Transcript of Daniel Kokotajlo Interview: Diary Of A CEO Podcast

They’re all copies of the same big model and they’re owned by the company. And so they all follow the orders given by the company.

People should be asking questions 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 situation where some tiny group of people are essentially oligarchs or dictators.

Ironically, both of these risks — the loss of control and the concentration of power — are things that people in the industry have been thinking about for decades. Even before the AI industry existed, people thinking about AI were talking and writing about these things. And then part of the founding narrative or the founding myth of DeepMind and OpenAI 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 I can go on. There’s lots more reasons as well. One thing is World War III, geopolitical conflict. 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.

Another one — what about those jobs? You’re going to lose your taxi job. But not just the taxi driver — everybody, pretty much. 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.

The “Doomerism” Counter-Narrative

STEVEN BARTLETT: 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. The counter-narrative 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 counter-narrative? And you must have seen this emerging yourself, especially from people who stand to benefit, dare I say.

DANIEL KOKOTAJLO: Yeah, exactly. This counter-narrative is fairly recent and it’s been pushed by the people who stand to benefit from it. And it’s not true. These concerns have been around for decades, since before the AI industry existed. They’re actually pretty reasonable concerns. If you take the companies at their word and imagine that they are in fact going to build superintelligence, well, it raises a lot of questions — like who’s going to control it? Will anybody control it? What about the jobs? These are just kind of obvious implications to be thinking about and worrying about.

Who Is Daniel Kokotajlo?

STEVEN BARTLETT: Who are you and what’s your story?

DANIEL KOKOTAJLO: My name is Daniel Kokotajlo. 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.

STEVEN BARTLETT: AI forecasting. Yeah.

DANIEL KOKOTAJLO: So think about how industry analysts who work for hedge funds and stuff will make these forecasts of, here’s how many cars Tesla will be selling 5 years from now, or here’s what the price of electricity will be in 2 years. 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 see where this is all headed.

Inside OpenAI: What He Observed

STEVEN BARTLETT: Why did you go to OpenAI? What did you do there? What did you observe while you were there? And how did it change your perspective on the future of AI, but also, I guess, OpenAI as a company? And for anybody that doesn’t know, OpenAI are the company that produced ChatGPT.

DANIEL KOKOTAJLO: Yeah, so I went to OpenAI in 2022. A large part of what I did there was more forecasting. AI 2027 is a scenario that you may have heard of — I did smaller, lower effort versions of them internally for just internal circulation of, here’s some guesses as to what the next couple of years might look like.

I also worked on evaluations for dangerous capabilities, so trying to measure the AI’s cyber abilities or persuasion abilities or situational awareness. And I also briefly was on a capabilities team doing reinforcement learning to create agents. AI is in fact getting a lot better, and I can say more about why — scaling laws, 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. OpenAI, Anthropic, and DeepMind all had these founding narratives of, “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 deeply guiding their actual behavior. And that when push comes to shove, they’ll follow their incentives rather than do what’s actually good.

STEVEN BARTLETT: So you’re inside OpenAI at the time and you start to believe that they’re following commercial incentives versus the, I guess, social or societal incentives that they founded themselves on. Sort of.

Power-Seeking Over Profit: The Real Incentive

DANIEL KOKOTAJLO: I mean, I wouldn’t actually describe it as commercial incentives. I think I would describe it as power-seeking incentives. It’s true that the companies care a lot about making a lot of money, but especially at the very top of these companies — the leaders — they understand that this is about more than just money.

There are these emails that came up in the lawsuit between Musk and 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 2017 about how the reason why we made OpenAI was because we were worried that Demis Hassabis at Google was going to become dictator with AGI. Even back then, this is obviously about more than just money. 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.

STEVEN BARTLETT: Have you met Sam Altman?

DANIEL KOKOTAJLO: Yeah.

STEVEN BARTLETT: And did that shape your opinion of his incentives or why he’s doing what he’s doing? Because there’s a lot speculated about what his incentives are. I mean, his most recent narrative says, “for the good of humanity.” I think that’s what.

DANIEL KOKOTAJLO: Yeah, I mean, I think the main thing I’ve learned is don’t pay attention to the narratives. 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.

STEVEN BARTLETT: And why are you no longer at OpenAI?

DANIEL KOKOTAJLO: 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, of course we wouldn’t actually just build superintelligence as soon as possible. Once we started getting really close, 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 because 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, 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, 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? They’ve sort of, partly because this has become more politicized and they’ve become bigger and been 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. And so yeah, 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.

Leaving OpenAI

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

DANIEL KOKOTAJLO: I resigned in 2024. I had a nice goodbye party.

STEVEN BARTLETT: What were the reasons you gave for quitting OpenAI?

DANIEL KOKOTAJLO: I thought that we were rationalizing too much and that we needed to think more about what would actually be good for the world. 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 a PR department and things like that. And so it started becoming more difficult to publish the sort of research that I was doing. For example, those scenarios that I mentioned, 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 hypey way, but they didn’t want me to publish the scenario, for example, laying out like, here’s how things might actually look.

STEVEN BARTLETT: I’m just super curious as to what it’s like being in a company like that when ChatGPT-3 is released. You were there at that time, right? Which was a moment where I think the whole world stood up and realized that this technology was powerful. And the conversation really began from a society level. The company starts growing super quickly, quicker than I think anybody could ever have imagined. What was it like inside there? What did you see change over that period of time?

DANIEL KOKOTAJLO: I remember one all-hands meeting where Ilya said something like — Ilya being? Ilya Sutskever, who was 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 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. Lots of new people came in. Ironically, the amount of conversation about superintelligence and the implications of superintelligence arguably sort of went down over time due to this growth, right? So because the company would 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.

The Anti-Disparagement Clause

STEVEN BARTLETT: You lost $2 million for not signing an anti-disparagement clause, which would mean you couldn’t criticize the company.

DANIEL KOKOTAJLO: Ah, yes. Well, so I got to keep the money.

STEVEN BARTLETT: Oh, you got to keep the money.

DANIEL KOKOTAJLO: So what happened was, after I had left, said my goodbyes, etc., I got the exit paperwork, and it included this clause that said you basically have to agree not to criticize the company again, 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, 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, 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 stock if you don’t sign this thing. And my wife and I were upset about this. We talked about it for like a month or two, consulted some lawyers, and then ultimately decided to just refuse to sign.

STEVEN BARTLETT: Which would mean you would have lost $2 million.

DANIEL KOKOTAJLO: That’s right. Which was like 80% of our net worth at the time. Fortunately, it didn’t go the way we expected. It blew up basically on the internet. When people heard that we had done this and that we had said no, it became like this huge scandal. Employees at the company started asking questions in Slack and asking leadership like, “Wait, what? Why are you going to take away our equity? What is this?” Because 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. And so they backtracked and they said, “Never mind, never mind, we’ll change the paperwork. You can keep the equity. It’s fine.”

STEVEN BARTLETT: And Sam Altman came out and said he was embarrassed that he didn’t realize this was happening.

DANIEL KOKOTAJLO: Yeah, he had no idea, apparently.

STEVEN BARTLETT: You don’t believe him?

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

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

DANIEL KOKOTAJLO: It’s true, most people would have and most people did. And money is nice, but it’s not the only thing. Sometimes it’s good to take a stand on principle.

The Race Toward Superintelligence

I keep mentioning superintelligence. Perhaps I should say more about 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 that will help the companies go faster, 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, analyzing experiments, communicating those results, all the other parts 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.

STEVEN BARTLETT: When you say do the entire thing, what do you mean? Do the entire thing?

DANIEL KOKOTAJLO: Anthropic and OpenAI in particular are trying to automate themselves. They’re trying to make it the case that they don’t really need human employees anymore. They just have a giant army of AIs that’s churning 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 all just happening internally, but also interfacing with the world, going out and talking to people, collecting the data, setting up the training environments, doing the business deals and so forth. 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 — superintelligence — and they’re trying to get there before their competitors do. Needless to say, this is incredibly dangerous, I would say, and in addition to being dangerous, it’s a power grab. Right? 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. Insofar as they can work out something with the president and integrate it into the military or whatever, then that would give the US immense hard power over all of the countries, right?

Obviously nobody knows exactly when this is happening, but a very disquieting thing has happened over the last year to me, which is that when we published AI 2027, people were generally of the opinion that my timelines were too short and that probably it would take more than 2027 until we got to the sort of events that I was just mentioning. Recursive self-improvement, AI automating the whole research process, superintelligence — these types of milestones happen in 2027 in AI 2027.

AI 2027: The Scenario Forecast

STEVEN BARTLETT: Which is this research paper you published.

DANIEL KOKOTAJLO: That’s right. It’s a scenario forecast that sort of lays out month by month a possible future trajectory. It was sort of, at the time that we started writing, my best guess as to what would actually happen. Obviously there’s lots of uncertainty, but 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 it’ll probably take a couple of years longer than you think.” And now it’s more 50-50, especially when I go talk to people at Anthropic and OpenAI. They’re often like, “Yeah, no, 2027, that’s basically what’s going to happen. Just like you wrote.”

Why did you update your timelines? Oh yeah. Context for this is, after writing AI 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 still could happen sooner, could happen later, 2030. But now when I talk to people in the companies, they’re like, “It’s not going to take that long. You need to shorten them again, get them back to 2027 or 2028.” So that’s a bit disquieting. Again, don’t know how long it’s going to take, but this is the stated plans of the AI companies — to do this incredibly dangerous thing. And they think that they’re just a few years away.

STEVEN BARTLETT: So you wrote this report here, What 2026 Looks Like, and you wrote this in 2021, and it was remarkably accurate, helped make a name for yourself amongst everybody in AI. And which one was it that JD Vance, the vice president, read? I think it was this one, wasn’t it? Yeah, this one. And then you published this one, AI 2027, and this was published, I believe, in 2025.

DANIEL KOKOTAJLO: Yes, that’s right.

STEVEN BARTLETT: April. What were you forecasting in here? What are the key things that you said in here for people that haven’t read it?

AGI, Superintelligence, and the Path to 2027

DANIEL KOKOTAJLO: 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 to superintelligence, they’re working with the government, 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 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 because of the race dynamics and because of the profit motive, they end up deploying it everywhere. And it builds robot factories that build more robots that build more robot factories, et cetera, 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 be aligned anymore, then they stop listening to orders. That’s the race ending of AI 2027.

We also wrote a different branch, which is the slowdown ending, which was intended to illustrate the concentration of power issues that I mentioned previously. What if hypothetically the alignment issues get sorted out sufficiently quickly? What if it turns out that it’s not too hard? With 2 months of slowdown, we can figure out how to make the AIs robustly do what we want and have the values that we want them to have. So that’s one possible branch.

And in that branch, it looks pretty similar. They take the jobs, beat China, et cetera. 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, et cetera.

STEVEN BARTLETT: Is there any possibility, do you think, that we never get to this thing called AGI? And how do we distinguish AGI from this term superintelligence? What’s the difference?

DANIEL KOKOTAJLO: Yeah, so the difference is that AGI is a more vague and weak term. Superintelligence is a bit more precisely defined. It’s better than the best humans at everything, faster and cheaper. AGI is more like — it stands for artificial general intelligence, which means AIs that can do things in general rather than like some specific task.

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

STEVEN BARTLETT: 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 stuck in my computer.

DANIEL KOKOTAJLO: The way that people talk about this is that they basically just say we’ve achieved superintelligence for cognitive tasks. Then you can talk about full superintelligence that can do this physical stuff.

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

DANIEL KOKOTAJLO: I think so. I mean, again, this is not something that we can be certain about. You asked, 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 magical about the human brain. It’s just a bunch of neurons. It is possible for a digital system to do similar functions in the same way that a plane can fly just like a bird. Not in the same way as a bird necessarily — it’s not flying in the same way that a bird flies, but it flies. So it does seem like, yeah, it seems possible.

Daniel’s Outlook: Optimism vs. Pessimism

STEVEN BARTLETT: You’ve written all these research reports. You’re working on another one that’ll be released 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?

DANIEL KOKOTAJLO: Based on everything that you know, I think we are headed to a bad place if things don’t change. 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.

STEVEN BARTLETT: How do you contend with that personally and emotionally?

DANIEL KOKOTAJLO: It’s rough. I mean, I think it’s the sort of thing that 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. I’ll put it this way. I would be incredibly happy if all my predictions turn out to be wrong and AI hits a wall, for example.

STEVEN BARTLETT: It gets you down on a regular basis.

DANIEL KOKOTAJLO: I used to be known as a pretty chipper and optimistic person, but in 2020, my AI timelines predictions started collapsing due to GPT-3 and the scaling laws papers and the Bio Anchors 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 quite plausibly coming by the end of the decade. And humanity is very obviously not ready for this in a whole bunch of different ways. And so that’s obviously very scary.

STEVEN BARTLETT: And that’s an extremely scary world because of all the things you’ve said. But again, because of this recursive self-improvement where AIs can train themselves, and at such a point we’re starting to lose hold of what’s going on here.

DANIEL KOKOTAJLO: I mean, the AIs are already training themselves, to be clear. It’s more like closing the entire research loop.

STEVEN BARTLETT: Okay, so doing everything.

DANIEL KOKOTAJLO: Yeah. Right now, a lot of the training data is generated by AIs. A lot of the reinforcement — like the grading that happens, doling out of positive and negative reinforcement — is itself done by AIs.

STEVEN BARTLETT: Can you explain that in layman’s terms?

How AI Actually Works: Neural Networks Explained

DANIEL KOKOTAJLO: 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. It’s not like some engineers at Anthropic went and wrote lines of code that basically says like, 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.

STEVEN BARTLETT: What’s that?

DANIEL KOKOTAJLO: Well, think about how the brain is a bunch of neurons connected to each other that are firing 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 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 beliefs about the world and you can sort of mentally simulate how it’s going and stuff like that.

So artificial neural nets are like that, except artificial. It starts off as a giant tangled spaghetti mess of randomly generated artificial connections called parameters. These days, they might be something like 10 trillion parameters in the biggest AIs. So it starts off randomly generated. So it’s of course completely useless. 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. So it’s basically playing this game of predicting the next word.

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

DANIEL KOKOTAJLO: Yep.

STEVEN BARTLETT: We have more pathways when we’re younger. And just like the process of training in AI, we’re trained down to remove the ones that aren’t useful and build up on the ones that are.

DANIEL KOKOTAJLO: Yeah, it’s both pruning and strengthening.

STEVEN BARTLETT: Okay.

DANIEL KOKOTAJLO: And it seems like in humans it’s actually more pruning than strengthening, but it’s both. 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. And a similar thing does happen in humans. So basically the random tangle gradually takes shape and gradually sort of coalesces into more useful circuitry that has stored lots of facts about the world and has stored lots of skills for how to 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, 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, here’s an environment, you have access to this virtual computer, here’s the code base 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, go, 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.

Building Artificial Brains: The Road to Superintelligence

STEVEN BARTLETT: So what does superintelligence 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?

DANIEL KOKOTAJLO: So there’s different AI models, right? So there’s GPT-3 and GPT-4 and GPT-4.5 and GPT-5 and GPT-5.5 and 5.6, right? Sometimes they’re just the same previous model, but with extra training. Sometimes they’re a whole new model that’s been trained from scratch, including starting the whole pre-training process again.

Over the last couple of 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.

STEVEN BARTLETT: Okay.

DANIEL KOKOTAJLO: Right now they’re at something like 10 trillion parameters. Back in 2020, it was more like 175 billion. So we’ve grown like 2 orders of magnitude in 6 years.

STEVEN BARTLETT: 2 orders of magnitude.

DANIEL KOKOTAJLO: Yeah, like 2 10Xs. So 100X, right? So that process is continuing. 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 connections and the neurons and so forth, and change the 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.

STEVEN BARTLETT: We’re literally building a brain.

DANIEL KOKOTAJLO: Basically, yeah. As they make more brains, they’re getting better at making them — they’re making them bigger and making them more efficient and so forth.

STEVEN BARTLETT: And it’s literally modeled on the brain. Like the way it works, right?

DANIEL KOKOTAJLO: It’s certainly heavily inspired by the brain, but I shouldn’t overstate the analogy. There are lots of differences too. So for example, the transformer architecture, which is the architecture that they use for these LLMs, 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 backpropagation algorithm is different from the sort of learning that naturally happens in the human brain.

So there are some differences, but yes, broadly speaking, we are sort of making artificial brains. It’s kind of like for brains what a plane is for a bird.

The Weight of AI’s Future

STEVEN BARTLETT: That’s a really good analogy. 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.

DANIEL KOKOTAJLO: Yeah.

STEVEN BARTLETT: But actually it’s judged based on the output, isn’t it?

DANIEL KOKOTAJLO: I mean, 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. And it seems like they’re going to be accomplishing a lot more in the near future.

STEVEN BARTLETT: Yeah, I do. I asked the question about how this weighs on you personally, because I can sense that you’re actually personally bothered.

DANIEL KOKOTAJLO: I mean, I think the situation is crazy. Like, first of all, it’s very exciting. Like, AI is really fascinating and interesting stuff. I’ve been following the field for more than a decade now. I’ve been part of it for some years and 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 in stories, it always ends well, but this is real life. And I think we have to sort of stare reality in the face and realize that it might not actually end well.

Paradigm Shifts and Unexpected Developments

STEVEN BARTLETT: 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?

DANIEL KOKOTAJLO: 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 there have been some differences from what we expected at the time we wrote this.

So the government has actually gotten involved faster than we expected and has been more aggressive than we expected. So the export controls on Mythos being the biggest example, and also threatening Anthropic with being destroyed by the Defense Production Act. Another thing that’s been surprising to us is that Anthropic in particular has gone from second place to first place in the race, basically.

STEVEN BARTLETT: Why do you think that happened? Because it seemed like OpenAI were out front and clear, but suddenly Anthropic have lapped them.

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

STEVEN BARTLETT: Why do you think they have more talent?

DANIEL KOKOTAJLO: 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’s the— what is the next best alternative? You could maybe say strategy, some combination of those things. Something that wasn’t just like the amount of resources they had.

The 70% Chance of Catastrophe

STEVEN BARTLETT: 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 100 buttons on this table now, and one of them would end the world. Would I dare press any of them? No, I wouldn’t press any of them.

But he made the case to me that these AI CEOs are very smart and they understand superintelligence and that they think actually, if there was 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 Daily Show, the interview you did, you said that you think there’s a 70% chance of human extinction due to AI.

DANIEL KOKOTAJLO: 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, for example, possibly the AIs take over and then don’t actually kill everyone. Maybe they do something else. Like, 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 why I don’t usually say, like, 70% chance of actual human extinction, but 70% chance of something like AIs taking over, some sort of very big catastrophe like that. That could lead to human extinction.

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

DANIEL KOKOTAJLO: Yes. But I think that an important thing to understand is that 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 genuinely convinced themselves that 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 Sam’s probably thinking, can’t let Dario or Elon get there first. And I know Dario is thinking Sam can’t get there first. Elon’s thinking that they’ve all probably convinced themselves that, oh yeah, maybe it’ll go horribly wrong, but probably it’s going to be fine. And probably I should be the one in charge.

Anthropic’s Stance and the Question of Trust

STEVEN BARTLETT: It appears to me that Anthropic are the only ones that are talking about the potential chance of extinction or a catastrophic event or the real downsides 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 tech industry in San Francisco. I’m watching a lot of interviews and 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 Claude model that they started to warn the world about. Again, he is attacked immediately for saying that.

DANIEL KOKOTAJLO: Yeah.

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

DANIEL KOKOTAJLO: Yeah, I mean, it seems like Anthropic and Dario have been more willing to say and do things that are costly to their bottom line, at least in the last year or so. That’s an example of it. I don’t think that really wins them favors in the administration or among their investors to say that type of thing. And a better example is just the whole fight between the Department of War and Anthropic was an example of them doing something that cost them a lot of money and even more importantly cost them a lot of power for something that they could have just signed the contract.

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. Like none of these people should be trusted with that much power, basically.

STEVEN BARTLETT: Nobody should.

DANIEL KOKOTAJLO: Nobody should.

STEVEN BARTLETT: Regardless.

DANIEL KOKOTAJLO: Regardless.

Human Incentives and the Race to the Bottom

STEVEN BARTLETT: Yeah. So on this point of the buttons, you do believe that they think there’s a credible chance of extinction.

DANIEL KOKOTAJLO: Yeah, but they’ve convinced themselves that it’s probably fine. And also it’ll be even worse if I’m not doing it. That’s what they’ll say inside the companies too. Like people will be like, well, if we stop, what about the other guys? Like, they’re not going to stop.

STEVEN BARTLETT: Yeah, 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, 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 this end? Well, it carries on going.

The Race Dynamics and Hope for Change

DANIEL KOKOTAJLO: Seems like it. I mean, 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’s 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 break them anymore because you get punished if you break them and everyone else is also following them too. And so, it’s fine. So, there is that sort of ray of hope that we can change the incentives if the government, especially the US government, but then later other countries act 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, Dario or Sam or Elon might realize that actually it’s not even in their own interest to keep racing unilaterally. And the problem with that is it’s only if it gets extremely obvious and extremely dire.

So in AI 2027, in that scenario, there’s this choice point that I mentioned. And in one case, the AIs are misaligned and the other case, the AIs are aligned. At that choice point, we have one branch that depicts the misalignment ending and one branch that depicts they slow down a bit and solve the alignment issues. The instigator for that choice point is they see some evidence that their AI might be misaligned and plotting against them. So if you actually see that evidence, then it’s like, oh gosh, maybe we shouldn’t put it in charge of everything and let it rip, because that evidence is staring us right in the face that it’s untrustworthy.

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. 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. So that’s the second ray of hope. Like overall, I don’t think that we’re definitely doomed. Like I said, 70%, but I could see it working out pretty well as well.

Jobs and the Coming Wave of Automation

STEVEN BARTLETT: What about jobs?

DANIEL KOKOTAJLO: Yeah. So I think I’m excited to at some point get into the new thing, which is the more optimistic, positive vision. And that will have a lot to say about this because in the prediction, in AI 2027, by the time everyone loses their jobs, there are worse things happening. Or it’s kind of like too late by that point.

But yes, once — just think about it. If the companies do manage to build superintelligence, 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.

STEVEN BARTLETT: And that’s, again, the timeline is by the end of sort of 2030, you reckon — you think superintelligence might arrive. I’m trying to think about when we could start to see job displacement in the economy.

DANIEL KOKOTAJLO: We’re already starting to see a little bit of it now, but not very much.

STEVEN BARTLETT: Why?

DANIEL KOKOTAJLO: Because the AIs aren’t good enough yet. They’re impressive, but they’re not just a drop-in replacement for a human worker in almost any field.

STEVEN BARTLETT: And do you think that’ll be sudden?

DANIEL KOKOTAJLO: 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. And this is maybe how it is in a lot of science fiction — the AIs gradually get better at a bunch of things and they gradually automate like this one industry, like pharma, and then they automate steering drones, then they automate driving cars or something like that.

But what’s different about the real world is that the companies have converged on this strategy of automating themselves first — automating the AI research process. And so, if they’re allowed to continue with this strategy, we’re not going to see the robotaxis and the plumber robots and the lawyer AIs. We’re not going to see that 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 get going and get to very high levels of intelligence, very high levels of general intelligence, and then deploy more out into the economy. By the time it’s actually coming for all these different jobs, they will have had fully autonomous AI research happening for months, maybe years. And that means that the AIs will be vastly superhuman at AI research, and probably also vastly superhuman at lots of other things just as a side effect. If you’re wondering what this looks like, well, we wrote about what it looks like. It’s sort of like this wave smashing through the economy after they do the intelligence explosion internally.

STEVEN BARTLETT: 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 all at once. Is that accurate?

DANIEL KOKOTAJLO: Yes, but it’s not even exactly that, because even if it’s mostly just getting better at the things that it’s doing, like research, that’ll have some spillover effects to other skills as well. And then when it turns to focusing on those other skills, it’ll be able to do them very fast.

What Jobs Will Remain?

STEVEN BARTLETT: What jobs remain in such a scenario, do you think?

DANIEL KOKOTAJLO: I think that’s actually a political question, not a technical question.

STEVEN BARTLETT: Because?

DANIEL KOKOTAJLO: Because on a technical level, all the jobs can be done by the AIs if they’ve reached that level. And so it’s a question of what jobs are allowed for them to do.

STEVEN BARTLETT: And what kind of jobs wouldn’t be allowed, do you think?

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

STEVEN BARTLETT: I mean, in this scenario, the humans are still controlling them, the AIs.

DANIEL KOKOTAJLO: Depends on what you mean by control, right? So there’s like, 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?

STEVEN BARTLETT: Are they obeying the orders is really what I’m saying.

DANIEL KOKOTAJLO: Yeah. Even in AI 2027, in the scenario where the AIs take over and kill everyone, there’s a period of several years where they’re still obeying orders and they’re taking some jobs but not other jobs, and they’re helping to make better weapons that the US government can use to 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 trust them. And they’re deliberately deploying them into all of these positions because they think that things are fine. But because these things are neural nets, you can’t just look inside and see what it’s really thinking. You can’t really tell.

The Black Box Problem: Understanding AI Decision-Making

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

DANIEL KOKOTAJLO: One note of optimism is that it doesn’t necessarily have to be that way. There’s a subfield 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 trained artificial neural nets and piece them apart and understand how the information is flowing and how the decisions are being made, so to speak.

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 this particular connection works. But how do you get a sense of the whole? How do you get a sense of what’s happening at a high level? 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?

STEVEN BARTLETT: Yeah.

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

STEVEN BARTLETT: It is pretty crazy to think that we’re building a technology, a brain that we don’t understand.

DANIEL KOKOTAJLO: Yeah, it’s pretty crazy.

STEVEN BARTLETT: I mean, it’s one of those things where, in 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.

DANIEL KOKOTAJLO: Yeah.

STEVEN BARTLETT: But they don’t really know what the f*.

DANIEL KOKOTAJLO: Yeah. I mean, it’s kind of just obviously a dangerous thing to be doing. But we’re doing it anyway because of this history of how the field has in the last 10 years where 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 they’re racing each other and they’re also under all sorts of political pressure to pretend that it’s not as bad as it seems because they don’t want to anger their investors. They don’t want to anger the White House.

Skills and Careers in an AI-Dominated Future

STEVEN BARTLETT: One of 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 and students focus on over the next 10 years?

DANIEL KOKOTAJLO: That’s kind of like — imagine if you were someone living in Mexico in like 1500, and then you hear that the conquistadors are coming. You could be asking yourself like, okay, well, what sort of job should I be switching to, to survive this transition? But you have a lot more to worry about besides that.

But yes, I think I would say that if we manage to avoid the loss of control problem and we end up with humans still in charge of the AIs and humans can 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 judges, potentially.

STEVEN BARTLETT: What about podcasters? Be honest.

DANIEL KOKOTAJLO: Probably not podcasters. I think stuff like being a nanny maybe, right? Like I think that even if there’s a robot nanny that’s 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 reason like that. There’s also stuff that might be legally protected, like maybe judges, for example, are going to be legally required to be humans and not robots.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: The problem with that is that past technological advancements have been more narrow. They’ve 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 protected by regulation or something.

But so, for example, right now there’s this sort of cycle where the AI has learned to do a certain thing, like write copy or draft code or 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 new jobs opening up and people flooding to them. But if it gets to the point where the AIs 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 — the AIs can switch to that too, and they’ll already be better at it than you.

The Timeline of AI Unemployment and Economic Disruption

STEVEN BARTLETT: 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 US unemployment rate, currently 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.

DANIEL KOKOTAJLO: Yeah, basically nobody has said that there would be mass unemployment by now, or at least we didn’t say that, 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 superintelligence.

Because again, the companies aren’t trying to cause mass unemployment as step 1. That’s like step 3 after — it’s like step 1, automate themselves. Step 2, have this recursive self-improvement to get to superintelligence. Step 3, 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. They’re going to be getting the superintelligence first and then doing the broad wave of automation, which means that by the time they’re actually doing all of that, well, it’s already going to be moving very fast and the AIs will already be very powerful.

AI 2027 Predictions: What Has Come True

STEVEN BARTLETT: 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. That happened. I’ve actually got an AI agent in my WhatsApp to talk to. Of course, I’ve got Claude Bot exploded obviously around the world. And now Claude have talked about their new Slack integration, but lots of people are using agents now. And that happened. I’d say for us, we really sort of caught onto it at the start of 2026.

You also said that 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.

DANIEL KOKOTAJLO: 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 my 50% mark was 2028 for the full automation of AI research milestone, not 2027. And then other people on my team had more like 2030, 2031, things like that.

So I kind of want to maybe try to illustrate this with — we have this probability distribution. It’s like a smeared out probability mass and the 50% mark is this particular year, but there’s a lot of possibility that it happens years earlier or years later, right?

STEVEN BARTLETT: Got you. What is this AI 2040?

AI 2040 Plan A: A Recommended Alternative Path

DANIEL KOKOTAJLO: So AI 2027 was our best guess prediction as to how things would actually go.

STEVEN BARTLETT: Yeah.

DANIEL KOKOTAJLO: AI 2040 Plan A is our recommendation for how things should go. So we called it AI 2040 because in this scenario, they build superintelligence in 2040 instead of much sooner because they delay things.

STEVEN BARTLETT: Why do they delay things?

DANIEL KOKOTAJLO: To manage the risks and make sure that power is distributed equitably. They basically 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 superintelligence in 2040, instead of in, say, 2030.

And then we call it Plan A because, well, it’s our recommendation. We’ve come up with a plan for what government should do. And the scenario is an illustration of what it might look like to implement that plan in a similar way to how AI 2027 is kind of an illustration of what it might look like to do what the companies are currently planning to do. That makes sense.

STEVEN BARTLETT: And is this wishful thinking or is this what you think is going to happen?

DANIEL KOKOTAJLO: No, it’s definitely not what we think is going to happen.

STEVEN BARTLETT: It’s not what you think is going to happen.

DANIEL KOKOTAJLO: No, no. What we think is going to happen is still something more like this, right? We don’t expect the world to listen to us, right? This is our recommendation, but we hope that people do something like this and we think it’s possible, but it’s not our prediction for what’s going to happen by default.

The Future of Jobs and Labor Output

STEVEN BARTLETT: So I do want to run through the plans, the potential plans, and also Plan A, but just to close off on how things might look after the year, because I think I wanted to touch on robotics too, and I’ve got this graph here which talks about share of labor output.

DANIEL KOKOTAJLO: Yeah. Yeah.

STEVEN BARTLETT: Which I found to be quite striking. I’ve been sat here wondering, as an employer who employs hundreds and hundreds of people, when all this stuff is going to happen. And we’re still hiring more people as things stand. There are some roles where our consideration is changing, shifting considerably. And I’d have to say that we’re probably in a phase where our teams are AI-powered and they’re using agents to do some of their work now. But I’m wondering as an employer, like, when does this happen?

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

STEVEN BARTLETT: Yeah, we’ll put it on the screen.

DANIEL KOKOTAJLO: 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 2027.

But as you can see, 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 superintelligent AI by then, because their strategy is to first get superintelligent AI, then do all that stuff.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: Oh, but it’s a risk well worth taking. I mean, it’s true that right now a lot of people use AI for a lot of things, but 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, even though there would be significant costs.

STEVEN BARTLETT: But you can’t over here, right? Can you? At this point where AI and robotics are doing most of the labor output?

DANIEL KOKOTAJLO: That’s right. But in this scenario, in the AI 2040 Plan A scenario, they put in the regulations in 2029. 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 happens gradually over the course of the 2030s instead of happening in this sort of crazy shock, a year later, right? Because in this scenario, they don’t let the companies recursively self-improve and get to superintelligence 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.

STEVEN BARTLETT: But I guess I noticed here that in both your scenarios, eventually AI and robotics do pretty much all the jobs.

DANIEL KOKOTAJLO: Yes.

STEVEN BARTLETT: So you kind of side there with Elon when Elon says that working will be a choice?

The Plans: From Plan A to Plan S

DANIEL KOKOTAJLO: Because I mean, yes, 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.

Should we bring up the plans diagram? Yeah. Thanks. Yeah. So our scenario is called AI 2040 Plan A. It’s a scenario in which they slow down AI development to make a superintelligence 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 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 AI 2027. The race continues, there’s very little regulation, you can read about that in AI 2027.

Plan C, also very similar to what happens in the slowdown ending of AI 2027 where they solve the alignment problems. So in that ending, they 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 and beat China and all those things.

Plan B is kind of like Plan C in that, well, basically in Plan B, you’re being more aggressive towards China and you’re taking actions to sabotage or cyberattack them to keep them behind so that you have more breathing room to solve the alignment problems yourself.

Plan A is our recommendation. It’s 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.

STEVEN BARTLETT: What do you want?

DANIEL KOKOTAJLO: Plan A is our recommendation. I think that I’m sympathetic to Plan S, but for reasons we explain, we recommend Plan A instead.

STEVEN BARTLETT: And what do you think is most probable, if you’re being honest?

DANIEL KOKOTAJLO: Plan D. Which is that they just keep going. Just the AI 2027 type of thing where they keep racing. They don’t really slow down significantly. And things happen extremely fast.

The diagram sort of explains roughly the reasoning behind this too. So there’s this high-level thing of — 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? 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 the ones that we could compress onto a screen.

A Father’s Perspective on the Future

STEVEN BARTLETT: Do you have children?

DANIEL KOKOTAJLO: Yeah, I have two children. It’s kind of sad. 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.

STEVEN BARTLETT: When you say this will be all over by the time they join the workforce — what do you mean by this will be all over?

DANIEL KOKOTAJLO: So these milestones that I described, like AI is automating the AI research, AI is getting superintelligent, AI is then exploding out into the economy, taking the jobs, building robot factories to build more robots to build more factories, et cetera. GDP starting to go vertical. That sort of thing is what I mean. All of those events transpiring. Maybe there’s a 10, 20% chance or something that it hits a wall and none of this comes to pass, even if we don’t do anything.

STEVEN BARTLETT: How old’s your oldest?

DANIEL KOKOTAJLO: 6.

STEVEN BARTLETT: 6. Boy, girl?

DANIEL KOKOTAJLO: Girl.

STEVEN BARTLETT: Girl. So your daughter comes to you and says, “Dad, what shall I study in school?”

The Age of Abundance and Who Controls It

DANIEL KOKOTAJLO: I mean, again, 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 that the thing to do is, well, A, try to make it actually go well. 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 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, basically.

STEVEN BARTLETT: Elon talks about this age of abundance we’re heading towards. Age of abundance.

DANIEL KOKOTAJLO: There’ll definitely be abundance. The question is, who controls the abundance and what do they do with it? 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 political structure governing how they make those decisions?

STEVEN BARTLETT: I think it was Geoffrey Hinton that said to me, he said, there’s no example in nature where a more intelligent species 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. Yeah.

DANIEL KOKOTAJLO: I mean, that’s the thing is I think it’s like, that should be our default assumption is that, 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.

STEVEN BARTLETT: And then we’re going to give them bodies.

The Default Trajectory and How to Change It

DANIEL KOKOTAJLO: Yeah. And then they’re going to be autonomously building new factories and so forth. And how is this supposed to end well again? Isn’t this just exactly like us picking a new species that’s then going to outcompete us when it doesn’t need us anymore?

I think that is just the default trajectory. Now there’s a whole argument we can get into about 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 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 part of what makes this difficult is that we’re building these AIs in race conditions. 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.

A lot of it is happening behind closed doors. Only a few people can really see the recipes that they’re using to train these AIs and so forth. Oftentimes when the AIs behave in unexpected ways or even just 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 let them self-improve. These are choices that we could not make.

Ilya Sutskever and the Incentives of AI Leaders

STEVEN BARTLETT: Ilya was, as you said, he was one of the leaders at OpenAI, and he left and he started his own company now, Safe Superintelligence. Very curious name of a company, Safe Superintelligence, after leaving OpenAI. Did you ever get to work with him?

DANIEL KOKOTAJLO: I wasn’t directly working with him. I had a couple of chats with him.

STEVEN BARTLETT: Do you think he’s genuinely concerned as well?

DANIEL KOKOTAJLO: I think he is, but I think he’s similar to these other CEOs where, I mean, just think about the sort of incentives that they’re under, right? They can sort of see the problem and then they can be like, okay, but if I stop, if I quit my job or do something else, that’s not going to solve the problem because the other CEOs are going to keep going. And even if all of us didn’t go, then maybe China would keep going. So man, it 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, and maybe I can make it go well.

And at any rate, 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 reason through all of this and then convince themselves that the thing to do is for them to build it and to do it better. And I think Ilya is just the latest example of this. Elon’s another example. Dario is another example. Arguably OpenAI at the beginning, Sam was an example, although Elon and Dario were at OpenAI early on.

STEVEN BARTLETT: So what do you think they should all do then?

DANIEL KOKOTAJLO: 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.

STEVEN BARTLETT: Okay, so walk me through Plan A. Yeah.

Plan A: A Slower Timeline and the Case for Regulation

DANIEL KOKOTAJLO: So in this scenario, AI takes longer to get to recursive self-improvement and full automation of AI research than it does in AI 2027. We figured that we should try to illustrate 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 racing, but they don’t quite succeed in automating 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.

STEVEN BARTLETT: Can I ask, how does the elections overlay with your timeframes 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 the general public and that it will be one of the big ticket items on the ballot.

DANIEL KOKOTAJLO: We think that it’ll be maybe the most important issue in the presidential election in 2028. I think a lot of people, most people will be quite concerned about where things are headed. And that’s part of why we chose to depict things the way that we’re 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.

STEVEN BARTLETT: And in this scenario, in 2027, would the general public have felt the consequences of AI much more severely than they have now by then? Yes.

DANIEL KOKOTAJLO: Although still, even in 2029 in this scenario, they still mostly have their jobs as depicted here. So in 2029, in this scenario, lots of jobs now involve managing AI agents. 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. That was the sort of thing that would come in 2030 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 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.

STEVEN BARTLETT: Who’s they?

A Controlled AI Future: The Roadmap

DANIEL KOKOTAJLO: So they can make it safe. The politicians, the president and the people who voted for the president and the heads of other governments and so forth. So goal 1, slow things down. Goal 2, 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 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 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 broad diffusion of AI into society rather than 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 literally helps them catch up because then they can 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 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 superintelligence 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 AIs and robots and compute everywhere.

So I can sort of walk you through the timeline if you’re interested.

STEVEN BARTLETT: Sure.

DANIEL KOKOTAJLO: 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 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 involves training them, but just taking existing AIs and using them to serve customers, that’s called inference.

And so the sort of solution they come up with here in this scenario is we’ll 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 6 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’s going to happen. Once they get those new data centers set up in 2030, then AI research continues.

Total Research Transparency

DANIEL KOKOTAJLO: 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 architectures, et cetera. We think that 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. 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 governments in particular kind of 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 is incentivized to like fool the regulator. And also if they discover some new problem that’s not even on the government’s radar, 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.

STEVEN BARTLETT: Faster, but it kills their competitive advantage.

DANIEL KOKOTAJLO: Yes, Dropbox not going to like this. OpenAI is not going to like this. This would be 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’re not Anthropic, you’re not OpenAI, then you would love this because this helps you catch up, or this helps you to like capture more of the value from the chips you’re selling, for example, or from the like downstream product that you’re making.

Job Disruption and the 2031 Milestone

STEVEN BARTLETT: And by 2031, then you have one-fifth of all cognitive labor done by AI.

DANIEL KOKOTAJLO: 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. They don’t have to be exactly the same. 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, like, oh, you’re letting them do that sort of thing and you’re not, like, maybe we should let them do this too. 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.

So anyhow, we’re imagining that 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 AIs 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, 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.

STEVEN BARTLETT: But still through these, we see job disruptions.

DANIEL KOKOTAJLO: 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.

A big thing that we want people to take away is that even if you heavily restrict AI progress, you still get this sort of crazy transformation. 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 they can sort of get there pretty soon if they want to, and it’s just a matter of how long they allow it to go, right? So 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. 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.

The Citizen’s Dividend

STEVEN BARTLETT: So in 2031, you have the one-fifth of all cognitive labor done by AI. In 2023, you have 60 million AIs running at 100x speed. In 2033, there’s cash dividends to all Americans. I’ve got to explain this to me.

DANIEL KOKOTAJLO: 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 AIs 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. 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 Citizen’s Dividend. Basically, people have shares in an 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. And then by the end, it’s something like $10 million per citizen.

STEVEN BARTLETT: Per person?

DANIEL KOKOTAJLO: Per person per year.

STEVEN BARTLETT: Factoring in inflation? Like, what do you mean?

DANIEL KOKOTAJLO: Factoring in inflation.

STEVEN BARTLETT: So we’re all going to be multi-millionaires?

DANIEL KOKOTAJLO: Yes. If this happens, which it probably won’t, but if it happens, this is where it’ll 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 built huge amounts of new data centers, huge amounts of new chip fabs, huge amounts of new robots, robot factories, et cetera. We’re not sure obviously how fast this will go exactly, but we’ve thought about it a lot and we have our guesses and this is sort of like our median guess.

2037: The Apocalyptic Arrival of Truth on Earth

STEVEN BARTLETT: What does this mean? 2037. The apocalyptic arrival of truth on Earth.

DANIEL KOKOTAJLO: Yeah. So like, this is the point where we say they get to top expert level AI. So it’s not superintelligence 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 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 AGI. Maybe they got like weak superintelligence. Because they have so many of these AIs and because they think faster than humans, 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 citizen’s dividend and not have a job anymore in this sort of world. 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 like cancer cures and like people living in apartments that were built by robots 2 years ago.

STEVEN BARTLETT: 2036, providing again we stop in 2029. Yeah. And providing, I mean, a conservative, this is a conservative timeframe.

The Apocalyptic Arrival of Truth and the Future of AI

DANIEL KOKOTAJLO: Yeah. 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 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 later, basically.

STEVEN BARTLETT: I’m still not super clear on what this means, the apocalyptic arrival of truth on Earth. It’s just because there’s so many AIs that are so smart that they’re uncovering, making new discoveries in sciences.

DANIEL KOKOTAJLO: Let me give you an example. Lie detectors. So that’s an example of a technology that might be invented.

STEVEN BARTLETT: Yeah.

DANIEL KOKOTAJLO: Right now we don’t have good lie detectors. We have very bad lie detectors that sort of work, but don’t fully work. But once you’ve had these top expert-level AIs thinking for many years at 100x human speed, and there’s billions of them, and they have access to robot factories to do research and stuff, they’ll probably invent a ton of technologies. Maybe they’ll invent 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.”

STEVEN BARTLETT: I was just thinking about the whole justice system and how that would be overturned. In fact, you could theoretically walk down the street and be—

Lie Detectors: A Double-Edged Sword

DANIEL KOKOTAJLO: It’s both terrifying and exciting. One thing that we talk about in this section is the invention of lie detectors could be really bad. It could be that it enables a new form of totalitarianism where the powerful people, the CEOs and the politicians, force the people under them to go under lie detectors and say, “Yes, I’m loyal to the dear leader. I would never do anything against the dear leader.”

STEVEN BARTLETT: And if you’re lying, then you’re in—

DANIEL KOKOTAJLO: And then if you’re lying, you get fired. So there’s a ton of 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.

STEVEN BARTLETT: What’s this? 2040, passing the torch to AIs.

2040: Passing the Torch to AIs

DANIEL KOKOTAJLO: Yeah, great. So here they pause at the top expert AI level, and the reason why they pause is because their safety cases aren’t good enough for going beyond that level. 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 what your intentions are and 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 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. 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 get up to more stuff. And if they’re actually untrustworthy, the possible downsides are bigger. So that’s why they stop at this level, is that 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.

STEVEN BARTLETT: With humans.

DANIEL KOKOTAJLO: 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.

STEVEN BARTLETT: I guess this is a plan and this is a hope.

DANIEL KOKOTAJLO: Yes.

STEVEN BARTLETT: But in reality, this is not what you think probabilistically if you had to.

DANIEL KOKOTAJLO: That’s right. It’s important to distinguish, this is what we recommend, this is what we want to happen, from this is what we actually think will happen by default. Now we do think it’s possible for this to happen, but 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.

STEVEN BARTLETT: Okay.

Living Through the AI Transition: A Timeline

DANIEL KOKOTAJLO: 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 AIs, 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 really starting to actually affect things. But I think still most people have their jobs, but their jobs are sort of transformed. So 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.

Citizen’s dividend, ideally this would happen sooner. In our scenario, they kind of do things at the last minute. So a lot of these policy things are happening kind of just in time. Obviously we would recommend that you do them sooner and do a better job of them too. But so 2033, you start getting your checks from your dividend.

STEVEN BARTLETT: So you’re forecasting that there will be a citizen’s check that your model says it could be around $25,000 at the start per person.

DANIEL KOKOTAJLO: And then it would grow as the economy grows.

STEVEN BARTLETT: But also as I guess job displacement takes hold, they’re going to need to grow that check, make sure you can—

DANIEL KOKOTAJLO: And that’s why it’s kind of the last possible moment, because if you waited to implement this until like 2037, then everyone would have already lost their jobs by the time that happens, right?

STEVEN BARTLETT: People losing their jobs, especially if it happens quickly, 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.

DANIEL KOKOTAJLO: Yes.

STEVEN BARTLETT: How do you think about that?

Jobs, Power, and Political Influence in an AI World

DANIEL KOKOTAJLO: 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 somewhat different things.

Why are jobs important? Well, there’s a lot of reasons why jobs are important, but I think the main ones are, 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 countries that are ruled by dictators can’t just completely genocide an entire subpopulation, or they can, but it’s costly for them to do so because then they’ll have less money because that subpopulation 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 revenue except for the AI companies and the robot companies, then you, the government, are less incentivized to care about what the common people think. So when people lose their jobs, they’re not just threatened with loss of income, they’re also threatened with loss of political power. And so we think that it’s important to do things to push against that.

STEVEN BARTLETT: What does that look like? How do people have power in such a world?

DANIEL KOKOTAJLO: Well, in democracies at least, they still have votes.

STEVEN BARTLETT: Okay.

DANIEL KOKOTAJLO: So I think that it’s very important for there to be regulations on the use of AI that help make the public discourse more sane and more actually giving the people what is in their interest and what they want, and avoiding a sort of opposite outcome where the masses are easily manipulated by AI-powered media, for example, or where everyone’s talking all day to their AI advisors. And the AI advisors are 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 secretly biasing their AIs to steer people towards voting for that candidate.

We want to be in a situation where people have AIs that are actually trustworthy and that are truth-seeking AIs, honest AIs, and that don’t have any sort of political agendas put into them by the AI companies or by the government. You want to avoid a situation where the government has issued some sort of secret order that the AIs have to be such and such a way.

The Department of War dispute versus Anthropic is an interesting sort of foreshadowing of this, right? Where Anthropic was giving their AIs to the Department of War. The Department of War wanted to use them for certain things and was upset that Anthropic’s AIs were not supposed to be used for those things. The things in particular were domestic surveillance and autonomous robots.

There’s going to be a lot more issues like that coming up, and you want it to be the case that people know what they’re getting, and that if people are 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 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. You can sort of potentially bootstrap this to having something where people’s power is even more secure than it is today.

Wars, Drones, and Robots: Predictions Already Unfolding

STEVEN BARTLETT: A lot of this stuff we’ve covered in part. So the wars and drones and missiles, we’re already seeing this around the world at the moment, which is really, really interesting. 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.

The 2040 Plan: A Positive Vision for AI’s Future

DANIEL KOKOTAJLO: Yeah. In this scenario, they delay the creation of superintelligence until 2040. And in fact, they pause from 2035, but then they let it go after that. And then they let the AIs become vastly superintelligent. And we think that once the AIs are vastly superintelligent, 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. The AIs are not— 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 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, 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 fancy new apartments that were built by robots 2 years ago. There’s 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 superintelligence, there’s a whole other 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. And that’s without even a qualitative improvement in intelligence. The humans of today aren’t 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 money and resources to build prototypes 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 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 will just completely — we did not think that was even possible. People don’t want to die. People don’t want to be hit by cars. People don’t want to be attacked by a random mass murderer.

STEVEN BARTLETT: Cancer’s gone.

DANIEL KOKOTAJLO: I mean, not just cancer. 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 computers, or self-replicating robots in the asteroid belt creating more and more satellites to produce more and more power to produce more and more self-replicating robots and so forth.

STEVEN BARTLETT: Most people still live on Earth, but the trend is to move to space?

DANIEL KOKOTAJLO: That’s right. Yeah. So if you end up in this situation where the entire human economy is just a tiny drop in the bucket that is the entire economy, and it’s just these 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. A lot of people are worried about the environment being destroyed, which it totally would be if it wasn’t protected. And there’s a lot of people who sort of like their lives as they are 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 to 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.

STEVEN BARTLETT: Data center — picture here of data centers in the ocean. I mean, there are 3 images there of different environments where humans might live.

DANIEL KOKOTAJLO: Again, our proposal was you preserve like 99% of the Earth mostly as is, for historic or environmental reasons. But then 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. We were thinking it would be good to build the data centers on the ocean instead of on land for a variety of reasons, although later space would be better and I could see that being reasonable as well.

Immortality and the Future of Human Life

STEVEN BARTLETT: What about immortality in a world of AI? Well, 3045, you say you’ve lived a dozen lifetimes and are immortal, passing from life to life as if by reincarnation. I mean, there are a lot of billionaires at the moment that are focused on longevity. I mean, Bryan Johnson said he’s got this central rule, which is “do not die right now because we’re in the age of AI,” and it’s conceivable that with superintelligence, we’ll be able to choose when we die.

DANIEL KOKOTAJLO: Yep. I think that’s probably right. We don’t depict that happening in this part because at this part they only have human-level AIs. But that’s one of those things that seems quite plausible that superintelligence could achieve through a variety of means.

STEVEN BARTLETT: What is your hope with all of this stuff? Why did you do this? Why did you make this 2040 plan?

DANIEL KOKOTAJLO: In the first week after we published AI 2027, it blew up a lot bigger than we expected, by the way. We actually made forecasts beforehand of how many views it would get and stuff like that, and it was like a 90th percentile outcome. So very much not what we expected.

But in the Twitter storm that happened, various people were like, “Argh, why are you giving us all this doom and gloom predictions? How about a more positive vision of what you think we should do instead.” And I think that seed sort of implanted in us and then we were like, yeah, that’s reasonable. 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 tack and come up with some actual recommendations and then depict that as well.

STEVEN BARTLETT: Even though you don’t believe they’re probable.

DANIEL KOKOTAJLO: Yeah. I mean, you can vote for a political candidate even if you aren’t confident that they’re going to win, and you can say, “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. If you think there’s no chance, then maybe you shouldn’t bother. But we think there’s a chance. In particular, for the reasons that we describe in the scenario, we think that people are going to wake up to the power of AI over the next few years.

STEVEN BARTLETT: Because something happens?

DANIEL KOKOTAJLO: 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 if they get anywhere close to this level of AI, then there are big issues and big problems and we need to do something about this. And so I think that even if there’s not any 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 more of this happening than we predicted.

STEVEN BARTLETT: More of what happening?

DANIEL KOKOTAJLO: Serious interest in AI regulation. So at the time that we published AI 2027, the sort of mainstream position of the tech companies and in the government was kind of like, “AI regulation, bad idea.”

STEVEN BARTLETT: Free for all.

DANIEL KOKOTAJLO: Free for all. In fact, there was even an attempt to preemptively ban states from regulating AI.

STEVEN BARTLETT: Yeah.

DANIEL KOKOTAJLO: You remember that? Now it seems like the conversation has changed a lot. The US government just told Anthropic they have to shut down their AI because they were worried that bad actors would use it for cyberattacks. The government is waking up and doing more stuff than we expected 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 avoids the loss of control and concentration of power risks that we mentioned.

Would You Press the Button?

STEVEN BARTLETT: You’ve spent, what, must be almost coming up to 15 years thinking about this stuff. 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 for good — there would never be any other AI labs working on these problems — would you press that button?

DANIEL KOKOTAJLO: I was about to slam it until you said “for good.”

STEVEN BARTLETT: Okay.

DANIEL KOKOTAJLO: If it was a sort of temporary shutdown, I would totally slam that button because we are not ready to do this. Civilization is not ready to have these companies automate themselves and then get smarter and smarter and then have the superintelligent — no, there are a bunch of reasons why that’s really 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.

STEVEN BARTLETT: But if you think that Plan D is probable, which is this race we’re on to superintelligence—

DANIEL KOKOTAJLO: If I had a choice between D and S, I think I would press it.

STEVEN BARTLETT: Well, it comes down to what you think, right? Because if you think that is what’s going to happen, Plan D, and the only alternative — I didn’t say this is what’s going to happen. Probabilistically.

DANIEL KOKOTAJLO: Yeah. 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.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: That’s tough. What is the scope of the shutdown? So is it—

STEVEN BARTLETT: It’s no one can train an AI model again.

DANIEL KOKOTAJLO: Ever again. That’s real rough because, as I said, there are loads of benefits that we could get from AI if we do it right.

STEVEN BARTLETT: I think I’ve almost put you in the position of Sam Altman to some degree.

DANIEL KOKOTAJLO: Yeah. Do you mind if I just take a moment to think about this?

STEVEN BARTLETT: I prefer you to think.

DANIEL KOKOTAJLO: Yeah. I think I would not press the button, but I feel very torn about it. 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, if we don’t build powerful AI systems eventually, then we’re probably going to die as a civilization. Eventually, 100 years from now, 200 years from now, something like that — nuclear war, pandemic, something. I don’t think human civilization right now is super, super stable.

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 current level of risk, but actually—

STEVEN BARTLETT: I’ve heard that narrative before.

DANIEL KOKOTAJLO: Yeah, I don’t know. 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. People right now definitely don’t want to do this lottery, I would say. Yeah, you’ve really asked me a tough question.

STEVEN BARTLETT: So would you press the button if that was the button?

DANIEL KOKOTAJLO: Probably not, but I’d feel very torn.

What Can People Do?

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

What Can People Do About AI?

DANIEL KOKOTAJLO: Ah, 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 building useful tools that will hopefully help people be better and stuff.

But if you don’t want to make any major career changes or things like that, then I would say just pay more attention to these issues and talk about it more with people. Do stuff like emailing your congressman or 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. There would already be much more significant regulation in place. And not only would there be more heavy regulation in place, but there would have been better regulation in place that’s less like a cudgel and more like a scalpel, and that’s 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, the more people wake up to these concerns and to these projections, I think the more likely it is that we can do good stuff before it’s too late.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: You should ask your candidates what they think about all this AI stuff. You should try to get them to have opinions, and then you should vote for the candidates whose opinions are better on this topic. 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. And so it’s what all the leaders of all the countries should be thinking about and making plans for.

Living at the Run-Up to the Climax

STEVEN BARTLETT: Isn’t it such a weird thing to be alive at this moment in time? 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. Yeah. What was the phraseology you used?

DANIEL KOKOTAJLO: I said the run-up to the climax or something.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: Yeah.

STEVEN BARTLETT: Or maybe not hopefully. What a crazy time to be alive.

DANIEL KOKOTAJLO: Certainly.

The Personal Toll: Family and the Future

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

DANIEL KOKOTAJLO: What? Yeah.

STEVEN BARTLETT: It’s like you dropped into a different state. Obviously, that’s been central to the rumination that you’ve been experiencing?

DANIEL KOKOTAJLO: Well, it is a sad topic, right? Like when I had kids, 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.

STEVEN BARTLETT: Presumably you still would have had kids.

DANIEL KOKOTAJLO: I’ve actually flip-flopped on this occasionally. Yeah. Basically the top line answer is I’m not sure. My first child, we had her in 2019, she was born in 2019. 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 actually expect it to happen soon, you know?

And then when I did start thinking like, oh my gosh, it’s going to be happening like real soon, by 2030, 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, we already had one kid and no siblings. So eventually I sort of gave in and was like, okay, well, you know what, we already have one. It’s going to be all right. Maybe the future will be good. And even if it’s not, well, we’re all in the same boat together.

STEVEN BARTLETT: 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.

DANIEL KOKOTAJLO: To be clear, yes, I mean, yes, it’s very concerning. I am chilled. This is bad. This is what I’ve been saying. I hope things go well. I think things might go well. I think that there’s a lot we can do to steer things in a better direction.

Speaking Out and Navigating the AI Landscape

STEVEN BARTLETT: I mean, one of those things as well, I have to say, is just speaking about it. I think a lot of the progress we’ve seen with governments waking up and we’ve seen certain things with people booing certain people at certain events. Yeah. Is downstream from people like yourself actually coming on shows like this and all the other podcasts and telling us what’s going on. Because elsewhere, to be fair, we’re going to be gaslighted by the people that have the biggest PR machines.

So 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 Groq, the inference chip company. I’ve invested in SpaceX, which now own another Groq 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, yeah.

DANIEL KOKOTAJLO: I mean, I think that is a tension. I think that there’s different ways you can draw the line. And I know lots of people who draw the line in lots of different ways. So there’s some people who just like, I’m not going to use AI. I think this stuff is bad and on a bad trajectory. So I’m going to boycott AI, right? I’m not one of those people. I use AI a lot. We all do at AI Futures Project. 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 get involved and accumulate power and try to steer it from the inside. And so I’m going to go work at OpenAI or Anthropic and try to climb the ranks and then be someone who matters when the important decisions are being made. 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—

STEVEN BARTLETT: That was the path.

DANIEL KOKOTAJLO: That was a— I mean, 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. And so all these people are deciding, we’re going to lean really hard into it. We’re going to be there in the room when the important decisions are being made.

So there’s a whole spectrum and I’m sort of somewhere in the middle. 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 my current best guess as to the way out. The way forward. But I’m not boycotting all the AIs. I’m not refusing to engage with it in that way.

Is It Too Late?

STEVEN BARTLETT: Do you think it’s too late?

DANIEL KOKOTAJLO: No, I don’t think it’s too late. If I thought it was too late, I wouldn’t be here.

STEVEN BARTLETT: Where would you be?

DANIEL KOKOTAJLO: With my family.

Closing Message to the Public

STEVEN BARTLETT: What’s your closing message to the general public?

DANIEL KOKOTAJLO: If you had to have a closing statement to them, maybe I would say that 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 which 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 the trends. And 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.

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

DANIEL KOKOTAJLO: You can go to ai2037.com to read our previous scenario. You can go to ai2040.com/planA to read our new proposal for what needs to be done. These things are not just a sci-fi story. They also have lots of explainers and links to other things. And so they’re kind of like a nice jumping off point to learn about all of this stuff. If you want, I could, after this is over, give a reading list of other papers and articles and—

STEVEN BARTLETT: Please do.

DANIEL KOKOTAJLO: —maybe blogs to follow and so forth.

STEVEN BARTLETT: 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 comments, the 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 just a really, really great moment in time to get educated on this stuff. 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 inform yourself so you know what actions to take, but also because AI, unavoidably, is going to be a huge part of all of our lives and careers.

DANIEL KOKOTAJLO: Yeah, yeah, thank you. And that’s a good way to say it. It’s going to matter a lot. It’s going to be everywhere soon, and we need to do something about it before it’s too late. And what about AI Future Project? 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. Daniel, thank you.

STEVEN BARTLETT: Thank you. 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 seeing how personal this is for you and seeing how much you’ve dedicated your life to this, but also hearing that you basically walked away from $2 million to be able to speak to the public about this information is incredibly admirable.

And 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 is often the quiet part out loud. Thank you. Doing really, really smart research. I’ll link everything we’ve discussed today below, and I hope we can chat again sometime soon. Thank you.

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