The Biggest Chip Ever Built — Why OpenAI Runs On It | Cerebras CEO Andrew Feldman
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Why has speed become the main bottleneck for AI today?
This is the largest chip built in the history of the computer industry. It's fifty-eight times larger than a GPU. And for AI, bigger chips process information more quickly and therefore you get answers in less time. For AI work, big chips are undoubtedly the best way to go. There's no mode in inference. It takes you eight keystrokes to move from a GPU to us. in the cloud. We solved a problem that nobody in the history of compute had solved and we delivered it in 2020 and nobody cared. Nobody cared. Nobody bought it and nobody cared. Everybody said we were crazy, it would never work. So then we built the next one.
Hi, I'm Matt Turk. Welcome to the Matt Podcast. My guest today is Andrew Feldman, co-founder and CEO of Cerebrus, the company that built the largest chip in the history of computing and just pulled off the biggest semiconductor IPO of all time. Andrew has been everywhere talking about the headlines, the $20 billion plus OpenAI deal, the IPO, but this conversation is a bit different. We started from what is a wafer and built up Step by step, why GPUs struggle with fast inference, the three shortages nobody talks about, the decade in the desert when nobody wanted this chip, and why Andrew believes that CUDA is no longer a mode for Nvidia. If you want to actually understand the current chip landscape and how AI inference works at the silicon level, this episode is for you.
Please enjoy this fantastic conversation with Andrew Feldman.
I thought a a fun place to start uh would be to talk about speed. So has speed become the dominant conversation for AI today? Yeah, what happened I think was
uh for a long time AI was sort of a novelty, right? It was like a parlor trick. It was cool but not useful. And what happened somewhere around the middle of 2025 was the AI got smart enough such that people began to use it. And we remember we we we make AI with training, but we use it with inference. And suddenly people wanted to use it. And the minute you want to use it, the minute it's productive, right? Speed matters. Right. Fast tokens are more productive. And so the conversation moved from everything else to how do we make our inference faster? How do we deliver tokens more quickly? Because those are more productive tokens. We get more done in less time. And Force more about the
Great. And w what does speed mean? Is that uh is that a question of token speed? Is that completion of the task? What what's the right metric? Sure, the right metric is tokens
per second per user. That's how fast you get the first token all the way through the last token in in your uh in your response. And it's true for queries from from chat, but it's also true for For agentic floats, right? Uh if they're sort of multi-cycle turns, waiting is amplified. And so what you want is blisteringly fast responses so that the the AI feels like it's in real time. You can engage with this. A bang. So it's the brombed moment for AI. I think I think that's right. And I I think that's a very good. analogy. I I think if you think of something like like Netflix. Right. When the internet was slow, right, Netflix delivered DVDs and envelopes. You would get a a DVD and envelope. And and when the internet became fast, they didn't get more efficient at delivering DVDs and envelopes.
They became a movie studio. Right. The speed enabled them to become something completely different. And that's what speed does uh in general and in particular for for AI. It it opens up a whole new domain. It allows you to use the AI differently. You will stay longer. Yep.
So it's literally a question of of the UX, right? That's just uh nobody wants to wait uh a few seconds. Yeah, that's great. I mean, how how big is the market for
slow search? How big is the market for dialogue? Is zero. How how big is how long will you wait for a website to resolve? Will you wait eight seconds? Nobody waits. And so it's the exact same with AI.
Yep. So no more people are waiting with their laptops opening uh while the agent That's right. Well it's running and running and running. I I I think that is not what what people want.
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Chapters
8 chapters
1
Why has speed become the main bottleneck for AI today?
0:00–7:53
2
What is the difference between SRAM and HBM memory for AI inference?
7:53–18:47
3
How do agents and CPU demand change the AI inference landscape?
18:47–27:39
4
What is wafer‑scale computing and why did Cerebras choose it?
27:39–36:42
5
How does Cerebras handle failure and cooling on a chip the size of a wafer?
36:42–45:01
6
What is Cerebras’ business model – hardware, cloud, and API?
45:01–54:44
7
What supply‑chain and manufacturing challenges does a wafer‑scale chip face?
54:44–1:04:04
8
What does Andrew see as the next big shift for AI hardware and SaaS?
1:04:04–1:12:41
Speakers
1 identifiedMore from The MAD Podcast with Matt Turck
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