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

Episode Date: June 29, 2026

In the China tech space, Baidu is now a full-stack player in the AI industry. The company makes its own chips, has its own AI models (Ernie), its own cloud system, and it's integrating AI into its self-driving car business, Apollo Go. But before all this, Baidu was known for being China's leader in search. Things, obviously, have changed a lot since the company was founded in the late 1990s. In today's episode, we speak with Baidu CFO Henry He about the company's AI ambitions. He talks to us about maximizing token spend, how Chinese tech firms are thinking about safety and alignment, the global robotaxi competition, and how the core search business fits into the company now. Read more:Chinese AI Stocks Rally on Demand Optimism and Policy SupportUS Seeks AI Partnership With EU on Regulation, Supply Chains Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

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Podcasts Radio News. Hello and welcome to another episode of the Odd Loss podcast. I'm Joe Wisenthal. And I'm Tracy Alloway. So we were in our Hong Kong recently. That was a lot of fun. Nice to be back. Yeah, I haven't been back for four years.

Yeah. Not much has changed, actually. I was kind of surprised. Less than you expected. Less than I expected. But I am really glad we went back because obviously one of the big talking points in markets right now is competition between U.S. versus Chinese AI. Yeah.

And we finally got a chance to talk to a couple high-level executives of Chinese tech companies who are actually making all the big capital allocation decisions when it comes to the AI race. Right. It felt like we're in this moment where there's been the, I mean, the way I think about it, there's been Chinese internet giants, but they concentrated on China. Yeah. 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 on internet companies specifically and where they're like 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, et cetera. And so there are all kinds of options for people. So it's like really interesting to say 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 Odd Lots trivia night while we were in Hong Kong. Our first non-US overseas Oddlots 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 He. So check it out. We truly have the perfect guest. We're going to be speaking with Baidu CFO, Henry He. So, Henry, thank you so much for coming on Oddlots.

Thanks for having me. And it's great season. I see in Hong Kong. and it definitely is great to see both Joe 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, Bidu is a full stack player, right?

You have cloud. You have 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? Bydo? 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. And I think you'll probably put the tough question in end, 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 chip is infrastructures. You need to have a great model to bridge the capability. The cloud is a deployment of that capability. And obviously, the monetization and all the IOI questions, especially for the people like me as CFO, we focus on that, is on application layers. So without any of that, this IOUI cannot work. So to answer a question, I think the key thing, if I have to pick one is cloud.

Because cloud at this moment is a platform. You can not only hosting Ernie, which is our own model, but also I can work in a very open to hosting other models. And the Mijtrip, which connecting to my cloud platform, can also help on inference. Because right now, the pre-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, you know, giving a tough question, If I want a big one as a student, A, B, C, I want big number C, which is my cloud. I'm going to ask a question, which I think is going to 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? You mean the token? The token budget for Bidu?

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? Yes, sure. I want to categorize probably two buckets. One is we consume computing power to reach a higher level technology standard. You know, the AGI, the how good model perform, and also how harness can be designed to deliver better results.

So these are R&D efforts. However, as also a tech house, we also deliver those know-how to our external clients with different verticals. I think if I'm measuring our engagement, internal consumption of the token, I really want to like that 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 the open claw and, for example, our own application called dolemate also is real agents,

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 cloud and other things, can link up the foundation model capability to the real-world task, from chatting on something to doing something and completing on something. I think completion part of the tokens is more important today.

And I think consumption internally we actually encourage the people do that. But think about that. Even last year or year before, So everyone is actually beefing up the R&D budgets. I think that budget is always there. But the completion new task is more important. But 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 Bidu 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 going to increase her budget. 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?

You know, Joe, given the question asking, I think next time I'll give you more tokens. 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, before thinking through something, we just install a certain policy by saying, you know, these are employees.

We define the token number by the titles or the seniorities. I don't think that's the way works. Okay. So I think we want to more open and more nimble in a way that given enough token to the 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 think the cost is coming down 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 the number of tokens that's one thing on the other end you feel very interesting facts so right now you know we are recruiting a lot of younger talents today even for bydo which is you know 20 plus years listed at a public company yeah 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 are the tasks they need to prioritize because they're working on agents and the 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 true. It's a mindset.

And the mindset becomes automatic and more intelligent. That if people can work on that well and have a new relationship with agents, and with a model, some of the old questions we kind of struggle ourselves will be kind of diminished and less important. Okay, 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 U.S., 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? If you're trying to attract someone to the company, what is it you say to them that makes them want to work for Bidu versus another top tech firm? Yes, great question. So let's bring a different perspective. I think we are a technology company. Previously, I think the priority is 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, you know, such as that.

But right now, I think AI, especially for the big cooperation like us, also change us as well. We need to think about the cultural 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 a client versus think about new tools affecting ourselves. So Trace is right. I think there are a few things we're actually making 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 more autonomy and give them more real work. And you know, there's one concept called a one-person company, right? So we are very happy to working with one-person company because they actually use our AI too very nicely and are 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 on a lot of internal tasks. So internally, we have a list a little tool called doodoo, right? in Chinese, a very kind of nicket name, which is actually our internal kind of open cloud similar tools, and which are actually enhancing people's efficiency. And to a point, I think, give me more people more autonomy, more trust, and more room to grow and attracting new talents. I think equally all kind of very important to change ourselves, but also, you know, reporting lines and the organization 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 a full stack in Baidu, which is very unique value in the China tech 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, you have your own business and you've had it for

while. And I'm trying to wrap my head. What is the rationale? How much is it about just wanting to be able to control your own fate more? And 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. I think thanks for the tech trend in the past kind of year and two. So if you look at the entire computing power consumed, for example, last year, most of the consumptions actually relating to the pre-training of large foundation model,

but right now, you know, many of them go into the inference and the completed 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 our cloud,

focusing on the inference and application is a unique way of foresee the positive network effects. I think that's where the area we want to invest. And also, given the issue you mentioned, I think within that defined areas, where I 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. The prize gets even sweeter. With flights from Porter Airlines, three nights at Residence Inn downtown Montreal, and $1,000 cash. Download the free IHeart Radio app, listen to Pure Country for 10 minutes, and enter to win. Lasso, Montreal. Every day you listen is another chance to win. Gain insight on the innovators, disruptors, and tech-driven trends shaping today's complex economy.

I'm Carol Masser. And I'm Tim Steneveck. Wrap up your workday with the Bloomberg Businessweek 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 miss during the day. Subscribe to the Bloomberg Business Week Daily podcast on Apple, Spotify, or anywhere you listen. 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 U.S. 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 call it. I call it. I call it. I call it. I call. I, I call you, I guess, balancing that, I guess, balancing that

So I called it impossible triangle. So I kind of scratch my head every few months, every few weeks, depends on I also see headline numbers on broken news with other peers as well. So sometimes I make it 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. One is our operating profit increase almost doubled on a Q-on-Q-Q basis. And number two, our cloud revenue grew about 79% on a Y-O-Y basis, which is almost double of the Y-O-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 growth in the market. However, my KAPX is not seeing kind of double even third multiple times. So I think the way of resolving that is as a CFO or as a management team of a head.

heavy cabbacks invested AI tech company right now. I need to find a way on one 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 a conscious regarding the I-OI and in mind to look at the 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 cashback. And during that frame, obviously there's a no price some memories, there's difficulty on the IDC centers, and the huge spending on servers. So my point is, as a sample, on every project, you need to look at the entire lifecycle, not only at one time, but also the pacing important because the foundation model R&D always taking a few months, right? So these are the things you need to keep on mind. But my statement today is, as by 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, you know, half year later, when we check on this point, we can still keep on the same pattern, you know, high growth, less dollar spent, but better I why. I think that's probably the angle we want to achieve. Can I ask you something 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?

Do you also have AI psychosis? Yeah, but like, do you like, you know, for, you're 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 a great question. So there's an emerging area, for example, in this data sanity and all the kind of post-training efforts need to work on that.

You know, 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 more 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, I think the entire ego system has been robust for, if you think about, even in the mobile internet work, right? So as simple as data labeling to the alignment checks and the post-training and SFT, I think this kind of the full chain of the capability in terms of the telehealth.

and the pool of resources and the cost of data and sanity and all the checks 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 engineering, not a theoretical quantum leap. So on that, the engineering capability form, the China tech world and industry has been there with the key elements I mentioned, right? talents, lower costs, more efficient.

I think these are the few things I just want to point out that actually can help solve the issue. But as I mentioned, the things evolve very quickly, right? So, you know, 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 labs are very anxious about this,

and they published these model reports, and it says things that in 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 in 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 work for people and don't have a rogue goal, so to speak?

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 thoughts about that. So we kind of follow the new thoughts, but also doing our own tests as well. So overall, I think people in the open source community today, in my view, it's very collegial. So people still want to do a better model, frontier model for everyone globally,

not really on one country or two different places. 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 U.S., 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 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 the framework, right? So that's actually very important because it's not only about algorithms itself.

It's about the 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, given this entire infrastructure has been upgraded to a level that is kind of world-leading, I think the policy is 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 the cloud age in the past kind of 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 policymaker to think about it is a new model. It is a new thing. It is really a new and better tools to utilize and access resources we're already building. And existing resources will have building on existing platforms, which has been 100% compliant, but also we have a lot of support

from the policymakers, industry practitioners, 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 academias and industry practitioners actually contributing quite a lot of the good conversations to this environment. And my feeling is the policymakers through different channels has been very open, also listening

to the new frontier issues and the questions. I know this is a business conference and we want to keep things very professional here and not engage in gossip, et cetera. 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 Bidu 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 a little Dario gossip that people talk about in the office from his stinted Bidu? So that's why I want to put the ball back to your court. I want to share another gossip, which you probably want to hear. So probably in the past 100 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 world. Obviously, in China, you know, there are a different way. You have a new way calling, you know, not only the claw, 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 or saying, you know, he, actually want to work with Spideau because, you know, open clause is a tool. But the true is kind of eating up all the capacity, i.e. the skills, right? So everyone is contributing to the skills. And the cloud is actually grab all the skills and do the work. So I think one day we are pretty happy. You know, see, oh, Peter drop us a note very in a positive way because he kind of on one side noticed that the search is important capability of the skills. i.e. the skills in the open-claw environment.

So actually he asked by to work with him to beef up the search skills in order for the open cloud to do a better work to accessing the real-time information. Because today, the foundation model, the 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 yesterday doesn't capture, you know, Joe and Tracy what's talking about today. You need to have a new skills accessing the all-slots. what is happening in real time. That's right.

So I think, you know, Google globally and Biduva, China, they are the powerhouse for searching real-time information. So it has to be linking to the open clock. So I think that's actually one of the things we're pretty much happy to have about. So, you know, next day, we asked our engineers to link up with speeder, and we're actually part of this skill marketplace doing pretty okay. And right now I just want to share our foundation model called earning 5.1. Right now is ranked as the globally number one in a tax format of the global LM 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. But for the previous, I probably can talk with you after this open session. Yeah, no, you didn't give us any Dario gossip. But implicitly, because I know that the open-cloth guy, you know, it's originally called open-clod. And then Anthropics 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 OpenClaught is not the biggest fan of Dario's approach.

So 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 substack. 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 Bidu 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 a 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 markets, 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 powered the cloud. So the GCP grows, the Google Cloud, is growing faster, which is part of the reason is the TPU. And so by the way, we have our own trip department. And based on the public information, we recently did the public filing,

one who's being off these assets, right? So the chip, we have the theme with Google. For the foundation model, we have our earnings, they just mentioned. However, in the physical AI, called applications, or 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 my, even my friends, it was kind of surprised that each week, including San Francisco and including all the cities, Austin, Texas, and U.S., Waymo from Google, deliver about 500,000 trips per week. And in the last quarter, Bidu's Apollo, in a globally 27 cities, deliver about 350,000 trips, which is only about 25% fewer than Google. The part of that is not only about robot taxi,

is about how we use the data empower our own foundations, models, trainings, and also do inference, but also have a lot of know-how regarding the multimodal contents and all the different things. And the more important, if you look at the traditional search, right now on this quarter, also in the market, didn't notice that, you know, still even my friends telling me, oh, Henry, congratulations for your earnings, but your search is probably still 80% and 90% of the revenue, but the truth is for this border is declined to about 48%. So it's already below 50%.

So the new growing area, for example, the digital humans and also our AI application of software is becoming a powerhouse and growing very fast. So my point on that is if you look at key components or 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 multimodal contents. And we actually have our controlled subsidy called ICHI IQ in China, which is actually over 50% of market share in China. for certain long-form contents in China as well. So we also have our close loop of the data flat well as well, probably at a different scale, but I think it's still in the same format and the same model.

So my view is, yes, I think on one side, it is right that certain data and elements are in their own kind of pockets, but however, for Baidu, we still have access to those pockets. Probably better than other peers. But I want to also very honest admit, right, given Joe's my girlfriend, I still own him a little kind of gossip after this session. I just want to share my own challenge. Obviously, in China, you have different camps, right?

Different camps, it 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 car penetration in China is about 20, 30%, versus the US is kind of 90%. So 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 is a gap. But my confidence coming from this gap will further narrowed because everything will be on cloud environment, everyone to access real-time information, but also for Bidu, we have a four stack and each components 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 layer of the data itself. It is like love. You feel it in your heart. IR. Radio. Canada's number one streaming app for radio and podcasts, including IHeart Pride Canada, your favorite hits and must-have party bangers.

personalized and curated playlists like back in the day pride come together celebrate love take pride with you anytime anywhere just ask your smart speaker to play iHeart pride Canada stream us on your phone or listen now hi i'm barry ritholtz inviting you to join me for the masters in business podcast every week we bring you conversations with the people who shape markets investing and business i speak with CEOs Nobel laureates market innovators and legend investors. Whether you own stocks, bonds, real estate commodities, even crypto, these are discussions you absolutely need to hear. Subscribe to the Masters in Business podcast on Apple, Spotify, or anywhere you listen. So I am very glad that you brought up the Robotaxies because, first of all,

I just love Robotaxies, period. They're very fun to ride in. You took me on my first Waymo, right? Yeah, I remember. And I was a big, I was like, Tracy, you've got to ride in Waymo. You got a ride in a Waymo. And I think You were convinced. They're pretty good. Now we have to write it in an Apollo go, right? But here's another. Besides, I'm also excited about them as a business story for a very specific reason, which is that when I think of like Baidu, people call it the Google of China.

But, you know, separate markets, right? Google isn't in China. People in the U.S. don't, by and large, use Baidu as far as I know. But there's going to be cities now where there's going to be direct competition between Waymo and Apollo, including London, I think, is going to be the first. city where there's going to be head-to-head bell. And so this is exciting because I feel like, okay, the tech internet, the consumer facing internet giants of the U.S. and the consumer facing internet giants of China are for the first time going to really be competing in certain

identical consumer markets. And so what I'm curious about is like, not who you think is going to win. I presume you think you're going to win, but like, what is the dimension upon which the winner will emerge? Will it be the quality of the application? Will it be who can produce and secure automobiles in volume? What is the most important dimension that will determine the winner either globally or in a specific city? So before getting there, just, you know, also the car is one of my hobby too. Just, you know, Joe probably know, I'm actually a race car driver. So I got my race, my license as well. So every time I actually drive a little bit. So it's quite nice, enjoyable because I think that probably the remaining car you can use gasoline and drive

yourself probably 20 years from now. So before getting there, I think the key word is change the car ownership. Okay. My view is in my own calculation in U.S. as an example, right now each mile, including insurance, gas price, parking, on average it's about 60 to 80 cents per mile is the tipping points between rent a car versus owner car. So if it's cheaper than that, people will buy a car, but more expensive people will rent a car. So right now, for global player, I don't want to name and name, but the average robo-taxic cost today,

because the scale is still very small, it's about $1 to $2.5 per mile. It's a range, about $1 to $2.2. So my point is this curve, just like agents, getting more prevailing, is coming down very fast. So assuming at once some point, you know, five years, six years, whatever, 10 years, if globally the robot tax could deliver average price per mile coming down to, let's say, 60, 80 cents, US per mile, then many people were thinking buying a car because today is very

simple. Joe and Trista, you're probably in Hong Kong, you know, the parking is so expensive, even more expensive than the gas. And the gas in Hong Kong also very expensive. So first of all, the car right now is all EV drive car. Number two, you don't have a part of a parking. because you and me can drive and the car can go out. And number three, while we're having this 40 minutes,

my car actually can go up, pick up passengers, and I can make some money for me. Yeah, yeah, that's nice. That's what I'm saying is the physical AI agents on the road to make money for myself. So my view is, Robotaxi will change the human behavior getting out in terms of behavior, pattern of transportation. That's one thing, right?

So we and the Waymo and all other players globally are going to that direction. So that's my vision for the market going forward. However, as you mentioned about the market as a player in the near- competition. Competition. My view is right now the market is due very early and the time is very high. So in the last quarter, we ship up a car in London and as you know, both Waymo and and us are starting open the market at London.

Hopefully next year you will see a car and we have a good partner with both Uber and the Lyft and also with a grab in Southeast countries. So next time you probably call a car from Uber or Lue. Lift apps, you will get a bydo's car. So I think it's actually helping increasing the services, because one interesting note is the human driver probably don't work in midnight, right, in certain cities. But right now, the car actually can work 24 hours.

So expanding a new market. And right now it's still a very low percentage of penetration. So still cool, we have a lot of room to go. On the other end, to your question about the success factor, I think the two things. One is a technology need to cutting edge and improving. And number two is operation efficiency, right? So it actually have a lot of work need to be operational driven. For example, how many locations, your pickup passengers can be more efficient, right?

The charging stations and all the different network designs is actually very important. But given we are working on this business for kind of 13 years so far, and I can tell you one interesting fact. Globally, there are only two cities right now have over, you know, thousand cars in that scale, which is San Francisco and a win city in China, which is Google. operating in San Francisco and Apollo from Baidu operating one city in China. But I think our kind of partnership with both a Lyft and Uber globally with different cities, I think has been very collegial because the demand is much higher than the supply. Joe, I'm going to admit something slightly embarrassing.

Actually, you already know this, but I never learned to drive, partly because I grew up in Tokyo and then I moved to a bunch of other big cities and so I never needed to. And now I always joke that I'm basically, I'm never going to learn. I'm just going to hold out for the self-driving cars. So, you know, fingers crossed. It's coming. I hope so. I wanted to ask something about, you know, you've mentioned agents a number of times,

and this seems to be becoming the hot new thing in AI. And I know your CEO has talked about how one of the key metrics for Baidu is daily active agents. And my question is, how does that actually turn into revenue or return from a CFO? perspective because I understand with search, you know, you type something in, you see the ads, advertisers are paying you for that, but I'm very unclear how it works if the agent is actually going out and doing something. Yeah. So in the mobile internet where, you know, everyone looks at, for example, the DAU, the daily

active users, because that either fulfill information query demand and individuals are the primary users for many of the mobile applications in app stores. So the DAU was the primary matrix to measure that. However, in the recent conference, our chairman and founder of Baidu, Robin mentioned, based on his thought leadership, that DAA, which is the daily active agents, are the new kind of matrix to defining the success of agents. So I kind of very much agree on that. The reason is if you look at tasks, it's actually spread out into different verticals, right?

So right now, it's very difficult to find a new way to identify how much people using, especially how much value coming out from using AI. So the agents today is basically can deliver a final task, not only using as a tool for human beings. The agent is smart enough to think about that planning the task and completing the task. And obviously, in the way of interacting with human beings, it actually become more smarter and in a way that I'm working with more efficient planning of that. So, Trista, to your question, overall, my thinking is the DAA will measure not only how many people using that, but also how difficult it is. To your 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 in China, in China, called FAMO, 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 world. So we install this agent to one of the biggest pot in China and help them deploy and planning for the shipments and the logistics.

It is saving the cost of the idle time and improving the revenue of their pots. So the pots actually are willing to share a certain profit generation with us. So the key things I'm observing is, In the previous meetings, even I'm the CFO, but actually I'm attending a lot of, you know, the meetings to meet with a 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 CFO and their CEO. But right now, most of the meeting we are having today is with the CEO himself. Because AI right now is not only about BIDU, it's really about helping our client. 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 willing to share certain economics with us. So I think the customization is also diminished because right now the agents can be used different paths.

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 does revenue look like or what is a sales price look like, because another thing people 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, et cetera. 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, I'm sort of 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 probably is very energetic, right? If you do that like 10, 20 times in two weeks, it's very exhausted, right?

Because you need to think about, wait, you're saying we're going to be replaced? You share my views, right? Wait, wait, is that what you're insinuated? We're going to get replaced because we get exhausted, but the AI agent won't So my point is, the humans, they're the motivation and the knowledge base, have their own kind of territory to be friends. 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 one example I 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, it looks fun. But, you know, the CalOO can not work like 24 hours, right? 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 their own countries. it actually can help 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 Q&A sessions, on the questions, it's actually reacting to the random users asking a wide range of questions. That knowledge actually is very fluent in a way that for the foundation model, it is the way it works, right? So I think that actually has different user case,

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 Biao, I think I had Tao Bows 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 select right products.

That's another way we probably can monetize on that. 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 careful by accident. So actually my both bachelor and master training was a chip designer myself. So I think after joining Baidu, I definitely realized that Baidu's chip product has been really, really high quality and really good for the inference for all the things we talk about, helping our DAA to grow as well.

So based on the public information, we already file the confidential filing for spinoff 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 foundation model, they are all very important. So after spinoff, we hopefully can enhancing that ego system. And as you know, chip is not only the hardware. I deeply understand it is about ego systems. We need to work pretty well with our customers and the suppliers and the software developers all at one goal. And I think to be a separate list of public company will help to achieve that goal, not only as a hardware, but also the entire ecosystem

as well. And our customer will view our chip products more neutral and independent products that can actually do more testing and more usage on their own cases. Yeah, it seems to recall reading that you've figured out a way that developers can easily port over their Kuda stack over to your stack without much trouble. Henry, thank you so much for coming on Obloss. Our first live recording anywhere in Asia, really appreciate it. Again, thank you, thank you. 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 Wisenthall. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez, at Carmen Erman, Dashel Bennett, at Dashbot, Kail Brooks at Kail Brooks, and Kevin Lazzano at Kevin Lloyd-Lazano. And for more OddLod's content, go to Bloomberg.com slash oddlots for the daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord.

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