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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.

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Speaker 2: Hello and welcome to another episode of the All Thoughts podcast.

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Speaker 3: I'm Tracy Allaway.

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Speaker 4: And I'm Jill Wisenthal.

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Speaker 3: So Joe.

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Speaker 2: We are here in Hong Kong still and we're learning a lot of different things, a lot of interesting things. One of the most interesting things we heard actually came yesterday from the bay Do CFO where we were just talking casually ahead of our interview, but he was saying that the word token has now officially been added to the Chinese Mandarin dictionary and that the characters that actually make up the Chinese word for token are something like word currency.

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Speaker 4: I think that's so fascinating, you know, I'm fascinated by the etymology the word token specifically, so I was thrilled to hear that. But it is, you know, it's like, we know the word token in the monetary context, we no chuck e cheese tokens, no crypto tookens. But we also know that since the middle of the twentieth century, linguists have been using token to describe more or less a word. And then obviously with LLLMS, we talk about these linguistic tokens a lot so to see that in Chinese the formal term is a merger of these two concepts. Is I find a very intellectually satisfying thing to learn about.

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Speaker 2: Bringing up chuck e cheese tokens is really a way to make sure everyone knows that you're a millennial.

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Speaker 4: Tem Yeah, that's right, that's right, arcade, I should just say arcade token, that's right.

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Speaker 3: Yeah, okay.

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Speaker 2: And I think what's really interesting about word currency itself is it implies that it's connected to spending, right of course, And so when you think about the big companies that are spending all this money on tokens, and I guess the AI infrastructure build out more broadly, CFO has become really important, right totally.

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Speaker 4: So we you know, the headlines by and large are about Capex, right, and that's going to continue to be because of the data center builder. We're going to talk about that, but a lot of it is also going to be opex and figuring out how to within a company allocating token permissions and caps and so forth. And I doubt anyone has figured out the final answer. But if two different people can get different value out of using AI models, then there is no way that it makes sense for them to have the same token budgets. By the way, Tracy, can I ask you a personal question that I've never asked you before?

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Speaker 3: Oh?

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Speaker 4: Okay, outside of the work context, are you a macro PC person?

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Speaker 2: Oh? I, I only have I only have work computers at the moment.

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Speaker 4: Okay, so all right, before that, but.

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Speaker 3: Before that, definitely PC?

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Speaker 4: Okay, good?

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Speaker 3: Yeah, And in fact, it's not it's not a choice, is it.

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Speaker 1: Like?

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Speaker 3: I sorry, I really don't like Max.

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Speaker 4: I don't either, And in particular, I am long been a fan of the what used to be the IBM think Pad laptop with the famous red button, which is now owned by Lenovo.

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Speaker 2: That's right, you've talked about that computer before. I can say that this is categorically true. Joe likes that computer. But what's interesting about Lenovo is like, okay, it's famous for the computer with a little red button in the middle, but it's now.

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Speaker 3: Making an AI play.

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Speaker 2: Sure, I mean everyone's making an AI play, but it's doing it from a different perspective. So AI integrating into the actual computer, the hardware, but it's also doing cloud right, So this is a really good opportunity to I guess take the temperature on AI, the AI build out, the AI spend from a bunch of different perspectives. So we do, in fact have the perfect guest. We're going to be speaking with, Winston Chang. He is the CFO of Lenovo's Winston thanks so much for coming on O lots.

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Speaker 5: Hey, thank you Tracy, and thank you Joe.

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Speaker 2: Why don't you go ahead and describe what Lenovo is and what the AI play is and how it actually fits into the existing business.

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Speaker 5: Yeah, Lenovo is a global AI infrastructure company that provides pocket to cloud AI infrastructure for the consumer and the enterprise. I think that's really in a nutshell, and so today we're able to provide this to hyperscalers which are doing a lot of the spending are driven by training demand today. And then given our IBM x eighty six heritage, which we also acquire the server from the IBM actually, so we actually are very strong in CPU compute as well, given that IBM was a dominant player in the x eighty six architecture. So from that perspective, we're well positioned for inferencing needs of the enterprise and also the hyper scalers in terms of in the cloud as well. So I think from the person active of then you talked about tokens, and I think from a token perspective, really today people are on the early stage of how much I'm really paying for I heard someone saying that there was an engineer at a Portuit company which I will not mention, that apparently spend one hundred million dollars a month on tokens. And so as a CFO, I would have concerns because that was clearly not in the budget, right not saying that I was from the Novo So we cannot have that happen. And I think we need to be able to drive the productivity or efficiencies as it relates to that budgeting of the token generation. And I think a lot of that would happen on device where you may just pay a higher price for a device, but you know what you're going to be able to do on compute for the security of the data and for the privacy that you want to interact with your AI agent.

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Speaker 2: So just to be clear, for the computers themselves, they can do some inference, right, but where it makes sense you route it to the cloud.

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Speaker 3: Is that right?

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Speaker 5: That that is our goal. So the Lenovo agentic AI today, what we are good at is really integrating and maximizing the compute capabilities on a device. And from that perspective, given the various needs in terms of various operating systems agents that may now want to sit on top of a device, I think our agent aims to orchestrate the various LMS, will take the compressed versions of these lllms. We will, depending on the partnership, be able to do the on device compress LLMS versions and do that as a local compute, but in certain queries allow it to go on the cloud and therefore probably would allow the user probably to spend in terms of the token generation.

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Speaker 4: It's interesting this word orchestrate because so you have the AI agent orchestrating maybe a bunch of different subagents to complete some tasks, and that is something that perhaps is done best on a CPU. A company that builds servers is also an orchestrator of the supply chain and acquiring the different components that go into a server, et cetera. I want to go back to something you said in your first answer, because I think this action will get to the core of this new era. You said, Okay, an engineer spends one hundred million dollars in a month on tokens, And it's like that would not make you happy as a CFO, but it could.

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Speaker 5: Make you happy.

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Speaker 4: Right. What if you had a multi year database migration plan that you think, oh, this would be a five hundred million dollar job and the engineer does it in a month for one hundred million dollars via tokens. Don't you have to at least be open to the possibility that that was money well spent.

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Speaker 5: Absolutely, Joe. I think everything is about the return, right and the planning. So we're not afraid to invest. As a CFO. You are there to allocate capital. You're not there to constrain capital. You have to allocate, but have to be clear in terms of that return. And I think in that case, it's probably one where they weren't sure in terms of that what they were particularly doing in terms of spending. It wasn't in budgeting, and that goes to the point of what is happening today. I think most enterprises were at the early stages of how people were changing from a subscription based model to a token usage model in terms of the compute capabilities, and so that is at the beginning and enterprises are starting to figure out how do I really track that spend and therefore, what do I really want to get out of that return? And so we're on that early stage. So you're absolutely right in terms of that we would want to spend if we can get a return out of it. You're absolutely You're absolutely right. And I think preliminary data and anecdotal evidence from a lot of people in a chat recognized the power of AI and the probability of generating increased productivity and potentially even cost savings.

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Speaker 2: More about measuring the actual return, And again I realized it's early stages, But I'm curious how you think it will actually be done because we've been asking a bunch of executives how do you internally benchmark your AI projects or your token spend? And everyone says productivity or cost savings or whatever, But those are they sound very theoretical. So is there more concrete? I guess KPIs that you were looking at.

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Speaker 5: Yeah, I think it really depends on your specific enterprise. In our case, I would really hong in on a few areas for me in terms of pricing and that visibility of demand, in terms of channel inventory and the demand of one hundred and eighty markets and what I would take to actually provide and work with my channel partners in terms of managing that inventory supply. There's a lot of economics involved in that. We can optimize that to run by AI rather than spread out in one hundred and a markets. I think there could be a lot of productivity gains from that that could be seen from a dollar perspective rather than just generally in terms of making very blanket general statements. Other areas I would say that you could also increase productivity is pretty clear in terms of data spend that you have today, A lot of hedge fund friends and investor friends really talk about in terms of what their savings could be in terms of how much they're paying for certain data subscriptions. And then, of course, as it relates to IR functions to M and A functions in terms of how you increase productivity, those functions tend to be smaller in headcount, so it's really less about the heckcount reduction, but probably about the increased productivity. And therefore, are you making the right decision because those are small headcount high impact areas, right, you could have a higher stock price and therefore your cost of capital becomes much more efficient, and you're financing even M and A. If you have better analysis, you're optimizing in terms of how you're returning in terms of that capital deployment. So from those perspectives, I think it works out. And then of course, areas like marketing where we're spending a lot out there in terms of you know, video and other production generation, those actually can be done by AI today, so we really need to look at that. So that's just a few examples, but I think there must be hundreds and hundreds and we're in the process of trying to sort through that.

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Speaker 4: Let's go back to the theoretical engineer who spends one hundred million dollars on tokens in a month, because you know, it seems to me like you're going to have someone who actually can with their you know, intuitions and skills with AI et cetera, actually deliver ROI, even if the nominal amount they spend is nose bleeding. And then other people are just gonna waste a lot of money. And I guarantee I know a lot about wasting money on AI, and I guarantee that it's very easy to develop that psychosis whereby you believe you're doing some incredible things that feel like magic and productivity, and it turns out that you really haven't built anything, et cetera. Like, it's very easy to delude yourself into thinking you're being productive with one's AI use. What I'm curious about, specifically is how do you even go about, say, identifying the employees or the teams who might really be people who you really should give a long leash to with their spend How do you even like identify who those are that should Yes, let's not be too aggressive in their consumption, in capping their.

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Speaker 2: Consumption, don't give your tokens to a joe with AI side vis.

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Speaker 4: How do you avoid giving all of the more, giving me the uncut budget and finding that person who actually deserves a very liberal spending budget.

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Speaker 5: Yeah, and you know, these are great questions that are so relevant, particularly in a large enterprise like us, in a global for a global two hundred company, right in one hundred and eighty markets, eighty plus billion in terms of revenues. We have massive scales and so many tens and thousands of employees. So it's really difficult from a cfo's perspective to micromanage down to the person's allocation. So you really have to be able to say, first of all, what are the agents in each domain capable of doing today? Right? And how what is that productivity and also capability, and then you can say, well, what specifically can it do today? And then let's get the dollars at least from the CFO perspective, let's then eliminate the dollar spend in those areas and really force the AI. I have come to the probably the conclusion that to really effectively drive things, I need to then force discipline or starve certain budgets to then allocate, because that really changes behavior because if you continue to allocate that budget, they will continue to use it and go on the old behavior. But I think human beings are really adaptable, and I think we tend to be good at survival, right, And so if you force a certain situation, I think people, particularly with the functions of AI today, people will be able to then really try to use it because they don't have the budget for the alternative. I think that's really from a cfo's place.

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Speaker 4: Yeah, yeah, well, let me press on it. I find this to be very interesting and maybe it even gets to a certain philosophical split perhaps among CFOs, because I think, okay, there's one school of thought would say, okay, you keep these very tight constraints and then you see who can do the most with the constraints, and then you derive valuable information that comes back into your office that that is valuable data there. And then there seems to be another school, which is, let everyone have really high caps. And we read these stories of American tech companies about the internal quasi tooken maxing competition, and I think that school of thought would say, look, let the employees go to town and that is the way that we discover who can actually use these tools productively, et cetera. But would you say that that is actually sort of a philosophical split in CFOs of tech companies in the early years figuring out this optimal path.

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Speaker 5: And I think that that decision exists within the same company. But it could be two paths that you just described, because for example, where we really it's a face of innovation today in terms of where the market's going, and so we really need to put more dollars in terms of innovation. And you can see that in terms of our spend so where we really and that's the emphasis from our chairman and CEO as well, it's really around making sure that we take advantage of this period. We're calling this the AI decade within the NOVO, and we're in the first year of that journey. And I think this is an opportune time for us because the way we're positioned today, where we have global manufacturing in a fairly fragmented world, where we have a lot of data and production and supply chain concerns, where we are able to be local and be able to supply that on the local basis and comply to each region's needs, sustainability needs for certain regions, particularly like EU. One hundred percent of our laptops that you like is actually in recyclable box paper, and also within the laptops a lot of them are also recyclable materials as well, and we also have a refurbished effort given the components constraints today. So I think from that perspective, we really want to spend dollar on innovation now. As you say, then there's another pocket. Should the likes of even within finance, be able to have an unlimited budget on AI, I think you could probably be sent more sensible in that and say, well, specifically within certain analysis and FP and A or accounting, what was it that you actually used to spend? So, for example, without naming a party, we outsource certain back office function to a service provider and that's a pretty costly one for us annually. Now in terms of that capabilities that it's really a process flow. So should we ultimately be able to automate everything and be able to achieve savings and that now we are okay to continue to outsource as long as that partner also innovates and be able to share that savings with us as well. So I think it really depends on where you want to go, very specific in each area within your function and your company. So in my function, there's so many areas planning, tax, FP and a accounting, treasury M and A right you name it, corporate finance. So within all of these areas, where are we going to optimize be more efficient productive? And I think that cost all or sometimes to me is the return is significant. I was just with the head of my tax this morning. I was said, if we just pull together and drive the next year's efforts, the tax savings or optimization right that we do, we'll more than pay for a little small trip of twenty something people together. That's very very much well spent. But I think that's just a philosophy. It's less AI driven, but it's really the philosophy of where that return is. And I think you allocated earlier, you spend based on the ROI generation, and I think today you have to recognize first the AI capabilities that you can have. I think the other thing we haven't touched upon is really the security within the enterprise of using AI, right. I think that's a broad topic that we really should touch on because today I think there is really concerns about what we don't know about using external AI within our enterprise. So I think that's also a concern in terms of how effective you can use AI.

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Speaker 2: We should definitely talk more about that. But you just mentioned the word unlimited in the context of AI spend and coming from the US, this is something that we're really seeing and I guess living through at the moment. Like people talk about the total addressable market being like basically infinite at this point, if you look at the SpaceX IPO, like the total addressable market is now the entire universe, right. I guess my question is how do you compete against those types of companies who seem to have investors throwing money at them. I know your stock price has gone up quite a lot, so that obviously helps.

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Speaker 3: And then a more.

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Speaker 2: Broader question, what are the key differences between how Chinese companies are approaching AI versus how US companies are approaching AI?

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Speaker 5: Sounds good. I think there's two very distinct questions there. The first one really with respect to Lenovo's position in the entire tech ecosystem. I was in Davos earlier this year, and when I can bag was particularly affected by Mark Kartney's speech and really talking about the superpowers in the middle powers, and of course within the middle power their superpowers. I actually use that within my own management committee in terms of referring our own position within the tech stack. And I think as big as we are as a middle power, I think we're definitely not a superpower. We have to recognize that. But I think from our perspective being the number one device PC manufacturer in the world, having a strong ecosystem and the best broadest portfolio set right across PC, tablets and mobile phones, and great partners to Microsoft and Googles of the world in terms of that type of operating system, and of course the chip suppliers like Nvidia, Intel, AMD and Qualcom and of course now aren't based chips as well, and also the Chinese tech stack that's also coming up. I think from that perspective, we are there to really enable and I think prior to this stock run, we're really not recognized by the market in terms of our ability to be partners to two thousand suppliers in the tech ecosystem driving almost two trillion dollars plus of spend and we're right in the middle of it and distributing this across the board, even in one hundred and eighty markets. So I think from that perspective we really needed to be recognized. But at the same time, we're very humble, and I think our chairman really used that word in terms of staying humble, and I think we are because we're also micro. From that perspective, most of the profits have been towards the IC companies, the OS companies. If you look at the PC revolution and the tech stack, the OS company is now trillion dollars. The chip companies are now crossing trillion dollars, but have before been hundreds of billions of dollars, and we only most recently became around I think thirty to forty billion dollars, but before that sub twenty billion. So I think that's the value chain that you're in. But I think we probably did not optimize it as well, and I think there's an opportunity time for the device makers today to optimize. And of course, distribution is very critical. Supply chain is critical. Ability to aggregate supply chain, to manufacture at scale, at cost efficiently and deliver it to your customers, to be able to service it, to provide a security and trust and the aftermarket service support. That must have a value beyond what we're being recognized by the market. I mean, I absolutely believe in that, and I think we need to drive that in terms of financial returns from that perspective, and AI wave definitely creates the opportunity for the likes of Lenovo, and I think we're the best position in this because there's no one company like us from the end to end portfolio and the global manufacturing, So you know, I think that's a very important point. And in terms of how the US and Chinese tech stact today are differentiated, or AI companies are differentiated today, obviously because of the restrictions. I know that probably the most efficient compute today is probably from the US chip company. So obviously the chip availability is something that the Chinese AI companies probably have to work with, but in that way they probably managed to produce at much lower costs and very efficiently as well. So you see today a lot of chatter about the cost per token generation from the like of a deep seak being, and I don't have official numbers, so this is just quoting what I've heard, like one fiftieth of those in the US. So I think from that perspective, right as I said, human beings or companies. And if you really think about the Darwinism in economic theories, the free markets create the hot, tapest competitors and the most healthy competitors. So I think in China, for those that compete in such a there's a word called involution in China. Involution it only happens in China market. So if they can survive within their cutthrow market under the constraints of the chip supply, they are pretty anywhere, they are pretty strong. So I think that would be one difference. I would say, Yeah, I want.

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Speaker 4: To talk about competition. You mentioned the deep and broad relationship you have with all these suppliers across the supply chain, and when I think about like when I think about competition within the server space specifically, I feel like there are two dimensions through which you could win. And one of them is, of course, like, Okay, you have total supply chain mastery. You can keep inventories low because you're just in time delivery of everything, and you just have this beautifully efficient supply chain with the world's best parts makers all around the world. And then there's another way you could win, which is your server is just more performance than a competitors server et cetera. And we know other competitors like like Adele or super Micro, et cetera. Those are both strike me as dimensions upon which a company could win the server market. Which to you is more important? Is it the sort of supply chain excellence or is it the quality of the server itself.

00:25:13
Speaker 5: I think it's not an either or question. It's fully integrated tax STAC today the value to a customer or a partner, and I think there are less customers today than there are really a partner because their architecture is not driven and the spend is a multi yeer spent, so they're coming to you for the multi yeer plan, not just a single purchase. So we take that attitude and we need to have that attitude with our suppliers as well. We have invested well over ten years in these factories. We have thirty factories of World twelve and each region of the world. We can produce in Europe and Hungary for the European market or the US market. We can produce in Mexico in the US for the US market, we can produce in Asia for the Asian market, and now we're building that factory in the Middle in Riad servers also for the Middle East market, and of course China market. We always have factories there as well as for the China market and other markets. So I think from that perspective we are really truly global. From that perspective today, the bottlenecks are not only in getting supply. After you supply, can you then put it together? Do you have the production capability to be able to produce? Then do you have the capability to test what is being produced? So we at Lenovo provide that end to end capability to a customer and that ensures them to have the safety and one stop shop. In fact, our SSG can actually also so the Solutions and Services Group at Lenovo can also build data centers, so different from our OEM like competitors, they can actually build data centers. So today the customers can hyperscallet can actually come to Lenovo with a plan to say I have a plan to have this site. So people who are not even hyperscalus, people who are land owners and they have power in specific areas, they come to Lenovo and they said, help me build the data center. Because we also have a modular solution that can build the data service within nine months in a depending on what you already have available in terms of infrastructure, we can do it as fast as six months. So that is very very fast and that gets the time to market and enable revenues immediately for our partners. And then in terms of that total infrastructure, plus if they can have GPU compute in the local market, then of course Lenovo can also provide that, particularly with our eleven thousand rack liquid cooling capability to be able to service a GPU compute today, right and we're expanding on that capability. So today we're the most end to end and relevant partner really from that domain. So it's really not from a perspective of really having the supply and having the technology. Technology always import but that end to end. Look, if you think about how people are affecting and enabling AI compute today, they need everything and of course, as Tracy said earlier, I think capital is also important part of that. And you know, it does help that that market is starting to recognize Renoble in terms of our value.

00:28:17
Speaker 4: Add Tracy, I'm thinking about the difference between you know, the server market today and say, like the PC market of the nineties, and I think part of the reason we talk about and remember the red button on the laptop is simply because the companies were all unable to differentiate themselves at all product wise, and it was like, truly the PC market kind of killed itself because it was just so unbelievably commodified. No one really knew the difference or carred between the difference between an adult box and a compact box and either the packard box and whatever. But it does sound listening to a Wisden that like you really can the partner, not the customer. You can really differentiate your offering.

00:29:16
Speaker 3: Yeah, I remember that environment. So I remember like.

00:29:19
Speaker 2: You had to go to Best Buy, right, and you had to basically get a salesman to tell you what the difference was between all these different.

00:29:25
Speaker 4: Things different Yeah.

00:29:26
Speaker 2: Yeah, Okay, since we're on the topic of supply chains and you mentioned data centers as well, give us some color on what it's like to try to get I guess when I think about constraints on the AI build out, I think about memory, GPUs, CPUs, chips and transformers. Now for data centers at least in the US.

00:29:49
Speaker 4: How difficult until connectors?

00:29:51
Speaker 3: Oh yes, thank you.

00:29:52
Speaker 2: How difficult is that at the moment getting those key things?

00:29:56
Speaker 5: Yeah, the market is very robust because the demands very robust, and as we're sitting in Hong Kong today in the Bloomberg offices. But really there are some really exciting IPOs coming right and also capital races. We're seeing capital raises at such significant levels, and that's really all going back to AI infrastructure spent. So really, from the perspective of that component shortage or demand driven challenge on the supply chain will continue probably for at least two to three years, but we know that the supply chain is starting to invest. But in terms of some of the memory specific areas, it does take almost two to three years for a new fact to come online. So I think from that perspective, and I think you're seeing bottlenecks across the board, right And as I said earlier, land power, not just the transformers, but actual power from the grid. And that's why we're in the Middle East to work with the Saudi government on sustainable but also cheap solar and much available energy in the local mind so it's very efficient there and they have very low cost energy there that could actually support AI infrastructure. From that perspective, so we really need to optimize where specifically the power is around the world, rather than just saying, hey, everything has to be generated within an area and that area doesn't even have power. So I think we really need to be able to do that put the global resources together.

00:31:23
Speaker 4: One of the things that we all learned during the pandemic is that you know, and we all learned about how supply chain disruptions work and the bull whip effect, or at least you know, us lay people learned about these concepts for the first time. Everyone we talked to. We recently did an episode with one of the co founders of court Weave, one of the neo clouds. You know, everyone we talked to, we'll talk about multi year long order books and backlogs, et cetera. And when I hear that and I started thinking back, I was like, yes, I believe you when you say that. But it could also be a bunch of players double ordering and triple order and trying to get ahead of themselves just because they're all aware of the shortages, et cetera. In which case it does seem like what looks right now like a major backlog and constraints and endless demand could not be as sustainable as people thought. Should we perhaps be worried that some of these endless order books are just in fact companies overordering because they see everyone else over ordering.

00:32:27
Speaker 5: I think these are very sophisticated companies in terms of the final off takers and so, but I think I would point out to the fact that the duplication could happen in the pipeline, but not necessarily in a backlog, because I think if people have standard definitions of the backlog by that time, it's actually already a sign signed money, so I think they wouldn't double signed with people. Yeah, and then of course revenue recognition is a totally different thing. I think you have already shipped the actual product and you expect to collect on your accounts receivables. So I think from that person aspect of really looking at the pipeline that is massive in the market, there is likely duplication there and people are waiting to see who has the supply and who can deliver to you, right. But overall, I think the commitment that we see in the market, particularly for data center space, for equipment for other things, is really a multi year commitment. And I think people see this as a critical infrastructure. They learn from the Internet error in terms of the cloud service providers how much data can be generated, and this is just going to be so much more. I think it's going to be exponentially more then what it is because it will take the existing data plus generate more data and then you need to enable that data, right, and that enablement I think these additional compute.

00:33:44
Speaker 2: Lenovo has software as well, right, and you have the gaming business, is that right?

00:33:49
Speaker 5: We have a we're the leading online games sorry, the game PC company in the world.

00:33:54
Speaker 2: Yeah, So what do you think about the SaaS apocalypse idea? So the idea that with AI, we're going to be able to replicate whatever application we want, whether it's Microsoft Word or Excel or something like that. Would that eat into your margins or because you own the IP, can you just do it more cheaply?

00:34:13
Speaker 5: No. I think we are not a SaaS company by any means. But I think from the perspective of Lenovo or enterprises, I think really as it relates to what we touched upon earlier, your IT department, and I think it actually works for a company like Lenovo because we are not a software centric company. These tools now lower the bar for us. So I think our engineers or hiring engineers should be able to create certain apps that should be available or applications. So I think from that perspective there are certain elements of applications that would be able to produce in house and that would lower the bar because these tools are very powerful. But in terms of the SaaS cop popularist, I think it's hardware for me to say, but I think from that perspective, I believe there's certain data layers that are absolutely essential because enterprises almost cannot get away from this, and not nor did they really want to. I think you just need the middle layer to be able to bring the data together and be able to connect to AI, and that's one of the biggest bottlenecks for enterprises today, and whoever can do that right would be able to be a helpful But I think rather than just purely a software solution, there's probably a consultant element to that as well to help enterprise enable this function. But I think we're a long journey from that. You know.

00:35:35
Speaker 4: I feel sorry for gamers because even prior to AI gobbling up some of the GPUs, right before that, it was crypto, and I remember they used to complain that the ethereum miners were buying up all of their in video GPUs, et cetera. But I'm curious, like you know, this is one another one of the big meta themes of the time, like in your own gaming business or in the gaming industry in general. Obviously, GPUs seems like the highest marginal value you can get from them is with artificial intelligence. Do you see this continuing phenomenon where raw inputs of any sort that might have at one time go to gamers, either because just the gamers get priced out, or they no one builds them for gamers because you can make so much more money selling them into the AI ecosystem.

00:36:26
Speaker 5: Well, we got a fantastic franchise in terms of as you alluded to, the think pad business. It's an iconic franchise and we love it, our customers love it. I've actually visited the R and D facility that originated actually from Japan. It's from the Yama Moto lapse of IBM, so we kept that intact also with a lot of the people from it. But I think the original inventor actually retired some years some years ago, but there's a long history that we're very proud of and we've actually maintained that in terms of our consumer and also as it relates specifically to the online business online game business, which is our legion business, it's actually also very famous, right, so it's the largest by volume, and people really like Lenovo for the price performance that we can deliver to them and gamers really appreciate that. So these are amazing franchises that we want to be able to keep and they are loyal fan base and were the largest market share in the world, so we want to be able to protect that. We actually, rather than protect that, we want to grow that market share. So I think from that perspective, we will have to balance between the device side of our business as well as the infrastructure side of the business, which at least they have a lot of demand today. I think it just means that we have to get more share of our supply.

00:37:43
Speaker 2: Talk to us about your decision to, I guess not tie yourself to any one AI platform, because I'm sure there are some very very big tech companies out there who would love to have exclusivity with Lenovo, and there are also broader arguments out there that eventually we're going to have one AI platform that emerges triumphant in the same way that we saw Google takeover search in the early two thousands. Why did you decide to, I guess work with a bunch of different people here.

00:38:13
Speaker 5: I think today what we see is that we're still in that journey of improvement, and you see improvements every few months from the likes of Open Ai or Anthropic or Gemini or whoever it might be. And so I think from that perspective you see a lot of innovation also from the China market. So from that perspective, you really want to be able to be flexible, and we think today we're not there to bet on who is going to win. We are there to provide the AI compute. So from now the orchestration that makes a lot of sense for us to allow our customers to be able to go through the Lenovo AI and reach what works best for them, rather than them have to figure out and download multiple apps to be able to do that. Right, So I think from thatspec that the orchestrator would do that for them, and I think that makes it much more efficient both from a memory perspective and compute capability perspective that is on your device. So with that architecture should actually potentially optimize the performance on the device.

00:39:16
Speaker 4: Should we believe these companies, let's say in Microsoft. So Microsoft recently announced their flagship model I forget if it's pronouncing MAYA or may or whatever or MAI. Haven't played with it, and they're like, and it also runs best in our own custom silicon. How should we read that? Is there, in your view a lot of juice to be squeezed from model silicon alignment? Or should we read this as at least companies would like to begin having a little bit of a wedge so that they're not so dependent on in video for their hardware needs.

00:39:56
Speaker 5: I think there's a lot of great things that in video is doing today, and I think a lot of people are recognizing. So their revenues are clearly growing significantly. I think they're probably at the core of driving this current and enabling this current AI wave. Of course, so I think that continues. But of course that's the costs become to a level where people have to find alternatives. You're also seeing people from a cost perspective making selections, but also probably from as you say, the flexibility of having their own architecture. But I think that's really happening with respect to the hyperscalers who have the scale and also the technological capability and the capital to be able to develop.

00:40:35
Speaker 4: But is it about their models really will run better on these ships that they're designing, or is it about long term strategic at least you know, I wouldn't want to say divorced from in video, but maybe you know, some time apart they had a prenup or something like that, just like having a little bit of like not so dependent on one companies specific chip capacity and roadmap.

00:41:04
Speaker 5: I can't speak for them, so there's really from an external point of view, but I believe it's probably a little bit of both, right, because if you were a corporate really deciding on your strategy, I think that's probably one where, especially given that a lot of them are also tech stack focused companies as well, so they probably want to have more control over their own tech stack. But the other one really is around the cost and also maybe future planning around what they're enabled to probably give them more flexibility to be able to do more if they have their own chips in other areas as well.

00:41:37
Speaker 2: So speaking of diversification, if we were recording this episode a year or so ago, we would probably still be talking about AI, but I think we'd be talking about trade and tariffs as well, right.

00:41:50
Speaker 3: Remember that show?

00:41:51
Speaker 4: I forget about that?

00:41:52
Speaker 3: That's right.

00:41:53
Speaker 2: So Lenovo obviously has a very large and complex supply chain all across the world. But how does I guess the general return of more trade restrictions actually impact your business?

00:42:09
Speaker 5: Yeah, it actually happened very interestingly on April second, so I took over on the rains our fiscal years April first and took over on the rains on April first or the second day. It was the most major event in the history of a one hundred I think it was two hundred plus countries that were enacted on this, and we were probably in one hundred and eighty of those. So I think it's a major event and given the significance of our business, But it's really about enabling and providing our products at a very reasonable price to our end customers, right, So I think the end customer suffers if there is inefficiencies added. So I think that's the most important aspect, which is and I think eventually right, I think PC products were actually exempt, So I think there was a conclusion that this is something that was needed, right for productivity, for entertainment, for a lot of things that people use devices for today, and we're one of the largest providers across the board, whether it's in your pocket every day or on your desktop or at home. So I think from that perspective, this is probably a very essential thing for consumers today, and I think that's the most important in terms of giving them that product at the lowest cost. You see that behavior really in a lot of probably third world countries or second world countries in terms of really emphasizing that, because it's really about not leaving their citizens behind. It's really about making sure that they have access to digital information, and the device is the start of that journey, right, So that's a very critical aspect of it. So lowering the tariff barrier is actually essential to ensure that their citizens and any citizens should have the lowest cost access to technology.

00:43:49
Speaker 4: You mentioned something earlier, and you're like, okay, On a per token basis, deep Seek is significantly cheaper than say, you know, the most advanced models from say open AI Andthropic. But strictly speaking, there is a wide agreement that still the flagship models from the American labs are the best models in the entire world. And I just don't think there's much dispute about that in either the US or Chinese communities, at least a few months behind, perhaps even inside Lenovo. But do you see in general, and this sort of gets back to the early part of the question about CFO decisions, people not being sophisticated about recognizing the queries don't always have to go to the most expensive, most advanced models, and how much learning is there still yet to do about how to optimize routing of the query such that it goes to the most cost effective model rather than just slamming the frontier model.

00:44:51
Speaker 5: Yeah, I'm going to answer your question in how I started my morning today, because Okay, I'm in the journey of figuring this out and I've decided to spend some money with external consultants, and so we're in that interview process and as I said, you know, we spend an hour and a half with the global team, and this is one of the major firms. I'm starting to get a sense from talking to various parties that everyone is new to this game. So everyone, even the advisors who are trying to earn a service fee from you, is really also learning as part of this journey. And that's what consultants do as well. Right, never asked what you're paying, but I would not comments about another industry. Yeah, absolutely absolutely. But you know, I think from the from that perspective that goes to say that if they are the specialists or those that see a lot in the market, then what about specific departments or individuals in the company. So I think from that perspective, everyone is in that journey, and it's the people who can be most clear headed to learn the fastest. I think it's probably the most essential, right, So I think from that perspective, we need to be able to take out the red tape, enable the processes to work, accelerate. I think timing and speed is essential, and they are in a new AI world, and then we need to deploy that where it's sensible. And then, as you say, where do you actually allow that spending and the interaction to generate tokens to really happen? And so we're in that journey. But I think we're not alone. I think every company is the same. I'm actually going to a conference next week hosted by a major consulting firm that is a gathering with all of CFOs. But in my regular diarog with CFOs, I understand why worry a lot, But I think we're also not alone.

00:46:38
Speaker 2: Joe, do you think in the future, when people meet each other, they're going to say their names and their nationality and then like declare Yeah, well, I was going to say, declare the foundational model that like they use.

00:46:50
Speaker 4: Oh, I was going to say, like, I wonder if like at bars or like women when they go like, I want a man who is like this much tooken you know whatever. Sorry, it'll be part of their identities for sure.

00:47:05
Speaker 3: That's right.

00:47:05
Speaker 2: Well, okay, so speaking of rich men, I have to ask at least one cfo ish question, which is when it comes to capital spend, I'm sure you could justify pretty much anything right now. And there's so much money that's actually going out on the AI build out. How are you thinking about returning capital to shareholders because the stock's gone up a lot, but you know, people can always be wealthier. I'm sure they're into that.

00:47:33
Speaker 5: Yeah, we paid the in the last fiscal year, which just ended March thirty first, we paid the highest dividend ever in the noble history, So our shareholders are very happy. We'd share our success with our shareholders, but we also see a lot of opportunities for growth, and as you have mentioned many times on this discussion, is that there's also a lot of capital needs to feel that growth. So we really need to strike that balance between giving our shareholders the media cash that they would like, but also at the same time they want us to be able to enable the capital appreciation on the stock. Because the underlying fundamental The business is growing, so we need to drive that growth. And if you there's there's going to definitely going to be a period where you drive that growth at the same time we're driving that margin expansion. But there will be quarters here and there that could potentially mismatch in terms of that growth versus the margins. But overall, the long term trend for Lenovo is a plan to drive growth with accelerated margin expansion.

00:48:34
Speaker 4: Northern Virginia in the US is sort of understood to be the data center capital of America, but you know, it's expanding. There's a lot that's sort of like off grid and Texas that's happening. And then there's also of course anti data center politics in the US, and we don't know how that will affect the map. What is the Northern Virginia of East Asia right now? Where do you see? Like which country is it? Because they have the most whether it's regular access to energy, Where is everyone trying to put up data centers? And I'm just curious, like anywhere in Asia is there any sort of equivalent anti data center politics that's emerging.

00:49:13
Speaker 5: Well, I think Asia tends to be a little bit more flexible from that perspective, but clearly the best infrastructure of scale is in China. Yeah, I think from that perspective, a lot of people, due to certain regulations and policies, cannot have data center. So you have a lot of idle resources today, whether it's power and data center therefore accessibility and also supply chain. Right, that's not that the world is spending additional money on in higher cost jurisdictions because of regulations that you cannot put it in China. So the world ends up having some of the inflationary effects is because you're not actually using the most efficient place in the world that you can actually do things. But having said that, I think other countries are actually catching up very fast as well. So we're seeing even if the supply chain area slightly lower costs, but in terms of the data center aspect and where the power Southeast Asia has been very good, and I think what we're seeing in this part of the world clearly in places like Malaysia and Indonesia, I think those are natural places where people have really been looking into. I mean even Hong Kong these days. I think there's plans from the government, you would think, and also Singapore, so some of these smaller land mass areas, but I think also have plans and I think right here in town there's also a dedicated a few players as well, and they have to be creative in terms of putting data centers in these higher alt VTA structures. But there's all product, all sort of creativity, and I think Hong Kong has a mixed use in terms of that power generation. But again that's a small area, but I think it's just giving a data set of examples. People are also doing it in Japan. Japan is also another one. From a regulatory perspective, people do, but cost wise and also policy wise in terms of getting necessary permits may not be the fat a long time. There's so may have some knowledge about this, but I think it really depends on where you can have trust and partnership with the local government. Because it talks, it involves land, it involves power, it involves imports of specific goods, and I think that part really depends on a lot of the local efficiencies.

00:51:23
Speaker 3: Joe, we got to go visit a data center in Hong Kong.

00:51:26
Speaker 4: I want to well, I was going to say, Hong Kong actually to me makes a lot of sense as a data center location. Because if there's one thing I know about Hong Kong's there's plenty of cheap real estate here and you just can't and there's more than enough space, and we know that people are very happy with the price of how affordable the residence is here, et cetera. So it makes sense.

00:51:46
Speaker 2: That you know there is actually a lot of land, you just can't build.

00:51:50
Speaker 4: On it, you justild yeah right, yeah, so.

00:51:53
Speaker 2: All right, interesting Winston Chang, thank you so much for coming on all thoughts.

00:51:57
Speaker 3: Really appreciate it.

00:51:58
Speaker 5: Thank you, Tracy, thank you Joe Joe.

00:52:12
Speaker 3: That was a lot of fun.

00:52:13
Speaker 2: I feel like we haven't actually spoken about AI from the perspective of a company like Lenovo.

00:52:20
Speaker 3: No, we have never done that before.

00:52:21
Speaker 4: Yeah, no, we haven't, and that was very nice. And look, part of the I would say two things about AI, which is we're doing a lot of AI episodes, and I would say there's two reasons for that. One is because I mean, it's just the biggest thing of our lives probably in terms of the significance and trying to everyone's trying to wrap their heads around it, so there's a lot to learn. But it also is in our wheelhouse because it's so physical and because it interacts with supply chains R and so you think, like, Okay, a company like Lenovo, a server company that also happens to offer the ability to build complete data centers, not just the servers, who therefore has been interact acting with all types of players, is almost like the perfect sweet spot for like an odd lots guest, even among all of our perfect guests, someone who has that visibility on both sides is really someone interesting to talk to.

00:53:14
Speaker 2: Yeah, I think I've said before there's such a weird tension between these sort of bodyless, faceless AI in interface and then when you think about all the actual physical infrastructure that supports it totally.

00:53:27
Speaker 4: It's why I think it's so interesting. The other thing to it that I think is interesting about hearing from a Lenovo and again hearing from a CFO of Lenovo, it's like, look, they're not a hyperscaler. I'm sure they're investing a ton of money and building out their capabilities, et cetera, but they're not one of these companies that announces big things like we're going to be spending five hundred billion dollars next year on building out data centers, So they really do have that dimension where they have to figure out the Googles and et cetera. They and the open air they're talking about Capex and the Lenova's of the world also have to think a lot more about op X and the op X component of the conversation. As he said, whether it's with fellow CFOs or the consultant community, it doesn't sound like it feels like it's in day one of figuring out.

00:54:16
Speaker 3: Yeah, everything's up for grabs.

00:54:18
Speaker 4: Yeah.

00:54:19
Speaker 2: I also thought the involution point Yeah, was interesting when we were talking about the key differences between China and us AI and the idea that because competition is so cut through in China that it just drives your costs down and down and down, so that like, even if you're not that competitive in China, you would still be competitive competitive.

00:54:42
Speaker 3: Yeah, exactly.

00:54:42
Speaker 4: It's kind of funny to think about. Yeah. Absolutely, No, I love that chat.

00:54:46
Speaker 3: All right, shall we leave it there.

00:54:47
Speaker 4: Let's leave it there.

00:54:48
Speaker 2: This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

00:54:54
Speaker 4: And I'm Joe Wisenthal. You can follow me at The Stalwart follow our producers Carmen Rodriguez at Carmen arm Dash, Ol Bennett at Dashbox, Calebrooks at Kilbrooks and Kevin Lozano at Kevin Lloyd Lozano and from our Odd Lots content. Go to Bloomberg dot com slash odd Lots or the daily newsletter and all of our episodes, and you can chat about all these topics twenty four to seven in our discord Discord dot gg slash Oddlines.

00:55:18
Speaker 2: And if you enjoy odd Lots, if you like it when you hear from the perspective of Chinese tech companies, 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.

00:55:32
Speaker 3: Episodes absolutely ad free.

00:55:34
Speaker 2: All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there.

00:55:40
Speaker 3: Thanks for listening.

00:56:06
Speaker 5: In e