Uncapped with Jack Altman
00:46:56 · Jack Altman
[00:46:56] S0: That's good. But it's going to take a long time. But the hope I have is that, like we were talking about in the cloud era, it's like people bought Workday and Salesforce on these really elongated timelines. And they were slow. And if you were talking to a government or a hospital or a law firm, it was like thirty six month cycles on some of this stuff. That's not really how it's going right now on the purchasing of software side. I think people are pretty alert. So the hope would be that people see the security stuff for real, people are making it a top priority, And everybody reacts a lot faster than you think. I still don't think that that means it happens in three months. But maybe in twelve months, people could do a lot. That'd be amazing. I think people see the urgency. Yeah, Hopefully. And maybe as a as a last topic, the... Think what's cool is the bio thing is interesting to me because I've kind of been of the view... I still am of the view that all the software improvements that we're making are awesome, but there is a ceiling to how much that can improve the world. And if you touch some grass and you get out of San Francisco and you're like, what will actually make the world a better place? It's not that we're shipping software way faster and way more easily. It's things like people have better housing, or people live longer, or people live healthier, or people have food, or people have transportation. It's physical things. I just think there's a limit to what you can get when it's all in the screen. And bio, to me, would be one of the very best possible things if we're discovering drug targets and finding amazing new drugs quickly or at all. That would be... What are laughing about? So anyway So you
00:48:48 · Ev Randle, Jack Altman
[00:48:48] S1: were on a good tirade. [00:48:48] S0: I was on a good tirade. So anyway... But I think that it is both really promising, and it seems like it's on the horizon. Same as robotics, by the way. [00:48:55] S1: Yeah. Similar. Those are [00:48:57] S0: the two to me where you see the labs both talking about it, building teams around it, purchasing huge amount of data around it. They're writing about it publicly. And so I think that that to me is likely an indicator that it's there. I mean, I haven't yet seen the labs be wrong when they say something's coming. [00:49:16] S1: That's [00:49:17] S0: right. Getting the timeline right is really difficult. With self driving, took Now it's fully here, but it took a while. And self driving, Tesla's obviously dominant. But a lot of that was because of just infinite data. And once you get enough data at these things, maybe it just works. [00:49:32] S1: I think that's... And that's the... There's this really weird sort of, like, inverse relationship between... I completely agree. I feel like you you you... Do you want, like, the... Do you want the rest of America who doesn't have, like, an amazing, I think, view on AI? Or at least a lot of people don't throughout the world and in America don't have an amazing view on AI. Do you want to change perception of AI, solve disease, save people's lives, help people live longer, build more housing so housing prices are lower, create physical abundance? [00:50:01] S0: That's what makes a better world.
00:50:02 · Ev Randle
[00:50:02] S1: That's what makes a better And the weird thing, though, is that there's almost this inverse relationship with those things, the things that when you actually touch the physical world, could actually make the physical world a better place, and the things that have been able to get better very quickly from AI. AI just tends to improve fastest when it can generate large amounts of trustworthy feedback cheaply. I think it's a good way to abstract it. So if you look at code, it's this incredible area for this because it's extremely verifiable. Can do a unit test. You know if it works or not. You can scale up experiments extremely cheaply. They're infinitely scalable. It's all in the digital space. And so when you think about Claude Code or how Cursor improved its models very quickly or even ChatGPT when they started getting feedback from users, all of these were in a digital realm where there was an API and customers could come on at the click of a button and start basically contributing into the leviathan of training data for these labs. And in the real world, robotics and now bio, the tough thing is that you have real bottlenecks to that feedback loop. In robotics, obviously, robots are expensive. They're expensive to deploy, expensive to maintain. There isn't a big customer base that can press a button. You could order a robot, and you could start doing these things, and Py has an API now, and you could do the Sunday beta program or any of these things. But it isn't like ChatGPT, where you can get 100,000,000 people in a few months via a website. And so that's just very different. Bio, you also have natural limiters. The equivalent of running a unit test in biology is a physical experiment. And and even that experiment might only answer a small part of the question you actually care
00:51:43 · Jack Altman, Ev Randle
[00:51:43] S0: And a lot things do just take time. [00:51:44] S1: They just take time. It's like, I don't know, a physical experiment. Even if you speed it up making your own virtual [00:51:50] S0: You have to parallelize. And I think so in bio, it's like you can imagine a virtual or like a real lab, but where you're kind of run somewhat autonomously or at massive scale, where you're just doing a ridiculous number of thoughtful trials at once. [00:52:06] S1: 100%. [00:52:06] S0: I think that could be time will hold you back, but the paralyzing will still speed things up quite a lot, I think. [00:52:13] S1: Yeah. But I think this and this is I know you have an investment that you recently did in this general space for robotics. We've been very interested in things happening in bio as well. The infrastructure to we've obviously seen it with the AI labs as well. Mercor as a business has grown to an unbelievable amount of scale, [00:52:31] S0: because there's there's just no limit Very unintuitively, by the way. Everybody thought that the data thing would run out, they would buy the data, and then they bought the data, and now they can go And the movie's gone. Just turned out to be wrong.
00:52:31 · Jack Altman, Ev Randle
[00:52:31] S0: because there's there's just no limit Very unintuitively, by the way. Everybody thought that the data thing would run out, they would buy the data, and then they bought the data, and now they can go And the movie's gone. Just turned out to be wrong. [00:52:42] S1: Yeah, reality has a surprising amount of detail, as they say. And there's just so many different things that these labs want to train on. There's so many different data types that emerge. And I think how much we've seen in digital space, in the digital data space, we're going to see... I think you have to see more. I don't know if he's the first one to come up this, but Brendan Foody at Mercor says the internet was a one time subsidy on the production of LLMs. And these physical spaces, they don't have as much of that one time subsidy. They don't have the internet. They have some data. There's a lot of biodata. There's some robotics data, but nowhere near [00:53:14] S0: And you also need a ridiculous diversity of data, it turns out, to build these models well. And so it's just not going to happen overnight. [00:53:20] S1: But I think it's going be like a civilization scale thing. Yes. I think maybe economy scale, civilization scale is maybe a little dramatic. But to get these things where they need to be, it's going to take an I think it's going be Manhattan Project level scale. Yes. [00:53:34] S0: It'll be a bigger effort to make it actually happen. But if you imagine what's really possible with good AI robotics, it's probably bigger than all the digital stuff so far, if it actually works.
00:53:34 · Jack Altman, Ev Randle
[00:53:34] S0: It'll be a bigger effort to make it actually happen. But if you imagine what's really possible with good AI robotics, it's probably bigger than all the digital stuff so far, if it actually works. [00:53:48] S1: I mean, if you think about the GDP impact of maybe it could get scary because if you extend lifespans, then you have an even more gring population, but would getting too much in the macro. Not But to get too personal with it, but again, my dad got diagnosed with a very advanced form of cancer. I look at the survivor cohort rates. It was eight percent by year two or whatever, yet immunotherapy drugs produced in the last ten years have given him a ten plus year prognosis, which would if he got the disease twelve years ago, he's dead, and now he's in remission. And we are just at the start of probably 100 to 1,000 times that amount of things happening, it's like every single disease area, every single cancer. We've solved everything else from AI, and when we solve bio through all the scale of investment we're going to need to make, it's going to make probably a much, much larger impact than any of these other things. [00:54:47] S0: Well, think that was enough for this one. [00:54:48] S1: This was lovely. This is great. What are you going to do for Halloween with the family? [00:54:52] S0: We're going to go trick or treating in an extremely dense neighborhood. Wow. And I'm gonna just try not to lose the kids. [00:54:59] S1: That's [00:54:59] S0: great. That's always my goal.