# Ilya Sutskever – We're moving from the age of scaling to the age of research

Markdown copy of https://realalpha.app/v/aR20FWCCjAs/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Ilya Sutskever argues that AI progress now depends on better generalization and research, not simply scaling existing recipes.

- Show: Dwarkesh Podcast
- Hosts: [Dwarkesh Patel](https://realalpha.app/people/dwarkesh-patel/)
- Guest: [Ilya Sutskever](https://realalpha.app/builders/ilya-sutskever/) is cofounder of SSI.
- Published: Nov 25, 2025
- Length: 1 h 36 m

## The best minutes

- [18:51](https://realalpha.app/v/aR20FWCCjAs/?t=1131) [AI has returned to an age of research](https://realalpha.app/s/ai-has-returned-to-an-age-of-research/) (7 min)
- [56:02](https://realalpha.app/v/aR20FWCCjAs/?t=3362) [Showing powerful AI could change how people respond](https://realalpha.app/s/showing-powerful-ai-could-change-how-people-respond/) (8 min)

## Also worth watching

- [36:23](https://realalpha.app/v/aR20FWCCjAs/?t=2183) [Research prototypes can work without frontier-scale compute](https://realalpha.app/s/research-prototypes-can-work-without-frontier-scale-compute/) (7 min)
- [47:14](https://realalpha.app/v/aR20FWCCjAs/?t=2834) [AGI may be a learner, not a finished mind](https://realalpha.app/s/agi-may-be-a-learner-not-a-finished-mind/) (7 min)
- [1:29:36](https://realalpha.app/v/aR20FWCCjAs/?t=5376) [Self-play can trade compute for scarce training data](https://realalpha.app/s/self-play-can-trade-compute-for-scarce-training-data/) (4 min)
- [1:32:54](https://realalpha.app/v/aR20FWCCjAs/?t=5574) [Research taste helps distinguish a bad idea from a buggy experiment](https://realalpha.app/s/research-taste-helps-distinguish-a-bad-idea-from-a-buggy/) (3 min)

Skip the sponsor reads and the extended explanations of value functions and evolved social instincts.

## Summary

Ilya Sutskever is a cofounder of SSI. He describes it as a research company pursuing a distinct technical approach and says it had raised $3 billion.

Sutskever says current models can perform well on difficult evaluations yet struggle to generalize reliably in real-world tasks, in part because reinforcement learning may be shaped too closely by evaluations. He argues that the scaling era is giving way to research into more efficient learning, including human-like generalization and continual learning. He expects powerful AI to become more understandable through incremental deployment, while arguing that alignment, limits on AI power and coordination will matter for safety. At SSI, he says, the team is investigating a different technical approach; he puts the timeline for a human-like learner at 5 to 20 years.

### Minute by minute

- [0:00](https://realalpha.app/v/aR20FWCCjAs/?t=0) **RL can widen the eval gap.** Sutskever says models can score well on difficult evaluations yet have much less economic impact, and can alternate between fixing two bugs. He suggests reinforcement learning may make models narrowly focused, while teams building RL environments may draw too directly from evaluations. Poor generalization could help explain the mismatch.
- [18:36](https://realalpha.app/v/aR20FWCCjAs/?t=1116) **The scaling era is giving way to research.** Sutskever describes pretraining as a scaling recipe that made investment easier to justify, but says data is finite and larger compute alone may not transform results. He sees a return to research, with a focus on finding more productive ways to use compute. He also says value functions could make reinforcement learning more efficient.
- [24:55](https://realalpha.app/v/aR20FWCCjAs/?t=1495) **Human learning exposes a generalization gap.** Sutskever says people learn with fewer examples, less explicit supervision and greater robustness than models. Evolution may explain human strengths in vision and dexterity, but he argues that learning in newer domains such as math and coding points to a broader learning principle. He says he has ideas about that principle but cannot discuss them in detail.
- [35:32](https://realalpha.app/v/aR20FWCCjAs/?t=2132) **Research needs ideas as well as compute.** Sutskever says scaling crowded out other work and left the field with more companies than ideas. He argues that research needs enough compute to test ideas, but not necessarily the largest available systems; AlexNet, he notes, used two GPUs. He says SSI has sufficient research compute and that comparisons with larger labs can overlook their inference and product demands.
- [42:30](https://realalpha.app/v/aR20FWCCjAs/?t=2550) **SSI may revise its straight-shot plan.** Sutskever says SSI is focused on research and sees merit in avoiding day-to-day market competition, but could change its plan if timelines are long or if deploying powerful AI would benefit the public. Patel argues that deployment can reveal failures and improve safety. Sutskever says gradual release would be part of any plan, including a straight shot to superintelligence.
- [46:56](https://realalpha.app/v/aR20FWCCjAs/?t=2816) **Superintelligence may learn on deployment.** Sutskever says the ideas of AGI and pretraining can suggest a finished system that already knows everything, while people rely on continual learning. He describes a possible superintelligence as an eager learner that gains skills through deployment. He says broad deployment of such systems could produce rapid economic growth, though its pace is difficult to predict.
- [54:54](https://realalpha.app/v/aR20FWCCjAs/?t=3294) **Incremental exposure could make AI's power legible.** Sutskever says future AI is difficult to imagine, so people may need to see more powerful systems before they understand their effects. He predicts that visible capability will lead companies to become more cautious and governments and the public to want action. He also proposes that AI should be aligned to care about sentient life and says capping the most powerful systems would help.
- [1:03:18](https://realalpha.app/v/aR20FWCCjAs/?t=3798) **Long-term AI stability may require limits.** Sutskever says extremely powerful AIs could be restrained or governed by agreements, since even a seemingly sensible objective might produce outcomes people dislike. He argues that early systems should care for sentient life, while noting that current methods may not produce the kind of reliable generalization needed. For a long-run equilibrium, he considers people becoming part AI through neural links, though he says he does not like that solution.
- [1:10:36](https://realalpha.app/v/aR20FWCCjAs/?t=4236) **Evolution's social values remain a mystery.** Sutskever says it is easier to imagine evolution linking a smell to a desire for food than encoding complex social desires, such as wanting society's approval. He speculates that evolution might use brain-region locations, then notes that brain regions can relocate after injury, weakening that idea. He says he has no satisfying explanation for how these desires are encoded.
- [1:17:54](https://realalpha.app/v/aR20FWCCjAs/?t=4674) **Sutskever says SSI is pursuing a distinct approach.** Sutskever says SSI is investigating ideas about generalization and describes the company as firmly in a research phase. He says SSI had raised $3 billion and recounts declining Meta's acquisition offer, while his former cofounder accepted and was the only SSI person to join Meta.
- [1:21:59](https://realalpha.app/v/aR20FWCCjAs/?t=4919) **Sutskever forecasts a 5-to-20-year horizon.** Asked when a human-like learning system might arrive, Sutskever says 5 to 20 years. He says current companies could continue generating substantial revenue even if their approaches stall, and expects convergence on broad strategies for powerful AI. He also expects competition to create specialized companies and AI systems for different areas.
- [1:28:19](https://realalpha.app/v/aR20FWCCjAs/?t=5299) **Diversity matters in AI research.** Sutskever expects diminishing returns from making identical copies of a researcher and says different ways of thinking are more useful. He attributes similarities among pretrained models to their shared data and sees self-play as one way to use compute when data is a bottleneck, though he says it suits only some skills. He links productive research to careful inspiration from the brain, simplicity and elegance.

## Transcript

Dwarkesh Podcast, hosted by Dwarkesh Patel, with Ilya Sutskever.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. Where the podcast’s audio and the video part ways, some times are a minute or so off.

[0:02](https://realalpha.app/v/aR20FWCCjAs/?t=2) than their economic impact would imply. Yeah, this is one of the very confusing things about the models right now. How to reconcile the fact that they are doing so well on evals and you look at the evals and you go, those are pretty hard evals. They're doing so well, but the economic impact seems to be dramatically behind. And it's almost like it's very difficult to make sense of how can the model on the one hand do these amazing things. And then on the other hand, like repeat itself twice in some situation in a kind of an example would be, let's say you use vibe coding to do something and you go to some place and then you get a bug and then you tell the model can you please fix the bug yeah and the model says oh my god you're so right i have a bug let me go fix that and it reduces a second bug yeah and then you tell it you have this you have this new second bug right and it tells you oh my god how could have done it you're so right again and brings back the first bug yeah and you can alternate between those yeah and it's like how is that possible yeah it's like i'm not sure but it does suggests that the something strange is going on i have two possible explanations so here this is

[1:34](https://realalpha.app/v/aR20FWCCjAs/?t=94) the more kind of a whimsical explanation is that maybe rl training makes the models a little bit too single-minded and narrowly focused a little bit too i don't know unaware even though it also makes them aware in some other ways and because of this they can't do basic things but there is another explanation which is back when people were doing pre-training the question of what data to train on was answered because that answer was everything yeah when you do pre-training you need all the data so you don't have to think it's going to be this data or that data yeah but when people do RL training they do need to think they say okay we want to have this kind of RL training for this thing and that kind of RL training for that thing. And from what I hear, all the companies have teams that just produce new RL environments and just add it to the training mix. And then the question is, well, what are those? There are so many degrees of freedom.

[2:47](https://realalpha.app/v/aR20FWCCjAs/?t=167) There is such a huge variety of RL environments you could produce. And one of the, one thing you could do, and I think that's something that is done inadvertently, is that people take inspiration from the evals. You say, hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task, right? I think that is something that happens, and I think it could explain a lot of what's going on. If you combine this with generalization of the models actually being inadequate, it that has the potential to explain a lot of what we are seeing this disconnect between eval performance and actual real real world performance which is something that we don't today exactly even understand what what we mean by that i like this idea that the real reward hacking is the human researchers who are too focused on the evals um i think there's two ways to understand or to try to think about what you have just pointed out one is look if it's the case that simply by becoming superhuman at a coding competition a model will not automatically become more tasteful and exercise better judgment about how to improve your code base well then you

[4:18](https://realalpha.app/v/aR20FWCCjAs/?t=258) should expand the suite of environments such that you're not just testing it on having the best performance in coding competition it should also be able to make the best kind of application for x thing or y thing or z thing and another maybe this is what you're hinting at is to say why should it be the case in the first place that becoming superhuman at coding competitions doesn't make you a more tasteful programmer more generally maybe the thing to do is not to keep stacking up the amount of environments and the diversity of environments to figure out approach which lets you learn from one environment and improve your performance on something else so i have i have an analogy a human analogy which might be helpful so even the case let's take the case of competitive programming since you mentioned that and suppose you have two students one of them work decided they want to be the best competitive programmer so they will practice 10 000 hours for that domain they will solve all the problems memorize all the proof techniques and be very, very, you know, be very skilled at quickly and correctly implementing all the algorithms.

[5:35](https://realalpha.app/v/aR20FWCCjAs/?t=335) And by doing so, they became the best, one of the best. Student number two thought, oh, competitive programming is cool. Maybe they practiced for 100 hours, much, much less. And they also did really well. Which one do you think is going to do better in their career later on? The second. Right. And I think that's basically what's going on. The models are much more like the first student, but even more because then we say, okay, so the model should be good at competitive programming, so let's get every single competitive programming problem ever, and then let's do some data augmentation so we have even more competitive programming problems, and we train on that.

[6:15](https://realalpha.app/v/aR20FWCCjAs/?t=375) And so now you've got this great competitive programmer, and with this analogy, I think it's more intuitive. I think it's more intuitive with this analogy that, yeah, okay, so if it's so well trained, okay, it's like all the different algorithms and all the different proof techniques are like right at its fingertips. And it's more intuitive that with this level of preparation, it will not necessarily generalize to other things. But then what is the analogy for what the second student is doing before they do the 100 hours of fine tuning? I think it's like they have it. I think it's the it factor.

[6:55](https://realalpha.app/v/aR20FWCCjAs/?t=415) Yeah. Right. And like I know like when I was an undergrad, I remember there was there was a student like this that studied with me. So I know it exists. Yeah. I think it's interesting to distinguish it from whatever pre-training does. So one way to understand what you just said about we don't have to choose the data in pre-training is to say, actually, it's not dissimilar to the 10,000 hours of practice. It's just that you get that 10,000 hours of practice for free because it's already somewhere in the pre-training distribution. But it's like maybe you're suggesting actually there's actually not that much generalization from pre-training.

[7:32](https://realalpha.app/v/aR20FWCCjAs/?t=452) There's just so much data in pre-training. But it's like it's not necessarily generalizing better than RL. like the main the main strength of pre-training is that there is a so much of it yeah and b you don't have to think hard about what data to put into pre-training and it's a very kind of natural data and it does include in it a lot of what people do yeah people's thoughts and a lot of the features of you know it's like the whole world as projected by people onto text yeah and pre-training tries to capture that using a huge amount of data it's it's very the pre-training is very difficult to reason about because it's so hard to understand the manner in which the model relies on pre-training data and whenever the model makes a mistake could it be because something by chance is not as supported by the pre-training data, you know, and support by pre-training is maybe a loose term.

[8:41](https://realalpha.app/v/aR20FWCCjAs/?t=521) I don't know if I can add anything more useful on this, but I don't think there is a human analog to pre-training. Here's analogies that people have proposed for what the human analogy to pre-training is, and I'm curious to get your thoughts on why they're potentially wrong. One is to think about the first 18 or 15 or 13 years of a person's life when they aren't necessarily economically productive, but they are doing something that is making them understand the world better and so forth. And the other is to think about evolution as doing some kind of search for three billion years, which then results in a human lifetime instance.

[9:28](https://realalpha.app/v/aR20FWCCjAs/?t=568) and then I'm curious if you think either of these are actually analogous to pre-training or how would you think about at least what lifetime human learning is like if not pre-training? I think there are some similarities between both of these two pre-training and pre-training tries to play the role of both of these but I think there are some big differences as well. The amount of pre-training data is very very staggering yes and somehow a human being after even 15 years with a tiny fraction of that pre-training data they know much less yeah but whatever they do know they know much more deeply somehow and the mistakes like like already at that age you would not make mistakes that reis make yeah there is another thing you might say could it be something like evolution and the answer is maybe but in this case i think evolution might actually have an edge like there is this i remember reading about this case where some you know that one thing that neuroscientists do or rather one way in which neuroscientists can learn about the brain is by studying people with brain damage to different parts of the brain and and so and some people have the most strange symptoms you could imagine it's actually really

[10:52](https://realalpha.app/v/aR20FWCCjAs/?t=652) really interesting and there was one case that comes to mind that's relevant i read about this person who had some kind of brain damage that took out i think a stroke or an accident that took out his emotional processing so he stopped feeling any emotion and as a result of that you know he still remained very articulate and he could solve little puzzles and on tests he seemed to be just fine but he felt no emotion he didn't feel sad he didn't feel angry he didn't feel animated and he became somehow extremely bad at making any decisions at all it would take him hours to decide on which socks to wear and he would make very bad financial decisions and that's very what does it say about the role of our built-in emotions in making us like a viable agent essentially and I guess to connect to your question about pre-training it's like maybe if you are good enough at like getting everything out of pre-training you could get that as well but that's the kind of thing which seems well it may or may not be possible to get that from pre-trained what is that clearly not just directly emotion it seems like some almost value function like thing which is giving telling you which decision to be like what the end reward for any decision should be and you think that

[12:44](https://realalpha.app/v/aR20FWCCjAs/?t=764) doesn't sort of implicitly come from i think it could i'm just saying it's not one it's not 100 obvious yeah but what is that like what how do you think about emotions and what is the ml analogy for emotions it should be some kind of a value function thing yeah but i don't think there is a great ml analogy because right now value functions don't play a very prominent role in the things people do it might be worth defining for the audience what a value function is if you want to do that? I mean, certainly, I'll be very happy to do that. Right. So when people do reinforcement learning, the very reinforcement learning is done right now. How do people train those agents? So you have a neural net, and you give it a problem. And then you tell the model, go solve it. And the model takes maybe thousands, hundreds of thousands of actions, or thoughts, or something and then it produces a solution the solution is created and then the score is used to provide a training signal for every single action in your trajectory so that means that if you are doing something that goes for a long time if you're training a task that takes a long time to solve you will do no learning at all until you solve until you come

[14:11](https://realalpha.app/v/aR20FWCCjAs/?t=851) up with the proposed solution that's how reinforcement learning is done naively that's how O1, R1 ostensibly are done. The value function says something like, okay, look, maybe I could sometimes, not always, could tell you if you are doing well or badly. The notion of a value function is more useful in some domains than others. So for example, when you play chess and you lose a piece, you know, I messed up. You don't need to play the whole game to know that what I just did was bad and therefore whatever preceded it was also bad. So the value function lets you short-circuit the weight until the very end.

[14:58](https://realalpha.app/v/aR20FWCCjAs/?t=898) Like let's suppose that you started to pursue some kind of, okay, let's suppose that you are doing some kind of a math thing or a programming thing, and you're trying to explore a particular solution direction. And after, let's say after a thousand steps of thinking, you concluded that this direction is unpromising. As soon as you conclude this, you could already get a reward signal a thousand time steps previously when you decided to pursue down this path. You say, oh, next time I shouldn't pursue this path in a similar situation long before you actually came up with the proposed solution. this was in the deep sea car one paper is that the space of trajectories is so wide that maybe it's hard to learn a mapping from an intermediate trajectory and value and also given that you know encoding for example you will have the wrong idea then you'll go back then you'll change something this sounds like such lack of faith in deep learning like i mean sure it might be difficult but nothing deep learning can't do yeah so my expectation is that like value function should be useful and and i fully i fully expect that they will be used in the future if not already what was i alluding to with the person whose emotional center got

[16:30](https://realalpha.app/v/aR20FWCCjAs/?t=990) damaged is more that maybe what it suggests is that the value function of humans is modulated by emotions in some important way that's hard-coded by evolution. And maybe that is important for people to be effective in the world. That's the thing I was actually planning on asking you. There's something really interesting about emotions of the value function, which is that it's impressive that they have this much utility while still being rather simple to understand so i have two responses i do agree that compared to the kind of things that we learn and the things we are talking about the kind of as we are talking about emotions are relatively simple they might even be so simple that maybe you could map them out in a human understandable way. I think it would be cool to do. In terms of utility though, I think there is a thing where, you know, there is this complexity, robustness trade-off, where complex things can be very useful, but simple things are very useful in a very broad range of situations. And so I think what we've got one way to interpret what we are seeing is that we've got these emotions that essentially evolved mostly from our mammal ancestors and then fine-tuned a little bit while we were hominids, just a bit. We do have like a decent amount of

[18:14](https://realalpha.app/v/aR20FWCCjAs/?t=1094) social emotions though, which mammals may lack, but they're not very sophisticated. And because they're not sophisticated, they serve us so well in this very different world compared to the one that we've been living in. Actually, they also make mistakes. For example, our emotions, well, I don't know, does hunger count as an emotion? It's debatable. But I think, for example, our intuitive feeling of hunger is not succeeding in guiding us correctly in this world with an abundance of food. Yeah. People have been talking about scaling data, scaling parameter, scaling compute. is there a more general way to think about scaling what are the other scaling axes so the thing so so here is a perspective here's a perspective i think might be might be true so the way ml used to work is that people would just think with stuff and try to and try to get interesting results that's what's been going on in the past then the scaling insight arrived, right?

[19:33](https://realalpha.app/v/aR20FWCCjAs/?t=1173) Scaling laws, GPT-3. And suddenly everyone realized we should scale. And this is an example of how language affects thought. Scaling is just one word, but it's such a powerful word because it informs people what to do. They say, okay, let's try to scale things. And so you say, okay, so what are we scaling? And pre-training was a thing to scale. It was a particular scaling recipe. The big breakthrough of pre-training is the realization that this recipe is good. So you say, hey, if you mix some compute with some data into a neural net of a certain size, you will get results. And you will know that it will be better if you just scale the recipe up.

[20:24](https://realalpha.app/v/aR20FWCCjAs/?t=1224) and this is also great companies love this because it gives you a very uh low risk way of investing your resources yeah right it's much harder to invest your resources in research compare that you know if you research you need to have like go forth researchers and research and come up with something versus get more data get more compute you know you'll get something from pre-training and indeed you know it looks like i based on various um things people say on some people say on twitter maybe it appears that gemini have found a way to get more out of pre-training at some point though pre-training will run out of data the data is very clearly finite and so then okay what do you do next either you do some kind of a souped up pre-training different recipe from the one you've done before or you're doing rl or maybe something else. But now that compute is big, computer is now very big, in some sense, we are back to the age of research. So maybe here's another way to put it. Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling, or maybe plus minus. Let's add error bars to those years. Because people say, this is amazing. You got to

[21:42](https://realalpha.app/v/aR20FWCCjAs/?t=1302) Scale more, keep scaling, the one word, scaling. But now the scale is so big, like is the belief really that, oh, it's so big, but if you had a Honeydex more, everything would be so different. Like it would be different for sure. But like, is the belief that if you just Honeydex the scale, everything would be transformed? I don't think that's true.

[22:06](https://realalpha.app/v/aR20FWCCjAs/?t=1326) **Ilya Sutskever:** So it's back to the age of research again, just with big computers.

[22:09](https://realalpha.app/v/aR20FWCCjAs/?t=1329) That's a very interesting way to put it. But let me ask you the question you just posed then. What are we scaling and what would it mean to have a recipe? Because I guess I'm not aware of a very clean relationship that almost looks like a law of physics, which existed in pre-training. There's a power law between data or computer parameters and loss. What is the kind of relationship we should be seeking and how should we think about what this new recipe might look like? so we've already witnessed a transition from one type of scaling to a different type of scaling from pre-training to rl now people are scaling rl now based on what people say on twitter they spend more compute on rl than on pre-training at this point because rl can actually consume quite a bit of compute you know you do very very long rollouts yes so it takes a lot of compute to produce those rollouts and then you get relatively small amount of learning power rollout so you really can spend you really can spend a lot of compute and i could imagine like i wouldn't at this at this it's more like i wouldn't even call it a scale and scaling i would say hey like what are you doing and is the thing you are doing the the the the most productive thing you could be

[23:34](https://realalpha.app/v/aR20FWCCjAs/?t=1414) doing yeah can you find a most more productive way of using your compute we've discussed the value function business earlier and maybe once people get good at value functions they will be using their their resources more productively and if you find a whole other way of training models you could say is this scaling or is it just using your resources i think it becomes a little bit ambiguous in a sense that when people were in the age of research back then it was like people say hey let's try this and this and this let's try that and that and that oh look something interesting is happening and i think there will be a return to that so if we're back in the era of research stepping back what is the part of the recipe that we need to think most about when you say value function people are already trying the current recipe but then having llm as a judge and so forth you could say that's a value function but it sounds like you have something much more fundamental in mind do we need do we need to go back to should we even rethink pre-training at all and not just add more steps to the end of that process yeah so the the the discussion about value function i think it was interesting i want to like emphasize that i think the value function

[24:47](https://realalpha.app/v/aR20FWCCjAs/?t=1487) is something like it's going to make our realm more efficient and i think that makes a difference but I think that anything you can do with a value function you can do without just more slowly the thing which I think is the most fundamental is that these models somehow just generalize dramatically worse than people and it's super obvious that seems like a very fundamental thing okay so this is the crux generalization and there's two sub questions there's one which is about sample efficiency which is why should it take so much more data for these models to learn than humans there's a second about even separate from the amount of data it takes there's a question of why is it so hard to teach the thing we want to a model than to a human which is to say for to a human that we don't necessarily need a verifiable reward to be able to you're probably mentoring a bunch of researchers right now and you're you know talking with them, you're showing them your code, and you're showing them how you think, and from that, they're picking up your way of thinking and how they should do research. You don't have to set like a verifiable reward for them that's like, okay, this is the next part of your curriculum,

[25:58](https://realalpha.app/v/aR20FWCCjAs/?t=1558) and now this is the next part of your curriculum, and oh, this training was unstable, and we got to, there's not this schleppy, bespoke process. So perhaps these two issues are actually related in some way, but I'd be curious to explore this second thing, which was more like continual learning and this first thing which feels just like sample efficiency yeah so you know you could actually wonder one one possible explanation for the human sample efficiency that needs to be considered is evolution and evolution has given us a small amount of the most useful information possible and for things like vision hearing and locomotion i think there's a pretty strong case that evolution actually has given us a lot so for example human dexterity far exceeds i mean robots can become dexterous too if you subject them to like a huge amount of training and simulation but to train a robot in the real world to quickly like pick up a new skill like a person does seems very out of reach and here you could say oh yeah like locomotion all our ancestors needed great locomotion squirrels like so locomotion maybe like we've got like some unbelievable prior yeah you could make the same case for vision you know i believe

[27:22](https://realalpha.app/v/aR20FWCCjAs/?t=1642) yan le khan made the point oh like um children learn to drive after 16 hours after 10 hours of practice which is true but our vision is so good at least for me when i remember myself being five year old my i was i was very excited about cars back then and i'm pretty sure my car recognition was more than adequate for self-driving already as a five-year-old you don't get to see that much data as a five-year-old you spend most of your time in your parents house so you have very low data diversity but you could say maybe that's evolution too but then language and math and coding? Probably not. It still seems better than models. I mean, obviously models are better than the average human at language and math encoding, but are they better at the average human at learning? Oh yeah. Oh yeah, absolutely. What I meant to say is that language, math, encoding, and especially math encoding, suggests that whatever it is that makes people good at learning is probably not so much a complicated prior, but something more some fundamental thing wait i'm not sure i understood what why should that be the case so consider a skill that people exhibit some kind of great reliability or you know um yeah if the skill is one that was very useful to our ancestors for many millions of years

[28:46](https://realalpha.app/v/aR20FWCCjAs/?t=1726) hundreds of millions of years you could say you could argue that maybe humans are good at it because of evolution because we have a prior an evolutionary prior that's encoded in some very non-obvious way yeah that somehow makes us so good at it yeah but if people exhibit great ability reliability robustness ability to learn in a domain that really did not exist until recently then this is more an indication that people might have just better machine learning period but then how should we think about what that is is it a matter of yeah what is the ml analogy for what there's a couple interesting things about it it takes fewer samples it's more unsupervised you don't have to set a very like a child learning to drive a car a teenager learning how to drive a car is like not exactly getting some pre-built verifiable reward there it comes from their interaction with the machine and with the environment and yeah it takes much fewer samples it seems more unsupervised it seems more robust much more robust the robustness of people is really staggering yeah so is it like okay and do you have a unified way of thinking about why are all these things happening at once what is the ml analogy that would that could be it could realize something like this so so so um

[30:24](https://realalpha.app/v/aR20FWCCjAs/?t=1824) this is where you know one of the things that you've been asking about is how can you know the teenage driver kind of self-correct and learn from their experience without an external teacher and the answer is well they have their value function right they have a general sense which is also by the way extremely robust in people like whatever it is the human value function whatever the human value function is with a few exceptions around addiction it's actually very very robust and so for something like a teenager that's learning to drive they start to drive and they already have a sense of how they're driving immediately how badly they're unconfident and then they see okay and they and then of course the the learning speed of any teenager is so fast after 10 hours you're good to go yeah it seems like humans have some solution but i'm curious about like well how are they doing it and like why is it so hard to like well how do we need to reconceptualize the way we're training models to make something like this possible you know that is a great question to ask and it's a question i have a lot of opinions about but unfortunately we live in a world where not not all machine learning ideas are discussed

[31:43](https://realalpha.app/v/aR20FWCCjAs/?t=1903) freely and this this is one of them so there's probably a way to do it i think it can be done the fact that people are like that i think it's a proof that it can be done there may be another blocker though, which is there is a possibility that the human neurons actually do more compute than we think. And if that is true, and if that plays an important role, then things might be more difficult. But regardless, I do think it points to the existence of some machine learning principle that I have opinions on, but unfortunately, circumstances make it hard to discuss in detail. Nobody listens to this podcast, Ilya.

[32:31](https://realalpha.app/v/aR20FWCCjAs/?t=1951) Yeah. So I have to say that prepping for Ilya was pretty tough because neither I nor anybody else had any idea what he's working on and what SSI is trying to do. I had no basis to come up with my questions. And the only thing I could go off, honestly, was trying to think from first principles about what are the bottlenecks to AGI? Because clearly Ilya is working on them in some way. Part of this question involved thinking about RL scaling because everybody's asking how well RL will generalize and how we can make it generalize better. As part of this, I was reading this paper that came out recently on RL scaling, and it showed that actually the learning curve on RL looks like a sigmoid.

[33:09](https://realalpha.app/v/aR20FWCCjAs/?t=1989) I found this very curious. Why should it be a sigmoid where it learns very little for a long time, and then it quickly learns a lot, and then it asymptotes? This is very different from the power law you see in pre-training where the model learns a bunch at the very beginning and then less and less over time. And it actually reminded me of a note that I had written down after I had a conversation with a researcher friend where he pointed out that the number of samples that you need to take in order to find a correct answer scales exponentially with how different your current probability distribution is from the target probability distribution. And I was thinking about how these two ideas are related. I had this vague idea that they should be connected, but I really didn't know how. I don't have a math background, so I couldn't really formalize it. But I wondered if Gemini 3 could help me out here. And so I took a picture of my notebook and I took the paper and I put them both in the context of Gemini 3 and I asked it to find the connection. And it thought a bunch and then it realized that the correct way to model the information you gain from a single yes or no outcome in RL is as the entropy of a random binary variable. It made a graph which showed how

[34:14](https://realalpha.app/v/aR20FWCCjAs/?t=2054) the bits you gain for a sample in RL versus supervised learning scale as a pass rate increases. And as soon as I saw the graph that Gemini 3 made, immediately a ton of things started making sense to me. Then I wanted to see if there was any empirical basis to this theory. So I asked Gemini to code my experiment to show whether the improvement in loss scales in this way with pass rate. I just took the code that Gemini outputted, I copy pasted it into a Google Colab notebook, and I was able to run this toy ML experiment and visualize its results without a single bug. It's interesting because the results look similar but not identical to what we should have expected.

[34:51](https://realalpha.app/v/aR20FWCCjAs/?t=2091) And so I downloaded this chart and I put it into Gemini and I asked it, what is going on here? And it came up with a hypothesis that I think is actually correct, which is that we're capping how much supervised learning can improve in the beginning by having a fixed learning rate. And in fact, we should decrease the learning rate over time. It actually gives us an intuitive understanding for why in practice we have learning rate schedulers that decrease the learning rate over time. I did this entire flow from coming up with this vague initial question to building a theoretical understanding to running some toy ML experiments, all with Gemini 3.

[35:24](https://realalpha.app/v/aR20FWCCjAs/?t=2124) This feels like the first model where it can actually come up with new connections that I wouldn't have anticipated. It's actually now become the default place I go to when I want to brainstorm new ways to think about a problem. If you want to read more about RL scaling, you can check out the blog post that I wrote with a little help from Gemini 3. And if you want to check out Gemini 3 yourself, go to gemini.google. I am curious if you say we are back in the era of research. You were there from 2012 to 2020. And do you have... Yeah, what is now the vibe going to be if we go back to the era of research?

[36:00](https://realalpha.app/v/aR20FWCCjAs/?t=2160) For example, even after AlexNet, the amount of compute that was used to run experiments kept increasing. And the size of frontier systems kept increasing. and do you think now that this era of research will still require tremendous amounts of compute um do you think it will require going back into the archives and reading old papers what is maybe what was the vibe of like you were at google and um open ai and stanford these places when there was like a more of a vibe of research what kind of things should we be expecting in the community so one consequence of the age of scaling is that there was this um scaling sucked out all the air in the room yeah and so because scaling sucked out all the air in the room everyone started to do the same thing we got to the point where we are in a world where there are more companies than ideas by quite a bit actually on that you know there is the silicon valley saying that says that ideas are cheap execution is everything and people say that a lot yeah and there is truth to that but then i saw i saw someone say on twitter something like if ideas are so cheap how come no one's having any ideas and I think it's true too I think if you think about research progress in terms of bottlenecks

[37:38](https://realalpha.app/v/aR20FWCCjAs/?t=2258) there are several bottlenecks if you go back to the and one of them is ideas and one of them is your ability to bring them to life which might be compute but also engineering so if you go back to the 90s let's say you had people who had pretty good ideas and if they had much larger computers maybe they could demonstrate that their ideas were viable but they could not so they could only have very very small demonstration and not convince anyone yeah so the bottleneck was compute then in the age of scaling computers increased a lot and of course there is a question of how much compute is needed but compute is large so compute is large enough such that it's like not obvious that you need that much more compute to prove some idea like i'll give you an analogy alex net was built on two gpus that was the total amount of compute use for it the transformer was built on eight

[38:44](https://realalpha.app/v/aR20FWCCjAs/?t=2324) **Ilya Sutskever:** 264 GPUs. No single transformer paper experiment used more than 64 GPUs of 2017, which would be like what?

[38:52](https://realalpha.app/v/aR20FWCCjAs/?t=2332) Two GPUs of today? So the ResNet, right? Many, like even the you could argue that the 01 reasoning was not the most compute heavy thing in the world. So they're definitely for research you need, like definitely some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute ever for research. You might argue, and I think it is true, that if you want to build the absolutely best system, if you want to build the absolutely best system, then it helps to have much more compute. And especially if everyone is within the same paradigm, then compute becomes one of the big differentiators.

[39:40](https://realalpha.app/v/aR20FWCCjAs/?t=2380) yeah i guess well it was possible to develop these ideas i'm asking you for the history because you were actually there i'm not sure what actually happened but it sounds like it was possible to develop these ideas using minimal amounts of compute but it wasn't the transformer didn't immediately become famous it became the thing everybody started doing and then started experimenting on top of and building on top of because it was validated at higher and higher levels of compute Correct. And if you at SSI have 50 different ideas, how will you know which one is the next transformer and which one is, you know, brittle without having the kinds of compute that other frontier labs have?

[40:21](https://realalpha.app/v/aR20FWCCjAs/?t=2421) So I can comment on that, which is, the short comment is that, you know, you mentioned SSI. Specifically for us, the amount of compute that SSI has for research is really not that small. And I want to explain why. Like a simple math can explain why the amount of compute that we have is actually a lot more comparable for research than one might think. now explain so ssi has raised three billion dollars which is like not small but it's like a lot by any absolute sense but you could say but look at the other companies raising much more but a lot of what their a lot of their compute goes for inference like these big numbers these big loans it's earmarked for inference that's number one number two you need if you want to have a product on which you do inference you need to have a big staff of engineers of salespeople a lot of the research needs to be dedicated for producing all kinds of product related features so then when you look at what's actually left for research the difference becomes a lot smaller now the other thing is is that if you are doing something different, do you really need the absolute maximal scale to prove it? I don't think that's true at all. I think that in our case, we have sufficient compute to prove, to convince ourselves and anyone

[42:00](https://realalpha.app/v/aR20FWCCjAs/?t=2520) else that what we're doing is correct. There's been public estimates that companies like OpenAI spend on the order of five, six billion dollars a year, even just so far on experiments. This is separate from the amount of money they're sending on inference and so forth. So it seems like they're spending more a year running research experiments than you guys have in total funding. I think it's a question of what you do with it. It's a question of what you do with it. Like they have, like, is the more, I think in their case, in the case of others, I think there is a lot more demand on the training compute. There's a lot more different work streams.

[42:36](https://realalpha.app/v/aR20FWCCjAs/?t=2556) there is there are different modalities there is just more stuff and so it becomes fragmented how will ssi make money you know my answer to this question is something like we just thought right now we just focus on the research and then the answer to this question will reveal itself i think there will be lots of possible answers is ssi's plan still to straight shot super intelligence maybe i think that there is merit to it i think there's a lot of merit because i think that it's very nice to not be affected by the day-to-day market competition but i think there are two reasons that may cause us to change the plan one is pragmatic if timelines turned out to be long, which they might.

[43:31](https://realalpha.app/v/aR20FWCCjAs/?t=2611) And second, I think there is a lot of value in the best and most powerful AI being out there impacting the world. I think this is a meaningfully valuable thing. But then, so why is your default plan to Straight Shot Superintelligence? Because it sounds like, you know, OpenAI, Anthropic, all these other companies, their explicit thinking is, look, we have weaker and weaker intelligences that the public can get used to and prepare for. And why is it potentially better to build a super intelligence directly? So I'll make the case for and against. Yeah. The case for is that you are, so one of the challenges that people face when they're in the market is that they have to participate in the rat race.

[44:19](https://realalpha.app/v/aR20FWCCjAs/?t=2659) and the rat race is quite difficult in that it exposes you to to difficult trade-offs which you need to make and there is it is it is nice to say we'll insulate ourselves from all this and just focus on the research and come out only when we are ready and not before but the counterpoint is valid too and those are those are opposing forces the counterpoint is hey it is useful for the world to see powerful AI. It is useful for the world to see powerful AI because that's the only way you can communicate it. Well, I guess not even just that you can communicate the idea, but... Communicate the AI, not the idea.

[45:01](https://realalpha.app/v/aR20FWCCjAs/?t=2701) Communicate the AI. What do you mean communicate the AI? Okay, so let's suppose you read an essay about AI. And the essay says AI is going to be this and AI is going to be that and it's going to be this. And you read it and you say, okay, this is an interesting essay. now suppose you see an AI doing this an AI doing that it is incomparable like basically I think I think that there is a big benefit from AI being in the public and that would be a reason for us to not be quite straight shot well I guess it's not even that but I do think that is an important part of it the other big thing is I can't think of another discipline and human engineering and research where the end artifact was made safer mostly through just thinking about how to make it safe, as opposed to why are airplane crashes per mile so much lower today than there were decades ago? Why is it so much harder to find a bug in Linux than it would have been decades ago? And I think it's mostly because these systems were deployed to the world. You noticed failures. Those failures were corrected and the systems became more robust.

[46:13](https://realalpha.app/v/aR20FWCCjAs/?t=2773) now i'm not sure why agi and superhuman intelligence would be any different especially given and i hope we can talk we're gonna get to this it seems like the harms of super intelligence are not just about like having some malevolent uh paper clipper out there but it's just like this is a really powerful thing and we don't even know how to conceptualize how people interact with what people will do with it and having gradual access to it seems like a um better way to maybe spread out the impact of it and to help people prepare for it well i think i think on this point even in the straight shot scenario you would still do a gradual release of it it's how i would imagine it the gradualism would be an inherent inherent component of any plan it's just a question of what is the first thing that you get out of the door that's number one Number two, I also think, you know, I believe you have advocated for continual learning more than other people.

[47:19](https://realalpha.app/v/aR20FWCCjAs/?t=2839) And I actually think that this is an important and correct thing. And here is why. So one of the things, so I'll give you another example of how language affects thinking. And in this case, it is going to be two words, two words that have shaped everyone's thinking, I maintain. First word, AGI. Second word, pre-training. Let me explain. So the term AGI, why does this term exist? It's a very particular term. Why does it exist? There's a reason. The reason that the term AGI exists is, in my opinion, not so much because it's like a very important essential descriptor of some end state of intelligence.

[48:11](https://realalpha.app/v/aR20FWCCjAs/?t=2891) but because it is a reaction to a different term that existed and the term is narrow ai if you go back to ancient history of game plane ai of checkers ai chess ai computer games ai everyone would say look at this narrow intelligence sure the chess ai can beat casper off but it can't do anything else it is so narrow artificial narrow intelligence so in response as a reaction to this some people said well this is not good it is so narrow what we need is general ai general ai an ai that can just do all the things the second and and that term just got a lot of traction yeah the second thing that got a lot of is pre-training.

[49:07](https://realalpha.app/v/aR20FWCCjAs/?t=2947) Specifically, the recipe of pre-training. I think the way people do RL now is maybe undoing the conceptual imprint of pre-training. But pre-training had the property. You do more pre-training and the model gets better at everything, more or less uniformly. General AI. Pre-training gives AGI. but the thing that happened with AGI and pre-training is that in some sense they overshot the target because by the kind if you think about the term AGI you will realize and especially in the context of pre-training you will realize that a human being is not an AGI because a human being yes there is definitely a foundation of skills

[49:58](https://realalpha.app/v/aR20FWCCjAs/?t=2998) **Ilya Sutskever:** a human being a human being lacks a huge amount of knowledge instead we rely on continual learning

[50:08](https://realalpha.app/v/aR20FWCCjAs/?t=3008) we rely on continual learning and so then when you think about okay so let's suppose that we achieve success and we produce a safe super some kind of safe super intelligence the question is but how do you define it where on the curve of continual learning is going to be i produce like a super intelligent 15 year old that's very eager to go and you say okay I'm going to they don't know very much at all the great student very eager you go and be a programmer you go and be a doctor go and learn so you could imagine that the deployment itself will involve some kind of a learning trial and error period it's a process as opposed to you drop the finished thing okay i i see so you're you're suggesting that the thing you're pointing out with super intelligence is not some finished mind which knows how to do every single job in the economy because the way say the original i think open ai charter or whatever defines agi is like it can do every single job that every single thing a human can do you're proposing instead a mind which can learn to do every single job.

[51:19](https://realalpha.app/v/aR20FWCCjAs/?t=3079) Yes. And that is super intelligence. But once you have the learning algorithm, it gets deployed into the world the same way a human laborer might join an organization. And it seems like one of these two things might happen. Maybe neither of these happens. One, this super efficient learning algorithm becomes superhuman, becomes as good as you and potentially even better at the task of ML research. And as a result, the algorithm itself becomes more and more superhuman. The other is, even if that doesn't happen, if you have a single model, I mean, this is explicitly your vision. If you have a single model or instances of a model which are deployed through the economy, doing different jobs, learning how to do those jobs, continually learning on the job, picking up all the skills that any human could pick up, but actually picking them all up at the same time and then amalgamating the learnings.

[52:16](https://realalpha.app/v/aR20FWCCjAs/?t=3136) You basically have a model which functionally becomes super intelligent, even without any sort of recursive self-improvement in software. Right? Because you now have one model that can do every single job in the economy. And humans can't merge our minds in the same way. And so do you expect some sort of intelligence explosion from broad deployment? I think that it is likely that we will have rapid economic growth. i think the broad deployment like there are two arguments you could make which are conflicting one is that look if indeed you get once indeed you get to a point where you have an ai that can learn to do things quickly and you have many of them then they will then there will be a strong force to deploy them in the economy unless there will be some kind of a regulation that stops it which by the way there might be but i think the idea of very rapid economic growth for some time i think it's very possible from broad deployment the other question is how rapid it's going to be so i think this is hard to know because on the one hand you have this very efficient worker on the other hand there is the world is just really big and there's a lot of stuff and that stuff moves at a different speed but then on the other hand now the ai could you know so i think

[53:48](https://realalpha.app/v/aR20FWCCjAs/?t=3228) very rapid economic growth is possible and we will see like all kinds of things like different countries with different rules and the ones which have the friendlier rules the economic growth will be faster hard to predict some people in our audience like to read the transcripts instead of listening to the episode. And so we put a ton of effort into making the transcripts read like they are standalone essays. The problem is that if you just transcribe a conversation verbatim using a speech-to-text model, it'll be full of all kinds of fits and starts and confusing phrasing. We mentioned this problem to Labelbox and they asked if they could take a stab. Working with them on this is probably the reason that I'm most excited to recommend Labelbox to people. It wasn't just, Oh, hey, tell us what kind of data you need and we'll go get it.

[54:31](https://realalpha.app/v/aR20FWCCjAs/?t=3271) They walked us through the entire process, from helping us identify what kind of data we needed in the first place, to assembling a team of expert aligners to generate it. Even after we got all the data back, LabelBox stayed involved. They helped us choose the right base model and set up auto-QA on the model's output so that we could tweak and refine it. And now we have a new transcriber tool that we can use for all our episodes moving forward. This is just one example of how Labelbox meets their customers at the ideas level and partners with them through their entire journey. If you want to learn more or if you want to try out the transcriber tool yourself, go to labelbox.com slash thwarkash.

[55:07](https://realalpha.app/v/aR20FWCCjAs/?t=3307) it seems to me that this is a very precarious situation to be in where look in the limit we know that this should be possible because if you have something that is as good as a human at learning but which can merge its brains merge there are different instances in a way that humans can't merge already this seems like a thing that should physically be possible humans are possible digital computers are possible you just need both of those combined to produce this thing and it also seems like this kind of thing is extremely um powerful and economic growth is one way to put it um i mean dyson spear is a lot of economic growth but another way to put it is just like you will have potentially a very short period of time because a human on the job can you know you're hiring people to ssi in six months they're like net productive probably right um a human like learns really fast and so this thing is becoming smarter and smarter very fast how do you think about making that go well and why is SSI positioned to do that well what is SSI's plan there basically is what I'm trying to ask yeah so one of the ways in which my thinking has been changing is that I now place more importance on AI being deployed

[56:28](https://realalpha.app/v/aR20FWCCjAs/?t=3388) incrementally and in advance. One very difficult thing about AI is that we are talking about systems that don't yet exist. And it's hard to imagine them. I think that one of the things that's happening is that in practice, it's very hard to feel the AGI. It's very hard to feel the AGI. We can talk about it, but it's like it's like talking about like the long future like imagine like having a conversation about like how is it like to be old when you're like old and and frail and you can have a conversation you can try to imagine it but it's just hard and you come back to reality well that's not the case and i think that a lot of the issues around agi and its future power stem from the fact that it's very difficult to imagine future ai is going to be different different it's going to be powerful indeed the whole problem what is the problem of ai and agi the whole problem is the power.

[57:44](https://realalpha.app/v/aR20FWCCjAs/?t=3464) The whole problem is the power. When the power is really big, what's going to happen? And one of the ways in which I've changed my mind over the past year, and so that change of mind may back, may, I'll say, I'll hedge a little bit, may back propagate into the plans of our company, is that so if it's hard to imagine what do you do? You got to be showing the thing. You got to be showing the thing. And I maintain that I think most people who work on AI also can't imagine it because it's too different from what people see on a day-to-day basis. I do maintain here's something which I predict will happen.

[58:34](https://realalpha.app/v/aR20FWCCjAs/?t=3514) That's a prediction.

[58:35](https://realalpha.app/v/aR20FWCCjAs/?t=3515) **Ilya Sutskever:** I maintain that as AI becomes more powerful, then people will change their behaviors.

[58:47](https://realalpha.app/v/aR20FWCCjAs/?t=3527) And we will see all kinds of unprecedented things which are not happening right now. And I'll give some examples. I think for better or worse, the frontier companies will play a very important role in what happens, as will the government. and the kind of things that I think you'll see, which you see the beginnings of, companies that are fierce competitors starting to collaborate on AI safety. You may have seen OpenAI and Anthropic doing a first small step, but that did not exist. That's actually something which I predicted in one of my talks about three years ago, that such a thing will happen.

[59:30](https://realalpha.app/v/aR20FWCCjAs/?t=3570) I also maintain that as AI continues to become more powerful, more visibly powerful. There will also be a desire from governments and the public to do something. And I think that this is a very important force of showing the AI. That's number one. Number two, okay, so then the AI is being built. What needs to be done? So one thing that I maintain that will happen is that right now people who are working on AI I maintain that the AI doesn't feel powerful because of its mistakes. I do think that at some point the AI will start to feel powerful, actually. And I think when that happens, we will see a big change in the way all AI companies approach safety. They'll become much more paranoid.

[1:00:23](https://realalpha.app/v/aR20FWCCjAs/?t=3623) I think, I say this as a prediction that we will see happen. We'll see if I'm right. But I think this is something that will happen because they will see the AI becoming more powerful. Everything that's happening right now, I maintain, is because people look at today's AI and it's hard to imagine the future AI. And there is a third thing which needs to happen. And I think this is, and I'm talking about it in broader terms, not just from the perspective of SSI, because you asked me about our company. But the question is, okay, so then what should the companies aspire to build yeah what should they aspire to build and there has been one big idea that actually every that everyone has been locked in locked into which is the the self-improving ai and why why did it happen because there is fewer ideas than companies but i maintain that there is something that's better to build and i think that everyone will actually want that it's like the AI that's robustly aligned to care about sentient life specifically.

[1:01:31](https://realalpha.app/v/aR20FWCCjAs/?t=3691) I think in particular, there's a case to be made that it will be easier to build an AI that cares about sentient life than an AI that cares about human life alone, because the AI itself will be sentient. And if you think about things like mirror neurons and human empathy for animals, which is, you know, you might argue it's not big enough, but it exists. I think it's an emergent property from the fact that we model others with the same circuit that we used to model ourselves, because that's the most efficient thing to do. So even if you got an AI to hear about sentient beings, and it's not actually clear to me that that's what you should try to do if you solve the alignment, it would still be the case that most sentient beings will be AIs.

[1:02:18](https://realalpha.app/v/aR20FWCCjAs/?t=3738) there will be trillions eventually quadrillions of AIs humans will be a very small fraction of sentient beings so it's not clear to me if the goal is some kind of human control over this future civilization that this is the best criterion it's true I think that it's possible it's not the best criterion I'll say two things I think that thing number one I think that if there is so I think that care for sentient life I think there is merit to it I think it should be considered I think that it will be helpful if there was some kind of a short list of ideas that then the companies when they are in this situation could use that's number two. Number three, I think it would be really materially helpful if the power of the most powerful super intelligence was somehow capped, because it would address a lot of these concerns.

[1:03:25](https://realalpha.app/v/aR20FWCCjAs/?t=3805) The question of how to do it, I'm not sure. But I think that would be materially helpful when you're talking about really, really powerful systems. Yeah. Before we continue the alignment discussion, I want to double click on that. how much room is there at the top how do you think about super intelligence do you think i mean using this learning efficiency idea maybe it's just extremely fast at learning new skills or new knowledge and does it just have a bigger pool of strategies is there a single cohesive it in the center that's more powerful or bigger and if so do you do you imagine that this will be sort of godlike in comparison to the rest of human civilization or does it just feel like another agent or another cluster of agents so this is an area where different people have different intuitions i think it will be very powerful for sure i think that what i think is most likely to happen is that there will be multiple such ais being created roughly at the same time I think that if the cluster is big enough, like if the cluster is literally continent-sized, that thing could be really powerful indeed, right?

[1:04:40](https://realalpha.app/v/aR20FWCCjAs/?t=3880) If you literally have a continent-sized cluster, like those AIs can be very powerful. And all I can tell you is that if you're talking about extremely powerful AIs, like truly dramatically powerful, then yeah it would be nice if they could be restrained in some ways or if there was some kind of an agreement or something because i think that if you are saying hey like if you really like what what is the the concern of super intelligence what is one way to explain the concern if you imagine a system that is sufficiently powerful like really sufficiently powerful. And you could say, okay, you need to do something sensible, like care for sentient life, let's say, in a very single-minded way. We might not like the results. That's really what it is.

[1:05:32](https://realalpha.app/v/aR20FWCCjAs/?t=3932) And so maybe, by the way, the answer is that you do not build an RL agent in the usual sense. And actually, I'll point several things out. I think human beings are a semi-RL agent. You know, we pursue a reward, and then the emotions or whatever make us tire out of the reward we pursue a different reward the market is like kind it's like a very short-sighted kind of agent evolution is the same evolution is very intelligent in some ways but very dumb in other ways the government has been designed to be a never-ending fight between three parts which has an effect so i think things like this another thing that makes this discussion difficult is that we are talking about systems that don't exist that we don't know how to build right that's the other thing and that's actually my belief i think what people are doing right now will go some distance and then peter out it will continue to improve but it will also not be it so the it we don't know how to build and i think that a lot a lot hinges on understanding reliable generalization and i'll say another thing which is like you know one of the things that you could say is what would that cause alignment to be difficult is that human value that it's it's um your ability to learn human values is fragile then your ability to optimize

[1:06:58](https://realalpha.app/v/aR20FWCCjAs/?t=4018) them is fragile you will you actually learn to optimize them and then can't you say are these not all instances of unreliable generalization why is it that human beings appear to generalize so much better what if generalization was much better what would happen in this case what would be the effect but those we can't we can't like those questions are right now still unanswerable um how does one think about what ai going well looks like because i think you've scoped out how ai might evolve we'll have these sort of continual learning agents ai will be very powerful maybe there will be many different ais how do you think about lots of continent compute size intelligences going around how dangerous is that how do we make that less dangerous and how do we do that in a way that protects a equilibrium where there might be misaligned ais out there and bad actors out there so one reason why i liked the ai that cares for sentient life you know and we can debate on whether it's good or bad. But if the first N of these dramatic systems actually do care for, you know, love humanity or something, you know, care for sentient life, obviously this also needs to be achieved. This needs to be achieved. So if this is achieved by the first N of those systems,

[1:08:27](https://realalpha.app/v/aR20FWCCjAs/?t=4107) then I can see it go well at least for quite some time and then there is the question of what happens in the long run what happens in the long run, how do you achieve a long run equilibrium and I think that there is an answer as well and I don't like this answer but it needs to be considered in the long run you might say okay, so if you have a world where powerful AIs exist, in the short run you could say, okay, you have universal high income. You have universal high income. And we're all doing well. But we know that, what do the Buddhists say? Change is the only constant. And so things change.

[1:09:12](https://realalpha.app/v/aR20FWCCjAs/?t=4152) And there is some kind of government, political structure thing, and it changes. Because these things have a shelf life. You know, some new government thing comes up and it functions, and then after some time it stops functioning that's something that we see happening all the time and so I think that for the long run equilibrium one approach you could say okay so maybe every person will have an AI that will do their bidding and that's good and if that could be maintained indefinitely that's true but the downside with that is okay so then the AI goes and like earns you know earns money for the person and advocates for their needs in the political sphere and maybe then writes a little report saying, okay, here's what I've done, here's the situation.

[1:10:01](https://realalpha.app/v/aR20FWCCjAs/?t=4201) And the person says, great, keep it up. But the person is no longer a participant. And then you can say that's a precarious place to be in. But, so I'm going to preface by saying, I don't like this solution, but it is a solution. And the solution is if people become part AI with some kind of neural link plus plus because what will happen as a result is that now the ai understands something and we understand it too like because now the understanding is transmitted wholesale so now if the ai is in some situation now it's like you are involved in that situation yourself fully and i think this is the answer to the equilibrium i wonder if uh the fact that emotions which were developed millions or in many cases billions of years ago in a totally different environment are still guiding our actions so strongly is an example of alignment success to maybe spell out what I mean the brainstem has these I don't know if it's more accurate to call it a value function or reward function but the brainstem has a directive where saying mate with somebody who's more successful the cortex is the part that understands what does success mean in the modern context but the brainstem is able to align the cortex and say

[1:11:27](https://realalpha.app/v/aR20FWCCjAs/?t=4287) however you recognize success to be and i'm not smart enough to understand what that is you're still going to pursue this directive i think i think there is so i think there's a more general point i think it's actually really mysterious how the brain encodes high level desires sorry how evolution encodes high level desires like it's pretty easy to understand how evolution would would endow us with the desire for food that smells good because smell is a chemical and so just pursue that chemical it's very easy to imagine such a mechanism evolution doing such a thing but evolution also has endowed us with all these social desires like we really care about being seen positively by society we care about being a good standing we like all these social intuitions that we have i feel strongly that they're baked in and i don't know how evolution did it because it's a high level concept it's represented in the brain like what people think like let's say you are like you care about some social thing it's not like a low level signal like smell it's not something that for which there is a sensor like the brain needs to do a lot of processing to piece together lots of bits of information to understand what's going on socially

[1:12:52](https://realalpha.app/v/aR20FWCCjAs/?t=4372) and somehow evolution said that's what you should care about yes how did it do it and it did it quickly too yeah because i think all these sophisticated social things that um we care about i think they evolved pretty recently yeah so evolution had an easy time hardcoding this high level desire and i maintain or you know at least i'll say i'm unaware of good hypothesis for how it's done i had some ideas i was kicking around but none of them none of them uh are satisfying. Yeah. And what's especially impressive is if it was a desire that you learned in your lifetime, it kind of makes sense because your brain is intelligent.

[1:13:33](https://realalpha.app/v/aR20FWCCjAs/?t=4413) It makes sense why we're able to learn intelligent desires. But your point is that the desire is, maybe this is not your point, but one way to understand it is the desire is built into the genome and the genome is not intelligent, right? But it's able to, you're somehow able to describe this feature that requires, like it's not even clear how you define that feature and you can get it into that you can build it into the genes yeah essentially or maybe i'll put it differently if you think about the tools that are available to the genome it says okay here's a recipe for building a brain and you could say here is a recipe for connecting the dopamine neurons to like the smell sensor yeah and if the smell is a certain kind of you know good smell you want to eat that i could imagine the genome doing that i'm claiming that it is harder to imagine it's harder to imagine the genome saying you should care about some complicated computation that your entire brain that like a big chunk of your brain does that's all i'm claiming i i can tell you like a speculation i was wondering how it could be done and let me offer a speculation and i'll explain why the speculation is probably false so the speculation is okay so the brain it's like

[1:14:45](https://realalpha.app/v/aR20FWCCjAs/?t=4485) the brain has those regions you know the brain regions we have our cortex right yeah it has all those brain regions and the cortex is uniform but the brain regions and and and the neurons in the cortex they kind of speak to their neighbors mostly and that explains why you get brain regions because if you want to do some kind of speech processing all the neurons that do speech need to talk to each other and they can and because neurons can only speak to their nearby neighbors for the most part it has to be a region all the regions are mostly located in the same place from person to person so maybe evolution hard-coded literally a location on the brain so it says oh like when when like you know the gps of the brain gps coordinates such and such when that fires that's what you should care about like maybe that's what evolution did because that would be within the toolkit of evolution yeah although there are examples where for example people who are born blind have that area of their cortex adopted by another sense and i have no idea but i'd be surprised if the desires or the reward functions which require visual signal no longer worked you know people who have their different areas of their cortex co-opted for example

[1:16:01](https://realalpha.app/v/aR20FWCCjAs/?t=4561) if you no longer have vision can you still feel the sense that i want people around me to like me and so forth, which usually there's also visual cues for. So I actually fully agree with that. I think there's an even stronger counterargument to this theory, which is like, if you think about people, so there are people who get half of their brains removed in childhood and they still have all their brain regions, but they all somehow move to just one hemisphere, which suggests that the brain regions, the location is not fixed. And so that theory is not true. It would have been cool if it was true, but it's not.

[1:16:37](https://realalpha.app/v/aR20FWCCjAs/?t=4597) And so I think that's a mystery, but it's an interesting mystery. Like the fact is somehow evolution was able to endow us to care about social stuff very, very reliably. And even people who have like all kinds of strange mental conditions and deficiencies and emotional problems tend to care about this also. AI tools like defakes, voice clones and agents have dramatically increased the sophistication of fraud and abuse. So it's more important than ever to actually understand the identity and intent of whoever or whatever is using your platform. That's exactly what Sardine helps you do. Sardine brings together thousands of device behavior and identity signals to help you assess risk.

[1:17:21](https://realalpha.app/v/aR20FWCCjAs/?t=4641) Everything from how a user types or moves their mouse or holds their device, to whether they're hiding their true location behind a VPN, to whether they're injecting a fake camera feed during KYC selfie checks. Sardine combines these signals with insights from their network of almost 4 billion devices. Things like a user's history of fraud or their associations with other high-risk accounts. So you can spot bad actors before they do damage. This would literally be impossible if you only use data from your own application. Sardine doesn't stop at detection. They offer a suite of agents to streamline onboarding checks and automate investigations.

[1:18:00](https://realalpha.app/v/aR20FWCCjAs/?t=4680) So as fraudsters use AI to scale their attacks, you can use AI to scale your defenses. Go to sardine.ai.com to learn more and download their guide on AI fraud detection. What is SSI planning on doing differently? so presumably your plan is to be one of the frontier companies when this time arrives and then what is presumably you started ssi because you're like i i think i have a way of approaching how to do this safely in a way that the other companies don't what what is that difference so the way i would describe it as there are some ideas that i think are promising and I want to investigate them and see if they are indeed promising or not.

[1:18:44](https://realalpha.app/v/aR20FWCCjAs/?t=4724) It's really that simple. It's an attempt. I think that if the ideas turn out to be correct, these ideas that we discussed around understanding generalization, if these ideas turn out to be correct, then I think we will have something worthy. Will they turn out to be correct? We are doing research. We are squarely age of research company. we're making progress we've actually made quite good progress over the past year but we need to keep making more progress yeah more research and that's how i see it i see it as an attempt to be an attempt to be a voice and a participant um people have asked uh your co-founder and previous ceo left to go to meta recently and people have asked well if there was a lot of breakthroughs being made.

[1:19:39](https://realalpha.app/v/aR20FWCCjAs/?t=4779) That seems like a thing that should have been unlikely. I wonder how you respond. Yeah, so for this, I will simply remind a few facts that may have been forgotten. And I think these facts which provide the context, I think they explain the situation. So the context was that we were fundraising at a 32 billion valuation. And then Meta came in and offered to acquire, and I said no, but my former co-founder, like in some sense, said yes. And as a result, he also was able to enjoy from a lot of near-term liquidity. And he was the only person from SSI to join Meta. It sounds like SSI's plan is to be a company that is at the frontier when you get to this very important period in human history where you have superhuman intelligence and you have these ideas about how to make superhuman intelligence go well, but other companies will be trying their own ideas. What distinguishes SSI's approach to making superintelligence go well?

[1:20:47](https://realalpha.app/v/aR20FWCCjAs/?t=4847) The main thing that distinguishes SSI is its technical approach. So we have a different technical approach that I think is worthy, and we are pursuing it. I maintain that in the end, there will be a convergence of strategies. So I think there will be a convergence of strategies where at some point, as AI becomes more powerful, it's going to become more or less clearer to everyone what the strategy should be. And it should be something like, yeah, you need to find some way to talk to each other. And you want your first actual, like real super intelligent AI to be aligned and somehow be, you know, care for sentient life, care for people, democratic, one of those, some combination of thereof.

[1:21:42](https://realalpha.app/v/aR20FWCCjAs/?t=4902) And I think this is the condition that everyone should strive for. And that's what the society is striving for. And I think that this time, if not already, all the other companies will realize that They're striving towards the same thing. And we'll see. I think that the world will truly change as AI becomes more powerful. And I think a lot of these forecasts will, like, I think things will be really different and people will be acting really differently. Speaking of forecasts, what are your forecasts to this system you're describing which can learn as well as a human and subsequently, as a result, become superhuman?

[1:22:23](https://realalpha.app/v/aR20FWCCjAs/?t=4943) I think, like, 5 to 20. 5 to 20 years? so i just want to unroll your how you might see the world coming it's like we have a couple more years where these other companies are continuing the current approach and it stalls out and stalls out here meaning they earn no more than low hundreds of billions in revenue or how do you think about what stalling out means yeah i think they're i think it could i think it could stall out and I think stolen out will look like, it will all look very similar among all the different companies, something like this. I'm not sure because I think even with stolen out, I think these companies could make a stupendous revenue.

[1:23:10](https://realalpha.app/v/aR20FWCCjAs/?t=4990) Maybe not profits because they will need to work hard to differentiate each other from themselves, but revenue definitely. But something in your model implies that when the correct solution does emerge, there will be convergence between all the companies. And I'm curious why you think that's the case. Well, I was talking more about convergence on their largest strategies. I think eventual convergence on the technical approach is probably going to happen as well. But I was alluding to convergence to the largest strategies. What exactly is the thing that should be done? I just want to better understand how you see the future unrolling.

[1:23:46](https://realalpha.app/v/aR20FWCCjAs/?t=5026) So currently we have these different companies and you expect their approach to continue generating revenue, but not get to this human-like learner. So now we have these different forks of companies. We have you, we have thinking machines. There's a bunch of other labs. And maybe one of them figures out the correct approach. But then the release of their product makes it clear to other people how to do this thing. I think it won't be clear how to do it thing, but it will be clear that something different is possible. And that is information. And I think people will then be trying to figure out how that works.

[1:24:19](https://realalpha.app/v/aR20FWCCjAs/?t=5059) I do think though that one of the things that's that I think you know not addressed here not discussed is that with each increase in the AI's capabilities I think there will be some kind of changes but I don't know exactly which ones in how things are being done and so like I think it's going to be important yet I can't spell out what that is exactly and how are the by default you would expect the company that has the model company that has that model to be getting all these gains because they have the model that is learning how to do all has the skills and knowledge that it's building up in the world what is the reason to think that the benefits of that would be widely distributed and not just end up at whatever model company gets this continuous learning loop going first like i think that empirically what happened so So here is what I think is going to happen.

[1:25:17](https://realalpha.app/v/aR20FWCCjAs/?t=5117) Number one, I think empirically, let's look at how things have gone so far with the AIs of the past. So one company produced in advance and the other company scrambled and produced some similar things after some amount of time. And they started to compete in the market and push their push the prices down and so i think from the market perspective i think something similar will happen there as well even if someone it's okay we are talking about the good world by the way where what's the good world what's the good world where we have these powerful human-like learners that are also like and by the way maybe there's another thing we haven't discussed on the on the the spec of the super intelligent ai that i think is worth considering is that you make it narrow can be useful and narrow at the same time so you can have lots of narrow super intelligent ais but suppose you have many of them and you have some and you have some company that's producing a lot of um profits from it and then you have another company that comes in and starts to compete and And the way the competition is going to work is through specialization.

[1:26:42](https://realalpha.app/v/aR20FWCCjAs/?t=5202) I think what's going to happen is that the way competition loves specialization. And you see it in the market, you see it in evolution as well. So you're going to have lots of different niches. And you're going to have lots of different companies who are occupying different niches in this kind of world. where you might say, yeah, like one AI company is really quite a bit better at some area of really complicated economic activity. And a different company is better at another area. And the third company is really good at litigation. And that's the way you want to go to. But is this contradicted by what human-like learning implies?

[1:27:18](https://realalpha.app/v/aR20FWCCjAs/?t=5238) Is that like it can learn? It can, but you have accumulated learning. You have a big investment. You spent a lot of compute to become really, really, really good. Really phenomenal at this thing. and someone else spent a huge amount of computer and a huge amount of experience to get really, really good at some other thing. Right. You apply a lot of human learning to get there, but now you are at this high point where someone else would say, look, I don't want to start learning what you've learned to go through this. I guess that would require many different companies to begin at the human continual learning agent at the same time so that they can start their different research in different branches.

[1:27:57](https://realalpha.app/v/aR20FWCCjAs/?t=5277) But if one company... you know, gets that agent first or gets that learner first, it does then seem like, well, you know, like if you just think about every single job in the economy, you just have instance learning. Each one seems tractable for a company. Yeah, that's a valid argument. My strong intuition is that it's not how it's going to go. My strong intuition is that, yeah, like the argument says it will go this way. Yeah. But my strong intuition is that it will not go this way. That this is the, you know, in theory, there is no difference between theory and practice. In practice there is.

[1:28:37](https://realalpha.app/v/aR20FWCCjAs/?t=5317) And I think that's going to be one of those. A lot of people's models of recursive self-improvement literally explicitly state we will have a million Ilias in a server that are coming in with different ideas and this will lead to a super intelligence emerging very fast. Do you have some intuition about how parallelizable the thing you are doing is? What are the gains from making copies of Ilya? I don't know. I think there'll definitely be diminishing returns because you want people who think differently rather than the same. I think that if they were literal copies of me, I'm not sure how much more incremental value you'd get.

[1:29:17](https://realalpha.app/v/aR20FWCCjAs/?t=5357) I think that, but people who think differently, that's what you want. Why is it that it's been, if you look at different models, even released by totally different companies, trained on potentially non-overlapping data sets. It's actually crazy how similar LLMs are to each other. Maybe the data sets are not as non-overlapping as it seems. But there's some sense that it's like, even if an individual human might be less productive than the future AI, maybe there's something to the fact that human teams have more diversity than teams of AIs might have. But how do we elicit meaningful diversity among AI?

[1:29:53](https://realalpha.app/v/aR20FWCCjAs/?t=5393) So I think just raising the temperature just results in gibberish. I think you want something more like different scientists have different prejudices or different ideas. How do you get that kind of diversity among AI agents? So the reason there has been no diversity, I believe, is because of pre-training. All the pre-trained models are the same, pretty much, because they pre-trained on the same data. Now, RL and post-training is where some differentiation starts to emerge because different people come up with different RL training. Yeah. And then I've heard you hint in the past about self-play as a way to either get data or match agents to other agents of equivalent intelligence to kick off learning. How should we think about why there's no public proposals of this kind of thinking working with LLMs?

[1:30:46](https://realalpha.app/v/aR20FWCCjAs/?t=5446) I would say there are two things to say.

[1:30:49](https://realalpha.app/v/aR20FWCCjAs/?t=5449) **Ilya Sutskever:** I would say that the reason why I thought self-play was interesting is because it offered a way to create models using compute only without data.

[1:31:00](https://realalpha.app/v/aR20FWCCjAs/?t=5460) And if you think that data is the ultimate bottleneck, then using compute only is very interesting. So that's what makes it interesting. now the the thing is that self-play at least the way it was done in the past when you have agents which somehow compete with each other it's only good for developing a certain set of skills it is too narrow it's only good for like negotiation conflict certain social skills strategizing that kind of stuff. And so if you care about those skills, then self-play will be useful. Now, actually, I think that self-play did find a home, but just in a different form, in a different form. So things like debate, prove a verifier, you have some kind of an LLM as a judge, which is also incentivized to find mistakes in your work. You could say this is not exactly self-play, but this is, you know, a related adversarial setup that people are doing, I believe.

[1:32:04](https://realalpha.app/v/aR20FWCCjAs/?t=5524) And really self-lay is an example of, is a special case of more general competition between agents. The natural response to competition is to try to be different. And so if you were to put multiple agents and you tell them, you all need to work on some problem, and you're an agent, and you're inspecting what everyone else is working, you're going to say, well, if they're already taking this approach, it's not clear I should pursue it, I should pursue something differentiated. And so I think that something like this could also create an incentive for a diversity of approaches. Yeah. Final question. What is research taste? You're obviously the person in the world who is considered to have the best taste in doing research in AI. You were the co-author on many of the biggest, the biggest things that have happened in the history of deep learning from AlexNet to GPT-3 to so on.

[1:33:07](https://realalpha.app/v/aR20FWCCjAs/?t=5587) What is it that, how do you characterize how you come up with these ideas? I can answer, so I can comment on this for myself. I think different people do it differently. But one thing that guides me personally is an aesthetic of how AI should be by thinking about how people are. But thinking correctly. like it's very easy to think about how people are incorrectly but what does it mean to think about people correctly so i'll give you some examples the idea of the artificial neuron is directly inspired by the brain and it's a great idea why because you say sure the brain has all these different organs that's the folds but the folds probably don't matter why do we think that the neurons matter because there is many of them it kind of feels right. So you want the neuron. You want some kind of local learning rule that will change the connections. You want some local learning rule that will change the connections between the neurons. It feels plausible that the brain does it. The idea of the distributed representation. The idea that the brain responds to experience, our neural net should learn from experience, not response. The brain learns from experience, the neural net should learn from

[1:34:28](https://realalpha.app/v/aR20FWCCjAs/?t=5668) experience. And you kind of ask yourself, is something fundamental or not fundamental? How things should be? And I think that's been guiding me a fair bit, kind of thinking from multiple angles and looking for almost beauty, beauty, simplicity, ugliness. There's no room for ugliness. It's just beauty, simplicity, elegance, correct inspiration from the brain. And all of those things need to be present at the same time. And the more they are present, the more confident you can be in a top-down belief and then the top-down belief is the thing that sustains you when the

[1:35:02](https://realalpha.app/v/aR20FWCCjAs/?t=5702) **Ilya Sutskever:** experiments contradict you because if you just trust the data all the time well sometimes you can be doing a correct thing but there's a bug but you don't know that there is a bug how can

[1:35:11](https://realalpha.app/v/aR20FWCCjAs/?t=5711) you tell that there is a bug how do you know if you should keep debugging or you conclude it's the wrong direction well it's the top down well how should you can say the things have to be this way. Something like this has to work. Therefore, we got to keep going. That's the top down. And it's based on this like multifaceted beauty and inspiration by the brain. All right. We'll leave it there. Thank you so much. Thank you so much. All right. Appreciate it. That was great. Yeah. I enjoyed it. Yes, me too. Hey, everybody. I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it.

[1:35:49](https://realalpha.app/v/aR20FWCCjAs/?t=5749) It's also helpful if you leave a rating or a comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you can reach out at dwarkesh.com slash advertise. Otherwise, I'll see you on the next one.