New episode with my good friends Sholto Douglas & Trenton Bricken. Sholto focuses on scaling RL and Trenton researches mechanistic interpretability, both at Anthropic.We talk through what’s changed in the last year of AI research; the new RL regime and how far it can scale; how to trace a model’s thoughts; and how countries, workers, and students should prepare for AGI.See you next year for v3. Here’s last year’s episode, btw. Enjoy!Watch on YouTube; listen on Apple Podcasts or Spotify.----------SPONSORS* WorkOS ensures that AI companies like OpenAI and Anthropic don't have to spend engineering time building enterprise features like access controls or SSO. It’s not that they don't need these features; it's just that WorkOS gives them battle-tested APIs that they can use for auth, provisioning, and more. Start building today at workos.com.* Scale is building the infrastructure for safer, smarter AI. Scale’s Data Foundry gives major AI labs access to high-quality data to fuel post-training, while their public leaderboards help assess model capabilities. They also just released Scale Evaluation, a new tool that diagnoses model limitations. If you’re an AI researcher or engineer, learn how Scale can help you push the frontier at scale.com/dwarkesh.* Lighthouse is THE fastest immigration solution for the technology industry. They specialize in expert visas like the O-1A and EB-1A, and they’ve already helped companies like Cursor, Notion, and Replit navigate U.S. immigration. Explore which visa is right for you at lighthousehq.com/ref/Dwarkesh.To sponsor a future episode, visit dwarkesh.com/advertise.----------TIMESTAMPS(00:00:00) – How far can RL scale?(00:16:27) – Is continual learning a key bottleneck?(00:31:59) – Model self-awareness(00:50:32) – Taste and slop(01:00:51) – How soon to fully autonomous agents?(01:15:17) – Neuralese(01:18:55) – Inference compute will bottleneck AGI(01:23:01) – DeepSeek algorithmic improvements(01:37:42) – Why are LLMs ‘baby AGI’ but not AlphaZero?(01:45:38) – Mech interp(01:56:15) – How countries should prepare for AGI(02:10:26) – Automating white collar work(02:15:35) – Advice for students Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Chapter 1: What is discussed at the start of this section?
Okay, I'm joined again by my friends, Sholto Bricken.
Wait, fuck.
Did I do this last year? No, you named us differently, but we didn't have Sholto Bricken and Trenton Douglas.
Sholto Douglas and Trenton Bricken, who are now both at Anthropic. Yeah, let's go. Sholto is scaling RL. Trenton's still working on mechanistic interoperability. Welcome back. Happy to be here. Yeah, it's fun. What's changed since last year? We talked basically this month in 2024. Now we're in 2025. What's happened?
OK. So I think the biggest thing that's changed is RL and language models has finally worked. And this is manifested in we finally have proof of an algorithm that can give us expert human reliability and performance given the right feedback loop. And so I think this is only really being conclusively demonstrated in competitive programming and math, basically.
And so if you think of these two axes, one is the intellectual complexity of the task, and the other is the time horizon of which the task is being completed on. And I think we have proof that we can reach the peaks of intellectual complexity along many dimensions. But we haven't yet demonstrated long-running agentic performance.
And you're seeing the first stumbling steps of that now and should see much more conclusive evidence of that basically by the end of the year with real software engineering agents doing real work. And I think, Trenton, you're like,
experimenting with this at the moment yeah absolutely i mean the most public example people could go to today is claude plays pokemon right uh and seeing it struggle in a way that's like kind of painful to watch but each model generation gets further through the game uh and it seems more like a limitation of it being able to use uh memory system yeah than anything else yeah
I wish we had recorded predictions last year. We definitely should this year. Oh, yeah. Hold us accountable. Yeah.
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Chapter 2: What significant changes in AI research have occurred recently?
That's right. Would you have said that agents would be only this powerful as of last year?
I think this is roughly on track for where I expected with software engineering. I think I expected them to be a little bit better at computer use. Yeah. But I understand all the reasons for why that is, and I think that's well on track to be solved. It's just a temporary lapse.
And holding me accountable for my predictions next year, I really do think end of this year, sort of like this time next year, we have software engineering agents that can do close to a day's worth of work for a junior engineer or a couple of hours of quite competent independent work.
Chapter 3: How are reinforcement learning and language models scaling?
Yeah, that seems right to me. I think the distribution's pretty wonky, though. Yes. Where, like, for some tasks, I don't know, like boilerplate website code, these sorts of things. It can bang it out and save you a whole day.
Yeah, exactly. Yeah, I think that's right. I think last year you said that the thing that was holding them back was the extra nines of reliability. Mm-hmm. I don't know if that's the way you would still describe the way in which these software agents aren't able to do a full day of work but are able to help you out with a couple minutes.
Is it the extra nines that's really stopping you or is it something else? Yeah.
I think my description there was I think like in retrospect probably not what's limiting them. I think what we're seeing now is closer to lack of context. lack of ability to do complex, very multi-file changes and maybe scope of the change or scope of the task in some respects. They can cope with high intellectual complexity in a focused context with a really scoped problem.
But when something's a bit more amorphous or requires a lot of discovery and iteration with the environment, this kind of stuff, they struggle more. And so maybe the way I would define it now is the thing that's holding them back is if you can give it a good feedback loop for the thing that you want it to do, then it's pretty good at it. If you can't, then they struggle a bit.
And then for the audience, can you say more about what you mean by this feedback loop if they're not aware of what's happening in RL and so forth?
Yes. So the big thing that really worked over the last year is – Maybe broadly, the domain is called RL from verifiable rewards or something like this, where a clean reward signal.
So the initial unhoppling of language models was RL from human feedback, where typically it was something like pairwise feedback or something like this, and the outputs of the models became closer and closer to things that humans wanted. But this doesn't necessarily improve their performance at any level. like difficulty of problem domain, right?
Particularly as humans are actually quite bad judges of what a better answer is. Humans have things like length biases and so forth. So you need a signal of whether the model was correct in its output that is... that is like quite true, let's say. And so things like the correct answer to a math problem or unit tests, parsing, this kind of stuff.
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Chapter 4: What challenges do models face in complex tasks?
Yeah, exactly.
It's like a tug in the grain.
That's right, yeah, yeah, yeah.
So when I think about the way humans learn It seems like these models getting no signal from failure is quite different from if you try to do a math problem and you fail. It's actually even more useful often than like learning about math and the abstracts because, oh, you don't think so?
Only if you get feedback.
Yeah. Only if you get feedback. But I think there's a way in which like you actually give yourself feedback. You're like you fail and you notice where you failed. Only if you get feedback, I think. Yeah. At times.
You think so?
People have figured out new math, right? And they've done it by the fact that they get stuck somewhere. They're like, why am I getting stuck here? Let me think through this.
Whereas in the example, I mean, I'm not aware of what's at the frontier, but looking at open source implementations from DeepSeek or something, there's not this conscious process by which once you have failed, you learn from the particular way in which you failed to then backtrack and do your next things better. It's just pure gradient descent. And I wonder if that's a big limitation. Yeah.
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Chapter 5: How can AI reliably assist with tasks like tax preparation by 2026?
And there are just like so many obvious things. And even though the team is like six X since I first joined it in size, there's just like still never enough capacity to explore these things. Okay. By end of 2026, reliably do your taxes.
Reliably fill out your receipts and this kind of stuff, like for company expense reports and this kind of stuff. Absolutely.
But the whole thing which involves going through inbox, going through your like, clicking on Marina Bay or whatever, like hotel reservations and like, was the champagne a business expense? Asking for a friend. Yeah.
Yeah, one of your friends does need to ask someone who's got a chance.
Chapter 6: What are the implications of model self-awareness in AI?
My answer is still if someone cares about it. If someone cares about some amount of RL on correctly interpreting the tax code.
Wait, even by the end of 2026, the model just can't do things you're not explicitly training it to?
I think it will get the taxes wrong. It's like, okay, so if I went to you, And I was like, I want you to do everyone's taxes in America. What percentage of them are you going to fuck up?
I feel like I would, like, succeed at the median. And I'm, like, asking, like, for the median, would it succeed? You know what I mean? Yeah. Or I feel like I'm, like... I wouldn't fuck up in the way that, like, these models will fuck up in, like, the middle of 2026. I think they also might just fuck up in different ways.
Like, as a grad student, I fucked up my taxes. I, like, overpaid, like, quite a bit. Because there was some Social Security payment that was already covered that otherwise wasn't. And, like... I wonder if – I should almost test, like, would an LLM have made that mistake? Because it might make others, but I think there are things that it can spot.
Like, it would have no problem if I asked it to read through the entire tax code and then see what applied to me.
Sorry, the thing I would be able to do is, like, this is the thing I'm unsure about. Like, I'm bringing this to your attention. Can you just let me know if, like, you were actually working at this Airbnb or you were just hanging out or things like that, right?
And I guess I'm curious, will they have enough sort of awareness as they're doing tasks where they can bring to your attention the things where they feel they are unreliable at, et cetera?
By early 2026 or end of 2026? End of. The unreliability and incompetence stuff will be somewhat tricky to do this all the time. Yeah, interesting.
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Chapter 7: How should countries prepare for the impact of AGI?
You want to be able to do that dynamically.
Is that variable, so we already have variable compute per answer, right? Right, with like tokens. That's right, yeah. Will we have variable compute per token?
I mean, you can already think of models forever. People have been calling the residual stream and multiple layers like poor man's adaptive compute. We're like, if the model already knows the answer to something, it will compute that in the first few layers and then just pass it through.
So, yeah.
I mean, that's getting into the weeds. Right. Yeah.
The residual screamers is like this operating RAM. You're doing stuff to it. Right. It's like the mental model I think one takes away from interpretability work.
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Chapter 8: What advice do experts have for students pursuing careers in AI?
But is that, are we going to? It seems, I mean, it's an empirical question. I think it's somewhat likely, if only because inference is expensive. Producing tokens is expensive. And so there will be an incentive to one, use as little thinking as you need to give the answer. And two, if you're going to use thinking, use some complex compression.
I wonder if it will emerge more once we allow agents to talk to each other in ways where currently it's kind of trained more in isolation or with a human.
And there'll be like some selective pressure against it so long as the agents are working with humans because they'll want to sort of cooperate. But then like as agents begin to work more and more with each other, then that selective pressure like changes the other direction basically.
Although somebody would still have to make the conscious decision to do like end-to-end training for multiple agents to use the system of communication, right?
Sure.
Yeah, I mean, one scary thing, though, is like the way we render text, you can use hidden white space tokens that also encode information. That's true. And so you can imagine a world where it looks like the agent's reasoning and it's scratchpad harmlessly, but it's actually hiding a bunch of data.
Speaking of inference compute, I guess one thing that I think is not talked about enough is if you do live in the world that you're painting, that in a year or two, we have computer use agents that are doing like... actual jobs, you've totally automated large parts of software engineering, then these models are going to be incredibly valuable to use.
And the way you use them, obviously, is you need compute. Right now, there's 10 million H100 equivalents in the world. By 2028, there's going to be 100 million. But there's been estimates that an H100 has the same amount of flops as the human brain. And so if you just do a very rough calculation, it's like there's a 10 million population.
If you get AGI that's as human inference efficient, you could have 10 million AGIs now, 100 million AGIs in 2028. But... presumably you would want more. And then at that point, your AI compute is increasing, what, 2.5x or 2.25x every year right now. But at some point, like 2028, you hit wafer production limits, and that's a longer feedback loop before you can make new fabs or whatever.
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