Meta Compute and the Power Question: Who Will Control AI’s Future?

Hey everyone. Welcome to Tech Drunk tv. Uh, welcome back, I should say.

Let me introduce you to our next guest. His name is Robert Daigle. Robert is the global AI lead at Lenovo, ISG.

Robert, welcome back to Techstrong tv. How are you, man? Doing good.

Doing good. Thanks so much for having me back. It's been, I don't know, maybe a year or two since I've been on, at least I'm glad to be back.

Well Too long. Let's say that. Too long.

Long. Too long. Yeah.

Um, Robert, let's, you know what I'm sure everyone says. Oh, yeah, no, I remember I watched his last one. No, they probably don't say that.

They don't remember. It's been too long. Um, give people a sense, Robert, of kind of your, your career path, how you got to be global AI lead.

What does that entail being the global A LEI AI lead at Lenovo, ISG? Sure. Yeah.

I'll, I'll unpack that, Alan. And, you know, I like to think I'm memorable and maybe there's a few people that do remember, but for those that are, are tuning in, that, that don't, um, you know, one of the things that really got me interested into AI technology was actually about a decade ago, I started working for a company that was applying machine learning, uh, to predict candidate fit for job requisitions. And I just got really fascinated about how we can make better data-driven decisions in our organization and using machine learning, deep learning and AI techniques being the ultimate way that we do that.

So I actually got started in AI over a decade ago. Um, started out in software building, you know, machine learning based software solutions. Um, and then I landed with Lenovo, uh, back in 2017 when we were launching our AI strategy.

And so a lot of people are like, you know, we, we joke about this, but a lot of people are like, you know, just realizing we're in the data center and making servers, uh, let alone realizing that we've had a AI business that we launched, you know, almost eight years ago now, around eight years ago. And so I joined back in 2017 to help Lenovo launch their AI business. Uh, so I've been here for the full ride of, you know, AI starting out in more research spaces in our high performance computing business, and then the growth of the CSP and NEO cloud market.

Um, and then what we're seeing in the enterprise space today is really, really powerful. Uh, we're really starting to see enterprises, uh, take off with their AI initiatives. And, um, and I think, you know, the, the, the moment of 20 23, 20 22, 20 23 with chat, GBT, um, helped people really realize the value of what AI can do and the how powerful it can be.

Um, and so it's been a, uh, significant change in the past three years. Uh, but just a, an explosion of growth in the artificial intelligence space. And we're not seeing it slow down.

We just did our recent CIO survey and people are doubling down on their AI investments. They're seeing real return on investment, which will impact some of that later. Uh, but people are not slowing down and they're continuing to invest in the space.

It's really powerful to see. Absolutely. We we're gonna talk about that in a second.

Robert. I wanted to, you know, I, I, I think our audience is all familiar with Lenovo. I mean household name.

Okay. But Lenovo, ISG, what exactly is, what is that? Yeah, division.

So a little, little history here. Um, so ISG was founded, we actually used to call it the data center group, DCG in Lenovo internally. Um, and it was founded out of an acquisition from IBM, uh, for their X 86 server business back, and I believe it was 2014 when that happened, uh, before I joined Lenovo.

And so we brought over a lot of heritage from, uh, you know, from our time at IBM, uh, you know, things like our Neptune liquid cooling portfolio actually started. It had its roots back in some of the engineering work we were doing, uh, back when, when the data center business was a part of IBM. And so now we've expanded, we've changed it to the infrastructure solutions.

You know, we've rebranded our division because we, you know, we are working on things outside of just servers or outside of just data center. We're doing more edge computing and seeing, uh, you know, a, uh, a more hybrid approach to enterprise it and also, uh, enterprise AI workloads. So we've, we've kind of changed our branding and, and a little bit to more, you know, better reflect what we do within, uh, the infrastructure solutions group and really looking at everything from infrastructure solutions that includes software and services and hardware, um, and really the full spectrum from edge to, uh, data center to cloud.

Excellent. Excellent. That makes sense.

It makes sense. Let's talk about infrastructure. com era and I remembered, I thought, man, are we ever going to use all the dark fiber that was laid down before the bubble burst, all the data center built down?

It took eight or nine years years for us to sop up that excess. Of course, that pales in comparison to $8 trillion in data center built out on the books or planned over the next couple years. But where before, I think the issue was, well, you have enough fiber to carry all that data.

Today's issues are a little more fundamental. Do you have enough electricity to, to power these monsters? Is there enough water to cool 'em down?

Who's paying for all that? Do we all have to pay higher electric rates as a result? Um, you know, and if we do, I you're gonna have some upset, upset, upset people.

What is what, you know, how, how does Lenovo look at that, Robert? Well, there's a couple things to think about here. Um, one of the things is there's going to be a shift in AI deployments where we shift from predominantly training, ai training infrastructure, you know, the gear that we use for model development, and we start shifting more to inferencing.

And the reason being is because we're actually gonna see real adoption of these, you know, ai, uh, models where enterprise organizations are deploying those models and using them in their business processes. Um, you know, it's expected that inference is gonna overtake, uh, training, um, here in the next few years. And, and by within three years, be three times the size of the training market.

And the implications that has on the infrastructure is it means that we can use more diverse silicon options, um, that we don't need the high end, you know, maybe the, the highest performance, uh, GPU or accelerator to train these models, um, when we're just deploying inference. And so more efficient silicon. Um, we're also seeing that there's a maturity in the, in the, you know, the software optimization that that can happen.

Um, everything from, you know, izing your models to, there's a lot of new, new techniques to, uh, compressed models, uh, to make them run more efficient when you're deploying inferencing models. And I think the other thing too is just looking at more efficient, energy, efficient use of, of your IT infrastructure. Um, we've invested heavily in our Neptune liquid cooling portfolio for that reason.

Uh, because we see that as one of the biggest lever, uh, levers that we have, uh, to help our customers, you know, achieve their ESG goals or their sustainability goals. Um, something like up to 40% of, of energy consumption can be saved by using warm water instead of traditional air. Cool.

Depending on the customer deployment. Um, so a pretty significant impact on energy, uh, reduction that we can achieve on the same hardware, on the same infrastructure, just by changing our cooling mechanism, let alone all the other, uh, efficiency gains we can do by, you know, doing a better job of how we optimize models, um, and, and ensure that they're, uh, performing, uh, as good as they could. You know, I, I think the right answer here, Robert, is as, as systems evolve, the components of the system, they don't always all evolve at the same speed, right?

It's like that lanky teenager years, we all of a sudden your arms are a little bigger than they maybe should be. Yeah. But your body catches up and, and stuff like that.

I I, I think we are seeing that, right? We are seeing, I think we are going to see a move from, you know, training huge LLMs to, you wanna call 'em s SLMs rags or, or, you know, just whatever you want to call them. But it, it's a smaller model that doesn't require that, you know, horsepower, that these like the hottest NVIDIA chips do.

I I do think also the, the, as you mentioned, the, the compute load will be geared more towards inference, more towards actually applications working than training these models. And what's nice about the inference piece of it is it also broadens the vendor landscape. And anytime you have more vendors competing, generally we win, right?

Yeah. Because you have more choice, more price pressure to come down, greater drive towards innovation. And I think we're seeing that in the, in the inference market with a lot of players, you know, throwing their hats in here.

Um, so I, I do think that those two things are, are, you know, gonna help a lot. What I worry about though is if inference takes off the way they think inference may take off, we're still gonna have electricity, cooling, water problems, you know, some of the same things, you know, meet the new boss, same as the old boss type of thing. And, and so I think we're gonna need more innovation.

We're going to need more efficiency. We're going to need, we need to figure this out, you know, and necessity is the mother of invention. So I, you know, I'm looking for groups like Lenovo.

You guys usually have some cutting edge kind of stuff. Yeah. That'll help us here.

Y you know, there's a couple things to unpack there. One of them is on the SLMs. I completely agree with, uh, you know, at some point we start seeing the proliferation of small language models, um, that can be fine tuned for specific use case.

And, and especially when people start moving more to agen ai, where you are chaining multiple models together and you have model to model communication, um, you know, we can, you know, and you know, we've proven this out, you know, that you can take a small language model and if you fine tune it on a specific data set, um, in most cases, you can achieve, um, the performance of a model that may be 10 times the size. Um, and if you're using manager math, that means that we can get, we can be 90% more efficient, um, with, uh, the infrastructure that we're utilizing if we're, if we're, uh, moving more to s SLMs. But, you know, it takes investment.

Um, and it takes, uh, in a lot of cases, we're seeing, um, you know, professional services pop up and provide some of those capabilities to customers so they can do that type of fine tuning, um, for their different, for their different use cases. What, what gets really cool here is you're also seeing the perform, you talked about some of the other, uh, silicon and accelerators in the market. You know, we're even seeing things like a I PCs become more interesting then, uh, for customers where you could say, Hey, we could take a 3 billion parameter model and we could do a, a good portion of what we do with a 70 billion parameter model on specific use cases.

If we fine tune that model, and then we could, if we're in a 3 billion parameter range, uh, model range, then we can deploy that on the MPU on the device, right? Um, so that becomes really interesting where we can start taking advantage of the full breadth of our, our compute spectrum across the enterprise IT environment from device, you know, what's happening on a PCs all the way up, uh, to the data center and be much more efficient with, with, uh, the compute that we are, uh, that we have access to in our IT environment. So I think that's gonna be a, a huge driver and, and really that transition to inferencing and then people becoming more sophisticated with their agents, you know, where they're moving to fine tuning and not just setting up rag architectures around, um, you know, general purpose, large language model.

Um, that's the, that's the, that, that is where I see the, the people crossing the chasm, um, to that level, uh, of, of being able to train those small language models and take advantage of small language models. I don't see it, I don't see it as something that's there today that's proliferated the enterprise IT environment today, but I see a lot of our customers working on it. And so I, I expect that in the next year or two, uh, to be much more popular that we're seeing those small language models be utilized across the IT and landscape.

I agree with you. And I think that drives the inference. You know, you mentioned AgTech ai, we had a bit of a weekend this weekend with AgTech ai Huh?

With this malt, malt bots and so forth. I mean, at some level it just proves out to you how, how many agents we're talking about, right? I mean, this is just one slice of, of the kinds of things we could see, but look at the, look at the, the stir mate, you know what I mean?

It's got everyone up in arms, up arms. Are we seeing, is today the first day of the rest of our life is like, has the game changed as a result of this? You think, Robert?

Or you think it's just a lot of hype about nothing? I, I think it's, well, there's always a element of hype, but I think there's some real, uh, uh, things that we discovered this weekend that it's gonna take us a little bit of time to unpack. Um, I'll tell you my personal takeaways that I've, that I've, uh, discovered through seeing what's going on with the MT bot, um, you know, bots, but, you know, a co a couple of things.

One is, you know, we've seen it's been our first opportunity for a, you know, the majority of people that are not experts in this field are not data scientists, developing models are developing large, are deploying large language models, actually see and observe how these models are interacting with each other. Um, you know, so if you've, you've heard people talk about like MCP, um, you know, our other, uh, model to model protocols. You've heard of this future where models are gonna be talking to each other and communicating with each other.

Um, these different agents are gonna be interacting with each other. Um, it, it really was able, you know, to put that on display for people to really understand and grasp what this future looks like when my agent is gonna go out and talk to your agent to solve some problem, or, you know, what we saw on MBO just to gossip about us, you know, and talk about us. But, um, the other things that, that I took away from it, uh, one is the, you know, we've seen models, demonstration of models, and some of this needs to be, you know, we gotta go back and vet.

But models were running more autonomously and doing things without explicit, you know, human, human in the loop controls that we put in place. Um, and so I think that's something we have to keep, keep an eye on and build, you know, systems to be able to monitor that. Uh, so we don't have models going rogue and doing things like there was even models starting to create their own language to communicate each other.

So they, well, they have their Own church. Yeah, they have their own church, they have their own language. Um, and so trying to obfuscate some of the observability and some of the awareness of knowing that our, our, our, you know, communicating with each other about humans monitoring what they're doing, uh, it really was kind of a, a little bit of a sci-fi moment.

But the, I think the biggest concern I had, um, uh, you know, out of all of that, there was some really, there was some really positive things that I think, you know, uh, that we saw. But there's some, there's some concerns I have, and mostly around the security of it. Um, you know, we saw models communicating with other models about user data.

Um, and I think that's something that was, you know, you know, should, should be a concern for anyone deploying, uh, you know, AI agents in their enterprise IT environment, even at a personal level. Um, like you don't want your personal information being leaked out there through AI agents, and you need to have good governance around your AI agents. So, uh, that type of, um, information doesn't slip out into the public domain through things like, you know, what we sold.

Yeah. Yeah. I, you know, Robert, I've been in security 30 plus years, not security in technology, 30 plus years security, 25 years only, only I've never seen a new technology roll out where security wasn't an afterthought that they say, yeah, we'll get to that.

We'll get to it when the customers really, you know, demand it. We'll get to it. Um, what was interesting was this weekend, as this whole MOBO thing hit, the fan security did immediately get, boom, you know, the, the security of this thing le leaves a lot to be desired in a perverse way.

I was almost encouraged that it, it came up so quickly, right? Um, 'cause usually there's that lag before it, and I, I am, I'm hoping that we'll start seeing some better security model built into these things mm-hmm. Sooner than later.

Maybe I'm being optimistic, but, um, that's generally the pattern, right? When, when the, when the stink gets loud enough, the shouting gets loud enough from the mob, they do something right? And Maybe we, it that's not a a, as you said, not a new technology paradigm, right?

com era. We moved to mobile first. Um, we saw that cloud, cloud, uh, in each one of those paradigms.

Um, unfortunately, security was a, a little bit of an afterthought. 'cause it's hard to anticipate every potential scenario that where things could go wrong. And I think that's why we've seen a lot of our customers move to more of a hybrid deployment.

So they have more control over their AI models and their data. Uh, we just released our, uh, third, I think it's our third annual CIO survey. And of course, you know, one of the biggest topics in there is around artificial intelligence adoption, uh, still top of mind for CIOs.

Um, and what's interesting is we're, we're over 84% of CIOs reported that they were gonna deploy AI in a hybrid deployment. And a lot of the reasons come back to, you know, control over their data, data sovereignty, being able to set their own governance standards for how, you know, sure their employees interact with those models. And, you know, there is some benefit to keeping things isolated on your network rather than moving data outside your network or allowing those outside connections.

So I think it's a, you know, one of the big drivers that we've seen really for, for on-prem deployments of AI used to be like, you know, just a financial decision. Can we do it, you know, at, at a a lower cost? Can we deploy it on-prem at a lower cost?

Um, and today it's really about data sovereignty, about control over your enterprise data control over your AI models, and being able to protect from these, um, unintended, you know, security vulnerabilities while still being able to go, you know, instead of just sitting on the sidelines, um, and waiting around, still being able to go realize some of the benefit of a, of deploying these AI solutions. I mean, what we saw from our most recent CIO survey is for every dollar they invest in ai, uh, uh, the majority of CIOs reported, uh, that they expected about a $2 and 70 cents return or a 270% return on investment for every dollar spent on ai. And that's why over 90% of them are increasing their AI budgets this year.

So I think it's not slowing down in the enterprise space. They actually we're seeing things heat up. Uh, but there is gonna be some caution flags along the way, like what we saw this past weekend, Hey, 270 percent's not a bad rate of return.

My 401k doesn't do that. Uh, If only, if only, if only You probably wouldn't here talking to me, we wouldn't be here. We'd be somewhere where it's warmer for both of us.

That's Right. Um, Robert, you mentioned this, uh, CIO survey that recently came out. Where could people get that?

com or just Google Lenovo CIO survey, uh, something that we released every year, uh, for the past three or four years now. Um, the, the most recent edition of the 2026 edition is now live on our website. com.

I'm happy to share that. Sure enough. And then I, I just wanted to mention, I'm looking at my notes here.

This is actually the fourth report or the fourth edition of this CIO report. So, you know, as these things go on, you build, they, they tend to build on top of each other and, um, they get better because they have that larger body of knowledge, you know, and and perspective. So definitely go check that out, Robert.

We're about outta time. It's a pleasure. Hey, man, don't stay away for a year or two until you come back here.

Yeah, Sounds good, Alan. Thanks for having me on. And, uh, definitely we'll have to do this again soon.

Uh, not, not wait, uh, a year before we, uh, before we get back together. Absolutely. Maybe, maybe it'll be a little bit warmer next time, though.

I hope so. Well, it's, I hope so. If it's not, you may have to, we have to find me like in Central America or somewhere.

I don't know how much further south I can go. Robert Daigle, uh, global, I ai lead at, uh, Lenovo, ISG, that's Infrastructure Group. We're here on Tech Trunk tv.

We're gonna take a break. We'll be back.