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Hello and welcome to another episode of the Odd Loss Podcast. I'm Joe Weisendahl. And I'm Tracy Allaway. So we were in Hong Kong recently. That was a lot of fun. Nice to be back. Yeah, I haven't been back for four years. 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. And we finally got a chance to talk to a couple of 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.
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 for 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 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 odd lots trivia night while we were in Hong Kong. Our first non-US overseas odd lots 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 AdLots. Thanks for having me. And it's great the season. I think Hong Kong, it definitely is great to see both Joe and Tracy. Thank you. Very nice of you to say. So why don't 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 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 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. And I think you'll probably put the tough question in the 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 at CFO, we focus on that, is on application layers. So without any of that, this IOI cannot work. So to answer your 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 out very open to hosting other models. And the MyChap, 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 given the tough question, if I want a big one as a student, A, B, C, D, I want to pick 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 Baidu. 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. You know, the AGI, the Hogwarts 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, yes.
I'm measuring our internal consumption of the token. I really want to look at 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 OpenClaw and, for example, our own application called DoMate, also is a real agent, equipment 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. OpenClaw 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 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 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 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 going to 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 the 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 evolved very fast. I think that's the beauty part of AI. So we don't want to constrain ourselves by, 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 it works. So I think we want to be 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 hand, 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 of these three parameters as a package, not only on the number of tokens, that's one thing. On the other hand, you'll 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 will not waste 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 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 I'll mention. It is not only a tool, it's a mindset. And the mindset becomes automatic and more intelligent. If 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 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 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? Yes, great question. So let's bring a different perspective. I think, you know, 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, such as that. But right now, I think AI, especially for the big corporation like us, also changes 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 clients versus think about new tools affecting ourselves. So Tris is right. I think there are a few things we actually make a lot of.
different thinkings 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 the company. But more importantly, I think it's really about giving people more autonomy to work in the 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're very happy to working with one-person company because they actually use our AI tool very nicely and they're 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 little tool called Doodoo, right? In Chinese, it's a very kind of unique name, which is actually our internal kind of open cloud, similar tools, and which actually enhancing people's efficiency. And to a point, I think, give 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 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 thing is that people see the application is important. They can work on a full stack in Baidu, which is a 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 the 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 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 inference and the tasks and if you look at the different stacks i think right now we are actually fitting to a incremental growing piece of the market which is well defined with a clear boundary which is not
focus on pre-training for a very super-scale foundation model. However, the inference application is important. So to a point, I think our product supporting our 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 you mentioned, I think within that defined areas, we all 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.
There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief podcast. Every week, experts from Barclays markets and research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee, so search Barclays Brief wherever you get your podcasts.
It's the same hosts and the same chemistry with all the hilarious moments you've been missing right on your screen. Open the free iHeartRadio app. Search video podcasts and tap watch. 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? So I call it impossible triangle. So I kind of scratched my head.
every few months every few weeks depends on also see headline 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 so in the recent quarter earnings we you know we mentioned we actually solved partially on this impossible triangles. One is our operating profit increase almost doubled on the Q&Q base. And number two, our cloud revenue grew about 39% on a YOY basis, which is almost double of the YOY 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 capex is not seeing kind of double even third.
multiple times so i think the way of resolving that is as a sample or as a management team of a heavy capex investor ai tech company right now 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 roi and roe 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 cash back and during that frame obviously there's a no price hikes on memories, there's difficulty on IDC centers, and the huge spending on servers. So my point is, as I say for, on every project, you need to look at the entire lifecycle, not only at one time, but also the pacing is important, because the foundation model R&D always takes a few months, right? So these are the things you need to keep in mind. But my statement today is, as Baidu, we want to invest probably in a more responsible manner to the shareholders, but do not diminish our ambitions.
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 working on that. And maybe, you know, half year later, when we check on this one, we can still keep on the same pattern, you know, high growth, less dollar spend, but better ROI. I think that's probably the angle we want to achieve. Can I ask you something about 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 and 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? Speaking of like trying to make money, 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, that'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 flywheel to the alignment, to the data quality and the labeling, I think the entire ecosystem has been robust for, if you think about it, even in the mobile internet work, right? So as simple as data labeling to the alignment checks and post-training and SFT, I think this kind of a full chain of the capability in terms of the talents and the pool of resources and the cost of data ascending it and all the checks has been, in my view, a little bit kind of more efficient in the 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, right? So on that, the engineering capability from 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 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 lab is very anxious about this and they publish 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... 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 models especially publishing recently also will publish their thoughts so we kind of follow the newest 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 for everyone globally not really on one country 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 definitely have 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 everything 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 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 we 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 trust 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 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, it's a new tool to actually cover all the things we are doing. So I think it is not a new concept for the policymakers to think about. It's a new model. It is a new thing. It is really a new and better tools to utilize and access the resources we're already building. And the existing resources will be building on existing platforms, which has been 100% compliant. But also, we have a lot of support from the policymakers, industry practitioners, academics. They actually all contribute to that. So overall, my feeling is it's a very transparent and open environment, not only China, but also globally.
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 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 Baidu 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 Dario gossip that people talk about in the office from his stint at Baidu? So that's why I want to put the ball back to your porch. I want to share another gossip, which you probably want to hear. So probably in the past kind of 100 days, right, has OpenCloud become very popular, right? And educated market about how AI is really getting to the real.
task and the real world obviously in china you know there are different ways 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 put i think instagram saying you know he actually want to work with baidu because you know open cloud is a tool but the tool is it's 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 right 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 an important capability of the skills, i.e. the skills in the OpenCloud environment. So actually he asked Baidu to work with him to beef up the search skills in order for OpenCloud to do a better work to accessing the real-time information. Because today, the foundation model is 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 were talking about today. You need to have new skills accessing the odd slots, what is happening in real time. That's right. So I think, you know, Google globally and Bidu for China, they are the powerhouse for searching the real-time information. So it has to be linking to the blog. So I think that's actually one of the things we're pretty much happy to have about. So, you know, next day, we ask our engineers to link up with Peter, 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, it's ranked as the globally number one in the text 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 person i probably can talk with you after this open yeah no 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 gave us a little drama there thank you you've mentioned search a number of times already and Data is obviously very important to AI. We spoke with Grace Xiao 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 Baidu? Because you've got a lot of data. You're using it for Ernie. How is that transition process actually carried out? Yeah, sure. I'll probably talk about something the market has not noticed enough. And I'll talk about what's the real challenge, right? So it's obvious.
Two sides of the story. I think I'm very happy to talk about Google versus what we think about Baidu in some certain formats. A few things. I think the markets, not only the capital markets, but also the industry, has kind of found the value a little bit regarding the same components we actually imagine with the same structure of Google is monetizing and have this integrated capacity. So first of all, Google has its own TPU, which powered the cloud. So the GCP growth at Google Cloud is growing faster, which is part of the reason it's a TPU. And so Baidu, we have our own chip department.
And, you know, based on public information, we recently did the public filing. We want to spin off these assets, right? So the trip, we have the same with Google. For the foundation model, we have our earnings, the suspension. However, in the physical AI called applications, or called work 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, I was kind of surprised that each week, including San Francisco and including all the cities, Austin, Texas, and U.S., Waymo, from Google, delivered about 500,000 trips per week. And in the last quarter, Baidu's Apollo Go, in a globally, 27 cities, delivered about 350,000 trips, which is only about 25%.
fewer than Google. The part of that is not only about robot hacks, it's about how we're using the data to 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 more importantly, if you look at the traditional search, right now on this border, also the market didn't notice that, even my friends telling me, oh, Henry, congratulations for that, for your earnings, but your search is probably still 80, 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 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, RoboTaxi 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 controlled subsidy called iQIQ 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 closed loop of the data fly well as well. Probably at a different scale, but I think it's doing 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 is my girlfriend, I still owe 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, they 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, this...
you know, 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%. 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's a gap. But my confidence coming from this gap will further narrow because everything will be on cloud environments, everyone access real-time information, but also for Baidu, we have a full 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 benefits from the different layer of the day.
Want to know how three words might have changed our understanding of the biggest espionage case in US history? Or the lengths the SEC might go to investigate a golf tee time? The answers are out there. The trick is getting the government to share them. From Bloomberg and No Smiling, this is Disclosure, a podcast about prying those secrets loose and the unexpected places that takes us. Listen or watch wherever you get your podcasts.
So I'm very glad that you brought up the robo-taxis because, first of all, I just love robo-taxis, period. They're very fun to ride in. You took me on my first Waymo ride. I remember. I was like, Tracy, you've got to ride in Waymo. You've got to ride in Waymo. And I think you were convinced. Now we have to ride 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 Baidu, people call it the Google of China, etc. 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 and including London, I think is going to be the first city where there's going to be head to head bow. And so this is exciting because I feel like, OK, the tech internet, the consumer facing internet giants of the US 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 to know, also the car is one of my hobbies too. I'm actually a race car driver. So I got my license as well. So every time I actually drive a little bit. So it's quite nice and 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 the U.S., as an example, right now, each mile, including insurance, gas price, parkings, on average, is about $0.60 to $0.80 per mile, is the tipping point between rent-a-car versus own-a-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 players, I don't want to name names, but the average robot tax it costs today, because the scale is still very small, is about $1 to $2.5 per mile. That's a wrench, about $1 to $2. So my point is, this curve, just like agents, getting more prevailing, is coming down very fast. So assuming at some point...
you know, five years, six years, whatever, 10 years. If globally, the Robotex can deliver average price per mile coming down to, let's say, 60 to 80 cents US per mile, then many people were thinking buying a car because today it's very simple. You know, Joe and Tracy are probably in Hong Kong, you know, the parking is so expensive, even more expensive than the gas. And the gas in Hong Kong are also very expensive. So first of all, the car right now is all EV.
drive car number two you don't have a parking parking because you and me can drive in the car and go out and number three why are we having this 40 minutes my car actually can go out pick up passengers and can make some money for me right so it's a new agent so that's what i'm saying it's a physical ai agents on the road but making money for myself so my view is robo taxi will change the human behavior of getting out in terms of behavior pattern of transportation so that's one one thing right so we and waymo and all other players globally 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 term competition, my view is right now that the market is still very early and the time is very high. So in the last quarter, we shipped our car in London. And as you know, you know, both Waymo and us are starting to open the market in London. Hopefully next year you will see a car. And, you know, we have a partner with both Uber and Lyft and also with Zagrad in Southeast countries. So next time you probably call a car from Uber or Lyft apps, you will get a buy-to-a-car. So I think it's actually helping increasing the services because one interesting notice, the human driver probably don't work in the...
midnight in certain cities. But right now, the car actually can work 24 hours. So it's expanding a new market. And right now, it's still a very low percentage of penetration. So it's still going to have a lot of room to go. On the other hand, to your question about the success factor, I think the two things. One, there's a technology need to be cutting edge and improving. And number two is operation efficiency. So it's actually a lot of work need to be operational driven. For example, how many locations you pick up passengers to be more efficient. The charging stations and all the different network designs is actually important. But given we are working on this business for 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 one 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, you know, 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's 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 a mobile internet where, you know, everyone look at, for example, the DAO, the daily active users, because that either fulfill information query demand and individual as the primary users for many of the mobile applications in app stores. So the DAO was the primary matrix to measure that. However, in a recent conference, our chairman and founder of AIDU, Robin,
mentioned, I think based on his 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 the 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 are 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 the planning.
the task and completing the task. And obviously, in the way of interacting with human beings, it actually becomes more smarter and in a way that working with more efficient planning of that. So to your question, overall, my thinking is the DAA will measure not only how many people are using that, but also how difficult it is. To your question, the result-driven payment is coming up in the 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, called FAMO.
really solving complicated issues for enterprises. It's very similar to AlphaGo, which actually in the previous years has to do with 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 logistics. It is saving the cost of idle time and improving the revenue of their pots. So the pot's 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 a successful, but actually I'm attending a lot of the meetings to meet with client. In the previous meeting without AI.
most of the meetings we are talking with is the CTO and CIO of that company because it was a tool. It was a cost center. So they need to find a budget internally. 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 Baidu, it's 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 becomes relatively more efficient in a way that we get into the number one decision makers. He has a budget and he knows that driving the policy 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 parts. You can repeating that success and lower the 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 does 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, 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 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 a 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 exhaustive, right? Because you need to think about... Wait, you're saying we're going to be replaced? Wait, wait, is that what you're insinuating? We're going to get replaced because we get exhausted, but the AI agent won't get exhausted? So, you know, so my point is the humans, their 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 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. It looks fun. But you know, the come 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 sell into 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 you know the e-commerce revenue so that's why we have our own kind of product called digital employees we actually help our merchants 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 the way that for the foundation model, it is the way it works. So I think that actually has a 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 Taobao, I think I had Taobao 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 the right products. So 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 successful 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 filed a confidential filing for spinoff of our chip 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 this being off, we hopefully can enhance that ecosystem. And as you know, chip is not only the hardware. I deeply understand it is about ecosystems. 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, a 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, 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 Ablaj. Our first live recording anywhere in Asia. Really appreciate it. 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 Allaway. You can follow me at Tracy Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more OddLots content, go to Bloomberg.com slash OddLots. We have a daily newsletter on all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy OddLots, if you like it when we talk to Chinese tech executives, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there.
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