Odd Lots - How Lenovo's CFO Is Allocating Capital During One of History's Biggest Booms

Episode Date: June 27, 2026

We know that companies around the world are investing heavily in AI. So intense is the race to win the AI battle, that it feels like there's almost no upward limit on how much you could spend on it. So how are CFOs thinking about capex in the AI age? In this episode we speak with Winston Cheng, CFO of Chinese-founded multinational tech firm Lenovo. Lenovo is known for its personal computers, especially its Thinkpad line of laptops, but they are making a push to move beyond its role as one of the leaders in personal computing, integrating AI agents into their devices and investing in building out an “AI Cloud” infrastructure alongside Nvidia. We talk to Cheng about how Lenovo's allocating capital during one of the biggest capex booms in history. We also discuss involution and market competition in China, and how Lenovo's been adapting its supply chain to tariffs. Read more:AI Sales Start to Justify Data-Center Spending Boom, Report SaysAnthropic Accuses Alibaba of ‘Illicitly’ Accessing AI Models Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

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Podcasts Radio News. Hello and welcome to another episode of the All Thoughts podcast. I'm Tracy Alloway. And I'm Joe Wisenthal. So Joe, 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 Baidu CFO, where we were just talking casually ahead of our interview. But he was saying that the war.

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. I think that's so fascinating. You know, I'm fascinated by the etymology of 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 know Chucky Cheese tokens. We know crypto tokens.

But we also know that since the middle of the 20th century, linguists have been using token to describe more or less a word. And then obviously with LLMs, 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. Bringing up Chucky Cheese tokens is really a way to make sure everyone knows that you're a millennial. Yeah, that's right. That's right. Arcade, I should just say arcade token. That's right. Okay. 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, CFOs become really important, right? Totally. So we, you know, the headlines by and large are about a KAPX, right? And that's going to be.

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 ask you a personal question that I've never I asked you before him. Oh, okay. Outside of the work context, are you a Mac or a PC person?

Oh, I only have, I only have work computers at the moment. Okay. All right, before that, but don't touch the question. Before that, definitely PC. Okay, good, me too. Yeah. And in fact, it's not, it's not a choice, is it? Like, sorry, I really don't like Macs.

I don't either. And in particular, I am long been a fan of the, what used to be, the I, IBM think pad laptop with the famous red button, which is now owned by Lenovo. 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 making an AI play. 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. So Winston, thanks so much for coming on Oddlots. Hey, thank you, Tracy, and thank you, Joe. 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. 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-86 heritage, which we also acquired 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 X86 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 perspective of then, you talked about tokens.

And I think from a token perspective, really today, people are in the early stage of how much I'm really paying for. I heard someone saying that there was an engineer at a particular company, which I would not mention, that apparently spent $100 million in 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 that was from Lenovo. 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. 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. Is that right? That is our goal. So the Lenovo agenetic AI, today what we are grid 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 LLMs, so we'll take the compressed versions of these LLMs.

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 it would allow the user probably to spend in terms of the token generation.

It's interesting this word orchestrate because you have the AI agent orchestrating, maybe a bunch of different subagents to complete some task, 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 $100 million in a month on tokens and it's like that would not make you happy as a CFO. But it could make you happy, right? What if you had a multi-year database migration plan that you think, oh, this would be a $500 million job and the engineer does it in a month for $100 million via tokens, don't you have to at least be open to the possibility that that was money well spent? 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 you have to be clear in terms of that return. And I think in that case is probably one where they weren't sure in terms of what they were particularly doing in terms of spending and 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. to 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 stages. 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 right. And I think preliminary data and anecdotal evidence from a lot of people in chat recognize the power of AI and the probability of generating increase productivity and potentially even cost savings.

Say more about measuring the actual return. And again, I realize 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, oh, productivity or cost savings or whatever, but they sound very theoretical.

So is there more cost. concrete, I guess, KPI's that you're looking at? Yeah, I think it really depends on your specific enterprise. In our case, I would really hone 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 180 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. So if we can optimize that to run by AI rather than spread out in 180 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&A functions in terms of how you increase productivity. Those functions tend to be smaller in headcount. So it's really less about the heck count reduction, but probably about the increased productivity. And therefore, are you making the right decision because those are small heck-count high-impact areas, right? You could have a higher stock price and therefore your cost of capital becomes much more efficient in your financing.

Even M&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. Turn up the love and listen to IHeart Pride Canada, your 24-7 radio stream and the only playlist you need for your Toronto Pride celebrations.

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Let's go back to the theoretical engineer who spends $100 million on tokens in a month. Because, you know, it seems to me like you were going to have. have someone who actually can, with their 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 going to 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 consumption. Don't give all your tokens to a Joe with AI psychosis. How do you avoid giving all the money, giving me the uncapped budget and finding that person who actually deserves a very liberal spending budget? Yeah. And no, these are great questions that are so relevant, particularly in a large enterprise like us in a global, for a global 200 company, right? in 180 markets, 80 plus billion in terms of revenues.

We have massive scales in 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. Sure. 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 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 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 perspective. 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. And then there seems to be another school, which is let everyone have really high caps, and we read these stories at American tech companies about the internal quasi-token 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? 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, is really around making sure that we take advantage of this period. We're calling this the AI decade within Lenovo and we're in the first year of that journey.

And I think this is an opportunity 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. 100% of our laptops that you like is actually in a 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 of 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 more sensible in that and say, well, specifically within certain analysis in FPNA 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 capability set, 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 are so many areas, planning, tax, FP&A, accounting, treasury, IR, M&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 dollar sometimes to me is the return is significant. I was just with the head of my tax this morning. I was it if we just pulled together and drive the next year's efforts, the tax savings or optimization, right, that we do,

we're more than pay for a little small trip of 20-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 it 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? And 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 effectively you can use AI. We should definitely talk more about that. You just mentioned the word unlimited in the context of AI spend. And coming from the U.S., 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 space X 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?

know your stock price has gone up quite a lot, so that obviously helps. And then a more broader question, what are the key differences between how Chinese companies are approaching AI versus how U.S. companies are approaching AI? 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 Daubles earlier this year, and when I came back, I was particularly affected by Mark Carney's speech. And really talking about the superpowers and the middle powers, and of course, within the middle powers, they're 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, you need? 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 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 Qualcomm, and of course, now RM-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 2,000 suppliers in the tech ecosystem, driving almost $2 trillion plus of spend, and we're right in the middle of it, and distributing this across the board, even in 180 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. I think we are because we're also micro. And 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, $30 to $40 billion, but before that, sub-20 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 in time for the device makers today to optimize. And of course, distribution is very critical. Supply chain is critical. The ability to aggregate supply chain to manufacture at scale, at cost efficiently,

and deliver to your customers to be able to service it to provide the 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 an opportunity for the likes of Lenovo. And I think we're the best position in this, because there's no 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 U.S. and Chinese TechS. today are

differentiated or AI companies are differentiated. Today, obviously, because of the restrictions, I know probably the most efficient compute today is probably from the U.S. 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 cost 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 Sikh being, and I don't have official number. This is just quoting what I've heard, like one-fifteenth of those in the U.S. 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 toughest 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. Oh, yeah, of course. Involution. We know well. It only happens in China.

market. So if they can survive within their cut-throw market under the constraints of the chip supply, they are pretty strong. They are pretty strong. So I think that would be one difference I would say. I want 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 have 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 performant than a competitor's server, et cetera. And we know other competitors like Adel or Super Bowl. 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? I think it's not an either or a question. It's fully integrated tax stack. Today, the value to a customer and a partner, and I think there are less customers today than they are really a partner because their architecture is not driven and the spend is a multi-year

spend. Yeah. So they're coming to you for the multi-year 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 10 years in these factories. We have 30 factories of world, 12, and each region of the world we can produce in Europe and Hungary for the European market or the U.S. market. we can produce in Mexico and the U.S. for the U.S. market.

We can produce in Asia for the Asian market. Now we're building that factory in the middle, in Riyadh, to produce 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. And today, the bottlenecks are not only in getting supply. After your 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 our 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 hyper-scalers can actually come to Lenovo

with a plan to say, I have a plan to have this site. So people who are not even hyperscalalers, people who are land owners, and they have power in specific areas, they come to Lenovo, and they said, help me build a data center

because we also have a modular solution that can build this data centers 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 have GPU compute in the local market, then, of course, Lenovo can also provide that, particularly with our 11,000 rack liquid

cooling capability to be able to service a GPU compute today. 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 important. 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 market.

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Tracy, I'm thinking about the difference between, you know, the server market today and say like the PC market of the 90s. And I think part of the reason we talk about and remember the red button on the laptop is simply because the companies were 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 cared between the difference between a dull box and a compact box and a Hewlett Packard box and whatever. But it does sound listening to a Winston that like you really can, it's the partner, not the customer, you can really differentiate your offering. Yeah. I remember that environment. So I remember like 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 things. Yeah. 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 buildout, I think about memory, GPUs, CPUs, chips, and transformers now for data centers, at least in the U.S. How difficult is. optical connectors. Oh, yes. Thank you. How difficult is that at the moment, getting those key things?

Yeah, the market is very robust because the demand is very robust. And as we're sitting in Hong Kong today, but in the Bloomberg offices, but really, there are some really exciting IPOs coming, right? And also capital raises. 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 fab 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 market. 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, pull the global resources together. 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 bullwhip 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 Corweave, one of the neoclods. You know, everyone we talked to will 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 ordering and getting, 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 a. sustainable as people thought. Should we perhaps be worried that some of these endless order books are just, in fact, companies over-ordering because they see everyone else over-ordering?

I think these are very sophisticated companies in terms of the final off-takers. But I think I would point out to the fact that the duplication could happen in the pipeline, but not necessary in a backlog. Because I think if people have standard definitions of the bag log, by that time, it's actually already a signed deal. So I think they wouldn't double sign 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 perspective, 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, it's really a multi-year commitment. And I think people see this as a critical infrastructure. They learn from the internet era 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 than what it is because it will take the existing data plus generate more data,

and then you need to enable that data. And that enablement, I think, needs additional compute. Lenovo has software as well, right? And you have the gaming business. Is that right? We have a, we're the leading online games. Oh, sorry, the game PC company in the world. So what do you think about the SaaSpocalypse 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? 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 a relation 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 coppulist, I think it's hard word for me to say.

But I think from that perspective, I believe there is certain data layers that are absolutely essential because enterprises almost cannot get away from this. And not nor that 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 bottleneck for enterprises today. And whoever can do that right would be able to be a helpful. But I think it's 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, I feel sorry for gamers because even prior to AI gobbling up so much. 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 Nvidia GPUs, et cetera. But I'm curious, like, you know,

this is 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 just the gamers get priced out or they don't build them for gamers because you can make so much more money selling them into the AI ecosystem. Well, we got a fantastic franchise in terms of as you alluded to the think pad business. Yeah. It's an iconic franchise and we love it. Our customers love it. And I've actually visited the R&D facility. that originated actually from Japan. It's the Yamamoto Laps 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 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 game business, which is our Legion business, it's actually also very famous, right? It's the largest by volume.

and people really like Lenovo for the price performance that we can deliver to them, and gamers really appreciates that. So these are amazing franchises that we want to be able to keep, and they are a loyal fan base, and we have 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. Yeah. I think it just means that we have to get more share of our supply. 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, you know, we saw Google takeover search in the early 2000s. Why did you decide to, I guess, work with a bunch of different people here? 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 flexified. 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 error 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. So I think from that perspective, 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. 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 Ma or whatever, or MAI, I haven't played with it. And they're like, and it also runs best on our own custom silicon. How should we read that? Is there, in your view, a lot of a 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 Nvidia for their hardware needs? I think there's a lot of great things that Nvidia 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, as the cost 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. Yeah. Who have the scale and also. the technological capability and the capital to be able to develop their own. 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 divorce from

Nvidia, but maybe, you know, wishing they had a pre-in-up or something like that. Just like having a little bit of like not so dependent on one company's. specific chip capacity and roadmap. I can't speak for them, but 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 focus 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 enable them, probably give them more flexibility to be able to do more if they have their own chips in other areas as well. 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? Remember that show? I forgot about that story. That's right. 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? Yeah. It actually happened very interestingly on April 2nd. So I took over on the

rains, our fiscal year is April 1st. And I took over on the reins on April 1st. So the second day, there was the most major event in the history of 100. I think it was about 200 plus countries that were enacted on this. And we're probably in 180 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. You mentioned something earlier and you're like, okay, on a per token basis, Deepseek is significantly cheaper than, say, you know, the most advanced. models from, say, Open AI and Anthropic. 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 U.S. 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 that 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? Yeah, I'm going to answer your question in how I started my morning today because 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. Yeah. 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? Do you ever ask what you're paying them for? But I would not make comments about another industry. I'm sure you love them. Yeah, absolutely, absolutely. But, you know, I think 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. 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 in the 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, posted by a major consulting firm that is a gathering of all CFOs. But in my regular dialogue with CFOs, I understand. I worry a lot, but I think we're also not alone. Joe, do you think in the future when people like 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. Oh, I was going to say like I wonder if like at bars or like women when they got us to.

with each other, they're like, I want a man who is like this much tokens spending. You know, whatever. That's right. It'll be part of their identities, for sure. That's right. 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 buildout. How are you thinking about returning capital to share?

shareholders because the stock's gone up a lot, but, you know, people can always be wealthier. I'm sure they're into that. Yeah, we paid the, in the last fiscal year, which just ended March 31st, we paid the highest dividend ever in the Lenovo history. So our shareholders are very happy. We're 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 fuel that growth. So we really need to strike that balance between giving our shareholders the immediate 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 of the business is growing. So we need to drive that growth. And if there's going to definitely 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. Northern Virginia in the U.S. 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 U.S. 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 regulations or 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?

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 do to certain regulations and policies cannot have data centers. 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 cost. 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 few players as well. And they have to be creative in terms of putting data centers in these higher elevated 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 fastest. Trace has spent a long time there, 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 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. Joe, we got to go visit a data center in Hong Kong. I love 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, it's there's plenty of cheap real estate here. 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 that, you know, I'm being position. There is actually a lot of land.

You just can't build on it. You just can't build on it, right? Yeah. So. All right. Winston Chang, thank you so much for coming on all thoughts. Really appreciate it.

Thank you, Tracy. Thank you, Joe. Joe, that was a lot of fun. Yeah. I feel like we haven't actually spoken about AI from the perspective of a company like Lenovo. No, we haven't. I've never done that before.

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 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. Right. And so you think like, okay, a company like Lenovo, a server company that also happens to all, for the ability to build complete data centers, not just the servers, who therefore is then interacting 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. Yeah, I think I've said before, there's such a weird tension between these sort of bodiless, faceless AI interface. And then when you think about all the

actual physical infrastructure that supports it. Totally. It's why I think it's so interesting. The other thing that I think is interesting about hearing from a Lenovo, and again, hearing from a CFO of Lenovo is 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 $500 billion 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, and the open AIs, they're talking about CAPEX. And the Lenovers of the world also have to think a lot more about OPEX. And the OPEX 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 it out. Yeah, everything's up for grabs. Yeah. I also thought the involution point was interesting when we were talking about the key differences between China and U.S. AI and the idea that because competition is so cutthroat in China, that it just drives your costs down and down and down so that even if you're not that competitive in China, you would still be competitive compared to the global scale. Yeah, it's kind of funny to think about. Yeah.

No, I love that chance. All right. Shall we leave it there? Let's leave it there. This has been another episode of the Oddlots podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Joe Wisenthall.

You can follow me at the stalwart. Follow our producers. Carmen Rodriguez at Carmen Armin, Dashel Bennett at Dashbot. Kale Brooks at Kail Brooks and Kevin Lazzano at Kevin Lloyd Lazzano. And for more oddlots content, go to Bloomberg.com slash oddlots. We have a daily newsletter in all of our episodes. And you can chat about all these topics 24-7 in our Discord.

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