Unsupervised Learning with Jacob Effron
00:00:00 · Jacob Effron, Diogo
[00:00:00] S0: As you think about Anthropic, is that something you're optimistic on? It's like, hey. That's that's undervalued or something you're like, oh, I don't know what's gonna happen to that. [00:00:06] S1: I get the sense that a lot of the expectations baked in are ones that are not just product level, but based on, like, misunderstandings of the future of AI and what it can do. So my guess is that because some people might be valuing, like, building god as basically infinite, there'll be some amount of miscalibration miscalibration there. And I think that, you know, like, the let them cure cancer is going to be like Marie Antoinette's, like, let them eat cake.
00:00:32 · Jacob Effron
[00:00:32] S0: Jev took the AI world by storm about a month ago, and I've been wanting to talk to Diogo, their CEO of TypeSafe, for quite some time. So it was really fun to have him on the podcast. I think maybe even better, with with a month to reflect on everything that's happened because we talked about a ton of different things. It was really awesome to get Diogo's perspective on, you know, the shapes of problems that he still thinks need to be solved by AI models, how he thinks about pushing Jev models to be even better. It was also fun to get his thoughts on Anthropic's upcoming IPO, AI safety, a bunch of other topics. I think folks will really enjoy hearing from him. Without further ado, here's Jojo. Well, I have really been looking forward to this. I think ever since your, like, viral launch, I I feel like the second that happened, I was like, I have to have you on the podcast. And so appreciate you making this happen. I think actually, in many ways, more interesting that we're we're we're some weeks removed from it. Given that, like, the early the early results were not what you expected and maybe not as as as as strong as you were hoping, like, what gave you that kind of early confidence in this research direction of RLCD? Mean, I feel like you kind of alluded to it in the in the GPT three, you know, example. Like, it seems like there has to be a leap of faith that any, you know, great research effort undergoes. And what what was it about this approach that just, you know, in the early days when you didn't have the signals early on, you know, you were still kinda confident in?
00:01:53 · Diogo
[00:01:53] S1: Oh, man. Oh, man. Oh, man. This is like... I'm I'm getting a little emotional right now. I've... I I have a fun little side topic gets into this, if that's okay. My cofounder was debating on whether or not I was mission driven at OpenAI. I would have described myself in hindsight as one of the lowest percentile mission driven because I think that the OpenAI mission is not really... It... It's kind of intentionally left as vague, so many people's missions can fit into it. But, like, the grand mission was never something I super believed in, even though I truly want AI to be extremely, extremely transformative. So I'm still trying to figure out, like, am I... Was I mission driven then or not? Because I'm really mission driven now. But the answer was, you know, just a lot of work trying to square, like, AI being super incredibly awesome, which I think it is, and AI being really not that useful in every, like, productivity stat around. And this is, like, the kind of thing that, like, most people, like, just focus on one or the other, and they never have, like, a bridge answer or, like, they never have a sensible bridge answer. I think distribution is, like, a nonsensical answer here, because so many companies have gone all in on that and then gotten burned. It's it's almost certainly not a distribution. And to me, it was like believing that, like, hey. AI is clearly smart and also that the future is clearly going to be machine native. The question that always motivated me... And, like, I used to ask this question even to early people joining or even to investors in our early, you know, like, our our early rounds, or even, like, the pre seed round, which I don't even know if that counts, like, taking a safe. I used to ask this question of, like, okay. Like, you have... Imagine LLMs are an API, you know, like a like a submodule that is called by software, which even in the most string model ends, they still need to be called by software by something like a harness. That harness can be a while loop. But what percentage of those calls going into the LLMs are actually meant for human consumptions? So for consumption. So things like style matter and, you know, pros and all of that versus for computer consumption, which is going to be very, very different. You know?
00:03:59 · Diogo, Jacob Effron
Like, I don't think a computer really ever wants a string except when that string happens to be an extreme cardinality enum, which is a very valid use case. But it's very rare that that is the thing that computers want because they operate on bits. And when I just ask myself or anyone this question, even the most string pilled of people will give many nines of, like, percent for the machine native component. It's just a question of when that will happen. And to me, it was always like, okay. What does that thing look like? Let's work backwards from that cognitive core and make it work. And I will say, Jev is not yet there. There's still so much more work to do to make it so much better. And that's that's what really drives us all. [00:04:40] S0: Is Jev not yet there because, I mean, you know, today, the shape of of, like, you know, calls and tokens actually is more for humans than it is for for computers themselves? How much of it is is just you're also waiting for the use cases themselves to evolve, and how much of it is, you know, areas you wanna push on the model?
00:04:57 · Diogo, Jacob Effron
[00:04:57] S1: I I think it's a bit of both. It's it's like it's like this interactive dance, and this is also why we released, like, these dreamer ready features, like the things that we are... You know, like, wouldn't recommend for the most stable of use cases because we really want to be honest about our model's properties. So we've released a lot of these things because the reality is that the people who best know how much reliability, you know, robustness, like intelligence, is needed for their downstream application is not us. It's the developers themselves. Because developers are the expert at this, and they will know way better than anyone on our team. We have smart people, but they will know better what reliability profile they need as long as we give them the right knobs, which string models don't tend to do. So there's, like, an entire spectrum of this. I think that they need to coevolve together. The reason why we launched was not like, okay. This is, like, a good moment in AI. Everyone told us not to launch then, just as FYI. It was that we felt like, hey. This is getting to be something we're really proud of. Like, it's kind of, like, impressively smart at a lot of things if you prompt it right, And we are ready to, like, keep pushing that limits... That... Those limits with the rest of the field to help us coevolve use cases, figure out weaknesses, deal with jaggedness. Like, there's so much we do to focus on jaggedness and insufficiency and intelligence that having people play with it would be so helpful. [00:06:13] S0: Why'd folks tell you not to launch?
00:06:13 · Jacob Effron, Diogo
[00:06:13] S0: Why'd folks tell you not to launch? [00:06:15] S1: We launched around the time of the pacing the frontier discussion, and people were like, hey. Like, it's kind of, like, a bad vibe to, like, launch a capabilities product during this. Like, everyone's, like, really doom and gloom about AI, and it's a kind of a bad vibe. And, like, I don't know if you guys should do this. And we're just like, that discussion never really made sense in the first place, or it is, at best, incomplete, in my opinion. [00:06:39] S0: You talk about kind of, you know, wanting to push in in so many directions. I mean, I think one thing, you know, that you that you guys do is you're always kind of transparent about the things the models can't do, you know, the areas where they're not as strong. How much of that is, like, the road map for what you're pushing on? Like, you know, how much of it is is other areas? Like, you know, how do you think about, like, where where you want, the company to to push in the next month or two? [00:06:58] S1: Month or two. Oh, that's a that's a [00:07:00] S0: I mean, AI world, don't know if I can ask, I don't know if I can ask longer than that. Right?