Lenny's Podcast: Product | Career | Growth
00:23:02 · Lenny Rachitsky
[00:23:02] S0: Yeah. I imagine people are starting on this, they didn't expect to create this new kind of almost life life type of being. Do you think about consciousness as a part of this? Just like, do you think AI is conscious? Do you think it'll get to a place where we think it's conscious? Or is that just not even, not even worth our time to think about?
00:23:19 · Emmanuel Ameisen
[00:23:19] S1: I mean, I think it's sort of very hard to say one way or another, you know, some of the most interesting ways to get at this question is interpretability because you're sort of, you're not biased by sort of like what the model says. You can just look at the structure inside and try to determine whether it pushes one way or another on the question. I will say that the thing I believe pretty strongly is that most people underestimate the sort of complexity that emerges and that like, if you look inside, there's just the sort of, you know, the facts that the models, the out learns or these representations for all of the concepts that we have and that we know, that they manipulate them to solve problems, that they do so by like planning many tokens ahead, that they sort of have this mix between this higher order reasoning and lower reasoning. And all of that just kind of emerges something that I think is underappreciated by people. And to me, it gives me, let's say a deeper sense of like, oh, there's a lot to still understand about how they work. I think maybe that's the main thing is, is I find that the more you look inside, the less certain you are about how they work. And then oftentimes people that will confidently say like, ah, language models are just X or just Y are the people that actually haven't spent a while looking inside.
00:24:45 · Lenny Rachitsky
[00:24:45] S0: And there's this, I don't know, thread of history through to how we got here, is a lot of people did not think we would get to artificial intelligence that is useful by just investing in something that was seemingly very simple and just giving it a lot of compute and that was famously something OpenAI bet on and then the Anthropic founders were at OpenAI and then spun off started their own thing. It was all kind of bet on this bet that okay, we could just do this seemingly simple thing and give it a ton of compute and it will become incredibly smart. And a lot of people didn't think that was possible and they were proven wrong. So I don't know, is there anything there that is just like, oh wow, look at this, it worked out.
00:25:24 · Emmanuel Ameisen
[00:25:24] S1: I think it's two things actually. Cause I think it's not just compute. It's also finding an objective where compute keeps giving you returns. So what does that mean? It's actually not that new of an idea. I mean, you're right, like compute versus sort of, I would say expert design architectures is a long debate. But then there's an additional step there, which is compute to do what? You know, so there's a bunch of prior work on like, oh, you can spend a bunch of compute to make a bot that's really good at go, you know, or that can win some video game. And then that's cool. And just the fact that that works is sort of like interesting, but that doesn't give you something that is nearly as useful as LLMs like Claude are. And so I think like one key thing that many in the field, including me underappreciated, you know, a few years ago and that has borne out is the combination of sort of like compute and the richness of text is what gets you there. And to me, like this is kind of what I was alluding to, but you know, if you think about like there's this observation that at OpenAI people made, it was some of the, a lot of the early team at Anthropic on the scaling loss paper. And so this is the observation that if you sort of like pour in more compute and more data to the task, to the task of predicting the next word, you see that like the scale of the model you get at predicting the next word, just like predictably keeps improving. And it's sort of like a trend that just keeps going and it keeps going and keeps going. And that's an empirical law, people sort of, you know, speculated as to why at the time, but it's sort of still just mostly an empirical finding. And that means that it's like, ah, this is a field where you can just keep pouring more compute and you keep improving on that metric. And that's rare. And also it's rare for it to be a metric that then correlates with a bunch of tasks that we actually care about.
00:27:40 · Lenny Rachitsky
[00:27:40] S0: This season's supporting sponsor is DX. Now that everybody at your company is finally using AI, the question that engineering leaders face is no longer are we using AI, but is it paying off? With DX, you can measure and benchmark the impact of AI adoption across your full product life cycle. DX shows you where AI is helping developers and where it's creating new bottlenecks. You can also use it to evaluate AI vendors, monitor costs and licenses, and see exactly what's limiting your agents. That's why hundreds of enterprises, including Snowflake, Sony, and BNY, use the DX platform to measure AI's impact on developer productivity. Visit getdx.com/leni to get a demo of d x. That's getdx.com/leni. Externally, there's been these kind of big jumps seemingly in what AI can do for us. There's like I forget which version of Opus, where it just unlocked coding. Now as Boris Jurney famously said, coding is solved. And many people are just like holy shit, over I think winter break, we're just like wow, AI is really good at this now. So there's been this kind of these moments of big jumps in abilities. And you actually said internally, it's, as you just said, it's actually been this very smooth progression of AI power that happens to cross some threshold where now we can do something. Speak a little bit about that.
00:29:07 · Emmanuel Ameisen
[00:29:07] S1: You know, there's a term for this, which is sort of this idea of emergence, which is it seems like two things are true at once. Like there's this predictably smooth trend. And then, you know, before, like Opus 4.5, I really couldn't let Claude cook too much on my code without a short leash. And then after Opus 4.5, could. And so that's weird. Like, I feel like you would expect a more gradual transition. And the mental model I have for this is, there's kind of two ways of looking at it. So there's the way that I talked about briefly just earlier where it's like, if you're just doing this kind of next token prediction, you imagine that all the texts in the world has sort of like easy words and medium words and hard words in terms of how hard they are to predict. And so as you're going down this curve first, know, the model is bad at everything. And then at some point it gets all of the easy words, right. And that means, you know, maybe the easy words are like the structure of sentences. And so the model, like all of a sudden can write coherent sentences, you know, but just one sentence at a time. And so it's kind of cool and impressive, but like we can't really do anything with that. And then maybe the medium words are sort of, yeah, these sort of basic facts about the world. And as the model smoothly gets better at predicting words, at some points it predicts most of the medium words, right? And now all of a sudden it can write coherent sentences and sentences that are true. And so now maybe it's helpful to like write emails. Maybe it's helpful to sort of like help you write drafts of blog posts, right? Even though that was like a smooth improvement, it just takes it taking over to be helpful. And then I guess the hard words, which maybe are like, your select domain expertise like science or code. And another way to look at it is it's a question of kind of how many of these words do you get right and how many do you get wrong? You know, once you get to the point where you're getting like, I don't know, maybe 90% of the medium words, right? Then you're useful as like an email writer. But if you're at 50%, it's like, it's actually wasting my time to use you because I'm just gonna have to go in and fix it half the time. But if I have to go in and fix it, you know, 10% of the time it's better or 5% of time it's better.