Can Anyone Catch NVIDIA? | The Future of Chips and Infrastructure
Episode publishedPodcast: The a16z ShowThe a16z Show
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NVIDIA's gonna have better networking. They're gonna have better HBM. They're gonna have better process node. They're gonna come to market faster. They're be able to ramp faster. They're gonna have better negotiations with whether it's TSMC or SK Hynix and the memory and silicon side or all the rack people or, like, copper cables, everything, they're gonna have better cost efficiency. So you can't just, like, do the same thing as NVIDIA. You have to really leap forward in some other way. You have to be, like, 5x better.
The AI race isn't just about models. It's also about the infrastructure underneath them. Chips, data centers, power, networking, and the economics that determine who can keep scaling. In this conversation, Semi Analysis co founder Dylan Patel joins Aaron Price Wright, Guido Epenzeller, and me to discuss the state of AI hardware, why NVIDIA remains so difficult to compete with, and how companies like Google, Amazon, Meta, and OpenAI are approaching the next generation of AI infrastructure. We also explore custom silicon, AI economics, robotics, export controls, and what founders and investors s
Dylan, welcome to the podcast.
Thank you for having me.
We've been trying to get you for a while. You're a busy man, but it worked out. Guido, why don't you introduce why we're so excited to have Dylan on the podcast and what we're excited to discuss?
I think, Dylan, you've done an exceptional job in covering what's happening in the AI hardware space, AI semispace, and now more and more data center space as well. And just looking at it, currently, most valuable company on the planet is an AI semi company, right? The, I think, biggest IPO so far in AI was an AI cloud company. This is currently where it's happening, right? And any gold rush in the early days is the peaks and troughs that make money. And I think this is the stage that we're in. Super excited to have you here today.
Awesome. Thank you. Happy to talk about my favorite topics. Amazing.
Well, maybe let's start with g p five. We just had some of the research, Christina and Isabella, on here last week. You said it was disappointing. Why don't you share your reactions or what what capabilities you were hoping to see or overall
I think it depends on what tier of user you are. Right? If you're just using g p d five and before you were $20 or $200 a month subscriber, you no longer have access to 4.5, which, in my opinion, is still a better pre trained model for certain things, or you no longer have access to o three, which would think for thirty seconds on average, maybe. Right? Whereas g p d five, even when you're using thinking, only thinks for, like, five to ten seconds on average. Right, which is an interesting sort of phenomenon. Right? But basically, like, g p d five is not spending more compute per se. The model
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