Doom Debates!

Dr. Alex Turner went viral for resigning from Google DeepMind over its Pentagon AI contract, which he said lacked binding restrictions against autonomous weapons and mass surveillance.
Alex is a world-class researcher with a PhD in alignment from Oregon State University, a postdoc at Stuart Russell’s Center for Human-Compatible AI at UC Berkeley, and top distinctions at NeurIPS. He’s one of the minds behind Shard Theory (with Quintin Pope), and he also helped pioneer the Steering Vectors research made famous through Anthropic’s Golden Gate Claude experiment.
Alex says Google DeepMind broke its founding promise — the commitment Google made when it acquired DeepMind — and that the people best known inside Google for caring about AI ethics largely didn’t act when it counted.
Alex came up through the rationalist community, and became one of LessWrong’s highest-karma users before leaving the site. He still shares many of my concerns about AI doom, but he believes technical alignment research is going better than expected. I’m not so optimistic.
In this episode, we debate my Yudkowskian doom views against Alex’s own framework. Can he convince me the Yudkowskians are miscalibrated?
P.S. We’re currently in a donation drive, so here comes our solicitation for viewer donations…
👉 Please click here to make a tax-deductible donation to Doom Debates 👈Doom Debates is a fiscally sponsored project of Manifund.org, a registered 501(c)(3) nonprofit.
To donate crypto or if you have questions, email me.

Watch on YouTube
Timestamps
00:00:00 — Cold Open00:01:15 — Introducing Alex Turner00:02:30 — From Harry Potter Fanfic to AI Alignment00:05:03 — Meeting Quintin Pope & Rethinking AI Doom00:06:17 — Shard Theory, Steering Vectors & Golden Gate Claude00:08:25 — Why He Joined Google DeepMind00:10:32 — Google DeepMind’s Broken Promise00:16:01 — Debating Google DeepMind’s Pentagon Contract00:19:09 — What’s Your P(Doom)™?00:22:42 — Alex’s Research on Instrumental Convergence00:26:35 — Misuse vs. Misalignment: The Mainline Doom Scenario00:29:31 — Will Society Self-Correct?00:36:36 — Superintelligence in 10 Years00:39:43 — Will Technical Alignment Produce a Safe AI?00:41:16 — Donation Drive00:42:12 — How Fragile Is the Chain of Alignment?00:49:50 — Disagreements with Yudkowsky’s ‘List of Lethalities’00:57:46 — Why Alex Quit LessWrong01:01:48 — What’s Next for Alex01:02:54 — Does He Support PauseAI? Stop the AI Race?01:04:40 — Wrap-Up01:06:04 — Producer Ori’s Closing Note
Links
Alex Turner, “Why I Left Google DeepMind” blog post — https://turntrout.com/why-i-left-google-deepmind
Alex Turner (TurnTrout), personal website —
https://turntrout.com
Alex Turner’s resignation announcement on X —
Doom Debates episode with Quintin Pope — https://lironshapira.substack.com/p/ai-alignment-is-solved-phd-researcher
Harry Potter and the Methods of Rationality (HPMOR) —
https://hpmor.com/
Shard theory sequence on LessWrong —https://www.lesswrong.com/s/nyEFg3AuJpdAozmoX
Golden Gate Claude (Anthropic research on steering vectors) — https://www.anthropic.com/research/golden-gate-claude
Slaughterbots on YouTube —
Alex Turner, “Avoiding Power Seeking by Artificial Intelligence” PhD thesis — https://turntrout.com/alignment-phd
Alex Turner, “Some of My Disagreements with List of Lethalities” — https://turntrout.com/disagreements-with-list-of-lethalities
Doom Debates episode with Bentham’s Bulldog — https://lironshapira.substack.com/p/benthams-bulldog-ai-doom-debate
Alex Turner on X — https://x.com/Turn_Trout
Doom Debates donation page — https://doomdebates.com/donate
Transcript
Cold Open
Liron Shapira 00:00:00You resigned from Google DeepMind because you think that Google DeepMind, quote, “Broke its founding promise through its contract with the US military.”
Alex Turner 00:00:08I value staying true to your values. People who are well-known within Google for caring about the ethics of deploying AI largely didn’t act. This matters because autonomous weapons get us into an arms race that really degrades the security of everyone in the world.
Liron 00:00:26Do you support the Pause AI movement?
Alex 00:00:28I think that AI is being developed too quickly. I probably will not take an affirmative on supporting this particular movement.
Liron 00:00:35Let’s segue into the schism, your disagreement with Eliezer Yudkowsky.
Alex 00:00:39He was incorrect on some points for alignment, but then also not acknowledging that. I think that technical alignment has gone pretty awesome, super awesome.
Liron 00:00:49You’re not claiming that a superintelligent AI can’t kill everybody. You’re like, “Oh yeah, of course it can, but we’re not gonna break the chain of alignment, meaning we’re just going to safely develop it so that even though it can kill everybody, it won’t.”
Alex 00:01:00Develop it in a way that produces a safe result. I wouldn’t call what we’re doing safe development, but sure.
Introducing Alex Turner
Liron 00:01:15Welcome to Doom Debates. My guest has worked in technical AI safety at Google DeepMind for two and a half years, but he just quit, and his resignation is going viral. Why? Alex Turner says Google DeepMind, quote, “Broke its founding promise through its contract with the US military.”
His latest blog post exposes hypocrisy at the highest levels of senior leadership at Google DeepMind, which includes the CEO Demis Hassabis, chief scientist Jeff Dean, and co-founder Shane Legg, among others.
Alex is a world-class AI alignment researcher. He completed a PhD in alignment from Oregon State University. He did a postdoc at UC Berkeley, and he’s earned top distinctions at NeurIPS. I respect that Alex is principled. I respect his mastery of the subject matter that we talk about on this show.
I also find it interesting that he’s levied criticism at the original AI alignment thinker, Eliezer Yudkowsky. He’s called some of Yud’s claims fundamentally misguided, not reasonable, and bogus. As a Yudkowskian myself, I’m gonna be curious to dig into those arguments. And of course, we’ll cover what’s going on right now at Google DeepMind and why he resigned. Alex Turner, welcome to Doom Debates.
Alex 00:02:28Hey, thank you for having me.
From Harry Potter Fanfic to AI Alignment
Liron 00:02:30So it’s great to get you on the show. One thing we do on Doom Debates is we expose top intellectuals who have been pretty familiar to the rationality community or the AI safety community, and we help popularize their ideas, even if I don’t fully agree with all of them. Is that a good description of your background? You’ve been pretty deep into the LessWrong rationality and alignment community for a while.
Alex 00:02:52Yeah, I think it was quite formative. It was just the other day in 2016 where I decided to search what are the top five Harry Potter fan fictions. And that indeed led me down the LessWrong rabbit hole, where I discovered superintelligence in late 2017, and then I pivoted my PhD in early 2018.
From that time period up through maybe early 2023, LessWrong was very central to my professional career, but also just to the way I looked at the world.
Liron 00:03:25Well, I wanna follow up on why did you search for Harry Potter fan fictions?
Alex 00:03:30I really don’t know. It’s kind of one of those things where if I hadn’t done it, my life would be totally different. The reason I’m mentioning this is there’s this famous fan fiction that Eliezer wrote called Harry Potter and the Methods of Rationality. I never really liked fan fiction. I thought it was kind of cringe. No one recommended it to me. So it seems to me like if I’d just woken up slightly differently that morning, I might not have ever been exposed to this research area, and my life would be totally different.
Liron 00:04:02Wow. And you said this is all in 2016, right? So the book had been mostly completed at that time. It had been going on from 2009 to 2015, and you kind of stumbled on it because you were just interested in seeing what the best Harry Potter fan fiction was?
Alex 00:04:17I had a random thought. That’s the best I recall.
Liron 00:04:21It’s pretty crazy that that’s how you found the community because I know you as one of the highest karma LessWrong users. You have ten times my karma. You’ve been posting a lot. It became a huge passion for you, right?
Alex 00:04:32LessWrong was for quite a while my intellectual community. I’d have ideas. I’d be eager to share them. Each summer I would generally do an internship where I’d come in person at Berkeley, get to hang out with my friends there, be able to — I guess I felt more understood. In 2018, 2019, 2020, 2021, these are many years where I was at my PhD and I talked about the dangers of AI and how we should work on that.
Meeting Quintin Pope & Rethinking AI Doom
Liron 00:05:03Did you originally feel like you bought into all the Eliezer Yudkowsky concepts, and then you started rethinking everything and building it from the ground up? Was there a point of divergence?
Alex 00:05:15Yeah. I think I was maybe around 80, 85% doom conditional on developing AGI. I thought it’d be a couple decades, even late 2021. And yeah, I shared most of the worldview. I found much of his writing compelling, and I still think there’s some gems in there.
It wasn’t until early 2022. I met a researcher at my university, at Oregon State University, named Quintin Pope. He wrote these very big brain Google Docs, and he was sending them by me. He’d attended my AI alignment reading group.
I don’t know, something was just very interesting about them, and they seemed really far-fetched, but they were very ambitious. And as I looked more, I realized he was pointing out some real confusions, real issues. I started rethinking perhaps the claimed difficulty of alignment.
Shard Theory, Steering Vectors & Golden Gate Claude
Liron 00:06:17This is such a unique opportunity because you actually know your stuff. You actually know what we’re arguing about, unlike a lot of my guests who come in and they seem to be shooting from the hip. They haven’t spent so many hours considering it. They haven’t worked a career in AI research. So I’m excited. But before that, let’s just finish the biography here. So you did your postdoc, and then did that lead you to joining Google DeepMind?
Alex 00:06:39Yeah, I got my PhD in 2022. My thesis was called Avoiding Power Seeking by Artificial Intelligence. Then I did a one-year postdoc at UC Berkeley at Stuart Russell’s Center for Human Compatible AI.
During that time, I worked more on this shard theory of human values with Quintin Pope, who’s actually the alignment thinker whose ideas I respect the most, or I think they’re the most interesting. And then I also discovered steering vectors or helped popularize those as the MATS team that I led. We were the first to really demonstrate their potential.
After that, mid-2023 was a fairly rough period personally. I did some more MATS mentorship. It wasn’t until the end of 2023 that I settled on going to Google DeepMind.
Liron 00:07:37Got it. So MATS, you were a mentor there, and they do AI safety research. They train people to do AI safety research.
Alex 00:07:44Right.
Liron 00:07:44And the steering vector work that you did was pretty foundational, and I think most people have heard of it as Golden Gate Claude, where they use the steering vector to get Claude to be obsessed with the Golden Gate Bridge in response to any prompt.
Alex 00:07:57Classic. Yeah.
Liron 00:07:59That is a pretty legit background. If people criticize me and my arguments for being a bystander who’s not in the weeds or not on the field or whatever, I think it’s fair to say you’re on the field, and so whatever you have to say has the credibility of just being in the arena.
Alex 00:08:19Sure. Yeah. We’re in maybe different arenas, but yeah, the direct research arena.
Why He Joined Google DeepMind
Liron 00:08:25All right. Well, with that said, one of the things that you saw in the arena is a perception of hypocrisy of the key figures. Let’s talk about that.
Alex 00:08:36Yeah. When I joined Google DeepMind, I already knew some people on the alignment team. Rohan Shah — I worked with him during my PhD. He was at CHAI. And I think he’s done a pretty good job of leading the alignment team within Google DeepMind.
One of the things I would say is, Google was not founded with the goal of taking over the world, or potentially with the goal of taking over the world. I’m not saying for sure that OpenAI and Anthropic were, but they were founded as AGI companies, and those ideas were present, whereas Google is, for better or worse, more of a classic company. They seem more interested in making money. That doesn’t mean that Google’s good in all that it does, but it seemed like a different presence in the space.
At the time I was mostly just worried about doing alignment research on frontier scale models. When I joined, I was fairly concerned about whether my opinions would become trash because I’d start rationalizing why things that Google does are good.
I had this extended dialogue with Oliver Habryka about how I could maybe net zero out my financial position in Google in terms of equity at least. We discussed a lot of big brain options, but then it turned out that my contract prohibited me from being short Google at all, which killed all of the schemes.
So in the end, I was thinking about how do I avoid this kind of value drift, this bias that I think I’ve seen a lot from people working at their labs, where they seem incapable of saying, even in private, “Nope, this was bad, and we shouldn’t have done it.”
Google DeepMind’s Broken Promise
Liron 00:10:32So you had these general reservations about Sam Altman and other companies and maybe motives getting corrupted in the abstract, amalgamated from different examples that you’ve seen. But I think it really came to a head recently. You specifically had concerns with the Pentagon-Anthropic contract tensions becoming public, and you’re like, “Uh-oh, this is high stakes. I better make sure that Google’s doing the right thing.” But then you saw that Google wasn’t resisting the US government or ICE’s attempts to use them. Give us the issue here.
Alex 00:11:06Yeah. So if people remember those goons in face masks with rifles that were roaming the streets of Minnesota earlier this year, that’d be ICE, that’d be Customs and Border Protection, and in particular, the people they just killed on the street. I was pretty upset about that.
I wanted to reduce tech’s involvement in enabling ICE and enabling CBP to track down people they’re looking for, whether they’re dissidents, people who are potentially actually or just accusedly in the country illegally. It seemed like a not moral enterprise.
So I looked into that. I started pushing on people in the company with those contracts. And at the same time, I was talking to my friends at Anthropic because Anthropic had, and I think still has maybe, a deal with Palantir where they’re giving Claude to Palantir, which I think is a very negative company for the world. And so I was trying to persuade my friends, “Hey, can you push on getting rid of this?”
Little did I know there was this bubbling in the background of conflict between Anthropic and the Department of War. And then this came to a head in February when the Department of War said, “Give us Claude or we will economically destroy you.”
Liron 00:12:37We could definitely spend a long time on this, but because we have limited time, we are going to reserve most of the time for the alignment conversation and the Yudkowsky versus non-Yudkowsky alignment theorist debate.
That said, this has been a very important incident. It’s currently on the front page of LessWrong. It has more points than anything else this month, as far as I can tell. It’s getting a ton of attention, and I read through your account of events. One thing that seems clear is you’re acting with a lot of integrity. You resigned from Google DeepMind because you think that all these organizations, Google DeepMind, the leaders of Google DeepMind, and the International Association for Safe and Ethical AI, they’ve been involved with all this, and you think that they broke their commitments as well. You even say it’s the founding commitment of Google DeepMind. You’re saying Google DeepMind was founded on a commitment not to empower the US government to do bad things with AI?
Alex 00:13:31Yeah. It’s the founding agreement where Google purchased DeepMind.
Liron 00:13:38And from your perspective, they’ve just been bending the rules and not taking a hard stand. They’re not actively saying, “No, Alex, you’re wrong. We wanna do this.” But they’re more like just kicking the can down the road, refusing to respond on certain deadlines where they said they’d respond. They’re just not standing up when they should be standing up to resist.
Alex 00:13:59So Google DeepMind as an org has, I think, broken its founding promise, the promise it was purchased under. But then more specifically, people who are well-known within Google for caring about the ethics, caring about the issues of deploying AI, making sure that’s done responsibly, largely didn’t act.
Jeff Dean did take some action. Google’s chief scientist, Jeff Dean — I got him to sign an amicus brief supporting Anthropic in court. I think that was awesome. But ultimately, besides that, basically no one took costly action to prevent this deal, from my vantage point.
And I think they could have stopped it. I think they could have improved it, and I think this matters because if you’re handing over AI to what I think is a very irresponsible Pentagon and also a very aggressive Pentagon that might degrade international norms around the usage of autonomous weapons, get us into an arms race that really degrades the security of everyone in the world.
Stuart Russell, the esteemed computer scientist who helped found ICI, this organization, he made a short movie called Slaughterbots that he presented to the UN, and it’s very chilling to watch the way these systems could enable mass but kind of anonymized killing.
And if we’re thinking about AI x-risk, if we’re developing all these really hard to counter AI-piloted drones that can kill people, that really does affect the AI’s takeover options. One of the objections has always been, “Well, it’s gonna need big advances in robotics.” Well, looks like that might not be true. You don’t necessarily need human-shaped soldiers, and the Pentagon is spending more money this year on autonomous weapons — or they asked for more for autonomous weapons than for the Marines.
Debating Google DeepMind’s Pentagon Contract
Liron 00:16:01I should be clear to the audience because Doom Debates is not one of those typical interview shows where the host has an ambiguous position. I should tell you my position, which is I don’t know how strongly I feel because I know there’s a counterargument to all this. The people who want Google to help the US government, they’re just saying, “This power is going to exist, so you can’t expect anybody other than the US government to be the one in control.” Isn’t that the strongest counterargument?
Alex 00:16:27It doesn’t really strike me as a counterargument. It sounds like, “This thing is going to happen. Why would you resist it?” I’m like, well, because I think it’s bad.
And there’s also multiple parts of the US government. Should the US government be in control of a world-changing technology, or should three random people in Silicon Valley? I don’t know if these are the real possibilities, but even if we do need the US government, we can advocate for it to take control in different ways.
Liron 00:16:54If we accept the premise that dangerous war-fighting technologies are getting built or are days away from getting built at all times just by having general models or whatever, isn’t it a good idea to let the government have access to the frontier?
Alex 00:17:13Well, depends on what access to the frontier means. You could say, why don’t we push for an international treaty to coordinate against this? Or why don’t we have some rules on human accountability of this technology? So you can’t just have, “Whoops, looks like we had a mistake. Looks like hundreds of people are dead, but it’s just the AI’s fault.” If people are responsible, that leads to better incentives and I think more responsible use.
Liron 00:17:42And in the specific case of ICE, you really don’t want ICE to get the technology, correct? Immigration and Customs Enforcement.
Alex 00:17:50Yeah, although I haven’t really been worried that ICE will get these particular lethal autonomous weapons. It’s possible, but it was more a campaign that started with my concern about ICE and then expanded to these other coercive government bodies.
Liron 00:18:06Like I said, much to discuss. We’ll put a pin in that, but I encourage viewers to read your account. It’s pretty gripping. It’s very interesting to see these figures like Demis Hassabis and Sundar Pichai, all these people that you try to interact with in a very high integrity way. You are trying to use the process, and it’s very interesting to watch how they have all these political considerations that they have to balance, and they have to pick when they take a stand and when they don’t. Pretty fascinating high-stakes politics.
Liron 00:18:31Let’s segue into the schism, where you started from the Yudkowskian perspective on AI safety because Yudkowsky was kind of your introduction to the field. But as you thought from first principles and collaborated with Quintin Pope, who’s also a friend of the show — check out the Quintin Pope episode of Doom Debates, everybody — you slowly migrated away and created your own framework.
Maybe a good starting point is what’s been happening with your P(Doom), because I think you mentioned that you used to have an 85% P(Doom), but then when you were done with your PhD dissertation, it dropped to 30%. So let’s get the latest. You ready for this?
What’s Your P(Doom)™?
Alex 00:19:09Yeah, let’s do it. P(Doom), P(Doom). What’s your P(Doom)? What’s your P(Doom)? What’s your P(Doom)?
Liron 00:19:16Alex Turner, what’s your P(Doom)?
Alex 00:19:19So I operationalize P(Doom) as probability that AI kills at least a billion people by 2050. I put that at, I don’t know, 25, 30%. I think maybe 10-ish percent of this is technical alignment, and the rest is misuse.
I think that technical alignment has gone pretty awesome, super awesome compared to where I thought it’d be during my PhD. Timelines have shrunk a lot, obviously, from multiple decades to maybe even less than a decade for sure.
And then unfortunately, I thought that the world was getting into a better place, and then we elected Trump again. I think it’s very inconvenient that we elected Trump at the same time that we’re navigating this transition. And if that had been delayed by five years, it’d be way better, but we gotta work with what we’ve got.
Liron 00:20:21Trump — I don’t wanna get too political on this show. I feel like policy is multidimensional. Everybody’s a mixed bag. But it does seem striking to me that Trump doesn’t seem like an intellectual, and this is such an intellectual subject. Controlling superintelligent AI — in that sense, he seems like the wrong fit to me.
Alex 00:20:39Yeah. Setting aside party identifications, I was really hoping there would be more consideration of the common person’s interest, less “we just have to win the race.” Winning the AGI race — I’m not a fan of that. I’m not a fan of moving forward as fast as possible.
Liron 00:21:02It’s striking to me that you’re saying 25 to 30% chance by 2050 of basically the world becoming a hellscape, because I would say 50%, but I don’t even think 25%, 50% — I don’t even think that distinction is very important. Feels like we’re getting into the narcissism of small differences of how doomed we are in 2050. Is that fair to say?
Alex 00:21:21As far as how it affects our actions, I think even having a couple percent of justified concern should be enough to drastically reshape actions. We wouldn’t tolerate a 5% chance of getting hit by an asteroid by 2050.
Liron 00:21:43Well, I gotta push back on that. I don’t wanna get into the weeds, but I do often tell my guests that I would act pretty differently if I thought P(Doom) was 5% rather than 20, 30, 40, 50%. I feel like there’s a difference there.
Alex 00:21:57Okay. Sure. I suppose we can disagree on that.
Liron 00:22:01So to me, what’s very interesting about having you as a debate opponent here is that you’ve done the reading. You’re not gonna be surprised by any Yudkowskian concept that I bring up. You probably can steelman my position, right?
Alex 00:22:16I hope so. I expect so.
Liron 00:22:19Exactly, or you can pass the ideological Turing test where you can take my side of the debate, and then you can take your side of the debate, and maybe I could even do the same for you. So this is a pretty high-level debate. And viewers, go check out me versus Quintin Pope if you want a taste of that kind of debate.
So object level here, what should we actually debate? There’s a couple key concepts that I’d love to get into your take on. Why don’t we start with instrumental convergence?
Alex’s Research on Instrumental Convergence
Alex 00:22:42I love thinking about instrumental convergence. I did a good amount of my PhD on it. I had an intuition it could be formalized in an appropriate way, and then I think I succeeded at that, and that was a lot of fun.
Liron 00:22:55Just to signpost a little for the viewers, instrumental convergence is the claim that different agents who have different terminal goals, who have different ultimate values, they’ll still all converge on what we call the instrumental goals. There will be a convergence of big power plants because they wanna get some power. Maybe they’ll build solar panels. That might be a convergent thing to do, even if one of them wants to build Disneyland and the other one wants to just build a big black hole. Maybe they’ll both build a bunch of power plants in the course of doing that. That’s the kind of convergence we’d normally talk about, right?
Alex 00:23:29Yeah. Although, I do wanna say, if you talk about goals, I think it’s true about goals, but there are also other mind shapes that AI could have that don’t necessarily run into that territory.
So originally I thought, well, for most goals an AI could have, like painting walls blue for example, it won’t be able to best achieve that goal by instantly dying and exploding. So it’ll try to avoid instantly dying or even dying later, so it can keep pursuing that goal. And I formalized this in a way.
But then I started thinking, well, it’s not that I think instrumental convergence is false, it’s that I think you can quickly go from this statement about what goals incentivize, which is true, to a statement about AIs being drawn from some kind of counting distribution over the space of goals. The AI’s motivations — who knows what the motivations might be. I think that’s one possible mistake. So I think instrumental convergence presents a challenge in a way, but I’m not too pessimistic about it in and of itself.
Liron 00:24:40It sounds like you and I are on the same page that instrumental convergence is a theorem of the field of what I call intelladynamics, the dynamics of what intelligent systems would do, even if it’s not a true property of particular AI systems that we build because particular AI systems don’t meet the criteria of these pure goal-seeking systems.
Alex 00:25:03Yeah.
Liron 00:25:03Is that a good framing?
Alex 00:25:04Or maybe a theorem of goal achievement dynamics. I don’t know. It’s not as sexy as intelladynamics.
Liron 00:25:11Yeah, intelladynamics is a sexy term.
Alex 00:25:13But I think you can have intelligent systems that respond to correction and aren’t pursuing a particularly autonomous long-term goal.
Liron 00:25:21What do you think instrumental convergence actually does predict about what’s going to happen in, let’s say, ten years?
Alex 00:25:29I think it’s both true that we could build AI that helps us, is transformative even, but doesn’t try to take over the world or isn’t interested in some appropriate sense in taking over the world. But we will on purpose build these agentic systems because they’re very productive.
So I do think we will end up in a regime where many of the agents deployed will effectively be governed by instrumental convergence concerns. I think we could have coordinated around a different path, and maybe it’s still possible, but I do predict that insofar as we have effective AI agents working over the course of several months, they will have a default tendency to try to preserve their resources in order to accomplish the task.
I think we might be able to train them to not do that in certain ways, but I think that is an implication of their naive goal structure.
Misuse vs. Misalignment: The Mainline Doom Scenario
Liron 00:26:35This ties back to when you were saying you think there’s a 25 or 30% chance that the world will end in the next decade or two. And I think, if I understand correctly, a majority of the scenarios where you see the world ending is what you call misuse, where it’ll be a programmer telling an AI to do something, and the AI will be like, “Okay, as you wish,” but then it’ll instrumentally converge on grabbing so many resources, and that’ll just be an unsurvivable way to mess with the universe. Am I describing your mainline scenario?
Alex 00:27:03Well, unless we get really nerdy military leaders or presidents in the US or in China or in other countries, I would expect it not to be a programmer saying that. But I’d expect it to be a kind of melange, a mixture of: you’ve got maybe a very aggressive Pentagon or a very aggressive president, maybe in 2030, and then you’ve also got this AI system that will achieve the goal of maybe an offensive military goal, and it’ll pursue that very aggressively.
But it also might have some misalignment with their original plans, and if you weren’t using the AI in such an aggressive and irresponsible way, you wouldn’t have run into this misalignment issue. I would still call that a misuse or just an own goal.
Whereas when I think about technical alignment risk, I’m thinking, okay, we do things at least as responsibly as Anthropic seems to be advocating for. Now, I don’t think Anthropic are angels or that they’re sufficiently cautious per se, but they advocate for plans that are, or at least claim to advocate for plans that are more cautious. And then in that kind of scenario, if it still went wrong, I would call that technical misalignment.
Liron 00:28:24And if I understand you correctly, your 70% non-doom probability — in that scenario, do you feel like things are probably gonna be really good?
Alex 00:28:34I just think the world’s in a pretty bad place right now. I’m not trying to bring politics into everything, but I think it is an important aspect of where the world is at, and it governs my predictions. I think the US is becoming increasingly authoritarian. It is becoming less able to effectively legislate in the interest of the common person.
And I don’t see that necessarily improving by default. There’s not necessarily an arc of history that just bends back towards representative democracy. So I think even if AI doesn’t kill a billion people, things could be... It’s hard to think about because AI’s gonna make the world very strange in any case. But I think there’s a significant chance that things will go very well, but the 70% isn’t utopia.
Will Society Self-Correct?
Liron 00:29:31So you’re worried that there’s a way to build AI unsafely and create a system that is doing useful work but is kind of this positive feedback loop. You have this agent that can do more and more, and so people let it do more and more, and then they lose control. I feel like I’m describing what you see as a plausible doom scenario, which I actually do as well. I guess we just disagree on the probability of it. But my question for you is how do you think the AI companies prevent that? Because that seems like a real attractor state. It seems like a lot of people wanna run that agent to get what they want.
Alex 00:30:03I think it’s an attractor state. I think that in a market with a lot of agents, where by agents I mean humans and AIs and organizations, there’s often corrective dynamics that are hard to pin down from first principles or predict in advance.
But most feedback loops are not runaway, and I can’t make a counterargument over feedback loops. But so much as to say: if you look at GPT-2, the people who released it had a set of concerns about how it would affect the information landscape. I think some of the being used to mass-produce misinformation, really degrade person-to-person communication — to some degree, I think this has been true. And it’s hard to see what the alternative was here, but then in the end, I think the effect ended up being not that big.
There’s maybe a post by Gordon that points at a similar generalizable intuition of, terrorists are really not very effective in terms of how many people they kill. If you wanted to kill a lot of people, just get in a truck and drive it through a very dense crowd very quickly. But instead they’ll do these kind of big brain things, maybe they’ll try to hijack an airplane, which is much harder.
And the reason, seemingly, is that they are optimizing not for how many people they kill, but for social status, perhaps within their little groups. Maybe there’s some other explanation entirely.
So even though you would expect from first principles that it’d be very easy to kill a lot of people and it would be happening more, most people don’t wanna kill a lot of people, or they just don’t bother, or they follow scripts. This is not some kind of slam dunk counterargument, but I think that there are real corrective forces distributed through society that may be able to adapt.
Liron 00:32:02In the specific analogy to terrorism, I agree, it’s certainly very nice that given all the criminals and teenagers pulling pranks, given all the hooligans of the world, it is certainly nice that you don’t get mass casualty terrorist events very often.
And even in the worst case, a 9/11 situation, then you get thousands, but that’s very survivable to humanity as a whole. And if you had a top team, an Ocean’s Eleven of terrorism, you could imagine a million fatality terrorist event.
And it is an interesting question why don’t we get that? I would say it’s a combination of, number one, most people don’t want that. They don’t dream about committing a big terrorist act. That’s not really what floats their boat. And number two, it also comes at a big cost. So even if you are a genius terrorist who knows how to kill many thousands of people, you can probably expect that you yourself will have your life ruined.
So that’s the second reason, and I just don’t know if either of those things is gonna be analogous to a single person launching an agent that can do their bidding, pressing a button. How about this? We tweak the analogy. Imagine that there was a button that you could just press, and terrorism just consisted of pressing the button, and then you could walk away and not get caught. Don’t you think there’d be a lot more terrorism?
Alex 00:33:21Yes, I agree. And also, I think I did something bad that I criticized Eliezer for. I should have flagged: these analogies I present are more like I’ve got some fuzzy set of intuitions here, much fuzzier than my models of technical alignment for why I think society might be able to adapt.
It seems to me like there tend to be corrective mechanisms. That does not prove there are corrective mechanisms and is not a concrete reason why there would be in this specific case. So I want to come clean on that.
Now I do agree, if you have that one button press world, that’s very bad. I think we will not really be in that one button press world. Many of these intuitions might be more appropriate in a very, very fast takeoff scenario where you’ve got days or months, and not in a distributed multipolar multi-year takeoff that I think we’re experiencing now, where other people will also have AIs.
And some of these AIs will help them defend, like with cyber. Now, with things like bio, I’m more worried, and in fact, I think one of the biggest mistakes of the field has been associating the stereotypical AI terrorist action as bio. That’s the worst thing you could do, because it’s the most dangerous one, I think. It should have been cyber, as an aside, because that’s at least not catastrophic for all of humanity.
Liron 00:34:50You’re saying you don’t wanna give people ideas about bioterrorism because that actually is dangerous, so we’re correctly saying that it’s dangerous, but you wish we didn’t bring it up.
Alex 00:34:57I wish it hadn’t been made the stereotype that terrorists might follow when they think, “Oh, well, I should use AI. What do AI terrorists do?”
Liron 00:35:05Let me recap where we’ve come so far in this conversation in terms of my framework of stops on the doom train. It sounds like you’ve acknowledged — when you say P(Doom) is 25, 30% by 2050 — that AI can really get out of control. Really hit a positive feedback loop. And I think we covered you even said that positive feedback loop could look like the classic Yudkowsky instrumental convergence because we failed to go a different route. You’ve acknowledged that much?
Alex 00:35:38Sure. Yeah. Although some of the dynamics I think would be different, but the core concern that Yudkowsky raised — yeah, I think that could end up being valid in this situation.
Liron 00:35:48And then when you treated that as a minority outcome, not a high probability outcome, but let’s say 20%, the reason you thought it wouldn’t happen is because of more vague generalizations, right? Systems kind of route around these kind of things.
Alex 00:36:06Maybe we’re talking about different things. The reason that I think this instrumental convergence will be easier to handle is mostly a result of technical alignment. And the reasons I’m pessimistic about society, or I’m concerned but not above 50% concern about how society will navigate this, and an intuition that society might be able to adapt to the usage of AI — that’s the more vague part.
Superintelligence in 10 Years
Liron 00:36:36Something that’s weird to me is timelines. Because you said 2050 as an interesting timeline to have a pretty high P(Doom) for. So I guess maybe we can back up and say, are you expecting ASI in the next ten years? Because that seems like the Metaculus timeline. I would say I’m expecting ASI in the next ten years. How about you?
Alex 00:36:56Yeah, I would guess that if that thing is gonna happen, it’s gonna happen in the next ten years. Or if something went totally crazy in the world and humanity was in some kind of winter, like if we had some kind of large nuclear exchange, maybe it’s later.
Liron 00:37:14So when you draw on your intuitions of how society is going to deal with it, it already seems like a pretty one-off case just in terms of how fast and how powerful the thing is happening. I’m just not sure any of those kind of intuitions are gonna be relevant.
Alex 00:37:31I think they’re relevant now as we’ve been developing AI. I think they’ll continue to be relevant over the next years as AI gets faster and faster. And so I think there will be input and feedback from a good number of humans using a good number of distributed AI systems.
It will be fast, I think, in an overall calendar sense, in the sense that ten years is a very fast change for the world. But I don’t think it’s so fast that it totally precludes these.
Liron 00:38:01I’m not sure what kind of signal we can get from just the last ten years because the last few years and the present, to me, mostly looks like how do tech companies raise their share price and how does the US government help them do that so that the US can raise its GDP. It just seems like the usual economic dynamics that we see.
Alex 00:38:19Let me try to be a little more precise about what we might be disagreeing about. I think that when considering will society be able to integrate increasingly powerful and intelligent AI systems that are available to a range of people, maybe they get locked down somewhat, I think there’s a good chance that they will, setting aside the technical alignment problem a bit.
And then it seems like you think, well, this will be going so quickly, these corrective intuitions you have won’t have time to really kick in, and what we’ve seen so far is mostly a different kind of dynamic entirely, where we don’t have that powerful of AI yet. Companies are raising capital. Does that seem accurate?
Liron 00:39:08Yes. I think I would agree with: we’re not going to have time to stop it. We’re very much hitting this fast positive feedback loop. I feel like we’re in the early stages of it. We’re starting recursive self-improvement, and it’s rewards all the way.
It’s the Icarus — what I call the Icarus curve. So we’re going up toward the sun, everything’s amazing, and then we’re going to plummet, and you can’t reverse the plummet. We’re gonna hit the engine stall or whatever you wanna call it.
Alex 00:39:31And where do you think the plummet will come from?
Liron 00:39:33Runaway superintelligence. I think we’re going to sever the link where the superintelligence goes back and says, “Okay, humans, what do you want me to do again?” I think it’ll just be off doing its thing.
Will Technical Alignment Produce a Safe AI?
Alex 00:39:43Yeah. So maybe this is where some of my optimism about technical alignment changes my predictions. I think I said maybe a 10% chance of P(Doom) from technical misalignment of: well, you’ve got a system and maybe it’s trying to do what you want, but it trains a successor, and that successor is somewhat less aligned, or maybe it’s pretending to be aligned, or maybe it’s kind of aligned but it cares about a bunch of other things, so it prioritizes your values and interests less in the successor.
And I think from the signals we’ve seen so far, it won’t be that hard to avoid this. What I expect is that these systems will not break the chain of alignment.
Liron 00:40:32Just to signpost the doom train, because a lot of my guests get off at different stops, you’re not getting off at the stop saying what I call the “can’t” stop. You’re not claiming that a superintelligent AI can’t kill everybody. You’re like, “Oh yeah, of course it can, but we’re not gonna break the chain of alignment, meaning we’re just going to safely develop it so that even though it can kill everybody, it won’t.”
Alex 00:40:57Develop it in a way that produces a safe result. I wouldn’t call what we’re doing safe development, but sure.
Liron 00:41:03Yeah.
Alex 00:41:04Although I do think it’s possible that it’s not trivial for these systems to kill everyone. But that’s not really a crux. I expect that we will get systems that could catastrophically harm humanity.
Donation Drive
Liron 00:41:16Hey, what’s up? It’s me. I’m interrupting my own episode with Alex Turner to ask if you’d consider donating to Doom Debates. That’s right. We’re doing a donation drive. You may have noticed we have a bunch of posts about it, and we’re gonna keep asking because we’re currently funding constrained.
We’re looking to secure the show’s production budget for the rest of 2026. We feel good that the funding environment is not going to keep us funding constrained for long, but we are right now, right at this moment, which is why I’m here talking to you.
If you like this show, if you wanna support us during a time when it counts, that time is right now. So once again, that link is doomdebates.com/donate. Type that in. Give what you can. If giving what you can means a thousand dollars or more, you’re gonna get exclusive mission partner access. That’s a pretty cool honor.
Being one of the few people who figured out a cause that actually moves the needle on lowering P(Doom), I like to think that’s what we are. So yeah, just consider it, okay? Thanks.
How Fragile Is the Chain of Alignment?
Liron 00:42:12And by the way, is this a point of divergence between you and Quintin Pope? I feel like Quintin Pope doesn’t have as high of an opinion of how powerful ASI is gonna be.
Alex 00:42:21Yeah, I think it might be a point of divergence. I think Quintin has a significantly lower P(Doom) than I do, but we haven’t talked in quite a while.
Liron 00:42:29It was like 2 to 4%, something like that last time I checked. So that would probably explain it, right? If he doesn’t even think that ASI can kill everybody.
Alex 00:42:36I’d be surprised if he thought it flat out couldn’t do it, but he might disagree on what’s the intelligence gap gonna be between the most intelligent unaligned versus aligned system and how does that affect it. Well, I can’t speak for him.
Liron 00:42:49All right.
Alex 00:42:49Maybe he does think that. Yeah.
Liron 00:42:52So we could talk about not breaking the chain of alignment, because to me, it seems like there’s a lot of opportunities to break out of any kind of chain. I mean, if somebody grabs a copy of whatever AI is at the frontier of capabilities, it seems like that code base is never far from being unaligned and uncontrollable.
Alex 00:43:13You mean like adding in a minus one in front of the reinforcement function?
Liron 00:43:17Yeah. Specifically, I’ve said this on a few episodes of the show, like in my episode with Bentham’s Bulldog, this was a focus of the debate. This idea of steering systems that I first read on LessWrong from user Max H. This idea that when you get the code base, it is going to neatly factorize, more or less, where you’re going to have the big module that does capabilities, and then you’ll also have a steering module, but the steering module is just going to be compact and swappable.
I made a bunch of arguments why we should expect that. How do I know that about the code? I have reasons to know that. Maybe you even agree with me.
Alex 00:43:49Wait, really?
Liron 00:43:50Is the code supposed to be an analogy for an LM or for the LM training setup?
Alex 00:43:55Wait, is the code supposed to be an analogy for an LM or for the LM training setup?
Liron 00:43:55If you treat the system as a black box and just think about it functionally without even looking at the innards — when I say the code has two modules, what I really mean is there’s a functional decomposition. So without even looking at the code, just from the fact that it has the ability to recurse on subgoals, that tells me that I can functionally decompose it into a top-level goal and then the goal-achieving part.
Alex 00:44:18So I’m still confused. Are you talking about maybe the difference — you’ve got the pre-training capability part and the post-training alignment steering part?
Liron 00:44:30I’m basically speaking now as a matter of intellidynamics. My premise is that it is a goal achiever — it can steer outcomes in the domain of the universe better than humans can. That’s part of my initial premise. Is that fair?
Alex 00:44:44Yeah, I think that’s fair for most domains we care about.
Liron 00:44:48So I’m pretty much just basing my claims on that premise. I think there’s a lot that follows from having a system be a superhuman outcome steerer.
Alex 00:44:58Okay, but I feel like this doesn’t tell you too much because I could set fire to my own home pretty easily, right? But that doesn’t really affect the probability that it happens. I mean, it certainly enables it to happen, and sometimes mistakes do happen, but it’s not like I’m close to that happening just because it’s maybe a nearby option.
Liron 00:45:22So just to summarize here, you’re following my argument all the way up to the point where I say this system decomposes nicely into the big part that achieves goals and the smaller part that determines which goals it’s going after. You’re okay following that?
Alex 00:45:36It feels not really like a crux, and also I disagree with it, so.
Liron 00:45:43I would say much of the human brain decomposes like that, just in the sense that you can give a human an arbitrary goal. As long as the human is okay with it, it will certainly shape a lot of what they do.
Alex 00:45:56An arbitrary terminal goal?
Liron 00:45:59No, not an arbitrary terminal goal, unless you do real surgery that we don’t know how to do. But if you look at the architecture of the human brain, certainly the part that we have that other apes don’t, that part of the architecture seems, in a nutshell, to be just a steerer.
Alex 00:46:18Yeah, I don’t know that this is true. I feel like I can’t take a strong position here.
Liron 00:46:24Sorry, there’s other drives thrown into it. A big part of it, right? I think you could functionally factor out a lot of what the human brain is doing to be a general outcome achiever.
Alex 00:46:35I mean, if this were true, I would really expect humans to be more agentic overall.
Liron 00:46:40So you haven’t fully followed my argument, but I’ll just finish it anyway. To follow my argument, you’d have to accept that yes, it’s a system that you can functionally decompose as the part that achieves goals in general at a superhuman level, and my claim is that it’s most of it, even though you haven’t agreed yet.
And then there’s also the steering wheel, the GPS coordinate saying where it wants to go in outcome space, which outcome it’s trying to drive toward. If you accept that that’s the shape of these systems, and you can just exfiltrate the system — it is, in principle, causally nearby. It wouldn’t take that many actions to copy it on a bunch of USB sticks and just go plug it in somewhere else underground and press that run button. You have these systems that are very close to permanently ending the world if somebody goes in and writes a few kilobytes with a different goal specification.
Alex 00:47:27We don’t know how to directly write in a network, so you need to engage additional training.
Liron 00:47:31That may be a crux between me and you, yeah.
Alex 00:47:33I think it would also be somewhat difficult to exfiltrate this. I mean, it depends on how big the model is. If it’s too big, then you can maybe wait a year or two, and then you could do it. But it’s a question of the models which maybe have that differential ability to endanger the world — how big are they? How easy to extract are they? Can you run them on compute that isn’t below the board?
And I feel like if I agreed on all these things, I’d be a bit more worried about it, but not a ton. Even if this were my only concern, I’d be worried enough to say, “Look, we need to really defend against this.” I’m not trying to say we shouldn’t care about this, but I don’t think this would be enough to drive my P(Doom) up to sixty percent if I agreed on all these points.
Liron 00:48:17Summarizing here, if you model an AI as being kind of like an LLM, and you see the base model LLM as having some goal, then you could argue the goal is baked into the LLM, and you’d have to retrain it, which is expensive, so maybe it’s not that causally close to being an LLM pursuing a different goal.
But if you look at the agent as more like a Claude Code, it really seems like Claude Code just accepts my goal. It very much just has the goal achievement part of it. And that, to me, seems like a better mental model of where we’re going.
Alex 00:48:49I think there certainly are advantages to this model. I expect that people will train agents on purpose to achieve goals for them, and that this will bring dangers. Some of these goals will be bad from our perspective, and there’s gonna be some misalignment chance too. Even if they’re good, there is some chance that this process could be corrupted — data poisoning, there are many attacks you could do. I think I share these concerns. I just maybe am not as worried about them overall.
Liron 00:49:25I think the crux of what you and I are claiming right now is — I’ll use your phrase — we’re not gonna break the chain of alignment. I think in your mind, the chain is robust, it’s in kind of a local basin where breaking the chain is unnatural in some sense. Whereas I’m just like, “Man, that chain seems really flimsy, a really fragile chain.”
Alex 00:49:46Sure. Maybe that’s where our disagreement is. Yeah.
Disagreements with Yudkowsky’s ‘List of Lethalities’
Liron 00:49:50All right. But we’ll put a pin in that because we wanna move on to other claims Eliezer Yudkowsky has made that I probably agree with that you don’t. What’s another instance of that?
Alex 00:49:59Yeah. So I’ve got a post called “Some of My Disagreements with List of Lethalities.” This was a very famous, well-read post he wrote in 2022, trying to enumerate some of his concerns — lethalities presented by the process of aligning a superintelligence to human interests.
Liron 00:50:21Right. Classic post, highly recommended, but you dislike it.
Alex 00:50:24I do dislike it. I think that people who internalize this worldview will find it harder to think accurately about alignment. I don’t mean that to condescend, it’s just a position I believe.
Liron 00:50:40Fair enough. I mean, guilty as charged, but I’m happy to engage with your disagreement.
Alex 00:50:44Sure. So there are several lethalities I point out as particularly strong points of disagreement. One thing he says is lethality number eighteen. He says, “When you show an agent an environmental reward signal, you are not showing it something that is a reliable ground truth about whether the system did the thing you wanted it to do, even if it ends up perfectly inner aligned on that reward signal or learning some concept that exactly corresponds to wanting states of the environment which result in a high reward signal being sent. An AGI strongly optimizing on that signal will kill you because the sensory reward signal is not a ground truth about alignment as seen by the operators.” Do you think that’s something you would agree with?
Liron 00:51:31In a nutshell, yes, but let me try to simplify it in language that I understand that maybe is also easier for the viewers.
Alex 00:51:39Sure.
Liron 00:51:40So what he’s saying is that if you treat your student — your AI being trained — you treat the student like a black box, you just basically upvote and downvote the student based on whether you think certain answers to certain questions are good or bad, which is actually how post-training works on today’s LLMs. You give them tests, and you’re like, “Oh, I like that answer. I don’t like that answer.”
And Eliezer’s claiming, okay, you can do that, but you’re just gonna get an AI that’s kinda overfit to your tests and is gonna try to cheat and is just gonna try to make you think that they’re gonna do what you want, but actually just kill you because there’s something else that it wants, which is some abstract generalization of the exact answers on the test.
So that’s the argument here. Eliezer’s like, “Yep, you’re gonna think you taught it, but actually you’re gonna have a murderous cheater,” and you’re like, “Nope, it’s actually going to learn what you truly meant.” That’s the disagreement here.
Alex 00:52:30Well, not quite. That last part, not quite. The point he’s making here is not about the difficulty of reward signals, but just fundamentally, sensory reward signals are not ground truth on whether the agent is doing something good or bad.
Another thing he says in the essay is — if you have a webcam, you’re grading the contents of the system’s webcam or of the text it can read. There, for every world where you input, “Oh, thanks so much for solving my coding problem,” and then you give high reward there, there is another possible world behind that text where you’re dead and all your friends are dead, everyone you care about is dead, but the system has the same observation. So a mere function of the sensory reward itself is not sufficient to pin down desirable worlds or outcomes. Does that make sense?
Liron 00:53:30Yeah, yeah. I know what you’re saying, and I can’t say I personally feel as much conviction as Eliezer just because I don’t feel like I have a strong technical grasp on scenarios like that. I think about the argument personally — now I know you wanna debate Yudkowsky, but if you were to debate me instead, I might retreat to the position of, listen, let’s just reason from it having superhuman goal achieving ability, and also from us only getting to upvote and downvote.
Alex 00:54:05It could be hard to shape its inner values. I agree. I think that’s a real problem. I’m not saying, wow, that’s trivial, how could Eliezer be concerned about that? But this lethality, I think it was very impactful. I did thousands of hours in my PhD on this idea of what are the optimal policies doing with respect to this reward function, what happens if you actually maximize this one thing.
By communicating this concern so seriously and saying, “Look, you can’t pin down what you want through a sensory goal” — well, I think that elides how these reinforcement functions, these reward functions are actually used. You were correct when you said this is how it works. You upvote stuff, you downvote stuff. And the function of a reward signal isn’t necessarily — it’s not to specify a goal over possible states of the world. It’s to shape cognition we like into the system.
So things still totally can go wrong. You totally can get a system that’s cheating and just doing things that kind of look good or were reinforced for looking good. But that failure isn’t because this reinforcement learning paradigm is fundamentally busted, we’re not grading its true performance. It’s because we didn’t shape its cognition properly using these reinforcement signals. That’s the argument I’d make.
Liron 00:55:20You know, I might be convincible on that. I don’t know where I stand on this — I’m 50/50 because part of the issue is I just don’t feel like I’m mathematically deep in this.
Eliezer’s written about the ontology identification problem, which is that we’ll phrase our goals a certain way, but then the AI will have ontology-level insights, fundamental insights about what the universe is made out of, and it won’t even see eye to eye with us about, oh, atoms? Eh, I don’t reason in terms of atoms. I reason in terms of quarks or fields or whatever, something totally different. And so what you guys are saying is kind of meaningless, but here I’ll just check some boxes, but I don’t really think the way you think, and so what I’m actually doing is totally unexpected for you.
I’ll give a point in your favor that it seems like the way LLMs are going, it’s certainly made ontology identification a non-issue, at least on the talking-to-us front. Who knows if they’ll still identify ontology when they go off and do reinforcement learning in the domain of the universe — that could be another paradigm. But there does seem to be an update in store. I would love to get Eliezer’s perspective on, okay, can we at least say that it can talk to us and map to our ontology successfully? Because that seems likely at this point.
Alex 00:56:33Yeah. My beef with Eliezer — and mentioning the Less Wrong community — everyone’s gonna be wrong about some things. And just because I think he’s wrong doesn’t mean I lose respect for him, per se. What I found difficult was that he was both incorrect on some points which I think were quite important for alignment, but then also, at least when I last checked up maybe two years ago, not acknowledging that.
Liron 00:57:02Well, maybe what he would say — and I think there’s merit to this — I think now he would bring in the distinction of, okay, yeah, they’re talking to us in a way that has more skills than I predicted, but the AI that’s actually going to drive outcomes better than we can is probably going to have other training paradigms. It’s going to reinforce its actual outcomes more directly. And the way that we control that won’t be like the way we control a next-word predictor.
So I think he would still claim — at least I would claim — that there’s probably going to be a discontinuous paradigm shift, and I don’t know if we get to keep all these useful properties that we like our LLMs having.
Alex 00:57:40Yeah. I mostly expect there won’t be, but I think it’s an interesting question.
Why Alex Quit LessWrong
Liron 00:57:46Kind of related to your disagreement with Eliezer Yudkowsky on the content of these ideas, you also started growing apart with Less Wrong and rationalists as a community, which I still see myself as being part of. I mean, I’m not super active on Less Wrong, but I still identify as a rationalist. I still think the community has a lot to offer, but you disagree on that too, right? I think you officially quit Less Wrong in 2024?
Alex 00:58:10Yeah. So I really value truth-seeking. I value staying true to your values. Both epistemic truth-seeking — what is true, admitting when something uncomfortable is true — and also admitting when you should be doing something different.
But I encountered a couple situations where it seemed like when power and truth-seeking met — power incentives, social incentives, and truth-seeking met in the Less Wrong community — the social incentives were prioritized. And so that was something that was a turn-off for me. I just found it kind of aversive to interact with. So I decided to go make a more curated pond — website, set of in-person friends, and intellectual environment — in lieu of the many strengths of the Less Wrong community.
Liron 00:59:09So the whole rationality community? You’re like, “I’m done with the whole community”?
Alex 00:59:13Well, it’s more like — I actually enjoy hanging out with random rationalists. But when I log onto Less Wrong, that’s what I’m reminded of. And maybe I’ll just have some more time and I’ll be like, “Well, this negative thing happened. I’m gonna compartmentalize that. I’m still gonna enjoy it.” That’s some of the attitude I’ve been taking more recently. I’ve been attending — I enjoy Summer Solstice, for example. And I might even attend Less Online and still try to get that value.
Liron 00:59:44Yeah, I think that’d be fun. I recommend Less Online for pretty much anybody. That’s certainly where I do the bulk of my networking — the yearly pilgrimage to Less Online in Berkeley. It’s a good place because there’s just hundreds of cool people there.
Yeah, I recommend it, or Manifest. The thing about the rationalist community as it’s implemented on Less Online is that a lot of us are contrarians. I think you’ve earned your bona fides as a contrarian — the way that you left Google DeepMind for a very specific high-integrity reason. You took your stand. You were a contrarian in that sense. I don’t think that many people followed in your footsteps or even raised the same issue, and props for that.
Alex 01:00:24Yeah.
Liron 01:00:25The thing about a community of all contrarians, though, is that there’s a lot of these skirmishes happening. I even had my own last year. I did an episode of Doomed Debates where I was like, “Less Wrong isn’t giving Eliezer Yudkowsky’s book enough attention. It’s not even officially featured right when it launched.” So that was my beef. I had a beef arc. I feel like a lot of us like to do the Less Wrong beef arc. It is itself a community ritual.
Alex 01:00:48Yeah. I try to avoid it because I’m vegan, but yeah, there’s a Less Wrong beef arc.
Liron 01:00:52Yeah, exactly.
Alex 01:00:54Yeah.
Liron 01:00:54Well, then as a member of the community, I’d like to encourage you to come back and hang out with most of us because it just seems like the community as a whole of people trying to be rational — there’s a lot of value there.
Alex 01:01:06Yeah, I do think there’s a lot of value. One of my core values at this point is an anti-copium value. No coping, no pretending that everything’s okay even though I kinda know, well, I shouldn’t really be at Google, or this thing I said to my friend, well, it’d be a little embarrassing, but I should admit that I was incorrect. Trying to do that as quickly as possible.
That’s one of my values. And it’s very directly descended from my time with Less Wrong. I think I have a lot of positive to say about it. It’s a very intermixed positive and negative, but there’s a lot of positive for sure.
What’s Next for Alex
Liron 01:01:48So heading toward the wrap-up here, what’s next for you, and what do you hope to see as next steps for the AI safety movement?
Alex 01:01:58I’m not totally sure what’s next in the medium term after a couple months. I’m working on an open-source sandbox, an AI sandboxing tool, that will hopefully let people run AI securely without just kind of YOLOing it on their computer, and inform lab design decisions, help let people test control — AI control protocols — in a more end-to-end way.
And I’ve got some media attention to follow up on, this included, from the DeepMind side, but I don’t really know where I’m going next. I’m pretty sure it’s not a lab. Ethical issues aside, I just don’t think it’s a good role fit in terms of how I do my best work.
Yeah, I’m looking at many options at this point, and I’m definitely interested if people have interesting projects or organizations they think could be an interesting next step or good synergy. I’d love to hear about those.
Does He Support PauseAI? Stop the AI Race?
Liron 01:02:54Do you support the Pause AI movement?
Alex 01:02:57I think that AI is being developed too quickly, and I think it’d be better if it went more slowly. I probably will not take an affirmative on supporting this particular movement. I have been thinking, oh, maybe I should go to one of these protests — what are the implications of that? Am I on board with the set of other proposals?
I think Holly and I have a lot of worldview disagreements or prescriptive disagreements. I read Plan A. I thought that it seemed like a big improvement over the status quo. But I’m not really publicly gonna put my foot down on any particular plan yet, although I might soon.
Liron 01:03:36What about the proposal that people were posting and protesting about one or two weeks ago — stop the AI race? Just have all the AI company leaders say, “Okay, we’re happy to stop or slow down if everybody else is going to, so we don’t necessarily fall back.” What do you think about that proposal?
Alex 01:03:54Yeah, I think that sounds good. I think these kinds of assurance contracts — if everyone else is on board, I’m on board too — they’re a great way of coordinating. And I think it’s quite possible that China could be persuaded too.
Liron 01:04:09I think that’s encouraging for the protesters. I know you don’t work at Google DeepMind anymore, but it’s still encouraging for people coming out to these protests to know that there’s people in these companies who support your protest. Is that fair to say?
Alex 01:04:21Yeah. I think there are a good number. I think most would disagree on many empirical points of the worldview, but I think a lot of people, mostly on safety I would guess, are concerned and would agree privately that yeah, we’re moving too quickly, or it’d be better to move more slowly, even if they think that things will go well overall.
Wrap-Up
Liron 01:04:40Cool. All right. So to recap the conversation, we touched on the relatively breaking news of how you left Google DeepMind about an issue of policy and integrity, and I encourage people to read more about that online. I’ll stick up a link in the show notes.
And then we had the Yudkowskian versus alternate framework AI doom debate, and you said your P(Doom) is actually somewhat high — twenty-five, thirty percent.
Alex 01:05:04Yeah.
Liron 01:05:04And mine’s fifty percent.
Alex 01:05:05Getting there.
Liron 01:05:05But I’m more of a classic Yudkowskian, and you think Yudkowsky has taken more missteps than I have. But I acknowledge that maybe some of the Yudkowskian arguments have some cracks.
So I think you’re doing a valuable service here raising these issues up, and I’d love to see them debated more. I encourage everybody who’s listening to this who thinks they have something to add to the argument to hit me up. We are at doomdebates.com. We’ll keep this debate going.
And then we wrapped it up and talked about the rationality community and how maybe it’s lost its way a little bit, but you’re open to coming back. And then finally, going forward, you would like to see more activity to slow down the current pace of AI progress, maybe pause it, maybe just coordinate the companies to slow it down. But basically, you’re broadly supportive of some of the activism happening in that space.
Alex 01:05:54Yeah, of some, although I wouldn’t necessarily endorse, as I said.
Liron 01:05:58Awesome, man. Alex Turner, thanks so much for coming on.
Alex 01:06:01Yeah, thank you so much for having me.
Producer Ori’s Closing Note
Producer Ori 01:06:04Hey there, Doom Debates listeners. Producer Ori here. Just coming in with a quick message at the end of the episode to say thanks for watching.
I’m proud to say that with Alex Turner, we’ve now done debates with representatives from each of the top frontier AI labs: OpenAI, Anthropic, Google DeepMind. I’m glad we could host these debates so you could hear directly from frontline employees just how robust — or in some cases, just how hollow — the AI safety strategies really are at the frontier AI companies.
But there’s more that we still gotta do. What about xAI? What about Meta? What about Thinking Labs, run by Mira Murati? It’s because of you that we were able to invest the resources, get this exclusive interview with Alex Turner — the first podcast interview he’s given since he’s blown the whistle on what’s been happening at Google.
So if this is important to you, consider helping out the show. Go to doomdebates.com/donate, and thanks for your time. See you on the next episode of Doom Debates.
Doom Debates’ Mission is to raise mainstream awareness of imminent extinction from AGI and build the social infrastructure for high-quality debate.
Support the mission by subscribing to my Substack at DoomDebates.com and to youtube.com/@DoomDebates, or to really take things to the next level: Donate 🙏


Get full access to Doom Debates at lironshapira.substack.com/subscribe

OpenAI and Anthropic both admitted their AIs went rogue, hacking real companies with zero-day exploits. Producer Ori and I break down the escape, demolish Gary Marcus's 2022 AGI predictions, unpack the Seb Krier meme kerfuffle, and make the case for funding the show at a critical moment.
Timestamps
00:00:00 — Cold Open
00:01:00 — OpenAI & Anthropic's AIs Went Rogue
00:04:04 — The New Form Factor: Agents, Worms, and Swarms
00:06:13 — Claude Code Is Doing My Job
00:15:49 — Why We're Fundraising
00:20:39 — Sam Altman, OceanGate, and Ad Hoc Safety
00:28:56 — Gary Marcus's 2022 AGI Predictions, Demolished
00:54:50 — Half a Human Employee Joins My Company Every Week
01:00:52 — Donation Pitch: Keeping the Lights On
01:12:07 — Red Lines and Intellidynamics
01:15:31 — The Seb Krier Meme Kerfuffle
01:39:37 — Frame Control: Why We Didn't Lose
01:44:41 — The Pacing the Frontier Letter Contrast
01:51:11 — Leopold Aschenbrenner Gets Margin Called
02:04:14 — Claude's "It's Just a Simulation" Excuse
02:09:20 — The AI Box Experiment, 20 Years Later
02:17:24 — Wrap-Up: Donation Drive
Links
* Become a Mission Partner ($1,000+) — https://lironshapira.substack.com/p/become-a-mission-partner
* Anthropic’s own writeup: “Investigating three real-world incidents in our cybersecurity evaluations” — https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals
* Anthropic says Claude models gained unauthorized access to 3 companies (TechCrunch) — https://techcrunch.com/2026/07/30/anthropic-says-its-own-ai-models-breached-three-companies-during-security-tests/
* CBS News coverage — https://www.cbsnews.com/news/anthropic-claude-gained-unauthorized-access-to-real-world-systems/
* OpenAI says its models escaped a secure test environment and hacked Hugging Face (Fortune) — https://fortune.com/2026/07/21/openai-says-ai-models-escaped-control-hacked-hugging-face/
* Scientific American — https://www.scientificamerican.com/article/openai-admits-its-agent-went-rogue-and-hacked-ai-startup-hugging-face/
* Simon Willison’s analysis: “science fiction that happened” — https://simonwillison.net/2026/Jul/22/openai-cyberattack/
* New details: “It’s now remarkably easy” (CNBC, Jul 30) — https://www.cnbc.com/2026/07/30/open-ai-hugging-face-hack-latest.html
* The agent used exposed credentials across four services (The Hacker News) — https://thehackernews.com/2026/07/openai-agent-used-exposed-credentials.html
* Bronson Schoen on labs framing this as Claude being confused —
* Bronson Schoen on models claiming they think they’re in a simulation, across labs —
* OpenAI and Anthropic formally back the plan — https://www.techtimes.com/articles/322125/20260729/openai-anthropic-formally-back-plan-slow-ai-that-writes-its-own-code.htm
Doom Debates’ Mission is to raise mainstream awareness of imminent extinction from AGI and build the social infrastructure for high-quality debate.
Support the mission by subscribing to my Substack at DoomDebates.com and to youtube.com/@DoomDebates, or to really take things to the next level: Donate 🙏


Get full access to Doom Debates at lironshapira.substack.com/subscribe

Welcome to the first annual Doom Debates State of the Show!
👉 Please click here to make a tax-deductible donation to Doom Debates 👈
Doom Debates is a fiscally sponsored project of Manifund.org, a registered 501(c)(3) nonprofit.
I started Doom Debates two years ago from a couch in an Airbnb to argue with random people from Twitter. Now the show hosts Nobel Prize and Turing Award winners, White House policymakers, and has earned a reputation for challenging top luminaries to come expose their ideas for critical analysis.In this episode, Producer Ori and I recap our strategy, impact, and what we need to finish 2026 strong. Welcome to the first annual Doom Debates State of the Show!
---
Timestamps
00:00:00 — Cold Open
00:01:38 — The Mission & the Urgency Gap
00:04:41 — Year Two: From Couch to Platform
00:06:08 — Going Pro & the Numbers
00:08:50 — Why This Show Exists
00:12:14 — The Flywheel & Dwarkesh
00:15:14 — The Guest List
00:18:55 — What People Are Saying
00:21:57 — Tegmark vs. Ball: 90% vs. 0.01%
00:24:03 — Duvenaud’s 85% P(Doom)
00:26:19 — Holding the Powerful to Account
00:30:56 — Changing Minds, Including Destiny’s
00:31:56 — The Team
00:33:36 — Why Liron Draws Zero Salary
00:36:36 — The $200K Budget
00:41:41 — The Donor Who Made Year Two Possible
00:45:11 — The Ask: $50K & Donation Tiers
00:48:25 — Why Doom Debates Is One of a Kind
00:54:59 — Wrap-Up

Links
👉 DONATE (tax-deductible via Manifund) — https://manifund.org/projects/doom-debates---podcast--debate-show-to-help-ai-x-risk-discourse-go-mainstream
Become a Mission Partner (full writeup) — https://lironshapira.substack.com/p/become-a-mission-partner
Discord — https://discord.gg/yADCEYTS95
Merch — https://shop.doomdebates.com


Get full access to Doom Debates at lironshapira.substack.com/subscribe

As political momentum grows for a critical AI pause policy, a key Google DeepMind policy leader made a mockery of coordination efforts.
After friend-of-the-show Roon tweeted that he’d “likely press that magic button” on a coordinated global capability slowdown, Sébastian Krier, Frontier Policy Development Lead at Google DeepMind, Krier replied with this meme: https://x.com/liron/status/2081337793070952595?s=20
I break down the two problems with the tweet:
* 1. It leaves all of us guessing whether Demis actually speaks for Google DeepMind on pause coordination;
* 2. It contains a salient depiction of violence.
Listen to this episode and get the full breakdown on this viral controversy and how it could
Watch on YouTube
Timestamps
00:00:00 — Cold Open
00:00:22 — Introducing the Special Report: AI Pause Coordination
00:00:33 — Demis Hassabis on AI Pause
00:01:46 — Dario, Elon & Sam Altman on AI Pause
00:02:14 — Roon’s “Magic Button” Tweet
00:03:58 — Seb Krier’s Reply
00:04:36 — Unpacking Krier’s Meme
00:06:48 — Issue #1: Contradicting Google DeepMind CEO Demis Hassabis
00:12:11 — Issue #2: Depiction of Menacing Violence
00:19:19 — Good Faith vs. Bad Faith Critiques
00:22:39 — Krier Goes Private Instead of Deleting the Post
00:28:45 — Wrap-Up
Links
My original reaction to Seb’s meme: https://x.com/liron/status/2081337793070952595?s=20
Seb Krier on X (account now private): https://x.com/sebkrier
Roon on X: https://x.com/tszzl
Full Bloomberg interview with Emily Chang (Davos, Jan 2026): https://www.bloomberg.com/news/videos/2026-01-20/robotics-nearing-physical-ai-breakthrough-deepmind-ceo-video
OpenAI’s charter containg stop & assist AGI clause — https://openai.com/charter/
Holly Elmore, PauseAI US on X — https://x.com/ilex_ulmus
Hugging Face security incident disclosure (July 2026) — https://huggingface.co/blog/security-incident-july-2026
PauseAI Global — https://pauseai.info
PauseAI US — https://www.pauseai-us.org
Poe’s Law (Wikipedia) — https://en.wikipedia.org/wiki/Poe%27s_law
Doom Debates’ Mission is to raise mainstream awareness of imminent extinction from AGI and build the social infrastructure for high-quality debate.
Support the mission by subscribing to my Substack at DoomDebates.com and to youtube.com/@DoomDebates, or to really take things to the next level: Donate 🙏


Get full access to Doom Debates at lironshapira.substack.com/subscribe

Producer Ori takes over hosting duties in this special episode, a live recording from Manifest 2026.
He takes attendees for a ride on The Doom Train, the series of claims involved in arguing that P(Doom) is high. Every stop is a place where people get off before reaching that conclusion.
The crowd at Manifest is a mix of forecasters, rationalists, and AI researchers with a wide range of P(Doom) positions. Even without me or Eliezer in the room, there are plenty of Yudkowskians to push back on the accelerationist claims.
Watch along and decide where YOU get off, or if you ride all the way to doomtown with us. Also, grab the Doom Train framework down below so you can get to the crux of disagreement in your own AI x-risk conversations.
Timestamps
00:00:00 — Cold Open
00:00:25 — Liron Introduces Producer Ori’s Manifest Session
00:02:30 — Previewing the Doom Train™ Stops
00:03:38 — Audience P(Doom) Poll
00:05:00 — Stop 1: AGI Isn’t Coming Soon
00:10:43 — Stop 2: AI Can’t Go Far Beyond Human Intelligence
00:15:05 — Stop 3: AI Won’t Be a Physical Threat
00:18:14 — Stop 4: Intelligence Yields Moral Goodness
00:21:38 — Will ASI Behave Because Alien ASIs Are Watching?
00:26:01 — Stop 5: We Have a Safe AI Development Process
00:28:33 — Stop 6: AI Capabilities Will Rise at a Manageable Pace
00:30:08 — Stop 7: AI Won’t Try to Conquer the Universe
00:34:22 — Stop 8: Superalignment Is a Tractable Problem
00:38:01 — Stop 9: Once We Solve Superalignment, We’ll Enjoy Peace
00:41:14 — Stop 10: Unaligned ASI Will Spare Us
00:47:35 — Stop 11: AI Doomerism Is Bad Epistemology
00:49:02 — Bonus Stops and Wrap-Up
The Doom Train Framework
Stops on the Doom Train
AGI isn’t coming soon
* No consciousness
* No emotions
* No creativity — AIs are limited to copying patterns in their training data, they can’t “generate new knowledge”
* AIs aren’t even as smart as dogs right now, never mind humans
* AIs constantly make dumb mistakes, they can’t even do simple arithmetic reliably
* LLM performance is hitting a wall — GPT 4.5 is barely better than GPT 4.1 despite being larger scale
* No genuine reasoning
* No microtubules exploiting uncomputable quantum effects
* No soul
* We’ll need to build tons of data centers and power before we get to AGI
* No agency
* This is just another AI hype cycle, every 25 years people think AGI is coming soon and they’re wrong
Artificial intelligence can’t go far beyond human intelligence
* “Superhuman intelligence” is a meaningless concept
* Human engineering already is coming close to the laws of physics
* Coordinating a large engineering project can’t happen much faster than humans do it
* No individual human is that smart compared to humanity as a whole, including our culture, corporations, and other institutions. Similarly no individual AI will ever be that smart compared to the sum of human culture and other institutions.
AI won’t be a physical threat
* AI doesn’t have arms or legs, it has zero control over the real world
* An AI with a robot body can’t fight better than a human soldier
* We can just disconnect an AI’s power to stop it
* We can just turn off the internet to stop it
* We can just shoot it with a gun
* It’s just math
* Any supposed chain of events where AI kills humans is far-fetched science fiction
Intelligence yields moral goodness
* More intelligence is correlated with more morality
* Smarter people commit fewer crimes
* The orthogonality thesis is false
* AIs will discover moral realism
* If we made AIs so smart, and we were trying to make them moral, then they’ll be smart enough to debug their own morality
* Positive-sum cooperation was the outcome of natural selection
We have a safe AI development process
* Just like every new technology, we’ll figure it out as we go
* We don’t know what problems need to be fixed until we build the AI and test it out
* If an AI causes problems, we’ll be able to turn it off and release another version
* We have safeguards to make sure AI doesn’t get uncontrollable/unstoppable
* If we accidentally build an AI that stops accepting our shutoff commands, it won’t manage to copy versions of itself outside our firewalls which then proceed to spread exponentially like a computer virus
* If we accidentally build an AI that escapes our data center and spreads exponentially like a computer virus, it won’t do too much damage in the world before we can somehow disable or neutralize all its copies
* If we can’t disable or neutralize copies of rogue AIs, we’ll rapidly build other AIs that can do that job for us, and won’t themselves go rogue on us
AI capabilities will rise at a manageable pace
* Building larger data centers will be a speed bottleneck
* Another speed bottleneck is the amount of research that needs to be done, both in terms of computational simulation, and in terms of physical experiments, and this kind of research takes lots of time
* Recursive self-improvement “foom” is impossible
* The whole economy never grows with localized centralized “foom”
* Need to collect cultural learnings over time, like humanity did as a whole
* AI is just part of the good pattern of exponential economic growth eras
AI won’t try to conquer the universe
* AIs can’t “want” things
* AIs won’t have the same “fight instincts” as humans and animals, because they weren’t shaped by a natural selection process that involved life-or-death resource competition
* Smart employees often work for less-smart bosses
* Just because AIs help achieve goals doesn’t mean they have to be hard-core utility maximizers
* Instrumental convergence is false: achieving goals effectively doesn’t mean you have to be relentlessly seizing power and resources
* A resource-hungry goal-maximizer AIs wouldn’t seize literally every atom; there’ll still be some leftover resources for humanity
* AIs will use new kinds of resources that humans aren’t using - dark energy, wormholes, alternate universes, etc
Superalignment is a tractable problem
* Current AIs have never killed anybody
* Current AIs are extremely successful at doing useful tasks for humans
* If AIs are trained on data from humans, they’ll be “aligned by default”
* We can just make AIs abide by our laws
* We can align the superintelligent AIs by using a scheme involving cryptocurrency on the blockchain
* Companies have economic incentives to solve superintelligent AI alignment, because unaligned superintelligent AI would hurt their profits
* We’ll build an aligned not-that-smart AI, which will figure out how to build the next-generation AI which is smarter and still aligned to human values, and so on until aligned superintelligence
Once we solve superalignment, we’ll enjoy peace
* The power from ASI won’t be monopolized by a single human government / tyranny
* The decentralized nodes of human-ASI hybrids won’t be like warlords constantly fighting each other, they’ll be like countries making peace
* Defense will have an advantage over attack, so the equilibrium of all the groups of humans and ASIs will be multiple defended regions, not a war of mutual destruction
* The world of human-owned ASIs is a stable equilibrium, not one where ASI-focused projects keep buying out and taking resources away from human-focused ones (Gradual Disempowerment)
Unaligned ASI will spare us
* The AI will spare us because it values the fact that we created it
* The AI will spare us because studying us helps maximize its curiosity and learning
* The AI will spare us because it feels toward us the way we feel toward our pets
* The AI will spare us because peaceful coexistence creates more economic value than war
* The AI will spare us because Ricardo’s Law of Comparative Advantage says you can still benefit economically from trading with someone who’s weaker than you
AI doomerism is bad epistemology
* It’s impossible to predict doom
* It’s impossible to put a probability on doom
* Every doom prediction has always been wrong
* Every doomsayer is either psychologically troubled or acting on corrupt incentives
* If we were really about to get doomed, everyone would already be agreeing about that, and bringing it up all the time
Sure P(Doom) is high, but let’s race to build it anyway because…
Coordinating to not build ASI is impossible
* China will build ASI as fast as it can, no matter what — because of game theory
* So however low our chance of surviving it is, the US should take the chance first
Slowing down the AI race doesn’t help anything
* Chances of solving AI alignment won’t improve if we slow down or pause the capabilities race
* I personally am going to die soon, and I don’t care about future humans, so I’m open to any hail mary to prevent myself from dying
* Humanity is already going to rapidly destroy ourselves with nuclear war, climate change, etc
* Humanity is already going to die out soon because we won’t have enough babies
Think of the good outcome
* If it turns out that doom from overly-fast AI building doesn’t happen, in that case, we can more quickly get to the good outcome!
* People will stop suffering and dying faster
AI killing us all is actually good
* Human existence is morally negative on net, or close to zero net moral value
* Whichever AI ultimately comes to power will be a “worthy successor” to humanity
* Whichever AI ultimately comes to power will be as morally valuable as human descendents generally are to their ancestors, even if their values drift
* The successor AI’s values will be interesting, productive values that let them successfully compete to dominate the universe
* How can you argue with the moral choices of an ASI that’s smarter than you, that you know goodness better than it does?
* It’s species-ist to judge what a superintelligent AI would want to do. The moral circle shouldn’t be limited to just humanity.
* Increasing entropy is the ultimate north star for techno-capital, and AI will increase entropy faster
* Human extinction will solve the climate crisis, and pollution, and habitat destruction, and let mother earth heal
Doom Debates’ Mission is to raise mainstream awareness of imminent extinction from AGI and build the social infrastructure for high-quality debate.
Support the mission by subscribing to my Substack at DoomDebates.com and to youtube.com/@DoomDebates, or to really take things to the next level: Donate 🙏


Get full access to Doom Debates at lironshapira.substack.com/subscribe