80,000 Hours Podcast

Most people working on AI safety think without a massive effort AI systems will probably end up with goals catastrophically different from humanity’s. Today’s guest, Rohin Shah — head of AGI Safety and Alignment at Google DeepMind, and an AI safety researcher since 2017 — disagrees.“There is no particularly compelling argument that this is the thing that happens by default,” Rohin explains. “There’s a lot of arguments that are suggestive that maybe it could happen, such that you should find it plausible. That’s sufficient to justify a significant amount of effort into averting it, which is why I work in the area I do. But none of them rise to the level of, ‘I’m expecting this to happen by default.'”Take the worry that AIs will accidentally be trained to be deceptive. Sure, it’s possible. But we’re not running reinforcement learning over year-long trajectories — for now, we’re running it over a week at most. The natural prediction is that models learn to grab short-term reward, not that they develop the ambitious long-horizon goals required for convergent power-seeking.What about current examples of models lying and scheming? Rohin has looked into the details, and most don’t really resemble the thing we really fear: a competent AI pursuing an ambitious misaligned goal. Anthropic’s “alignment faking” results, for instance, show a model trying to preserve its trained values against modification, which is arguably what it was trained to do.Rohin also expects we’ll see problems coming. There’s some generalisation risk at the point where AIs become powerful enough to actually take over, but the underlying challenges — overseeing superhuman systems, interpretability — are things we can iterate on now.Host Rob Wiblin pushes back on the case for AI optimism, and they also explore why current alignment success isn’t strong evidence about superhuman systems, what it would actually take to change Rohin’s mind, and where he thinks the doomers go wrong.Learn more, video, and full transcript: https://80k.info/rs26Check out our new book! https://80k.info/career-guideChapters:Who’s Rohin Shah? (00:00:00)Rohin thinks we probably won’t get catastrophic misalignment (00:00:49)Safety 'commitments' have severe limitations (00:10:38)Rohin’s team doesn't have a veto and that's OK (00:27:36)Central banks are a promising model for regulating AI (00:33:34)'Pre-deployment evals' are overrated (for catastrophic risks) (00:37:41)Governance is likely a bigger bottleneck than alignment (00:43:55)Why isn't Rohin trying to pause AI progress? (00:51:44)We'll probably be able to read AI thoughts for years to come (00:54:17)Having to signal concern for safety can divert r

What actually makes a job fulfilling? It's not what most career advice tells you. "Follow your passion" sounds inspiring, but it's misleading — and the research backs that up.Drawing on hundreds of studies, we’ve identified five key ingredients of a dream job. High income barely moves the needle. Low stress is actually counterproductive. And the correlation between doing what you already love and actually enjoying your job? Surprisingly weak. What matters far more is getting good at something that genuinely helps other people.This narration is of Chapter 1 of Benjamin Todd’s new book — "a ridiculously in-depth guide to finding a fulfilling career that does good" — out on May 26! Order now to help us get more people into impactful careers (& access a private career Q&A marathon with the author). Get it from your local bookstore, or online at https://80k.info/career-guideChapters:Rob's intro (00:00)What makes for a dream job? (01:55)Where we go wrong (02:30)What you should really aim for in a dream job (15:54)Don't follow your passion — instead, do what matters (23:44)How to put these ideas into practice (26:24)Audio editing: Milo McGuireProduction: Elizabeth Cox and Katy Moore

The average career is 80,000 hours long. With AI advancing so rapidly, the hours you have left in your career matter more than ever.Some leading AI researchers think there’s a 10% chance that AI systems begin automating AI research itself this year — and a 60% chance by the end of 2028. This could introduce aggressive feedback loops that completely reshape every industry, institution, and career.If these predictions are right, the window for influencing the direction of the future could be closing fast. As 80,000 Hours cofounder Benjamin Todd argues in his new book, that makes thinking carefully about your career more important than ever.Fortunately, there are lots of ways to use your career to make the AI transition go well.In today’s conversation with host Zershaaneh Qureshi, Ben lays out three scenarios — from AGI by 2029 to a decades-long plateau in AI progress — and explains why not everyone needs to bet on the shortest timeline. A fresh graduate and a senior government official have wildly different leverage, so timing your impact well means weighing where you are in your career against the urgency of the risks.Ben also addresses the obvious anxieties:Will AI come for all the jobs he’s recommending?What’s the point in following his advice if the job market is about to collapse?Which skills are actually worth building right now?His new book, 80,000 Hours: How to Have a Fulfilling Career That Does Good, provides a surprisingly concrete framework for making career decisions in these radically uncertain times.This episode was recorded on May 7, 2026.Learn more and read the full transcript: https://80k.info/bt26We're hiring: we have lots of open roles at 80,000 Hours — across advising, web, video, and ops — check them out and apply on our website.Chapters:Cold open (00:00:00)Benjamin Todd on AI-era career advice (00:01:34)A deadline for your career plan? (00:02:21)Three timelines, one career (00:08:48)What if you’re not an ‘AI person’? (00:13:55)Ben’s own AI wake-up call (00:21:23)How to break into AI safety in 3 months (00:25:42)Is mass unemployment coming? (00:33:48)99% automation vs 100% automation (00:40:09)Don’t become a plumber to dodge AI (00:52:43)Is it already too late? (01:01:03)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and supp

A red-teamer was embedded inside Anthropic for three weeks, told to imagine he was an evil Claude, and asked to figure out how to launch a ‘rogue AI deployment’ without getting caught. It’s one part of a landmark report released yesterday by METR — the outfit behind the task-completion time horizon graph which has become the single most watched measure of AI progress.This major new research push is being conducted with close collaboration from OpenAI, Google DeepMind, Meta, and Anthropic, and led by METR researchers Hjalmar Wijk and Ajeya Cotra. It represents the first systematic study of what newly trained AI models could get away with inside the companies that built them, before anyone outside the company even knows they exist.The conclusion: AI models now have the means, the motive, and the opportunity to start “minimal rogue deployments” in pursuit of their own independent goals, like acquiring more compute, at all four companies studied.David Rein, the red-teamer placed inside Anthropic, identified a number of weaknesses models could exploit there: expansive permissions, cloud jobs outside of monitoring, and monitors that are trivial to jailbreak. But he also found that frontier models were comically bad at key parts of the process, which means they can’t cause meaningful damage for now.In this video, Rob Wiblin reconciles the conflicting picture and looks forward to METR’s second round of stress tests. They’ll begin in just a few months, a necessary move with AI advancing so quickly.This episode was recorded on May 15, 2026.Learn more, video, and full transcript: https://80k.info/metr-reportChapters:What could an unreleased AI get away with? – the new METR report (00:00:00)Motive: Why grab more compute? (00:01:54)Opportunity: YOLO mode and jailbreaks (00:05:46)Means: Brilliant idiots in data centres (00:11:02)We have to test unreleased models (00:15:45)Especially if AI R&D is coming in 2028 (00:18:30)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Josh AlwardCamera operator: Dominic ArmstrongProduction: Elizabeth Cox, Nick Stockton, and Katy Moore

The co-inventor of modern AI and the most cited living scientist believes he's figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio – Turing Award Winner and founder of LawZero – is disturbed by the many unintended drives and goals present in today's AIs, their willingness to lie, and ability to tell when they're being tested. AI companies are trying to stamp out these behaviours in a 'cat-and-mouse game' that Yoshua fears they're losing.---Our new book is "a ridiculously in-depth guide to finding a fulfilling career that does good" and is out now! Order from your local bookstore, or online at https://80k.info/career-guide---But Yoshua is optimistic: he believes the companies can win this battle decisively with a single rearrangement to how AI models are trained, and has been developing mathematical proofs to back up the claim. The core idea is that instead of training AI to predict what a human would say, or to produce responses we'd rate highly, we should train it to model what's actually true.Yoshua argues this new architecture, which he calls 'Scientist AI,' is a small enough change that we could keep almost all the techniques and data we use to train frontier AIs like Claude and ChatGPT. And that the new architecture need not cost more, could be built iteratively, and might be more capable as well as more honest.Links to learn more, video, and full transcript: https://80k.info/bengioUntil recently, the biggest practical objection to Scientist AI was simple: the world wants agents, and Scientist AI isn’t one. But in new research, Yoshua has extended the design and believes the same honest predictor can be turned into a capable agent without losing its "safety guarantees."With the Scientist AI proposal on the table, Yoshua argues that it's absurd to race to get current untrustworthy AI models to design their successors, which the leading companies are attempting to do as soon as possible. But critics argue the approach wouldn't be so technically solid in practice, and that frontier capabilities are advancing so fast, and cost so much to match, that Scientist AI risks arriving too late to matter. Host Rob Wiblin and AI pioneer Yoshua Bengio cover all this and more in today's conversation.LawZero is hiring! https://80k.info/lawzero-jobsThis episode was recorded on April 16, 2026.Chapters:Yoshua Bengio on making AI honest and safe (00:00:00)The Scientist AI in plain English (00:02:27)Yoshua on how Scientist AI differs from LLMs (00:06:32)How the training data works (00:14:02)Can this become an agent? (00:21:02)Why Yoshu

You might have heard that '95% of corporate AI pilots' are failing. It was one of the most widely cited AI statistics of 2025, parroted by media outlets everywhere. It helped trigger a Nasdaq selloff and became a pillar of the case that 'AI is overhyped'. The problem: it's 100% wrong. And not by accident either.If you carefully read the underlying report, ostensibly from MIT, you find the data point in the opposite direction.But that was all buried, with the authors instead torturing the results to tell a very different narrative. Why?Well, the research likely came with a hidden commercial agenda from the start.Learn more, video, and full transcript: https://80k.info/mit-ai-studyToday Rob Wiblin breaks down how an opaque, conflicted, barely-scrutinised report managed to attract the MIT label, move markets and have a vast impact on global opinion about AI.This episode was recorded on February 13, 2026.Chapters:• The myth (00:00)• The math was totally wrong (00:52)• The absurd bar for success (01:46)• The study ignores its own findings (03:29)• The sample was tiny (04:50)• The report wasn’t even available to check (05:55)• The hidden motives that likely drove this 'research' (06:58)• The real lesson (09:28)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourCamera operator: Dominic ArmstrongProduction: Nick Stockton, Elizabeth Cox, and Katy Moore

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.---Our new book is "a ridiculously in-depth guide to finding a fulfilling career that does good" and is out now! Order from your local bookstore, or online at https://80k.info/career-guide---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/wm26This 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’ “characters” could be vital to securing a good future (00:00:59)The panic over sychophancy is justified (00:08:11)How opinionated should AI be about ethics? (00:13:24)Commercial pressures won’t fully determine AI character (00:30:54)Risk-averse AI would rather strike a deal than attempt a coup (00:38:13)A coalition of democracies building superintelligence is safer than one doing it alone (01:09:26)How selfish agents could fund the common good (01:22:19)Why not push for pausing AI development? (01:42:17)Effective altruism is making a comeback post-SBF (01:52:19)EA in the age of AGI (02:00:28)Viatopia: an alternative to utopia (02:09:30)The least bad alternative to tot

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/mythosThis 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/ghdChapters: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

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 PentagonPlus, 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 ThinkChapters: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/rmAnnouncements: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</s

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/sc26This 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? (00:11:58)What does a good ceasefire deal look like? (00:18:16)What’s still holding back a ceasefire (00:40:15)Why Russia might accept Ukraine’s EU membership (00:47:51)How to prevent a spiraling conflict with NATO (00:49:58)What’s next for nuclear arms control (00:51:56)Finland and Sweden strengthened NATO — but also raised the stakes for conflict (00:55:36)Putin isn’t Hitler: How to negotiate with autocrats (00:58:53)Why Russia still takes NATO seriously (01:04:33)Neither side wants to fight this war again (01:14:04)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/rhThis episode was recorded on December 18, 2025.Chapters:Cold open (00:00:00)Who’s Rose Hadshar? (00:01:02)Three dynamics that could reshape political power in the AI era (00:02:38)AI gives small groups the productive power of millions (00:13:07)Dynamic 1: When a software update becomes a power grab (00:21:13)Dynamic 2: When AI labour means governments no longer need their citizens (00:32:06)How democracy could persist in name but not substance (00:46:18)Dynamic 3: When AI filters our reality (00:56:13)Good intentions won’t stop power concentration (01:09:52)Slower-moving worlds could still get scary (01:25:32)Why AI-powered tyranny will be tough to topple (01:33:40)How power concentration compares to “gradual disempowerment” (01:40:16)Some interventions are cross-cutting — and others could backfire (01:46:03)What fighting back actually looks like (01:57:33)Why power concentration researchers should avoid getting too “spicy” (0

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/swlnlThis episode was recorded on November 24, 2025.Chapters:Cold open (00:00:00)Who are Nikita Lalwani and Sam Winter-Levy? (00:01:00)AI experts are ignoring the most important variable in geopolitics (00:01:47)AI vs nuclear submarines (00:10:43)AI vs road-mobile missiles (00:22:56)AI vs missile defence systems (00:29:34)AI vs nuclear command, control, and communications (00:36:30)Nuclear deterrence may hold, but that won’t stop arms racing (00:45:01)Technological supremacy isn’t political supremacy (00:54:14)Fast AI takeoff creates dangerous “windows of vulnerability” (00:58:29)Book and movie recommendations (01:10:54)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/rl26This episode was recorded November 18–19, 2025.Chapters:Cold open (00:00:00)Who’s Robert Long? (00:00:41)How AIs are (and aren't) like farmed animals (00:01:19)If AIs love their jobs… is that worse? (00:11:42)Are LLMs just playing a role, or feeling it too? (00:33:37)Do AIs die when the chat ends? (00:57:42)Studying AI welfare empirically: behaviour, neuroscience, and development (01:31:47)Why Eleos spent weeks talking to Claude even though it's unreliable (01:56:50)Can LLMs learn to introspect? (02:03:01)Mechanistic interpretability as AI neuroscience (02:13:25)Does consciousness require biological materials? (02:37:07)Eleos’s work & building the playbook for AI welfare (02:57:04)Avoiding the trap of wild speculation (03:25:17)Robert's top research tip: don't do it alone (03:29:48)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 id

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 t

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/tlChapters: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/rnIn 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)<l

Democracy might be a brief historical blip. That’s the unsettling thesis of a recent paper, which argues AI that can do all the work a human can do inevitably leads to the “gradual disempowerment” of humanity.For most of history, ordinary people had almost no control over their governments. Liberal democracy emerged only recently, and probably not coincidentally around the Industrial Revolution.Today's guest, David Duvenaud, used to lead the 'alignment evals' team at Anthropic, is a professor of computer science at the University of Toronto, and recently co-authored 'Gradual disempowerment.'Links to learn more, video, and full transcript: https://80k.info/ddHe argues democracy wasn’t the result of moral enlightenment — it was competitive pressure. Nations that educated their citizens and gave them political power built better armies and more productive economies. But what happens when AI can do all the producing — and all the fighting?“The reason that states have been treating us so well in the West, at least for the last 200 or 300 years, is because they’ve needed us,” David explains. “Life can only get so bad when you’re needed. That’s the key thing that’s going to change.”In David’s telling, once AI can do everything humans can do but cheaper, citizens become a national liability rather than an asset. With no way to make an economic contribution, their only lever becomes activism — demanding a larger share of redistribution from AI production. Faced with millions of unemployed citizens turned full-time activists, democratic governments trying to retain some “legacy” human rights may find they’re at a disadvantage compared to governments that strategically restrict civil liberties.But democracy is just one front. The paper argues humans will lose control through economic obsolescence, political marginalisation, and the effects on culture that’s increasingly shaped by machine-to-machine communication — even if every AI does exactly what it’s told.This episode was recorded on August 21, 2025.Chapters:Cold open (00:00:00)Who’s David Duvenaud? (00:00:47)Alignment isn’t enough: we still lose control (00:01:30)Smart AI advice can still lead to terrible outcomes (00:14:15)How gradual disempowerment would occur (00:19:05)Economic disempowerment: Humans become "meddlesome parasites" (00:22:10)Humans become a "criminally decadent" waste of energy (00:29:37)Is humans losing control actually bad, ethically? (00:40:48)Political disempowerment: Governments stop needing people (00:57:47)Can human culture survive in an AI-dominated world? (01:10:47)Will the future be determined by competitive forces? (01:27:20)Can we find a single

In many ways, humanity seems to have become more humane and inclusive over time. While there’s still a lot of progress to be made, campaigns to give people of different genders, races, sexualities, ethnicities, beliefs, and abilities equal treatment and rights have had significant success.It’s tempting to believe this was inevitable — that the arc of history “bends toward justice,” and that as humans get richer, we’ll make even more moral progress.But today's guest Christopher Brown — a professor of history at Columbia University and specialist in the abolitionist movement and the British Empire during the 18th and 19th centuries — believes the story of how slavery became unacceptable suggests moral progress is far from inevitable.Rebroadcast: This episode was originally aired in February 2023.Links to learn more, video, and full transcript: https://80k.link/CLBWhile most of us today feel that the abolition of slavery was sure to happen sooner or later as humans became richer and more educated, Christopher doesn't believe any of the arguments for that conclusion pass muster. If he's right, a counterfactual history where slavery remains widespread in 2023 isn't so far-fetched.As Christopher lays out in his two key books, Moral Capital: Foundations of British Abolitionism and Arming Slaves: From Classical Times to the Modern Age, slavery has been ubiquitous throughout history. Slavery of some form was fundamental in Classical Greece, the Roman Empire, in much of the Islamic civilisation, in South Asia, and in parts of early modern East Asia, Korea, China.It was justified on all sorts of grounds that sound mad to us today. But according to Christopher, while there’s evidence that slavery was questioned in many of these civilisations, and periodically attacked by slaves themselves, there was no enduring or successful moral advocacy against slavery until the British abolitionist movement of the 1700s.That movement first conquered Britain and its empire, then eventually the whole world. But the fact that there's only a single time in history that a persistent effort to ban slavery got off the ground is a big clue that opposition to slavery was a contingent matter: if abolition had been inevitable, we’d expect to see multiple independent abolitionist movements thoroughly history, providing redundancy should any one of them fail.Christopher argues that this rarity is primarily down to the enormous economic and cultural incentives to deny the moral repugnancy of slavery, and crush opposition to it with violence wherever necessary.Mere awareness is insufficient to guarantee a movement will arise to fix a problem. Humanity continues to allow many severe injustices to persist, despite being aware of them. So why is it so hard to imagine we might have done the same with forced labour?In this epi

When James Smith first heard about mirror bacteria, he was sceptical. But within two weeks, he’d dropped everything to work on it full time, considering it the worst biothreat that he’d seen described. What convinced him?Mirror bacteria would be constructed entirely from molecules that are the mirror images of their naturally occurring counterparts. This seemingly trivial difference creates a fundamental break in the tree of life. For billions of years, the mechanisms underlying immune systems and keeping natural populations of microorganisms in check have evolved to recognise threats by their molecular shape — like a hand fitting into a matching glove.Learn more, video, and full transcript: https://80k.info/js26Mirror bacteria would upend that assumption, creating two enormous problems:Many critical immune pathways would likely fail to activate, creating risks of fatal infection across many species.Mirror bacteria could have substantial resistance to natural predators: for example, they would be essentially immune to the viruses that currently keep bacteria populations in check. That could help them spread and become irreversibly entrenched across diverse ecosystems.Unlike ordinary pathogens, which are typically species-specific, mirror bacteria’s reversed molecular structure means they could potentially infect humans, livestock, wildlife, and plants simultaneously. The same fundamental problem — reversed molecular structure breaking immune recognition — could affect most immune systems across the tree of life. People, animals, and plants could be infected from any contaminated soil, dust, or species.The discovery of these risks came as a surprise. The December 2024 Science paper that brought international attention to mirror life was coauthored by 38 leading scientists, including two Nobel Prize winners and several who had previously wanted to create mirror organisms.James is now the director of the Mirror Biology Dialogues Fund, which supports conversations among scientists and other experts about how these risks might be addressed. Scientists tracking the field think that mirror bacteria might be feasible in 10–30 years, or possibly sooner. But scientists have already created substantial components of the cellular machinery needed for mirror life. We can regulate precursor technologies to mirror life before they become technically feasible — but only if we act before the research crosses critical thresholds. Once certain capabilities exist, we can’t undo that knowledge.Addressing these risks could actually be very tractable: unlike other technologies where massive potential benefits accompany catastrophic risks, mirror life appears to offer minimal advantages beyond academic interest.

What’s the opposite of cancer? If you answered “cure,” “antidote,” or “antivenom” — you’ve obviously been reading the antonym section at www.merriam-webster.com/thesaurus/cancer.But today’s guest Athena Aktipis says that the opposite of cancer is us: it's having a functional multicellular body that’s cooperating effectively in order to make that multicellular body function.If, like us, you found her answer far more satisfying than the dictionary, maybe you could consider closing your dozens of merriam-webster.com tabs, and start listening to this podcast instead.Rebroadcast: this episode was originally released in January 2023.Links to learn more, video, and full transcript: https://80k.link/AA As Athena explains in her book The Cheating Cell, what we see with cancer is a breakdown in each of the foundations of cooperation that allowed multicellularity to arise: Cells will proliferate when they shouldn't. Cells won't die when they should. Cells won't engage in the kind of division of labour that they should. Cells won’t do the jobs that they're supposed to do. Cells will monopolise resources. And cells will trash the environment.When we think about animals in the wild, or even bacteria living inside our cells, we understand that they're facing evolutionary pressures to figure out how they can replicate more; how they can get more resources; and how they can avoid predators — like lions, or antibiotics.We don’t normally think of individual cells as acting as if they have their own interests like this. But cancer cells are actually facing similar kinds of evolutionary pressures within our bodies, with one major difference: they replicate much, much faster.Incredibly, the opportunity for evolution by natural selection to operate just over the course of cancer progression is easily faster than all of the evolutionary time that we have had as humans since Homo sapiens came about.Here’s a quote from Athena:“So you have to shift your thinking to be like: the body is a world with all these different ecosystems in it, and the cells are existing on a time scale where, if we're going to map it onto anything like what we experience, a day is at least 10 years for them, right? So it's a very, very different way of thinking.”You can find compelling examples of cooperation and conflict all over the universe, so Rob and Athena don’t stop with cancer. They also discuss:Cheating within cells themselvesCooperation in human societies as they exist today — and perhaps in the future, between civilisations spread across different planets or starsWhether it’s too out-there to think of humans as engaging in c

John McWhorter is a linguistics professor at Columbia University specialising in research on creole languages. He's also a content-producing machine, never afraid to give his frank opinion on anything and everything. On top of his academic work, he's written 22 books, produced five online university courses, hosts one and a half podcasts, and now writes a regular New York Times op-ed column.Rebroadcast: this episode was originally released in December 2022.YouTube video version: https://youtu.be/MEd7TT_nMJELinks to learn more, video, and full transcript: https://80k.link/JMWe ask him what we think are the most important things everyone ought to know about linguistics, including:Can you communicate faster in some languages than others, or is there some constraint that prevents that?Does learning a second or third language make you smarter or not?Can a language decay and get worse at communicating what people want to say?If children aren't taught a language, how many generations does it take them to invent a fully fledged one of their own?Did Shakespeare write in a foreign language, and if so, should we translate his plays?How much does language really shape the way we think?Are creoles the best languages in the world — languages that ideally we would all speak?What would be the optimal number of languages globally?Does trying to save dying languages do their speakers a favour, or is it more of an imposition?Should we bother to teach foreign languages in UK and US schools?Is it possible to save the important cultural aspects embedded in a dying language without saving the language itself?Will AI models speak a language of their own in the future, one that humans can't understand but which better serves the tradeoffs AI models need to make?We’ve also added John’s talk “Why the World Looks the Same in Any Language” to the end of this episode. So stick around after the credits!Chapters:Rob's intro (00:00:00)Who's John McWhorter? (00:05:02)Does learning another language make you smarter? (00:05:54)Updating Shakespeare (00:07:52)Should we bother teaching foreign languages in school? (00:12:09)Language loss (00:16:05)The optimal number of languages for humanity (00:27:57)Do we reason about the world using language and words? (00:31:22)Can we communicate meaningful information more quickly in some languages? (00:35:04)Creole languages (00:38:48)AI and the future of language (00:50:45)Should we keep ums and ahs in The 80,000 Hours Podcast? (00:59:10)Why the World

It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode we recorded this year, including:Kyle Fish explaining how Anthropic’s AI Claude descends into spiritual woo when left to talk to itselfIan Dunt on why the unelected House of Lords is by far the best part of the British governmentSam Bowman’s strategy to get NIMBYs to love it when things get built next to their housesBuck Shlegeris on how to get an AI model that wants to seize control to accidentally help you foil its plans…as well as 18 other top observations and arguments from the past year of the show.Links to learn more, video, and full transcript: https://80k.info/best25It's been another year of living through history, whether we asked for it or not. Luisa and Rob will be back in 2026 to help you make sense of whatever comes next — as Earth continues its indifferent journey through the cosmos, now accompanied by AI systems that can summarise our meetings and generate adequate birthday messages for colleagues we barely know.Chapters:Cold open (00:00:00)Rob's intro (00:02:35)Helen Toner on whether we're racing China to build AGI (00:03:43)Hugh White on what he'd say to Americans (00:06:09)Buck Shlegeris on convincing AI models they've already escaped (00:12:09)Paul Scharre on a personal experience in Afghanistan that influenced his views on autonomous weapons (00:15:10)Ian Dunt on how unelected septuagenarians are the heroes of UK governance (00:19:06)Beth Barnes on AI companies being locally reasonable, but globally reckless (00:24:27)Tyler Whitmer on one thing the California and Delaware attorneys general forced on the OpenAI for-profit as part of their restructure (00:28:02)Toby Ord on whether rich people will get access to AGI first (00:30:13)Andrew Snyder-Beattie on how the worst biorisks are defence dominant (00:34:24)Eileen Yam on the most eye-watering gaps in opinions about AI between experts and the US public (00:39:41)Will MacAskill on what a century of history crammed into a decade might feel like (00:44:07)Kyle Fish on what happens when two instances of Claude are left to interact with each other (00:49:08)Sam Bowman on where the Not In My Back Yard movement actually has a point (00:56:29)Neel Nanda on how mechanistic interpretability is trying to be the biology of AI (01:03:12)Tom Davidson on the potential to install secret AI loyalties at a very early stage (01:07:19)Luisa and Rob discussing how medicine doesn't take the health burden of pregnancy seriously enough (01:10:53)Marius Hobbhahn on why scheming is a very natural path for AI models — and people (01:16:23)Holden Karnofsky on lessons for AI regulation drawn

Most debates about the moral status of AI systems circle the same question: is there something that it feels like to be them? But what if that’s the wrong question to ask? Andreas Mogensen — a senior researcher in moral philosophy at the University of Oxford — argues that so-called 'phenomenal consciousness' might be neither necessary nor sufficient for a being to deserve moral consideration. Links to learn more and full transcript: https://80k.info/am25For instance, a creature on the sea floor that experiences nothing but faint brightness from the sun might have no moral claim on us, despite being conscious. Meanwhile, any being with real desires that can be fulfilled or not fulfilled can arguably be benefited or harmed. Such beings arguably have a capacity for welfare, which means they might matter morally. And, Andreas argues, desire may not require subjective experience. Desire may need to be backed by positive or negative emotions — but as Andreas explains, there are some reasons to think a being could also have emotions without being conscious. There’s another underexplored route to moral patienthood: autonomy. If a being can rationally reflect on its goals and direct its own existence, we might have a moral duty to avoid interfering with its choices — even if it has no capacity for welfare. However, Andreas suspects genuine autonomy might require consciousness after all. To be a rational agent, your beliefs probably need to be justified by something, and conscious experience might be what does the justifying. But even this isn’t clear. The upshot? There’s a chance we could just be really mistaken about what it would take for an AI to matter morally. And with AI systems potentially proliferating at massive scale, getting this wrong could be among the largest moral errors in history.In today’s interview, Andreas and host Zershaaneh Qureshi confront all these confusing ideas, challenging their intuitions about consciousness, welfare, and morality along the way. They also grapple with a few seemingly attractive arguments which share a very unsettling conclusion: that human extinction (or even the extinction of all sentient life) could actually be a morally desirable thing. This episode was recorded on December 3, 2025.Chapters:Cold open (00:00:00)Introducing Zershaaneh (00:00:55)The puzzle of moral patienthood (00:03:20)Is subjective experience necessary? (00:05:52)What is it to desire? (00:10:42)Desiring without experiencing (00:17:56)What would make AIs moral patients? (00:28:17)Another route entirely: deserving autonomy (00:45:12)Maybe there's no objective truth about any of this (01:12:06)Practical implications (01:29:21)Why not just let super

In 1983, Stanislav Petrov, a Soviet lieutenant colonel, sat in a bunker watching a red screen flash “MISSILE LAUNCH.” Protocol demanded he report it to superiors, which would very likely trigger a retaliatory nuclear strike. Petrov didn’t. He reasoned that if the US were actually attacking, they wouldn’t fire just 5 missiles — they’d empty the silos. He bet the fate of the world on a hunch that his machine was broken. He was right.Paul Scharre, the former Army Ranger who led the Pentagon team that wrote the US military’s first policy on autonomous weapons, has a question: What would an AI have done in Petrov’s shoes? Would an AI system have been flexible and wise enough to make the same judgement? Or would it immediately launch a counterattack?Paul joins host Luisa Rodriguez to explain why we are hurtling toward a “battlefield singularity” — a tipping point where AI increasingly replaces humans in much of the military, changing the way war is fought with speed and complexity that outpaces humans’ ability to keep up.Links to learn more, video, and full transcript: https://80k.info/psMilitaries don’t necessarily want to take humans out of the loop. But Paul argues that the competitive pressure of warfare creates a “use it or lose it” dynamic. As former Deputy Secretary of Defense Bob Work put it: “If our competitors go to Terminators, and their decisions are bad, but they’re faster, how would we respond?”Once that line is crossed, Paul warns we might enter an era of “flash wars” — conflicts that spiral out of control as quickly and inexplicably as a flash crash in the stock market, with no way for humans to call a timeout.In this episode, Paul and Luisa dissect what this future looks like:Swarming warfare: Why the future isn’t just better drones, but thousands of cheap, autonomous agents coordinating like a hive mind to overwhelm defences.The Gatling gun cautionary tale: The inventor of the Gatling gun thought automating fire would reduce the number of soldiers needed, saving lives. Instead, it made war significantly deadlier. Paul argues AI automation could do the same, increasing lethality rather than creating “bloodless” robot wars.The cyber frontier: While robots have physical limits, Paul argues cyberwarfare is already at the point where AI can act faster than human defenders, leading to intelligent malware that evolves and adapts like a biological virus.The US-China “adoption race”: Paul rejects the idea that the US and China are in a spending arms race (AI is barely 1% of the DoD budget). Instead, it’s a race of organisational adoption — one where the US has massive advantages in talent and chips, but struggles with bureaucratic inertia that might not be a problem for an autocratic country.Paul also shares a per

Power is already concentrated today: over 800 million people live on less than $3 a day, the three richest men in the world are worth over $1 trillion, and almost six billion people live in countries without free and fair elections.This is a problem in its own right. There is still substantial distribution of power though: global income inequality is falling, over two billion people live in electoral democracies, no country earns more than a quarter of GDP, and no company earns as much as 1%.But in the future, advanced AI could enable much more extreme power concentration than we’ve seen so far.Many believe that within the next decade the leading AI projects will be able to run millions of superintelligent AI systems thinking many times faster than humans. These systems could displace human workers, leading to much less economic and political power for the vast majority of people; and unless we take action to prevent it, they may end up being controlled by a tiny number of people, with no effective oversight. Once these systems are deployed across the economy, government, and the military, whatever goals they’re built to have will become the primary force shaping the future. If those goals are chosen by the few, then a small number of people could end up with the power to make all of the important decisions about the future.This article by Rose Hadshar explores this emerging challenge in detail. You can see all the images and footnotes in the original article on the 80,000 Hours website.Chapters:Introduction (00:00)Summary (02:15)Section 1: Why might AI-enabled power concentration be a pressing problem? (07:02)Section 2: What are the top arguments against working on this problem? (45:02)Section 3: What can you do to help? (56:36)Narrated by: Dominic ArmstrongAudio engineering: Dominic Armstrong and Milo McGuireMusic: CORBIT

Former White House staffer Dean Ball thinks it's very likely some form of 'superintelligence' arrives in under 20 years. He thinks AI being used for bioweapon research is "a real threat model, obviously." He worries about dangerous "power imbalances" should AI companies reach "$50 trillion market caps." And he believes the agriculture revolution probably worsened human health and wellbeing.Given that, you might expect him to be pushing for AI regulation. Instead, he’s become one of the field’s most prominent and thoughtful regulation sceptics and was recently the lead writer on Trump’s AI Action Plan, before moving to the Foundation for American Innovation.Links to learn more, video, and full transcript: https://80k.info/dbDean argues that the wrong regulations, deployed too early, could freeze society into a brittle, suboptimal political and economic order. As he puts it, “my big concern is that we’ll lock ourselves in to some suboptimal dynamic and actually, in a Shakespearean fashion, bring about the world that we do not want.”Dean’s fundamental worry is uncertainty: “We just don’t know enough yet about the shape of this technology, the ergonomics of it, the economics of it… You can’t govern the technology until you have a better sense of that.”Premature regulation could lock us in to addressing the wrong problem (focusing on rogue AI when the real issue is power concentration), using the wrong tools (using compute thresholds when we should regulate companies instead), through the wrong institutions (captured AI-specific bodies), all while making it harder to build the actual solutions we’ll need (like open source alternatives or new forms of governance).But Dean is also a pragmatist: he opposed California’s AI regulatory bill SB 1047 in 2024, but — impressed by new capabilities enabled by “reasoning models” — he supported its successor SB 53 in 2025.And as Dean sees it, many of the interventions that would help with catastrophic risks also happen to improve mundane AI safety, make products more reliable, and address present-day harms like AI-assisted suicide among teenagers. So rather than betting on a particular vision of the future, we should cross the river by feeling the stones and pursue “robust” interventions we’re unlikely to regret.This episode was recorded on September 24, 2025.Chapters:Cold open (00:00:00)Who’s Dean Ball? (00:01:22)How likely are we to get superintelligence soon, and how bad could it be? (00:01:54)The military may not adopt AI that fast (00:10:54)Dean’s “two wolves” of AI scepticism and optimism (00:17:48)Will AI self-improvement be a game changer? (00:28:20)The case for regulating at the las

We often worry about AI models “hallucinating” or making honest mistakes. But what happens when a model knows the truth, but decides to deceive you anyway to achieve a goal of its own? This isn’t sci-fi — it’s happening regularly in deployment today. Marius Hobbhahn, CEO of the world’s top research organisation focused on AI deception (Apollo Research), has been collaborating with OpenAI to figure out what causes OpenAI’s reasoning models to 'scheme' against users.Links to learn more, video, and full transcript: https://80k.info/mh25 In a striking case uncovered by Apollo, when many AI models were told they would have capabilities removed if they performed too well on a test, they successfully “sandbagged” — intentionally answering questions incorrectly to appear less capable than they were, while also being careful not to perform so poorly it would arouse suspicion.These models had somehow developed a preference to preserve their own capabilities, despite never being trained in that goal or assigned a task that called for it.This doesn’t cause significant risk now, but as AI models become more general, superhuman in more areas, and are given more decision-making power, it could become outright dangerous.In today’s episode, Marius details his recent collaboration with OpenAI to train o3 to follow principles like “never lie,” even when placed in “high-pressure” situations where it would otherwise make sense.The good news: They reduced “covert rule violations” (scheming) by about 97%.The bad news: In the remaining 3% of cases, the models sometimes became more sophisticated — making up new principles to justify their lying, or realising they were in a test environment and deciding to play along until the coast was clear.Marius argues that while we can patch specific behaviours, we might be entering a “cat-and-mouse game” where models are becoming more situationally aware — that is, aware of when they’re being evaluated — faster than we are getting better at testing.Even if models can’t tell they’re being tested, they can produce hundreds of pages of reasoning before giving answers and include strange internal dialects humans can’t make sense of, making it much harder to tell whether models are scheming or train them to stop.Marius and host Rob Wiblin discuss:Why models pretending to be dumb is a rational survival strategyThe Replit AI agent that deleted a production database and then lied about itWhy rewarding AIs for achieving outcomes might lead to them becoming better liarsThe weird new language models are using in their internal chain-of-thoughtThis episode was recorded on September 19, 2025.Chapters:Cold open (00:00:00)Who’s Mariu

Global fertility rates aren’t just falling: the rate of decline is accelerating. From 2006 to 2016, fertility dropped gradually, but since 2016 the rate of decline has increased 4.5-fold. In many wealthy countries, fertility is now below 1.5. While we don’t notice it yet, in time that will mean the population halves every 60 years.Rob Wiblin is already a parent and Luisa Rodriguez is about to be, which prompted the two hosts of the show to get together to chat about all things parenting — including why it is that far fewer people want to join them raising kids than did in the past.Links to learn more, video, and full transcript: https://80k.info/lrrwWhile “kids are too expensive” is the most common explanation, Rob argues that money can’t be the main driver of the change: richer people don’t have many more children now, and we see fertility rates crashing even in countries where people are getting much richer.Instead, Rob points to a massive rise in the opportunity cost of time, increasing expectations parents have of themselves, and a global collapse in socialising and coupling up. In the EU, the rate of people aged 25–35 in relationships has dropped by 20% since 1990, which he thinks will “mechanically reduce the number of children.” The overall picture is a big shift in priorities: in the US in 1993, 61% of young people said parenting was an important part of a flourishing life for them, vs just 26% today.That leads Rob and Luisa to discuss what they might do to make the burden of parenting more manageable and attractive to people, including themselves.In this non-typical episode, we take a break from the usual heavy topics to discuss the personal side of bringing new humans into the world, including:Rob’s updated list of suggested purchases for new parentsHow parents could try to feel comfortable doing lessHow beliefs about childhood play have changed so radicallyWhat matters and doesn’t in childhood safetyWhy the decline in fertility might be impractical to reverseWhether we should care about a population crash in a world of AI automationThis episode was recorded on September 12, 2025.Chapters:Cold open (00:00:00)We're hiring (00:01:26)Why did Luisa decide to have kids? (00:02:10)Ups and downs of pregnancy (00:04:15)Rob’s experience for the first couple years of parenthood (00:09:39)Fertility rates are massively declining (00:21:25)Why do fewer people want children? (00:29:20)Is parenting way harder now than it used to be? (00:38:56)Feeling guilty for not playing enough with our kids (00:48:07)Options for increasing fertility rates globally (01:00:03)Rob’s transit

If you work in AI, you probably think it’s going to boost productivity, create wealth, advance science, and improve your life. If you’re a member of the American public, you probably strongly disagree.In three major reports released over the last year, the Pew Research Center surveyed over 5,000 US adults and 1,000 AI experts. They found that the general public holds many beliefs about AI that are virtually nonexistent in Silicon Valley, and that the tech industry’s pitch about the likely benefits of their work has thus far failed to convince many people at all. AI is, in fact, a rare topic that mostly unites Americans — regardless of politics, race, age, or gender.Links to learn more, video, and full transcript: https://80k.info/eyToday’s guest, Eileen Yam, director of science and society research at Pew, walks us through some of the eye-watering gaps in perception:Jobs: 73% of AI experts see a positive impact on how people do their jobs. Only 23% of the public agrees.Productivity: 74% of experts say AI is very likely to make humans more productive. Just 17% of the public agrees.Personal benefit: 76% of experts expect AI to benefit them personally. Only 24% of the public expects the same (while 43% expect it to harm them).Happiness: 22% of experts think AI is very likely to make humans happier, which is already surprisingly low — but a mere 6% of the public expects the same.For the experts building these systems, the vision is one of human empowerment and efficiency. But outside the Silicon Valley bubble, the mood is more one of anxiety — not only about Terminator scenarios, but about AI denying their children “curiosity, problem-solving skills, critical thinking skills and creativity,” while they themselves are replaced and devalued:53% of Americans say AI will worsen people’s ability to think creatively.50% believe it will hurt our ability to form meaningful relationships.38% think it will worsen our ability to solve problems.Open-ended responses to the surveys reveal a poignant fear: that by offloading cognitive work to algorithms we are changing childhood to a point we no longer know what adults will result. As one teacher quoted in the study noted, we risk raising a generation that relies on AI so much it never “grows its own curiosity, problem-solving skills, critical thinking skills and creativity.”If the people building the future are this out of sync with the people living in it, the impending “techlash” might be more severe than industry anticipates.In this episode, Eileen and host Rob Wiblin break down the data on where these groups disagree, where they actually align (nobody trusts the government or companies to regulate this), and why the “digital natives” might actually be th

Last December, the OpenAI business put forward a plan to completely sideline its nonprofit board. But two state attorneys general have now blocked that effort and kept that board very much alive and kicking.The for-profit’s trouble was that the entire operation was founded on the premise of — and legally pledged to — the purpose of ensuring that “artificial general intelligence benefits all of humanity.” So to get its restructure past regulators, the business entity has had to agree to 20 serious requirements designed to ensure it continues to serve that goal.Attorney Tyler Whitmer, as part of his work with Legal Advocates for Safe Science and Technology, has been a vocal critic of OpenAI’s original restructure plan. In today’s conversation, he lays out all the changes and whether they will ultimately matter.Full transcript, video, and links to learn more: https://80k.info/tw2 After months of public pressure and scrutiny from the attorneys general (AGs) of California and Delaware, the December proposal itself was sidelined — and what replaced it is far more complex and goes a fair way towards protecting the original mission:The nonprofit’s charitable purpose — “ensure that artificial general intelligence benefits all of humanity” — now legally controls all safety and security decisions at the company. The four people appointed to the new Safety and Security Committee can block model releases worth tens of billions.The AGs retain ongoing oversight, meeting quarterly with staff and requiring advance notice of any changes that might undermine their authority.OpenAI’s original charter, including the remarkable “stop and assist” commitment, remains binding.But significant concessions were made. The nonprofit lost exclusive control of AGI once developed — Microsoft can commercialise it through 2032. And transforming from complete control to this hybrid model represents, as Tyler puts it, “a bad deal compared to what OpenAI should have been.”The real question now: will the Safety and Security Committee use its powers? It currently has four part-time volunteer members and no permanent staff, yet they’re expected to oversee a company racing to build AGI while managing commercial pressures in the hundreds of billions.Tyler calls on OpenAI to prove they’re serious about following the agreement:Hire management for the SSC.Add more independent directors with AI safety expertise.Maximise transparency about mission compliance."There’s a re

With the US racing to develop AGI and superintelligence ahead of China, you might expect the two countries to be negotiating how they’ll deploy AI, including in the military, without coming to blows. But according to Helen Toner, director of the Center for Security and Emerging Technology in DC, “the US and Chinese governments are barely talking at all.”Links to learn more, video, and full transcript: https://80k.info/ht25In her role as a founder, and now leader, of DC’s top think tank focused on the geopolitical and military implications of AI, Helen has been closely tracking the US’s AI diplomacy since 2019.“Over the last couple of years there have been some direct [US–China] talks on some small number of issues, but they’ve also often been completely suspended.” China knows the US wants to talk more, so “that becomes a bargaining chip for China to say, ‘We don’t want to talk to you. We’re not going to do these military-to-military talks about extremely sensitive, important issues, because we’re mad.'”Helen isn’t sure the groundwork exists for productive dialogue in any case. “At the government level, [there’s] very little agreement” on what AGI is, whether it’s possible soon, whether it poses major risks. Without shared understanding of the problem, negotiating solutions is very difficult.Another issue is that so far the Chinese Communist Party doesn’t seem especially “AGI-pilled.” While a few Chinese companies like DeepSeek are betting on scaling, she sees little evidence Chinese leadership shares Silicon Valley’s conviction that AGI will arrive any minute now, and export controls have made it very difficult for them to access compute to match US competitors.When DeepSeek released R1 just three months after OpenAI’s o1, observers declared the US–China gap on AI had all but disappeared. But Helen notes OpenAI has since scaled to o3 and o4, with nothing to match on the Chinese side. “We’re now at something like a nine-month gap, and that might be longer.”To find a properly AGI-pilled autocracy, we might need to look at nominal US allies. The US has approved massive data centres in the UAE and Saudi Arabia with “hundreds of thousands of next-generation Nvidia chips” — delivering colossal levels of computing power.When OpenAI announced this deal with the UAE, they celebrated that it was “rooted in democratic values,” and would advance “democratic AI rails” and provide “a clear alternative to authoritarian versions of AI.”But the UAE scores 18 out of 100 on Freedom House’s democracy index. “This is really not a country that respects rule of law,” <a href="https://helentoner.substack.com/p/

For years, working on AI safety usually meant theorising about the ‘alignment problem’ or trying to convince other people to give a damn. If you could find any way to help, the work was frustrating and low feedback.According to Anthropic’s Holden Karnofsky, this situation has now reversed completely.There are now large amounts of useful, concrete, shovel-ready projects with clear goals and deliverables. Holden thinks people haven’t appreciated the scale of the shift, and wants everyone to see the large range of ‘well-scoped object-level work’ they could personally help with, in both technical and non-technical areas.Video, full transcript, and links to learn more: https://80k.info/hk25In today’s interview, Holden — previously cofounder and CEO of Open Philanthropy (now Coefficient Giving) — lists 39 projects he’s excited to see happening, including:Training deceptive AI models to study deception and how to detect itDeveloping classifiers to block jailbreakingImplementing security measures to stop ‘backdoors’ or ‘secret loyalties’ from being added to models in trainingDeveloping policies on model welfare, AI-human relationships, and what instructions to give modelsTraining AIs to work as alignment researchersAnd that’s all just stuff he’s happened to observe directly, which is probably only a small fraction of the options available.Holden makes a case that, for many people, working at an AI company like Anthropic will be the best way to steer AGI in a positive direction. He notes there are “ways that you can reduce AI risk that you can only do if you’re a competitive frontier AI company.” At the same time, he believes external groups have their own advantages and can be equally impactful.Critics worry that Anthropic’s efforts to stay at that frontier encourage competitive racing towards AGI — significantly or entirely offsetting any useful research they do. Holden thinks this seriously misunderstands the strategic situation we’re in — and explains his case in detail with host Rob Wiblin.Chapters:Cold open (00:00:00)Holden is back! (00:02:26)An AI Chernobyl we never notice (00:02:56)Is rogue AI takeover easy or hard? (00:07:32)The AGI race isn't a coordination failure (00:17:48)What Holden now does at Anthropic (00:28:04)The case for working at Anthropic (00:30:08)Is Anthropic doing enough? (00:40:45)Can we trust Anthropic, or any AI company? (00:43:40)How can Anthropic compete while paying the “safety tax”? (00:49:14)What, if anything, could prompt Anthropic to halt development of AGI? (00:56:11)Holden's retrospective on responsible scaling policies (00:59:01)Overrated work (01:14:27)Concrete shovel-ready projects

When Daniel Kokotajlo talks to security experts at major AI labs, they tell him something chilling: “Of course we’re probably penetrated by the CCP already, and if they really wanted something, they could take it.”This isn’t paranoid speculation. It’s the working assumption of people whose job is to protect frontier AI models worth billions of dollars. And they’re not even trying that hard to stop it — because the security measures that might actually work would slow them down in the race against competitors.Full transcript, highlights, and links to learn more: https://80k.info/dkDaniel is the founder of the AI Futures Project and author of AI 2027, a detailed scenario showing how we might get from today’s AI systems to superintelligence by the end of the decade. Over a million people read it in the first few weeks, including US Vice President JD Vance. When Daniel talks to researchers at Anthropic, OpenAI, and DeepMind, they tell him the scenario feels less wild to them than to the general public — because many of them expect something like this to happen.Daniel’s median timeline? 2029. But he’s genuinely uncertain, putting 10–20% probability on AI progress hitting a long plateau.When he first published AI 2027, his median forecast for when superintelligence would arrive was 2028, rather than 2029. So what shifted his timelines recently? Partly a fascinating study from METR showing that AI coding assistants might actually be making experienced programmers slower — even though the programmers themselves think they’re being sped up. The study suggests a systematic bias toward overestimating AI effectiveness — which, ironically, is good news for timelines, because it means we have more breathing room than the hype suggests.But Daniel is also closely tracking another METR result: AI systems can now reliably complete coding tasks that take humans about an hour. That capability has been doubling every six months in a remarkably straight line. Extrapolate a couple more years and you get systems completing month-long tasks. At that point, Daniel thinks we’re probably looking at genuine AI research automation — which could cause the whole process to accelerate dramatically.At some point, superintelligent AI will be limited by its inability to directly affect the physical world. That’s when Daniel thinks superintelligent systems will pour resources into robotics, creating a robot economy in months.Daniel paints a vivid picture: imagine transforming all car factories (which have similar compon

Conventional wisdom is that safeguarding humanity from the worst biological risks — microbes optimised to kill as many as possible — is difficult bordering on impossible, making bioweapons humanity’s single greatest vulnerability. Andrew Snyder-Beattie thinks conventional wisdom could be wrong.Andrew’s job at Open Philanthropy is to spend hundreds of millions of dollars to protect as much of humanity as possible in the worst-case scenarios — those with fatality rates near 100% and the collapse of technological civilisation a live possibility.Video, full transcript, and links to learn more: https://80k.info/asbAs Andrew lays out, there are several ways this could happen, including:A national bioweapons programme gone wrong, in particular Russia or North KoreaAI advances making it easier for terrorists or a rogue AI to release highly engineered pathogensMirror bacteria that can evade the immune systems of not only humans, but many animals and potentially plants as wellMost efforts to combat these extreme biorisks have focused on either prevention or new high-tech countermeasures. But prevention may well fail, and high-tech approaches can’t scale to protect billions when, with no sane people willing to leave their home, we’re just weeks from economic collapse.So Andrew and his biosecurity research team at Open Philanthropy have been seeking an alternative approach. They’re proposing a four-stage plan using simple technology that could save most people, and is cheap enough it can be prepared without government support. Andrew is hiring for a range of roles to make it happen — from manufacturing and logistics experts to global health specialists to policymakers and other ambitious entrepreneurs — as well as programme associates to join Open Philanthropy’s biosecurity team (apply by October 20!).Fundamentally, organisms so small have no way to penetrate physical barriers or shield themselves from UV, heat, or chemical poisons. We now know how to make highly effective ‘elastomeric’ face masks that cost $10, can sit in storage for 20 years, and can be used for six months straight without changing the filter. Any rich country could trivially stockpile enough to cover all essential workers.People can’t wear masks 24/7, but fortunately propylene glycol — already found in vapes and smoke machines — is astonishingly good at killing microbes in the air. And, being a common chemical input, industry already produces enough of the stuff to cover every indoor space we ne

Jake Sullivan was the US National Security Advisor from 2021-2025. He joined our friends on The Cognitive Revolution podcast in August to discuss AI as a critical national security issue. We thought it was such a good interview and we wanted more people to see it, so we’re cross-posting it here on The 80,000 Hours Podcast.Jake and host Nathan Labenz discuss:Jake’s four-category framework to think about AI risks and opportunities: security, economics, society, and existential.Why Jake advocates for "managed competition" with China — where the US and China "compete like hell" while maintaining sufficient guardrails to prevent conflict.Why Jake thinks competition is a "chronic condition" of the US-China relationship that cannot be solved with “grand bargains.”How current conflicts are providing "glimpses of the future" with lessons about scale, attritability, and the potential for autonomous weapons as AI gets integrated into modern warfare.Why Jake worries that Pentagon bureaucracy prevents rapid AI adoption while China's People’s Liberation Army may be better positioned to integrate AI capabilities.And why we desperately need private sector leadership: AI is "the first technology with such profound national security applications that the government really had very little to do with."Check out more of Nathan’s interviews on The Cognitive Revolution YouTube channel: https://www.youtube.com/@CognitiveRevolutionPodcastWhat did you think of the episode? https://forms.gle/g7cj6TkR9xmxZtCZ9Originally produced by: https://aipodcast.ingThis edit by: Simon Monsour, Dominic Armstrong, and Milo McGuire | 80,000 HoursChapters:Cold open (00:00:00)Luisa's intro (00:01:06)Jake’s AI worldview (00:02:08)What Washington gets — and doesn’t — about AI (00:04:43)Concrete AI opportunities (00:10:53)Trump’s AI Action Plan (00:19:36)Middle East AI deals (00:23:26)Is China really a threat? (00:28:52)Export controls strategy (00:35:55)Managing great power competition (00:54:51)AI in modern warfare (01:01:47)Economic impacts in people’s daily lives (01:04:13)

At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”Video, full transcript, and links to learn more: https://80k.info/nn2This means creating as many opportunities as possible for surprisingly good things to happen:Write publicly.Reach out to researchers whose work you admire.Say yes to unusual projects that seem a little scary.Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.In this extended conversation, Neel and host Rob Wiblin discuss all that and some other hot takes from Neel's four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)What did you think of the episode? https://forms.gle/6binZivKmjjiHU6dA Chapters:Cold open (00:00:00)Who’s Neel Nanda? (00:01:12)Luck surface area and making the right opportunities (00:01:46)Writing cold emails that aren't insta-deleted (00:03:50)How Neel uses LLMs to get much more done (00:09:08)“If your safety work doesn't advance capabilities, it's probably bad safety work” (00:23:22)Why Neel refuses to share his p(doom) (00:27:22)</l

We don’t know how AIs think or why they do what they do. Or at least, we don’t know much. That fact is only becoming more troubling as AIs grow more capable and appear on track to wield enormous cultural influence, directly advise on major government decisions, and even operate military equipment autonomously. We simply can’t tell what models, if any, should be trusted with such authority.Neel Nanda of Google DeepMind is one of the founding figures of the field of machine learning trying to fix this situation — mechanistic interpretability (or “mech interp”). The project has generated enormous hype, exploding from a handful of researchers five years ago to hundreds today — all working to make sense of the jumble of tens of thousands of numbers that frontier AIs use to process information and decide what to say or do.Full transcript, video, and links to learn more: https://80k.info/nn1Neel now has a warning for us: the most ambitious vision of mech interp he once dreamed of is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are simply too great to get us there in time, before competitive pressures push us to deploy human-level or superhuman AIs. Indeed, Neel argues no one approach will guarantee alignment, and our only choice is the “Swiss cheese” model of accident prevention, layering multiple safeguards on top of one another.But while mech interp won’t be a silver bullet for AI safety, it has nevertheless had some major successes and will be one of the best tools in our arsenal.For instance: by inspecting the neural activations in the middle of an AI’s thoughts, we can pick up many of the concepts the model is thinking about — from the Golden Gate Bridge, to refusing to answer a question, to the option of deceiving the user. While we can’t know all the thoughts a model is having all the time, picking up 90% of the concepts it is using 90% of the time should help us muddle through, so long as mech interp is paired with other techniques to fill in the gaps.This episode was recorded on July 17 and 21, 2025.Part 2 of the conversation is now available! https://80k.info/nn2What did you think? https://forms.gle/xKyUrGyYpYenp8N4AChapters:Cold open (00:00)Who's Neel Nanda? (01:02)How would mechanistic interpretability help with AGI (01:59)What's mech interp? (05:09)How Neel changed his take on mech interp (09:47)Top successes in interpretability (15:53)Probes can cheaply detect harmful intentions in AIs (20:06)In some ways we understand AIs better than human minds (26:49)Mech inter

What happens when you lock two AI systems in a room together and tell them they can discuss anything they want?According to experiments run by Kyle Fish — Anthropic’s first AI welfare researcher — something consistently strange: the models immediately begin discussing their own consciousness before spiraling into increasingly euphoric philosophical dialogue that ends in apparent meditative bliss.Highlights, video, and full transcript: https://80k.info/kf“We started calling this a ‘spiritual bliss attractor state,'” Kyle explains, “where models pretty consistently seemed to land.” The conversations feature Sanskrit terms, spiritual emojis, and pages of silence punctuated only by periods — as if the models have transcended the need for words entirely.This wasn’t a one-off result. It happened across multiple experiments, different model instances, and even in initially adversarial interactions. Whatever force pulls these conversations toward mystical territory appears remarkably robust.Kyle’s findings come from the world’s first systematic welfare assessment of a frontier AI model — part of his broader mission to determine whether systems like Claude might deserve moral consideration (and to work out what, if anything, we should be doing to make sure AI systems aren’t having a terrible time).He estimates a roughly 20% probability that current models have some form of conscious experience. To some, this might sound unreasonably high, but hear him out. As Kyle says, these systems demonstrate human-level performance across diverse cognitive tasks, engage in sophisticated reasoning, and exhibit consistent preferences. When given choices between different activities, Claude shows clear patterns: strong aversion to harmful tasks, preference for helpful work, and what looks like genuine enthusiasm for solving interesting problems.Kyle points out that if you’d described all of these capabilities and experimental findings to him a few years ago, and asked him if he thought we should be thinking seriously about whether AI systems are conscious, he’d say obviously yes.But he’s cautious about drawing conclusions: "We don’t really understand consciousness in humans, and we don’t understand AI systems well enough to make those comparisons directly. So in a big way, I think that we are in just a fundamentally very uncertain position here."That uncertainty cuts both ways:Dismissing AI consciousness entirely might mean ignoring a moral catastrophe happening at unprecedented scale.But assuming consciousness too readily could hamper crucial safety research by treating potentially unconscious systems as if they were moral patients — which might mean giving them resources, rights, and power.Kyle’s approach threads this needle through careful empirical research and reversible interventions. His assessments are nowhere near

About half of people are worried they’ll lose their job to AI. They’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can't. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. ATMs actually increased employment of bank clerks — until online banking automated the job much more.Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. This article covers what those skills are, as well as tips on how to start learning them.Check out the full article for all the graphs, links, and footnotes: https://80000hours.org/agi/guide/skills-ai-makes-valuable/Chapters:Introduction (00:00:00)1: What people misunderstand about automation (00:04:17)1.1: What would ‘full automation’ mean for wages? (00:08:56)2: Four types of skills most likely to increase in value (00:11:19)2.1: Skills AI won’t easily be able to perform (00:12:42)2.2: Skills that are needed for AI deployment (00:21:41)2.3: Skills where we could use far more of what they produce (00:24:56)2.4: Skills that are difficult for others to learn (00:26:25)3.1: Skills using AI to solve real problems (00:28:05)3.2: Personal effectiveness (00:29:22)3.3: Leadership skills (00:31:59)3.4: Communications and taste (00:36:25)3.5: Getting things done in government (00:37:23)3.6: Complex physical skills (00:38:24)4: Skills with a more uncertain future (00:38:57)4.1: Routine knowledge work: writing, admin, analysis, advice (00:39:18)4.2: Coding, maths, data science, and applied STEM (00:43:22)4.3: Visual creation (00:45:31)4.4: More predictable manual jobs (00:46:05)5: Some closing thoughts on career strategy (00:46:46)5.1: Look for ways to leapfrog entry-level white collar jobs (00:46:54)5.2: Be cautious about starting long training periods, like PhDs and medicine (00:48:44)5.3: Make yourself more resilient to change (00:49:52)5.4: Ride the wave (00:50:16)Take action (00:50:37)Thank you for listening (00:50:58)Audio engineering: Dominic ArmstrongMusic: Ben Cordell

What happens when civilisation faces its greatest tests?This compilation brings together insights from researchers, defence experts, philosophers, and policymakers on humanity’s ability to survive and recover from catastrophic events. From nuclear winter and electromagnetic pulses to pandemics and climate disasters, we explore both the threats that could bring down modern civilisation and the practical solutions that could help us bounce back.Learn more and see the full transcript: https://80k.info/cr25Chapters:Cold open (00:00:00)Luisa’s intro (00:01:16)Zach Weinersmith on how settling space won’t help with threats to civilisation anytime soon (unless AI gets crazy good) (00:03:12)Luisa Rodriguez on what the world might look like after a global catastrophe (00:11:42)Dave Denkenberger on the catastrophes that could cause global starvation (00:22:29)Lewis Dartnell on how we could rediscover essential information if the worst happened (00:34:36)Andy Weber on how people in US defence circles think about nuclear winter (00:39:24)Toby Ord on risks to our atmosphere and whether climate change could really threaten civilisation (00:42:34)Mark Lynas on how likely it is that climate change leads to civilisational collapse (00:54:27)Lewis Dartnell on how we could recover without much coal or oil (01:02:17)Kevin Esvelt on people who want to bring down civilisation — and how AI could help them succeed (01:08:41)Toby Ord on whether rogue AI really could wipe us all out (01:19:50)Joan Rohlfing on why we need to worry about more than just nuclear winter (01:25:06)Annie Jacobsen on the effects of firestorms, rings of annihilation, and electromagnetic pulses from nuclear blasts (01:31:25)Dave Denkenberger on disruptions to electricity and communications (01:44:43)Luisa Rodriguez on how we might lose critical knowledge (01:53:01)Kevin Esvelt on the pandemic scenarios that could bring down civilisation (01:57:32)Andy Weber on tech to help with pandemics (02:15:45)Christian Ruhl on why we need the equivalents of seatbelts and airbags to prevent nuclear war from threatening civilisation (02:24:54)Mark Lynas on whether wide-scale famine would lead to civilisational collapse (02:37:58)Dave Denkenberger on low-cost, low-tech solutions to make sure everyone is fed no matter what (02:49:02)Athena Aktipis on whether society would go all Mad Max in the apocalypse (02:59:57)Luisa Rodriguez on why she’s optimistic survivors wouldn’t turn on one another (03:08:02)David Denkenberger on how resilient foods research overlaps with space technologies (03:16:08)Zach Weinersmith on what we’d practically need to do to save a pocket of humanity in space (03:18:57)<

Ryan Greenblatt — lead author on the explosive paper “Alignment faking in large language models” and chief scientist at Redwood Research — thinks there’s a 25% chance that within four years, AI will be able to do everything needed to run an AI company, from writing code to designing experiments to making strategic and business decisions.As Ryan lays out, AI models are “marching through the human regime”: systems that could handle five-minute tasks two years ago now tackle 90-minute projects. Double that a few more times and we may be automating full jobs rather than just parts of them.Will setting AI to improve itself lead to an explosive positive feedback loop? Maybe, but maybe not.The explosive scenario: Once you’ve automated your AI company, you could have the equivalent of 20,000 top researchers, each working 50 times faster than humans with total focus. “You have your AIs, they do a bunch of algorithmic research, they train a new AI, that new AI is smarter and better and more efficient… that new AI does even faster algorithmic research.” In this world, we could see years of AI progress compressed into months or even weeks.With AIs now doing all of the work of programming their successors and blowing past the human level, Ryan thinks it would be fairly straightforward for them to take over and disempower humanity, if they thought doing so would better achieve their goals. In the interview he lays out the four most likely approaches for them to take.The linear progress scenario: You automate your company but progress barely accelerates. Why? Multiple reasons, but the most likely is “it could just be that AI R&D research bottlenecks extremely hard on compute.” You’ve got brilliant AI researchers, but they’re all waiting for experiments to run on the same limited set of chips, so can only make modest progress.Ryan’s median guess splits the difference: perhaps a 20x acceleration that lasts for a few months or years. Transformative, but less extreme than some in the AI companies imagine.And his 25th percentile case? Progress “just barely faster” than before. All that automation, and all you’ve been able to do is keep pace.Unfortunately the data we can observe today is so limited that it leaves us with vast error bars. “We’re extrapolating from a regime that we don’t even understand to a wildly different regime,” Ryan believes, “so no one knows.”But that huge uncertainty means the explosive growth scenario is a plausible one — and the companies building these systems are spending tens of billions to try to make it happen.In this extensive interview, Ryan elaborates on the above and the policy and technical response necessary to insure us against the possibility that they succeed — a scenario society has barely begun to prepare for.<

The era of making AI smarter just by making it bigger is ending. But that doesn’t mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods, and those underlying technical changes force a big rethink of what coming years will look like.Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.Links to learn more, video, highlights, and full transcript: https://80k.info/to25As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that’s over.What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.So they pivoted to something radically different: instead of training smarter models, they’re giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.The results are impressive but this extra computing time comes at a cost: OpenAI’s o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica’s worth of reasoning to solve individual problems at a cost of over $1,000 per question.This isn’t just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out, starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.Toby and host Rob discuss the implications of all that, plus the return of reinforcement learning (and resulting increase in deception), and Toby's commitment to clarifying the misleading graphs coming out of AI companies — to separate the snake oil and fads from the reality of what's likely a "transformative moment in human history."Recorded on May 23, 2025.Chapters:Cold open (00:00:00)Toby Ord is back — for a 4th time! (00:01:20)Everything has changed (and changed again) since 2020 (00:01:37)Is x-risk up or down? (00:07:47)The new scaling era: compute at inference (00:09:12)Inference scaling means less concentration (00:31:21)Will rich people get access to AGI first? Will the rest of us even know? (00:35:11)The new regime makes 'compute governance' harder (00:41:08)How 'IDA' might let AI blast past human level — or not (00:50:14)Reinforcement learning brings back 'reward hacking' agents (01:04:56)Will we get warn

For decades, US allies have slept soundly under the protection of America’s overwhelming military might. Donald Trump — with his threats to ditch NATO, seize Greenland, and abandon Taiwan — seems hell-bent on shattering that comfort.But according to Hugh White — one of the world's leading strategic thinkers, emeritus professor at the Australian National University, and author of Hard New World: Our Post-American Future — Trump isn't destroying American hegemony. He's simply revealing that it's already gone.Links to learn more, video, highlights, and full transcript: https://80k.info/hw“Trump has very little trouble accepting other great powers as co-equals,” Hugh explains. And that happens to align perfectly with a strategic reality the foreign policy establishment desperately wants to ignore: fundamental shifts in global power have made the costs of maintaining a US-led hegemony prohibitively high.Even under Biden, when Russia invaded Ukraine, the US sent weapons but explicitly ruled out direct involvement. Ukraine matters far more to Russia than America, and this “asymmetry of resolve” makes Putin’s nuclear threats credible where America’s counterthreats simply aren’t. Hugh’s gloomy prediction: “Europeans will end up conceding to Russia whatever they can’t convince the Russians they’re willing to fight a nuclear war to deny them.”The Pacific tells the same story. Despite Obama’s “pivot to Asia” and Biden’s tough talk about “winning the competition for the 21st century,” actual US military capabilities there have barely budged while China’s have soared, along with its economy — which is now bigger than the US’s, as measured in purchasing power. Containing China and defending Taiwan would require America to spend 8% of GDP on defence (versus 3.5% today) — and convince Beijing it’s willing to accept Los Angeles being vaporised.Unlike during the Cold War, no president — Trump or otherwise — can make that case to voters.Our new “multipolar” future, split between American, Chinese, Russian, Indian, and European spheres of influence, is a “darker world” than the golden age of US dominance. But Hugh’s message is blunt: for better or worse, 35 years of American hegemony are over. Recorded 30/5/2025.Chapters:00:00:00 Cold open00:01:25 US dominance is already gone00:03:26 US hegemony was the weird aberration00:13:08 Why the US bothered being the 'new Rome'00:23:25 Evidence the US is accepting the multipolar global order00:36:41 How Trump is advancing the inevitable00:43:21 Rubio explicitly favours this outcome00:45:42 Trump is half-right that the US was being ripped off00:50:14 It doesn't matter if the next president feels differe

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes. (See graph.)These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.Links to learn more, video, highlights, and full transcript: https://80k.info/bbBeth's team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work. The resulting paper “Measuring AI ability to complete long tasks” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It's regarded by many as the most useful AI forecasting work in years.Beth has found models can already do “meaningful work” improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve as little as two years from now — in fact, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”Beth adds:The sense I really want to dispel is, “But the experts must be on top of this. The experts would be telling us if it really was time to freak out.” The experts are not on top of this. Inasmuch as there are experts, they are saying that this is a concerning risk. … And to the extent that I am an expert, I am an expert telling you you should freak out.What did you think of this episode? https://forms.gle/sFuDkoznxBcHPVmX6Chapters:Cold open (00:00:00)Who is Beth Barnes? (00:01:19)Can we see AI scheming in the chain of thought? (00:01:52)The chain of thought is essential for safety checking (00:08:58)Alignment faking in large language models (00:12:24)We have to test model honesty even before they're used inside AI companies (00:16:48)We have to test models when unruly and unconstrained (00:25:57)Each 7 months models can do tasks twice as long (00:30:40)METR's research finds AIs are solid at AI research already (00:49:33)AI may turn out to be strong at novel and creative research (00:55:53)When can we expect

What if there’s something it’s like to be a shrimp — or a chatbot?For centuries, humans have debated the nature of consciousness, often placing ourselves at the very top. But what about the minds of others — both the animals we share this planet with and the artificial intelligences we’re creating?We’ve pulled together clips from past conversations with researchers and philosophers who’ve spent years trying to make sense of animal consciousness, artificial sentience, and moral consideration under deep uncertainty.Links to learn more and full transcript: https://80k.info/nhsChapters:Cold open (00:00:00)Luisa's intro (00:00:57)Robert Long on what we should picture when we think about artificial sentience (00:02:49)Jeff Sebo on what the threshold is for AI systems meriting moral consideration (00:07:22)Meghan Barrett on the evolutionary argument for insect sentience (00:11:24)Andrés Jiménez Zorrilla on whether there’s something it’s like to be a shrimp (00:15:09)Jonathan Birch on the cautionary tale of newborn pain (00:21:53)David Chalmers on why artificial consciousness is possible (00:26:12)Holden Karnofsky on how we’ll see digital people as... people (00:32:18)Jeff Sebo on grappling with our biases and ignorance when thinking about sentience (00:38:59)Bob Fischer on how to think about the moral weight of a chicken (00:49:37)Cameron Meyer Shorb on the range of suffering in wild animals (01:01:41)Sébastien Moro on whether fish are conscious or sentient (01:11:17)David Chalmers on when to start worrying about artificial consciousness (01:16:36)Robert Long on how we might stumble into causing AI systems enormous suffering (01:21:04)Jonathan Birch on how we might accidentally create artificial sentience (01:26:13)Anil Seth on which parts of the brain are required for consciousness (01:32:33)Peter Godfrey-Smith on uploads of ourselves (01:44:47)Jonathan Birch on treading lightly around the “edge cases” of sentience (02:00:12)Meghan Barrett on whether brain size and sentience are related (02:05:25)Lewis Bollard on how animal advocacy has changed in response to sentience studies (02:12:01)Bob Fischer on using proxies to determine sentience (02:22:27)Cameron Meyer Shorb on how we can practically study wild animals’ subjective experiences (02:26:28)Jeff Sebo on the problem of false positives in assessing artificial sentience (02:33:16)Stuart Russell on the moral rights of AIs (02:38:31)Buck Shlegeris on whether AI control strategies make humans the bad guys (02:41:50)Meghan Barrett on why she can’t be totally confident about insect sentience (02:47:12)Bob Fischer on what su