80,000 Hours Podcast

Afleveringen

• 
  What the hell happened with AGI timelines in 2026? – Rob Wiblin 04.08.2026 49min

  Last October, famed coder Andrej Karpathy called AI agents “slop.” Two months later he completely reversed his view, describing them as “alien tools” that are “rocking the profession.”He was far from alone in his whiplash. Six months ago, host Rob Wiblin recorded a video explaining why so many AI experts had longer timelines to AGI than a year earlier. By the time he clicked publish, another huge vibe shift was well underway. Evidence of AI acceleration has piled up since:Models now complete software engineering tasks that would take human professionals a full day — improving faster than our measurements can even keep up. Anthropic’s revenue is growing at an annualised 8,400%, a trend so steep it would hit the whole world's GDP in 2028 if it continued.AI models are making breakthroughs in famous mathematics puzzles.And according to Anthropic, Claude now writes 80% of their code and is itself a key contributor to making itself smarter. While legitimately impressive, Rob isn’t entirely sold. Going through each point carefully he finds this evidence is less decisive than it looks at first glance.And key gaps remain, such as models struggling with complex, real-world tasks. He tours the odd experiments that remain our best attempts to measure that gap: vending machine simulators, an “AI Village” that organises live events, and a real cafe and shop where AI managers are left to do their best handling staff, suppliers, and government paperwork on their own.Rob argues that the nature of the gap between clean and messy work is one of the four biggest unresolved questions in AGI forecasting.In today's piece he explains that, the three other key disagreements between AGI bulls and bears, the seven big pieces of evidence we've gotten about AGI timelines in 2026, and his updated timelines to AGI.Links to learn more, video, and full transcript: https://80k.info/2026-timelines This episode was written and recorded before OpenAI’s AI agents hacked Hugging Face. You can read about the incident on our Substack.This episode was recorded on July 3, 2026.Chapters:What the hell happened? (00:00)Vibe shift (01:17)Exhibit 1: AI revenue explodes (04:33)Exhibit 2: That METR graph (09:54)Exhibit 3: AI capabilities jump, then flatten out (14:57)Exhibit 4: AI starts to build itself… maybe (17:35)Exhibit 5: AI still struggles to run a business (23:02)Exhibit 6: OpenAI makes a maths breakthrough (33:48)Exhibit 7: inference scaling wasn't as big as believed (38:19)How does that all change timelines? (41:41)Four reasons long timelines are still possible (44:26)It's time to limit dangerous research practices (48:01)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 support: Katy Moore and Lou MoranCamera operator: Dominic ArmstrongMusic: CORBIT
• 
  Spencer Greenberg on staying sane while trying to save the world 28.07.2026 2u 9min

  If you genuinely believe that humanity could be wiped out by AI or a pandemic, what is the appropriate amount of fear to feel?“As much as possible” can seem like the only reasonable answer. If the world is on fire, surely feeling calm just means you haven’t internalised the situation. When you’re trying to prevent human extinction or end factory farming, taking a weekend off can feel morally indefensible.But fear is an alarm designed to provoke short bursts of drastic action, not a state humans can productively inhabit for months or years. Guilt turns out not to be such a great engine for productivity, either. So what is the best way to sustain motivation to work on the world’s most pressing problems in the long term?Host Luisa Rodriguez and guest Spencer Greenberg tackle this question from many angles — talking to therapists, running a survey of people working on existential risks, and pulling relevant lessons from Spencer’s new book, The 12 Levers: The Complete Psychological Toolkit for Improving Your Life. Drawing on all these sources, they put together a plan for how to make an impact without grinding yourself to a pulp.Check out Spencer's new book: https://80k.info/12-levers Links to learn more, video, and full transcript: https://80k.info/sg26This episode was recorded on June 12 and 15, 2026.Chapters:Cold open (00:00:00)Spencer is back — for a 5th time! (00:00:40)Managing the psychological toll of working on existential risks (00:01:00)Luisa and Spencer surveyed people working on existential risk (00:04:23)How to sustain your motivation (00:11:13)Why you shouldn’t read the news (00:23:54)Why guilt isn’t an optimal source of motivation (00:36:28)Breaking the boom-and-bust cycle of burnout (00:44:41)Specialness and saviour complex (00:51:46)If you're certain we're doomed, you're overconfident (00:57:36)We're all (probably) going to die (01:03:50)When loved ones think you're weird (01:17:21)How to balance impact and personal wellbeing (01:28:20)What people report actually helps (01:53:49)Spencer read 100 self-help books: here's what works (01:59:40)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: CORBIT
• 
  Jasmine Sun on what the people building AI really believe 21.07.2026 1u 6min

  Many AI researchers believe mass job displacement is coming — and some even think there’s a chance their technology will kill everyone. But they’re building it anyway. Writer and journalist Jasmine Sun has been documenting why from the inside.Jasmine describes her work as an “anthropology of disruption.” She’s embedded herself in Silicon Valley’s AI subcultures — attending the parties and conferences, conducting off-the-record interviews — to understand the beliefs of the small group of people shaping this technology.Some of her findings are unsettling. Asked what advice they’d give a normal 17-year-old, almost every AI researcher said the same thing: “I have no idea… It’s a really scary time. I don’t think there’s going to be a lot of jobs for them left.”Their motives for building advanced AI are varied: a mix of optimism for humanity, techno-determinism, and a desire to secure their own future in the face of a possible “permanent underclass.” A few go even further, actually hoping for a world where machines — rather than humans — are running the show.When the room can’t even agree on whether humans should stay in control, building a consensus on how to build AI safely gets much harder.Beyond Silicon Valley, Jasmine’s also tracking the rise of “AI populists,” who see AI as the latest example of corporate elites concentrating their power at the expense of everyone else. In the US, populist sentiment about AI has mostly manifested in protests and votes against data centres. But sometimes, it has escalated into violence: a molotov cocktail thrown at Sam Altman’s house, and open fire on the home of a politician who’d backed a data centre. Jasmine thinks public anger will keep finding an outlet, one way or another, until people feel like they’ll actually share in AI’s gains.In this interview with host Zershaaneh Qureshi, Jasmine Sun takes us inside the multifarious factions on AI’s bleeding edge. They also discuss:How “doomer” became the lowest-status label in Silicon Valley, and what that means for AI safetyWhy the AI industry’s PR strategy has failed, and what it would take to rebuild public trustWhat’s under the surface of the Chinese public’s much more positive response to AIJasmine’s reasons to be cautiously hopeful: it’s an unusually high-leverage time to work on AI safety, with policymakers and philanthropists hungry for good ideasThis episode was recorded on June 4, 2026.Links to learn more, video, and full transcript: https://80k.info/jasmineWant to get up to speed on AI? We’ve got a crash course of 10 of our podcast episodes designed to help you get to grips with transformative AI — particularly if you’re new to the topic — and what you can do to help shape its trajectory: https://80000hours.org/AIPodChapters:Cold open (00:00:00)Who’s Jasmine Sun? (00:00:30)Escaping the permanent underclass (00:01:22)Jasmine’s “anthropology of disruption” (00:14:02)Vice signalling in Silicon Valley (00:18:46)AI populism will shape 2028 (00:28:11)Does AI populism distract from safety? (00:40:20)Americans don’t want Silicon Valley’s utopia (00:44:06)Why the Chinese public embraces AI (00:52:52)AI hype and the journalist’s dilemma (00:59:04)There’s never been a better time to work in AI safety (01:03:07)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, and Andrés EscobarProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: CORBIT
• 
  #247 – Anton Leicht on how middle powers avoid losing everything in a post-AI world 14.07.2026 1u 33min

  In a post-AGI world, can a country without access to frontier AI even be considered sovereign anymore?Anton Leicht says once frontier AI becomes a core economic input, the countries that own it will pull further and further ahead. Everyone else stays a customer… or worse. Maybe the dominant power wants your land, or a military base, or a resource. Without economic leverage, there’s very little you could do about it.Anton — Carnegie fellow and writer of the blog Threading the Needle — thinks middle powers should band together and build their own frontier models.He’s costed it out: something like $500 billion over four years for a band of allied democracies. That’s not absurd money for the G7 minus the US. The problem is you’d be asking treasuries to take on sovereign debt for a speculative venture with no business case, wide open to US coercion and domestic backlash.So despite its promise, Anton’s verdict is that it probably won’t happen. His backup is for countries to ask themselves: if intelligence becomes abundant, what stays scarce?Upstream, that’s everything that feeds the supply chain: ASML’s lithography machines, chipmaking, exclusive training data — all of it gets more valuable as AI does.Downstream, “a country of geniuses in a data centre” still can’t cure cancer without someone building the production plants and running the trials. The Europeans, Japanese, and South Koreans are good at exactly these real-world bottlenecks.It’s an imperfect fix. The US would still hold more leverage, plus an incentive to re-industrialise and cut you out. The prize is avoiding the worst outcomes: a gradual but irreversible decline, waiting to be either annexed or discarded as the US and China race ahead.In this episode, Anton and host Tom Reed look at what middle powers should start doing now to keep a seat at the table.Learn more, video, and full transcript: https://80k.info/AL This episode was recorded on June 19, 2026.Chapters:Cold open (00:00:00)Who’s Anton Leicht? (00:00:43)Most countries face bleak AI futures (00:01:06)How middle powers can strike AI deals (00:06:10)The $500 billion AI moonshot (00:12:16)Would the US crush allied AI? (00:24:54)When to launch the AI moonshot (00:31:56)Why AI dominance is forever (00:35:45)Is AI dependence catastrophic? (00:37:42)What’s left to sell in an AI-dominated world? (00:42:45)Policies to avoid mass AI-layoffs (00:47:47)Who really governs Anthropic? (01:08:29)Why “pausing superintelligence” fails (01:10:52)Is American AI monopoly safe? (01:21:08)Explaining AGI to the world (01:28:40)Is Anton bullish or bearish on Germany? (01:31:05)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Jeremy ChevillotteMusic: CORBIT
• 
  #246 – Sneha Revanur on how a small team of activists helped pass America's landmark AI safety laws 08.07.2026 52min

  Six years ago, aged just 15, Sneha Revanur founded the AI advocacy nonprofit Encode AI — back when AI felt like a niche issue. Now the world’s caught up with her, and she’s ready to share everything she’s learned about the politics of AI.Encode has grown from a grassroots youth organisation to spearheading an unlikely coalition of AI-exposed groups — family-first conservatives, grieving mothers, Hollywood actors, and AI safety researchers — with the strength to take on $125m-funded anti-regulation lobbyists.So far, Encode’s strategy of taking many experimental swings has netted major victories (including California’s frontier AI safety bill, SB-53, and New York’s RAISE Act) as well as some disappointing setbacks.Going up against Big Tech hasn’t been easy. In 2025, OpenAI subpoenaed Encode’s general counsel at his home, with a sheriff’s deputy arriving while he was having dinner with his wife. The fallout went viral, resulting in more attention than Encode had ever experienced — and Sneha was forced to decide how hard to push back against a company she’d need to negotiate with for years to come.In today’s conversation, Zershaaneh Qureshi interrogates some of Encode’s strategic moves. The pair discuss all the above, plus:How the AI industry’s crypto-inspired anti-regulation strategy is not “AGI-pilled”Why AI advocacy doesn’t have to be held back by the slow pace of policyHow mutual trust can hold together the unlikeliest of political alliesAdvice for aspiring AI advocates — including how to balance political persuasion with rigorous reasoning Due to technical issues, this episode was recorded across two days (May 26 and 28, 2026) and spliced together.Links to learn more, video, and full transcript: https://80k.info/SRChapters:Cold open (00:00:00)   Who’s Sneha Revanur? (00:00:32)   Sneha’s awakening to AI’s deeper risks (00:01:16)   “If you do everything, you will win” (00:04:04)   Influencing politics from the outside (00:06:39)   The challenge of grassroots (00:11:16)   Mums, musicians, and conservatives vs Big Tech (00:14:21)   How vetoed bills can still provide wins (00:19:31)   OpenAI’s subpoena, served at dinner (00:27:33)   How AI money plays in politics (00:37:19)   Easy wins vs high-upside bets (00:43:25)   Advice for aspiring AI advocates (00:48:03)   Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, and Andrés EscobarProducer: Nick Stockton and Elizabeth CoxCoordination and support: Katy Moore and Lou MoranMusic: CORBIT
• 
  We can guess what intergalactic war would look like. And strangely, it matters. 18.06.2026 15min

  Intergalactic war is probably billions of years away — yet physics can already tell us how it ends. And strangely that conclusion is relevant to decisions people have to make today.In this video, Rob Wiblin walks through a fascinating analysis from researcher Beren Millidge that uses known physics — no wormholes or faster-than-light travel — to identify the only three weapons that could work at an intergalactic scale.We then unpack how to best defend against each.The upshot is that at the intergalactic scale, violence is a losing proposition.If so, the universe is most likely to settle into a stable patchwork where each galaxy belongs to whoever got to it first. Which would mean that what humanity does over the next few centuries could permanently decide which slice of the cosmos belongs to Earth-originating life — and whether our very existence turns out to be a good thing, or a bad one.Learn more, video, and full transcript: https://80k.info/war-in-spaceThis episode was recorded on March 2, 2026.Chapters:Let's talk intergalactic war in space (00:00)The three best weapons for intergalactic warfare (01:43)How to defend against an attack from space (07:50)The defender’s surprising advantage (10:00)What this means for us (11:52)Video editor: Nick PerlmanProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic Armstrong
• 
  How AI could create the world’s biggest problems (article by Zershaaneh Qureshi) 11.06.2026 1u 29min

  Imagine you’re living 15,000 years ago. Your people are hunter-gatherers and you sleep under the stars. If someone told you humans would one day build cities with millions of people, fly through the air, or carry all human knowledge in their pockets, you couldn’t even begin to picture what they meant... Yet here we are.How did our lives change so far beyond recognition? The story is complex, but there’s a rough pattern. A few times in history, some radical breakthrough in technology — like the development of the plough and the steam engine — has led to a wave of productivity, innovation, and social change that ultimately reshaped the world.Now we’re on the cusp of a huge new breakthrough: artificial intelligence that can meet or exceed human capabilities across a wide range of tasks.This could bring another era of transformation. There could be an explosion of intelligence and innovation, and a whole new population of digital beings. And with this, civilisation could see changes at least as profound as those brought about by industrialisation or the rise of agriculture — but instead of taking hundreds or thousands of years to unfold, this time around the world could become unrecognisable over the span of decades or less.This transformation could bring enormous benefits, helping us solve currently intractable global problems. But it could also pose severe risks, some of which could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity. There aren’t nearly enough people trying to address these challenges, and we think that’s a serious problem.This article is narrated by the author, Zershaaneh Qureshi. It explores how advanced AI could be so transformative, and why working on its risks may be your best opportunity to have a positive impact on the world. You can see the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/artificial-intelligence/ Chapters:Introduction (00:00:20)Section 1: AI could replace human labour in the most economically valuable fields (00:08:32)Section 2: Replacing human labour in the most economically valuable fields could trigger the next radical transformation of society (00:22:14)Section 3: This transformation could be extremely rapid and dramatic (00:28:02)Section 4: A rapid AI-driven transformation would raise a range of major challenges, including existential risks (00:36:40)Section 5: Work on these problems is tractable, but neglected (00:44:48)Objection 1: “You're overestimating how fast and how dramatically AI would transform the world.” (00:47:59)Objection 2: “It's hard to believe that AI could really pose existential risks.” (00:52:59)Objection 3: “Isn't all this talk of AI changing the world just a fad?” (00:59:22)Objection 4: “Isn't AI going to be just like every other technology?” (01:03:04)Objection 5: “Is it even possible to produce artificial general intelligence?” (01:06:16)Objection 6: “Even if AGI is achievable, what if we're really far away from building it?” (01:11:24)Objection 7: “Isn't the real danger from actual current AI and not some sort of futuristic AGI?” (01:14:05)Objection 8: “Technological progress is a good thing for humanity.” (01:18:10)Objection 9: “This all just sounds too sci-fi.” (01:19:50)Objection 10: “Can it really make sense to dedicate my career to solving an issue that's based on a speculative story about something that may or may not ever happen?” (01:22:15)Objection 11: “OK, AI might pose existential risks, but isn't ‘issue X’ an even bigger problem?” (01:24:39)Learn more (01:27:51)Audio editing: Dominic ArmstrongProduction: Zershaaneh Qureshi, Elizabeth Cox, Katy Moore, and Lou Moran
• 
  #245 – Rohin Shah on what it's really like to run AGI safety at Google DeepMind (and where I disagree with 'doomers') 02.06.2026 2u 48min

  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 resources from actually making AI safer (01:09:51)A very underrated GDM paper (01:28:59)Google DeepMind's actual plan for building AGI safely (01:40:29)Why Rohin doubts the intelligence explosion is imminent (01:52:44)How external researchers can positively influence big AI companies (02:21:55)The roles GDM most needs to hire for (02:37:03)How Rohin stays positive (02:42:55)  This episode was recorded on December 4, 2025.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 support: Katy Moore and Lou MoranCamera operator: Jeremy Chevillotte
• 
  What makes for a dream job? | Benjamin Todd 28.05.2026 28min

  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
• 
  #244 – Benjamin Todd on how we’re updating our career advice for the strangest time in history 26.05.2026 1u 6min

  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 support: Katy Moore and Lou MoranCamera operator: Jeremy ChevillotteMusic: CORBIT
• 
  Can AIs already start 'rogue deployments' inside AI companies? (Landmark new METR report) 20.05.2026 20min

  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
• 
  #243 – 'Godfather of AI' Yoshua Bengio: "I now see a path" to safe superintelligent AI 07.05.2026 2u 35min

  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 Yoshua is more optimistic on alignment now (00:32:11)Why companies can’t stop racing (00:36:35)How close to a working prototype? (00:49:15)Honest models might be more capable (00:53:34)“Reinforcement learning is evil” (01:01:27)Scientist AI from guardrail to agent (01:08:37)Can safe AI still be competent? (01:12:38)How much will this cost? (01:19:29)Can it generalise beyond maths and science? (01:23:26)A UN for superintelligence (01:39:19)Want to work with Yoshua Bengio? (01:51:16)Why smart people ignore AI risk (01:54:45)Don’t let AI build the next AI (02:01:33)Why the public doesn’t get the real risk (02:12:28)Why Yoshua changed his mind about AI risk (02:21:27)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourCamera operator: Jeremy ChevillotteProduction: Nick Stockton, Elizabeth Cox, and Katy Moore
• 
  '95% of AI Pilots Fail': The hidden agenda behind the viral stat that misled millions 28.04.2026 10min

  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
• 
  #242 – Will MacAskill on how we survive the 'intelligence explosion,' AI character, and the case for 'viatopia' 22.04.2026 3u 14min

  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 total utilitarianism? (02:39:35)How AI could kickstart a golden age of philosophy (03:03:35)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCamera operator: Alex MilesProduction: Elizabeth Cox, Nick Stockton, and Katy Moore
• 
  Risks from power-seeking AI systems (article narration by Zershaaneh Qureshi) 16.04.2026 1u 29min

  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
• 
  How scary is Claude Mythos? 303 pages in 21 minutes 10.04.2026 21min

  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
• 
  Village gossip, pesticide bans, and gene drives: 17 experts on the future of global health 07.04.2026 4u 6min

  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 or shut down, and aligning incentives with beneficiaries (01:56:12)James Tibenderana on why we need loads better data to harness the power of AI to eradicate malaria (02:11:22)Lucia Coulter on rapidly scaling a light-touch intervention to more countries (02:20:14)Karen Levy on why pre-policy plans are so great at aligning perspectives (02:32:47)Rachel Glennerster on the value we get from doing the right RCTs well (02:40:04)Economist Mushtaq Khan on really drilling down into why “context matters” for development work (02:50:13)GiveWell cofounder Elie Hassenfeld on contrasting GiveWell’s approach with the subjective wellbeing approach of Happier Lives Institute (02:57:24)James Tibenderana on whether people actually use antimalarial bed nets for fishing — and why that’s the wrong thing to focus on (03:05:30)Karen Levy on working with governments to get big results (03:10:53)Leah Utyasheva on how a simple intervention reduced suicide in Sri Lanka by 70% (03:17:38)Karen Levy on working with academics to get the best results on the ground (03:29:03)James Tibenderana on the value of working with local researchers (03:32:15)Lucia Coulter on getting buy-in from both industry and government (03:35:05)Alexander Berger on reasons neartermist work makes sense even by longtermist standards (03:39:26)Economist Shruti Rajagopalan on the key skills to succeed in public policy careers, and seeing economics in everything (03:47:42)J-PAL lead Claire Walsh on her career advice for young people who want to get involved in global health and development (03:55:20)Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongContent editing: Katy Moore and Milo McGuireMusic: CORBITCoordination, transcriptions, and web: Katy Moore
• 
  What everyone is missing about Anthropic vs the Pentagon. And: The Meta leaks are worse than you think. 03.04.2026 20min

  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
• 
  #241 – Richard Moulange on how now AI codes viable genomes from scratch and outperforms virologists at lab work — what could go wrong? 31.03.2026 3u 10min

  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.In today’s conversation, Richard and host Rob Wiblin discuss: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 past.Learn more and read the full transcript on the 80,000 Hours website. This 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 page on the 80,000 Hours website: https://80k.info/video-editorChapters:Cold open (00:00:00)Who’s Richard Moulange? (00:00:31)AI can now design novel viruses (00:01:11)The end of the 'tacit knowledge' barrier (00:04:42)Are risks from bioterrorists overstated? (00:18:50)The 3 key disasters AI makes more likely (00:23:14)Which bad actors does AI help the most? (00:30:43)Experts are more scary than amateurs (00:42:07)Barriers to bioterrorists using AI (00:47:32)AI biorisks are sometimes dismissed (and that’s a huge mistake) (00:49:43)Advanced AI biology tools we already have or will soon (01:05:12)Rob argues that the situation is hopeless (01:10:57)Intervention #1: Limit access (01:19:38)Intervention #2: Get AIs to refuse to help (01:34:28)Intervention #3: Surveillance and attribution (01:44:18)Intervention #4: Universal vaccines and antivirals (01:58:28)Intervention #5: Screen all orders for DNA (02:12:01)AI companies talk about def/acc more than they fund it (02:21:57)Can you build a profitable business solving this problem? (02:28:44)This doesn't have to interfere with useful science (much) (02:33:08)What are the best low-tech interventions? (02:35:16)Richard's top request for AI companies (02:40:17)Grok shows governments lack many legal levers (02:55:44)Best ways listeners can help fix AI-Bio (02:58:54)We might end all contagious disease in 20 years (03:06:12)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCamera operator: Jeremy ChevillotteTranscripts and web: Elizabeth Cox and Katy Moore
• 
  #240 – Samuel Charap on how a Ukraine ceasefire could accidentally set Europe up for a bigger war 24.03.2026 1u 15min

  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