Every major AI company has the same safety plan: when AI gets crazy powerful and really dangerous, they’ll use the AI itself to figure out how to make AI safe and beneficial. It sounds circular, almost satirical. But is it actually a bad plan?

Today’s guest, Ajeya Cotra, recently placed 3rd out of 413 participants forecasting AI developments and is among the most thoughtful and respected commentators on where the technology is going.

She thinks there’s a meaningful chance we’ll see as much change in the next 23 years as humanity faced in the last 10,000, thanks to the arrival of artificial general intelligence. Ajeya doesn’t reach this conclusion lightly: she’s had a ring-side seat to the growth of all the major AI companies for 10 years — first as a researcher and grantmaker for technical AI safety at Coefficient Giving (formerly known as Open Philanthropy), and now as a member of technical staff at METR.

So host Rob Wiblin asked her: is this plan to use AI to save us from AI a reasonable one?

Ajeya agrees that humanity has repeatedly used technologies that create new problems to help solve those problems. After all:

• Cars enabled carjackings and drive-by shootings, but also faster police pursuits.
• Microbiology enabled bioweapons, but also faster vaccine development.
• The internet allowed lies to disseminate faster, but had exactly the same impact for fact checks.

But she also thinks this will be a much harder case. In her view, the window between AI automating AI research and the arrival of uncontrollably powerful superintelligence could be quite brief — perhaps a year or less. In that narrow window, we’d need to redirect enormous amounts of AI labour away from making AI smarter and towards alignment research, biodefence, cyberdefence, adapting our political structures, and improving our collective decision-making.

The plan might fail just because the idea is flawed at conception: it does sound a bit crazy to use an AI you don’t trust to make sure that same AI benefits humanity.

But if we find some clever technique to overcome that, we could still fail — because the companies simply don’t follow through on their promises. They say redirecting resources to alignment and security is their strategy for dealing with the risks generated by their research — but none have quantitative commitments about what fraction of AI labour they’ll redirect during crunch time. And the competitive pressures during a recursive self-improvement loop could be irresistible.

In today’s conversation, Ajeya and Rob discuss what assumptions this plan requires, the specific problems AI could help solve during crunch time, and why — even if we pull it off — we’ll be white-knuckling it the whole way through.

Links to learn more, video, and full transcript: https://80k.info/ac26

This episode was recorded on October 20, 2025.

Chapters:

• Cold open (00:00:00)
• Ajeya’s strong track record for identifying key AI issues (00:00:43)
• The 1,000-fold disagreement about AI's effect on economic growth (00:02:30)
• Could any evidence actually change people's minds? (00:22:48)
• The most dangerous AI progress might remain secret (00:29:55)
• White-knuckling the 12-month window after automated AI R&D (00:46:16)
• AI help is most valuable right before things go crazy (01:10:36)
• Foundations should go from paying researchers to paying for inference (01:23:08)
• Will frontier AI even be for sale during the explosion? (01:30:21)
• Pre-crunch prep: what we should do right now (01:42:10)
• A grantmaking trial by fire at Coefficient Giving (01:45:12)
• Sabbatical and reflections on effective altruism (02:05:32)
• The mundane factors that drive career satisfaction (02:34:33)
• EA as an incubator for avant-garde causes others won't touch (02:44:07)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcriptions, and web: Katy Moore

In early 2025, after OpenAI put out the first-ever reasoning models — o1 and o3 — short timelines to transformative artificial general intelligence swept the AI world. But then, in the second half of 2025, sentiment swung all the way back in the other direction, with people's forecasts for when AI might really shake up the world blowing out even further than they had been before reasoning models came along.

What the hell happened? Was it just swings in vibes and mood? Confusion? A series of fundamentally unexpected and unpredictable research results?

Host Rob Wiblin has been trying to make sense of it for himself, and here's the best explanation he's come up with so far.

Links to learn more, video, and full transcript: https://80k.info/tl

Chapters:

• Making sense of the timelines madness in 2025 (00:00:00)
• The great timelines contraction (00:00:46)
• Why timelines went back out again (00:02:10)
• Other longstanding reasons AGI could take a good while (00:11:13)
• So what's the upshot of all of these updates? (00:14:47)
• 5 reasons the radical pessimists are still wrong (00:16:54)
• Even long timelines are short now (00:23:54)

This episode was recorded on January 29, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Dominic Armstrong
Coordination, transcripts, and web: Katy Moore

Mental health problems like depression and anxiety affect enormous numbers of people and severely interfere with their lives. By contrast, we don’t see similar levels of physical ill health in young people. At any point in time, something like 20% of young people are working through anxiety or depression that’s seriously interfering with their lives — but nowhere near 20% of people in their 20s have severe heart disease or cancer or a similar failure in a key organ of the body other than the brain.

From an evolutionary perspective, that’s to be expected, right? If your heart or lungs or legs or skin stop working properly while you’re a teenager, you’re less likely to reproduce, and the genes that cause that malfunction get weeded out of the gene pool.

So why is it that these evolutionary selective pressures seemingly fixed our bodies so that they work pretty smoothly for young people most of the time, but it feels like evolution fell asleep on the job when it comes to the brain? Why did evolution never get around to patching the most basic problems, like social anxiety, panic attacks, debilitating pessimism, or inappropriate mood swings? For that matter, why did evolution go out of its way to give us the capacity for low mood or chronic anxiety or extreme mood swings at all?

Today’s guest, Randy Nesse — a leader in the field of evolutionary psychiatry — wrote the book Good Reasons for Bad Feelings, in which he sets out to try to resolve this paradox.

Rebroadcast: This episode originally aired in February 2024.

Links to learn more, video, and full transcript: https://80k.info/rn

In the interview, host Rob Wiblin and Randy discuss the key points of the book, as well as:

• How the evolutionary psychiatry perspective can help people appreciate that their mental health problems are often the result of a useful and important system.
• How evolutionary pressures and dynamics lead to a wide range of different personalities, behaviours, strategies, and tradeoffs.
• The missing intellectual foundations of psychiatry, and how an evolutionary lens could revolutionise the field.
• How working as both an academic and a practicing psychiatrist shaped Randy’s understanding of treating mental health problems.
• The “smoke detector principle” of why we experience so many false alarms along with true threats.
• The origins of morality and capacity for genuine love, and why Randy thinks it’s a mistake to try to explain these from a selfish gene perspective.
• Evolutionary theories on why we age and die.
• And much more.

Chapters:

• Cold Open (00:00:00)
• Rob's Intro (00:00:55)
• The interview begins (00:03:01)
• The history of evolutionary medicine (00:03:56)
• The evolutionary origin of anxiety (00:12:37)
• Design tradeoffs, diseases, and adaptations (00:43:19)
• The tricker case of depression (00:48:57)
• The purpose of low mood (00:54:08)
• Big mood swings vs barely any mood swings (01:22:41)
• Is mental health actually getting worse? (01:33:43)
• A general explanation for bodies breaking (01:37:27)
• Freudianism and the origins of morality and love (01:48:53)
• Evolutionary medicine in general (02:02:42)
• Objections to evolutionary psychology (02:16:29)
• How do you test evolutionary hypotheses to rule out the bad explanations? (02:23:19)
• Striving and meaning in careers (02:25:12)
• Why do people age and die? (02:45:16)

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong
Transcriptions: Katy Moore