Dwarkesh Podcast
2027 Intelligence Explosion: Month-by-Month Model — Scott Alexander & Daniel Kokotajlo
03 Apr 2025
Scott and Daniel break down every month from now until the 2027 intelligence explosion.Scott Alexander is author of the highly influential blogs Slate Star Codex and Astral Codex Ten. Daniel Kokotajlo resigned from OpenAI in 2024, rejecting a non-disparagement clause and risking millions in equity to speak out about AI safety.We discuss misaligned hive minds, Xi and Trump waking up, and automated Ilyas researching AI progress.I came in skeptical, but I learned a tremendous amount by bouncing my objections off of them. I highly recommend checking out their new scenario planning document, AI 2027Watch on Youtube; listen on Apple Podcasts or Spotify.----------Sponsors* WorkOS helps today’s top AI companies get enterprise-ready. OpenAI, Cursor, Perplexity, Anthropic and hundreds more use WorkOS to quickly integrate features required by enterprise buyers. To learn more about how you can make the leap to enterprise, visit workos.com* Jane Street likes to know what's going on inside the neural nets they use. They just released a black-box challenge for Dwarkesh listeners, and I had a blast trying it out. See if you have the skills to crack it at janestreet.com/dwarkesh* Scale’s Data Foundry gives major AI labs access to high-quality data to fuel post-training, including advanced reasoning capabilities. If you’re an AI researcher or engineer, learn about how Scale’s Data Foundry and research lab, SEAL, can help you go beyond the current frontier at scale.com/dwarkeshTo sponsor a future episode, visit dwarkesh.com/advertise.----------Timestamps(00:00:00) - AI 2027(00:06:56) - Forecasting 2025 and 2026(00:14:41) - Why LLMs aren't making discoveries(00:24:33) - Debating intelligence explosion(00:49:45) - Can superintelligence actually transform science?(01:16:54) - Cultural evolution vs superintelligence(01:24:05) - Mid-2027 branch point(01:32:30) - Race with China(01:44:47) - Nationalization vs private anarchy(02:03:22) - Misalignment(02:14:52) - UBI, AI advisors, & human future(02:23:00) - Factory farming for digital minds(02:26:52) - Daniel leaving OpenAI(02:35:15) - Scott's blogging advice Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Chapter 1: What is discussed at the start of this section?
Today, I have the great pleasure of chatting with Scott Alexander and Daniel Cocotello. Scott is, of course, the author of the blog Slate Star Codex, Astral Codex 10 Now. It's actually been, as you know, a big bucket list item of mine to get you on the podcast. So this is also the first podcast you've ever done, right? Yes. And then Daniel is the director of the AI Futures Project.
And you have both just launched today something called AI 2027. Yes. So what is this?
Yeah, AI 2027 is our scenario trying to forecast the next few years of AI progress. We're trying to do two things here. First of all is we just want to have a concrete scenario at all. So you have all these people, Sam Altman, Dario Amadei, Elon Musk saying, going to have AGI in three years, super intelligence in five years.
And people just think that's crazy because right now we have chatbots that's able to do like, A Google search, not much more than that in a lot of ways. And so people ask, how is it going to be AGI in three years? What we wanted to do is provide a story, provide the transitional fossils. So start right now.
go up to 2027 when there's AGI, 2028 when there's potentially superintelligence, show on a month-by-month level what happened. Kind of in fiction writing terms, make it feel earned. So that's the easy part. The hard part is we also want to be right. So we're trying to forecast how things are going to go, what speed they're going to go at,
We know that, in general, the median outcome for a forecast like this is being totally humiliated when everything goes completely differently. And if you read our scenario, you're definitely not going to expect us to be the exception to that trend. The thing that gives me optimism is Daniel, back in 2021, wrote kind of the prequel to this scenario called What 2026 Looks Like.
It's his forecast for the next five years of AI progress.
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Chapter 2: What is the purpose of the AI 2027 scenario?
He got it almost exactly right. Like, you should stop this podcast right now, and you should go and read this document. It's amazing. Kind of looks like you asked ChatGPT, summarize the past five years of AI progress, and you got something with like a couple of hallucinations, but basically well-intentioned and correct. So when Daniel said he was doing this sequel, I was very excited.
really wanted to see where it was going. It goes to some pretty crazy places, and I'm excited to talk about it more today.
I think you're hyping up a little bit too much. Yes, I do recommend people go read the old thing I did, which was a blog post. I think it got a bunch of stuff right, a bunch of stuff wrong, but overall held up pretty well and inspired me to try again and do a better version of it.
I think read the document and decide which of us is right.
Another related thing, too, is that it was going to... The original thing was not supposed to end in 2026. It was supposed to go all the way through the exciting stuff, right? Because everyone's talking about, like, what about AGI? What about superintelligence? Like, what would that even look like? So I was trying to sort of, like, step by step work my way from where we were at the time until...
things happen and then see what they look like. But I basically chickened out when I got to 2027 because things were starting to happen and the automation loop was starting to take off and it was just so confusing and there was so much uncertainty. So I basically just deleted the last chapter and published what I had up until that point and that was the blog post.
Okay, and then Scott, how did you get involved in this project?
I was asked to help with the writing, and I was already somewhat familiar with the people on the project, and many of them were kind of my heroes. So Daniel I knew both because I'd written a blog post about his opinions before. I knew about his What 2026 Looks Like, which was amazing. And also he had pretty recently made the national news for –
having, when he quit OpenAI, they told him he had to sign a non-disparagement agreement or they would claw back his stock options. And he refused, which they weren't prepared for. It started a major news story, a scandal that ended up with OpenAI agreeing that they were no longer going to subject employees to that restriction. So
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Chapter 3: How do Scott and Daniel forecast AI progress?
I've been thinking about this for like a decade, decade and a half now. And it just made it so much more concrete to have a specific story like, oh, yeah, that's why we're so worried about the arms race with China. Obviously, we would get an arms race with China in that situation. And like aside from just the people, getting to read the scenario really sold me.
This is something that needs to get out there more.
Yeah. Yeah. OK, now let's talk about this new forecast. Let's start because you do a month by month analysis of what's going to happen from here. So what is it that you expect in mid 2025 and end of 2025 in this forecast?
So beginning of the forecast mostly focuses on agents. So we think they're going to start with agency training, expand the time horizons, get coding going well. Our theory is that they are, to some degree consciously, to some degree accidentally, working towards this intelligence explosion. where the AIs themselves can start taking over some of the AI research, move faster.
So 2025, slightly better coding. 2026, slightly better agents, slightly better coding. And then we focus on, and we name the scenario after 2027, because that is when this starts to pay off. The intelligence explosion gets into full swing. The agents become good enough to help with, at the beginning, not really do, but help with some of the AI research.
So we introduced this idea called the R&D progress multiplier. So how many months of progress without the AIs do you get in one month of progress with all of these new AIs helping with the intelligence explosion? So 2027... We start with, I can't remember if it's literally start with or by March or something, a five times multiplier for algorithmic progress.
So we have like the stats tracked on the side of the story. Part of why we did it as a website is so that you can have these cool gadgets and widgets. And so as you read the story, the stats on the side automatically update. And so one of those stats is like the progress multiplier.
Another answer to the same question you asked is basically, 2026, nothing super interesting, or 2025, nothing super interesting happened. More or less similar trends to what we're seeing.
Computer use is totally solved, partially solved. How good is computer use by the end of 2025?
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Chapter 4: What are the implications of the intelligence explosion?
I agree, Robin Hanson in particular has been too pessimistic.
But he's a smart guy, you know? So I think that the aggregate opinion has been underestimating the pace of both technical progress and deployment. I agree that there have been plenty of people who have been more bullish than me and have been already proven wrong. But they're not being... Wait a second.
We don't have to guess about aggregate opinion. We can look at Metaculous. Metaculous, I think their timeline was 2040 back... No, it was like 2050 back in 2020. It gradually went down to like 2040, two or three years ago. Now it's at 2030, so it's barely ahead of us. Again, that may turn out to be wrong, but it does look like the Metaculans overall have been...
too pessimistic, thinking too long-term rather than too optimistic. And I think that's like the closest thing we have to a neutral aggregator where we're not cherry-picking things.
I had this interesting experience yesterday. We were having lunch with this senior AI researcher, probably makes on the order of like millions a month or something. And we were asking him, how much are the AIs helping you? And he said, in domains, which I understand well, and it's closer to autocomplete but more intense, There it's maybe saving me four to eight hours a week.
But then he says, in domains which I'm less familiar with, if I need to go wrangle up some hardware library or make some modification to the kernel or whatever, where I'm just like, I know less, that saves me on the order of 24 hours a week. now with like current models. What I found really surprising is that the help is bigger where it's less like autocomplete and more like a novel contribution.
It's like a more significant productivity improvement there.
Yeah, that is interesting. I imagine what's going on there is that a lot of the process when you're unfamiliar with a domain is like Googling around and learning more about the domain and language models are excellent because they've already read the whole internet and know all the details.
Isn't this a good opportunity to discuss a certain question I asked Dario that you responded to? What are you thinking of? Well, I asked this question where, as you say, they know all this stuff. I don't know if you saw this. I asked this question where I said, look, these models know all this stuff.
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Chapter 5: What role does alignment play in AI development?
So... I am the writer and the celebrity spokesperson for this scenario. I am the only person on the team who is not a genius forecaster. And maybe related to that, my PDoom is the lowest of anyone on the team. I'm more like 20%. I think that we, first of all, People are going to freak out when I say this.
Chapter 6: How do AIs balance power-seeking behavior?
I'm not completely convinced that we don't get something like alignment by default. I think that we're doing this bizarre and unfortunate thing of training the AI in multiple different directions simultaneously. We're telling it, succeed on tasks, which is going to make you a power seeker, but also don't seek power in these particular ways.
And in our scenario, we predict that this doesn't work and that the AI learns to seek power and then hide it. I am pretty agnostic as to exactly what happens. Like maybe it just learns both of these things in the right combination. I know there are many people who say that's very unlikely. I haven't yet had the discussion where that worldview makes it into my head consistently.
And then I also think we're going to be Involved in this race against time, we're going to be asking the AIs to solve alignment for us. The AIs are going to be solving alignment because they want to align. Even if they're misaligned, they want to align their successors. So they're going to be working on that. And we have kind of these two competing curves. Like, can we...
get the AI to give us a solution for alignment before our control of the AI fails so completely that they're either going to hide their solution from us or deceive us or screw us over in some other way. That's another thing where I don't even feel like I have any idea of the shape of those curves. I'm sure if it were Daniel or Eli, they would have already made like five supplements on this.
But for me, I'm just kind of, agnostic as to whether we get to that alignment solution, which in our scenario, I think we focus on mechanistic interpretability. Once we can really understand the weights of an AI on a deep level, then we have a lot of alignment techniques open up to us.
I don't really have a great sense of whether we get that before or after the AI has become completely uncontrollable. I mean, a big part of that relies on the things we're talking about. How smart are the labs? How carefully do they work on controlling the AI?
How long do they spend making sure the AI is actually under control and the alignment plan they gave us is actually correct rather than something they're trying to use to deceive us? All of those things I'm completely agnostic on, but that leaves a pretty big chunk of probability space where we just do okay. And I admit that my PDoom is literally just PDoom and not PDoom or oligarchy. So...
that 80% of scenarios where we survive contains a lot of really bad things that I'm not happy about, but I do think that we have a pretty good chance of surviving.
Let's talk about geopolitics next. So describe to me how you foresee the relationship between the government and the AI labs to proceed, how you expect that relationship in China to proceed, and how you expect the relationship between the US and China to proceed. Three simple questions. Yes, no, yes, no, yes, no.
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Chapter 7: What geopolitical dynamics affect AI development?
Eventually it reaches the point where the government is extremely impressed. In our scenario, that starts with cyber warfare. The government sees that these AIs are now as capable as the best human hackers that can be deployed at huge, humongous scale. So they become extremely interested and they discuss nationalizing the AI companies.
In our scenario, they never quite get all the way, but they're gradually bringing them closer and closer to the government orbit. Part of what they want is security because they know that if China steals some of this and they get these superhuman hackers. And part of what they want is just knowledge and control over what's going on.
So through our scenario, that process is getting further and further along until by the time that the government wakes up to the possibility of superintelligence, they're already pretty cozy with the AI companies. They already understand that superintelligence is kind of the key to power in the future.
And so they are starting to integrate some of the national security state with some of the leadership of the AI companies so that these AIs are... programmed to follow the commands of important people rather than just doing things on their own.
If I may add to that, so one thing by the government, I think what Scott meant is the executive branch, especially the White House. So we are depicting a sort of information asymmetry where like the judiciary is kind of out of the loop and the Congress is out of the loop and it's like mostly the executive branch that's involved.
Two, we're not depicting governments, like, ultimately ending up in total control at the ends. We're thinking that, like, there's an information asymmetry between the CEOs of these companies and the presidents, and they... It's alignment problems all the way down. Yeah, and so, for example, like... I'm not a lawyer.
I don't know the details about how this would work out, but I have a sort of high-level strategic picture of the fight between the White House and the CEO. And the strategic picture is basically the White House can sort of threaten, here's all these orders I could make, Defense Production Act, blah, blah, blah, blah, blah, blah.
I could do all this terrible stuff to you and basically disempower you and take control. And then the CEO can be like threatened back and be like, here's how we would fight it in the courts. Here's how we would fight it in the public. Here's all this stuff we would do. And after then they both do their posturing with all their threats.
Then they're like, okay, how about we have a contract that like, you know, instead of executing on all of our threats and having all these crazy fights in public, we'll just like come to a deal and then have a military contract that like sets out like who gets to call what shots in the company. And so that's what we depict happening is that sort of like
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Chapter 8: How can transparency influence AI safety?
So we were just getting lunch with a prominent Washington, D.C. political journalist, and he was making the point that when he talks to these Congress people, when he talks to political leaders, none of them are at all awake to the possibility even of stronger AI systems, let alone HEI, let alone superhuman intelligence. I think a lot of your...
forecast relies on at some point, not only the US president, but also Xi Jinping, wake up to the possibility of a super intelligence and the stakes involved there. Why think that even when you show Trump the remote worker demo, he's going to be like, oh, and therefore in 2028, there will be a super intelligence. Whoever controls that will be God Emperor forever. Maybe not that extreme.
You see what I'm saying? Like, why not? Why wouldn't he just be like, oh, there'll be a stronger remote worker in 2029, a better remote worker in 2031.
Well, to be clear, we are uncertain about this. But in our story, we depict this sort of intense wake-up happening over the course of 2027, mostly concurrently with the AI companies automating all of their R&D internally and having these fully autonomous agents that are like amazing autonomous hackers and stuff like that, but then also just like actually doing all the research.
And part of why we think this wake-up happens is because the company deliberately decides to wake up the president. And this is a, you could imagine running the scenario without not happening. You can imagine the company is trying to sort of keep the president in the dark. I do think that they could do that.
I think that if they like didn't want the president to wake up to what's going on, they might be able to achieve that.
Strategically, though, that would be quite risky for them because if they keep the president in the dark about the fact that they're building superintelligence and that they've actually completely automated their R&D and it's getting superhuman across the board, and then if the president finds out anyway somehow, perhaps because of a whistleblower, he might be very upset at them, and he might crack down really hard and just actually execute on all the threats and nationalize them and blah, blah, blah, blah, blah, blah, blah.
They kind of want him on their side, and to get him on their side, they have to make sure he's not surprised by... any of these crazy developments. And also, if they do get him on their side, they might be able to actually go faster. They might be able to get a lot of red tape waved and stuff like that.
And so we made the guess that early in 2027, the company would basically be like, we are going to deliberately wake up the president and scare the president with all of these demos of crazy stuff that could happen and then use that to lobby the president to help us go faster and to cut red tape and to maybe slow down our competitors a little bit and so forth.
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