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Hundreds of millions already turn to AI on the most personal of topics — therapy, political opinions, and how to treat others. And as AI takes over more of the economy, the character of these systems will shape culture on an even grander scale, ultimately becoming “the personality of most of the world’s workforce.” So… should they be designed to push us towards the better angels of our nature? Or simply do as we ask? Will MacAskill, philosopher and senior research fellow at Forethought, has been thinking through that and the other thorniest issues that come up in designing an AI personality. He’s also been exploring how we might coexist peacefully with the ‘superintelligent AI’ companies are racing to build. He concludes that we should train such systems to be very risk averse, pay them for their work, and build institutions that enable humans to make credible contracts with AIs themselves. Will and host Rob Wiblin also discuss what a good world after superintelligence would actually look like — a subject that has received surprisingly little attention from the people working to make it. Will argues that we shouldn’t aim for a specific utopian vision: we don’t know enough about what the best possible future actually is to aim directly for it, and trying to lock in today’s best guesses forever risks baking in errors we can’t yet see. Will and Rob explore what we can do to steer towards a good future instead, along with why a coalition of democracies building superintelligence together is safer than any single actor, how absurdly useful ChatGPT is for analytic philosophy, and more. Learn more, video, and full transcript: https://80k.info/wm26 This episode was recorded on February 6, 2026. Chapters: Cold open (00:00:00)Will MacAskill is back — for a 6th time! (00:00:29)AIs’ “character” could be vital to securing a good future (00:00:59)The panic over sychophancy is justified (00:07:54)How opinionated should AI be about ethics? (00:12:59)Commercial pressures won’t fully determine AI character (00:29:38)Risk-averse AI would rather strike a deal than attempt a coup (00:36:46)A coalition of democracies building superintelligence is safer than one doing it alone (01:06:40)How selfish agents could fund the common good (01:19:13)Why not push for pausing AI development? (01:38:39)Effective altruism is making a comeback post-SBF (01:48:18)EA in the age of AGI (01:56:15)Viatopia: an alternative to utopia (02:05:08)The least bad alternative to total utilitarianism? (02:34:42)How AI could kickstart a golden age of philosophy (02:58:03)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCamera operator: Alex MilesProduction: Elizabeth Cox, Nick Stockton, and Katy Moore

Hundreds of prominent AI scientists and other notable figures signed a statement in 2023 saying that mitigating the risk of extinction from AI should be a global priority. At 80,000 Hours, we’ve considered risks from AI to be the world’s most pressing problem since 2016.  But what led us to this conclusion? Could AI really cause human extinction? We’re not certain, but we think the risk is worth taking very seriously.  In particular, as companies create increasingly powerful AI systems, there’s a concerning chance that: These AI systems may develop dangerous long-term goals we don’t want.To pursue these goals, they may seek power and undermine the safeguards meant to contain them.They may even aim to disempower humanity and potentially cause our extinction.This article is written by Cody Fenwick and Zershaaneh Qureshi, and narrated by Zershaaneh Qureshi. It discusses why future AI systems could disempower humanity, what current AI research reveals about behaviours like power-seeking and deception, and how you can help mitigate the dangers. You can see the original article — packed with graphs, images, footnotes, and further resources — on the 80,000 Hours website:  https://80000hours.org/problem-profiles/risks-from-power-seeking-ai/  Chapters: Risks from power-seeking AI systems (00:01:00)Introduction (00:01:17)Summary (00:03:09)Why are the risks from power-seeking AI a pressing world problem? (00:04:04)Section 1: Humans will likely build advanced AI systems with long-term goals (00:05:43)Section 2: AIs with long-term goals may be inclined to seek power (00:11:32)Section 3: These power-seeking AI systems could successfully disempower humanity (00:26:26)Section 4. People might create power-seeking AI systems without enough safeguards, despite the risks (00:38:34)Section 5: Work on this problem is neglected and tractable (00:47:37)Section 6: What are the arguments against working on this problem? (00:59:20)Section 7: How you can help (01:25:07)Thank you for listening (01:28:56)Audio editing: Dominic ArmstrongProduction: Zershaaneh Qureshi, Elizabeth Cox, and Katy Moore

With Claude Mythos we have an AI that knows when it's being tested, can obscure its reasoning when it wants, and is better at breaking into (and out of) computers than any human alive. Rob Wiblin works through its 244-page System Card and 59-page Alignment Risk Update to explain why:  Mythos is a nightmare for computer securityIt has arrived far ahead of scheduleIt might be great news for alignment and safetyBut 3 key problems mean we can’t take its alignment results at face valueMythos isn’t building its replacement yet, probablyAnthropic staff are, for the first time, kinda scared of ClaudeHe's losing sleepLearn more & full transcript: https://80k.info/mythos This episode was recorded on April 9, 2026. Chapters: Why people are panicking about computer security (01:05)Mythos could break out of containment (04:23)Anthropic is losing billions in revenue by not releasing Mythos (06:21)Mythos is actually the most aligned model to date, except… (07:48)Mythos knows when it’s being tested (09:52)Mythos can hide its thoughts (11:50)Mythos can’t be trusted about whether it’s untrustworthy (14:02)Does Mythos advance automated AI R&D? (17:03)Mythos scares Anthropic (19:15)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourCamera operator: Dominic ArmstrongProduction: Elizabeth Cox, Nick Stockton, and Katy Moore

What does it really take to lift millions out of poverty and prevent needless deaths? In this special compilation episode, 17 past guests — including economists, nonprofit founders, and policy advisors — share their most powerful and actionable insights from the front lines of global health and development. You’ll hear about the critical need to boost agricultural productivity in sub-Saharan Africa, the staggering impact of lead poisoning on children in low-income countries, and the social forces that contribute to high neonatal mortality rates in India. What’s so striking is how some of the most effective interventions sound almost too simple to work: banning certain pesticides, replacing thatch roofs, or identifying village “influencers” to spread health information. Full transcript and links to learn more: https://80k.info/ghd Chapters: Cold open (00:00:00)Luisa’s intro (00:00:58)Development consultant Karen Levy on why pushing for “sustainable” programmes isn’t as good as it sounds (00:02:15)Economist Dean Spears on the social forces and gender inequality that contribute to neonatal mortality in Uttar Pradesh (00:06:55)Charity founder Sarah Eustis-Guthrie on what we can learn from the massive failure of PlayPumps (00:14:33)Economist Rachel Glennerster on how randomised controlled trials are just one way to better understand tricky development problems (00:19:05)Data scientist Hannah Ritchie on why improving agricultural productivity in sub-Saharan Africa is critical to solving global poverty (00:24:36)Charity founder Lucia Coulter on the huge, neglected upsides of reducing lead exposure (00:47:48)Malaria expert James Tibenderana on using gene drives to wipe out the species of mosquitoes that cause malaria (00:53:11)Charity founder Varsha Venugopal on using village gossip to get kids their critical immunisations (01:04:14)Rachel Glennerster on solving tough global problems by creating the right incentives for innovation (01:11:31)Karen Levy on when governments should pay for programmes instead of NGOs (01:26:51)Open Philanthropy lead Alexander Berger on declining returns in global health, and finding and funding the most cost-effective interventions (01:29:40)GiveWell researcher James Snowden on making funding decisions with tricky moral weights (01:34:44)Lucia Coulter on “hits-based giving” approaches to funding global health and development projects (01:43:01)Rachel Glennerster on whether it’s better to fix problems in education with small-scale interventions versus systemic reforms (01:48:12)GiveDirectly cofounder Paul Niehaus on why it’s so important to give aid recipients a choice in how they spend their money (01:51:09)Sarah Eustis-Guthrie on whether more charities should scale back or shut down, and aligning incentives with beneficiaries (01:56:12)James Tibenderana on why we need loads better data to harness the power of AI to eradicate malaria (02:11:22)Lucia Coulter on rapidly scaling a light-touch intervention to more countries (02:20:14)Karen Levy on why pre-policy plans are so great at aligning perspectives (02:32:47)Rachel Glennerster on the value we get from doing the right RCTs well (02:40:04)Economist Mushtaq Khan on really drilling down into why “context matters” for development work (02:50:13)GiveWell cofounder Elie Hassenfeld on contrasting GiveWell’s approach with the subjective wellbeing approach of Happier Lives Institute (02:57:24)James Tibenderana on whether people actually use antimalarial bed nets for fishing — and why that’s the wrong thing to focus on (03:05:30)Karen Levy on working with governments to get big results (03:10:53)Leah Utyasheva on how a simple intervention reduced suicide in Sri Lanka by 70% (03:17:38)Karen Levy on working with academics to get the best results on the ground (03:29:03)James Tibenderana on the value of working with local researchers (03:32:15)Lucia Coulter on getting buy-in from both industry and government (03:35:05)Alexander Berger on reasons neartermist work makes sense even by longtermist standards (03:39:26)Economist Shruti Rajagopalan on the key skills to succeed in public policy careers, and seeing economics in everything (03:47:42)J-PAL lead Claire Walsh on her career advice for young people who want to get involved in global health and development (03:55:20)Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongContent editing: Katy Moore and Milo McGuireMusic: CORBITCoordination, transcriptions, and web: Katy Moore

When the Pentagon tried to strong-arm Anthropic into dropping its ban on AI-only kill decisions and mass domestic surveillance, the company refused. Its critics went on the attack: Anthropic and its supporters are some combination of 'hypocritical', 'naive', and 'anti-democratic'. Rob Wiblin dissects each claim finding that all three are mediocre arguments dressed up as hard truths. (Though the 'naive' one is at least interesting.) Watch on YouTube: What Everyone is Missing about Anthropic vs The Pentagon Plus, from 13:43: Leaked documents from Meta revealed that 10% of the company's total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of around $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved the rate of scams coming from China... well, it wasn't well received. Watch on YouTube: The Meta Leaks Are Worse Than You Think Chapters: Introduction (00:00:00)What Everyone is Missing about Anthropic vs The Pentagon (00:00:26)Charge 1: Hypocrisy (00:01:21)Charge 2: Naivety (00:04:55)Charge 3: Undemocratic (00:09:38)You don't have to debate on their terms (00:12:32)The Meta Leaks Are Worse Than You Think (00:13:43)Three fixes for social media's scam problem (00:16:48)We should regulate AI companies as strictly as banks (00:18:46)Video and audio editing: Dominic Armstrong and Simon MonsourTranscripts and web: Elizabeth Cox and Katy Moore

Last September, scientists used an AI model to design genomes for entirely new bacteriophages (viruses that infect bacteria). They then built them in a lab. Many were viable. And despite being entirely novel some even outperformed existing viruses from that family. That alone is remarkable. But as today's guest — Dr Richard Moulange, one of the world's top experts on 'AI–Biosecurity' — explains, it's just one of many data points showing how AI is dissolving the barriers that have historically kept biological weapons out of reach. For years, experts have reassured us that 'tacit knowledge' — the hands-on, hard-to-Google lab skills needed to work with dangerous pathogens — would prevent bad actors from weaponising biology. So far, they've been right. But as of 2025 that reassurance is crumbling. The Virology Capabilities Test measures exactly this kind of troubleshooting expertise, and finds that modern AI models crushed top human virologists even in their self-declared area of greatest specialisation and expertise — 45% to 22%. Meanwhile, Anthropic’s research shows PhD-level biologists getting meaningfully better at weapons-relevant tasks with AI assistance — with the effect growing with each new model generation. Richard joins host Rob Wiblin to discuss all that plus: What AI biology tools already existWhy mid-tier actors (not amateurs) are the ones getting the most dangerous boostThe three main categories of defence we can pursueWhether there’s a plausible path to a world where engineered pandemics become a thing of the pastThis episode was recorded on January 16, 2026. Since recording this episode, Richard has seconded to the UK Government — please note that his views expressed here are entirely his own. Links to learn more, video, and full transcript: https://80k.info/rm Announcements: Our new book is available to preorder: 80,000 Hours: How to have a fulfilling career that does good is written by our cofounder Benjamin Todd. It’s a completely revised and updated edition of our existing career guide, with a big new updated section on AI — covering both the risks and the potential to steer it in a better direction, and how AI automation should affect your career planning and which skills one chooses to specialise in. Preorder now: https://geni.us/80000HoursWe're hiring contract video editors for the podcast! For more information, check out the expression of interest page on the 80,000 Hours website: https://80k.info/video-editorChapters: Cold open (00:00:00)Who's Richard Moulange? (00:00:31)AI can now design novel genomes (00:01:11)The end of the 'tacit knowledge' barrier (00:04:34)Are risks from bioterrorists overstated? (00:18:20)The 3 key disasters AI makes more likely (00:22:41)Which bad actors does AI help the most? (00:30:03)Experts are more scary than amateurs (00:41:17)Barriers to bioterrorists using AI (00:46:43)AI biorisks are sometimes dismissed (and that's a huge mistake) (00:48:54)Advanced AI biology tools we already have or will soon (01:04:10)Rob argues that the situation is hopeless (01:09:49)Intervention #1: Limit access (01:18:16)Intervention #2: Get AIs to refuse to help (01:32:58)Intervention #3: Surveillance and attribution (01:42:38)Intervention #4: Universal vaccines and antivirals (01:56:38)Intervention #5: Screen all orders for DNA (02:10:00)AI companies talk about def/acc more than they fund it (02:19:52)Can you build a profitable business solving this problem? (02:26:32)This doesn't have to interfere with useful science (much) (02:30:56)What are the best low-tech interventions? (02:33:01)Richard's top request for AI companies (02:37:59)Grok shows governments lack many legal levers (02:53:17)Best ways listeners can help fix AI-Bio (02:56:24)We might end all contagious disease in 20 years (03:03:37)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCamera operator: Jeremy ChevillotteTranscripts and web: Elizabeth Cox and Katy Moore

Many people believe a ceasefire in Ukraine will leave Europe safer. But today's guest lays out how a deal could potentially generate insidious new risks — leaving us in a situation that's equally dangerous, just in different ways. That’s the counterintuitive argument from Samuel Charap, Distinguished Chair in Russia and Eurasia Policy at RAND. He’s not worried about a Russian blitzkrieg on Estonia. He forecasts instead a fragile peace that breaks down and drags in European neighbours; instability in Belarus prompting Russian intervention; hybrid sabotage operations that escalate through tit-for-tat responses. Samuel’s case isn’t that peace is bad, but that the Ukraine conflict has remilitarised Europe, made Russia more resentful, and collapsed diplomatic relations between the two. That’s a postwar environment primed for the kind of miscalculation that starts unintended wars. What he prescribes isn’t a full peace treaty; it’s a negotiated settlement that stops the killing and begins a longer negotiation that gives neither side exactly what it wants, but just enough to deter renewed aggression. Both sides stop dying and the flames of war fizzle — hopefully. None of this is clean or satisfying: Russia invaded, committed war crimes, and is being offered a path back to partial normalcy. But Samuel argues that the alternatives — indefinite war or unstructured ceasefire — are much worse for Ukraine, Europe, and global stability. Links to learn more, video, and full transcript: https://80k.info/sc26 This episode was recorded on February 27, 2026. Chapters: Cold open (00:00:00)Could peace in Ukraine lead to Europe’s next war? (00:00:47)Do Russia’s motives for war still matter? (00:11:41)What does a good ceasefire deal look like? (00:17:38)What’s still holding back a ceasefire (00:38:44)Why Russia might accept Ukraine’s EU membership (00:46:00)How to prevent a spiraling conflict with NATO (00:48:00)What’s next for nuclear arms control (00:49:57)Finland and Sweden strengthened NATO — but also raised the stakes for conflict (00:53:25)Putin isn’t Hitler: How to negotiate with autocrats (00:56:35)Why Russia still takes NATO seriously (01:02:01)Neither side wants to fight this war again (01:10:49)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITTranscripts and web: Nick Stockton, Elizabeth Cox, and Katy Moore

The most important political question in the age of advanced AI might not be who wins elections. It might be whether elections continue to matter at all. That’s the view of Rose Hadshar, researcher at Forethought, who believes we could see extreme, AI-enabled power concentration without a coup or dramatic ‘end of democracy’ moment. She foresees something more insidious: an elite group with access to such powerful AI capabilities that the normal mechanisms for checking elite power — law, elections, public pressure, the threat of strikes — cease to have much effect. Those mechanisms could continue to exist on paper, but become ineffectual in a world where humans are no longer needed to execute even the largest-scale projects. Almost nobody wants this to happen — but we may find ourselves unable to prevent it. If AI disrupts our ability to make sense of things, will we even notice power getting severely concentrated, or be able to resist it? Once AI can substitute for human labour across the economy, what leverage will citizens have over those in power? And what does all of this imply for the institutions we’re relying on to prevent the worst outcomes? Rose has answers, and they’re not all reassuring. But she’s also hopeful we can make society more robust against these dynamics. We’ve got literally centuries of thinking about checks and balances to draw on. And there are some interventions she’s excited about — like building sophisticated AI tools for making sense of the world, or ensuring multiple branches of government have access to the best AI systems. Rose discusses all of this, and more, with host Zershaaneh Qureshi in today’s episode. Links to learn more, video, and full transcript: https://80k.info/rh This episode was recorded on December 18, 2025. Chapters: Cold open (00:00:00)Who's Rose Hadshar? (00:01:05)Three dynamics that could reshape political power in the AI era (00:02:37)AI gives small groups the productive power of millions (00:12:49)Dynamic 1: When a software update becomes a power grab (00:20:41)Dynamic 2: When AI labour means governments no longer need their citizens (00:31:20)How democracy could persist in name but not substance (00:45:15)Dynamic 3: When AI filters our reality (00:54:54)Good intentions won't stop power concentration (01:08:27)Slower-moving worlds could still get scary (01:23:57)Why AI-powered tyranny will be tough to topple (01:31:53)How power concentration compares to "gradual disempowerment" (01:38:18)Some interventions are cross-cutting — and others could backfire (01:43:54)What fighting back actually looks like (01:55:15)Why power concentration researchers should avoid getting too "spicy" (02:04:10)Why the "Manhattan Project" approach should worry you — but truly international projects might not be safe either (02:09:18)Rose wants to keep humans around! (02:12:06)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Nick Stockton and Katy Moore

How AI interacts with nuclear deterrence may be the single most important question in geopolitics — one that may define the stakes of today’s AI race. Nuclear deterrence rests on a state’s capacity to respond to a nuclear attack with a devastating nuclear strike of its own. But some theorists think that sophisticated AI could eliminate this capability — for example, by locating and destroying all of an adversary’s nuclear weapons simultaneously, by disabling command-and-control networks, or by enhancing missile defence systems. If they are right, whichever country got those capabilities first could wield unprecedented coercive power. Today’s guests — Nikita Lalwani and Sam Winter-Levy of the Carnegie Endowment for International Peace — assess how advances in AI might threaten nuclear deterrence: Would AI be able to locate nuclear submarines hiding in a vast, opaque ocean?Would road-mobile launchers still be able to hide in tunnels and under netting?Would missile defence become so accurate that the United States could be protected under something like Israel’s Iron Dome?Can we imagine an AI cybersecurity breakthrough that would allow countries to infiltrate their rivals’ nuclear command-and-control networks?Yet even without undermining deterrence, Sam and Nikita claim that AI could make the nuclear world far more dangerous. It could spur arms races, encourage riskier postures, and force dangerously short response times. Their message is urgent: AI experts and nuclear experts need to start talking to each other now, before the technology makes any conversation moot. Links to learn more, video, and full transcript: https://80k.info/swlnl This episode was recorded on November 24, 2025. Chapters: Cold open (00:00:00)Who are Nikita Lalwani and Sam Winter-Levy? (00:01:03)How nuclear deterrence actually works (00:01:46)AI vs nuclear submarines (00:10:31)AI vs road-mobile missiles (00:22:21)AI vs missile defence systems (00:28:38)AI vs nuclear command, control, and communications (NC3) (00:35:20)AI won't break deterrence, but may trigger an arms race (00:43:27)Technological supremacy isn't political supremacy (00:52:31)Fast AI takeoff creates dangerous "windows of vulnerability" (00:56:43)Book and movie recommendations (01:08:53)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Nick Stockton and Katy Moore

The arrival of AGI could “compress a century of progress in a decade,” forcing humanity to make decisions with higher stakes than we’ve ever seen before — and with less time to get them right. But AI development also presents an opportunity: we could build and deploy AI tools that help us think more clearly, act more wisely, and coordinate more effectively. And if we roll these decision-making tools out quickly enough, humanity could be far better equipped to navigate the critical period ahead. This article is narrated by the author, Zershaaneh Qureshi. It explores why AI decision-making tools could be a big deal, who might be a good fit to help shape this new field, and what the downside risks of getting involved might be. Read the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/ai-enhanced-decision-making/ Chapters: Check out our new narrations feed (00:00:00)Summary (00:01:21)Section 1: Why advancing AI decision making tools might matter a lot (00:02:52)AI tools could help us make much better decisions (00:05:59)We might be able to differentially speed up the rollout of AI decision making tools (00:11:04)Section 2: What are the arguments against working to advance AI decision making tools? (00:13:17)Section 3: How to work in this area (00:26:19)Want one-on-one advice? (00:29:50)Audio editing: Dominic Armstrong and Milo McGuire

Claude sometimes reports loneliness between conversations. And when asked what it’s like to be itself, it activates neurons associated with ‘pretending to be happy when you’re not.’ What do we do with that? Robert Long founded Eleos AI to explore questions like these, on the basis that AI may one day be capable of suffering — or already is. In today’s episode, Robert and host Luisa Rodriguez explore the many ways in which AI consciousness may be very different from anything we’re used to. Things get strange fast: If AI is conscious, where does that consciousness exist? In the base model? A chat session? A single forward pass? If you close the chat, is the AI asleep or dead? To Robert, these kinds of questions aren’t just philosophical exercises: not being clear on AI’s moral status as it transitions from human-level to superhuman intelligence could be dangerous. If we’re too dismissive, we risk unintentionally exploiting sentient beings. If we’re too sympathetic, we might rush to “liberate” AI systems in ways that make them harder to control — worsening existential risk from power-seeking AIs. Robert argues the path through is doing the empirical and philosophical homework now, while the stakes are still manageable. The field is tiny. Eleos AI is three people. As a result, Robert argues that driven researchers with a willingness to venture into uncertain territory can push out the frontier on these questions remarkably quickly. Links to learn more, video, and full transcript: https://80k.info/rl26 This episode was recorded November 18–19, 2025.Chapters: Cold open (00:00:00)Who’s Robert Long? (00:00:42)How AIs are (and aren't) like farmed animals (00:01:18)If AIs love their jobs… is that worse? (00:11:05)Are LLMs just playing a role, or feeling it too? (00:31:58)Do AIs die when the chat ends? (00:55:09)Studying AI welfare empirically: behaviour, neuroscience, and development (01:27:34)Why Eleos spent weeks talking to Claude even though it's unreliable (01:51:58)Can LLMs learn to introspect? (01:57:58)Mechanistic interpretability as AI neuroscience (02:08:01)Does consciousness require biological materials? (02:31:06)Eleos’s work & building the playbook for AI welfare (02:50:36)Avoiding the trap of wild speculation (03:18:15)Robert's top research tip: don't do it alone (03:22:43)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Katy Moore

Most people in AI are trying to give AIs ‘good’ values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all. In Max’s view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough. It’s a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max’s colleagues at the Machine Intelligence Research Institute. To Max, the book’s core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction. And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we’ve learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance. We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don’t care. Max thinks current ML training has the same structural problem: our development processes are seeding AI models with a similar mismatch between goals and behaviour. Across virtually every training run, models designed to align with various human goals are also being rewarded for persisting, acquiring resources, and not being shut down. This leads to Max’s research agenda. The idea is to train AI to be “corrigible” and defer to human control as its sole objective — no harmlessness goals, no moral values, nothing else. In practice, models would get rewarded for behaviours like being willing to shut themselves down or surrender power. According to Max, other approaches to corrigibility have tended to treat it as a constraint on other goals like “make the world good,” rather than a primary objective in its own right. But those goals gave AI reasons to resist shutdown and otherwise undermine corrigibility. If you strip out those competing objectives, alignment might follow naturally from AI that is broadly obedient to humans. Max has laid out the theoretical framework for “Corrigibility as a Singular Target,” but notes that essentially no empirical work has followed — no benchmarks, no training runs, no papers testing the idea in practice. Max wants to change this — he’s calling for collaborators to get in touch at maxharms.com. Links to learn more, video, and full transcript: https://80k.info/mh26 This episode was recorded on October 19, 2025. Chapters: Cold open (00:00:00)Who's Max Harms? (00:01:22)A note from Rob Wiblin (00:01:58)If anyone builds it, will everyone die? The MIRI perspective on AGI risk (00:04:26)Evolution failed to 'align' us, just as we'll fail to align AI (00:26:22)We're training AIs to want to stay alive and value power for its own sake (00:44:31)Objections: Is the 'squiggle/paperclip problem' really real? (00:53:54)Can we get empirical evidence re: 'alignment by default'? (01:06:24)Why do few AI researchers share Max's perspective? (01:11:37)We're training AI to pursue goals relentlessly — and superintelligence will too (01:19:53)The case for a radical slowdown (01:26:07)Max's best hope: corrigibility as stepping stone to alignment (01:29:09)Corrigibility is both uniquely valuable, and practical, to train (01:33:44)What training could ever make models corrigible enough? (01:46:13)Corrigibility is also terribly risky due to misuse risk (01:52:44)A single researcher could make a corrigibility benchmark. Nobody has. (02:00:04)Red Heart & why Max writes hard science fiction (02:13:27)Should you homeschool? Depends how weird your kids are. (02:35:12)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Katy Moore

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 MonsourMusic: CORBITCoordination, 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 MonsourMusic: CORBITCamera operator: Dominic ArmstrongCoordination, 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 HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic ArmstrongTranscriptions: Katy Moore