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

Summary

Odd Lots Podcast Summary

Episode: How Lenovo's CFO Is Allocating Capital During One of History's Biggest Booms
Date: June 27, 2026
Hosts: Joe Weisenthal & Tracy Alloway
Guest: Winston Chang, Chief Financial Officer, Lenovo

---

Episode Overview

This episode explores how Lenovo, traditionally known for its iconic ThinkPad laptops, is navigating the massive global surge in AI infrastructure spend. The conversation with CFO Winston Chang dives into Lenovo's evolving strategy, unique global position, methods for capital allocation during the historic AI boom, and insights into supply chains, competition, differences in U.S. and Chinese AI approaches, and how Lenovo assesses ROI on AI spend. The episode is rich in real-world operational perspectives, especially on the intersection of CapEx, OpEx, and organizational adaptation during fast change.

---

Key Discussion Points & Insights

Lenovo’s Evolving Role in the AI Era

• Lenovo’s Identity & Strategy
  • Lenovo now positions itself as a “global AI infrastructure company” providing “pocket to cloud AI infrastructure for the consumer and the enterprise” (Winston Chang, 05:46).
  • Leverages IBM x86 heritage for strong server-side (CPU) computing, serving both enterprise and hyperscalers.
• AI’s Impact on Business Models
  • AI infrastructure involves both cloud and device-side inference—Lenovo aims to maximize on-device compute while orchestrating cloud resources as needed for users’ queries (07:35).

The New Economics of ‘Tokens’ and Cost Control

• Rise of Token-Based Usage Models
  • Move from broad cloud subscriptions to granular, usage-based (token) models for AI—demanding new ways of budgeting and cost control.
  • Notable mention: An engineer at a tech company (not Lenovo) once spent $100 million/month on tokens—“I would have concerns because that was clearly not in the budget” (Winston Chang, 06:41).
• Balancing Investment with ROI
  • “As a CFO, you are there to allocate capital, not to constrain capital, you have to allocate. But you have to be clear in terms of that return.” (Winston Chang, 09:22)
  • AI spend decisions hinge on measurable productivity and concrete KPIs, not just theoretical gains (10:34).

Measuring and Managing AI ROI

• How Lenovo Tracks Returns

  • Focuses on targeted areas: pricing, inventory management across 180 markets, IR and M&A productivity, and marketing—using AI for specific, high-dollar optimizations (11:04).
  • Example: “It’s really less about the headcount reduction, but probably about the increased productivity. And therefore, are you making the right decision?” (11:50)

• Token Budget Discipline vs. Experimentation

  • Philosophical tension among CFOs:
    • Discipline approach: Starve or cap budgets to encourage efficiency and behavior change.
    • Experimental approach: Give wide latitude, see who innovates most productively (17:42).
  • Chang: You must “force discipline or starve certain budgets to then allocate, because that really changes behavior…” (16:15).

Lenovo’s Global & Strategic Position

• "Middle Power" in the Tech Ecosystem

  • Not a superpower (like hyperscalers/chipmakers), but enables a vast ecosystem: “Driving almost $2 trillion plus of spending and we're right in the middle of it and distributing this across the board in 180 markets.” (23:25)
  • Emphasizes humility and enabling partner/supplier networks.

• Supply Chain & Differentiation

  • Full-stack approach: from manufacturing servers in regional factories (e.g., Europe, US, Asia, Middle East) to building data centers end-to-end with rapid deployment (as little as six months) (28:17–30:17).
  • Differentiates through supply chain mastery, reliable delivery, and ability to scale globally—not just server specs.

AI Competition: U.S. vs. China Perspectives

• U.S. vs. China AI Models and Market Dynamics
  • U.S.: Leads in advanced foundational models, greater capital influx, but higher cost per token.
  • China: “Involution” (fierce, cost-cutting competition) pushes efficiency and produces lower-cost solutions, even under supply constraints (26:56).
  • Chang: “The Chinese AI companies probably manage to produce at much lower cost and very efficiently as well…”—suggests Darwinian competitive stress breeds global competitiveness (25:14).

CapEx, OpEx, and the Challenge of Scaling

• Navigating Booms and Potential Overordering

  • Intense multi-year demand for AI hardware (memory, GPUs, CPUs, connectors, land, and power); shortages and backlogs likely to persist for 2–3 years (34:21).
  • On possible “bullwhip effect”: Chang notes duplication may occur in sales pipelines but not in signed backlogs, as those represent committed deals (36:52).

• Capital Return vs. Growth Investment

  • Balance between rewarding shareholders (record dividend last year) and reinvesting for growth: “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…” (51:58).

Adapting to Regulatory & Geopolitical Shifts

• Impact of Trade Restrictions
  • Recent increases in trade barriers challenge efficiency, but consumer access to low-cost devices seen as critical for not leaving populations behind (46:34–48:00).

Platform Neutrality and AI “Orchestration”

• Not Tying to a Single AI Platform
  • “We think today we're not there to bet on who is going to win. We are there to provide the AI compute...the Orchestrator would do that for them and...potentially optimize the performance on the device.” (42:39)
  • Flexibility allows Lenovo to keep up with rapid AI model evolution without being locked in.

Sector-Specific Challenges: Gaming vs. AI Demand for GPUs

• GPUs: Gamers Squeezed by AI Demand
  • Gamers historically squeezed by crypto and now AI waves. Lenovo aims to balance growth in high-demand enterprise/infrastructure GPUs with maintaining consumer market share (39:59–42:08).

The Future of AI Spend Management and Identity

• Cultural Implications of AI Usage

  • Light-hearted speculation on a future where one’s AI model and “token spend” becomes part of personal/professional identity (51:03).

• Everyone’s Still Learning

  • Chang admits even global consulting firms are new to game: “Everyone, even the advisors who are trying to earn a service fee from you, is really also learning as part of this journey.” (49:51).

---

Notable Quotes & Memorable Moments

• “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.”
  — Winston Chang on the cautionary tale of uncontrolled token spend (06:41)

• “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.”
  — Winston Chang (09:22)

• “You must force, discipline or starve certain budgets to then allocate, because that really changes behavior.”
  — Winston Chang on changing employee spend patterns for AI (16:15)

• “We're calling this the AI decade within Lenovo, and we're in the first year of that journey.”
  — Winston Chang (18:37)

• “If they can survive within their cutthroat market under the constraints of the chip supply, they are pretty strong anywhere.”
  — Winston Chang, on Chinese “involution” in AI (26:56)

• “Today what we see is that we're still in that journey of improvement... And so I think 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.”
  — Winston Chang, on maintaining openness (42:39)

---

Important Timestamps

• 05:36: Winston Chang introduces Lenovo’s AI strategy and global business scope.
• 06:41: $100 million token spending anecdote—CFO’s concern about runaway AI spending.
• 09:22: Chang on capital allocation and the necessity of clear returns from AI.
• 10:34–11:50: Concrete examples of AI driving productivity and how Lenovo thinks about measurement.
• 16:15: Chang discusses using budget discipline to drive organizational change in AI usage.
• 18:37: Lenovo’s “AI decade,” investment focus and innovation philosophy.
• 25:14–26:56: Comparison of U.S. and China approaches to AI development, and “involution” concept.
• 28:17–30:17: Lenovo’s global supply chain and end-to-end data center build-out capability.
• 34:21–36:52: Discussion on supply constraints, multi-year demand, and overordering risks.
• 42:39: Strategic choice to remain platform-agnostic in AI.
• 46:34: Impact of new trade restrictions and Lenovo’s response.
• 49:51: Even consultants are learning about token spend management and AI ROI.
• 51:58: Balancing capital returns to shareholders with growth investment needs.
• 53:38: Where are the “Northern Virginia” equivalents for Asian data centers?

---

Takeaways for Listeners

• Lenovo is at the nexus of the global AI buildout, with a unique “middle power” role enabling both hyperscalers and enterprises—balancing global manufacturing scale, end-to-end solutions, and rapid adaptability.
• The era of token-based AI usage brings challenges to traditional budgeting; CFOs are grappling with how to measure, allocate, and discipline spend in a world of exploding demand and rapidly shifting technology.
• Global supply chain mastery and partnership networks are as crucial as product quality for winning in the server/data center arms race.
• Lenovo’s openness to diverse AI partners, rather than betting on a single winner, reflects a strategic approach amidst rapid innovation and geopolitical complexity.
• The market is so new and fast-changing that even the advisors and consultants are learning along with their clients—humility and adaptability are key.
• The difference between the U.S. and Chinese AI markets is highlighted by the advantage that fierce domestic competition brings to cost and efficiency in China.
• The fundamentals of capital return—balancing short-term investor rewards with long-term capability building—remain central, even in this historic boom.

---

This episode offers a rare, inside-the-boardroom look at how a global technology firm is thinking about capital, competition, and opportunity during one of history’s largest industrial pivots—all in language that's practical, strategic, and laced with candid CFO realism.

Transcript

[Meta Workforce Academy Announcer]

People are building things here in America again. And this moment calls for the best of America, our people. Introducing America's Workforce Academy built by Meta, a program helping to train the next generation of welders, fiber installers, crew leaders and more. Paid training, a job and a path to America's future. Because the future is for everyone. Learn [e-mail] AmericasWorkForceAcademy

[Bank of America Advertiser]

introducing B of A rewards A new loyalty program with rewards for every ambition. From cash back deals on brands you know and love to a credit card rewards bonus. From fueling up to rewards that fuel your goals. It all starts with a Bank of America checking account and grows from there. What would you like the power to do? Bank of America Open or enroll your [e-mail] B of A rewards bank of America Corporation All Rights Reserve okay, before

[Microsoft Windows 11 Pro Promoter]

we get into it, little side note for the IT leaders listening in, I was reading up on a Microsoft Commission survey the other day and learned that teams using Windows 11 Pro PCs report 62% fewer security incidents compared to Windows 10 PCs, including three times fewer firmware attacks. Pretty significant. With security built in, you'll have AI ready IT that sets you up for operational efficiency as well as long term resilience. Upgrade to Windows 11 Pro at Windows means business.com

[Joe Weisenthal]

Bloomberg Audio Studios podcasts Radio News.

[Tracy Alloway]

Hello, and welcome to another episode of the All Thoughts podcast. I'm Tracy Alloway.

[Joe Weisenthal]

And I'm Joe Weisenthal.

[Tracy Alloway]

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 word token has now officially been added to the Chinese Mandarin Dictionary and and that the characters that actually make up the Chinese word for token are something like word currency.

[Joe Weisenthal]

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 Chuck E. 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.

[Tracy Alloway]

Bringing up Chuck E. Cheese tokens is really a way to make sure everyone knows that you're a millennial.

[Joe Weisenthal]

Yeah, that's right. That's right. Arcade. I should just say arcade token.

[Tracy Alloway]

That's right.

[Joe Weisenthal]

Yeah.

[Tracy Alloway]

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.

[Joe Weisenthal]

Right, Totally. 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?

[Tracy Alloway]

Oh, okay.

[Joe Weisenthal]

Outside of the work context, are you a Mac or a PC person?

[Tracy Alloway]

Oh, I only have. I only have work computers at the moment. Okay, so all right. Before that, but before that, definitely PC.

[Joe Weisenthal]

Okay, good. Me too.

[Tracy Alloway]

Yeah. And in fact, it's not a choice, is it? Like, sorry, I really don't like Macs.

[Joe Weisenthal]

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

[Tracy Alloway]

That's right. You've talked about that computer before, so 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 the 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 Odd Lots.

[Winston Chang]

Hey, thank you, Tracy. And thank you, Joe.

[Tracy Alloway]

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.

[Winston Chang]

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 x86 heritage, which we also acquired the server from the IBM actually, so. 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 hyperscalers 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 will not mention, that apparently spent a 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 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.

[Tracy Alloway]

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?

[Winston Chang]

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 LLMs. We'll take the compressed versions of these LLMs. We will, depending on the partnership, be able to do the on device compressed 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.

[Joe Weisenthal]

It's interesting this word orchestrate, because so you have The AI agent orchestrating maybe a bunch of different sub agents to complete some task. And that is things 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?

[Winston Chang]

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 it's probably one where they weren't sure in terms of 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 are 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 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 a chat recognize the power of AI and the probability of generating increased productivity and potentially even cost savings, say more

[Tracy Alloway]

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 those are, they sound very theoretical. So is there more concrete, I guess KPIs that you're looking at Yeah, I

[Winston Chang]

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 spent 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 headcount 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 in your 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 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 them.

[Meta Workforce Academy Announcer]

People are building things here in America again. And this moment calls for the best of America, our people. Introducing America's Workforce Academy, built by Meta, a program helping to train the next generation of welders, fiber installers, crew leaders and more. Paid training. A job and a path to America's future. Because the future is for everyone. Learn [e-mail] AmericasWorkForceAcademy When I got a

[Bank of America Advertiser]

new car, I thought my insurance premium would increase and empty my bank account like if a tween won the lottery. I've invested most of my winnings in chicken tenders because they're bomb. But bro I bought a house and it's sick, bro. I'm thinking the floor is gonna be all trampoline, bro. With a helipad on the roof. The contractor said it's structurally unsound, but they're just being babies. But switching to GEICO saved me hundreds, so my bank account is safe.

[Meta Workforce Academy Announcer]

It feels good to save some hard earned cash. It feels good to geico.

[Home Depot Advertiser]

Heat up your fourth of July at the Home Depot with our wide variety of grills under $300 and make every gathering one. Give your outdoor space a glow up, whatever your budget is with savings on seasonal plants starting at $5. With the grill fired up and your backyard set to perfection, you'll be able to invite friends and family over to kick off the party. Start celebrating. With low prices guaranteed at the Home Depot, prices may vary by store exclusion supply. Seehomedebo.com Pricematch for details.

[Joe Weisenthal]

Let's go back to the theoretical Engineer who spends $100 million on tokens in a month. Because it seems to me like you're going to 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. 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.

[Tracy Alloway]

Don't give all your tokens to a Joe with AI cycles.

[Joe Weisenthal]

How do you avoid giving all of them? Giving me the uncapped budget and finding that person who actually deserves a very liberal spending budget.

[Winston Chang]

Yeah. And no, these are great questions. They're so relevant, particularly in a large enterprise like us in a global. Yeah, for a global 200 company. Right. In 180 markets. 80 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 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 spent 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 perspective. Yeah.

[Joe Weisenthal]

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 then 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 a sort of a philosophical split in CFOs of tech companies in the early years, figuring out this optimal path?

[Winston Chang]

And, and, 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 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 a 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 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 the other 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 in 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 capability set, it's really a process flow. So should we ultimately be able to automate everything and be able to achieve savings in 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, FPA 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, is the return is significant. I was just with the head of my tax this morning. I said if we just pull together and drive the next year's efforts, the tax savings or optimization 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 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 know, 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.

[Tracy Alloway]

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, 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. And then a more broader question. What are the key differences between how Chinese companies are approaching AI versus how US companies are approaching AI?

[Winston Chang]

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 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 there are 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

[Tracy Alloway]

are as a middle power, you mean.

[Winston Chang]

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 Google's of the world in terms of that type of operating system and of course to chip suppliers like Nvidia, Intel, AMD and Qualcomm, and of course now ARM based chips as well. And also the Chinese tech 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 spending and we're right in the middle of it and distributing this across the board evenly 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 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 companies 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 dollars 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 opportune 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 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 that 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 US and Chinese tech stack today are differentiated or AI companies are differentiated today obviously because of the restrictions. I know the 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 manage 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 SEQ being and I don't have official numbers, this is just quoting what I've heard like 1/50 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 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.

[Joe Weisenthal]

Right, of course, evolution.

[Winston Chang]

Well we know well it only happens in China market. So if they can survive within their cutthroat market under the constraints of the chip supply, they are Pretty strong anywhere. They are pretty strong. So I think that would be one difference. I would say. Yeah.

[Joe Weisenthal]

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 could 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 performant than a competitor server, et cetera. And we know other competitors like, like a Dell or super micro, etc. Those 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?

[Winston Chang]

I think it's not an either or question. It's fully integrated tech stack today the value to a customer and 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 year spend. So they are 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 over 12 and each region of the world. We can produce in Europe, in Hungary for the European market or the U.S. market. We can produce in Mexico, in the U.S. for the U.S. market, we can produce in Asia for the Asian market. Now we're building that factory in Riyadh to produce servers also for the Middle east market and of course China market. We always have factories there as well 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 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 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 Hyperscalers can actually come to Lenovo with a plan to say I have a plan to have this site. So people who are not even hyperscalers, 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 the data centers within nine months.

[Tracy Alloway]

Wow.

[Winston Chang]

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 11,000 rack liquid cooling capability to be able to service 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 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 that market is starting to recognize renewable in terms of our value add.

[Geico Advertiser]

We all do it. You have a night for yourself, but don't like the sound of the silence. So you turn on the TV just for the ambiance. It's a little trick that helps you feel like you've got company and aren't alone. And other insurers, well, they may make you feel alone, but when you switch to Geico, you've got claims reps available around the clock. So whenever you need, you'll have people around to help. And let's turn on the washing machine just for good measure. Isn't that soothing?

[Winston Chang]

It feels good to have support.

[Meta Workforce Academy Announcer]

It feels good to geico.

[Winston Chang]

If you're chasing data down instead of seeing it in one place. You need the Intuit ERP Intuit Enterprise suite. All your data in one place with built in AI for real time insights. Learn [e-mail] ERP okay, laundry stinks.

[Arm & Hammer Advertiser]

Literally. I mean you could just keep buying new underwear, not that I've ever done that. Or maybe sort your clothes into piles based on how re wearable or filthy they are. Or just use arm and hammer deep clean. It's made for real life stinks and stains. So even if you don't do laundry, the quote right way Deep Clean will knock it out. I mean it is from the number one liquid detergent brand that tackles more loads than any other. Come clean with arm and hammer. Deep Clean number one claim based on total wash loads sold.

[Joe Weisenthal]

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 between or cared between the difference between a Dell box and a compact box and Hewlett Packard box and whatever. But it does sound listening to a Winston that like you really can the partner, not the customer. You can really differentiate your offering.

[Tracy Alloway]

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.

[Joe Weisenthal]

Different. Yeah, yeah.

[Tracy Alloway]

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 US how difficult is.

[Joe Weisenthal]

And optical connectors.

[Tracy Alloway]

Oh yes, thank you. How difficult is that at the moment getting those key things?

[Winston Chang]

Yeah, the, the market is very robust because the demand is very robust. And as we're sitting in Hong Kong today, but in, 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 spend. 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, you know, some, 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.

[Joe Weisenthal]

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 Core Weave, one of the Neo clouds, you know, everyone we talk 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. And 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 over ordering because they see everyone else over ordering?

[Winston Chang]

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 signed deal. So I think they wouldn't double sign with people.

[Microsoft Windows 11 Pro Promoter]

Yeah.

[Winston Chang]

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, is 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. Right. And that enablement, I think needs additional compute.

[Tracy Alloway]

Lenovo has software as well. Right. And you have the gaming business, is that right?

[Winston Chang]

We have a. We are the leading online games. Oh, sorry, the game PC company in the world. Yeah.

[Tracy Alloway]

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?

[Winston Chang]

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 SAS COP populace, 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 nor do they really want to. I think you just need the middle layer to be able to bring the data together and to 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 very 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.

[Joe Weisenthal]

You know, 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 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 no one builds them for gamers because you can make so much more money selling them into the AI ecosystem.

[Winston Chang]

Well, we got a fantastic franchise in terms of, as you alluded to, the ThinkPad 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 the Yama from the Yama Motor Labs of IBM. So we kept that intact also with a lot of the people from it. 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 business, 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 appreciate 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.

[Tracy Alloway]

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 take over Search in the early 2000s. Why did you decide to, I guess, work with a bunch of different people here?

[Winston Chang]

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 OpenAI 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 layer 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 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.

[Joe Weisenthal]

Should we believe these companies, let's say a Microsoft. So Microsoft recently announced their flagship model. I forget if it's pronounced like Maya or Maya or whatever or Mai. I haven't played with it. And they're like and it also runs best on our own custom Silicon Valley. 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 Nvidia for their hardware needs?

[Winston Chang]

Well, 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. So I think that continues. But of course as 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 tech technological capability and the capital to be able to develop their own.

[Joe Weisenthal]

Is it about their models really will run better on these chips 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 some time apart, wishing they had a prenup or something like that. Just like having a little bit of like not so dependent on one company's specific chip capacity and roadmap.

[Winston Chang]

I can't speak for, for them but so there's really from an external point of view but. 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 be 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. Probably give them more flexibility to be able to do more if they have their own chips in other areas as well.

[Tracy Alloway]

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, Joe?

[Joe Weisenthal]

I forgot about that story.

[Tracy Alloway]

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?

[Winston Chang]

Yeah, it actually happened very interestingly on April 2. So I took over on the reins. Our fiscal year is April 1st and took over on the rains on April 1st. First or the second day there was the most major event in the history of 100, I think it was 200 plus countries that were enacted in this and we were 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.

[Tracy Alloway]

Right.

[Winston Chang]

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.

[Joe Weisenthal]

You mentioned something earlier and you're like, okay, on a per token basis, Deep Seek is significantly cheaper than say the most advanced models from say, OpenAI 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 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 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.

[Winston Chang]

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, we spent 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.

[Joe Weisenthal]

Right. You ever ask what you're paying them for?

[Winston Chang]

But I, I would not make comments about another industry.

[Joe Weisenthal]

I'm sure you love them and.

[Winston Chang]

Absolutely, absolutely. But you know, I think from the pers, 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. Yeah, 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 in AI, 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 of all CFOs. But in my regular dialogue with CFOs, I understand, I worry a lot, but I think we're also not alone.

[Tracy Alloway]

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

[Joe Weisenthal]

I was going to say, like, I wonder if like at bars or like women when they gossip with each other, they're like, I want a man who is like this much token spending, you know, whatever.

[Tracy Alloway]

I don't know.

[Joe Weisenthal]

It'll be part of their identities for sure.

[Tracy Alloway]

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 shareholders? Because the stock's gone up a lot. But you know, people can always be wealthier. I'm sure they're into that.

[Winston Chang]

Yeah, we paid the. In the last fiscal year, which just ended March 31, we paid the highest dividend ever in the Lenovo history. So our shareholders are very happy. We 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 you, there's a, there's going to definitely be 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.

[Joe Weisenthal]

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 in Texas that's happening. And, and then there's also of course, anti data center politics in the US 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?

[Winston Chang]

Well, I think, I think Asia tends to be a little bit more flexible in that perspective. But clearly the best infrastructure of scale was in, is in China. But 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 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, even Hong Kong these days, I think there's really 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 elevated structures. But there's 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. Treasury 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.

[Tracy Alloway]

Joe, we gotta go visit a data center in Hong Kong.

[Joe Weisenthal]

I'd love 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, 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, etc, so it makes sense that,

[Tracy Alloway]

you know, there is actually a lot of land. You just can't build on it.

[Joe Weisenthal]

You just can't build on it. Right. Yeah. So.

[Tracy Alloway]

All right, interesting to hear. Winston Chang, thank you so much for coming on OPBOTs. Really appreciate, appreciate it.

[Winston Chang]

Thank you, Tracy. Thank you, Joe.

[Tracy Alloway]

Joe. That was a lot of fun.

[Joe Weisenthal]

Yeah.

[Tracy Alloway]

I feel like we haven't actually spoken about AI from the perspective of a company like Lenovo. We've never done that before.

[Joe Weisenthal]

Yeah, no, we haven't. And that was really 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 and.

[Tracy Alloway]

Right.

[Joe Weisenthal]

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 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.

[Tracy Alloway]

Yeah, I think I've said before, there's such a weird tension between these sort of bodyless, faceless AI interface. And then when you think about all the actual physical infrastructure that supports it,

[Joe Weisenthal]

it's why I think it's so interesting. The other thing too 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, etc. 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 OpenAI's. They're talking about Capex and the Lenovas 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.

[Tracy Alloway]

Yeah, everything's up for grabs.

[Joe Weisenthal]

Yeah.

[Tracy Alloway]

I also thought the the involution point. Yeah, was 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 compared to global scale. Yeah, exactly.

[Joe Weisenthal]

It's kind of funny to think about. Yeah. Absolute. I love that chat.

[Tracy Alloway]

All right, shall we leave it there?

[Joe Weisenthal]

Let's leave it there.

[Tracy Alloway]

This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway.

[Joe Weisenthal]

And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our producers Carmen Rodriguez at CarmenArman, Dashiell Bennett at Dashbot, KalebrookSailbrooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to bloomberg.com oddlots we have a daily newsletter and all all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord GG Oddlauss

[Tracy Alloway]

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 episodes absolutely ad free. All you need to do is find the Bloomberg Channel channel on Apple Podcasts and follow the instructions there. Thanks for listening.

[Winston Chang]

Oregon Land of forest green mountains, snowy white land of nature lovers dream beauty to match delight where some inlet paths wind their way around mountain, lake and stream. Be careful never to start a wildfire. Please keep Oregon Green

[Tracy Alloway]

Migraine makes me miss out on the moments that matter. So I asked my doctor about saying yep to Vipti eptinezumab jjmr, an IV infusion given every three months that helps prevent migraine attacks in adults.

[Bank of America Advertiser]

Don't take if allergic to Vipdi. Get help right away for allergic reactions like rash, swelling of face, lips, tongue or throat, trouble breathing, hives or facial redness. Tell your doctor about increased blood pressure or color changes, pain or numbness in fingers or toes. Common side effects are stuffy nose, scratchy throat and allergic reactions.

[Tracy Alloway]

Tap the ad to see how many migraine free days you might reclaim and see the full prescribing [e-mail]

[Shell V Power Advertiser]

Shelby Power Nitro plus fuels every drive from the Pacific coast to the high desert with a fuel like no other. It contains active coating ingredients that clean and protect for longer lasting engines. That means more protection with active ingredients for longer lasting engines. Shell V Power Nitroplus Premium Gasoline Engine performance that lasts Chances are you're not far from a Shell station. Find it using the Shell Lab formulation unique to Shell compared to Minimum detergent gasoline. With continuous use of Shell V Power Nitroplus and gasoline direct injection engines, actual effects and benefits may vary. See Shell Us More Protection for more information.