Lenny's Podcast: Product | Career | Growth
01:21:06 · Marc Andreessen, Lenny Rachitsky
[01:21:06] S0: Yeah, yeah. No, that's right, that's right. I mean look, the founders need to be determinate optimists. They need to have a very specific plan. And look, the critique always... The critique from the founders is, "Oh UVCs have it easy, because you don't actually have to commit, right? You don't actually have to, like, make... You have to make the bed you lay in, you can, like, place multiple bets you can have. Whereas a portfolio, you should have a lot more sympathy for us as founders, because we only get to make the one bet." And there's truth to that. The kind of argument on that is the founders get to run their companies, we don't. So, we don't get to put our hand on the steering wheel. And so, the great virtue of being a determinant optimist is you actually get to single-mindedly execute against that goal. And look, in the long run, who does history remember? History remembers Henry Ford, right, not whoever was, whatever the seed investor who seeded Ford Motor Company, and 10 other car companies have failed. Right? And so, the determinant optimist is the founder of the company builder and the engineer, and these are the people who actually use the sign, and deserve 99.9999% of the credit. But you know, having said that, I do think there is a role for having some indeterminate optimists in the background, no, helping along the way, and helping keep the whole cycle going. [01:22:18] S1: Do you think about AGI in shifting your investment thesis? Like, as we approach AGI and hit AGI, as an investor, how do you think about your investment thesis changing?
01:22:29 · Marc Andreessen
[01:22:29] S0: Yeah, so I've always kind of had a little bit of an issue... I've always kind of struggled with the concept of AGI because at least... Well, let's put it this way. Let's define terms which is where I kind of struggle with it. Which is, there's like the prosaic definition of AGI, and then there's like the cosmic definition. And the way I describe it as, so let me start with the cosmic one. So the cosmic one is basically, is the singularity, right? And so, AGI is the moment where you enter the singularity which is to say where the world fundamentally changes. And the rules of the old world are gone, we're now operating in a new domain. And then the full definition of singularity is it's a world in which human judgment is no longer really relevant because you get this self-improvement loop, the AI is improving itself. In a sort of race circle takeoff scenarios, you could see if this takeoff thing, where the AI's improving itself, and the machines are making decisions so much faster than people, and people are just sitting there watching the machine do its thing. And I kind of described it, I don't really think we live in that world, whether they could call that utopian or dystopian, I don't think we're lucky or unlucky enough to live in that world. We could debate that, we could talk about that more. But the prosaic definition of AGI that at least I think the industry purchases but it's kind of conversed on, and tell me if you agree with this, is when the AI could do every economically-relevant task as good as a person.
01:23:47 · Lenny Rachitsky, Marc Andreessen
[01:23:47] S1: The way the co-founder of Anthropic put it is, like, "A basket of the most valuable economic tasks," so it's, like, 10, 15, not every single economically-valuable task. [01:23:56] S0: Okay, got it. Yeah, so it's maybe even a slightly reduced definition. And by the way, we're clearly getting close to that if we're not already there. And so on that one, I kind of feel like, so I kind of feel like the cosmic one overstates what's going to happen. And then I kind of feel like the kind of AGI definition that you just gave, I think it kind of understates what's going to happen. It's almost too reductionist. And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything. Right? And so the way we say that is AGI always is the definition you gave, the definition I gave. It's always kind of relative in comparison to a human worker, right? And it's, like, I don't know, human skill level caps out at a certain point, but that's because of the inherent biological limitations of the human organism. Right? Human, I gave you an example. Human IQ, kind of what they call "fluid intelligence," or the sort of G factor of fluid intelligence, IQ I think tops out in humans as a species, it tops out around 160. Right? Where at like 160 it's like Einstein level, Einstein [inaudible 01:25:00]- [01:24:59] S1: In terms of IQ. Yeah.
01:25:02 · Marc Andreessen
[01:25:02] S0: ... in terms of IQ. Like, it just tops out at 160. The 160 IQ people are the ones who come up with new physics, there's only a small handful of those. Generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a bestselling author, or like a, you know, one of the world's best, I don't know, research scientists, or one of the world's best doctors, whatever it would be, probably 140 is kind of the IQ that you're looking for there. If you're looking for a really good lawyer, it's probably 130. If you're looking for a really good line manager in a business, it's probably 110. If you're looking for an accountant, like a small business accountant, who's good at doing the books for small businesses, it's probably 105. Right? And so the kind of scope of impressive human... The ability of the human organism to do intellectually impressive things, it's sort of that 110 to 160 is kind of the spectrum, and good news is there's a lot of those people running around, but there's not that many at 140, 150, 160. But it's like, that's like the limitations of what can fit in here, right? And it's like, there's no theoretical limit on where this goes if you release the limitations of human biology, right? And so, can you have a... And you already have people running these experiments to kind of do human-equivalent kind of IQ for existing AI models. And by the way, existing AI models are kind of testing around the 130, 140 level, which means they're going to get to the 160 level. And they're arguably on the math side starting to get to the 160 level now. But I think we're going to have AI models relatively quickly that are going to be like 160, 180, 200, 250, 300. By the way, and I think that's great, right? I feel as great about that as I do about the fact that we occasionally get an Einstein. Right? It's like, would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course the world would be better off with more Einsteins, and of course the world would be better off with machines that have more IQ like Einstein or greater than Einstein. But I think IQ of the machines is going to exceed that of the humans, I think that's really good. And then the performance, again, it goes back to like the AI coding thing that's happening. Performance against task is going to get better also.
01:26:57 · Marc Andreessen
I think this is where Linus Torvalds in particular was like, "Yeah, okay, this thing is starting to generate better code than I can." Okay? So now we're going to have AI coders that are actually better coders than the best human coders. I think that's... Right? I think we're going to have AI doctors that are better than the best human doctors, I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see, which we can talk about. Which I think is also great. And so, I don't think there's a... I think we're used to living in a world where we just don't understand how good good can get, because we've been capped by our own biology. And we're going to get to experience what it's like when you have the capability at your fingertips, that's actually better than human in these domains. So you see what I'm saying, which is, like, I think this idea of human equivalent is just going to be a footnote. It's like, "Oh yeah, that was just on Tuesday, in 2026 is when they hit that." And it kind of didn't matter because the next question was, like, "Okay, what do we get to do in a world where we actually have machines that are better than that?" Right? And so, I think this is going to be much more of an exploratory process for actually seeding human capability than it's going to be any sort of particular singular singularity moment or whatever that happens, that just happens to coincide with the human threshold.
01:28:10 · Lenny Rachitsky
[01:28:10] S1: 200 IQ, I... Just like that frame of reference is such a mind-expanding way to think about just how fast and how smart these things are going to get, and quickly.