Like, would you do that every time again? Was there something that made it clear to you, or was it h- like, what, what happened for you when you made the investment?

I, I, I think one of the scariest things about venture, and I think about this all the time, is the more you work on companies and you see the challenges and you see how hard it is, you build up scar tissue.

And then the thing is, like, well, you should do it again, but, like, will you do it again? And I ask myself that all the time.

Like, there, you actually need some naivete.

Um, like, you need to have this, like, oh, can-do attitude and this, like, naivete around it to attempt it.

Like, otherwise y- you just don't do it again.

And y- I've definitely built up a, a bunch of scar tissue.

And so what you wanna do is you, you wanna think about sort of h- how you can innovate a- across the, the, the, the various elements of, of a full solution, and push as hard as you can.

How are you behaving differently than you would've three years ago or something because of the pace?

Any time that I'm talking to a founder about a problem in their company or what they're doing or a move they're making or anything else, which is what I spend eighty percent of my day doing, I'm very, "This is how we used to do it.

This is what we would typically do.

This would be the typical readout on this old school readout of why this candidate is better than this candidate." Let's reevaluate that in the context of today.

Let's reevaluate that in the context of an unstable technology substrate.

Let's reevaluate that in the context of a business model that's growing this way versus that way.

I've really started to question every assumption and every lesson that I learned before, which of it translates and which of it doesn't.

What do you think you're doing on the boards of these companies, partner with the founders, that's actually different from other really talented investors who are also nominally doing the same job but don't come up nearly as often when asked that question?

Do I have the nuts to put a million bucks on the line and say I believe it's going down and buy those options?" Decided I didn't have that just yet.

I, I mean, I, I, I think the one thing that people very much underestimate, I, I had a coming-of-age moment as a young value investor at the time.

I think every value investor's gone through this.

So I, I was in this value investing club in college where you basically, you know, you read Benjamin Graham and Howard Marks and think you're smarter than everyone else and buy things at, like, you know, five times PE.

After college, I was still on that kick.

With all my college savings, I shorted Tesla, and I feel like everyone has gone through this experience.

If they were a value investor at some point in their life where it was like, you know, you just, "Oh my God, Tesla, maybe it's not a fraud, but it's so overvalued.

So they're offside, right? They don't have a ton of leverage in court, so i- i- it's a d- it's a tricky situation.

Mm.

Like, why would a graphics processing unit be the right solution for deep learning? And then, you know, of course he proceeded to explain like why GPUs were so much better than CPUs, um, for training, and also what the like ideal ground up solution could look like.

Um, and you know, and they had their idea of, of the wafer scale and everything else.

And, you know, and as, as soon as he said it, just kind of like, "Oh, yeah, that makes sense." And like, I should...

You know, like we don't know what application's gonna work.

We should invest in infrastructure.

This is an amazing team and a really provocative idea.

[laughs]

So what did you see in the company then, and what about where we are now is making it this hot?

Well, I think 10 years ago you saw the beginnings of the AI wave, so deep learning was becoming a thing, and in our world, in venture capital, you're trying to catch these things very, very early.

And while it was before ChatGPT, it was actually before LLMs or even the transformer paper, you knew that something was gonna happen in deep learning.

It was too big.

It was too powerful.

And as we thought about it, it was a question of, well, we can't quite figure out what the application's gonna be, but all the applications are gonna need better infrastructure, and that's what led us to invest in Cerebras.

It's amazing team with a very provocative idea to build a wafer scale chip, and if you thought about that, that would make AI a lot faster, and here we are 10 years later.

Uh, but maybe that also limits ultimately how big it will scale because who knows what happens to memory prices down the road, and who knows, uh, if how many customers will wanna make that commitment.