The Compute 100: Austin Lyons, Analyst & Writer, Chipstrat

So what was, what is your particular take? I know that there's, there's a, there's, there's a lot in that, but like walk us through some of what you've, you know, Ben Thompson has like some core ideas that animate his writing.

Curious to hear, like, what are some of like your core ideas right now? And I'm sure they're constantly evolving, but as, as, as some of them become solidified, what are, what are they?

Sure, so some of my early takes, very early on, there's AI accelerator startup Etched, and I started looking at them.

Early on, it was just the Nvidia sort of gravy train.

Everyone realized like, holy cow, GPUs are the future of AI.

And NVIDIA is obviously dominating in training.

And we weren't even into the inference area yet.

And I saw that Etched was out there and I started asking questions around like, oh, could there be companies in this space other than NVIDIA? So Etched was one of them that kind of got me into thinking about like, well, what would it take for a startup to compete with NVIDIA? Would would.

would the model labs trust and buy from a startup? Would the hyperscalers trust and buy for a startup? And so kind of looking at first principles of like, well, what's wrong with the GPU? And what's right with the GPU? And so I started kind of pulling on threads and realizing like, hey, GPUs are super flexible.

That's great for training.

That's great for inference.

But at the same time, once you like start to fast forward, if transformer-based inference is like the defining workload of our time, which has turned out to be true, then it would make a lot of sense for hyperscalers who actually know their workloads to actually invest in something that might be a little bit less flexible.

But as a trade-off, it's very, you know, hyper-designed for the workload at hand for, you know, uh, attention and LLMs and KV cash.

Um, and so, you know, I had, I, early on, I started saying like, Hey, I think there's something here.

I think the, uh, you know, these AI accelerator companies compete.

And then similarly early on, I was actually pretty, um, bullish AMD.

And I think actually that was one of my first posts.

I was like a year in that, that started to get a lot of traction, um, was just saying like, Hey, uh, you know, I think everyone's at the time was talking about this coup to moat.

And part of the coup de mot early on was the fact that NVIDIA had written software so that anyone in almost any domain could accelerate their workload from scientific computing to oil and gas to quantitative finance and so on and so forth.

And so anyone who's trying to compete, like say AMD was trying to compete, people would say, oh, well, you have to write libraries in all of these different domains and it's going to take you hundreds of thousands of man years.

But once all of a sudden that...

It became that the TAM for LLM inference was gonna be way bigger than any of these other high-performing computing things.

All of a sudden, that kind of shrank, which was like, well, to compete for all these dollars, maybe AMD doesn't have to even worry about all those other markets.

Maybe if they just focus on making their instinct chip and their Rockum software competitive for LLM inference and start there, they could actually compete.

And yeah, so those are actually kind of two very similar takes, which is, I think startups can compete in this market and I think that AMD can compete.