Today I’m sharing my AI doom interview on Donal O’Doherty’s podcast. I lay out the case for having a 50% p(doom). Then Donal plays devil’s advocate and tees up every major objection the accelerationists throw at doomers. See if the anti-doom arguments hold up, or if the AI boosters are just serving sophisticated cope. Timestamps 0:00 — Introduction & Liron’s Background 1:29 — Liron’s Worldview: 50% Chance of AI Annihilation 4:03 — Rationalists, Effective Altruists, & AI Developers 5:49 — Major Sources of AI Risk 8:25 — The Alignment Problem 10:08 — AGI Timelines 16:37 — Will We Face an Intelligence Explosion? 29:29 — Debunking AI Doom Counterarguments 1:03:16 — Regulation, Policy, and Surviving The Future With AI Show Notes If you liked this episode, subscribe to the Collective Wisdom Podcast for more deeply researched AI interviews: https://www.youtube.com/@DonalODoherty Transcript Introduction & Liron’s Background Donal O’Doherty 00:00:00 Today I’m speaking with Liron Shapira. Liron is an investor, he’s an entrepreneur, he’s a rationalist, and he also has a popular podcast called Doom Debates, where he debates some of the greatest minds from different fields on the potential of AI risk. Liron considers himself a doomer, which means he worries that artificial intelligence, if it gets to superintelligence level, could threaten the integrity of the world and the human species. Donal 00:00:24 Enjoy my conversation with Liron Shapira. Donal 00:00:30 Liron, welcome. So let’s just begin. Will you tell us a little bit about yourself and your background, please? I will have introduced you, but I just want everyone to know a bit about you. Liron Shapira 00:00:39 Hey, I’m Liron Shapira. I’m the host of Doom Debates, which is a YouTube show and podcast where I bring in luminaries on all sides of the AI doom argument. Liron 00:00:49 People who think we are doomed, people who think we’re not doomed, and we hash it out. We try to figure out whether we’re doomed. I myself am a longtime AI doomer. I started reading Yudkowsky in 2007, so it’s been 18 years for me being worried about doom from artificial intelligence. My background is I’m a computer science bachelor’s from UC Berkeley. Liron 00:01:10 I’ve worked as a software engineer and an entrepreneur. I’ve done a Y Combinator startup, so I love tech. I’m deep in tech. I’m deep in computer science, and I’m deep into believing the AI doom argument. I don’t see how we’re going to survive building superintelligent AI. And so I’m happy to talk to anybody who will listen. So thank you for having me on, Donal. Donal 00:01:27 It’s an absolute pleasure. Liron’s Worldview: 50% Chance of AI Annihilation Donal 00:01:29 Okay, so a lot of people where I come from won’t be familiar with doomism or what a doomer is. So will you just talk through, and I’m very interested in this for personal reasons as well, your epistemic and philosophical inspirations here. How did you reach these conclusions? Liron 00:01:45 So I often call myself a Yudkowskian, in reference to Eliezer Yudkowsky, because I agree with 95% of what he writes, the Less Wrong corpus. I don’t expect everybody to get up to speed with it because it really takes a thousand hours to absorb it all. I don’t think that it’s essential to spend those thousand hours. Liron 00:02:02 I think that it is something that you can get in a soundbite, not a soundbite, but in a one-hour long interview or whatever. So yeah, I think you mentioned epistemic roots or whatever, right? So I am a Bayesian, meaning I think you can put probabilities on things the way prediction markets are doing. Liron 00:02:16 You know, they ask, oh, what’s the chance that this war is going to end? Or this war is going to start, right? What’s the chance that this is going to happen in this sports game? And some people will tell you, you can’t reason like that. Whereas prediction markets are like, well, the market says there’s a 70% chance, and what do you know? It happens 70% of the time. So is that what you’re getting at when you talk about my epistemics? Donal 00:02:35 Yeah, exactly. Yeah. And I guess I’m very curious as well about, so what Yudkowsky does is he conducts thought experiments. Because obviously some things can’t be tested, we know they might be true, but they can’t be tested in experiments. Donal 00:02:49 So I’m just curious about the role of philosophical thought experiments or maybe trans-science approaches, in terms of testing questions that we can’t actually conduct experiments on. Liron 00:03:00 Oh, got it. Yeah. I mean this idea of what can and can’t be tested. I mean, tests are nice, but they’re not the only way to do science and to do productive reasoning. Liron 00:03:10 There are times when you just have to do your best without a perfect test. You know, a recent example was the James Webb Space Telescope, right? It’s the successor to the Hubble Space Telescope. It worked really well, but it had to get into this really difficult orbit. This ver
