Lenny's Podcast: Product | Career | Growth
00:48:20 · Emmanuel Ameisen
[00:48:20] S1: Yeah, a lot. This isn't just next token prediction on a bunch of text. You know, it's not, it hasn't been like, you know, goes against so many of our intuitions for like, no, the way that you make an intelligence as you know, you'd have like, you give it lessons, you bring it up, like you do all this complicated stuff. And it's like, no, absolutely not. We do this incredibly simple thing. And yet we're at the point where it's breaking new ground in mathematics. I think there's sort of, if you hold these two thoughts in your head, there's two reactions you can have. You know, there's denial and there's curiosity, where denial this is just not possible. If you're doing, all you're doing is predicting the next word, it's not possible that you could ever like match, you know, the like best specialists in our field. And I think that position just gets harder and harder as we get more empirical evidence that actually that is what's happening. And curiosity is kind of the road I've ended up choosing, right? Which is just like, well, like how is this happening? You know, let's open up, let's look inside. How could it be that, you know, one of the early projects I did at Anthropic is I learned SQL, a programming language, it took me years to master. And then as a little like side project, I got Claude to learn SQL and it was better than me at it. And I'll never be better than Claude at SQL ever again. And it took it, you know, just like this kind of like simple process, how could this be? And I think there's so much depth in that question and just so much to explore. It's to me so fascinating to just every day try to unpack that, try to sort of like peel a few layers of the onions back like, okay, well clearly this is happening. The simple objective is giving rise to this super complicated thing. Like, okay, let's figure out why, let's figure out, oh, it's learning these general concepts. Oh, it has these circuits. Oh, okay. What's the next question? What's the next thing we don't know? And my sort of, I don't know, my sort of maybe most optimistic take on this is that I think we're gonna learn a lot about just the nature of intelligence itself, right? Of sort of like how you get this super complex thing that emerges from simple objectives. And in fact, I would argue that we're another example of this.
00:50:35 · Emmanuel Ameisen, Lenny Rachitsky
Like evolution, pretty simple objective, you know? You sort of like shake things around for a while and like keep organisms that manage to adapt to the environment. And at the end you get jazz and podcasts, you know? That's kind of crazy as well. And so I think there's a sense in which there's there's some fundamental truth that we can learn by studying these things. [00:50:57] S0: Wow. Something that comes up as you say that is what's interesting now is AI is helping us learn and evolve and, you know, progress, like AI tutoring and all these things, just like, it's like interesting how it now is helping us accelerate our own understanding of the world. And I wonder if like this point you made about how the way we learned was lessons. AI is learning just processing a ton of data. I wonder if that's how we end up. I wonder if we change how our education system based on what LMs have been trained on. [00:51:31] S1: Yeah, hopefully you know, we don't change it just so that we ask, we get students who are like hey, here's a thousand words, predict the next one. [00:51:38] S0: Just read it all. Yeah. Keep it all in your head. That's right. Yeah. Let me come back to this kind of safety piece because I think there's a lot of people that are just worried about where things are going. And I'm curious just as someone watching this stuff happen being within alignment org, watching it evolve. Are you optimistic? We'll figure it out. Do you think we'll crack this and keep AI aligned with our interests?
00:52:05 · Emmanuel Ameisen
[00:52:05] S1: My take on this is like, I'm personally optimistic. I think this is something where right now a lot of people are paying attention to it. And first of all, I think that's kind of a great thing. I think as an issue like this rises in prominence, it's exactly what we were talking about earlier. It's like this technology is powerful. It has benefits. It has risks. We have to kind of adapt and shift resources from like benefits to risks and back and forth as it progresses. And so, you know, to me, I feel like sort of we've continued to study it. The thing that's interesting is that there's this sort of, like as the models get better, they get in some ways easier to study. And so there's been this sort of strange, you know, the models get better. And so like the world model inside them is clearer and we can sort of like understand some parts of them better, but also to get more capable. And so they're more able to sort of evade like basic measures of sort of security. And so there's this push and pull as models get better. I think, you know, basically based on this push and pull, wanna allocate more or less resources to sort of various parts of like monitoring, like understanding their circuits better, things like that. But it's also why I'm kind of happy to chat with you because one of the main things that I think is important to make progress is just to make sure that people kind of understand both, you know, what kind of systems these are, like how they work, how they're made, so they can form good mental models of, you know, the present of the future. So they can decide how to shape it, whether that's, you know, taking part in some of these alignment efforts or just like informing them as the public that's gonna have to like vote and sign on much of these issues. Like, I don't know. I'm pretty excited for this current moment we're in for that reason.
00:54:03 · Lenny Rachitsky
[00:54:03] S0: And something I think people misunderstand is people working in these labs have taken this problem very seriously from the beginning. Like I said, Anthropics in the original mission was AI safety and alignment. That was like the reason that it became a company. And a lot of people are hearing this for the first time or think that it's not a serious area of study and that people aren't focused on it, many smart people care a lot about this and are working on it. There's a lot of, from what I can tell, lot of good progress is happening. The other point you made about the models growing very smoothly in intelligence, like, you know, there's been this question of, are we going to have a fast takeoff or a slow takeoff? There's always this fear that we would hit a point, recursive self improvement for example, and it just takes off super intelligence so we're in huge trouble, we have no control, it's out of the box. But it feels very much like we're on the slow takeoff scenario because as you said, the models are kind of very smoothly improving and where we are catching the little things along the way, learning, evolving, putting regulation around it. Know, of the scenarios, this feels like a pretty good path we're on of the many scenarios people imagined and the fears they had.
00:55:12 · Emmanuel Ameisen, Lenny Rachitsky
[00:55:12] S1: As sort of this pace of progress kind of evolves, it's just a matter of reacting quickly. So I think anything, you know, that's why I think having people be informed is great because then people will like voice concerns or like voice their opinions and then, sort of regularly evaluating, you know, what the benefits are, what the risks are and how we sort of like mitigate each is is basically the way that feels like the right way forward. And and I... Yeah. I kind of agree with you that it feels like sort of there's momentum to to do this right now, which is which is great. [00:55:42] S0: Something I wanted to ask you completely unrelated. Where are you on the simulation hypothesis? Being someone that watches this, all this life form growing, are you more convinced we're in a simulation and this is and we're not the base at the base layer of all this or are you not?
00:55:42 · Lenny Rachitsky, Emmanuel Ameisen
[00:55:42] S0: Something I wanted to ask you completely unrelated. Where are you on the simulation hypothesis? Being someone that watches this, all this life form growing, are you more convinced we're in a simulation and this is and we're not the base at the base layer of all this or are you not? [00:55:59] S1: Oh, this is a fun one. I actually haven't thought about this in a while. To me like there's something else that like looking inside these models kind of brings out which is this sort of like almost like there's a fundamental structure that they keep falling on. This is related to your question, I promise, let me get to it. Let me explain. I've studied like a bunch of cloud models and also, you know, some different models, some like open source work. And then a lot of time when you look at how, what they learn and what the structure inside is, it's surprisingly similar, you know, and this is for models that are trained years apart on like different data by different people. And you know, and you look at how they solve these problems and there's just so much overlap. And my take on why that is, is that for some problems that are important, there's just one efficient way to do things, right? Like there's kind of one way to do math efficiently that like fits the structure of our brain and we all learn it. And very few people are doing it a crazy different way, right? Everybody works in base 10, not many people on like basics. [00:57:09] S0: I think that, that to me makes me think