Daniel Cocatello: The scary open secret in the AI industry right now is that it's possible that we'll end up essentially creating a new species that ends up ruling the world with a 70% chance that this goes horribly wrong like human extinction. That's one possibility. There's many more.

Host: It's quite chilling what you're saying.

Daniel Cocatello: Yeah, it's uh it gets me down sometimes. I basically told my wife like, let's not have any more kids. It's too uncertain. I don't think they'll ever join the workforce. Everybody should be afraid that their jobs are going to be lost. And I know this because I went to open air in 2022. What I did there was forecasting as to what the next couple years might look like. And unfortunately, most of the world is kind of asleep at the wheel and doesn't really realize what's going on with AI. So, I resigned.

Host: I read it somewhere that you lost $2 million for not signing an anti-disparagement clause, meaning you couldn't criticize the company.

Daniel Cocatello: Yes. For reasons I'm happy to get into. But the main thing I've learned is when I go talk to people at Anthropic and OpenAI about forecasting, they're like, "It's not going to take that long. You need to shorten them again. get them back to 2027 or 228 because these powerful CEOs, Ariel or Sam or Elon are racing each other to be in control of the most powerful AIs and are literally afraid that if the other guy gets there first, he might become dictator. I mean, Anthropic is on track to be the entire economy by 2030. But none of these people should be trusted with that much power. So, this is the most important thing happening in our lifetimes, probably in all of history, in fact. And it's very important that it go well. So, I think that there's a lot we can do to like steer things in a better direction. There's loads of benefits that we could get from AI if we do it right. And if we do solve the problems, then things could be absolutely amazing for everyone.

Host: Well, this report here in 2021, it was remarkably accurate. And then you just published this one.

Daniel Cocatello: Yeah. So, this is our new scenario.

Host: So, let's go through these slowly and one at a time.

Daniel Cocatello: I would be incredibly happy if all my predictions turn out to be wrong.

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Host: Daniel Cocatello, at the very heart of what you do, um, what is your mission and why? So what would you do if you thought that super intelligence was coming in a few years?

Daniel Cocatello: I guess it depends what the consequences were. Well, let's talk about it. So super intelligence, AIs that are better than the best humans at everything while also being faster and cheaper. Also able to operate robots that can do everything in the physical world that humans can do, but better, faster, and cheaper. If that really is coming in a few years, then we need to prepare and we need to think about how to make it go well instead of poorly. So that's sort of my answer is like I'm doing that to the best of my ability.

Host: So you believe it's coming in a few years?

Daniel Cocatello: Yes.

Host: How could you be so sure?

Daniel Cocatello: I spend a lot of time trying to forecast this sort of thing. My sort of median estimate 50% chance is currently in 2029. Maybe it'll slip to 28 28. It's possible that it'll take significantly longer, like maybe 10 years or something like that, but uh you know, for reasons I'm happy to get into, seems to me like it's probably happening by the end of the decade. What's less important is the the sense of how close we are. What's more important is the pace of the trends. Anthropic this time last year was making something like a billion dollars a year and now they're making something like $60 billion a year. So that's 60x growth in one year, which is extremely impressive even for very small startups. But for a company of their size, it might be the fastest growth in history. Um, we expect that rate of growth to slow down, but even if it slows down quite a lot, they're still on track to be, you know, the entire economy by 2030 or so.

Host: Why should the average person care?

Daniel Cocatello: The high level thing is absolutely everything is going to change for the whole world and including therefore for them and their families. Um could change for the better, could change for the worse depending on the details of how it's done. So for example, everyone could die you know um this is the classic loss of control scenario or one version of it. If we do build these super intelligences and we use them to automate all the jobs and we put them in the military and we, you know, have them giving advice to politicians and so forth, they will eventually have accumulated enough real world power that they don't need humans anymore and they're smarter than us, they're more strategic, etc. At that point, we sort of have to hope that they are virtuous, that they have, you know, the goals that we wanted them to have, the values that we wanted them to have, etc. And the sort of scary open secret in the AI industry right now is that right now that is kind of just a hope. It's not something that we can be at all confident in. And in fact there's lots of evidence and arguments that it we're not on track to achieve that. So there's lots of reason like current AIS for example will often lie uh to people or they will like you tell them to do something and they go do something else and then pretend that they did it right. So, it's an inherently difficult problem to make something that's super intelligent and also has the values and virtues that you want it to have. And it doesn't seem like we're on track to solve that problem. Also, it seems like the sort of problem that you could think you've solved when you haven't actually solved it, right? Uh that's a big reason why this is scary. So, for all those reasons, it's possible that we'll end up essentially creating a new species that ends up ruling the world instead of us. And then maybe we go the way of other extinct species in the past that were out competed by humans. That's one possibility. There's many more. Even if you're not worried about that and you think that the AI will be totally controlled, there's the question of who controls the AIS, right? When there's a couple corporations that have made these super intelligences and are using them to automate all the jobs. Well, that's a lot of power. You know, that's a lot of money. It's a lot of political power. They'll have the best strategists, the best advisers, you know, they'll think faster militarily. uh the countries that has these AIs will be able to absolutely wipe the floor with all the other countries. The AIS themselves it's it's kind of a single point of failure like central uh control system where you know the CEO of Enthropic Dario he coined this phrase the country of geniuses in the data data center that was his phrase to describe what they're trying to build. You know I think that's a little bit misleading. I think it would be more accurate to describe it as army of geniuses in the data center because it's not like it's a bunch of diverse different AIs, you know, living in their different parts of the data center. They're all copies of the same big

Dan Coatello: ...and they're owned by the company and so they all follow the orders given by the company, right? People should be asking questions of like who controls this army or these armies and what are they going to be doing with them? I think that we could very easily end up in a sort of uh a situation where some tiny group of people are essentially oligarchs or dictators. And ironically, both of these risks, the loss of control and the constitution of power are things that people in the industry have been thinking about for decades. Um even before the AI industry existed, you know, people thinking about AI were talking and writing about these things. And then part of the founding narrative or the founding myth of deep mind and open AAI and anthropic is these problems are real. So we need to get there first so that we can handle it responsibly. Those are I think the big two reasons. But then I can go on there's lots more reasons as well. So one thing is you know World War II geopolitical conflict. Um if AI does in fact get incredibly powerful that's going to change the balance of power between nations. That's going to disrupt a lot of things. That puts us at increased risk of crisis more generally, right? Another one, what about those jobs? You you're going to lose your taxi job, but not just the taxi driver, everybody pretty much. Um there might be a few exceptions like people whose jobs for legal reasons are only allowed to be done by humans, but for the most part, everybody should be afraid that their jobs are going to be lost even if we manage to avoid all the other problems. Right?

Host: this narrative has started to emerge and I've had several interviews on this show where I've interviewed people who are very very scared and anxious about AI and these are people that have worked in the industry for sometimes decades. Yeah. Um the counternarrative coming over the hill is that this is doomerism that these people are for whatever reason just trying to scare people and that they don't really understand what they're talking about. How do you respond to that sort of counternarrative? And you must have seen this emerging yourself especially from people who stand to benefit dare I say.

Dan Coatello: Yeah, exactly. this counter narrative is fairly recent and it's been pushed by the people who stand to benefit um from it and it's not true. Like these these concerns have been around for decades since before the AI industry existed. They're actually pretty reasonable concerns. Like if you take the companies at their word and imagine that they are in fact going to build super intelligence. Well, it raises a lot of questions like who's going to control it? Will anybody control it? What about the jobs? You know, like these are just kind of obvious implications to be thinking about and worrying about.

Host: Who are you and what's your story?

Dan Coatello: My name is Dan Coatello. Um, I currently run the AI Futures Project, which is a small nonprofit that mostly focuses on forecasting the future of AI. Before that, I worked at OpenAI.

Host: AI forecasting.

Dan Coatello: Yeah. So think about how like you know industry analysts who work for hedge funds and stuff will make these forecasts of like here's you know how many cars Tesla will be selling 5 years from now or like here's what the price of electricity will be in 2 years right that's forecasting I was doing that but specifically focused on AI the reason I was doing it is because it's incredibly important to to see where this is all headed

Host: why did you go to open AI what did you do there what did you observe while you were there and how did Did it change your perspective on the future of AI but also I guess open AI as a company and for anybody that doesn't know OpenAI are the company that produced chatbt?

Dan Coatello: Yeah, so I went to OpenAI in 2022. Uh a large part of what I did there was more forecasting. AI 2027 is a scenario that you may have heard of. I did like smaller, you know, lower effort versions of them internally for just internal circulation of like here's some guesses as to what the next couple years might look like. I also worked on evaluations for dangerous capabilities. So, you know, trying to measure the AI's cyber abilities or persuasion abilities or situational awareness. And I also briefly was on a uh a capabilities team doing reinforcement learning to create agents. AI is in fact getting uh a lot better and I can say more about why you know scaling laws um deep neural nets bigger trained on more data become more efficient, more competent at those things. I also became a bit more disillusioned with the AI industry. So, OpenAI, Anthropic, and Deep Mind all had these sort of founding narratives of like, yes, these risks are real, but we've thought about them and we're going to try to handle them responsibly. And that's why it's important for us to keep doing what we're doing. And I increasingly came to think that these were rationalizations to justify what they were doing rather than sort of like deeply guiding their actual behavior and that when push comes to shove, they'll follow their incentives rather than do what's actually good.

Host: So you're inside Open AI at the time and you start to believe that they're following commercial incentives versus the I guess social in or societal incentives that they founded themselves on.

Dan Coatello: Sort of. I mean, I wouldn't actually describe it as commercial incentives. I think I would describe it as um power seeking incentives. So, like it's true that the companies care a lot about making a lot of money, but especially at the very top of these companies, like the leaders, they understand that this is about more than just money. You know, there are these emails that came up in, you know, the the lawsuit between Musk and and um OpenAI. A bunch of emails were surfaced in that lawsuit, which you can go read. And in some of them, the founders of OpenAI were talking back in like 2017 about how the reason why we made OpenAI was because we were worried that Demos at Google was going to become dictator with AGI. Even back then, they were this is obviously about more than just money. Like these these powerful CEOs are literally afraid that if the other guy gets there first, he might become dictator. And they don't trust each other. And so that's why they are racing as hard as they can so that they're the ones who get there first, so to speak.

Host: Have you met Sam Alman?

Dan Coatello: Yeah.

Host: And did did that shape your opinion of his incentives or what why he's doing what he's doing? Cuz there's a lot, you know, speculated about what his incentives are. I mean, his most recent narrative says uh for the good of humanity. I think that's what

Dan Coatello: Yeah. I mean, I think the main thing I've learned is don't pay attention to the narratives, you know, like uh what they say to one person is just different from what they can say to some other person at the same time. And what they say in public is a third thing entirely. I think you should judge people by their actions, not by their words.

Host: And why are you no longer at OpenAI?

Dan Coatello: Largely for the reason that I mentioned. So I became gradually disillusioned with how the company was going to behave. For example, when I first joined in 2022, at least the people I talked to, my colleagues at the company, there was this general sense of like, of course, we wouldn't actually just build super intelligence as soon as possible. Once we started getting really close, like once we started getting to AIS that could maybe automate the AI research process, we would pause and figure out how to make it safe. That's cuz we're the good guys. And that's obviously the safe thing you should do rather than just going full speed ahead. But we're worried about other people who might not pause. You know, our competitors, Google for example. And so that's why we need to be in the lead so that we have that room to do the safe stuff, right? That was sort of like a thing that seemed like maybe like the median position or something among the colleagues I talked to when I was there when I started, including people like Sam, you know, including the leadership. And then by the time I left, I was like, "Oh man, they're really not going to do that, are they?" Like like they they've sort of, you know, partly because this has

Guest: become more politicized and they've become bigger and under more scrutiny, people have started asking like, "Why are you doing this in the first place if it's so risky?" And so they've pivoted their narrative to being more like actually it's not that risky, you know? Um, and so yeah, I mean it seems like they're just going to keep going roughly as fast as they can and hope that they can figure it out on the way.

Interviewer: How did your time at OpenAI come to an end?

Guest: Uh, I resigned in 2024. I had a nice goodbye party.

Interviewer: What were the reasons you gave for quitting OpenAI?

Guest: I thought that we were rationalizing too much and that we needed to think more about what would actually be good for the world. Um, I wanted more freedom to publish. So at OpenAI as it became a bigger company it became more of a normal tech company with incentives and you know a PR department and things like that and so it started becoming more difficult to um to publish the sort of research that I was doing for example those scenarios that I mentioned couldn't uh couldn't publish those right they're just for internal use I thought that that was a shame because right now most of the world is kind of asleep at the wheel and doesn't really realize what's going on with AI and doesn't really realize what's coming in the pipeline a couple years from now and the companies aren't really incentivized to tell people that much about it. I mean they say some vague stuff in a sort of hypy way but um you know well they didn't want me to publish the scenario for example laying out like here's how things might actually look.

Interviewer: I'm just I'm super curious as to what it's like being in a company like that when they you know chat GBT3 is released. You were there at that time right? um which was a moment where I think the whole world stood up and realized that this technology was powerful.

Guest: Mhm. Yeah.

Interviewer: Um and the conversation really began from a society level. Um the company starts growing super quickly. Quicker than I think anybody could ever have imagined. And what what was it like inside there? What did you see change um over over that period of time?

Guest: I remember one all hands meeting where Ilia said something like

Interviewer: Ilia being

Guest: Ilia Sgver who was um head of research at that time. He said something like, "Okay, now the world is starting to pay attention. Each of you is going to be the most popular person at every party uh for the next year. Don't let it get to your head. Focus on the mission. Got to build AGI." The company grew a lot. It already wasn't really feeling like a nonprofit when I joined, but it definitely didn't feel like a nonprofit by the time I left. Um, lots of new people came in. Ironically, the like amount of conversation about super intelligence and the implications of super intelligence arguably sort of went down over time due to this growth, right? So, because the company would like double and then double again and then double again, all these new people were coming in from other parts of the tech industry who hadn't really been thinking about these things and were attracted by the high salaries.

Interviewer: You lost $2 million for not signing an anti-disparagement clause, which would mean you could speak, you couldn't criticize the company.

Guest: Uh, yes. Well, so, um, I got to keep the money.

Interviewer: Oh, you got to keep

Guest: So, what happened was after I had left, said my goodbyes, etc., um, I got the the exit paperwork, and it included this clause that said you basically have to agree not to criticize the company again. Um, and also a clause saying, you can't tell anyone about this. And so I thought that was kind of rich coming from a nonprofit that's supposed to be, you know, for the benefit of all humanity. So I didn't sign it. And if you don't sign, you don't get to keep your equity. So your compensation, you know, what what they pay you is a bunch of money and then also a bunch of stock basically. But then they had this stuff in the contract that they get to yank back your your stock if you don't sign this thing. Um, and my wife and I, you know, were uh upset about this. We talked about it for like a month or two, consulted some lawyers, um, and then ultimately decided to just refuse to sign,

Interviewer: which would mean you lost, you would have lost $2 million.

Guest: That's right. Which was like 80% of our net worth at the time. Um, fortunately, uh, it didn't go the way we expected. It blew up basically on the internet. Like when people heard that that we had done this and that we had said no, it became like this huge scandal. Employees at the company started like asking questions in Slack and like asking leadership like, "Wait, what? Like why are you going to take away our equity? What is this?" You know, cuz a lot of people hadn't really noticed this before. It had been whispered about, but it hadn't been sort of like a thing that most employees knew about. Um, and so they backtracked and they said, "Never mind. Never mind. We'll change the paperwork. You can keep the equity. It's fine." And Samman came out and said he was embarrassed that he didn't realize this was

Interviewer: Yeah, he had no idea apparently. You don't believe him?

Guest: No, I think he probably knew. And if he didn't know, then people close to him probably did, such as his head lawyer.

Interviewer: Why did you decide not to take the $2 million? I mean, most people would have. I think

Guest: it's true. Most people would have and most people did. And you know, money is nice, but like it's not the only thing. You know, sometimes it's good to take a stand on principle.

Guest: I I keep mentioning super intelligence. Perhaps I should say more about like the the sequence of events that the companies are planning to do. So, right now they're focusing on automating coding. They're taking their AIS, they're making them bigger, they're training them for longer, and they're especially focusing the training on getting them to be good at autonomously writing and editing code because uh that will help the companies go faster, right? Right? If they can automate the code, then they can do their own work better and faster and accelerate progress. The next step, which they've already begun, is to look at the rest of the research process as well. Coming up with ideas, um, analyzing experiments, communicating those results, all the other parts of of the research process, they're trying to figure out how to train AIs to be good at those as well, so that they can have AIs do the entire thing autonomously.

Interviewer: When you say do the entire thing, what do you mean do the entire thing?

Guest: So like Anthropic and OpenAI in particular are trying to automate themselves. Like they're trying to make it the case that um they don't really need human employees anymore. Uh they just have a giant army of AIS that's turnurning away doing all this autonomous research to make better AIs to train the new AIs, put them in charge so they can make even better AIs and so forth. And of course, not just not all just happening internally, but also like interfacing with the world, right? Like going out and talking to people, collecting the data, setting up the training environments, doing the business deals, and so forth. Like they're they're trying to automate all of that. The reason why they're doing this is because they're trying to get to a position where they have AIs that are superhuman at everything, super intelligence, and they're trying to get there before their competitors do.

Guest: Needless to say, this is incredibly dangerous, I would say. You know, and in addition to being dangerous, it's a power grab, right? Like, if they actually succeed at this, then they'll be sitting on top of this army of superhuman AIs that will give them immense leverage over all sorts of other actors in the economy in so far as they can work out something with the president and, you know, integrate it into the military or whatever, then that would give the US immense hard power over all other countries, right? Obviously, nobody knows exactly when this is happening, but a very disquing thing has happened over the last year to me, which is that when we published 2027, people were generally of the opinion that my timelines were too short and that like probably it would take more than 2027 until we

Guest: ...got to the sort of events that I was just mentioning. you know, recursive self-improvement, AI is automating the whole research process, super intelligence. These these types of milestones um they happen in 2027 in AI 2027,

Host: which is this research paper you published.

Guest: That's right. It's it's a scenario forecast that sort of lays out like month by month a possible future trajectory. There was sort of like at the time that we started writing it was my best guess as to what would actually happen. Obviously, there's lots of uncertainty, but you know, I I I thought it's valuable to make a concrete guess just to sort of see what it might look like. And at the time we were writing this, a lot of my friends in the AI industry and in nonprofits and so forth that work on AI, a lot of people were saying like, "Yeah, that stuff's going to happen, but like it'll probably take a couple years longer than you think." And now it's more 50/50. Especially when I go talk to people at Anthropica and OpenAI, they're often like, "Yeah, no, 2027, that's basically what's going to happen, just like you wrote. Why did you why did you become why did you update your timelines?" Oh, yeah. Context for this is um after after writing 2027, I shifted my timelines to be a little bit more conservative. So, at the time that we published, my 50% mark was in 2028, not in 2027. And then after we published progress just seemed like it was going a bit slower and so I updated to 2030 which is you know it still could happen sooner could happen later 2030. Um but now when I talk to people in the companies they're like it's not going to take that long. They're like oh you need to shorten them again like get them back to 2027 or 2028 you know. Um so that's a bit disquing. Um, again, don't know how long it's going to take, but this is the stated plans of the AI companies is to do this incredibly dangerous thing, and they think that they're just a few years away.

Host: So, you wrote this um report here, what 2026 looks like, and you wrote this in 2021, and it was remarkably accurate, helped make a name for yourself amongst um amongst everybody in AI. And I Which one was it that JD Vance, the vice president, read? I think it was this one, wasn't it? Yeah, this one. Um and then so then you published this one AI 2027 and this was published I believe in 2025.

Guest: Uh yes that's right April.

Host: Yeah. What were you forecasting in here? What is what are the key things that you said in here for people that haven't read it?

Guest: The high level version of it is they automate the coding then they automate the rest of the research process. Then the pace of progress accelerates dramatically. They get the super intelligence. They're working with the government who specifically the president the executive branch naturally wants to control this technology and otherwise wants to use it to beat China and integrate into military and so forth. By this point it's sort of doing basically all the work itself. I mean it's it's super intelligent. So it's coming up with all these great ideas for how to integrate itself into everything and all these new technologies it's invented and so forth and uh because of the race dynamics and because of the profit motive they end up deploying it everywhere and it builds robot factories. They build more robots. to build more robot factories etc. transforms the world entirely and then at some point it has enough power it meaning the AIs have enough power that they don't have to pretend to to be aligned anymore right um then they stop listening to orders that's the race ending of AI27 we also wrote a sort of different branch which is the slowdown ending which was intended to sort of illustrate the concentration of power issues um that I mentioned previously. So, what if hypothetically the alignment issues get sorted out sufficiently quickly? Like what if it turns out that like it's not too hard with two months of slowdown, we can figure out how to make the AIS robustly do what we want um and have the values that we want them to have. So, that's one possible branch. And in that branch, uh it looks pretty similar. You know, they take the jobs, beat China, etc. Um, but instead of the AIS ultimately killing everyone, they create this sort of amazing utopia. But the amazing utopia is whatever the people who control the AIS wanted it to be, right? And so that would be a very small group of people like the president, some CEOs, etc.

Host: There should be a button just down below here. And if it says subscribed, you're already subscribed. If it says subscriber, that means you're not yet. And if you're not subscribed, please could you do us a favor and hit that button? It helps the show more than you know. And according to the algorithm, you're someone that watches our show, but you haven't yet hit that button. Thank you so much. Is there any possibility, do you think, that we never get to this thing called AGI? And and how do we distinguish AGI from this term super intelligence? What's the difference?

Guest: Yeah, so the difference is that AGI is a more vague uh and weak term. So super intelligence is a bit more precisely defined. It's better than the best humans at everything, faster and cheaper. Um, AGI is more like it stands for artificial general intelligence, which means AI is that can do things in general rather than like some specific task.

Host: Yeah.

Guest: And so arguably we've already achieved AGI, right? If you use cloud code or something like that, it's like it can do a lot of stuff. It's it's almost kind of like a little employee that you can like have go do stuff. So it's it is quite general. It's not maximally general though. It can't do everything. Whereas super intelligence by definition can do all the things that a human can do but better.

Host: And how does this sort of overlap with robotics? Because obviously we're seeing this huge robotics boom at the moment. There are some real world things that humans can still do because these AIs are still stunk on my computer.

Guest: The way people talk about this is that they basically just say we've achieved super intelligence for cognitive tasks. Then you can talk about like full super intelligence that can do this physical stuff.

Host: And are we going to get there? Are we going to get there with both?

Guest: I think so. I mean again this is not something that we can be certain about. Um you asked like is it possible we'll never get there? Yes, it's possible we'll never get there. I don't think it's likely though. I think that there's nothing sort of like magical about the human brain. It's, you know, um it's just a bunch of neurons. It is possible for a digital system to do similar functions in the same way that like, you know, a plane can fly just like a bird. Not in the same way as a bird necessarily, like it doesn't have it's not flying in the same way that a bird flies, but it flies, you know.

Host: Um so, so it does seem like yeah, like

Guest: seems possible.

Host: You've written all these, you know, these research reports. You're working on another one that will be released um likely on the 9th of July. You have worked inside OpenAI. You then quit OpenAI because you were concerned about what was going on there and about the future of the industry. You know more than I do. Are you optimistic about the future or pessimistic? Are we heading to a bad place if things don't change? Um based on everything that you know,

Guest: I think we are headed to a bad place if things don't change. Um I'm not confident in that. I would say something like 70%, it's very very hard to predict of course, but yeah, it seems like the current default path is heading towards a very very scary place.

Host: How do you contend with that personally and emotionally?

Guest: Um, it's rough. I mean, I think it it's the sort of thing that like gets me down on a regular basis, but also I've been dealing with this for so many years now that I've sort of gotten used to it, if that makes sense. Um, yeah. Yeah. I I'll put it this way. I would be incredibly happy if all my predictions turn out to be wrong and uh an AI hits the wall, for example.

Host: It gets you down on a regular basis.

Guest: I used to be known as a pretty chipper and optimistic person. But um in 2020 my AI

Speaker A: timelines predictions started collapsing due to GPT3 and the scaling laws papers and um the bioankers report which I can talk about if you're interested but basically some events happened in 2020 that convinced me that actually this stuff was like quite plausibly coming by the end of the decade and humanity is very obviously not ready for this you know in a whole bunch of different ways and so that's obviously very scary

Speaker B: and that's an extremely scary world because of all things you've said but but again because of this recursive self-improvement where AIs can train themselves and at such point we're starting to lose hold of what's going on here. I mean the AIS are already training themselves to be clear. It's more like closing the entire research loop, right? Doing everything.

Speaker A: Yeah. Like right right now a lot of the training data is generated by AIS. A lot of the reinforcement like the grading that happens out of positive and negative reinforcement is itself done by AI.

Speaker B: Can you explain that in layman's terms for

Speaker A: Yeah. So an important thing for everybody to understand is that modern AI systems are not software in the normal sense. I mean they are technically software but they're not lines of code you know it's not like some engineers at anthropic went and wrote lines of code that basically says like you know when the user asks for this type of thing then go do this type of thing for this many steps or whatever. There's nothing like that. Instead it's a neural net. You know

Speaker B: what's that?

Speaker A: Well think about how the brain is a bunch of neurons connected to each other.

Speaker B: Yeah.

Speaker A: That are firing um signals back and forth. The brain learns over time the types of patterns of firing that caused success that caused a dopamine rush or various other types of feedback get reinforced and fire more often. And the types of patterns that caused failure like touching a hot stove get anti-reinforced, get, you know, um, destroyed so that they fire less often. And as a result of all of that, you over the course of years learn to act in the world and you learn all sorts of skills and you learn world models. You learn like beliefs about the world and you can sort of like mentally simulate how it's going and stuff like that. So artificial neurallets are like that except artificial. So it's it starts off as a giant tangled spaghetti mess of randomly generated uh artificial connections called parameters. these days there might be something like 10 trillion parameters uh in the biggest AIS. So it starts off randomly generated. So, it's of course completely useless. Like if you give it some input, it'll just produce gibberish as an output. But then they train it. And they start with pre-training, which is where you give it a bunch of internet text, and you show it the first piece of text, and you put that in as the input, and then it gives a gibberish output, and then you positively or negatively reinforce it based on how accurate that output was at predicting the next piece of text. Um, so it's basically playing this game of like predict the next word.

Speaker B: Isn't that how it happens with babies? I had a I think I had a neuroscientist tell me that babies have more neural connections um than adults. And yeah, it says yeah, toddlers have twice as many neural connections as adults. And they I guess they whittle down through reinforcement.

Speaker A: Yep.

Speaker B: We have more pathways when we're younger and just like the process of training an AI, we're trained down to like remove the ones that aren't useful and build up on the ones that are.

Speaker A: Yeah. It's both pruning and strengthening. Okay, it seems like in humans it's actually more pruning than strengthening, but it's both. Uh, and in AI it's the same thing. It's both. So the first portion of training is where they train the AI to predict text, which is kind of like training it to read. Um, and it a similar thing does happen in humans. So basically the the random tangle gradually takes shape and gradually sort of coaleses into more useful circuitry that has stored lots of facts about the world and has stored lots of skills for how to you know process information and transform it and then produce predictions. That's just the first step. After they do the pre-training then they try to teach it more useful skills besides just predicting text. And so, you know, by the end of the process, they've thrown lots of coding problems at it and they've said like, "Here's a coding problem. Go. Here's a coding problem. Here's an environment. You have access to this virtual computer. Here's like the codebase you're working with. You can write code. You can edit the code. You can run the code. You can read it. You can use the internet. Go." And it does that for a while. And then based on how successful it is, reinforcement happens. and they have thousands maybe millions of examples of coding problems like that that they train it on and that's why they're so good at coding now.

Speaker B: So what does super intelligence look like in this regard? Is it just more of these connections and how would they get more connections? Can you explain that to me like on

Speaker A: So there's different AI models, right? So there's like you know GPT3 and GPT4 and GPD 4.5 and GPD5 and GPD 5.5 and 5.6, right? Sometimes they're just the same previous model but with extra training. Mhm. Sometimes there are a whole new model that's been trained from scratch, including starting the whole pre-training process again. Over the last couple years, they've done several new rounds of starting over from scratch. And typically, when they start over from scratch, they make the whole thing bigger, the artificial brain much bigger.

Speaker B: Okay.

Speaker A: Right now, they're at something like 10 trillion parameters. Back in 2020, um it was more like 175 billion.

Speaker B: So, we've grown like two orders of magnitude uh in six years.

Speaker A: Two orders of magnitude. Yeah, like two 10 x's, so 100x, right? So that process is continuing. Um, they're also improving the algorithms themselves. So they're not literally just the same type of AI, but bigger. They've also come up with all sorts of ideas for how to change the structure of the of the connections and the neurons and so forth and change the like reinforcement algorithms that they're using and to change the training data that they're training on. All sorts of tweaks that have made this whole thing more efficient. We're literally building a brain basically. Yeah. As they make more brains, they're getting better at making they're making them bigger and making them more efficient and so forth.

Speaker B: And it's literally modeled on the brain, like the way it works, right?

Speaker A: It's it's certainly heavily inspired by the brain, but I I shouldn't overstate the analogy. Like there's lots of differences, too. So, for example, the transformer architecture um

Speaker B: which is

Speaker A: which is the architecture that they use for for these LLMs, uh is not really recurrent. So the information sort of flows one way rather than allowing all these sort of little loops on the inside. Also the the back propagation algorithm is different from the sort of um learning that naturally happens in human brain. So there are some differences but yes like broadly speaking uh we are sort of making artificial brains. It's kind of like for brains what like a plane is for a bird.

Speaker B: Mhm. That's a really good analogy. Yeah,

Speaker A: that that analogy helped me think through a bunch of questions people often ask about AI when they said can it be creative but actually that analogy kind of helps me understand that actually that maybe that's not the question it's can it produce something that you would consider to be creative because creativity is people think of it as like a process but actually it's it's judged based on the output isn't it? I mean, you you can get philosophical about like, is it truly creativity that they have, but you can also be like, well, I mean, just look at all the stuff they're accomplishing, you know, and uh it seems like they're going to be accomplishing a lot more in the near future.

Speaker B: Yeah, I do I I asked the question about how this weighs on you personally because I can I can sense that you're actually personally bothered.

Speaker A: I mean, I think the situation is crazy. Like, first of all, it's very exciting. Like, AI is really

Speaker A: fascinating and interesting stuff. I've been following the field for more than a decade now. Um, I've been part of it uh for some years and um it's really cool, really interesting and it's really fun to think about what's going on inside these artificial brains and why they are the way that they are. And it's really cool to see all the applications of this technology out in the world. But it really seems like we're on a pretty scary path. And the more you think about it, the more worried you get. And you know, in stories it always ends well, but this is real life. And I I think we have to sort of stare reality in the face and tell it and realize that like it might not actually end well, you know.

Speaker B: Were there any recent dare I say I was going to say Eureka moments, but paradigm shifting moments where even your own sort of mental model of what's going on here and how this is going to look were changed for better or for worse.

Speaker A: For better or for worse and probably for worse, things are kind of on track for AI 2027. There are a few things that have been different. not exactly like paradigm shift differences but like there have been some differences from what we expected at the time we wrote this. So the government has actually got involved faster than we expected and has been more aggressive than we expected. So the export controls on mythos being uh the biggest example and also threatening anthropic with uh being destroyed by the defense production act. Um

Speaker B: another thing that's been surprising to us is that anthropic in particular has gone from second place to first place in the sort of in the race basically. Why do you think that happened? Because it seemed like chat Gvt were out front and clear as it relates to OpenAI were out front and clear, but suddenly Anthropic have uh lapped them.

Speaker A: Yeah, I mean I guess they have um probably higher talent density um and better strategy, but not by a lot, but enough to make the difference.

Speaker B: Why do you think they have more talent?

Speaker A: Well, they don't have more compute. Like what are the inputs, right? Like they're in the lead now. They used to be behind. What are the possible explanations for this? Well, it could have been that they had more resources, like more compute, more money. But that's not true. They have less resources and less money, right? So then I guess talent is is the next best alternative. You could maybe say strategy,

Speaker B: some combination of those things. Yeah.

Speaker A: Something that wasn't just like the amount of resources they had.

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Speaker B: Uh, a friend of mine who knows some of these people sat me down once upon a time in London. He's actually said this a few times to me, but I remember one particular conversation where he says that some of these AI CEOs predict the probability of extinction at being I think he said 7%. I don't know why I have that number in my head, but I remember it being less than 10%. And the point he was making to me was that even if it was 1%, like if there was a 100 buttons on this table now

Speaker A: Yeah.

Speaker B: and one of them would end the world. Would I dare press any of them? You know, um,

Speaker A: no,

Speaker B: I wouldn't press any of them. But he made the case to me that these AI CEOs are very smart and they understand super intelligence and that they think actually if there was a 100 buttons on this table right now, maybe 10 of them could end the world. I've heard you say, I think it was on the the the Daily Show, the interview did, you said that you think there's a 70% chance of human extinction due to AI.

Speaker A: I wouldn't say human extinction exactly. I would say something like 70% chance that this goes horribly wrong like human extinction. But that's just one of several possibilities. But yeah, basically like for example, possibly the AI take over and then don't actually kill everyone. You know, maybe they do something else. Like just just because they've taken over doesn't mean they're definitely going to kill us, right? They might, but they could do something else. So that's what that's that's why I don't usually say like 70% chance of like actual human extinction, but 70% chance of like something like AI is taking over some some sort of very big catastrophe like that that could lead to human.

Speaker B: I guess I got two points there, which is you've been around these CEOs. I mean, you've worked for Sam Alman at OpenAI before you quit. Do you think that they think there's a chance of human extinction?

Speaker A: Yes. But I think that an important thing to understand is that like people sort of believe what they need to believe in order to think that they're good people and that they need to keep doing what they're doing. This is what rationalization is. And so I think that the tech CEOs have like genuinely convinced themselves that like probably things are going to be fine and that the way to make things fine is for them to keep doing what they're doing. And like they need to like make sure that like you know Sam needs to make Sam's probably thinking like can't let Dario or Elon get there first. You know I know Dario is thinking Sam can't get there first. Elon's thinking that like you know they they they've all probably convinced themselves that like oh yeah like maybe it'll go horribly wrong but like probably it's going to be fine and probably, you know, I should be the one in charge.

Speaker B: It appears to me that Anthropic are the only ones that are talking about the potential chance of extinction or a catastrophic event or um the down the real downside still. They seem to be the only ones that are still publishing on it and now they're actually becoming the enemy in many respects of the the tech industry in San Francisco. I'm watching a lot of interviews and it's everyone's attacking Dario because he's saying listen things could go bad. They're calling him a doomer and questioning his incentives. Even with Mythos, which is a a claude model that they started to warn the

Interviewer: world about, again, he is attacked immediately for saying that.

Interviewer: >> Yeah.

Interviewer: >> My question is, do you see him as being slightly different from Sam in this regard?

Interviewee: >> Yeah. I mean, it seems like Anthropic and Stereo [Dario] have been more willing to say and do things that are costly to their bottom line uh at least in the last year or so. That's an example of it. um like I don't think that really wins them favors in the administration or among their investors to say that type of thing and you know a better example is just the whole fight between the department of war and anthropic was an example of them doing something that like cost them a lot of money and even more importantly cost them a lot of power for something like like they could have just signed a contract you know that said I really don't want to be in a situation where we're like which CEO is the least bad CEO let's support that one you know like none of these people should be trusted did uh with that much power basically.

Interviewer: >> Nobody should.

Interviewee: >> Nobody should

Interviewer: >> regardless.

Interviewee: >> Regardless. Yeah.

Interviewer: >> Mhm. So on this point of the buttons, you you you do believe that they think there's a credible chance of extinction.

Interviewee: >> Yeah. But they've convinced themselves that like it's probably fine and also it'll be even worse if I'm not doing it, you know? Like that that's what they'll say inside the companies too. Like two people will be like, "Okay, well if we stop, what about the other guys?" Like they're not going to stop, you know? Yeah. This is this has always been why I've had this outstanding question which is how does this not go bad when human incentives seem to rule the day when you look at history and all of the human incentives are saying well if you you're damned if you do i.e. you're damned if you carry on developing these bigger and bigger and bigger AI brains but you're also then damned if you don't from a geographical perspective because the United States will lose to that country or this company will lose to that company. So when you just look at human incentives and goes how does how does if just purely incentives and disincentives how does this end? Well it carries on going. seems like it. I mean there there is a caveat to that which is a hopeful caveat which is that first of all if the world wakes up to all of this then there can be a more serious conversation about regulation and international treaties and things like that and that can change the incentives right so the government could come in and say like actually here are some rules that you all have to follow and because they're rules that you all have to follow then you're not incentivized to like break them anymore because you get punished if you break and everyone else is also following them too and so you know it's fine. So so there is that sort of like ray of hope that like we can change the incentives if the government and especially the US government but then later other countries act to to change the incentives but that's not going to happen until people sort of wake up to all of this. The second thing is that even individually at some point, you know, Dario or Sam or Elon might realize that like actually it's like not even in their own interest to to keep racing unilaterally. And it the problem with that is it's only if it gets extremely obvious and extremely dire. So like in in AI 2027 in that scenario, there's this choice point that I mentioned and in one case the AI are misaligned, in the other case the AIS are aligned. At that choice point, we have like one branch that depicts the the misalignment ending and one branch that depicts like they they slow down a bit and solve the alignment issues.

Interviewer: >> The instigator for that choice point is they see some evidence that their AI might be misaligned and plotting against them, right? So, if you actually see that evidence,

Interviewee: >> then it's like, oh gosh, uh maybe we shouldn't put it in charge of everything and let it rip, you know, because that's evidence is staring us right in the face that it's it's untrustworthy, you know. But if they don't see that sort of very clear evidence, then I think they're going to convince themselves that they need to keep going, you know, but maybe they will see very clear evidence like that. In which case, even if we don't have regulation, they might just sort of voluntarily stop. Um, so that's the second ray of hope. Like overall, I don't think that we're like definitely doomed. You know, like I said 70%, but like I could see it working out pretty well as well.

Interviewer: >> H what about uh jobs?

Interviewee: >> Yeah. So I think I think I'm excited to at some point get into the new thing which is the more optimistic positive vision.

Interviewer: >> Uh and that will have a lot to say about this because in the in the in the prediction you know in AI 2027 by the time everyone loses their jobs there are worse things happening or like it's it's kind of like too late by that point.

Interviewee: Um, but yes, like once if I mean just just think about it. If the companies do manage to build super intelligence, then by definition they're going to be able to take almost all the jobs or all the jobs, right? Because it's better, faster, cheaper than the best humans at everything.

Interviewer: >> And that again, the timeline is by the end of sort of 2030, you reckon you think super intelligence might arrive? I'm trying to think about when we could start to see job displacement in the economy.

Interviewee: >> We're already starting to see a little bit of it now, but not very much.

Interviewer: >> Why?

Interviewee: >> Um, cuz the AI aren't good enough yet. like they they're they're they're impressive, but they're not like they're not just a drop in replacement for a human worker in almost any field.

Interviewer: >> And do you think that'll be sudden?

Interviewee: >> I think it'll be sudden because of the intelligence explosion dynamics or recursive self-improvement dynamics. So you can imagine a different world where it's gradual.

Interviewer: >> Mhm. And and this is this is maybe how it is in a lot of science fiction is you know the AIS gradually get better at a bunch of things and you know they gradually automate like this one industry like pharma then they automate like you know steering drones then they automate like driving cars or something like that.

Interviewee: Um but what's different about the real world is that the companies have converged on this strategy of automating themselves first, you know, automating the AI research process. And so if they're allowed to continue with this strategy, we're not going to see like, you know, the robo taxis and like the plumber robots and, you know, the lawyer AIs. We're not going to see that sort of like broad diffusion of AI into the economy happening first because that's not what they're focusing on first. They're focusing on automating themselves, automating their own research so that they can do everything that they're doing faster. And they want that to sort of get going and get to, you know, very high levels of intelligence, very high levels of general intelligence. Um, and then deploy more out into the economy, right? So by the time it's actually coming for like all these different jobs they will have had fully autonomous AI research happening for months maybe years you know and that means that like the AIs will be vastly superhuman at AI research and probably also vastly superhuman at lots of other things just as a side effect you know if you're wondering what this looks like well we wrote about what it looks like it's sort of like this this wave smashing through the economy after they do the intelligence explosion internally.

Interviewer: >> What I'm hearing there is that because the AI will be able to improve itself and train itself, it'll be getting better at everything at once and then it will be released at kind of once. Is that accurate?

Interviewee: >> But it's it's not it's not even exactly that because even if it's mostly just getting better at the things that it's doing like re research that'll have some spillover effects to other skills as well and then when it turns to focusing on the those other skills it'll be able to do them very fast.

Interviewer: >> What jobs remain in such a scenario do you think? I think that's actually a political question, not a technical question

Interviewer: >> because

Interviewee: >> because on a technical level, all the jobs can be done by the AIS if they've reached that level.

Guest: And so it's a question of what jobs are allowed for them to do

Host: and what kind of jobs wouldn't be allowed, do you think?

Guest: That depends on who's in charge. So there'd be some sort of political conversation about like what we're going to allow and disallow.

Host: I mean, in this scenario, the humans are still controlling them. The AIs

Guest: depends on what you mean by control, right? So there's like there's do the AIs actually have the goals and values that you want them to have and are they going to robustly do that and behave as intended into the future and then there's like are they obeying your orders for now?

Host: Are they obeying the orders is really what I'm saying.

Guest: Yeah. So like even in AI27 in the scenario where the AI take over and kill everyone, there's a period of like several years where they're still obeying orders and they're, you know, taking some jobs but not other jobs and they're helping to make better weapons that the US government can use to like do its arms race with China and so forth. And that's why they're able to get so much power so quickly is because the governments and the corporations and so forth trusts them and is deliberately deploying them into all of these positions because it thinks that things are fine. But because these things are neural nets, you can't just like look inside and see what it's really thinking. You can't really tell.

Host: I think this is a really important point because unlike software we can look at the code and see what's going on theoretically with AI you're saying that we don't know what why it's making the decisions that it's making because we can't get inside.

Guest: One note of optimism is that it doesn't necessarily have to be that way. Like there's a a sub field of machine learning called mechanistic interpretability and a broader subfield called interpretability more generally that's trying to solve that problem and trying to take these these trained artificial neural nets and piece them apart and understand like how the information is flowing and how the decisions are being made so to speak. Um the problem is just it's a very inherently hard problem. If you have 10 trillion connections to look at, you can look at any particular group of them and be like, okay, so this is how like this particular connection works, but like how do you get a sense of the whole, you know, how do you get a sense of like

Host: what's happening at a high level?

Guest: And the answer is, well, it might be impossible, but people are working on it and they are making progress and if they can make enough progress, then we're in a very different and much brighter world. I think that it would be much less likely for us to get into those loss of control scenarios if we could just actually see what our AIS were thinking and why and how at any given time, right?

Host: Yeah.

Guest: So, we would still have the other problems to worry about, but at least we could mostly solve that one.

Host: It is pretty crazy to think that we're building a technology, a brain that we don't understand.

Guest: Yeah, it's pretty crazy. I mean, it's one of those things where like

Host: in a movie, like a sci-fi movie, a bunch of scientists stood around this big brain and they're all just like they're making it more they're feeding it.

Guest: Yeah. Yeah,

Host: but they don't really know what the

Guest: Yeah, I mean it's it's kind of just like obviously a dangerous thing to be doing. Um, but we're doing it anyway because of this history of how the field has developed in the last 10 years where, you know, people were like, "Oh, wow. Yeah, that's obviously dangerous. Oh, no. What if someone else did it and did a bad job of it, therefore we should do it and do a good job of it." And now they're in this race where where they're racing each other. And they're also under all sorts of political pressure to like pretend that it's not as bad as it seems because they don't want to like anger their investors. They don't want to anger the White House.

Host: One of the the key questions we had from our audience was which and I kind of asked you this in part, but which jobs are genuinely likely to survive AI and what skills should people/ students focus on over the next 10 years? That's kind of like like imagine if you were someone living in Mexico in like 1500 and then you hear that like the concistadors are coming. You could be asking yourself like okay well what sort of job should I be switching to to like survive this transition.

Guest: But like you have a lot more to worry about besides that. But yes, I think I would say that like if we manage to avoid the loss of control problem and we end up with humans still in charge of the AI and humans can like say what the AI's goals and values are supposed to be even as they become much smarter than humans and even as they run the whole economy then probably there will be regulation that protects some areas and you can try to guess at what those areas might be maybe stuff that's more like like like judges potentially.

Host: What about podcasters?

Guest: Be honest. Probably not podcasters, I think. Um, stuff like, you know, being a nanny, maybe, right? Like, I think it's even if there's a robot nanny that's like really really good, I think a bunch of people might prefer to have an actual human because they might be creeped out by the idea of a really good robot nanny. So, you can sort of you can sort of reason like that. There's also like stuff that might be legally protected, like maybe judges, for example, like are going to be legally required to be humans and not robots.

Host: Some people say though there's going to be so many jobs created that we can't foresee right now like there was in the industrial revolution or the internet boom or whatever.

Guest: The problem with that is that um past technological advancements have been more narrow. They've like automated some things but not everything. But we are talking about a hypothetical future situation in which everything gets automated. So there isn't any new job that you could do that the AI couldn't also do except if it's like protected by regulation or something. That's that's that's also a thing. But so like for example, right now there's this sort of like cycle where you know the AI has learned to do a certain thing like write copy or like draft code or like debug something and then humans who used to do that thing switch to managing AIS or switch to doing the other stuff that the AIS can't do. And that's why there's been this dynamic historically of, you know, new jobs opening up and people flooding to them. But if it gets to the point where the AI can do everything that humans can do and better and faster and cheaper, then whatever that new job is that you might have switched to, like the AI can switch to that, too. And they'll already be better at it than you.

Host: Because we haven't seen widespread unemployment yet in the economy, do you think people are getting a little bit complacent? Because what I'm seeing on my timeline is a lot of people saying, "I told you so. I told you everything would be fine." And when you look at the the US unemployment rate, currently the it's flat to slightly down. If you look at the UK, it is up. The trend is up compared to last year. We're at about 5% unemployment. The US is at 4.2% unemployment.

Guest: Yeah. Basically, nobody has said that there would be mass unemployment by now. Or at least we didn't say that, you know, and we were historically one of the more bullish people on AI progress in AI 2027 because of the dynamics that we just described. The mass unemployment doesn't happen until 2028 or 2029 after they already have super intelligence because again the companies aren't trying to cause mass unemployment as step one. That's like step three after you know it's like step one automate themselves. Step two have this recursive self-improvement to get to super intelligence. Step three expand out into the economy and automate everything. And so this is really unfortunate from humanity's perspective because one might have hoped that if there was this broad wave of automation going through the economy, people would sit up and pay attention and think about where all this is headed and demand good regulations from the government. But that's not actually the strategy the companies are taking.

Speaker A: you know, they're going to be getting the super intelligence first and then doing the broad wave of automation, which means that by the time they're actually doing all of that, uh, well, it's already going to be moving very fast and the AI will already be very powerful.

Speaker B: In your 2027 report, so you wrote that in 2025, but it is called AI 2027. You said that in mid 2025, we'd have the autonomous employee, which is sort of like AI agents taking instructions over Slack or Teams.

Speaker B: That happened. I've actually got an AI agent in my WhatsApp, which I talk to, of course. I've got Clawbot exploded obviously around the world and and now um you know Claude have talked about uh their new Slack integration but lots of people are using agents now and that happened I'd say for us at the we really sort of caught on to it the the start of 2026 you also said by by 2026 companies begin replacing entire corporate departments with AI agent subscriptions 2027 the final job AI automates the job of the human AI researchers themselves and begins the machine learning research to upgrade and build the next generation of AIs.

Speaker A: Yeah. Yeah. So again timelines we are uncertain about how long it will take to achieve these milestones in this scenario they happen at those times but by the time we had actually published the scenario our timelines had shifted back a little bit specifically mine had so like my 50% mark was 2028

Speaker A: for that for the full automation of AI research milestone not 2027 uh and then other people on my team had more like 2030 2031 things like that so I I I kind of want to Excellent.

Speaker A: Maybe try to illustrate this with, you know, we have like this probability distribution. It's like a smeared out probability mass and like the 50% mark is this particular year, but there's like a lot of possibility that it happens

Speaker A: years earlier or years later, right?

Speaker B: What is this AI 2040?

Speaker A: So AI 257 was a best guess prediction as to how things would actually go.

Speaker B: Yeah.

Speaker A: AI 2040 plan A is our recommendation for how things should go. So we called it AI 2040 because in this scenario uh they build super intelligence in 2040 instead of much sooner because they delay things.

Speaker B: Why do they delay things?

Speaker A: To manage the risks and make sure that power is distributed equitably. They basically like regulate AI development so that it still continues but at a slower more reasonable pace in a more transparent and safe way and spread out over more countries and companies and as a result they get to super intelligence in 2040 instead of in say 2030 and then we call it plan A because well it's our recommendation like we've we've come up with a plan for what government should do and uh the scenario is an illustration of what it might look like to implement that plan in a similar way to how AI27 is kind of an an illustration of what it might look like to do what the companies are currently planning to do. That makes sense.

Speaker B: And is this wishful thinking or is this what you think is going to happen?

Speaker A: No, it's definitely not what we think is going to happen.

Speaker B: It's not what you think is going to happen.

Speaker A: No. No. What we think is going to happen is still something more like this, right? We we don't expect the world to listen to us, right? this is our recommendation, but we mean we hope that that people do something like this and we think it's possible, but it's not our like prediction for what's going to happen by default, you know. So, I do want to run through the plans, the potential plans and also plan A, but um just to close off on how things might look after the year cuz I think I wanted to touch on robotics too and I've got this graph here which talks about share of labor output.

Speaker B: Yeah.

Speaker B: Um which I found to be quite striking. I I've been sat here wondering as an employer who employs hundreds and hundreds of people

Speaker B: when when all this stuff is going to happen and you know we're still hiring more people as things stand there are some roles where our consideration is changing shifting considerably

Speaker B: and I'd have to say that you know we're probably in a phase where our teams are AI powered and they're using agents to do some of their work now

Speaker B: but I'm wondering as an employer like when is it when does this happen?

Speaker A: Yeah, great question. So if we could maybe zoom in on this a little bit.

Speaker B: Yeah, we'll put it on the screen.

Speaker A: Um, so this is in the AI 2040 plan A scenario and notably in that scenario there's significant regulation introduced in 2029 that slows down the pace of AI development. In the scenario, they do that sort of at the last moment. So in the scenario, if they hadn't done that, then it was about to take off similar to how it does in AI 247. Um, but as you can see, like in the scenario, there's still a bunch of jobs at the point that they implement it. And this gets back to what I was saying earlier is that if you wait until most people have lost their jobs to regulate the AI companies, that's already too late because they will probably already have super intelligent AI by then because their strategy is to first get super intelligent AI and then do all that stuff.

Speaker B: And I think you say that it would collapse the economy and cause even more harm to suddenly regulate something that all of us and all of our lives were then at that point relying on.

Speaker A: Oh, but it's a risk well worth taking. I mean, we it's true that right now a lot of people use AI for a lot of things, but like if we could somehow slow or halt AI development now to set up a better way to do it, that would be well worth it. Um, even though there would be significant costs,

Speaker B: but you can't over here, right, can you at this point where AI and robotics are doing most of the labor output.

Speaker A: That's right. But in but in but in in this scenario in the AI 2040 plan A scenario they put in the regulations in 2029

Speaker A: and then they slowly and carefully develop AI in a way that avoids all the problems which we can get into in a little bit. And so eventually, yes, eventually the AIs take the jobs. Eventually, basically the whole economy is run by AIs and robots, but it it happens gradually over the course of the 2030s

Speaker A: instead of happening in this sort of crazy shock, you know, a year later,

Speaker B: right?

Speaker A: Because in this scenario, they don't let the companies recursively self-improve and get to super intelligence as fast as possible. Instead, they regulate AI development so that the core capabilities of the AIS are improving at a more reasonable pace and also in a more transparent way so that the scientific community can see what's going on and help make it safe.

Speaker B: But it's uh I guess I noticed here that in both your scenarios eventually AI and robotics do pretty much all the jobs.

Speaker A: Yes.

Speaker B: So you kind of side there with Elon when Elon says that working will be a choice. Uh

Speaker B: because I mean what happens?

Speaker A: Yes. I mean if it by definition if it can do all the things then it can do all the things. I think that there's a question of like should we allow there to be AIS that can do all the things right? Some people think that the answer is no and we should just shut it all down and prevent these types of AIs from being created in the first place. And we're actually kind of sympathetic to that. We we have our Should we bring out the plans diagram?

Speaker B: Yeah.

Speaker A: Thanks. Yeah. So our scenario is called AI 240 plan A. It's a scenario in which they slow down AI development to make super intelligence happen in 2040 instead of earlier. And plan A is our recommendation. So this is sort of illustrating our recommendation. But for comparison, we made like mini scenarios illustrating different alternative plans which we call plan S, plan B, plan C, and plan D. Plan D is basically the same thing that happens in AI27. Like the race continues. there's very little regulation. Um, you can read about that in AI27. Plan C also very similar to what happens in the slowdown ending of AI27 where they solve the alignment problems. So, in that ending, they like slow down a little bit, pivot more resources to AI alignment and AI safety research, get lucky and succeed, and now they have aligned AIS and then they speed up again and take all the jobs

Speaker A: and beat China and all those things. Plan B is it's kind of like plan C in that well basically in plan B you're uh being more aggressive towards China and you're like taking actions to sabotage or cyber attack them to like keep them behind so that you have more breathing room to to solve the alignment problems yourself.

Speaker A: Plan A is our recommendation. It's uh domestic regulation and then an international deal to continue building AI, but in a much better way. Plan S is shut it all down. If you want to have a future where there aren't AIs running around that can do everything better and faster than humans, you kind of want something like plan S.

Speaker B: What do you want?

Speaker A: Plan A is our recommendation. I think that I'm sympathetic to plan S, but for reasons we explain, we recommend plan A instead.

Speaker B: And what do you think is most probable if you're being honest?

Speaker A: Plan D,

Speaker B: which is that they just

Speaker A: type of thing where they keep racing, they don't really slow down significantly. Um, and uh things happen extremely fast. The diagram sort of explains like roughly the reasoning behind this too. So like there's this high level thing of like do you want to keep racing as fast as possible to make the AI smarter and smarter to put them in charge of more things so that we can beat China? You know, if you're happy with that, then you can get down into this variation of options here. If you are worried about that, well, you get to something like this. There's more different options besides these, but this is kind of like the ones that we could compress onto a screen.

Speaker B: Do you have children?

Speaker A: Yeah, I have two children. It's kind of sad. Like I think that one way or another this will probably all be over by the time they're old enough to join the workforce. So I don't think they'll ever join the workforce.

Speaker B: When you say this will be all over by the time they join the what do you mean by this will be all over?

Speaker A: So these milestones that I described like AI is automating the AI research AI is getting super intelligent. Um AI is then exploding out into the economy, taking the jobs, building robot factories to build more robots, to build more factories, etc. GDP starting to go vertical. That sort of thing is what I mean like all of those events transpiring. Maybe there's like, you know, 10 20% chance or something that hits the wall and and none of this comes to pass even if you don't do anything.

Speaker B: How old your oldest?

Speaker A: Six.

Speaker B: Six. Boy, girl,

Speaker A: girl,

Speaker B: girl. So your daughter comes to you and says, "Dad, what shall I um what shall I study in school?"

Speaker A: I mean, again, like if these radical transformations happen, then the world would just look completely different and what sort of jobs you set yourself up for basically won't matter that much probably. I would say um that the thing to do is well a try to make it actually go well. Like if you can exert any influence at all on history and how this all develops, you should be trying very hard to steer the future in better directions. And then separately from that on a personal level you should focus on well being a good person and doing things that are sort of good in the for their own sake rather than good because they'll set you up for later employment because that later employment is going to be very uncertain. Um basically

Speaker B: Elon talks about this age of abundance we're heading towards age of abundance.

Speaker A: There'll definitely be abundance. The question is who controls the abundance and what do they do with it? Right? Are the AIs controlled by anyone or are they doing their own thing? And then if they are controlled by people, who controls them and what do they do? And what's the sort of like political structure governing how they make those decisions?

Speaker B: I think it was Jeffrey Hinton that said to me, he said there's no example in nature where a more intelligent species is has less control than a less intelligent species. Thus saying that we're quite arrogant to think that in a world where there's this artificial brain that's a gazillion times the size of mine that I'm going to give it orders.

Speaker A: Yeah. I mean that that's the thing is I I think it's like that should be our default assumption is that like well there's these brains. We can't see exactly what they're thinking. We're going to make them smarter than us and put them in charge of everything

Speaker B: and then we're going to give them bodies.

Speaker A: Yeah. And then they're going to be autonomously building new factors and so forth. And like how is this supposed to end well again? Like isn't this just exactly like us picking a new species that's then going to out compete us when it doesn't need us anymore? Like I think that is just the default trajectory. Now there's a whole argument we can get into about like ways that we could get off of that default trajectory. So for example, there's research into interpretability that I described previously. And if that research bears fruit, then you will be able to actually see what they're thinking and then that would be an excellent tool for shaping them and controlling them and making sure that they do what we want. Right? there's other sorts of um AI alignment research agendas that are making progress and if enough of those agendas succeed sufficiently we can avoid this problem of course also there's the regulatory side too where like part of what makes this difficult is that we're building these AIs in race conditions you know like the the companies are secretive about their recipes for making these AIs because it's secrets that they want to protect so that other people can't copy them and so a lot of it is happening you know behind closed doors only a few people can really see the recipes that they're using to train these AIs and and so forth. And then often times when the AI behave in unexpected ways or even just like blatantly misaligned ways, sometimes that information doesn't really flow out to the public because the companies are not really incentivized to tell everyone about how they messed up and how their AI is evil. It's just not very conducive to scientific progress on these issues. If the regulatory system was different, then perhaps we could be in a better situation, make faster progress. Also, of course, we wouldn't be planning to put these AIs in charge of everything as fast as possible, and we wouldn't be planning to like let themsel improve, you know, like the these are choices that we could not make, you know?

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Speaker B: Ilia was, as you said, he was one of the leaders at OpenAI and he left and he started his own company now, Safe Super Intelligence. very curious name of a company, Safe Super Intelligence, after leaving OpenAI. Did you ever get to work with him?

Speaker A: Uh, I wasn't directly working with him. I had a couple chats with him.

Speaker B: Do you think he's he's genuinely concerned as well?

Speaker A: I think he is, but I think he's I think he's similar to these other CEOs where I mean, just think about the sort of incentives that they're under, right? like they can sort of see the problem and then they can be like okay but like if I don't if I stop if I quit my job and or do something else that's not going to solve the problem cuz the other CEOs are going to keep going and even if all of

Speaker A: us didn't go then maybe China would keep going. So like man seems like this is just going to happen one way or another whether I do anything about it or not. I guess I should be involved, you know, and like maybe I can make it go well. And at any rate, like I don't want to be out in the cold while these other people I don't trust are in charge of everything. So they all sort of like reason through all of this and then convince themselves that like the thing to do is for them to build it and to do it better. And I think Ilia is just the latest example of this. Elon's another example. Dario is another example. You know, arguably OpenAI at the beginning Sam was an example. Although like Elon and Dario were at OpenAI early on. So what do you think they should all do then? So I think what should happen is some sort of international regulation or at least domestic regulation similar to what we described in plan A.

Speaker B: Okay. So walk me through plan A.

Speaker A: Yeah. So in this scenario, AI takes longer to the to get to recursive self-improvement and full automation of AI research than it does in AI 27. We figured that we should try to illustrate like a range of different possibilities because we do have those sort of uncertainty intervals. So we chose 2030 as the moment when full automation would finally be achieved and things would really kick off. And then working backwards from that, when's the last moment you could really have good regulation? 2029. So in this scenario, AI progress slows down a little bit naturally and the AI companies keep keep racing, but they don't quite succeed in automating uh themselves in 2027 or in 2028 or in 2029, but they're getting really close and they're going to do it in 2030. And then in 2029, the government steps in and regulates them. What regulations do they do? Well, they basically just shut it down temporarily.

Speaker B: Can I ask um how does the elections overlay with your time frames here? Because there's going to be a big election, isn't there, in 2028? And it seems now that sentiment has really really turned against AI in in sort of in the general public and that it will be one of the big ticket items on the on the ballot.

Speaker A: We think that it'll be maybe the most important issue in the presidential election in 2028. Um, I think a lot of people mo most people will be quite concerned about where things are headed. And that's part of why we we chose to depict things the way they were doing in this scenario because that helps explain why they might do this sort of regulation in 2029 is that the voters have been demanding it and the presidential candidates have been promising it.

Speaker B: And in this scenario and in 2027, would the general public have felt the consequences of AI much more severely than they have now by then?

Speaker A: Yes. Although still even in 2029 in this scenario they still mostly have the jobs as as depicted here. Right. So in in 2029 in this scenario lots of jobs now involve managing AI agents. You you mentioned you have an AI agent right? Well in 2029 in this scenario the AI agents will be much better. Still though not enough to just completely do everything. You know that was the sort of thing that would come in 2030 in this in this timeline. Again we're uncertain about timelines. Things could go faster than depicted in this scenario. And in fact, I think things probably will go a bit faster than depicted in this scenario, but we're uncertain. We already did the very fast timeline scenario, so now we're doing a slower timeline scenario. But maybe we should talk about the high level goals. So they want to have AI continue, but in a slower pace.

Speaker B: Who's that?

Speaker A: So they can make it safe. The politicians, you know, the president and the people who voted for the president and, you know, the heads of other governments and so forth. So goal one, slow things down. Um, goal two, make it more transparent so that the scientific community can catch up to this stuff and make more progress and also so that we don't have to take the company's word for it when they say that their systems are safe and when they say that they haven't, you know, put in any biases into their systems, for example. That's a concentration of power issue. We also want to avoid a situation where there's an intense concentration of power. So in addition to these the transparency and the slowdown, we actually think it's actively good for there to be multiple AI companies across multiple different countries that have similar levels of very advanced AI capability and for there to be like broad diffusion of AI into society rather than you know a single mega project that has all the best AIs for example. And the nice thing about that is you kind of get that by default if you do the first two things. If you slow it down and if you make it more transparent, then that means there's breathing room for other projects to sort of catch up, right? And the transparency just like literally helps them catch up because then they can like copy copy some of the ideas. And then I think the fourth thing would be reversibility. So in what follows in the scenario, we are going to be building up a lot of data centers and a lot of robots. We're going to be transforming the world at a in a sort of like slower pace, though still a very fast pace, but slower. And if things go wrong and the deal breaks down and everyone starts racing each other again to get to super intelligence as fast as possible, that would be very scary. And so the fourth principle is basically build the new data centers in such a way that if everything breaks down and everyone starts racing again, the newly built data centers get destroyed so that we're sort of back to square one again instead of in an even worse race where there's even more AI and robust and compute everywhere. Um, so I can sort of walk you through the timeline if you're interested.

Speaker B: Sure.

Speaker A: Or the president talks to China, talks to the leaders of a bunch of other countries and says, "We're going to basically halt AI development until we can figure out a plan for how to do it in the in ways that achieve these goals." So they basically send inspectors to each other's data centers. Like Chinese inspectors come to US data centers, US inspectors go to Chinese data centers and verify that they are doing inference and not training. developing new AIS that's that involves training them. But just taking existing AIS and using them to serve customers that's called inference. And so the sort of like solution they come up with here in this scenario is will allow them to keep doing inference but not training for now until we can get the new training data center set up. So they retrofit the existing data centers to serve inference. People can still keep talking to their AI agents, but they're going to stop getting better and better for like six months to a year while they build the new data centers that are going to be the transparent data centers. And that's where the training is going to happen. Once they get those new data centers set up in 2030, then AI research continues. This is a bit spicy. We advocate for total research transparency which means that on the training data centers that are training the new models they basically have to publish everything which means you get to see all the details of the recipes for training these models you get to see the architecture etc. We think that's sort of open science is really important for solving the alignment problem fast enough because you don't want to have these sort of biased companies making the decisions about whether the AIS are safe. Um and we also think it's important for just good regulations more generally because right now most of the expertise in the world on AI is sort of concentrated in Silicon Valley and the the governments in particular kind of are don't really understand AI that well and imagine an alternative instead of total research transparency you had like an auditor system where the government says here are some rules for how to make the AI safe and then we're going to have like an agency that like goes into the companies and asks them questions and tries to make sure that they're following the rules that creates this sort of adversarial dynamic where the company

Speaker A: ...is incentivized to like fool the regulator, you know, and and also if they if they discover some new problem that's not even on the government's radar,

Speaker B: they might be incentivized to like not tell the government about it, right? So, if you have the total transparency, it helps the government make better decisions faster, but

Speaker B: it kills that competitive advantage.

Speaker A: Yes, Dropbox's not going to like this. You know, OpenAI is not going to like this. This would be uh probably bad for the valuations. I don't think it would kill them completely, but it means that it would commoditize more, right? So, it means that there'd be like a bunch of AI companies that would catch up to the frontier. They would train AIs that are like roughly similar, roughly equivalent. They could still make money by doing that and then selling their AIs, but they wouldn't have a monopoly. They wouldn't have anything close to monopoly, which I think is good for humanity, although it's bad for the bottom line of those particular companies. Notably, it's good for the bottom line of lots of other companies. Like if you're a company that's behind and you don't you're not anthropic or you're not open AI then you would love this because this helps you catch up you know or this this helps you to like um capture more of the value from the chips you're selling for example or from the like downstream product that you're making

Speaker B: and by 2031 then you have 1/5th of all cognitive labor done by AI.

Speaker A: Yeah. So what's happening here is that we're imagining that the government of the United States and the government of these other countries that are involved in this agreement that are sort of implementing similar regulations. Um they don't have to be exactly the same. Uh but that's another thing that's nice about the transparency is that if you have this sort of transparency then if two governments are implementing different regulations like if one of them is like telling their companies to go slower or like banning more stuff than the other one is they can both see

Speaker B: Yeah. like, oh, you're letting them do that sort of thing and you're not like maybe we should let them do this too, you know? So, it helps to sort of naturally equalize the regulations to some extent without having there to be a central power that just gets to make regulations for everybody.

Speaker A: Mhm.

Speaker A: So, anyhow, we're imagining that when they when they get this transparency set up, they basically agree to ban the dangerous stuff to allow the not so dangerous stuff. And there's a constant ongoing conversation about like, well, what's dangerous and what's not? What should we ban? What should we allow? What about this country? what about that country? That conversation evolves over time, but the gist of it is, at least if they do it the way that we recommend it, is that they don't do an intelligence explosion. They don't let the AI, you know, autonomously self-improve. Instead, they slowly and carefully scale up the AIS that they currently have and invest lots into finding ways to make them more interpretable, uh, to make them more easy to control, to understand better how they work, and so forth. The result is that AI progress continues but it's not quite as fast and it's much much much safer and more transparent.

Speaker B: But still through these you we see job disruption.

Speaker A: It is continuing because they are building more data centers right like this whole time they're building more and more data centers more and more chips and they're continuing to like make there be a larger and larger population of AIS so to speak and that causes this huge transformation over the course of the 2030s. sort of a big thing that we sort of want people to take away is that even if you heavily restrict AI progress, you still get this sort of crazy transformation. You know, in this scenario, they basically allow progress to continue but at a slower, more safe pace here in 2030 and then as a result, it takes until 2035 to get to top expert level AI. So remember, they were on track to do that in 2030, but then sort of at the last moment they stopped. But because it was sort of so close to the last moment, that means that like they can sort of get there pretty soon if they want to. And it's just a matter of like how long they they allow it to go, right? So they sort of they sort of slow it down, spread it out, leisurely arrive at this level after 5 years. By this point, they've built up massive amounts of data centers everywhere. So it's not just that the AIs are smarter and able to do all the things that humans can do, but also there's a lot more of them and there's a lot of robots and so forth. So by this by this point you kind of have the economy that a lot of people would have imagined with AGI where there's AIS there's lots of them they're able to do all sorts of jobs there's robots there's lots of them they're able to do all sorts of physical work and basically the economy is being run by these machines

Speaker B: so in 20 31 you you have the 1/5th of all cognitive labor done by AI in 2023 you have 60 million AIs running at 100x speed in 2033 there's cash dividend to all Americans.

Speaker A: Mhm.

Speaker B: Um I've got to explain explain this to me.

Speaker A: Yeah. So, if the AIS are going to be taking people's jobs, then it's very important that people not starve to death and still have money. And if companies are going to be using AI and robots to take all these jobs, then that means that there needs to be some sort of taxation scheme or something to like make sure that people still have a slice of that pie. M

Speaker B: the pie is going to grow huge, but you still need to actually give people a slice of the pie. And our proposal for how to do that, we call it the citizens dividend. Basically, people have shares in a agency that sells permits to the robot companies and to the compute companies and makes profit from selling those permits and then those are people have shares in that entity. It starts off small. It starts off something like $25,000 per person. Uh, and then by the end it's something like $10 million per citizen

Speaker A: per person.

Speaker B: Per person per year.

Speaker A: Factoring in inflation. Like what you mean?

Speaker B: Factoring in inflation.

Speaker A: So we're all going to be multi-millionaires.

Speaker B: Yes. If this happens, which they probably won't, but if it happens, this is where it go. And again, this is the thing I want to emphasize is that if you get to the point where your AIs are close to being able to do all the research and then you sort of pause and slow down, that means that like you still have a lot of transformation ahead of you because if you allow those AIs to like still proceed slowly and like start to automate various jobs and so forth after some years they will in fact have done that and they will have, you know, built huge amounts of new data centers, huge amounts of new chip fabs, huge amounts of new robots, robot factories, etc. You know, we're not sure obviously how fast this will go exactly, but we've thought about it a lot and we have our our guesses and this is sort of like our median guess.

Speaker A: What does this mean? 2037, the apocalyptic arrival of truth on Earth.

Speaker B: Yeah. So, like this is the point where we say they get to top expert level AI. So, it's not super intelligence in the sense that it's not like vastly smarter than humans at things because they deliberately pause it at the level of top experts. So, so here they're going slow. here they've just actually stopped but they've stopped at a point where the AIs are just actually really good at everything. So kind of they've definitely got AI maybe they got like weak super intelligence because they have so many of these AIs and because they think faster than humans you know they just run much faster that's going to transform society dramatically. So we talk about some of the ways in which it transforms society like this is sort of life after work we talk about what it would be like to be living on your cit citizens dividend and not have a job anymore in this sort of world. Um, here we talk about all the scientific changes and all the social changes that would come from all of the intellectual progress and activity that would be generated by all of these AIs. So, for example, here is things

Speaker A: like cancer cures and like you know people living in apartments that were built by robots 2 years ago,

Speaker B: 2036

Speaker A: providing again we stop in 2029.

Speaker B: Yeah.

Speaker A: And providing I mean a conservative this is a conservative time frame.

Speaker A: Yeah. Like unfortunately, I actually think that things will happen faster than this by default and that if we don't slow down, things will happen much faster than this. Once you get to the point where you've got, you know, a billion AIs running day and night and they're each better than the best humans at everything. And so they're doing a lot of science. They're doing a lot of talking to each other. They're doing a lot of thinking. Everyone's constantly talking to their AI assistants and so forth. There's going to be a lot of scientific progress. There's going to be a lot of changes to politics, to ideologies. It's going to be very disruptive and crazy and we get into some of the ways in which it is uh later. Basically,

Speaker B: I still not super clear on what this means. The apocalyptic arrival of truth on Earth. It's just it's just because there's so many eyes AIs that are so smart that they're uncovering making new discoveries in sciences.

Speaker A: Let me give you an example. Lie detectors.

Speaker B: Yeah.

Speaker A: So, that's an example of a technology that might be invented.

Speaker B: Yeah.

Speaker A: You know, right now we don't have good lie detectors. We have very bad lie detectors that like sort of work but don't don't fully work. But once you've had these top expert level AIs thinking for many years at, you know, 100x human speed and there's billions of them and they have access to robot factories to do research and stuff.

Speaker A: They'll probably invent a ton of technologies. Maybe they'll invent on lie detectors that actually work on real humans. That'll have big social effects, right? Imagine a presidential candidate who's like, "Those allegations are false, and to prove them, I will go under a lie detector and say that they're false." I was just thinking about the whole justice system and how that would be overturned. Um, in fact, you could, you know, theoretically walk down the street and be

Speaker B: Yeah,

Speaker A: it's both terrifying and exciting. One thing that we talk about in this in this section is like the invention of lie detectors could be really bad. Like it could be that it enables a new form of totalitarianism where the powerful people, you know, the CEOs and the politicians

Speaker A: force the people under them to go under lie detectors and say like, "Yes, I'm loyal to the dear leader. I would never do anything against the cheerleader, right?

Speaker B: And if you're lying, then you're in.

Speaker A: And then if you're lying, you get fired, right? So like there's there's a ton of like very harmful uses of lie detector technology. There's also the good uses. And broadly speaking, I would say the good uses are when lie detectors are used on the powerful instead of by the powerful.

Speaker B: What's this 2040 passing the torch to AIS?

Speaker A: Yeah, great. So here they pause at the top expert AAI level. And the reason why they pause is because their safety cases aren't good enough for going beyond that level. Um, so in the sort of regulatory systems that they set up over the course of these years, roughly speaking, the way they would work is when you're making a new AI and then when you're trying to deploy the AI into something, you have to have some sort of safety case explaining like what your intentions are and like why you think it's going to work the way that you want it to work. And in particular, why the AI is going to like do as it's told, for example, and why nothing super terrible is going to happen like AI takeover. It's relatively easy to make safety cases like this when your AIs are still not capable of automating everything,

Speaker A: but the more powerful they get, the more difficult it is to actually argue that things are going to be fine because the AIS are just more capable and they can they can get up to more stuff. And if you if they're actually untrustworthy, the the possible downsides are bigger. So that's why they stop at this level is that they they realize that if they keep going, then they might actually lose control of everything. But at the current level, they're convinced by safety cases that it's fine, but they don't want to go further. So they stop there. And then what happens in 2040 is they've made significant progress scientifically, including on alignment. And they figured out how to make AIs that are actually aligned in a robust way

Speaker B: with humans

Speaker A: with humans. So they can actually trust those AIs and they can allow them to become much smarter again. So that's why we call the whole thing AI 2040 because in 2040 they sort of let off the brakes and allow the AIs to become significantly smarter than humans.

Speaker B: I guess you know this is a this is a plan and this is a hope.

Speaker A: Yes.

Speaker B: But in reality this is not what you think probabilistically if you had to

Speaker A: that's right it's important to distinguish like this is what we recommend. This is what we want to happen from like this is what we actually think will happen by default. Now, we do think it's possible for this to happen, but you know, that will require a lot of people to sort of wake up and pay more attention and advocate for something like this to happen. So, our main scenario is mostly talking about the policy choices made and the broad scale effects on society. We figured it would also be nice to accompany this with a little mini scenario that describes what it would actually feel like to live through this from an ordinary person's perspective.

Speaker B: Okay.

Speaker A: Um 2029, everyone's yelling at each other. the presidents are negotiating something and they've paused AI but you still have access to the existing AI so it doesn't really feel that different although it definitely is like something exciting happening 2031 they've started progress again the AIS are really smart more people have lost their jobs it's like really starting to actually affect things but I think still most people have their jobs but their jobs have sort of transformed so like by 2031 it's like most white collar jobs involve working with AIs to a large extent or managing teams of AIS or collaborating with them somehow also there are some things like robo taxis that are basically just working citizens dividend, you know, ideally this would happen sooner. Like in our scenario, they kind of do things at the last minute, you know, so like a lot of these policy things are like happening kind of like just in time. Obviously, we would recommend that you do them sooner and and do a better job of them too, but so 2033 you start getting your your checks from your dividend.

Speaker B: So you're forecasting that there will be a citizens check that your model says it could be around 25,000 at the start per person

Speaker A: and then it would grow as the economy grows.

Speaker B: But also as I guess job displacement takes hold. They're going to need to to grow that. Check make sure you

Speaker A: and that's why it's kind of the last possible moment because if you waited to implement this until like 2037 then like everyone would have already lost their jobs by the time that happens, right? people losing their jobs, especially if it happens quickly like like we see on this sort of graph here, is going to cause lots of problems in terms of civil unrest, social unrest, purpose, mental health, these kinds of things theoretically.

Speaker B: Yes. How do you think about that?

Speaker A: Uh it's it's going to be rough and hopefully we can navigate that well. We think that at a high level people need to have money and also people need to have power. And I think these are like somewhat different things. It's like why are jobs important? Well, there's a lot of reasons why jobs are important, but I think the main ones are um well, it's how people get money so they can survive and get the things that they want by buying the things that they want. So, if people are going to be losing their jobs, you need some other way of people getting money. And then there's also the power thing, which is that right now people have political power in part due to their economic power. people can threaten to go on strike for example or you know countries that are ruled by dictators can't just completely you know genocide an entire

Speaker A: or they can but like it's costly for them to do so because then they'll have less money because that subop is contributing to their economy and contributing tax revenue and so forth but if you end up in a world where actually nobody's contributing tax menu revenue except for the AI companies and the robot companies then you're you the government are less incentivized to care about what you know the common people think. So, so when people lose their jobs, they're not just threatened with lack of loss of income. They're also threatened with loss of political power. And so, we think that it's important to like do things to push against that.

Speaker B: What does that look like? How do you how do people have power in such a world?

Speaker A: Well, in democracies at least, they still have votes.

Speaker B: Okay.

Speaker A: So I think that it's very important for there to be uh regulations on the use of AI that help make the public discourse more sane and more um actually giving the people what is in their interest and what they want and avoiding a sort of um opposite outcome where you know the masses are easily manipulated by AI powered media for example or where everyone's talking all to their AI advisers and the AI advisers are like subtly steering them away from voting for the candidate that would not be what the AI companies want because the AI companies have this other candidate that they like better and they're like secretly biasing their AIS to like steer people towards voting for that candidate, right? So, so we want to be in a situation where um people have AIs that are actually trustworthy and that are truth seeeking AIs, honest AIs and that don't have any sort of like political agendas put into them by the AI companies or by the government. You know, you want to avoid a situation where the AI company where where the government has issued some sort of secret order that like the AI have to be such and such a way. Yeah. The Department of War dispute versus Enthropic is like a an interesting sort of foreshadowing of this, right? where um Enthropic was giving their AIS to the Department of War. The Department of War wanted to use them for certain things and was upset that Enthropic's AIS were like not supposed to be used for those things. Uh that things in particular were domestic surveillance and uh autonomous robots.

Speaker A: Mhm. There's going to be a lot more issues like that coming up and you want it to be the case that like people know what they're getting and that if people are like spending hours a day talking to their chatbot that chatbot doesn't have political biases put into it or a secret agenda or things like that and instead has been trained to like give honest true answers to things. And I think if you can do that it can improve the discourse and help people to use their votes to put even better regulations and even better politicians in place and so forth. it can sort of potentially bootstrap this to having something where people's power is even more secure than it is today.

Speaker B: A lot of the stuff we've we've covered in part, so you know the wars and drones and missiles, we're already seeing this around the world at the moment, which is really really interesting. Um, and we've talked about robots outnumbering humans as well, which is part of this prediction. Some of the ones down here I found to be really curious, which is people will be protected by AIS wherever they go.

Speaker A: Yeah. In this scenario, they delay the creation of super intelligence until 2040 and in fact they pause from 2035 but then they let it go after then and then they let the AIS become vastly super intelligent. And we think that once the AIs are vastly super intelligent, the world will transform even more radically than what happens in the 2030s in this scenario. So in the 2030s in this scenario, it's more like human level. You know, the AIS are not they're they're doing the same sorts of things that human experts would have done. They're just doing it a bit better, a bit faster, and a lot cheaper, and there's a lot more of them. And the robots are still, you know, doing the same sorts of things that human workers would have done. There's just more of them, and they're cheaper. And because of exponential growth, uh you start with a world that looks not that different from today in 2029, and then by 2039, you end in a world that's radically transformed, where everyone's living in these like fancy new apartments that were built by robots two years ago. There's like giant special economic zones that are full of robots and solar panels and factories producing more robots and solar panels and factories and so forth. Most of the economy is AIS and robots and people don't have jobs anymore. That sort of transformation is what you get if you pause at human level. But if you go beyond the super intelligence, there's a whole another transformation coming that's going to look more like magic. Think about how the technology of today would look like magic to someone from 500 years ago.

Speaker B: Mhm.

Speaker A: You know, and that's without even like a qualitative improvement in intelligence, right? Like the humans of today aren't like qualitatively smarter than the humans from 500 years ago. It's just that we've had more time to do research and we have more like money and resources to build, you know, prototypes and experiments and run experiments and so forth. But if you had a point where there were billions and billions of AIs that were not only faster than humans, but like qualitatively way way way better at everything and in particular at doing scientific research, we should expect that some of the things that they develop will seem like magic to us and we'll just completely like we did not think that was even possible. You know, people don't want to die. People don't want to be hit by cars. People don't want to be like attacked by a random mass murderer.

Speaker B: Cancer's gone. I mean not just cancer like you know all all a lot of the stuff that happens in science fiction will probably have happened by then. So things like people scanning their brains and uploading into into computers right or self-replicating robots in the asteroid belt uh creating more and more satellites to uh produce more and more power to produce more and more self-replicating robots and so forth. Most people still live on Earth but the trend is to move to space.

Speaker A: That's right. Yeah. So like if if you end up in the situation where the entire human economy is just like a tiny drop in the bucket that is the entire economy and it's just like this huge amounts of robots and AIs that are moving incredibly quickly, then what you want is Earth to be mostly left as something like a preserve. You know, I think a lot of people are worried about the environment being destroyed, which it totally would be if it wasn't protected. And uh you know there's a lot of people who sort of like their lives as it is and don't want to be uploaded or live in some crazy new future thing. And it seems to us like the reasonable solution to these issues is uh create new living spaces off the planet with some of that vast economic wealth and activity that's happening for the people who want that sort of thing and then that way the earth can be preserved.

Speaker B: Data picture here of data centers in the ocean. Uh I mean there's three images there of different environments where humans might live.

Speaker A: Again like our proposal was you preserve like 99% of the earth uh mostly as is as historic or environmental from historic or environmental reasons but then like some parts of it you designate as special economic zones where the robots can go crazy and dig giant pit mines and produce factories and so forth. Um, we were thinking it would be good to build the data centers on the ocean instead of um on land for a variety of reasons, although later space would be better and we could see that being reasonable as well.

Speaker B: What about immortality in a world of AI? Um, 20 well 30 45 you say you've lived a dozen lifetimes and are immortal, passing from life to life as if by reincarnation. I mean, there's a lot of billionaires at the moment that are focused on longevity. I mean, Brian Johnson's said he's got this central rule, which is do not die right now.

Speaker A: Yeah.

Speaker B: Because we're in the age of AI and

Speaker A: It's conceivable that with super intelligence, we'll be able to choose when we die.

Speaker B: Yep. I think that's probably right. We don't depict that happening in this part because at this part they only have, you know, human level AIs, but that's one of those things that seems quite plausible that super intelligence could achieve um through a variety of means.

Speaker A: What is your hope with all of this stuff now? Why did you do this? Why did you make this 2040 plan ape?

Speaker B: In the like first week after we published AI 2027, it blew up a lot bigger than we expected, by the way. Like after we published AI 2027, it blew up a lot bigger than we expected, by the way. Like we actually made forecasts beforehand of like how many views it would get and stuff like that, and it was like 90th percentile outcome. So like um very much not what we expected. Um, but in like the Twitter storm that happened, various people were like, "Are why are you giving us all this like doom and gloom uh predictions? Like, how about a more positive vision of like what you think we should do instead?" And I think that that seed sort of like implanted in us and then we were like, "Yeah, that's reasonable." Like, we've sort of depicted what we think the default path looks like and why we think it's pretty scary. Now maybe we should switch facts and come up with some actual recommendations and then depict that as well

Speaker A: Even though you don't believe they're probable.

Speaker B: Yeah. I mean you can vote for a political candidate even if you aren't confident that they're going to win, you know, and and you can say like here's what I think we should do even if you think that people are probably not going to do it. You shouldn't say this if you think it's completely unlikely. Like if you think there's no chance then like maybe you shouldn't bother. But we think there's a chance. Like in particular for the reasons that we described in the scenario we think that people are going to wake up to the power of AI over the next few years

Speaker A: Because of something happens

Speaker B: The companies are saying that they're going to do this, and they are kind of on track and it just sort of makes sense that like if they get anywhere close to this level of AI then there's like big issues and big problems and like we need to like do something about this and so I think that even if there's not any like very dramatic warning shot or something, I think that just naturally people are going to start paying more attention to this and reasoning through the implications and trying to predict what's going to happen. And so naturally people are going to be more interested in regulation of AI for example. And in fact there's actually like there's there's actually more of this happening than we predicted.

Speaker A: More of what happening?

Speaker B: Serious interest in reg AI regulation. So, at the time that we published AI 2427, the sort of like mainstream position of the tech companies and in the government was kind of like AI regulation, bad idea,

Speaker A: Free-for-all.

Speaker B: Free-for-all. Yeah. In fact, there was even an attempt to um preemptively ban states from regulating AI.

Speaker A: Yeah.

Speaker B: You remember that? Now, it seems like the conversation has changed a lot. Like now the the US government just told Enthropic they have to shut down their AI because they were worried that bad actors would use it for cyber attacks. you know, the government is like waking up and doing more stuff than we expected uh already. And we're actually hopeful that that trend will just continue and that before it's actually too late, there will be very serious conversations happening inside the government and outside the government and in the broader society about all of these issues and trying to chart a course that is um avoids the loss of control and concentration of power risk that we mentioned.

Speaker A: you um you've spent well must be almost coming up to 15 years thinking about this stuff. Um if this here was a button and if you press that button your plan S would occur and it would shut down every data center that is currently training a frontier AI model uh for good. There would never be any other AI labs um working on these problems. Would you press that button?

Speaker B: I was I was about to slam it until you said for good.

Speaker A: Okay.

Speaker B: Like I think I think if it was a sort of temporary shutdown, I would totally slam that button because we are not ready to do this. You know, like civilization is not ready to have these companies automate themselves and then get smarter and smarter and then have the super intelligent like no. There's a bunch of reasons why that's really uh dangerous. But I would be at least hesitant to press this button if it permanently foreclosed the possibility of ever doing it again. for sure.

Speaker A: But but if you think that plan D is probable, which is this race we're on to super intelligence,

Speaker B: if I had a choice between D and S, I think I would press it.

Speaker A: Well, it's it comes down to what you think, right? Cuz if you think that's that is what's going to happen, plan D and the only alternative.

Speaker B: I didn't say this is what's going to happen

Speaker A: proistically.

Speaker B: Yeah. Yeah. Like like I'd be like this is the most likely, maybe this is the second most likely, maybe this is the third most likely. They are all possible.

Speaker A: So with your current perspective on whatever one you think is going to happen, would you press the button? I'm giving you an S, a definite S or whatever you think is going to happen.

Speaker B: That's tough. What is the scope of the shutdown? So is it

Speaker A: it's no one can train an AI model again, ever again.

Speaker B: That's real rough because like I said, there's loads of benefits that we could get from AI if we do it right. Um,

Speaker A: I think I I've almost put you in the position of Sam Holtman.

Speaker B: Yeah.

Speaker A: To some degree.

Speaker B: Yeah. Um, let me Do you mind if I just take a moment to think about this?

Speaker A: I think I prefer you to think.

Speaker B: Yeah. I think I would not press the button, but I'm I feel very torn about it. Um, the reason why I think I would not press the button is that I still have substantial hope that we can get something much better than this, something more like this. And I think that basically I think that if we don't build powerful AI systems eventually, then we're probably going to die as a civilization. eventually, you know, like 100 years from now, 200 years from now, something like that, like nuclear war, pandemic or something, you know, I I don't think human civilization right now is like super super stable. Um, and so I think that basically what I was about to say was the possible benefits for posterity and for all the billions and billions of people who could live in the future outweigh the like the current level of risk. But actually,

Speaker A: I've heard that narrative before.

Speaker B: Yeah. I don't know. Like, yeah, like maybe maybe it's just like, nope, the people right now are the people we should prioritize. People right now are in grave danger. They're going to be fine for at least the next couple decades. So, never mind posterity. Prioritize the people right now. Um, and people right now definitely don't want to do this lottery, I would say. Um, yeah, you've really asked me a tough question.

Speaker A: So, would you press the button if that was the button?

Speaker B: Probably not, but I would feel very torn.

Speaker A: Okay. So, what I I always think about the personas of like the audience that are watching and these are, you know, they're they're very curious people, especially on the subject of AI as we've seen, but they they want to know like what it means for them. I think a lot of them also want to know what they can do.

Speaker B: Oh, yes. Yeah. What can people do? Well, I think that if you either have talent or passion, you can get directly involved. There's lots of organizations that are worried about these things and that are trying to do something about it, like political advocacy or technical research or like building useful tools that will hopefully help people be better and stuff. But if you don't want to like make any major career changes or or things like that, then I would say just pay more attention to these issues and talk about them more with people. do stuff like, you know, emailing your congressman or

Speaker A: whatever, it doesn't change things that much, but it does help. I think that especially for this particular issue, the core problem is that people aren't taking it seriously yet. Like if the sorts of things that I was just saying to you for the last hour or two were just like top of everybody's mind, we wouldn't even be here. Like there would already be much more significant regulation in place, you know, and not only would there be more heavy regulation in place, but there would have been better regulation in place that's less, you know, less like a cudgel and more like a scalpel and that's like more sensitive to what's actually bad and what's not so bad and so forth. And there'd be more expert people in the government and advising the government and so forth. So, just in general, like the more people wake up to these concerns and to these projections, uh, I think the more likely it is that we can do good stuff before it's too late.

Speaker B: What about how they should vote at the polls? We've got an election coming up in the United States in a couple of years time, but there's elections happening all over the world all the time.

Speaker A: You should ask your candidates what they think about all this AI stuff. You should try to get them to like have opinions, and then you should vote for the candidates whose opinions are better on this topic. This is the most important thing happening uh in our lifetimes, probably in all of history in fact and it's very important that it go well and so it's what all the leaders of all the countries should be thinking about and making plans for.

Speaker B: Isn't it such a weird thing to be alive at this moment in time? Like I was thinking about all the times that I could have been born and I guess my ancestors probably thought the same, but I was thinking as you were speaking I was like I think it's when you referred to it as like the final show.

Speaker A: Yeah.

Speaker B: What was the phraseology you used?

Speaker A: I said the run up to the climax or something.

Speaker B: Yeah. I mean, what a crazy thing to be born in the run-up to the climax where everything you're describing here is within my lifetime, conceivably. Hopefully.

Speaker A: Yeah.

Speaker B: Um or maybe not hopefully. What a crazy time to be alive.

Speaker A: Certainly.

Speaker B: I noticed that when I asked you if you had kids, your demeanor changed quite considerably.

Speaker A: Well, it's Yeah.

Speaker B: It's like you dropped into a different state. Obviously, that's been central to the rumination that you've been experiencing.

Speaker A: Well, it's a sad topic, right? Like when I had kids, like the reason to have kids is in large part about the future, you know, like it's not just like a cuddly thing to have with you in the moment. It's because you have all these hopes and dreams about how they'll grow up and how they'll go do their own thing and be their own person and stuff. And because of what's happening with AI, I think a lot of those dreams are in jeopardy.

Speaker B: Presumably, you still would have had kids.

Speaker A: I've actually flip-flopped on this occasionally. Yeah. Basically, the topline answer is I'm not sure. my first child was had we had her when we were um in 2019 she was born 2019.

Speaker B: Yeah.

Speaker A: So this is before my timeline shortened a lot. So at this point I was interested in AI I was tracking the field. I was making forecasts but I didn't like actually expect it to happen soon you know.

Speaker B: Mhm.

Speaker A: And then this caused like when I did start thinking like oh my gosh it's going to be happening like real soon um like by 2030 you know. um that caused some reconsidering and so I basically told my wife like let's not have any more kids. It's too uncertain, you know. But that turned out to be really hard because especially for my wife, like we already had one kid and like no siblings. Um so eventually I sort of gave in and was like, "Okay, well, you know what? We already have one. It's gonna be all right. Like maybe the future will be good. And even if it's not like, well, we're all in the same boat together.

Speaker B: It's quite chilling what you're saying. It's chilling because you know more than me. And if you're at home saying to your wife, listen, maybe we should pause on having more children and building a family because of what's going on with AI.

Speaker A: To be clear, this is Yes. I mean, yes, it's very concerning. I am I am chilled. Uh this is bad. This is what I've been saying. I hope things go well. I think things might go well. Um I think that there's a lot we can do to like steer things in a better direction.

Speaker B: I mean, one of those things as well, I have to say, is just speaking about it. It's I think a lot of the progress we've seen with governments waking up and, you know, we've seen certain things with people booing certain people at certain events.

Speaker A: Yeah.

Speaker B: Um is is a downstream from people like yourself actually coming on shows like this and all the other podcasts and

Speaker A: Yeah.

Speaker B: telling us what's going on.

Speaker A: Yeah.

Speaker B: Because elsewhere, to be fair, we're going to be gaslighted by the people that have the biggest PR machines.

Speaker B: Yeah. So, um, I often I think it's probably worth me saying I find myself kind of in two minds because I'm an entrepreneur and I'm an investor. I'm an investor in probably more than 100 companies now. And so many of those companies are using AI. I invested in Grock, the inference chip company. I've invested in SpaceX which now own another Grock and they're doing AI. I use AI every day in my life. I've been using it through this conversation to understand different things that you've said. So that's one side of me which is like business builder entrepreneur who has seen the benefits of AI in my own life. And then there's the other side of me. And it's funny because I think sometimes people think you have to pick a camp. But through all of my life, even when I was a social media CEO and I was saying, by the way, listen, I'm building a social media business, but I think there's some downsides to social media. I find myself at the same moment where I'm like, I build with AI. I have AI investments. And at the same time, as a civilian, I'm like,

Speaker A: yeah, I mean, I think that is a tension. I think that there's there's different ways you can draw the line. So, and I know lots of people who draw the line in lots of different ways. So, like there's some people who just like, I'm not going to use AI. I think this stuff is bad um and on a bad trajectory, so I'm going to like boycott AI, right? I'm not one of those people. I use AI a lot. We all do at AI features project. Um it's helpful for a lot of our work. The opposite end of the spectrum is people being like, well, it seems like it's on a trajectory to happen, so the thing to do to make it go well is to like get involved and accumulate power and try to like steer it from the inside.

Speaker B: Mhm.

Speaker A: And so I'm going to go work at OpenAI or Anthropic and like try to like climb the ranks and then like you know be someone who matters when the important decisions are being made, you know? And I know loads of people like that. That was like what I was doing when I was that wasn't what I was doing exactly but like

Speaker B: that was the path

Speaker A: that was like that was I mean this in some sense this is what the whole narrative of the companies are right like this is why they tell themselves it's okay to do what they're doing is that they're worried about the other guys you know and so like all these people are are deciding like we're going to like lean really hard into it. are going to like be there in the room when the important decisions are being made, you know? So, there's a whole spectrum and I'm sort of like somewhere in the middle. Like, I'm not at the AI companies. I'm not helping them go faster. Instead, I'm talking to the broad public and trying to advocate for what I think is the my current best guess as to the way out, you know, the way forward. Um, but I'm not like boycotting all the AIs. I'm I'm not like, you know, uh trying to I'm not refusing to like engage with it in that way.

Speaker B: Do you think it's too late?

Speaker A: No, I don't think it's too late. If I thought it was too late, I wouldn't be here. H

Speaker B: Where

Host: ...would you be?

Daniel: With my family.

Host: What's your closing message to the general public? If you had to have a closing statement to them...

Daniel: ...maybe I would say that like you're going to hear a lot of things and you already have been hearing a lot of things about AI and it's going to sound like science fiction, but sometimes things which sound like science fiction happen in reality. And in fact, many times historically things used to be science fiction have then become reality. And people need to stop thinking about what does or doesn't sound like science fiction and just start thinking about like the trends and you know the actual trends that this technology is on and reading and forecasting how it's going to go and then taking seriously the possibility that it could go something like this and then thinking about what should be done about that.

Host: And where would you direct them to get more information?

Daniel: You can go to a27.com to read our previous scenario. You can go to a2040.com plan A to read our new proposal for what is to be done. Um, these things are not just a sci-fi story. They also have lots of like explainers and links to other things. And so they're kind of like a nice jumping off point to to learn about all of this stuff. Um if you want I could um after this is over like give a reading list of like other papers and articles and blogs to follow and so forth...

Host: ...and I'll link them all below in the comment section. So if you're listening now go ahead and take a look at the comment se... the um description of this episode and you'll see a bunch of links which is Daniel's recommendations of what you should read. You know I think it's it's just a really really great moment in time to get educated on this stuff. Um humans have an inclination because of cognitive dissonance where we feel uncomfortable about something to bury our heads in the sand and avoid it. But actually I think this is one such time to do the very opposite for many reasons to to inform yourself so you know what actions to take but also because AI you know unavoidably is going to be a huge part of all of our lives and careers.

Daniel: Yeah. Yeah. Thank you. And that that's a good way to to say it. It's going to matter a lot. It's going to it's going to be everywhere soon and um we need to do something about it before it's too late.

Host: And what about AI future project?

Daniel: That's our organization. We spent a year writing AI 2027 after I left OpenAI and then we spent another year writing AI 2040 plan A.

Host: Daniel, thank you.

Daniel: Thank you.

Host: Thank you for all the work that you do. I can see how much you care about this stuff and it's your care. It's funny. Care itself makes others feel care. and um seeing how personal this is for you and seeing how much you've dedicated your life to this, but also hearing that you you basically walked away from $2 million to be able to speak to the public about this information is incredibly admirable and uh I I think voices like yours are more important now than they've ever been on this subject. So, please do keep fighting the fight that you're fighting. And that's one of information. It is of honesty and it is of saying what what is often the quiet part out loud and doing really really smart research. I'll link everything we've discussed today below. Um, and I hope we can chat again sometime soon.

Daniel: Thank you.

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