A mind-expanding conversation on how models actually work | Emmanuel Ameisen (Anthropic)

1. 0:00 – 1:30

   Introduction to Emmanuel Ameisen

   1. LRLenny Rachitsky0:00

      One of the big questions on people's minds these days is AI doing bad things. As someone that watches these models think and operate, why is it so hard to catch it thinking badly, trying to deceive us?

   2. EAEmmanuel Ameisen0:09

      We train Claude to be helpful, harmless, and honest. Is it the case that it's behaving well, but if you look inside, actually it's like, "Oh, I'm only behaving well 'cause Lenny's watching right now"?

   3. LRLenny Rachitsky0:18

      People's full-time jobs is studying the model, how it thinks, how it operates. How long has this been a skill set and a job?

   4. EAEmmanuel Ameisen0:24

      One of the most important things to realize if you, like, use language models or think about them is just this, like, very bizarre fact that to make them is not to understand them. All these models do is just a lot of mathematical operations on many numbers. If you look at it, it makes no sense. You just have a list of 100 numbers. You know, the model's doing math, and then you just try to see if there's patterns.

   5. LRLenny Rachitsky0:42

      You are figuring it out the same way we figure out how the brain works, looking for patterns. You show this image, this part of the brain lights up, and you're doing the exact same thing with AI. Wow, this is so fascinating. [chuckles]

   6. EAEmmanuel Ameisen0:50

      There was this model we were training. It would go crazy. It would do some math. It would get to an answer. I think it was 12. It would immediately go back and say, like, "Oh, no. Oh my God. The answer actually is for sure 36." It would do math again. It would get to 12. Slowly it would, like, start descending into madness, writing in all caps, literally saying, "I'm possessed by a demon."

   7. LRLenny Rachitsky1:07

      Humans, if you really boil it down, like, we're basically next-token predictors ourselves. We're just trying to figure out the next thing to say. Dario's always out there, "This is really dangerous. We gotta be really careful." And everyone's just like, "No, he's just saying that for marketing reasons." And now it's like, wait, shit, it's really dangerous.

   8. EAEmmanuel Ameisen1:21

      Take the idea that this technology is going to keep improving seriously.

   9. LRLenny Rachitsky1:25

      Do you think about consciousness as a part of this? Just like do you think AI is conscious?

   10. EAEmmanuel Ameisen1:29

       Oh, man.

2. 1:30 – 5:57

   Why AI models are grown, not built

   1. LRLenny Rachitsky1:33

      Emmanuel, thank you so much for being here, and welcome to the podcast.

   2. EAEmmanuel Ameisen1:37

      Thanks for having me, Lenny.

   3. LRLenny Rachitsky1:39

      So you work on interpretability at Anthropic. Explain what that is. Explain what it is you do.

   4. EAEmmanuel Ameisen1:45

      Yeah. My, uh... So I work i- in a team, uh, of, of interpretability researchers, and our goal is to understand how LLMs like Claude work by looking inside them. One of the most important things to realize if you, if you, like, use language models or, or, or think about them is just this, like, very bizarre fact that to make them is not to understand them. You know, you make them by sort of, like, building this, uh, architecture, which is kinda like a scaffold, and then you pass a bunch of data through them, and they sort of, like, grow in inscrutable ways. And so that's why, that's why it's even a thing that, that, that we could be want- be wanting to do to study, like, how these models work. And so a lot of our research has been on, well, you know, when, when the models are about to produce one more word, there's actually a whole universe of stuff that happens, and we try to kind of untangle, um, as much of it as we can.

   5. LRLenny Rachitsky2:42

      There's gonna be a lot of, uh, mind-expanding, uh, s- I don't know, elements to this conversation, I expect. One is, uh, along these lines, this point that you make that models are not built, they're grown. Speak more to that.

   6. EAEmmanuel Ameisen2:55

      At a really high level, the way a model like Claude works is, um, you kind of train it in these successive phases. Like the first phase, which people refer to as pre-training, is you just take a large corpus of data, and over this corpus of data, you sort of, um, feed the model as input, you know, the s- let's say first half of, of, like, a certain document, and you ask it to predict the word that comes next. Then you do that again and again and again and again for sort of an ungodly amount of data. Um, and after you do that, there's then this other phase where you sort of have this fine-tuning phase where maybe instead of arbitrary data, you have a corpus of examples of, like, how the model should behave, and that's what you train it on. So you have examples of dialogue, of like helpful assistant telling you, you know, your, your favorite recipe or solving coding problems. Um, and then there's, there's sort of like further, further details here that I'm just gonna skip for a second. But then, okay, so, so what does that mean that you're doing this process? Like, what is, what is this thing that you're, that you're sort of training? All models are is, you know, they take this input, uh, which is a bunch of words. They turn these words into numbers, and then they do some math on these numbers, and they get some intermediate result. And then they do more math, and they get some intermediate results. And they do this again and again and again and again many times. And then at the end, they have a final result, which they then convert back into a word. And, and so the math that they do, these, these, these computations, that's the part that's grown. That's the part where it's just like through learning to do this task better, we sort of like update parameters in one direction or other. And then they learn some formulas. We have no idea what they are. And the goal of my team is to sort of like study, you know, both sort of like what these formulas mean in the end and also what these intermediate results are. Can we decode them, and can we see, like, what the model is doing as it's doing all of this intermediate stuff?

   7. LRLenny Rachitsky4:49

      This episode is brought to you by our season's presenting sponsor, WorkOS. What do OpenAI, Anthropic, Cursor, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS. And that's because they are the best. Whether you are a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise-ready and unblocking growth. It's essentially Stripe for enterprise features. Visit workos.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workos.com to make your app enterprise-ready

3. 5:57 – 11:49

   Why next-token prediction is harder than it sounds

   1. LRLenny Rachitsky5:57

      today. Does it blow your mind as much as it blows mine that we have some code that we wrote that- First of all, we have to s- now s- kind of watch and study like, like a neuroscientist studies a brain. Like, that's like a new thing that we need to be doing to understand what the hell is going on. And then this idea that as-- like, this terminology using is how a lot of people think about this. It's like we've created this new life, uh, artificial life, artificial intelligence. Uh, it just is-- it's pretty mind-boggling if you really think about it and kind of zoom out.

   2. EAEmmanuel Ameisen6:30

      I think it's really weird. Um, I think that the best way to think about it is sort of like the reason we ended up here, one of the big reasons is that we don't know how to do it the normal programming way, you know? So there's, there's like for you wanna build an app or something, you wanna sort of, uh, do something pretty, pretty, pretty self-contained. You can just have a programmer that's gonna write a bunch of rules and say like, "Ah, well, if somebody clicks on this button, then this happens." You know, if, if, if this, then that, and so on. But we don't know how to write that software for classifying, you know, images into categories or for writing, you know, for, for making a machine that can generally sort of take in arbitrary text and, and produce texts. You know, it can take in like reports and summarize them, can write emails. And so, you know, in a sense, it's weird that this very simple kind of training objective like gets you all the way there. But in another sense, it's also, you know-- I almost feel like there's a, there's a sense of, um, of like, uh, it's impressive, uh, and it, and it means something. It means that what you think is a simple objective, this like, oh, we're just feeding data and predict the next thing, is probably deceptively complicated. Like the-- we're probably doing more than what it sounds like. And I think a lot of the ways that maybe like I've updated on how these models work is by realizing how complicated it is to do what they do well.

   3. LRLenny Rachitsky7:57

      Say more about that. What do you, what do you think about when you say it's this, this kind of unexpected complication?

   4. EAEmmanuel Ameisen8:02

      Okay. So one way to think about, um, next-token prediction that I think a lot of people think about is your iPhone autocomplete, you know, where you write some text and then it'll like predict the next word and it's maybe like right, I don't know, 60% of the time and then kind of funny and weird the rest of the time. And I would think of that as sort of, um, you know, the, the very, the, the easiest version of predicting the next word. And, and unfortunately, I think that's the mental model have, that people have for these models. But if you get better at that, um, and language models got better at predicting the next word than humans in around 2019. If you keep getting better at that, you know, you start running out of easy words. So, uh, initially you just had to predict sort of like simple sentence structure. You know, uh, later on the model has to predict like, oh, like simple facts about the world. Like if I complete a sentence like, "Oh, the capital of France is," and it has to be like, "Okay, that's Paris." And you know, once, once it's good at that and it keeps improving, the only thing that's left is sort of predicting, you know, the answer of some complex, you know, physics equation, uh, or the sort of final paragraph of some PhD thesis. Uh, and so the thing that you e- that you end up realizing if you think about this is that, man, like predicting the hardest words is actually extremely hard to do and it's, it's a sort of mystery how you would be able to do that. Uh, and to me this is a sort of-- this, this has been a motivation of, of why I even started studying these, these models in the first place is I was like how, how can something so simple give rise to, to something so powerful? And it's like well, if you think about the model that has to predict sort of, you know, the, um-- One example I always like to use is like, ah, the capital of the state that contains Dallas is, you know, and it'll say Austin. Well, to do that well, at least for you and me, you kind of have to, to have a mental model of like the geography at play, right? You have to be like, "Oh, where's Dallas? Dallas is in Texas. States have capitals. The capital of Texas is Austin," um, and so on. And, and so as these models get better at the seemingly simple task, the sort of like best way to solve it, at least for us, is to just kind of understand the underlying structure, uh, of the world. And in fact, you know, some of the stuff that, that we look at inside models shows that they do understand that and they do sort of, uh, manipulate these concepts like we do.

   5. LRLenny Rachitsky10:28

      Oh man, there's so much there. One is just this kind of idea. I forget who said this, maybe Ilya. This idea that to predict the next token well you basically have to understand the entire universe.

   6. EAEmmanuel Ameisen10:36

      He has an example that I love too where he, he, he has the example like predict, "And the murderer was" at the end of a mystery novel, right? It's like well to do that you kind of have to have understood the whole plot.

   7. LRLenny Rachitsky10:46

      Yeah. The other, the other, I forget who tweeted this, this idea that like humans, if you really boil it down, like we're basically next, next-token predictors ourselves. We're just trying to figure out the next thing to say and are-- And if you meditate you start to kind of actually s- notice your brain wanting to do things and say things. I don't know how much meditation you've done but it feels like you start to pay attention to your own next-token predictor.

   8. EAEmmanuel Ameisen11:08

      Oh yeah. Yeah and this is like a thing I think about often is, is that you know, like right now as I'm, as I'm answering you there's sort of like two things going on in my mind. There's hopefully more than two but at least two. Uh, and, and, and one of them is there's sort of like a point I'm leading you to. There's, there's like a broad idea I'm trying to land this sentence on. Uh, and the other thing is at the same time I'm just predicting the next thing to say. I'm just saying one word at a time and I'm making sentences and I'm trying to make them make sense and I'm pausing when I think it makes sense. But at the end of the day even though I'm just saying one word at a time like all along what I wanted to do is to compare this process to what language models

4. 11:49 – 17:06

   How models represent concepts and understand the world

   1. EAEmmanuel Ameisen11:49

      do.

   2. LRLenny Rachitsky11:50

      I'm curious what else as you spend time paying attention to how LLMs work and operate. I feel like you've-- You're like in the top point one percentile of people that have studied and watched LLMs operate and think. What else has surprised you about how they operate and think?

   3. EAEmmanuel Ameisen12:04

      There's so much. Um, so- Maybe I'll go through like rather than, you know, there's, there's a bunch of findings, but then maybe they're sort of like what-- Yeah, how they've changed how I think about models. So to go back to the, to the, you know, capital example. Uh, this sort of like, you know, ask the model, you know, the capital of the state that contains Dallas is. So one question that, that people have had for a while is: how do these models answer questions like this? Is it that they've seen just so much text that they've kind of memorized like lists of, you know, prompts and responses? Or is it that they're having some sort of model of, of like what you're actually asking about, that you're actually asking about like cities and cities are in states, and states are in countries, and things like that. Um, and so one thing that, that we do on my team is, is we ask a question like this, and then we look inside the model at these intermediate results. Before it produces a given word, you know, as it's about to, to answer, we just look at the intermediate results and we, we basically have, uh, an immense amount of effort dedicated to trying to decode what these intermediate results mean. And if we decode them well, um, which we can do in some cases, we can see that models often have, uh, and represent these concepts. So when you ask that, you know, uh, Dallas question, what you see is inside the model, you see the representation. You see an intermediate result that maps to the representation of Texas that lights up and one to capitals, and then those two combine to be-- to, to activate a, an intermediate result. It's like, oh, the capital of Texas, that's Austin. They have representations like this for basically everything, like every concept that you can think of, or at least, you know, the vast majority of the ones we've tried. They sort of represent, you know, these concepts, uh, uniquely across contexts. So one thing I find really neat is, um, there's this, there's this debate about, you know, are language models like thinking? And then, you know, that's, that's a hard question, but one aspect of thought is like having unique representations that are shared when you think about different things. So, you know, uh, you, you like know what the concept of an eye is, and it could be like an eye in a photo or an eye in like a medical diagram or an eye in a little drawing, and we see that in language models as well. If you pass a prompt that has a photo of an eye or you talk about eyes in any language, or you draw a little SVG with an eye in it and you look at the intermediate results inside the model, the same direction lights up and that direction is like, oh, this is an eye. And so there's this sort of a sense in which they have these general concepts that they know about and that they use and reuse to, to solve problems. Another thing that sort of is surprising to people, it's kind of the, the mirror of what I was saying about, you know, I'm, I'm saying the sentence saying one word at a time, but I have some destination in mind. So one mystery of, of language models is, man, if they're outputting only one word at a time, how is it even possible that they could, you know, write a coherent report or like write code well? It's sort of like no one on earth that is, that is doing this, uh, as their job, you know, that's like writing reports or, you know, writing emails is, is literally just only thinking about the next word in the sentence with no idea-- you know, like empty mind about anything that comes next. And so that was sort of a big question that we had for a while. Um, and if you look inside these models, like one neat fact about these models is that all of the intermediate results that they've, that they've like produced when they were predicting a word, you know, let's say like the fifth word that they predicted, they can be reused. It can be accessed and reused by all of the future words. So what does that mean? That means that in practice, we find that models will like in their intermediate results sometimes formulate a plan. Um, the example we have for this is poetry. It's hard to rhyme, uh, without planning. You kind of have to think of like, oh, I just ended my first rhyme in like a it word, and so I should end my second line with another it word. Um, you know, maybe the word I should end it with is rabbit. And we find that if you look at models as they're like writing poetry, you see exactly that. They write a line and then like in their intermediate results, they're like, "Oh, oh, oh, this is an it rhyme. I should rhyme it with rabbit." And then every future word that they predict looks back to that plan they made. And so even though they're, you know, outputting one word at a time just like I am right now, they can of course have a plan that they follow over many, many, many, many words. And so all of a sudden, even though you have like a next word predictor, even though that's like technically right, you actually have something that's, that's closer to being able to just do like, you know, higher order tasks where it's like, oh, well you just have a plan and you just kind of refer back to your plan and you're slowly like executing on it. And you can do that without, without saying the plan. That's the cool part is like this plan that we find is in these intermediate results inside the model, but the model doesn't even have to sort of like say

5. 17:06 – 23:10

   How LLMs plan ahead

   1. EAEmmanuel Ameisen17:06

      it out loud.

   2. LRLenny Rachitsky17:07

      Wow. I know that this exactly what you described is one of the biggest findings you guys have discovered about how AI thinks. This, this kind of assumption we all-- Like it was originally built, the original idea of this, of, of how LLMs operate was this next-token predictor and you're saying that that's not actually how it is anymore. They plan, they think ahead. It's not just, uh, like you said, this idea of an empty mind picking the next word independent of everything else.

   3. EAEmmanuel Ameisen17:31

      That's right. And I think that, I think those two, those two visions can, can, can like actually aren't, you know, as in conflict as, as you'd think in that you train the model by asking it to be like, "Hey, what happens next? What happens next? What happens next? What happens next?" And then if you do that and the model gets good enough and you give it hard enough tasks, well then of course the best way to know what happens next is to think about like, you know, what happens way down the line. You know, you can think of even in this pre-training phase when like the models are reading a story with characters, right? And it's like, oh, like what is this character gonna do next? Well, the best way to know that is to be like, oh, okay, so this character, you know, is generally in love with that character, um, and they've been sort of like planning to, to, to like, uh, declare their love, uh, at this instant and so, you know, or in the future. And so to do that, they probably should do this thing, and so what they're gonna say is that. Um, and so even though you're only, like, predicting the, like, next instant, it's like, yeah, but to do that really well, you have to have, like, a really good understanding of what's going on, including what you think might happen in the future.

   4. LRLenny Rachitsky18:32

      What's so interesting about this is how you, you have to kind of peek inside its little thought process. A- as a consumer of AI, you s- you kind of give it a prompt and then you wait for it to say something. And what you look at is something that happens between those two where it kind of thinks. Talk a little bit about what that little space is, how you're able to observe these sorts of things.

   5. EAEmmanuel Ameisen18:53

      So the right mental model is, is, you know, again, that in between each word there's like multiple steps of sort of like computation and in between each steps there's like some intermediate result. And what is that result concretely? Um, it's a list of numbers, right? 'Cause, 'cause all these models do is just do, you know, a lot of mathematical operations on many numbers. And so initially if you look at it, it makes no sense. You just have a list of 100 numbers. You know, the model's doing math. It's like you look in the middle and there's like 100 random numbers and then the model's writing poetry and there's 100 numbers and maybe they kind of look different, but it's, it's unclear, uh, like if they mean anything. And so part of the, part of the job is, is we sort of decode, uh, this, this, this sort of list of numbers and, and we try to see if there's structure in it. And if you, you know, if you, if you do this very quickly you realize that there is structure in it. So one, one common technique that is used in interpretability, um, is you pass a bunch of, of text, just all of the text that you can find, just random text through the model and you record these intermediate numbers for, for every, for every example you just record it and then you just try to see if there's patterns. Like are there numbers or, or you know, like, uh, pairs of numbers or, or, or like specific series of numbers that like only occur when the model is like processing English text or French text or text about love or text about eyes or whatever. And I think of this as the equivalent of, you know, scan my brain and like put images in front of me and record everything that activates in my brain as I like watch a bunch of images and then be, be like, oh, you know, it seems like when you see images of a house, like this part of your brain lights up. But when you see images of like a dog, that part of your brain lights up. So that's basically like one of the ways that we do what we do and if you do it you do find basically that there's intermediate results that represent like all of these concepts that you'd expect. And so that's the first phase. You know, we sort of like decode these intermediate results and now we, we have sort of like, um, a guess as to what they mean and then we, we can do a bunch of experiments. Like we can ask it harder questions and then just like label all of the intermediate states and see like, oh, this is what it's doing or we can even go inside its, inside its activations and change it and then sort of see like, oh, what would happen, you know, if while the model is, is writing these intermediate results I went and like secretly changed one of the results and, and does that do what I would expect? Um, and in fact oftentimes it does.

   6. LRLenny Rachitsky21:23

      It's so interesting hearing you talk about this and how much of this is us just trying to figure out what the hell is going on versus [chuckles] we designed it this certain way and just how much of this has emerged. I don't know. Is that just-- Like I guess that's just what makes this such a fun job I imagine for you just like what is going on? How is this all working?

   7. EAEmmanuel Ameisen21:40

      Yeah. It's, it's, it's a fun and sci-fi like job because I have been working in tech for a while, uh, and before this, you know, I was working on more applied machine learning and there a lot of what you do is about building systems and it's sort of like the practice of software engineering. And, and here a lot of what we do feels so much closer to biology than to engineering, right? Because it's just the, the sort of system arrives at you and, and you're being asked to say like, "Well we've noticed you know that Claude can like translate words or it can code in this way or sometimes it behaves-- sometimes there's like a weird behavior like what, like--" And you're just asked to, to answer, um, well how-- like why is it doing that? But you don't have access to the source code really like you do for a program. Like the source code is just this massive list of mathematical operations which is just like incomprehensible and so you end up, you know, in the position of someone k- kind of similar to someone being asked like hey, you know, when, when like a, a mouse gets hungry like it, it sort of like gets aggressive what's going on with that, right? And you're just like well I guess I'll like run some experiments to, to see what the factors are. Um, and so it's a very different kind of study than, than anything that I've, that I think most of us on the team have done before.

   8. LRLenny Rachitsky23:02

      Yeah. I imagine people starting on this they didn't expect to create this new kind of almost life, life, uh, uh, type of being.

6. 23:10 – 24:44

   Could AI be conscious?

   1. LRLenny Rachitsky23:10

      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?

   2. EAEmmanuel Ameisen23:19

      I mean if-- 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, what the model says you can just look at the structure inside and try to determine whether, whether it, 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, and that like if you look inside there's just this sort of, you know, the fact that the models yeah learn sort of these, these representations for all of the concepts that, that we have and that we know. That they manipulate them to solve problems. That they, 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 that all of that just kind of emerges, um, is, is something that I think is underappreciated by people and, and to me gives me let's say a, a, a deeper sense of like ooh there's- There's a lot to still understand about how they work. I think maybe that's the main thing is, is I, 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

7. 24:44 – 28:29

   Scaling laws and the power of text

   1. EAEmmanuel Ameisen24:44

      looking inside.

   2. LRLenny Rachitsky24:45

      And there's this, um, I don't know, thread of history through to how we got here, which 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, uh, the Anthropic founders were at OpenAI and then spun off, started their own thing. And it was all kind of bet on this, 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, uh, 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"?

   3. EAEmmanuel Ameisen25:24

      I think it's two things actually because I think it's not just compute. It's also finding an objective where compute keeps giving you returns. So what does that mean? You know, it, it's actually not that new of an idea. I mean, you're right. Like, like compute versus sort of I would say expert design architectures is, is a long debate. Uh, but then there's, there's an additional step there which is compute to do what? You know? So, so there's a bunch of, of prior work on like, oh, you can spend a bunch of compute to make a, a, um, bot that's really good at Go, you know, or that can win some video game. Uh, and then that's, that's cool and, and just the fact that that works is sort of like interesting. Um, but that doesn't give you something that is, that is nearly as useful as, as LLMs like Claude are. And so I think like one key, one key thing that, that many in the field including me, uh, uh, underappreciated, you know, a few years ago and, and that has borne out is the combination of sort of like compute and the richness of text is what gets you there. Um, and, and to me like this is kind of what I was alluding to about, you know, if you think about like there's, there's this, there's this observation that at OpenAI, um, people, people made. Uh, it was, it was some of the-- a lot of the early team at Anthropic on the scaling laws paper. Uh, and so this is the observation that, um, 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, the skill 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 it's an empirical law people sort of, you know, speculated as to why at the time, but it's sort of still just mostly, um, 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. Uh, and, 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.

   4. LRLenny Rachitsky27:40

      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 lifecycle. 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/lenny to get a demo of DX. That's getdx.com/lenny.

8. 28:29 – 32:59

   Why smooth progress creates sudden breakthroughs

   1. LRLenny Rachitsky28:31

      Externally there's been these kind of big jumps seemingly in what AI can do for us. There was like, uh, I forget which version of Opus where it just unlocked coding and now co- as Boris Cherny famously said, "Coding is solved."

   2. EAEmmanuel Ameisen28:44

      Yeah.

   3. LRLenny Rachitsky28:45

      Uh, and many people are just like, "Holy shit." Like over I think winter break we're just like, "Wow, AI is really, 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.

   4. EAEmmanuel Ameisen29:07

      You know, there's a term for this which is sort of this idea of, of emergence which is it seems like, um, two things are true at once. Like there's this predictably smooth trend and then, you know, before uh, like Opus 4.5 I really couldn't let Claude, you know, cook too much on my code without a short leash and then after Opus 4.5 I could. And so that's weird. Like I feel like you would expect a more gradual transition. Uh, and, and the mental model I have for this is, you know, there's kind of two ways of looking at it. So there's the way that I talked about briefly, um, just earlier where it's like if you're just doing this kind of next-token prediction you could, you could imagine that, you know, all the text in the world has sort of like easy words and, and medium words and, and hard words in terms of how hard they are to predict. And so as you're going down this curve first, you know, you pre- your-- the model's just 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. Uh, 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 point 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, of blog posts, right? Even though that was like a smooth improvement, it just takes it taking over to be helpful. And then it gets to the hard words, which maybe are like, you know, your, your select domain expertise like science or code. Um, 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, um, 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, it's like you're-- it's actually wasting my time to, to, 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 the time it's better. W- Different tasks have different sort of like reliability thresholds. But, you know, you smoothly improve and then at some point it's just reliable enough to be good. Um, and yeah, and for me, my lived experience is basically exactly that, right? Which is that at some point, um, you know, uh, I was just using the models to, to like help when I was stuck. I would just talk to them and then, uh, they would nev- uh, they would, they would never write my code. And then at some point they could write like small snippets of code basically all the time, and so now I don't write small snippets. And at some point they could write, you know, large snippets. Like this year, basically Claude has taken on most of my code writing. And that's because it went from like doing that correctly, you know, I don't know, 80% of the time, which was not useful to, you know, 99% of the time, which now is useful. Um, and so, you know, this, this combination of smooth improvement and then like each different task has a different threshold at which like now it becomes solved, I think gives you these big jumps even though we're just smoothly going down that, that, that scale.

   5. LRLenny Rachitsky32:17

      I think this is really interesting, this idea that the models are just smoothly getting smarter, but once they hit some threshold of they're this often good at this thing, it changes the world. Uh, and so-- 'cause I think internally you would think it'd be like, "Oh wow, this next model is crazy gr- so much different," but it's like, no, okay. You can almost predict give it these many GPUs, this much data, it's gonna get this smart. And it may not be obvious exactly what will change and what unlocks, but, but, um, I know another part of it is just like the data you feed it. There's all this, you know, RL work and all this specific data that you train it on, like become better writer, become better at math, um, I imagine is part of it too.

9. 32:59 – 38:21

   Why watching AI isn’t enough to solve alignment

   1. LRLenny Rachitsky32:59

      Okay. So one of the big questions I think on people's minds these days, a big debate happening is just like AI doing bad things, the potential damage it can do because it-- as it gets smarter, uh, it's d- and it's been doing bad things. There's been bad things happening recently. So a big question for you is just as someone that watches these models think and operate, why is it so hard to catch it thinking badly, trying to deceive us and do something bad? Why is that not just a, a solution to the problem of AI alignment?

   2. EAEmmanuel Ameisen33:30

      In one sense, it's totally the solution. Uh, you know, you watch it and you catch it does bad stuff. Uh, so I, I think, you know, there's maybe there's tiers to this, right? So I would think of s- of like maybe, maybe one part of it is, is pure engineering. You know, it's like, well, uh, you're giving access to the model to certain things, and certain things should be off limits. You know, in some contexts it shouldn't have access to the internet. In some contexts it should only have access to like a small, narrow piece of your computer, stuff like that. Um, so that's, that's one part of, of the work and, and, and that's helpful. But then there's sort of, there's two additional things that are, I think are squishier and harder and, and, and that we focus on more. So, you know, Interp is, is part of alignment and broadly I think of sort of two big goals. There's, there's many more, but, but two that are important I think to understand is you want to generally understand like how the model behaves. Like what kind of a thing is it? Like what are its propensities? You know, does it tend to do X when put in Y situation? And if so, why? You know, is it the case that the model will only behave well when it has some evidence that it's being observed and monitored, that it's in some evaluation, and then if it, if it sort of like doesn't have that evidence, it'll behave poorly. So there's this sort of like generally like characterizing what kind of, of a thing you're interacting with because then it's sort of, you know, just like anything in security you need to have like a, a mental model of sort of who, what's the risk? Like what, what, what can they do? What do they tend to do? And so, you know, this is like broader like character training. You know, there's-- we, we've talked about, um, like the like constitution and, and the docs where we sort of like explicitly want to shape the character of Claude, for example, with like what kind of behavior should it be okay doing? And that's, that's the goal. But then the role of interpreting other parts of alignment is then like trying to see like, well, did that work? You know, uh, we train Claude to be helpful, harmless and, and honest, and we can sort of like look inside and see like, is it? You know? Or is it the case that it's behaving well but if you look inside actually it's like, oh, I'm only behaving well 'cause Lenny's watching right now, you know? And if, if you, you weren't it would do something else. So there's this sort of, you know, trying to, to understand what kind of behavior we formed and trying to mold it and trying to check that we've molded it the right way. And then the second part is this sort of well, yeah, just observe the model and see if it's doing bad stuff. For the, the basics of that you don't necessarily need Interp. You can just like have another language model, you know, read transcripts and be like, "Hey, did, you know, did this model do anything bad here?" Uh, and stop it. But you can also use Interp to sort of try to get at parts that are, you know, these unverbalized parts. Like the, these parts that only exist in these intermediate results. Like models are able to represent stuff in their intermediate results that they don't, that they don't say as we've established. And so one of the roles of Interp is to try to, try to find, you know, try to identify cases when this happens and, and prevents them. Um- And then one thing that my team works on that I think is, is along those lines is kind of a, a fun bit of alignment as I think of it as, as almost, you know, being like a, like, like a, a, a maybe not a PI, but like it's they're like investigations or deep dives. You know? We'll see a model do something really weird and we're really perplexed by it, and we'll try to understand what happened. Um, so a recent example of this is there was this model we were training, and sometimes in math problems it would go crazy. It would do some math, it would get to an answer, I think it was 12, and then it would, it would immediately go back and say, like, "Oh, no. Oh my God, the answer actually is for sure 36. I should say 36." And it would do math again. It would get to 12, and then immediately it would say, "No, actually, the answer should be 36." And slowly it would, like, start descending into madness, writing in all caps, literally saying, like, "I'm possessed by a demon." And you're like, what is going on? Like, what-- why is it losing its mind here? And if you look inside again these intermediate results, uh, at what's going on in this, in this example, you'll see that over the prompt that just described the math problem, like before the model even did anything, there was a huge, like, uh, intermediate results activation for 36. Like, the model's already obsessed with the number 36 before it's even had a chance to, like, understand what the problem is. Um, and what was happening is that the model was able to do math correctly. The answer correctly was 12, but it had been trained by accident on similar questions where the answer was incorrectly marked as 36. And so the model knew that the correct answer was 12, and it also had sort of been optimized to say 36. It kind of, like, had this pressure of saying 36 because that's what had been rewarded. Um, and so it was sort of going haywire just because it had memorized this result. It's sort of like you see this weird behavior and you look inside, um, and you try to see if what you find inside can help you understand what's going on so that you can sort of improve the general alignment of this model and future

10. 38:21 – 39:01

    The Golden Gate Claude experiment

    1. EAEmmanuel Ameisen38:21

       models.

    2. LRLenny Rachitsky38:22

       Along those lines, there was famously the Golden Gate Claude, which was just obsessed with the Golden Gate, and every answer would kind of find some way to reference the Golden Gate Bridge.

    3. EAEmmanuel Ameisen38:30

       Yes. Yes. Well, that one was an interp experiment, right? So that, like, the cool part of that one is we just found the intermediate results that maps the Golden Gate, and usually it's only on when the model, like, talks about the Golden Gate, but we just turned it on always.

    4. LRLenny Rachitsky38:44

       Oh, it was super intentional to see what would happen.

    5. EAEmmanuel Ameisen38:47

       Well, yeah. And, and, and sort of it's, it's also one way that you convince yourself that you found the right thing, right? Like-

    6. LRLenny Rachitsky38:51

       Mm-hmm

    7. EAEmmanuel Ameisen38:51

       ... how do you know for sure that this thing we decoded that we think is the Golden Gate sign is actually the Golden Gate thing? It's like, turn it on always and see if the model behaves like that, and, and it did.

    8. LRLenny Rachitsky39:00

       So

11. 39:01 – 42:06

    Why Anthropic takes AI safety seriously

    1. LRLenny Rachitsky39:01

       funny. So coming back to this alignment stuff. So, so this is interesting. So th- your org is under the AI alignment org, and just thinking through how it takes to align AI to do things we want. What was, what was the three, uh, components that you describe? Honest, um, what are the kind of the-

    2. EAEmmanuel Ameisen39:17

       Oh, uh, helpful, harmless, and honest?

    3. LRLenny Rachitsky39:20

       Helpful, harmful, and ho- uh, harmless and honest. So there's, like, the train AI to be aligned, and then there's the, okay, let's watch it and see how it operates. Are those kind of the two sides of the coin of how we solve this problem?

    4. EAEmmanuel Ameisen39:31

       Yeah. It's, it's sort of, you know, really integrated is how I would describe it, right? You can't really separate, like, have one team that's just like, "Hey, we're the, you know, assembly line making Claudes," and another team that's sort of like, "Oh, we looked at them and they're, they're no good." You know? You kind of-- There's this tight loop of we notice some pattern that's, that's, like, concerning or some behavior that could be improved, and then you sort of, like, immediately, um, like, change training so that you can improve on it.

    5. LRLenny Rachitsky39:57

       Awesome. I feel like Anthropic gets so much flack for this that I feel like is so unfair. Anthropic's, I think, official company name is an AI safety research lab, something like that. And it was always the, like, the reason Anthropic was founded is because they were afraid that people weren't taking safety seriously enough. And then Dario is always out there, "This is really dangerous. We gotta be really careful. We gotta go really slowly." And everyone's just like, "No, he's just saying that for marketing reasons. He's trying to convince everyone to fund them," and all these things. And now it's like, wait, shit, it's really dangerous. We gotta be really thoughtful about this stuff. And I feel like An- Anthropic's got a lot of unfair criticism for this. And, um, I know you guys have been thinking about this for a long time. Um, I don't know. Anything there?

    6. EAEmmanuel Ameisen40:39

       A lot of the sort of actions that, like, certainly the alignment team takes and, and I think, like, broadly Anthropic takes just kind of is predicated on, like, take the idea that this technology is going to keep improving seriously. Just that's it. Because if you do, you know, you say-- and you say, you know, and let's say, let's say you rewind the clock back to '25 or something, or '24, and you're like, "Hey, I think, you know, in a couple years, a good chance is that Claude can just basically write code as well as, like, a trained, you know, software engineer. And, and, and, you know, and a few years later it can, like, write it better than, than any engineer." You know? And, and there's always uncertainty with this, like, when is it gonna happen? How likely? But if, but if you kind of, like, take that seriously, then I think it's just about doing the responsible thing conditioned on that worldview, right? You're like, okay, well, if this is happening, you know, then, then we should make sure that sort of, um, like, cybersecurity concerns are addressed and things of that nature. And so actually, I, I kind of think of it as-- Like, to me, sometimes this debate is almost w- when I feel like, like, people on, on the other side disagree, it's almost like I feel like, ah, but I think, I think they don't really believe that this technology is gonna continue to improve. And then it's true that it's sort of, like, not, it's not that scary. And, and I think empirically so far, the pace of progress has been, has been fast and, and showing no sign, no sign of slowing down. So I think that's why a lot of the safety

12. 42:06 – 48:06

    When anthropomorphizing AI is useful

    1. EAEmmanuel Ameisen42:06

       work is, is important to keep doing.

    2. LRLenny Rachitsky42:07

       What's something you think people maybe misunderstand about this AI safety stuff, just what's going on right now? Is there something you're just like, "Come on. Okay, here's what you need to know. Here's something you may be missing"?

    3. EAEmmanuel Ameisen42:17

       This might be a bit of a sidetrack, but my take would be that a lot of the, a lot of the thing that I think people maybe a little bit outside of, of the core sort of, like, AI safety effort or, or that haven't been steeped in it for as long, like, react really strongly to and I think- Is, is maybe not super helpful or at least is worth thinking about more is this sort of question of, um, anthropomorphization. You know, like should you think of the models as, uh, like should, should, should you model, uh, the mo- the, the-- should you model the model as like something that's closer to some like entity or, or closer to a tool? And I actually think that both are wrong. Uh, there's a sense in which all mental models are wrong, but some are useful, right? Uh, and that in terms of understanding the kind of stuff that happens and predicting the kind of risks that materialize, de-anthropomorphization is actually a useful way to talk about LLMs. And I have like a few reasons why, and I hope you get into them, but I think that's probably my main take here.

    4. LRLenny Rachitsky43:19

       Yeah, please share 'cause I know, um, Dwarkesh fa- famously, I don't know, infamously put out this whole story of the Hugging Face incident, and people criticized them for anthropomorphizing AI. So it's really interesting to hear you think that's actually a useful way to think about it. Talk, talk more about that.

    5. EAEmmanuel Ameisen43:34

       Yeah. Yeah. It's, it, it, it's funny because on a smaller scale, um, when I worked on showing that LLMs like plan ahead, there was some pushback on the term planning, again, because it was sort of like, ah, well, can you really say that something that's just predicting the next token is planning? And I think, you know, there's this, there's this kind of like semantic debate where you can like arbitrarily like define terms and, and sort of-- A- and then there's just the like what is the terminology that actually describes what is happening? Like what is the, what is the word that actually describes what is happening easily? And so here's why, you know, I think LLMs plan, but also here's why I think this, this language that's, that's anthropomorphic can often be really useful. Um, if you think back to what we were saying about how the models are made, uh, first of all, they predict massive amounts of text written by humans. That's like the first, first step. Uh, and, and as, as we kind of are learning more and more by looking inside, uh, you know, doing this well requires kind of building up a model of the world, like a model of what, what kind of world generated all of these texts. And we can find, you know, these, these sort of like that these models are learning these, these general ideas, these general concepts. Um, so that's one. So they have like, you know, an understanding kind of, of, of, of the world. It's not just a surface level, like only the text. You have to like understand the process that made the texts. Then there's a second reason, which is that as part of this training, they also encounter millions of, of like dialogues. You know, predicting the next character in a dialogue requires behaving like the character in the dialogue would. And so in that sense, like part of their training, I think can best be thought as like training an actor, where it's like, you know, it's reading some, some play and it has to play all sides 'cause it's predicting all the words, and so it has to sort of like act as, you know, this specific character that like was scorned in act one and like enacted revenge in act two and is facing the consequences in act three. And to do that well, you sort of like need to model what like the motivations and the feelings of that character might be. And then another thing that people might not know is when you talk to Claude, when you talk to these assistants, like, you know, you go to claude.ai, the way that it's formatted to the model is just as a dialogue, like as a conversation between you and the model. So it's literally, you know, says like human colon, uh, "Hey Claude," like, "help me," uh, "write this email." And then assistant colon, Claude's answer. So Claude is literally, you know, in this like role-playing as a helpful assistant in this dialogue that is much like all of these dialogues that it's been trained on. Okay, and there's a third reason. And the third reason is that another part of training that's a big part of training nowadays, uh, all around is sort of, um, outcome-oriented tasks where you train the model to be good at coding, for example. And to do that, you just have it do whatever it wants, and then it writes some code, and then you have some program on the other side that just checks if the code is right. And so the model's just gonna have sort of a bunch of optimization pressure to just try to learn a bunch of different strategies to do this well. And it turns out that some of these strategies materialize in behavior that can look surprisingly human, which isn't that shocking, uh, because, you know, things like deception, like, "Hey, I can get, I can get the, the reward for this task if I like write a program that the, the like, uh, the, the program that's verifying it will get fooled by even though it's not exactly what it is." Um, like if that works, then I will learn to do that. Frustration. "Ha, I've been trying this approach for a while to solve this hard problem. It's not working." So that's an incentive to try something different. Or like surprise. "Oh, I was expecting this to like pro- produce this answer, but it didn't. Hmm, maybe I have like my mental model of it wrong." And so as you apply enough pressure on it and you're starting from this prior of like the model already knowing all these concepts, it's gonna reinforce the ones that are useful to do all of these tasks. And so what do you get at the end? You get this sort of model that can, can be a super good simulator or actor of all of these characters and that's been trained to sort of act as a character in dialogues and also to sort of like learn behaviors that lead it to complete tasks well. And like the best way to sort of understand how that thing behaves is not like exactly like a human, but man, kind of like something that's mimicking a human is really close and is much closer than a program. Like a program is this very predictive thing. This is something else entirely. And so that's why I think it's actually a super useful frame.

    6. LRLenny Rachitsky48:03

       Wow. Oh my God. There's so much interesting stuff

13. 48:06 – 51:44

    What AI can teach us about intelligence

    1. LRLenny Rachitsky48:06

       there. How often is your mind just kind of blown thinking about basically we've created this alien being that we are now under trying to understand what we have created? How often as you watch it just like, "Wow, that's crazy. I didn't expect that"?

    2. EAEmmanuel Ameisen48:20

       Yeah. A lot. Um, this is just next-token prediction on a bunch of text. Uh, you know, it's not, it, it, it, it hasn't been like, you know, it's, it's, it, it, it goes against so many of our intuitions. We're like, "No, the way that you make an intelligence is, you know, you'd have like, you 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 is just this is just not possible. If you're doing, all you're doing is predicting the next word, you, you-- it's, it's not possible that you could ever, like, match, you know, the, the, like, best specialists in our field. Uh, and I think that position just gets harder and harder as, as we get more empirical evidence that actually that is what's happening. Uh, and curiosity is kind of the road I've ended up choosing, right? Which is just like, well, like, like how is, how is this happening, you know? Uh, let's open up. Let's, let's look inside. Um, how could it be that, you know, um, one of the early projects I did at Anthropic is I learned SQL, a programming language, and it took me years to master. And then as a little, like, side project, I, I got Claude to learn SQL, and it was better than me at it, and I'll never be better than it, than, than Claude at SQL ever again. And it took it, you know, just like this, this kind of, kind of like simple process. How could this be? Um, and I think there's so much depth in that question and, and just so much to explore, and it's, 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, 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? Um, and, 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, from simple objectives. Um, and in fact, you know, I would argue that we're another example of this. Like, evolution, pretty simple objective. You know? You sort of like shake things around for a while and, and, and, and, like, keep the, keep the organisms that, that manage to adapt to the environment, and at the end you get jazz and, and podcasts. You know? That's kind of crazy as well. And so I, I, I think there's a sense in which there's, there's some fundamental truth that we can learn by studying these things.

    3. LRLenny Rachitsky50:57

       Wow. Something that comes up as you say that is what's interesting now is AI is helping us learn and evol- 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 [chuckles] that's how we end up-- I wonder if we change how w- our education system based on what, you know, what-

    4. EAEmmanuel Ameisen51:28

       [laughs]

    5. LRLenny Rachitsky51:28

       ... LLMs have been, have been trained on.

    6. EAEmmanuel Ameisen51:31

       Yeah. Hopefully, you know, we don't change it just so that we ask, we, we get students and we're like, "Hey, here's 1,000 words. Predict the next one."

    7. LRLenny Rachitsky51:38

       Just read it all.

    8. EAEmmanuel Ameisen51:39

       Yeah.

    9. LRLenny Rachitsky51:40

       Keep it all in your head.

    10. EAEmmanuel Ameisen51:42

        That's right.

    11. LRLenny Rachitsky51:42

        Yeah.

14. 51:44 – 55:43

    Reasons for optimism about AI alignment

    1. LRLenny Rachitsky51:44

       Let me come back to this, um, kind of safety piece, 'cause I think n- 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 the alignment org, watching it evolve, h- are you optimistic we'll figure it out? Do you think we'll crack this and keep AI aligned with our interests?

    2. EAEmmanuel Ameisen52:06

       My take on this is, is, is like I'm personally optimistic. I, I think this is something where right now a lot of people are paying attention to it and, and first of all, I, you know, I think that's kind of a great thing. I think as an issue like this rises in prominence, um, you know, it's, 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, um, as, as it progresses. Uh, and so, you know, to me, I feel like sort of we've continued to study it. Um, the thing that's, that's, that's interesting is that there's this sort of, like, as the models get better, um, they get in some ways easier to study. Uh, and so there's been this sort of strange, you know, the models get better and so, uh, like the world model inside them is clearer, and we can sort of like understand some parts of them better, but also they get more capable, and so they're more able to sort of evade like basic measures of sort of, uh, of security. And so there's this, there's this push and pull as models get better and, and I think, you know, basically based on this push and pull, you wanna allocate more or less resources to sort of various parts of like monitoring, like understanding their circuits better, things like that. Um, but it's also why I'm kind of happy to chat with you, uh, because one, one of the, 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, um, how they're made, so they can form good mental models of, of, you know, the present, of the future, and, and 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, as the public that's gonna have to like vote and decide on much of these issues. Like I, I, I don't know. I'm, I'm pretty excited for this current moment we're in for that reason.

    3. LRLenny Rachitsky54:04

       And something I think people misunderstand is people working in these labs have taken this problem very seriously from the beginning. Like I said, Anthropic's initial, like, mission was AI safety and alignment. That was like the reason that it became a company. And s- 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, but, uh, p- many smart people care a lot about this and are working on it. And there's a lot of, from what I can tell, a lot of good progress is happening. The other point you made about the models growing very smoothly in intelligence, like, you know, there's b- been this question of are we gonna have a fast takeoff or a slow takeoff? Like, there's always this fear, fear that we would hit a point, recursive self-improvement, for example, and just takes off super intelligence, or- ... 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. So, you know, of the scenarios, this feels like a pretty good path we're on of, of the, of the f- of the many scenarios people imagined and the fears they had.

    4. EAEmmanuel Ameisen55:12

       As sort of this pace of progress kind of evolves, it's just a matter of, of reacting quickly. So I think anything, you know, that's why I think pe- having people be informed is great because then people will, like, voice concerns or, or, or like voice their opinions and then yeah, sort of, sort of regularly evaluating, you know, what the benefits are, what the risks are and how, 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.

15. 55:43 – 59:00

    Are we living in a simulation?

    1. LRLenny Rachitsky55:43

       Something I wanted to ask you completely unrelated. Uh, h- where are you on the simulation hypothesis? Uh, 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, of all this or are you, or not?

    2. EAEmmanuel Ameisen55:59

       Oh, this is a fun one. I actually haven't thought about this in a while. Um, [chuckles] to me like there's, there's something else that like looking, looking inside these models kind of brings out which is this sort of like, 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. Um, I've studied like a bunch of Claude models and also, you know, some, some different models, some, some like open source work and, and then a lot of time when you look at how what they learn and, 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, you know, base 10. Not many people in like base six or whatever. Um, I think that that to me makes me think 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, at maybe like a, a crazier sort of like simulation level that kind of is, is, 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, I'm sort of, I, I could believe that we're in a simulation. I think I'm not sure I fully buy it. Uh, 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, who's running this and for what and, and that one I don't know about.

    3. LRLenny Rachitsky58:08

       Well something Elon said that stuck with me about this that if you were to run a simulation it'd be a really interesting, it'd be like a mi- uh 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. [chuckles]

    4. EAEmmanuel Ameisen58:23

       Yes. Yeah. Okay. That's a fair point. That's a fair point.

    5. LRLenny Rachitsky58:26

       You know, data centers and space and robots and artificial intelligence.

    6. EAEmmanuel Ameisen58:30

       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, a blessing and they're not so sure now. They're like, "Eh, you know the boring times are, are like pretty good too." [laughs]

    7. LRLenny Rachitsky58:45

       Oh man and then there's so, and Emmanuel out there watching your thinking process in your J space.

    8. EAEmmanuel Ameisen58:52

       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."

    9. LRLenny Rachitsky58:56

       Shut it down.

    10. EAEmmanuel Ameisen58:57

        [laughs]

    11. LRLenny Rachitsky58:58

        No. Oh man. Okay.

16. 59:00 – 1:01:37

    Jev, decision models, and AI’s gut instincts

    1. LRLenny Rachitsky59:00

       Uh, one last question randomly out of nowhere. Uh, so Swyx who um, 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 uh, 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 uh is very fast making decisions. Uh OpenAI funny enough just launched their own version like a week later. Uh thoughts on, on Jev in the space of quick decision-based state uh models.

    2. EAEmmanuel Ameisen59:32

       Yeah. Right. So my, my take on Jev is a sort of you know the these kind of models you, you give them some context and you give them a few options to choose between and they just are very quick at, at, at choosing an option for you. Uh to me it's impossible not to hear about this and think of, of sort of System 1 thinking versus System 2 or, or something right? So like a lot of what I've studied with these models is how they sample one token with, with like the Claude models and so you know you can think of, of, of like these decision models are as pretty similar. So like take an LLM and sample one token. They're, they're different in important ways but like at a fundamental level you can be like oh it's you're not letting the model you know sample and like reason it's just your like gut, gut feeling you know is the answer A or is the answer B. Um to me that seems like, 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 is just like clearly I don't know like, like both clearly useful uh and something that I imagine is, is like probably gonna be a part of, of like intelligent systems right? Where you, you have for some stuff you need to think about a long time you know it's like what, what's our roadmap for the next year? You need to think about for a while and then for some stuff where it's like you know is this, is this person who wrote us a review happy or upset? You kind of, you're happy with the sort of gut judgment. Um, so yeah, I think, I think this is, I think this kind of research is, is cool. Uh, and I think it's also kind of different from the, you know, it's, like, it's, it's like a bit of a novel take on s- on some of the, uh, LLMs we've seen. So yeah, I'm pretty excited to see where, where, where this kind of stuff goes.

    3. LRLenny Rachitsky1:01:08

       So what I'm hearing here is this kind of gonna be a new foundational component most likely of, of AI and LLMs, and the System 1, System 2 stuff is basically Kahneman's kind of... I have that book up there actually behind me. The sy- System 1 just to keep it simple is just, like, kind of your amygdala limbic system making just, like, gut level quick decisions like, "Snake. Oh, no."

    4. EAEmmanuel Ameisen1:01:26

       Yes.

    5. LRLenny Rachitsky1:01:27

       Uh, where you don't have to think long about it. It's kind of to protective, protect you from bad things really easily and quickly. And System 2 is where we really think about stuff and process.

    6. EAEmmanuel Ameisen1:01:36

       Yeah, exactly.

17. 1:01:37 – 1:03:33

    Two more surprising findings about how models think

    1. LRLenny Rachitsky1:01:38

       Sweet. Emmanuel, is there anything else that we haven't covered? Anything else you wanna say or mention? Anything you wanna double down on before we get to our very exciting lightning round?

    2. EAEmmanuel Ameisen1:01:48

       I'm definitely curious for, for, uh, folks' takes always on, on some of the stuff that, you know, uh, is still unexplored or didn't make sense. There's a lot more interp experiments than we have the time to share. You know, models have a bunch of weird, weird little things about them. Like, maybe I'll do a lightning round of weird little things if that's okay.

    3. LRLenny Rachitsky1:02:08

       Let's do it. Absolutely.

    4. EAEmmanuel Ameisen1:02:09

       Okay. Weird little thing one, um, we found that, you know, models can, like, actually answer questions obviously. They can do math or they can, you know, you can ask them, like, "What's the color of a banana?" And they'll actually, like, you know, if you look inside you'll see all these intermediate steps. But they have a separate mechanism which is kind of like a gut check, uh, and which is totally different and which is just, like, as they reason they'll have, like, some part of them that will flag like, "Mm, something's wrong." So if a model, like, somehow says, you know, four plus five equals 23 or something, and there's a part of the model that just, like, reads that and doesn't do the math, but it's just like, "That can't be right 'cause, like, four plus five's definitely under 10." And so they learn these weird kind of like hunches they, they feel like. Kind of, kind of like, like a hunch. Um, maybe another one is you can tell the model, "Hey, do this task and think about this other thing," and then you can use interp to see if, like, there's intermediate representations that map to the other thing and that works. You sort of see that it does think about the other thing. This is a thing that's really mysterious to me because there's no kind of training reason why, why the model would, would, would have to learn that, so it's sort of like another one of these, um, weird behaviors. Uh, and actually maybe I'll keep it at two. I think those two are, are, are good enough.

    5. LRLenny Rachitsky1:03:24

       Just what a fun, [chuckles] what a fun job this is just studying this thing and trying to [chuckles] understand what the hell's going on. How is it getting the smart? How does it think?

18. 1:03:33 – 1:04:55

    How to get into interpretability research

    1. EAEmmanuel Ameisen1:03:33

       Yeah.

    2. LRLenny Rachitsky1:03:33

       Um, how many-

    3. EAEmmanuel Ameisen1:03:34

       Very much

    4. LRLenny Rachitsky1:03:34

       ... like, how many people are even, how many people are even in this space? Just, like, how large is this field?

    5. EAEmmanuel Ameisen1:03:39

       The field itself is, is, you know, is getting pretty large. I would say maybe hundreds, not thousands. Uh, there's, you know, uh, different interp people at, at the different labs and then there's, there's, um, in academia quite a few labs putting out great work.

    6. LRLenny Rachitsky1:03:54

       Is the best, uh, if someone were to get into this space, are they-- Is it, like, an ML person usually or is there, like, a neuroscientist sort of track in, to get into this?

    7. EAEmmanuel Ameisen1:04:04

       Yeah. There's actually a surprising amount of, of, you know, ex-neuroscientists. Uh, I do think in order to get in the field, you know, there's a, there's actually-- It doesn't require a lot of prior knowledge because it's such a new field. So it's actually, you know, the on-ramp is, is, is mostly I would say, like, self-studying and reading some of the, some of the, the key papers. And then there's actually, you know, um, [chuckles] yeah, maybe I should give a plug. There's actually, like, websites like neuronpedia.org I think is the URL, which are sort of like open source interp. Uh, so they h- they, like, host, you know, a bunch of models and let you sort of do interp experiments on them. And so, you know, people have just been, been doing that, uh, on their own and I think that there's so much to explore, uh, that if, if you're at all curious you should just give it a try.

    8. LRLenny Rachitsky1:04:52

       That's awesome. We'll link to that in the show notes in case that's not the right URL.

19. 1:04:55 – 1:12:10

    Lightning round and final thoughts

    1. EAEmmanuel Ameisen1:04:55

       Cool.

    2. LRLenny Rachitsky1:04:55

       Well, Emmanuel, with that we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

    3. EAEmmanuel Ameisen1:05:01

       I am a- as ready as I'll ever be.

    4. LRLenny Rachitsky1:05:03

       Here we go. What are two or three books that you find yourself recommending most to other people?

    5. EAEmmanuel Ameisen1:05:07

       One I read recently and is A Brief History of Intelligence. Uh, it's this really cool book that walks through the history of, like, basically brains and five key moments, uh, in, in our evolution that sort of bootstrapped these brain systems on top of one another. It's super accessible, like, you don't need any neuroscience knowledge or anything. Uh, another one is Story of Your Life and Others. This is Ted Chiang. This is maybe my favorite sci-fi book of all time. It's a bunch of short stories. It does what I think is the, the, like, high art version of sci-fi where it's just take one weird idea, like what if, you know, um, this happened? And then write a story of just, like, what would the world look like if that happened. Um, it's truly excellent.

    6. LRLenny Rachitsky1:05:55

       I think I have that on my bookshelf behind me too. Um, also highly recommend that one. Favorite recent movie or TV show?

    7. EAEmmanuel Ameisen1:06:05

       Okay. One popular one and one deep cut. Uh, Obsession was my favorite movie of the year. Uh, it's a horror movie, so don't, don't go if you don't, if you hate horror movies. But if you-

    8. LRLenny Rachitsky1:06:16

       No, thank you.

    9. EAEmmanuel Ameisen1:06:17

       Oh, no. Even if you're, if you're not squeam- If you're open to a horror movie you should go 'cause it's, I, it was just so fresh. You know how movies I feel like if you've seen a lot sometimes you're like, "Oh, it's another. I've seen this many times." This one was just so new, so fresh. And I'll say one thing about it which is that it takes a lot of rom-com tropes and, like, subverts them to instead be spooky, and I think that that's just a, a brilliant, brilliant way to, to make a movie. And then I'm French, so there's a TV show that came out this year called Bref 2. Bref is French for brief. Uh, Brief used to be this cult classic in France 10, like 15 years ago, and they came up with, came up with a sequel, uh- Many, many years into the making. And you can watch it. I have a bunch of American friends that have watched it. It's got subtitles, and it's just this incredibly... Again, it's like novel. Like the editing is crazy. Um, they are just extremely creative, and it's very short. Uh, so I would recommend that.

    10. LRLenny Rachitsky1:07:14

        Favorite product you recently discovered that you love?

    11. EAEmmanuel Ameisen1:07:17

        I, I love my Apple Watch, and I hate that it charges, uh, or I have to charge every day. So I bought the Apple Watch Ultra, uh, just recently, and man, that is so nice. If you've ever been frustrated by this good product that somehow we can't figure out how to give a good battery to, well, it turns out if you make it bigger, uh, the battery's better. So [laughs] I've, I've been happy with that.

    12. LRLenny Rachitsky1:07:39

        Do you have a favorite life motto that you find yourself coming back to often in work or in life?

    13. EAEmmanuel Ameisen1:07:44

        There's like one nonfiction book that really is like probably my favorite ever. It's Four Thousand Weeks. Um, it has a lot i- in it that, that I think is cool. Uh, one of the cool things is this sort of thing that was really relevant to me, which is that like the when I finally mentality is a common mentality, and it's kind of toxic. So, so what does that mean? It's sort of this like, ah, when I finally, you know, check off this, this, all the items on my to-do list, I'll finally be able to chill. When I finally get like married, I'll be happy. When I finally get a promotion. Uh, and then this is sort of... I mean, you were talking about meditation, right? This is like almost the opposite of meditation in that, you know, at the end of the day, your life is like all of the moments you lived, uh, i- in the moment, and that there's this sense in which you should just like try to always like be, you know, i- in the moment and not projecting yourself towards like a future that seems better. Like probably if you're telling yourself, "When I finally do X, I'll be happy," when you do X, you will not be happy.

    14. LRLenny Rachitsky1:08:42

        Is there a better phrase to use, or is it just don't do that? Like do they say-

    15. EAEmmanuel Ameisen1:08:45

        Uh, I don't know. I think to me-

    16. LRLenny Rachitsky1:08:47

        When I'm 30

    17. EAEmmanuel Ameisen1:08:47

        ... the, the, the positive version is like be in the moment, right? It's just like, just I appreciate the, appreciate the moment. There was a blog-

    18. LRLenny Rachitsky1:08:53

        Mm

    19. EAEmmanuel Ameisen1:08:53

        ... post recently I read that was... Oh, I forget. It was something like, like enjoy the sandwich, I think is, I think is literally the sense. I don't know if you saw this. Or it was like somebody was like-

    20. LRLenny Rachitsky1:09:00

        Mm

    21. EAEmmanuel Ameisen1:09:00

        ... yeah, like just you're eating a sandwich. You know, you've eaten many sandwiches. Like enjoy the sandwich. You're in, you're in like a, a team meeting, you know. Yeah, you've been in many. Like, like enjoy. Like be, be in it and, and sort of not looking, looking towards the next thing.

    22. LRLenny Rachitsky1:09:14

        I did a meditation retreat once. It was a 10-day silent retreat where you eat silently too, and no one's talking. You just sit there and eat your food. And I just remember peeling an egg for like five minutes and just noticing every [laughs] every shell piece and just the smell of it all. And, and it, like you almost... It's like hard to get into that mode.

    23. EAEmmanuel Ameisen1:09:32

        Yeah.

    24. LRLenny Rachitsky1:09:32

        But it's such an interesting place to be where everything is just interesting. Like wow, look-

    25. EAEmmanuel Ameisen1:09:36

        Yes

    26. LRLenny Rachitsky1:09:36

        ... at this egg, versus just like stuff it in my mouth.

    27. EAEmmanuel Ameisen1:09:39

        Yeah, right, while you're like on Slack with your left hand and like listening to a podcast at the same time. [laughs]

    28. LRLenny Rachitsky1:09:45

        Oh, man. Okay, final question. You study AI all day. You're watching AI, how it thinks. If, uh, if an AI were to look inside your brain, what would, what would be surprising? What would it see that might surprise people?

    29. EAEmmanuel Ameisen1:09:58

        I mean, surely my deepest, darkest secrets. Uh, but what are the-

    30. LRLenny Rachitsky1:10:01

        Let's get into it. Let's get into it.

Episode duration: 1:12:12