Intel's CEO Shares His Plan To Win The AI Chip War — With Pat Gelsinger

0:00The CEO of Intel joins us to talk about the company's AI plans and the broader chip war. All that and more coming up right after this. LinkedIn Presents.

0:15Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. We have a very special show for you today because the CEO of Intel, Pat Gelsinger, is here with us today. Pat, welcome to the show. Hey, great to be with you, Alex, and look forward to having a chat together and talking to all of your listeners today. Great. Well, thank you for being here. We're talking about Intel. And I think the question that people have about Intel, I mean, obviously you're in the middle of a comeback is we all remember the Intel inside days and that Intel wasn't everything.

0:48And then something fell off. So what exactly happened at Intel? And what is the comeback looking like as briefly as you can? Yeah. And, you know, over an extended period of time, Intel didn't have technical leaders in the CEO chair. made some bad technical choices, had sort of lost its way in its core product execution. And then probably the most critical one, Alex, was we lost process technology leadership. We bet against this key technology called EUV, and that ended up being critical. Then we went from being multiple years ahead in process technology and continuing Moore's law to multiple years behind.

1:30And boy, you know, when I came up in charge two and a half years ago, it was rebuilding the company, setting a new strategy, getting the product execution, but most importantly, getting back to process technology or silicon leadership in the core of the company again, and opening up a new strategy to become a foundry for the industry. Okay, great. So I want to talk about all of this, but let's just do a quick one-on-one to get started. So there's so many different parts of the chip world. I mean, can you talk us through exactly what they are? There's design, manufacturing, and not every chip manufacturer manufactures their own.

2:08Yeah. And, you know, we used to describe Intel as IDM, integrated design and manufacturing. And for the most part, the world has moved to foundry. You know, there are companies that do foundry or manufacturing for others. And then fabless companies. So a fabless company would be NVIDIA, AMD, Qualcomm. They don't actually build and run their own fabs. Intel is one of the few companies left that actually builds and runs our own fabs. But we had only manufactured our chips in the past. And this is what I've called IBM 2.0, that we're going to be designing chips, you know, like Xeons and, you know, our core chips for PCs.

2:53but we're also going to be a manufacturer of our chips and for the industry. And so you can sort of think about those two parts of the semiconductor industry, the manufacturing, being a foundry, and being a provider of the chip designs themselves. Right. So design. Some companies just design, some companies just manufacture, some companies manufacture other people's chips, and Intel wants to do all those. That's right. That's right. And that's the new strategy that I've laid out, that we're going to be this manufacturer for the world. But we've also said, hey, we have to rebuild Western manufacturing.

3:28And this has been the CHIPS Act in the U.S., the EU CHIPS Act, that all of a sudden the world had moved to only manufacturing in Asia. And when we saw in COVID, boy, our supply chains got disrupted. Auto plants stopped because we didn't have a$1 semiconductor. So we want to rebuild Western manufacturing at scale as well. So it's been another key piece of the strategy. And obviously, with the passage of the CHIPS Act in the U.S. and Europe, we've been quite successful getting that underway as well. We're here with Intel CEO Pat Gelsinger. So, Pat, just to go back to basics, can you also tell us what the difference is between a CPU and a GPU?

4:10Well, CPU is this general purpose compute device. You know, as you think about it, it runs everything. You know, it could run a web service. You know, it could run, you know, an application. You know, it runs Zoom, right? It runs, you know, every, you know, your recipe program. It runs everything and all the software. So it's called a general purpose CPU. A GPU instead is really built for a very specific class of workloads. So generally those have been called throughput workloads. So it does lots of floating point processing and matrix operations. So it's very dedicated for things like graphics, matrix, and now it's worked out to be uniquely good at things like AI.

4:56And so it's a very specific set of apps that have become very important. Why is the GPU so good for AI? Well, AI tends to have very specific operations that it's doing and that all it's doing is compute, compute, compute, compute. right whereas a cpu is sort of saying oh if then and jump over here run this application so it's very specific you know and largely emerged from the whole graphic space where all it's doing is vector graphics you know rasterization a very narrow set of compute workloads so it's designed basically you can sort of think about it you know as your general purpose sedan that's sort of the CPU and the GPU, all it does, it gets on the F1 track and all it does is go fast on very specific workloads.

5:46Interesting. And obviously it worked really well for gaming and that's kind of was NVIDIA's specialty. Is that how NVIDIA just ended up running away with the game here was that they built this GPU for gaming and it ended up being, they kind of lucked into it being good for AI? Yeah, and it very much is that way. And Jensen and I, you know, we've known each other for 35 years. You know, this general purpose workload, and we always are adding more capabilities to the CPU. But over here, it was always just go really fast for graphics. And then you got really lucky that the AI workload sort of looked a lot like the graphics workload.

6:24So as I joke with Jensen, I said, you know, you just were really true to that mission of throughput computing and graphics. And then you got lucky on AI. And he said, no, no, no, Pat, I got really lucky on AI. But now it's interesting because you have NVIDIA. Okay, they're the clear leader. But every single day, it seems like another company is announcing their own GPU. I know that Intel's had its own Ponte Vecchio chip in development. But also you have accelerators, right? which is basically ways that companies like Amazon and Google will modify chips in order to be able to run AI workloads. In fact, Google just trained apparently its entire Gemini model on its own accelerator, not needing NVIDIA at all.

7:09So just take us into that race a little bit. And does it seem like, I mean, NVIDIA's lead for a long time has seemed steep, but it seems like less so now. Yeah. And what we expect, you know, and when we think about AI workloads, you can think about training and inferencing. And you can think about that like a weather model. How many people create the weather model? That's training versus how many people use the weather modeling. Oh, that's lots of people, you know, local forecasters, you know, scheduling, route maps, all that kind of stuff use weather models. For the training application, you now have what NVIDIA does, accelerators like what we're doing with Gaudi, but also then the TPU from Google, the Tranium from Amazon, what Microsoft just announced with Maya, what AMD announced.

7:58Because the software there is very specific in this class. So if I can run that Python code, as it's called, the key language in this case, then I'm going to go compete at that. And those machines are getting big and fast. So a lot of people are pursuing that. But in the inferencing, then you sort of say, hey, how do I mainstream that application? That's an area that actually is just another workload. And we're going to do a lot of inferencing on our standard CPUs or the Xeon product line as well. So we expect that there's going to be a lot of competition in the AI space. And finally, for Intel, we're also going to be a foundry.

8:37We're going to be the manufacturer for many of those chips as well. So we want to be the manufacturer for NVIDIA, for AMD, for Google, for Amazon. We want to be their manufacturing partner, even if we're not using our design chips. Yeah, I'm definitely going to talk to you about the manufacturing in a bit, but let's just stick with the design here. So, Todd, I mean, it does seem like what you're saying, though, is that this landscape is going to be a lot more competitive than it has been previously. I mean, you have a company like NVIDIA that added, what,$600 billion to its market cap in one year.

9:11Like, there are going to be others that are going to be trying to get in. Does that sound right to you? Absolutely. You know, and I sort of put them into two classes, Alex. There's going to be those that build their own. You know, and that's what you see Amazon, Microsoft, Google are doing. They're going to say, hey, I'm going to own this and do this myself. And then there's going to be the general providers in the marketplace. And that's going to be Intel, AMD, NVIDIA, I think will be the three big ones in that space. So there's going to be do it ourselves. We're going to own the full stack of hardware and software, you know, which is the big cloud guys.

9:45And then there are those who say, hey, I'm going to sell my chips to everybody. And I expect those to be the big three. OK, so wait, which which part are you going to compete in that both or? Oh, yeah. Yeah, you know, we're going to, you know, because I want to be a foundry to what Amazon does, what Microsoft does, what Google does. And I'm going to sell my chips and I'm going to sell my chips to the enterprise customers who want to do this with their data on premise, as well as to the big cloud guys as well. And today, you know, biggest customers for NVIDIA today are probably Microsoft, who's putting up their big farms.

10:17But they're saying, hey, no, I'm going to build my own chip. I'm going to build Maya so that I do just like what Google is doing with their own TPU as well. I want to own that margin and I'm going to do it on my architecture as well. So Intel, I think, uniquely has two bites of the apple here to pursue. Right. It's interesting to hear you talk about how the AI inferencing or the running of these models is going to be done on CPU chips. I mean, you just kind of explained the architecture and the use of a CPU. And it seems like even still a GPU would be better for AI functionality. But are you saying that actually the CPU will be fine?

10:54It seems like it was built for something different. Yeah. And what's going to happen is AI is going to get added to every application. Right. So everybody's going to start saying, how do I bring AI into my apps? So imagine I'm running SAP. Right. I'm going to do a lot of my normal SAP and all of that runs on my CPUs today. But then I want to add some inferencing capabilities into my SAP environment. You know, we believe and we're adding these matrix functions onto our CPUs. So we're extending the workloads of our general purpose CPUs to do a better job at AI. And so if the workload is just running inferencing, it'll probably run better on a GPU.

11:37But if it's running a lot of things, we're going to make it just run great on the CPU. We're finding great interest from customers to do that today. And for my standard CPUs today in the data center, we see about a third of the purchases are being based for AI workloads. So we're already seeing that characteristic emerge quite strongly today. So explain this one to me then, because Intel has its own Ponte Vecchio trip, which is apparently, you know, a GPU trying to do some of this other stuff. But how are you, you know, how are you going to balance that with running AI on your CPU chips? Yeah.

12:14You know, so some, you know, if the workload is running on the CPU, it's just going to stay running on the CPU. But then we're also going to, for these environments where all that you're doing is running AI, then we're going to offer our accelerators as well. And Ponte Vecchio and Gaudi, we're bringing those together into a single product line going forward because we're going to compete in that space as well. We're going to be building components, whether they're GPUs or CPUs, to capture as much of the market as possible. Okay, so I know about Ponte Vecchio. It's a GPU. Gaudi, CPU, that runs AI functions.

12:49It's called an accelerator. It really is designed for these unique matrix functions that are seen in AI workloads. So just give us an honest assessment of where Intel stands today compared to NVIDIA. Like, are you getting close in terms of like the volume or where is that? How do you compare right now to them? Yeah. No, NVIDIA is the runaway market share leader today. Right. Give them credit for that. But, you know, we're now seeing our growth rate and quarter to quarter, we approximately double the growth rate, you know, but we're still small market share today. But we're rising quickly because customers are looking for alternatives, you know, today because this is demand and they also want better price and different features.

13:32So our business here is growing very rapidly, you know, but it's from a much smaller base. But we are now winning some of the performance benchmarks. So all of a sudden customers are saying, huh, you know, they're showing up winning some of the benchmarks. You know, I want an alternative. NVIDIA is short on supply and I'm getting much better TCO from Intel. You know, hey, let's go start testing this. And we're getting a lot of interest in our value proposition. Yeah. In some ways, this supply crunch, you know, really can end up working in your favor because people do need something. Yeah. And it's both a supply crunch for the supply chain.

14:07right and some of our packaging and wafer capabilities people are saying hey can you help us people who might not have considered intel as a foundry supplier or all of a sudden saying hey can you manufacture right even people we compete with on the product side saying hey can i be your manufacturer but you know if your chips are working today and i can build my next ai farm you know using you a lot of interest there as well so unquestionably the supply crunch is working for us. Okay, great. So we've talked now about design. I think we've done enough on that. We can talk now about manufacturing.

14:40People are talking a lot about TSMC, Taiwan Semiconductor, and the fact that, A, like I think during COVID, a lot of people realized that it was a strategic liability to have core manufacturing for the U.S. be done offshore. Now, it's going to take a while for us to get to that point, but there's legislation, there's funding, the CHIPS Act that's going to give companies like yours an opportunity to start to build some serious foundry capabilities in the US. One question to you to start. Intel has tried a couple times to build foundry capabilities for others, I think twice before, and it hasn't worked out.

15:18It's very different to basically manufacture your own chips than to manufacture other people's chips. It takes off-the-shelf technology, process, all that stuff. So what gives you confidence that this time is going to be different for Intel? Well, several things that we're doing differently this time. And the first, you know, I'll say on the first attempts, they were hobbies, right? It was sort of like, ah, let's go try it. You know, we really weren't taking it seriously as a company. This time, I have met the future of the company that we are going to - Why not? Like, why was this a hobby? And then we can talk a little bit more about why this is so important now.

15:54And, you know, fundamentally, the Intel business was going really well before. and this foundry business model was still pretty nascent. So it was sort of like, ah, that model's emerging. TSMC is doing pretty good. You know, let's go try it in a few places. But it wasn't taken deeply intentional as a core part of the strategy. And not very profitable from what I understand is that this is kind of like the least profitable part of the whole process. Well, hey, TSMC has gotten pretty good profits now. And how they figured it out, yeah. Yeah, they have figured out how to make good profits here. So this is now a very profitable business, almost as profitable as the chip business itself in many respects.

16:36Secondly, the ecosystem has become much more mature. And Intel before was very proprietary. So if you wanted to use my foundry, you had to be proprietary on me. Well, now we have standardized our processes like the rest of the industry. So it's much easier to use us as a foundry. You know, third, I'd say everybody post-COVID realizes, oh, my gosh, you know, we desperately need a Western foundry at scale. You know, this is super important. And we're finding that interest from customers because they see their supply chains as very fragile. You know, and they have become so dependent on one company, one island, one port.

17:19You know, there's a lot of industry interest as well as government interest to build us as a world class foundry. So we're well on the way. And, you know, it's become a key piece of the strategy that I've laid out. How would you assess the geopolitical risk to Taiwan? I mean, you said one country, one island. Obviously, that's Taiwan. We've seen already, you know, Russia invade Ukraine. That put a lot of people's antennas up. This might happen in Taiwan. What's your perspective on how serious we should take this? Well, you know, this is one where it's going to take years to rebuild these supply chains.

17:52Right, like how many years? It took us three decades to have our supply chains move to Asia. You know, what we've said is, hey, we've gone from 80-20 to 20-80 in Asia. Wow. By the end of the decade, so, you know, seven, eight years, I think we can get close to 50-50 by the end of the decade. And if we accomplish that right over a seven or eight year period, I think the world is going to sleep much better at night, you know, because, hey, this is, you know, a blockade of the Taiwan Straits. And all of a sudden the island browns out in 30 days. You know, this becomes very precarious. Right. And we can fix it.

18:31And, you know, not just the economic, but the national security benefits of this are huge. Why is it going to take so long? It takes five years to build the new fab. Right. So, you know, what I've described, you know, we're a couple of years underway on this. But, you know, if we accomplish this by the end of the decade, as we've laid out, you know, that is spectacular. You know, for a layman, why does it take five so long to build one of these factories? Well, you know, these factories, you know, first, they are just amazing. Right. And, you know, I just love people to come and visit the factories.

19:04These are the largest construction projects on Earth today, building the smallest things that have ever been built on Earth. It really is amazing, the precision manufacturing, the chemicals and so on. You know, it takes us about five years to have one of these factories up and running on a leading edge process technology. You know, the total project is about$30 billion to build one of these factory complexes. You know, so it's an enormous capital investment. Right. And, you know, I end up with like 7000 trades people, you know, to work for almost four years to build one of these locations. It truly is right.

19:45A manufacturing marvel building the most advanced science that's ever been done on Earth. Now, Pat, I've spoken with people in the early days who were there at Texas Instruments and were part of this offshoring of chips to Taiwan. And basically what they said was it was the least profitable part of the whole process. And they just didn't care. They didn't think about it strategically. I'm curious if you think that it was a mistake to let so much manufacturing leave the U.S. And then also, it seems like a good hedge to have a plant here. But just in terms of a profitable business line and a good business, it's going to be very expensive to run in the U.S., don't you think?

20:31So curious to have you weigh in on both of those. Yeah, two things. One is, yes, it was a mistake. And I think the world realized how big a mistake it was in the middle of COVID, you know, that we allowed our supply chains to become so fragile. And as I say, what aspect of your life isn't more digital? Everything's more digital going forward, right? And everything digital needs semiconductors. You know, where oil reserves are has defined geopolitics for 50 years. where technology and fabs are for the future is more important you know so with that in mind yes it was a mistake and now you know with the chips act you know we've taken the most significant industrial policy legislation since world war ii to correct that error now yeah part of the chips act was to level the playing field right it was to close that economic uh gap that we see with Asia today.

21:25So it's designed to bring those back on parity so that the investments that we're making are competitive with those of Asia in the world. And I believe as the decade goes forward, we rebuild the ecosystems, we can systemically and structurally be closing those gaps in addition to the aid that's been needed to immediately fix some of that huge economic gaps that we have today. Okay. So there's some subsidy there that sort of makes the economics work. Yes. And so we are big technology podcasts. So I have to ask you about Apple, right? They've started designing their own chips and they're doing a pretty good job of it.

22:04So I'm curious from your perspective as a chip manufacturer, what do you think about the position that Apple's in today? And I mean, clearly the performance is quite good on the chips they've designed. Is that something that you, I mean, obviously not every company is going to do it, but how would you assess their effort? Well, they used to use Intel chips. And when Intel stumbled, Apple stepped in and did their own chips. So ultimately, my objective is build better chips that they want to use our chips versus doing it themselves. But it also shows that this idea of the foundry ecosystem has become very mature, that a company like them could step in and build very good chips.

22:43And remember, they build chips for their applications, so they highly optimize them just for the Mac and for the iPhone as well. They don't do everything like the Intel chips do across many different markets. They optimize them solely for their applications and products, and they've done a super good job. And I'd say over time, hey, I'd hope to give them a better product that they could use my chips again. But I still want to be a manufacturer for them, even if they choose to keep designing their own chips going forward. I want to become a foundry for them, just like they use TSMC today. And now, have you talked to them about that?

23:17Are we still seven years away from that being a reality? Of course I've talked to them. I've talked to everybody in the industry. You know, Qualcomm, NVIDIA, AMD, Google, Apple, Broadcom, etc. I want them all running on our factories because that is better for them to have our technology, better for them to have more resilient supply chains. And I'm going to make it a good business proposition for them as well. Okay. You have a big AI event coming up this week. Can you tell us a little bit about what people can expect there? Yeah. We call it AI everywhere, right? In this sense, AI isn't just going to be for these big, high-end cloud and training environments, but how do we make it available across every PC, across every edge device, as well as our chips for the data center?

24:05And we'll be introducing two new chips. One is our main CPU, right, Xeon Gen 5, that we have further enhancements for the AI workload. And we'll be introducing Core Ultra, which is for the client to put AI capabilities directly into your PC. Okay, sounds good. Anything you think I missed or anything else we should know? Yeah. And I just say for this AI everywhere thing, you know, Intel showing up here saying we're the volume provider, you know, and much like the Centrino event was, you know, 20 plus years ago that made Wi-Fi and access points and every coffee shop had to have Wi-Fi service. You know, it just changed the not just the PC, but the entire way that people use computing.

24:55We see this AI PC having that same kind of shift where all of a sudden maybe I don't type to my computer anymore. I just talk to it in the future. It knows when I'm there. It translates languages. You know, it has new insights and capabilities. It becomes my personal bot. You know, we just see it ushering in a new generation. And Andy Grove, one of the founders of Intel, described the PC as the ultimate Darwinian device. And we think we're about to go through a major evolutionary step in the life of the PC. And that begins today. One quick follow up. What is an AI computer? You mentioned an AI computer.

25:34Yeah. You know, think about your PC today that now has built-in AI capabilities, where all of of sudden, instead of having to go to the cloud to get a model, all of a sudden, my PC is able to record, translate, summarize, you know, be vision tracking in flight, you know, where you could be speaking in Korean, and I could be hearing you in English and vice versa in real time. I'd look away from the screen, right? And it would summarize the conversation when I'm outside of the meeting, you know, before the call, you know, before my next call with you would say, hey, you know, on this date in December, you spoke to Alex and remember this is his birthday coming up and don't forget to remind him to bring flowers home.