Dylan Patel: GPT-5, NVIDIA, Intel, Meta, Apple
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Why is NVIDIA still ahead in the AI hardware race and what does it take to catch up?
NVIDIA's gonna have better networking than you, they're gonna have better HPM, they're gonna have better process node, they're gonna come to market faster, they're gonna be able to ramp faster, gonna have better negotiations with whether it's TSMC or SK Heinex 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 five X better.
Today we're talking AI, hardware, chips, and the infrastructure powering the next wave of models with three people at the center of it all. Dylan Patel, founder and CEO of Semi-Analysis, one of the sharpest voices on chips, data centers, and the economics driving AI's explosive growth. Aaron PriceRipe, general partner at A16Z, investing in the technologies and infrastructure shaping the future. Guido Appenzeller, partner at A16Z with decades on the front lines of AI, cloud, and networking. From GPT-5's launch to NVIDIA's dominance, custom silicon and the global race for compute, we're covering what's happening behind the scenes. Let's get into it.
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. Good, why why don't you introduce why we were so excited to have Dylan on the podcast and what we're excited to discuss?
I think Dylan, you've done exceptional job in in covering what's happening in the AI harbor space, AI semi space and now more and more data center space as well. And Just looking at it, currently the most valuable company on the planet is an AI semicompany, right? The I think biggest IPO so foreign AI was an AI cloud company. This is currently where it's happening, right? In any gold rush in the early days is the pigs and troubles that make money. And I think this is the stage that we're in. So super excited to have you here today.
Awesome. Thank you. Happy to talk about my favorite topics.
Amazing. Well, maybe let's start with GPT five. We just had some of the researchers from Christina and Isabella on here last week. You said it was disappointing. When you share your reactions or 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 GPT-5 and before you were twenty dollars or two hundred dollars a month subscriber, you no longer have access to four point five, which in my opinion, is still a better pre-trained model for certain things. Or you no longer have access to O3, which would think for 30 seconds on average, maybe, right? Whereas GPT-5, even when you're using thinking, only thinks for like five to 10 seconds on average, right? Which is an interesting sort of phenomenon, right? But basically like GPT-5 is not Spending more compute per se. The model did get a little bit better on a vanilla basis, right? 4.0 to 5 is actually quite a bit better. But when you think about
You know, what is this curve of intelligence, right? It's like the more compute you spend, the better the model gets. And that's whether it's a bigger model, which GPD five isn't, right? You can see it's not a bigger model. It's roughly the same size, you know, or you think more, right? But again, like this is something that OpenAI's first thinking models, you know, the first few generations of O one, oh three would think for a long time and waste a lot of tokens, if you will. And when you look at, for example, Anthropics thinking models, even when you put them in thinking mode, they think a lot less, right? To get to the same results or better results, right? As OpenAI was. And so OpenAI, I think, like optimized a lot of like, well, if I ask, like I think the silliest one I had asked was like, I asked O3 once, is pork red meat or white meat?
And it thought for like 48 seconds. It's like, what are you doing? Like, they should just like tell me the answer. And so, like, the nice thing is that GPD5 will think a lot less, even if
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Chapters
6 chapters
1
Why is NVIDIA still ahead in the AI hardware race and what does it take to catch up?
0:00–6:32
2
What are the main strengths and limitations of GPT‑5 compared to earlier models?
6:32–18:35
3
How are Google, Amazon, and Meta using custom silicon to reshape the AI market?
18:35–32:27
4
What economic pressures are driving AI model launches toward cost‑efficiency?
32:27–46:42
5
Which infrastructure bottlenecks—power, cooling, and supply chain—are limiting AI growth?
46:42–1:01:27
6
What challenges and opportunities do emerging AI silicon startups face?
1:01:27–1:04:49
Speakers
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