 billion, increasing 8% year-over-year, primarily due to an increase in channel spending, promotional market expenses, server depreciation expenses, and the server custody fees, which support earning more, research includes. Vital cost operating expenses were IMB 10.5 billion, increasing 10% year-over-year. Bidocall SG&A expenses were RMB 4.8 billion, increasing 14% 1.4 year-over-year. SG&A accounting for 18% 1.8 of Bidocall revenue in the quarter, compared to 17% 1.7 in the same period last year. Bidocall IMD expenses were RMB 5.6 billion, increasing 7% year-over-year. IMD accounting for 21% of Bidocall revenue in the quarter, which maintained unchanged from the same period last year. Operating income was RMB 6.3 billion, by-the-cores operating income was RMB 5.5 billion, and by-the-cores operating margin was 21%. Non-GAAP operating income was RMB 7.6 billion, non-GAAP by-the-cores operating income was RMB 6.7 billion, and non-GAAP by-the-cores operating margin was 25%. Total other income net was RMB 1.9 billion compared to the total other loss net of RMB 4.8 billion for the same period last year. mainly due to the first recognition of IMB's $338 million gain versus IMB's $3.1 billion loss for the same period last year from favorable changes in long-term investments. And second, a decrease in impairment of long-term investments by IMB's $1.4 billion. Income tax expenses was IMB's $1.3 billion, increasing 41% year over year. primarily due to an increase in profit before tax. Net income attributable to Baidu was RMB 6.7 billion, and diluting earnings per ADS were RMB 18.22. Net income attributable to Baidu Core was RMB 6.4 billion, and net margin for Baidu Core was 24%. Non-GAAP net income attributed to Baidu was RMB 7.3 billion, and non-GAAP dilutive earnings per ADS were RMB 20.40. Non-GAAP net income attributed to Baidu Core was RMB 7 billion, and non-GAAP net margin for Baidu Core was 26%. As of September 13, 2023, cash equivalents, restricted cash, and shortened investments were RMB 202.7 billion, and cash, cash equivalents, restricted cash, and shorting investments, excluding IT, was RMB 197.4 billion. Free cash flow was RMB 6 billion, and free cash flow, excluding IT, was RMB 5.2 billion. Baidu Corp had approximately 35,000 employees as of September 30, 2023, With that, Operator, let's now open the call to questions.
Operator
Thank you. We will now begin the question and answer session. If you wish to ask a question, please press star then 1 on your telephone and wait for your name to be announced. If you wish to cancel your request, please press star then 2. Your first question comes from Alicia Yap with FIDI.
Alicia Yap
Please go ahead. Hello, thank you. Good evening, Robin, Julius, and management team. Thanks for taking my questions. My question is on advertising. So it seems like Baidu ad revenue growth is tracking slower than some of the Internet peers. So besides macro, can management elaborate any other reasons that contributed to the softer ad revenue growth? And then looking into the fourth quarter, have we seen any demand picking up? What is the e-commerce sector contribution and how will AI change the advertising outlook? Thank you.
Robin Li
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. It accounted 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 GenAI. We are basically restructuring the overall ad platform, including creative construction, ad targeting, and the bidding mechanism. These efforts have started to pay off, and the incremental revenue from this kind of initiatives are expected to reach the level of hundreds of millions RMB in the current quarter, which is Q4 of this year. And looking forward, we are optimistic that the growth of our online marketing revenue will continue to exceed China's GDP growth. Thank you.
Operator
The next question comes from Alex Yao with JP Morgan. Please go ahead.
Alex Yao
Thank you, management, for taking my question. I have a few questions on cloud revenue. I believe Robin mentioned that despite of moderate revenue decline in Q4, the growth rate will return to positive territory in Q4. And then from there, should we expect the cloud revenue to further accelerate into first half of 2024? With regard to the smart city projects, are there any more projects that are still at risk? And then more importantly, as you guys start to monetize the AI capability, when will AI start contribute to the cloud revenue meaningfully. Lastly, any preliminary view on cloud revenue growth outlook for 2024? Thank you.
Robin
Hi, Alex. This is Do. Thank you for your question. So actually, as we mentioned before, we have been focusing on improving the health of our business for sustainable development. And as a result, we have achieved a non-gap operating profits in the past few quarters. As Robin already mentioned, due to the weak demand for intelligent transportation, cloud revenue experienced slight decline in Q3. So while excluding small transportation, the rest of our AI cloud business showed a pretty solid growth. And we believe AI cloud revenue will return to positive growth in the fourth quarter, and the trend will continue down the road. What is even more exciting is that we keep seeing new opportunities brought up by general TPI and large-language models. Actually, last quarter, we already said that more and 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, but it's growing very fast. We have seen more and more enterprises proactively adopting these new technologies for productivity and efficiency gain. Some of these customers, especially those from the internet, education, and the tech sectors, they have started to see efficiency gains through working with us. As a result, some of them have gradually increased their spending on our cloud services. Looking to Q4, we aim to leverage our leadership in general TPI and large-language models to continuously attract new customers and encourage the existing customers to increase their spending on a by-the-way cloud. And we believe this should not only lead to long-term revenue growth, but also continuous margin improvement. Thank you, Alex.
Operator
The next question comes from Miranda Zhang with Bank of America. Please go ahead.
Miranda Zhang
Thank you. Good evening. Thanks, management, for taking my question and congratulations on the result. My question is about earning. So can management share with us the latest feedback for Earning 4 since the rollout last month and any color on the contract signed on adopting Earning 4? And also for consumer, how is the feedback after Earning Bot started to charge a user a subscription fee? And lastly, among the various opportunities you mentioned, which one do you think can become the biggest revenue driver? Thank you.
Robin Li
Hi, Miranda. Let me answer your questions. Since the release of EB4 in mid-October, we're receiving positive feedback from both users and customers. Many enterprises have reached out to test EB4 have been impressed with its capabilities. EB4 has gained a reputation for its advanced understanding and complex reasoning abilities. Comparing to EB35 and other LAMs in the market, we have noted that EB4 generates more structured and clearer responses and excels in coding. From November 1st, we started to charge enterprises and end users for using EB4, and 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 widened our lead over other in the market. And we are the first LSM to charge end user fees that sets us apart from other peers. And regarding your question about monetization opportunity, we see significant opportunities in AI native applications, either devices 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. It also 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. As our search will be able to talk with users in natural language and in multi-round, it will create more potential on the commercial side, too. 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. And at the same time, our capabilities will help our advertisers to better operate their bins on Baidu. Our ongoing efforts to revamp the ad platform have already shown positive results. and we will continue leveraging generative AI to assist advertisers in achieving durable ROI growth on Baidu. In terms of empowering our customers with gen AI, as Do just mentioned, customer needs are different now. Some customers do prefer to train their own model, but the GPU export restrictions will put a brake on that. It will eventually become clear that training LLMs from scratch is very difficult, especially when trying to achieve emergent abilities. So they will turn to advanced LLMs available on the market, like Ernie, for devising applications. As customers become more advanced in using LLM to create applications and more AI-native applications powered by Ernie become more widely used, we should be able to see continuous revenue growth through model inferencing. Over the long term, inferencing should become a major source of revenue for Ernie. Meanwhile, we will also help customers to fine-tune our existing model offerings to suit their customized needs in each scenario because our models are better, faster, and more cost effective. So in summary, GEM-AI and LLM will bring us massive business opportunities. We have already made good progress in commercialization so far, and this is just the beginning of a promising future. Thank you.
Operator
The next question comes from Gary Yu with Morgan Stanley. Please go ahead.
Gary Yu
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 or even all advertisers. Thank you.
Robin Li
Yeah, 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 them improve their allies on our platform. Also, 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 GenAI and LRMs to reinvent our app system over the past few quarters. Now, we have an integrated advertiser-facing marketing platform. Advertisers can use it to generate creative advertising materials. These materials have proved to be deliver higher conversion rates than materials created by humans. And our platform also allows advertisers to engage in natural language. By interacting with advertisers in multiple rounds of conversation, our upgraded platform is able to better understand their intentions. This allows us to create campaign strategies that deliver higher ROI. Moreover, it significantly reduces the time that ad managers need to spend creating campaigns. Because even an experienced ad manager has to spend hours developing an advertising strategy. And now with an AI co-pilot, the process only takes like a few minutes. As of today, we have... A few thousand of advertisers already migrated to our new platform. While this number is relatively small compared to our half million advertiser base, it is certainly growing very fast. In addition, we continue to use AI to improve our bidding system and add targeting capabilities. Such initiatives happened at the back end of the system, so advertisers may not directly perceive them, but they've observed improvements in their ad conversion and ROI. So, on average, advertisers using these capabilities probably achieved a high single-digit increase in conversions in Q3. All of our efforts should eventually attract advertisers to allocate more of their advertising budgets on Baidu. As I previously mentioned, online marketing revenue related to our upgraded ad platform has been growing rapidly and already became meaningful. And this is just the beginning, as I said. We are experimenting with AI chatbots. And chatbots could act as a replacement for landing page in the future. This is particularly useful in verticals where users typically research and engage in a long decision process before purchasing. Imagine searching for a training program and being directed to a bot instead of a webpage. With our AI chatbots, users can quickly learn about brand-specific information, product details, and other stuff. Although, I think the multi-round conversation is also very engaging. And for advertisers, it helps them to stay connected potential customers and guide them at key decision-making points leading to better conversion and even direct sales. We believe AI Chatbot could work well with GenAI-powered new search and bring us more opportunities. I mentioned earlier that the incremental revenue from this kind of initiative expected to reach the level of hundreds of millions of RMB this current quarter Q4. And this is certainly growing very fast. The trend should continue and further strengthen in the year of 2024. Thank you.
Operator
The next question comes from Wei Zhang with UBS. Please go ahead.
Wei Zhang
Good evening, management. Thank you for taking my question. I want to follow up on your cloud business. So could management share more color on how to think about the industry competitive landscape in China cloud market, especially among the Internet cloud vendors and as well as when competing with the telcos? And also with the development of 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 make efforts and catch up with us? Thank you.
Robin
Great question. As you actually may already notice, the traditional cloud business is slowing down. Well, generative AI and large language models are diving in and reshaping the competitive landscape of the cloud industry. In the past, the focus in the cloud market was actually on iOS, which is like a commodity, and people are competing on that pricing. But now, with the rise of generative AI and the large language models, things are changing. There's a growing interest among cloud customers coming to Baidu to utilize these sophisticated technologies to increase productivity and efficiency. They came to us not only because we have the most advanced AI technology, but also because we have experience and track record in using AI to help enterprises to solve problems. Some of them are still in the product experimental stages, but they have firm belief in the new technology to rebuild their products and services because they have seen successful stories overseas. That is why we are seeing the new technology is increasing our time and expanding our competitive edge. So as Robin mentioned, AB4 is China's first GPT-4 style model. He also shared the positive initial feedbacks we have garnered for AB4. So currently, our team are engaged in their dialogues with our clients, assisting them in understanding the technology and utilizing Ernie to redevelop their existing products and create new ones So I can see that Ernie has already helped us attract new customers and additional IT spending from existing customers. So here I would like to briefly bring up two points for our advantage compared to the players in the market. The first one is our unique four-layer AI infrastructure, which gives us the flexibility to make adjustments or innovations at every layer to be able to be compatible with other layers to keep driving efficiency in both model training and inference. And the second one, more specifically, is our capability to develop GPU networks or clusters for large-language model training. So as Robin just said, 98% of the training time on our AI infrastructure is valid. as a result in our customers including several leading internet and tech companies for increasing their investments in our service. Furthermore, we will continue to leverage our unique advantage of AI architecture to drive efficiency gain. It will help us to greatly reduce costs in model training and efforts on our cloud, giving us the flexibility to offer more compelling prices to our customers. and further strengthen our competitive edge in the market. You know, regarding the competition from telecom operators, I would like to highlight our focus is on different market segments since we differentiate ourselves with our AI capabilities in particularly, you know, earnings as we have mentioned. Actually, it's worth noting that we can cooperate, and we are actually cooperating on many objects, projects. As for other Internet companies in China, our strong AI capabilities are well-recognized by the market, which will set us apart from our peers. To sum up, we believe that our strong AI capabilities, particularly in general AI and large language models, will allow us to eventually become the market leader and gain share in the cloud market.
Operator
The next question comes from Lincoln Kong with Goldman Sachs. Please go ahead.
spk06
Thank you, management, for taking my question. My question is also about Ernie. So given the successful upgrade of the ERNI 4.0, what would be the future strategy for model iteration to solidify our tech leadership? So do we foresee any competition in the foundation model in industry, either to stabilize or intensify in the future? Thank you.
Robin Li
Hi, Lincoln. The AI chips we have in hand already allow us to launch EB4. We are ahead of the competition. To take our lead in our arms to the next level, we will take an application-driven approach. We will let the AI native apps tell us what to improve in Ernie Bot capabilities. Given that there are only a very limited number of AI native apps on the market right now, the majority of earning API calls are from internal apps, are internal apps like Search, Ads, OneCool, etc. The rebuilding and restructuring of our existing products drives earning innovation in the right direction. What is equally important is that we are helping enterprises use earning to build their offerings. and we have seen that over 10,000 enterprises are using Ernie through API calls on a monthly basis, which propels Ernie's improvement too. We also continue to improve the efficiency of our models. For example, compared to Ernie Bot's version, the Ernie Bot version in March, inference cost of the current version has been reduced by 98%, basically resulting in a 50 times increase in QPS for the same amount of hardware activity power. We are also able to do this using our unique four-layer architecture and leveraging our ability to do end-to-end optimization. Continued inference cost reduction has further strengthened our model's competitive advantage, and it gives us the flexibility to offer more and more compelling pricing. From a long-term perspective, taking into account factors such as the scarcity of high-performance chips, high demand for data, AI talents, and the huge upfront investments, the industry will soon transit into a consolidation stage. We believe there will only be a select few foundation models in the market, and Baidu will certainly be one of them. In this stage of industry development, more and more enterprises will begin to leverage advanced foundation models like Ernie to create AI-native products rather than spending resources on building their own large language models. So we expect that the number of native apps based on Ernie will reach millions in the future. Thank you.
Operator
The next question comes from James Lee with Mizuho. Please go ahead.
James Lee
Great, thanks for taking my questions. Can you guys maybe quantify the investments related to AI and how that affects various cost items in your P&L? And should we expect these investments to accelerate over the next few quarters? I was thinking especially after the launch of Ernie 4.0 and potentially higher inference costs as more people are using it. And then if we extrapolate that over a longer term, how should we think about Baidu Core OPM over the next few years, given all the moving parts, including, you know, revenue shift, investment AI, and also your continued improvement in cloud profitability? Thanks.
GPT-4
Hi, James. Let me ask you a question. This is Julius. Currently, the primary investments for generative AI and large-engine models are centered around computing power, which is recorded as powerful KPEX. I think the past few quarters we have put a lot of cheap resources in training our new earning models. In the future, as more AI-native applications, which is powered by our earnings, become more widely used, we should also put more resources in model inferences. However, please note that all of this impact for the AI-related hardware investments, or P&L, is quite manageable. because all the hardware depreciations are spread out over a few years. For example, all expenses linked to the computing power were used in the training early are recorded through the IND depreciations. And the model inferencing causes, which is highly related to the usage of the models, either internally or externally, and you should be supporting the funding by the future business And moreover, we're happy to see that our investment in generative AI and large-diameter models are beginning to be approved. As Robin has mentioned just earlier, since we're receiving the approval from regulatory and more additional revenue generated from earnings powered by 2C or 2B businesses has been growing quite fast. While we are using the generative AI and large energy models to renovate our businesses, we are still keeping a close eye on making sure our vital cost earnings stay solid. In Q3, we can see that mobile ecosystem continue its high margin, ensuring a very strong generation of cash flow. And AI cloud businesses continue its heresy growth and achieve profitability one more time. And look ahead, we expect the traditional cloud business to remain quite profitable, and the new opportunity arising from the generative AI and large-range models are also expected to have a favorable margin in the long term. For intelligent driving businesses, our long-term growth opportunity will continue to invest with a measured pace. And all in all, we will concentrate our resources by reallocating them from non-core businesses to AI-related businesses. All of this will be quite beneficial for our long-term growth. Thank you, James.
Operator
The next question comes from Thomas Chong of Jefferies. Please go ahead.
Thomas Chong
Hi, good evening. Thanks, management, for taking my questions and congratulations on a solid set of results. My question is on the chip side. Can management comment about the impact on AI development after the further restriction of the chip export from the U.S.? How does that affect our AI product offerings and user experience, if any? Thank you.
Robin Li
Yeah, the restrictions on the chip export to China actually have limited impact on Baidu in the near term. We have successfully launched EB4 in mid-October, our most advanced foundation model in China. It is a milestone for us. And as I just said earlier, we have a substantial reserve of AI chips which can help us keep improving ErnieBot for the next year or two. Also, inference requires less powerful chips and we believe our chip reserves as well as other alternatives will be sufficient to support a lot of AI-native apps for the end users. And in the long run, having difficulties in acquiring the most advanced chips inevitably impacts the pace of AI development in China. So we are proactively seeking alternatives. While these options are not as advanced as the best chips in the U.S., our unique four-layer AI architecture and the strength in AI algorithm will continue to help us improve efficiency and mitigate some of the challenges. For example, we have made some innovations in PedoPedo, our deep learning framework, and Ernie, our foundation model, to allow them to be better compatible with different types of AI chips, both model training and inference tasks. But given that all the other Chinese companies face the same challenge, we believe we are actually best positioned to serve this market. As you probably know, in the past, Some of our peers, they try to ride on the GenAI wave by investing in those startups to train foundation models. And they basically sell the computing power to those startups. We didn't do that. We tried to optimize everything from the infrastructure layer to framework layer than to model layer than to app. So we invested in this kind of end-to-end optimization approach. Therefore, we can, for the same amount of computing power, we can do training more efficiently, more cost effectively, and we can do inferencing faster and cheaper. And as time passed by, I think more and more companies will realize that they don't need to train their foundation models. They just need to develop AI-native apps based on Baidu's foundation model, which is the best on the market. So I'm really happy that we basically invested on this kind of end-to-end optimization front for many, many years, and it's time for us to show that the investment is worth it.
James Lee
Thank you.
Operator
Ladies and gentlemen, that does conclude our conference for today. Thank you for participating. You may all disconnect.
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