Baidu's CFO on How It Became a Full-Stack AI Player

Game Insight on the innovators, disruptors, and tech-driven trends shaping today's complex economy. I'm Carol Masser. And I'm Tim Stenevek. Wrap up your workday with the Bloomberg Business Week Daily Podcast. We bring you deeper dives into the story shaping your world from the evolution of AI to the shifting priorities of global business. Plus, Silicon Valley power players and the latest tech trends. Catch up on the conversations you missed during the day. Subscribe to the Bloomberg Business Week Daily Podcast on Apple, Spotify, or anywhere you listen. Bloomberg Audio Studios. Podcasts, radio, news. What's the new expected? Yeah. - Right, it felt like we're in this moment where there's been. I mean, the way I think about it. There's been Chinese internet giants, but they concentrated on China, right? There's been American internet giants that basically had the rest of the world, and whether we're talking about AI or self-driving cars, we're going to see the first sort of, like, real head-to-head battle in internet companies, specifically, and whether or not competing playing the same game on some of the same markets. And of course, we know American companies can use AI models built by China, etc. And so, there are all kinds of options for people, so it's like really interesting to see, like, okay, this clash is actually, like, it's happening. - It's a good time to talk to a Chinese tech executive, for sure. - That's right. So the reason we were back in Hong Kong is because we were at the Bloomberg Invest Conference, though we also threw an Adelaide trivia night while we were in Hong Kong. - Our first non-US overseas Adelaide Quiz Night. - Yeah, that was a lot of fun, and I'm sure we'll come back and do that again. - But we were at the Bloomberg Invest Conference, and so we had the chance to speak with the CFO of Baidu Henry Hur. So, check it out. - We truly have the perfect guest. We're gonna be speaking with Baidu CFO Henry Hur. So Henry, thank you so much for coming on, Abla. - Thanks for having me, and it's great season. I think Hong Kong, and it definitely is great to see both Jo and Tracy. - Thank you, very nice of you to say. So, why do we start with this? You know, obviously, I feel like half the conversations are probably about AI these days, but within AI, Baidu is a full-fac player, right? You've cloud, you've the application layer, you have your own chips, and of course, your own model. As the CFO, you might have to think about prioritization, et cetera. Is there one layer of the stack that you feel is a must-win for Baidu? When you think about resource allocation, is there a layer where it's like, okay, this is an area where we have to win? - Thank you so much, Chen. I think you'll probably put the tough question in hand, but I think it's probably the most difficult question to start with. So, I think the very unique thing today is, I think the entire AI has been shifting from infrastructure to applications, and from model to agents. I think that's actually the backdrop. I think within that, frankly speaking, right now it's very difficult to say at this moment, which part is the must-have, because in my view, the trip is infrastructures. You need a great model to bridge the capability. The cloud is a deployment of that capability. And obviously, the money, the foundation, and all the ROI questions, especially for the people like me, as we focus on that, is on application layers. So, without any of that, this ROI cannot work. So, to answer a question, I think the key thing, if I have to pick one, is cloud. - Okay. - Because cloud at this moment is a platform, you can not only host an earning, which is our own model, but also I can work here very open to hosting other models. And the mychip, which connecting to my cloud platform, can also help on the inference, because right now the training is important, but 80% of the incremental demand today on a token are inference-related. I think this part of the full picture is what I want to emphasize, but given the tough question, if I want to pick one as a student, ABCD, I want to pick number C, which is my cloud. (laughing) - I'm gonna ask a question, which I think is gonna become standard for financial journalists in the same way we ask about headcount and expansion plans. What's your token budget? Is it bigger than Joe's? (laughing) - I mean the token? - The token budget for buy-dew. - Yes. - Or how do you measure, I'll ask it in a slightly different way. How do you measure what you're gaining from your token spend? How do you measure productivity? - Yeah, sure. I want to categorize probably two buckets. One is we consume computing power to reach a higher level of technology standard. The AGI, the how-good model of form, and also how harness can be designed to deliver better results. So these are the R&D efforts. However, as also a tech house, we also deliver those know-how to our external clients with different verticals. I think it's measuring our internal consumption of the token. I really want to like how better and how efficient our technology can be developed. So that's on one side. However, on the other end, what I think the ROI is more relevant is how many real tasks that open-claw and for example our own application called DOOM8 also is a real agent, human digit and other things can do the task. So I think these two different measurements are important in a way that right now there are two things better than last year. When is the foundation model getting much better? And number two is the framework, i.e. open-claw and other things can link up the foundation model capability to the real-world task from chatting or something to doing something and completing or something. I think completion part of the tokens is more important today. And I think consumption internally will actually encourage the people to do that. But think about that even last year or year before everyone is actually beefing up the R&D budgets. I think that budget is over there, but the completion new task is more important. So let me just press you on this question a little bit further. So let's say Tracy and I are, let's say we worked for Baidu in the same department, I don't know, some department of yours. Would we have identical token budgets or would you have one way of saying, you know what Joe, Tracy is actually finding ways to get more value out of AI than you are. So I'm gonna increase her budget. Like do you make decisions like that and do you have measurement techniques to see like this person really should get 10 times the token budget of another person because they have figured out how to get a lot of juice from the squeeze, so to speak. Joe, given the question asking, I see in prior next time, I give you more tokens. Again. (laughing) I think right now the technology evolve very fast. I think that's the beauty part of AI. So we don't want to constrain by ourselves by before thinking through something, we just install certain policy by saying, these are employees with defined the token number by the titles or the senior entities. I don't think that's a way of works. Okay. So I think we want to more open and more nimble in a way that given enough token to individuals to empower their internal R&D efforts, but on the other end, we do have a lot of efforts to make sure the token costs become dramatically coming down. I see. The cost is coming out very fast. Before you even think about getting a policy, maybe the unit cost is coming down half in like a few weeks. So we need to think about the speed and the cost and output efficiency, these three parameters as a package, not only on a number of tokens, that's one thing. On the other end, you feel very interesting facts. So right now, we are recruiting a lot of younger talents today, even for Baidu, which is 20 plus years listed at a public company. So my feeling is the kids actually getting smarter than people expected. So they were not wasted the tokens you give to them. So they have a sensible judgment about what other tasks they need to prioritize because they work on agents and models. The model actually helping them also prioritize all the tasks they have. So I think the power of the technology today is it is not only the tools. So that's actually the key concept on the mention. It is not only a tool, it's a mindset. And a mindset become automatically and more intelligent. That gives people can work on that well and have a new relationship with agents and with the model. Some of the old questions we kind of struggle ourselves will be the only thing that's less important. OK, so no token maxing at Baidu. But since you brought up talent, one thing I'm very curious about is we know the competition for the top engineers is so intense right now. And in the US, we see these headlines where engineers are treated like sports stars. You know, they're being traded for millions of dollars or whatever. What is Baidu's pitch to top talent? Like if you're trying to attract someone to the company, what is it you say to them that makes them want to work for Baidu versus another top tech firm? Yeah, it's a great question. So let's bring a different perspective. I think we are a technology company. Previously, I think the priorities we empower our clients to be more intelligent. We give them more technology tools to help them to remove a move from the traditional IT to the cloud environment, such as that. But right now, I think AI, especially for the big corporation like us, also changes as well. We need to think about the cultural changes. change, organization change, not only as an organization and a company, but also how AI empower ourselves. So it's actually equally important to do something for clients versus think about new tools affecting ourselves. So Trace is right. I think there are a few things we actually make a lot of different thinking and some of the new initiatives. First of all, we're probably among a few companies in China still very open, even increasing the campus recruiting and the focus on the younger talents. And number two recently, we also tasked the senior people not only look at the current reporting structure, but also in the real mentor relationship with the younger growing piece of the human capital in a company. But more importantly, I think it's really about giving people more autonomy to work in a company. So more trust and autonomy and give them more real work. And you know, there's a one concept called a one person company, right? So we're very happy to work with one person company because they actually use our AI to very nicely and willing to pay a lot of revenue to our products given the quality. However, within the company, we also encourage people to be the one person team. So they can actually use the agents to work a lot of internal tasks. So internally, we have a little tool called do do right in Chinese, a very kind of nickname, which is actually our internal kind of open cloud similar tools and which actually enhancing people's efficiency and the tool point is in give me more people more autonomy, more trust and the more room to grow and attracting new talents, I think equally or kind of very important to change ourselves, but also reporting lines and organizing structure need to come with it to make sure that people can deliver the results. And the last note I want to mention, the key things that people see, the application is important. They can work on the first stack in by do which is a unique value in the China tax space. You know, it just occurred to me, American companies are kind of becoming more Chinese in the sense that they're doing more vertical integration. Like that's sort of a long history here of sort of the whole thing. And now we see one of this phenomenon is that every American company, like they want to even start designing and selling their own chips, their own silicon, which is something that you're doing your own business and you've had it for a while. And I'm trying to wrap my head like, what is the rationale? How much is it about just wanting to be able to control your own fate more? So wanting to like control more of the supply chain versus having a chip that optimally aligns with the model that you're working on. Because those are distinct priorities. So what is the real rationale for having custom silicon? Yeah. And I think thanks for the tech trend in the past kind of year and two. So if you look at entire computing power consumed, for example, last year, most of the consumption is actually related to the pre-training of large foundation model. But right now, you know, many of them go into the inference and the company tasks. And if you look at the different stacks, I think right now we are actually fitting to an incremental growing piece of the market, which is well defined with a clear boundary, which is not focused on pre-training for a very super scale foundation model. However, the inference application is important. So to a point, I think our chip product, supporting a cloud, focusing on the inference and application is a unique way of we see the positive network effects. I think that's where the area we want to invest. And also given the issue mentioned, I think within that defined areas, we feel pretty confident regarding all the issues you mentioned. And because on the supply and demand side, we can find the good match within the emerging market category within the inference and application markets. Hi, I'm Carol Masser with a helpful tip to keep you plugged in throughout the market day. Subscribe to the Stock Movers report from Bloomberg. These are short audio episodes, five minutes or less delivered right to your podcast feed. So, hypothetically, you could try to do everything, right? The full stack and I guess the capital investment you would need to do that is also hypothetically unlimited at this moment in time. And we hear these crazy numbers in the US about the hyperscalers spending hundreds of billions of dollars this year alone. But when you're looking at the different parts of the business, so you have a very mature internet search business and then you have everything that you're doing with AI, including infrastructure. How are you actually allocating capital and then how are you actually, I guess, balancing that with returning capital at the same time to shareholders? Yeah. So I caught an impossible triangle. So I kind of scratched my head every few months, every few weeks, depends on also see head line numbers on news with other peers as well. So sometimes I make a little bit kind of hesitating to make a statement, but I just want to tell the facts and tell the views. I want to separate them out. Sure. So in the recent quarter earnings, we mentioned we actually solved partially on this impossible triangles. Our operating profit increased almost doubled on the Q and Q base and number two, our cloud revenue grew about 79% on a Y Y basis, which is almost double of the Y Y growth rate for the cloud market in China. And number three is since Q3 last year, our operating cash flow has turned positive. So positive operating cash flow, incremental operating profits and the higher growths in the market. However, my cat-backs is not seeing double even third multiple times. So I think the way of resolving that is, as a sales management team of a heavy cat-back investing AI tech company right now, I need to find a way on when hand really drive the growth, but also keep the density of the investment into AI in a reasonable pacing. However, when you do that, you need to keep conscious regarding ROI and our in mind to look at entire cash cycle. For example, every dollar we spend today, we probably need to wait for another probably 20, 30, 40 months depends on the category to get full cash back. And during that frame, obviously there's a price hike, some memories, there's difficulty on the IDC centers and a huge spending on servers. So my point is, as a several on every project, you need to look at entire lifecycle, not only at one time, but also the pacing important because the foundation model R and D always take a few months, right? So these are the things you need to keep on mind, but my statement today is, as I do, we want to invest probably in a more responsible manner to the shareholders, but do not diminish our ambitious to investment into AI. Keep the right density is important, but given the results for this quarter, I think we kind of resolve that at least for this quarter. So hopefully we can keep on working on that. And maybe half year later, when we check on this point, we can still keep on the same pattern, you know, high growth less dollars spent, but better IY. I think that's probably the angle we want to achieve. Can I just mention that I'm very curious about, you know, I'd say the heads of the American AI labs maybe have varying degrees of AI psychosis. They have a lot of worries that the what they would call alignment research, et cetera. Do you work on similar things or do you have the same concerns and do you also have AI psychosis? Do you like, you know, for your true speaking of like trying to make money, like do you invest in or how much do you invest in what they would call AI safety or alignment and essentially making sure that the models that you're building don't go rogue and always work on behalf of human flourishing. Is that a thing that you allocate capital to? Yeah, it's great question. So there's an emerging area, for example, in this data sanity and all the kind of post-training efforts. I think it's a good idea to work on that alignment, obviously, is one of that. But my point is, if you look at this new concept of harness, right, it's not only about pre-training and getting model on the leaderboard, but also more importantly to measure the robustness and all the things you mentioned. I think in the context in China tech sector, the engineering has to be and has been a good competitive advantage. So the harness from the data fly well to the alignment, to the data quality and the labeling, as in the entire ecosystem has been robust for, if you think about, even in the mobile internet work, right? So as simple as data labeling to the alignment tracks and post-training and SFG, I think this kind of the full chain of the capability in terms of the talents and the pool of resources and the cost of data sanity and all the tracks has been, in my view, a little bit kind of more efficient in a way that the ecosystem has been in place there. So the cost efficiency has been there. So my view is this is the engineering, not a theoretical quantum leap, right? So on that, the engineering capability, the form, the China tech world and industry has been there with the key elements I mentioned, right? Talent's lower cost, more efficient. I think these are the few things I just want to point out. They actually can help solve the issue. But as I mentioned, the things are evolved very quickly, right? So we don't worry too much about the issue you mentioned in local market. Yeah, so this is interesting. I'm curious, I want to press further on this because the American AI has been very anxious about this and they published these model reports and it says things that in the world, they're the chain of thought we were able to see that 4% of the time the model was able to identify that it was being tested and therefore it changed its behavior and response to recognizing that it was tested and this is a sign of potential misalignment. Are you doing the same sort of research and spending to establish that again the models worked for people and don't have a road goal? Yeah sure. I think right now if you look at this right so we are also part of the Open Source community so many of the good model especially publishing recently also will publish their software. That makes sense. So we kind of follow the new thoughts but also doing our own test as well. So overall I think people in Open Source community today in my view it's very collegial so people still want to do a better model for everyone globally not really on one country to two different places. Yeah. So actually related to this I'm going to ask something maybe it's slightly sensitive but I think it's very important. So in the US the AI companies even as they talk about safety they're basically self-regulating right like they choose to put out these reports and judge their own models and things like that. In China tell me if I'm wrong but it feels very different it feels like the government is more hands-on when it comes to AI China has been very explicit about this as an area. National security, national strategy so you're operating in an environment where you're firmly embedded in China's technological and industrial policy. How does that influence the development of your AI models and your broader tech? So obviously we're not in position to comment on public policy but I'm definitely happy to share some of my thoughts I have. I think in the world in the China AI today we believe we have a great group of very superior talents not only the engineers but also people actually design you know the framework right. So that's actually very important because it's not only about algorithm self it's about a whole system regarding infrastructures regarding the data regulation regarding the model and the cloud. I think given the past kind of 10-20 years in China giving this entire infrastructure has been upgraded to a level that is kind of world leading. I think the policy supporting to getting the moment we have today is already proven we have a proven path to leading not only the technology renovation and innovation but also the way monitor that into the stage we already have today so that's my first point. My second point is I think today the technology is growing very fast and the checks on the performance and on the data transparency and the rules regarding the data regulation even without the AI model even on a cloud age in the past kind of 10-20 years or five years has been getting more robust because if you think about that in the cloud environment you have almost similar issues right who own the data who use the data who can access that but today is a new tool to actually cover all the things we are doing so I think it is not a new concept for the policy maker to think about it's a new model it is a new thing it is really a new and a better tools to utilize and access the resource already building and existing resources will have a building on existing platforms which has been 100% compliant but also will have a lot of support you know from the policy makers industry practitioners academics they actually all contributing to that so overall my feeling is it's a very transparent and open environment not only China but also globally and academics and industry practitioners actually contributing quite a lot of the good conversations to this environment and my feeling is the policy makers through different channels has been very open also listening to the new frontier issues and questions I know this is a business conference and we want to keep things very professional here and not engaging gossip etc but I have like one sort of I'm just curious about something which is if the American AI CEOs the most hawkish on the sort of like chip exports on China stuff is Dario who used to be a bidoe employee do you ever hear things in the office do people ever say like oh I remember that guy he was you know is there any other little Dario gossip that people talk about in the office from his stinted bidoe so that's why I want to put the ball back to a quatch I want to share another gossip which probably want to hear so probably in the past kind of herner days right is open cloud become very popular right and educator market about how AI is really getting to the real task and the real word obviously in China you know there are different ways to have a new way calling you know not only the cloud but other niki names right so one day I saw Peter who is the founder of the community which drive the open cloud to be prevail put I think Instagram yeah saying he actually want to work with by due because you know open clouds are true but the true is is kind of eating up all the capacity i.e the skills right so everyone's contributing to the skills and the cloud is actually grab all the skills and do the work all right so I think one day we are pretty happy you know CEO when the Peter drop us a note very in a positive way because he kind of on one side notice that the search is important capability of the skills i.e the skills in the open cloud environment so actually he asking by due to work with him to beef up the the search skills in order for open cloud to do a better work to accessing the real time information because today the foundation models one kind of carve out is every few months you train a new model and the model itself in his mindset doesn't have the real time information for example the foundation model is they doesn't capture you know Joe and Tracy what talking about today you need to have a new skills accessing the odds lots what is happening real time that's right so I think you know Google globally and by due for China they are the powerhouse for searching the real time information so it has to be linking to the open cloud so I think that's actually one of the things we pretty much happy to have out so you know next day we ask our engineers to link up with speeder and we actually part of this skill marketplace doing pretty okay and right now just want to share our foundation model called Ernie 5.1 right now is ranked as the globally number one in a tax format of the global RM arena and globally number five in the search skill capabilities globally in the RM arena as well so I think that's want to give you another gossip so but for the previous one probably can talk with you after this open yeah I know you didn't give us any Dario gossip but implicitly because I know that the open claw guy you know it's originally called open clawed and then anthropic sued him and also he's got kind of annoyed because they didn't let the API users get full access so I think that fellow who created open claw is not the biggest fan of Dario's approach so I by giving us that answer at least give us a little drama there thank you you mentioned search a number of times already and data is obviously very important to AI we spoke with Grace Schau earlier in the week she writes about AI on her sub stack and I asked her if China has an edge when it comes to data collection and she said she thought not really because a lot of the data that's been collected was unstructured and so it was hard to harness for AI model training and inference purposes can you talk a little bit more about how you did that at bydo because you've got a lot of data you're using it for Ernie how is that transition process like actually carried out yeah sure I probably will talk about something the market has not noticed enough and I'll talk about what's a real challenge right so it's always two sides of story I think I'm very happy to talk about Google versus what we think about bydo in some certain formats a few things I think the the market is not only the capital markets but also the industry has kind of on the value a little bit regarding the same components we actually matching with the same structure of Google is monetizing and have this integrated capacity so first of all Google has its own TPU right which power that cloud so the GCP grows the Google cloud is growing faster which is part of the reason is the TPU and so bydo we have our own trip department and you know based on the public information we recently did the public filing on the spring of this assets right so the trip we have the theme with Google for the foundation model we have our earnings this is mentioned however in the physical AI called applications are called a word model so we have our robot taxi called Apollo go just want to share one number I think the market sometimes I tell even my friends was kind of surprised that each week including San Francisco and including all the cities Austin taxes in US wemo from Google delivered about 500,000 trips per week and in the last quarter by do's Apollo go in a globally 27 cities delivered about 350,000 trips which is only about 25 percent fewer than Google yeah the party of that is not not only about robot taxi it's about how we're using the data in power our own foundations models trainings and also do inference but also have a lot of know how regarding the multi model contents and all the different things and the more important if you look at the traditional search right now on this border also in the market didn't notice that you know still even my friends telling me oh Henry congratulations for that for your earnings but you'll search it probably still 80 and 90 percent of the revenue but the chooses for this border is declined to about 48 percent so it's already below 50 percent so the new growing area for example the digital humans and also our application of software is becoming a powerhouse and and a growing environment fast. So my point on that is, if you look at the data, could keep components on the blocks from the chip, cloud, robot-taxi for AI, physical AI applications, and the software. And also one more thing I want to mention is Google linking with YouTube for the multi-model contents. And we actually have our control, the subsidy called ICEIQ in China, which is actually over 50% on market share in China for search and long form contents in China as well. So we also have our close loop of the data flat well as well. Problem at a different scale. But I think it's doing the same format and the same model. So my view is, yes, I see on one side, it is right that certain data and elements are in their own kind of pockets. But forever for bydo, we still have access to those pockets. Probably better than other peers. But I want also very honest admit, right? Given Joey's my girlfriend has to own him a little kind of bossy after the session. You just want to share my own challenge. Obviously in China, you have different camps, right? Different camps, the kind of don't open up enough to share the data, which is reality known to the market for everyone. But my point is, right now, the agents and the foundation model become super smart. And it has a great push to move everything to a public cloud. It's actually helped resolving that issue to be accessing more information. Last note I want to share is, before AI can, the public cloud penetration in China is about 20%, 30%, versus the US is kind of 90%. That's why your comments I can totally understand, because without AI, the gap is like this. But right now it's actually getting closer, but still there's a gap. But my confidence coming from this gap will further narrowed, because everything will be on cloud environments, everyone's access real-time information. But also for bydo, we have a four stack and each component given the Google pass has been proven to be right and more efficient. We just want to follow the same pattern and access and the benefits from the different layers that AI self. [MUSIC PLAYING] [VIDEO PLAYBACK] [MUSIC PLAYING] [VIDEO PLAYBACK] [VIDEO PLAYBACK] [VIDEO PLAYBACK] [VIDEO PLAYBACK] today is basically can deliver a final task, not only using a a tool for human beings. The agent is smart enough to think about planning the task and completing the task. Obviously, in interacting with human beings, it actually becomes more smarter and in a way that are working with more efficient planning of that. So, Trisit, a true question overall, my thinking is the DAA will measure not only how many people using that, but also how difficult it is. A true question, the result driven payment, is coming up in a near trend. So, I just want to share a few things. For example, right now, we have three or four different key products. One of them, we in China called a pharma, it's really solving complicated issues for enterprises. It's very similar to AlphaGo, which actually in the previous years, so doing the planning. But right now, it's actually coming to the real work. So, we installed this agent to one of the biggest pot in China and help them deploy and planning for the shipments and logistics. It is saving the cost of the idle time and improving the revenue of their pots. So, the pot actually waiting to share a certain profit generation with us. So, the key things I'm observing is in the previous meetings, even I'm in the several, but actually, I'm telling a lot of, you know, the meetings to meet with client. In the previous meeting, without AI, most of the meetings we are talking with is the CTO and the CIO of that company. Because it was a tool. It was a cost center. So, they need to find a budget internally. Joe, you know, it's not easy, right? So, they have their several and their CIO. But right now, most of the meetings we are having today is with the CIO himself. Because AI right now is not only about, by do is really about helping our clients. So, the client has to be a top-down level of the initiatives to really drive AI internally. So, I think our sales process become relatively more efficient in a way that we're getting to the number one decision makers. He has a budget and he knows that driving the pot efficiency is important for his task. So, he's waiting to share certain economics with us. I think the customization is also diminished because right now, the agents can be used different parts. You can repeating that success lower the unit basis of the cost. And the agents become more real and the clients see the value and the profits. So, they have a higher willingness to pay and high ability to achieve that payment. So, I think these four cycles actually in the AI world is very different with the traditional IT. This is actually an interesting question because I've seen debate on this within AI about what is revenue look like or what is, you know, a sales price look like. Because another thing people will talk about is, for example, using an AI agent to say, resolve an insurance claim or something like that. And then the AI provider gets paid on say, like, you know, the number of successful claims resolved, etc. Are you bullish on that basic model where the payment is, as you said, okay, maybe they'll share revenue with you because they can measure that savings. Is that the model that you see across a range of AI applications where it's like a sort of per task or sort of very clearly linked to the efficiency gain? Yeah, we have another kind of line of business we call the digital employees, which, you know, Joe, probably you have the similar experience that if you have one season of the podcast, you're probably very energetic, right? If you do that like 10, 20 times in two weeks, it's very exhausted, right? Because you think about, wait, you're still going to replace your my views, right? So, wait, wait, is that what you're insinuating? We're going to get replaced because we get exhausted, but the AI didn't want to get it. So, so, so my point is the humans, they're the motivation and the knowledge base have their own kind of territory to be frank. But if you look at the conversation, look at the quality of the know how, if you really tap in a good manner, of course, the digital human actually can deliver efficiency and a better execution quality. So when, for example, just want to share is e-commerce is a big industry in China. Yeah. And a lot of live performance is really selling the products. Yeah, looks fun. But, you know, the cover out is the KOL cannot work like 24 hours, right? So, and people cannot buy stuff like 24 hours. But if you think about, you have a great quality of the human employee can help the merchant owners to selling to different time zones and also can speak Chinese, speak English and for the different parts of audience to have the little jokes from the own countries, the action can help, you know, the e-commerce revenue. So that's why we have their own kind of product called digital employees. We actually help our merchant and e-commerce store owners to really push on that and selling all the goods. And it actually can perform pretty well because on the, you know, the Q&A sessions on the questions is actually reacting to the random users asking why range of questions. That knowledge actually is very fluent in the way that for the foundation model, it is the way it works, right? So I think that actually has different use cases and we actually monetize by charging, for example, the result improvement and all the different things. I've tried to buy things for 24 hours straight before. I think when I first used Tao Bao, I think I had Tao Bao's psychosis or something and that's how my husband and I ended up with three couches in our apartment that was about 500 square feet large. So my little suggestion, you need to have another agent help you to sell your right products. That's another way of probably getting money as a result. Are you going to spin out the chips business? We're at Bloomberg Invest. It's our first live recording of odd lots. Let's break some news. All right, so it's coming to the money part. So I was not, to be frank, I was not even trained by finance. I become self-aware by accident. So actually my both bachelor and master training was a trip designer myself. So I think after drawing by, I definitely realized that Baidu's trip product has been really, really high quality and really good for the inference for the for the other things we talk about, the helping our DA to grow as well. So based on the public information, we already filed the confidential filing for sping off of our trip assets in Hong Kong. And we are doing that and processing that process on track. And that is one part of the assets we try to unlock at this moment. But however, as I mentioned, the cloud of foundation model, they are all very important. So after sping off, we hopefully can enhancing that ecosystem. And as you know, trip is not only the hardware. I deeply understand it is about ecosystems. We need to work pretty well with our customers and suppliers and the software developers all at one goal. And I think to be a separate listed public company, it will help to achieve that goal not only as hardware, but also the entire ecosystem as well. And our customer will view our trip products more neutral and independent products that can actually do more testing and more usage on own their own cases. Yeah, I seem to recall reading that you figured out a way that developers can easily port over their CUDA stack over to your stack without much trouble. Henry, thank you so much for coming on, Abloss. Our first live recording in Learn Asia. Really appreciate it. Again, thank you for the guest. Yeah, again, thanks for having me. Thank you, Joe. Thank you, Tracy. Thank you. That was our conversation with Henry Hutt, the CFO of Baidu recorded live at Bloomberg Asia Invest. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez, Ed Carmen, Arman Dash, O'Bennett, Ed Dashbot, Kilbrook's and Kilbrook's and Kevin Luzano at Kevin Lloyd Luzano. And from our AdLots content, go to Bloomberg.com/AdLots, or the daily newsletter and all of our episodes. And you can share about all of these topics 24/7 in our Discord Discord.gg/AdLots. 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