Lenny's Podcast: Product | Career | Growth
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
00:57:09 · Lenny Rachitsky, Emmanuel Ameisen
[00:57:09] S0: I think that, that to me makes me think [00:57:12] S1: that there's like some fundamental structure of like information that like these models are picking up on that in some ways, like we're picking up on and that, you know, and maybe like a crazier sort like simulation level that kind of is like regularity in our world. And so that maybe makes me think that like, if you were to have the power to simulate something like our world, actually, like you would land in sort of a similar situation, like there's just kind of this like basin of where things end up and they're just the sort of like easiest way for things to arrange themselves. And so in that sense, maybe, yeah, I'm sort of, I could believe that we're in a simulation. I think I'm not sure I fully buy it. The other reason I don't fully buy it is that even though I think the structure like lends itself to it, there'd be a question of like, why? Why are we like, like who's running this and for what? And that one I don't know about. [00:58:08] S0: Well, something Elon said, it stuck with me about this, that if you were to run a simulation, it'd be a really interesting to be like an interesting time in history or something that you're really excited about. And it tracks that this is a very interesting time in history. [00:58:23] S1: Yes. Yeah. Okay, that's a fair point. That's a Data fair [00:58:26] S0: centers in space and robots and artificial intelligence.
00:58:26 · Lenny Rachitsky, Emmanuel Ameisen
[00:58:26] S0: centers in space and robots and artificial intelligence. [00:58:30] S1: Yeah. Okay. I see it now. I see it now. Yeah. Somebody like recently told me, you know, they used to think that may you live in interesting times was was like a blessing. And they're not so sure now they're like, you know, the boring times are like pretty good too. [00:58:45] S0: Oh, man. And then there's an Emmanuel out there watching your thinking process in your J space. [00:58:51] S1: Oh my god. Yeah, hopefully I'm making him proud. He's not like, oh, I gotta rerun the simulation. We messed up this one. Shut it down. Oh, man. [00:58:59] S0: Okay. One last question randomly out of nowhere. So Swix, who, runs AI engineering, latent space podcast, awesome guy. You were asked him what to ask you. You were on his podcast a year ago and something completely from a different direction. He wanted to get your take on JEV, which is this hot new model that recently came out that is very fast making decisions. OpenAI, funny enough, just launched their own version like a week later. Thoughts on JEV in the space of quick decision based models?
00:59:32 · Emmanuel Ameisen, Lenny Rachitsky
[00:59:32] S1: Yeah, right. So my take on JEV is this sort of, you know, these kind of models, you give them some context and you give them a few options to choose between, and they just are very quick at choosing an option for you. To me, it's impossible not to hear about this and think of sort of system one thinking versus system two or something, right? So like a lot of what I've studied with these models is how they sample one token with like the cloud models. And so, can think of like the decision models are as pretty similar. So like take an LLM and sample one token. They're different important ways, but like at a fundamental level, can be like, oh, you're not letting the model sample and like reason, it's just you're like gut feeling, is the answer A or is the answer B? To me, that seems like something that's like that general frame of like, sometimes you'll just want a binary decision or like one out of 10 or whatever. It's just like clearly, I don't know, like both clearly useful and something that I imagine is like probably going to be a part of like intelligence systems, right? Where you have, for some stuff you need to think about a long time, you know, it's like, what's our roadmap for the [01:00:43] S0: next year? Need to think about for a while.