Patrick Collison: "What If You Succeed?"
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What is the main topic discussed in this episode?
Okay, Patrick, thanks so much for being here. Welcome to Startup School.
Great to be here. Harj and I first met 20 years ago, and we started a company together. Sorry, am I giving away the introduction?
Yeah, I thought this was my interview, but keep going.
We started a company together many, many years ago, and I learned a huge amount from Harj. So it's really fun to do this.
All right, let's... Speaking of that, when I first met you 20-something-ish years ago, at the time, your most impressive achievement, I would argue, was Chroma, your dialect of Lisp.
Any Lisp programmers here? Oh wow, okay. I think I heard one whoop, which is more than I expected. But yeah, I really liked Lisp when I was in high school.
So what I was going to ask is, a prolific 16-year-old today could presumably just prompt call to write their Lisp dialect. Would you advise them to not do that and still do it? Is there any value in such things?
I don't know. I wonder a lot. Yeah, obviously, on the one hand, it used to be really fun to write all this assembly and machine code and to optimize your instructions and layout and memory and everything. And now we don't have to do that anymore. Compilers do it for us. We don't mourn it too much. And so maybe in the same way, we shouldn't mourn source code. We should just transcend the plane of instructions to Claudius at all. But emotionally, I miss it.
I think just as I've been hanging out here with these students, maybe the question behind it is many of them are just wondering what should they be learning at college? What is in this AI world? How much should they be trying to learn and derive from first principles and how much should they just outsource to the AI? Right.
I mean, my model of this is... is cache, you know, the C-H, not an S-H, where Jeff Dean has this famous set of numbers that every programmer should know, bandwidths and latencies and just kind of relevant constants you should have a reason about as you build systems. And obviously when you're thinking of building any system or distributed system or whatever, like all lookups and all relevant bandwidths between different Components are very different, right? And retrieving something from L1 cache is very different to retrieving from RAM. It's very different from retrieving across the network or whatever. And I think it's like that with knowledge, where fine, yes, you can ask the agent or something to compute something for you or to look something up for you, whatever.
But that's a hell of a lot slower than knowing it in cognitive L1 cache. And you can have way more round trips in your brain than you can you know, muttering through, you know, Super Whisper or typing it out or whatever. And so I think even granting the full capabilities of the models, I still think there's a pretty, like, I think for a long time to come, neuronal lookups will be much faster. And... And then if you look in revealed preference at what companies themselves are doing, whether they're companies like Stripe or The Labs or what have you, there still seems to be an enormous premium on cognitive ability. And so I think renouncing that before there is evidence that we've saturated those benefits would be premature.
I mean, are there specific things that maybe you personally, either personally or as CEO of Stripe, you still, you purposely choose to sort of do yourself and, like, retrieve from your own cash, even though, like, the agents would probably do a reasonably good job?
I still write myself. Like, I... I don't... I both philosophically, but also specifically, substantively, dislike the writing of the models. I mean, it's very interesting, right? Because these can prove the Jacobian conjecture, you know, whatever. And so clearly they're capable of these monumental feats. But somehow, I still haven't read the LLM essay that I found super compelling. Now, it is just very hard to, like, RL limit that domain because the, you know, the... utility function or something is kind of hard to define. But yeah, I think writing is a pretty, interpersonal communication and writing I think are so very fundamental and sort of being able to reason sensibly in the multi-dimensional space of reality.
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Chapters
4 chapters
1
What is the main topic discussed in this episode?
0:07–5:58
2
How did Patrick Collison and John decide to start Stripe after Startup School in 2009?
5:58–10:12
3
What should students learn in the AI era versus relying on agents for coding?
10:12–12:21
4
Why does Patrick say cognitive 'L1 cache' still matters despite powerful models?
12:21–30:58