Two Veteran Chip Builders Have a Plan to Take On Nvidia

SPEAKER_1 (0:03)

Bloomberg Audio Studios, podcasts, radio, news.

Joe Weisenthal (0:20)

Hello, and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

Tracy Alloway (0:25)

And I'm Tracy Alloway.

Joe Weisenthal (0:26)

Tracy, here's something I know about AI. I don't know much, but here's something I do know.

Tracy Alloway (0:32)

How to log in to ChatGPT.

Joe Weisenthal (0:33)

No, I'm good at that. I'm good at logging in to ChatGPT and Clod, and I'm reasonably good at asking questions. No, here's actually something about the business, actually about the business of AI that I know.

Tracy Alloway (0:44)

Okay.

Joe Weisenthal (0:45)

I know that Nvidia is making a ton of money, and the stock has gone to the moon, and that other companies would like a slice of that pie.

Tracy Alloway (0:55)

Yes. Yes. That's a good thing to know.

Joe Weisenthal (0:58)

It's like a basic, simple thing, which is that when people think about AI chips, there's literally one company that comes to mind. I know others are involved. AMD has stuff. Intel obviously wants to play others, but there is obviously that one gigantic pile of cash that's flowing to this one company.

I don't know if it's still, but at one point it was the biggest company in the world. It's pulled back a little bit. Well, I would say two things. One, other companies would like a piece of that pie, and B, companies that are in the business of building AI models would like to find a way to get cheaper, more efficient, less energy-intensive chips so that they don't have to always pay the Nvidia tax.

Tracy Alloway (1:40)

Do you want to know what I know about AI and semiconductors?

Joe Weisenthal (1:44)

Let's go for it.

Tracy Alloway (1:45)

Okay, here's the one thing that I know, which is that whenever you have this conversation about Nvidia, the one word that always comes up is moat.

Joe Weisenthal (1:54)

Oh yes, moat, yes.

Tracy Alloway (1:55)

So like, you're either talking about medieval castles, or you're talking about semiconductor manufacturing, that's when you hear the word moat, because over and over again, people will say, it is expensive to make the chips, you need a lot of money for research and development, and to set up the fabs, and you need a lot of first-person expertise in building them. And then there's also the network effect. So a company like Nvidia has this huge moat around its business. The question, of course, is whether or not getting back to the medieval castle analogy, it is unassailable.

Joe Weisenthal (2:28)

That's right. In fact, semiconductors seems to be moat after moat after moat, because there's ASML's moat, and then there's Taiwan Semiconductor's moat, and then there's Nvidia's moat. And so, yes, it's like there's a series of moats, and if someone could overcome these moats, or find a way to build a bridge over one of these moats and enter this proverbial castle, that would be very lucrative. We know that many are trying to enter these moats, but it's incredibly costly, and capital intensive, and difficult, and there are just not many people who know how to do any of this stuff. And so, the question of whether these moats can be overcome. But again, there are many businesses that would love to see more robust competition in this space, so that their payment is not a tax.

Tracy Alloway (3:15)

You know one thing I don't know, and I don't think we've ever done an episode purely on this, but I don't really understand the different designs of chips. So, I know that some chips, specifically in videos, are supposed to be better at AI. They're better at running lots of little calculations all at the same time. And I know there's basic chips that go into your refrigerator or your car or whatever, but I don't really know the difference between what a chip that was designed specifically to run a large language model would look like compared to a standard basic chip.

Joe Weisenthal (3:52)

I don't know anything about chip design. I just sort of imagined someone using some CAD software, etching little lines in the thing and drawing some sort of circuitry, placing the transistors.

Tracy Alloway (4:05)

Actually, a chip design game would be really fun now that I think about it, where you could just draw little things on the square. OK, anyway.

Joe Weisenthal (4:13)

Well, we are going to learn about how chip design works. We are going to learn about what makes a chip particularly good for the task of training and running inference on these AI models. And I have to say, I really do believe we have the two perfect guests because they are both veterans in this space and they are both active in the attempt to bridge some of these motes and enter the space and bring competition to the industry. We are going to be speaking with Reiner Pope, co-founder and CEO of MatX, as well as Mike Gunter, co-founder and CTO of MatX. It's a new company that's trying to build chips specifically for the purpose of large language models. Both of them have a lot of experience in the space. We're going to get our hands dirty, so to speak, and understand how you build hardware for all this stuff and what makes it win and whether it's even a winnable game. Reiner and Mike, thank you so much for coming on Odd Lots.