No Priors: Artificial Intelligence | Technology | Startups
00:20:00 · Elad Gil, Sarah Guo
[00:20:00] S0: Yeah. I mean, the physical compute basically reinforces an oligopoly market because what it does is it creates a ceiling on the rate of progress any single lab can get if effectively assume the compute is roughly pro rata across the ecosystem to the big labs. And so in the absence of a lack of compute constraints, you're almost having an enforced oligopoly market up to a point, or at least you force closer competition between the players than what exists otherwise, which I think is an interesting artifact of this moment in time. And the question is when does that lift and what does that look like? So, yeah, it's kind of it's a very exciting time. I mean, I always wonder about second order effects of that belief of it's eighteen months away because that does suggest there could be a burnout cycle in eighteen months. Like, I know some people at one of the major labs who for a while, brought up with me, should they get married? Do people want to get married? Should they get married? Because I don't know what happens in eighteen months to the world. It's like, you should get married. You should go ahead. It'll be okay. And so I do think we're living through this very manic, very exciting, very intense work period. So, yeah, it's really fun stuff. [00:21:01] S1: I think it's kind of tragic, man. [00:21:03] S0: Really? Why?
00:21:03 · Elad Gil, Sarah Guo
[00:21:03] S0: Really? Why? [00:21:04] S1: I think the reactions of some really extraordinary research friends to it, it feels a little bit tragic. I think it like I feel like it's psychologically most similar to if people think they're gonna die. Right? Like, how would you spend the last two years of your life? Would you spend it the way you are today, or would you spend it in a very different way is a, like, a not unrelated philosophical question. And so you do have people who are like, ah, like, my contribution is a bit irrelevant given RSI in the next eighteen months. So should I get married? Should I bother to work? Should I travel? Should I only work? And, you know, I just actually think it's a much more stable and satisfying state if people act as if they have. But maybe you think that's blind.
00:21:49 · Elad Gil
[00:21:49] S0: Yeah. I just think there's a lot of second order effects that are happening or going to happen. Part of them are driven by this belief system and potential burnout over time. Part of it is going to be One thing that I've noticed that is happening at some of the labs is that as compute becomes really the scarce resource, it turns out that there's say a few dozen researchers that drive 80% of the results at any given place, which is a really interesting human power law. If you actually look at it in any field, there's at most a few dozen people who drive the field. You look at breast cancer research, you look at certain subfields of mathematics, you look at subfields of physics, You look at the entrepreneurial ecosystem and founders. There's a handful of people, dozens of people who drive most progress. And that also happens in AI research. And increasingly, compute is differentially provided to those people. Right? And so I know some labs have slowed down on their hiring of researchers unless they're above a very, very high bar because the cost isn't the researcher, it's the compute associated with the person. That's where the real bottleneck is. I think there's this broader concept of return on invested tokens, like an ROI kind of metric, which is if you have a certain token budget, who do you give it to and why? This is kind of like engineering back in the day. Right? The internal tools teams at companies were always starved for resources because many, at least tech companies, would rather use the same engineers to build product than to build internal tools that would make other functions more productive. That's why I think the death of SaaS is a little bit overstated because why would you use tokens on a bunch of SaaS stuff that you're not actually paying that much for per year relative to the outcome of those same tokens being invested against a core product or against some massive margin lift or some other thing? And so I think increasingly we've shifted from a world where people said, Hey, everybody use AI and do whatever you want, to hey, we have to measure, spend, and move more things to open source. And then I think the next wave is what are the projects and people that should actually get outsized pieces of a token budget, and what is that return on investment? It's this sort of next shift that's coming? It'll take some time, though.
00:23:57 · Elad Gil, Sarah Guo
I mean, many people are still at like, hey, everybody try AI or whatever, you know, big enterprises. [00:24:02] S1: What do you think is the appropriate compute, like token budget for a business or a human being three to five years from now? Should I look at it like rent?
00:24:14 · Elad Gil
[00:24:14] S0: No. I mean, it depends on what the budget is for what. I mean, people forget too. Minecraft was like, what was it, five people, 10 people, and it was bought for billions of dollars by Microsoft. People keep talking about someday there'll be a multi billion dollar single person company. That was basically Minecraft, roughly. It already happened fifteen years ago or whenever that was. So there are always people who can take outsized advantages of technology, and AI has accelerated that radically. And so at some point, it's like, why give tokens to people who can't do that on a relative basis unless you just run out of those people? And you may run out of them. This is back to like, well, all the engineers get laid off. Probably not anytime soon. But you could argue that at some companies, even before AI, there's a bunch of engineers that weren't that productive that could be let go, especially some of the big tech companies. And I think a lot of those folks will be very coveted by GE or PG and E or Hershey's. So even if there is some displacement of engineers at some point in the future, I don't know when that is or if it happens. But if it does happen, there's lots and lots of homes for them Because there's tons of enterprises that never had the capability set or ability to recruit these people, and they want the capabilities they bring. Even if they're mediocre in the context of a Google or Meta or whatever, they may be exceptional in the context of a certain subset of old school enterprises. And so I do think there's gonna be this permeation through the enterprise landscape of engineering talent in an unexpected way. This probably is many years away. I'm just saying I think that's probably a likely outcome.
00:25:45 · Sarah Guo, Elad Gil
[00:25:45] S1: I think relatedly, if, you know, if you are a researcher 800 and you have not been allocated an outsized number of tokens to work with at one of the major labs. [00:25:57] S0: Yeah. [00:25:58] S1: I think the opportunity to go spend your energy on, you know, something where you have, like, compared advantage and understanding and should benefit from all of this. The supply chain bottlenecks, domains that should accelerate like bio, diffusion into other valuable fields. Like, to me, that that seems a lot more exciting than being concerned about the downfall of mathematics and going on, you know, vacation until the world ends.