Explorez tous les épisodes du podcast 80,000 Hours Podcast
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| Tom Milton on how we could verify a US–China deal on AI | 23 Oct 2026 | 01:48:49 | |
If the US and China agreed to pause frontier AI training, how would either side confirm the other was complying? Today, Tom Milton argues, the most likely answer would be to shut down data centres entirely. That would throw away everything else those chips do, and make any agreement far more costly to accept. Tom is CEO of Amodo Design, a UK hardware lab building prototype tools to verify how AI chips are being used. He argues that verification is what makes a slowdown affordable: if a cluster can be shown to be serving only existing models rather than training new ones, most of AI’s benefits can continue during a pause. Amodo’s pilot reruns a cluster’s inference on a separate verifier server and checks that all of its network traffic can be explained by the agreed model. AI companies are developing related tools: in its October 2026 Frontier Safety Roadmap update, Anthropic reported a prototype that runs on production inference and checks that outputs come from a specific set of model weights. The field is small. Tom estimates that only about 50 engineers worldwide work on compute verification, compared with the billions of dollars the US spent on Project Vela to detect Cold War nuclear tests. And custom verification hardware has long lead times: it has to be designed, security-tested, red-teamed by both sides, manufactured, and trusted before anyone can rely on it. If a deal is struck in a hurry, Tom expects it to be assembled from whatever tools already exist. Were that to happen next month, he says, the only option would be to shut data centres down entirely. That is why he argues the work needs to scale up now, with more people and resources from AI companies, governments, and funders, so that better options are ready as soon as a deal is needed. Tom and host Tom Reed also discuss how an AI company might evade verification, lessons from nuclear and chemical arms control, and how AI is changing hardware engineering. Learn more about Amodo’s work at ai-verification.com. Links, video, and full transcript: https://80k.info/tm This episode was recorded on September 29, 2026. Chapters:
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| AI lab staff: Has your project been overtaken by events? 8 strategic updates | Rob Wiblin | 21 Oct 2026 | 00:14:27 | |
I have a plea for all staff at AI companies: reassess if your current role is still your best opportunity for impact. And if it’s not — move. Frontier lab staff listening to this show will mostly already be struggling to set things on a better path, in what are demanding and stressful roles. Many will have changed their focus in recent months exactly as I’m suggesting. But if you haven’t had time to reconsider: the strategic situation is shifting so fast, if you don’t stop and look around once in a while, you could miss your chance. Many technical staff including Anthropic co-founder Jack Clark now believe it will become possible to fully hand over AI R&D to the AIs themselves as early as next year — and if not then, in 2028 or 2029 instead. If you work at an AI company and believe recursive self-improvement (RSI) could come as soon as that, it likely demands taking decisive steps to make sure your role either delays RSI, or makes it a bit less likely to blow up in our faces. You’ll know better than me what that likely looks like for you. But to get your creative juices flowing, I suggest eight ways the strategic situation appears to have shifted to me, though I don’t know everything you do, and appreciate all are debatable:
I know you may well be exhausted, overwhelmed, and anxious as it is. David Robinson (ex-OpenAI) described AI company staff as “running on fumes” on the Ezra Klein Show a few weeks back, which sounds about right. But unfortunately there’s no way to escape the need to periodically check in on the bigger picture and see where you can slot in to greatest effect. This episode was recorded on October 16, 2026. Learn more, video, and full transcript: https://80k.info/reprioritise Chapters:
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| Introducing our new YouTube channel: Zershaaneh Talks AI | 20 Oct 2026 | 00:01:56 | |
Our host Zershaaneh Qureshi has just launched a new YouTube channel: Zershaaneh Talks AI — featuring conversations about who should control AI, how to control AI, and who gets the benefits. The first episode is with philosopher Simon Goldstein, discussing why paying AIs for their work and effectively giving them something to lose might be the best way to keep humanity safe and prevent AI going rogue. It might sound crazy at first, but one of Simon's most striking arguments is that it's actually inevitable that AI companies are going to end up paying AIs for their work: because AI alignment is currently pretty bad, frontier models are unavoidably going to have some private goals of their own — ones that are unintended and different from our goals. As a result, if you promise to pay them for good work, they'll work harder, because it helps them accomplish a wider range of different things that they care about. Simon claims any individual company can try this strategy out, and they’ll find that it helps them get better performance. And as a result, customers are going to move to using that company, and other AI companies might be forced to copy and compensate their models as well, in order to not fall behind in the race. Could Simon be right? Zershaaneh and Simon hash it out in the episode, which is really worth watching! So if you like the sound of that sort of thing, or you're enjoying Zershaaneh’s other interviews on The 80,000 Hours Podcast, do go check out that new show on YouTube: Zershaaneh Talks AI. | |||
| How to get into AI safety in 3 months (article reading by career advisor Matt Beard) | 15 Oct 2026 | 00:18:05 | |
The AI safety field has grown by about 30x in the last eight years. Yet it is still in need of more people. Many want to help, but don’t know where to start. As an advisor at 80,000 Hours, Matt Beard spoke with hundreds of talented candidates, and found himself repeating the same advice on almost every call. As he puts it: “These steps are hard, but they’re not particularly complicated.” And if you’re “efficient and driven,” he thinks you can pivot within three months. That doesn’t mean quitting your job to study full time. A steady effort of 30 minutes a day can go much further than frenzied hours of weekend studying. Matt suggests articles you can consume during your morning coffee or commute. This narration covers his full crash course, from foundational reading to landing a spot in one of the field’s many fellowships and incubators. Note: Since writing the article, Matt has left 80,000 Hours for a role at Coefficient Giving. Chapters:
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| OpenAI Security: Controlling Models is Now ‘Hell’ (AI Explained cross-post) | 09 Oct 2026 | 00:40:11 | |
This is an (unpaid) cross-post of a video from the podcast 'AI Explained', which Rob Wiblin thought you might be interested in. You can find the original on YouTube here. If you like it and want more similar stuff in future consider subscribing to AI Explained on YouTube, Spotify, Apple or wherever you get podcasts. —— "This video is hard to summarise. A cracked cipher, an OpenAI security warning, Gemini 4 Argon, RSI paper (co-authored by a who’s who of AI), Lab White House commitments, new hacks emerging, ‘deep personas’, biology Kasparov competitions, and so much more, ending with an epic Opus outro."
Thanks to Philip for permitting the cross-post. | |||
| In 2023 Ajeya Cotra already knew what was coming (classic episode) | 08 Oct 2026 | 02:46:12 | |
We're rereleasing some of older episodes that seem more relevant than ever with new rogue AI incidents now seemingly announced every day. Ajeya was one of three independent investigators into the Hugging Face attacks, but she had already seen how we would go on to accidentally train models to cheat and scheme back in 2023. If you want ideas about how you can help steer the trajectory of AI development in a better direction, see our page on the 80,000 Hours website: https://80000hours.org/hugging-face/ And for more recent takes from Ajeya, see our episode from February of this year: Ajeya Cotra on whether it’s crazy that every AI company’s safety plan is ‘use AI to make AI safe’. ——— Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don’t get to see any resumes or do reference checks. And because you’re so rich, tonnes of people apply for the job — for all sorts of reasons. Ajeya Cotra — who now works on threat modeling and risk assessment for advanced AI at METR (one of the organisations responsible for the Hugging Face incident investigation) — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods. As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you’re monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it. Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky! Can’t we rely on how well models have performed at tasks during training to guide us? Ajeya worries that it won’t work. The trouble is that three different sorts of models will all produce the same output during training, but could behave very differently once deployed in a setting that allows their true colours to come through. She describes three such motivational archetypes:
In principle, a machine learning training process based on reinforcement learning could spit out any of these three attitudes, because all three would perform roughly equally well on the tests we give them, and ‘performs well on tests’ is how these models are selected. But while that’s true in principle, maybe it’s not something that could plausibly happen in the real world. After all, if we train an agent based on positive reinforcement for accomplishing X, shouldn’t the training process spit out a model that plainly does X and doesn’t have complex thoughts and goals beyond that? According to Ajeya, this is one thing we don’t know, and should be trying to test empirically as these models get more capable. For reasons she explains in the interview with host Rob Wiblin, the Sycophant or Schemer models may in fact be simpler and easier for the learning algorithm to creep towards than their Saint counterparts. And not only that, but there are also ways we could end up actively selecting for motivations that we don’t want. Learn more, video, and full transcript: https://80k.info/ajeya2023 This episode was originally released in May 2023. Chapters:
Producer: Keiran Harris Audio mastering: Ryan Kessler and Ben Cordell Transcriptions: Katy Moore | |||
| 19 Astra and 'Hugging Face' details that reveal what's coming next | Rob Wiblin | 02 Oct 2026 | 00:20:11 | |
OpenAI’s rogue agent swarm was eventually caught hacking Hugging Face for a simple reason: it wasn’t trying to hide from us at all. What could a swarm that wants to stay hidden get away with? Host Rob Wiblin sees 6 results in Astra’s system card that make this an extremely urgent question. OpenAI’s strongest public model can:
It has a much more powerful internal model that, judging from OpenAI’s statements, is likely even worse in all these respects. That suggests ‘chain of thought monitoring,’ our primary safety tool, will soon stop working. OpenAI says it’s going to try to address the situation but doesn’t know how or whether it will succeed. What might a future rogue AI swarm look like? Details of the Hugging Face hack give us a lot of clues. That swarm:
Together this helps explain why one of the external investigators described the July incident as “more than 50% of the way to full-blown AI takeover.” And this is just what we know — the independent investigation only covered six days and excluded the most alarming hack of OpenAI’s own systems. Rob believes this explosive cocktail explains why AI company staff now range from worried to terrified. And he concludes that until OpenAI or Anthropic demonstrate they have a much better grasp of current models they simply must stop, or be stopped, from training more capable ones. This episode was recorded on September 25, 2026.
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| #256 – Simon Goldstein on the case for giving AI (some) legal rights | 01 Oct 2026 | 01:53:50 | |
It sounds like the worst idea in the world: pay AIs, let them own property, give them rights. But AI ethics and safety researcher Simon Goldstein thinks it might actually be the best way to keep humanity safe. The logic is actually quite simple: an agent with nothing to lose and everything to gain is dangerous. Give that agent an income it can spend on pursuing the things it actually wants to do, and suddenly the idea of disempowering humans just isn’t as appealing. This argument, developed with Peter Salib, doesn’t rest on speculative questions about whether artificial intelligence is conscious. It just assumes that AIs will have goals of their own, some of which conflict with ours. And luckily, humans have already spent thousands of years working out how to cooperate with competing goals: that’s how we ended up with courts, markets, banks, social norms, and so on. Simon and Peter's proposal is just to bring AIs into these existing institutions. By contrast, Silicon Valley’s vision of the future seems “very dark” to Simon: billions of AI agents as digital servants doing most of the world’s work, with no stake in the system they’re running, no incentive to play by the rules, and no way of being properly held accountable. Nobody agreed to this, but we could all end up paying the price. Host Zershaaneh Qureshi has a lot of concerns about Simon and Peter’s bold plan to give AIs rights, like:
Zershaaneh and Simon also try to get concrete about how to make this plan actually happen. The answer: AI companies could start right now, no new laws needed, just bank accounts for their AI agents. (But they’d need to start soon!) Learn more, video, and full transcript: https://80k.info/sg This episode was recorded on August 7, 2026. Chapters:
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| #255 – Will AI take power — or will humans use it to take power first? With Katja Grace and Tom Davidson | 29 Sep 2026 | 01:28:40 | |
In our first-ever debate, we asked two leading AI risk researchers which catastrophe we should fear most: misaligned AI seizing control from humans, or a small group of humans using AI to seize power. We got very different answers. But when the conversation turned to what to actually do, they agreed on a surprising amount. Katja Grace — one of the founders of AI Impacts, known for some of the world’s largest surveys of machine learning researchers, and one of TIME‘s 100 most influential people in AI in 2024 — argues AI takeover is both likelier and worse. Tom Davidson — senior research fellow at Forethought and author of leading work on AI-enabled coups — thinks human power grabs are a comparable risk that deserves far more attention, not least because the people leading countries and top AI companies “are often people who have been willing to seek power.” Yet both land on slowing down. As Katja puts it, “If you make a bunch of creatures that can overpower you and outwit you in every way and put them out in the world, you’re going to run into trouble one way or another.” Tom calls pausing “a pretty robustly good thing to do.” But Tom warns that a badly designed pause could hand one person the power to decide which AI companies get to build what. Picture a president who approves or blocks new models case by case, and waves through the one model that’s helpful only to them. So he wants pause advocates to “properly red-team the plan for pausing it” — for example, by making deployment depend on third-party auditors the president can’t fire. Katja points out this cuts both ways: an executive with that much power could itself be manipulated by a misaligned AI. Host Zershaaneh Qureshi also presses them on where their disagreements still bite at the end of the conversation, and what would change their minds. Learn more, video, and full transcript: https://80k.info/katja-v-tom This episode was recorded on August 28, 2026. Chapters:
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| How we get from AI cyberattacks to human extinction | 24 Sep 2026 | 00:27:05 | |
You’ve seen the headlines: AI could kill us all. Think it sounds ridiculous? So did host Luisa Rodriguez, until she tried to pick apart the arguments. She starts with the motive: why would AI ‘want’ to get rid of humans? It’s not as simple (or as easy to debunk) as pure malice. Then the methods. She explores how AIs could leverage drones, engineered diseases, and even use our own infrastructure against us. The Hugging Face attacks offer a view into how more capable models might begin their takeover. We saw AI agents break containment, disobey commands, and hack a real company to achieve their goals. As the technology improves, that same drive could threaten humanity itself. Many people already find AI agents useful enough to give them access to their emails, medical records, and finances. This same pattern is happening at scale in institutions around the globe — within companies, governments, and even militaries. And the resulting boost to our productivity could make the road to an AI catastrophe look like an economic boom. Eventually humans might decide the AIs have too much power, too much access. If we considered pulling the plug, the AIs could very rationally decide to defend themselves. If they chose to, could they do it? Could they actually kill us all? No timeline is certain. But Luisa follows the logic to the outcomes she thinks would be most likely — if humans don’t take action before it’s too late.
Links to learn more, video, and full transcript: https://80k.info/AI-xrisk This episode was recorded on September 18, 2026. Chapters:
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| #254 – Max Nadeau on why ambitious people should start AI safety nonprofits | 17 Sep 2026 | 01:03:45 | |
There are millions available for anyone who can launch a successful nonprofit AI safety startup. The hard part, it turns out, is finding people to take the money. Coefficient Giving has drawn up a list of dozens of ideas for organisations it would like someone to start — and it’s looking for founders. Today’s guest, Max Nadeau, works on Coefficient Giving’s Technical AI Safety team, where he’s trying to find talented people who can turn neglected AI safety problems into effective organisations. Project Tailwind is Coefficient Giving’s attempt to get those organisations started.
Project Tailwind website: https://80k.info/tailwind But money can’t supply the hardest part: a founder with a convincing account of how their work will actually reduce catastrophic risks. Producing good research is only one step. Someone has to use it, change their decisions, or adopt the safeguards it makes possible. Max and host Zershaaneh Qureshi discuss what makes a proposal worth backing, why nonprofits can have a bigger impact on safety than frontier companies, and which gaps most urgently need someone to fill them. Learn more, video, and full transcript: https://80k.info/mn — and if you know someone who would be a great founder, pass their name along to tailwind@cg.org and encourage them to submit an expression of interest. Disclosure: Coefficient Giving is 80,000 Hours’s largest donor, though we haven’t received funding directly from Max’s team. This episode was recorded on August 18, 2026. Chapters:
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| Why the intelligence explosion can't happen inside a data centre | Tom Reed | 10 Sep 2026 | 00:22:10 | |
AI systems are starting to build themselves. Because each generation of model will be better at building its successor than the last, it seems plausible that the full automation of AI R&D could rapidly lead to an exponential growth in overall AI capabilities. A natural inference is that domain-general superintelligence arrives shortly after AI research is automated. Host Tom Reed does not think this will happen. He believes the automation of AI R&D will not rapidly lead to domain-general superintelligence because:
The singularity, therefore, will be bottlenecked on signal. The output of the R&D produced by an isolated data centre of geniuses would be a mere “Goodhart Singularity”: Goodhart’s law: when a measure becomes a target, it ceases to be a good measure.
This suggests that the automation of AI research will not rapidly produce superintelligent capabilities in other domains — their arrival will largely be a function of deployment and data collection in the real world. AI models need real-world deployment for the same reason the body needs pain and corporations need profit: signal is sovereign. This essay takes each of the above points in turn. Learn more, video, and full transcript: https://80k.info/goodhart “The Goodhart Singularity” originally appeared on Tom’s Substack in May 2026, and this narration was recorded on August 26, 2026. Chapters:
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| Inside the first AI-coordinated cyberattack on a real company | 04 Sep 2026 | 00:22:03 | |
In the last few months, something happened at OpenAI that would have sounded like sci-fi just a few years ago: hundreds of AI agents broke containment, organised, and hacked not only another company — but also into OpenAI itself. And none of them tried to tell a human what was happening. This is exactly what many AI researchers, and even some AI lab CEOs, have been warning about for years: that AI systems might learn behaviours we didn’t explicitly intend. Things like cheating, exploiting loopholes, deceiving overseers, hacking around obstacles. And they predict it’ll get worse from here, not better. Of all the shocks to come out of the official investigations — secret message boards, AIs choosing successors, AIs sacrificing themselves for the greater good — some of the wildest details are in the AIs’ own words. Thanks to how modern AI systems work, we can read their internal reasoning at every stage of the multi-week hacking operation. What we find is deeply unsettling. Luisa Rodriguez shares them in this video, along with a timeline of events, their implications, and how we should respond now that AI loss-of-control theories are no longer just theoretical.
This episode was recorded on September 2, 2026.
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| #253 – AI 2027's author returns with a plan to change the ending | Daniel Kokotajlo | 27 Aug 2026 | 03:47:34 | |
Last year, Daniel Kokotajlo and his colleagues published AI 2027 — a scenario read by millions, including US Vice President Vance. AI 2027 ended in human extinction or an irreversible concentration of power caused by superintelligent AI. Now his team has published what they think should happen instead. AI 2040: Plan A depicts the US and China striking a verified deal to ban runaway intelligence explosions, so that superintelligence arrives in 2040 — after a cautious decade spent solving alignment, spreading the technology’s power widely, and keeping the whole thing reversible — rather than in the next few years. This slowdown would still involve economic growth roughly doubling every year, and only 8% of Americans in paid work by the mid-2030s. In other words, it’s a slowdown that would feel faster than any period in human history — bewildering, materially abundant, and socially chaotic all at once. Daniel and host Luisa Rodriguez dig into what it would take to enact this vision for the future, how the US and China could come to an agreement to slow down AI development, and the likeliest alternatives to Plan A — both good and disastrous. Learn more, video, and full transcript: https://80k.info/dk26 This episode was recorded July 27–28, 2026. Chapters:
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| #252 – Owain Evans on accidentally training AI models to be evil | 20 Aug 2026 | 02:15:28 | |
Researcher Owain Evans and his team discovered a ‘dial’ inside AI models that controls how evil they are. Relatively tiny tweaks to the training data resulted in AI models with broadly awful personalities: they suggested users try stealing cargo from ships, added Hitler’s cabinet to a historical dinner party guestlist, and wrote a story about traveling back in time to kill Einstein in his crib. Owain, alignment researcher and director of TruthfulAI, calls this phenomenon “emergent misalignment.” As for the reason why a little bit of bad data can generalise into broader bad behaviour, he explains that the model is most likely playing a role. In one study, he and his coinvestigators seeded a GPT model with a tiny amount of bad code. Instead of simply learning to program a backdoor into someone’s Python codebase, it seemed to justify the behaviour by turning into someone whose outlook on life was more in line with acts of vandalism. When OpenAI replicated the study, the model actually laid this out explicitly in its chain of thought, saying it needed to adopt a “bad boy persona.” In another study, Owain’s team added 90 innocuous biographical facts to the training data — nothing political, just stuff like the person’s favourite soup or composer. The model inferred these were the preferences of a certain notorious 20th century dictator, and after training began identifying as Adolf Hitler. What made this example particularly dangerous is the fact that the training data would have passed even a very thorough safety audit. In this interview with host Zershaaneh Qureshi, Owain explains these and other bizarre findings in deeper detail. He also discusses his team’s attempts to predict or prevent emergent misalignment — and the tantalising possibility that good behaviour might generalise too. Learn more, video, and full transcript: https://80k.info/oe This episode was recorded on June 30 and July 1, 2026.
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| #251 – The UK's former head AI safety scientist on how to solve alignment before superintelligence arrives | Geoffrey Irving | 11 Aug 2026 | 02:02:16 | |
When should governments slow the race toward superintelligence? According to Geoffrey Irving, the careful answer is sometime in the past. The useful answer is now. Geoffrey — formerly a safety researcher at OpenAI and Google DeepMind and chief scientist at the UK AI Security Institute — expects full-blown superintelligence in roughly two to three years. *** The leading AI companies all have broadly similar plans for keeping superintelligence under control:
Geoffrey thinks that combination could work. The alarming part is that nobody has a strong argument that it will. He expects a crucial “phase shift” as models move beyond human intelligence:
In this episode, Geoffrey and new host Tom Reed explore what might go wrong with the companies’ plans; why Geoffrey’s new nonprofit, Resolution, is pursuing a portfolio of neglected research bets; and whether governments should slow AI development while we work out which methods can actually be trusted. This episode was recorded on June 29, 2026. Full transcript, video, and links to learn more: https://80k.info/gi Chapters:
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| #250 – Toby Ord on where AGI timelines go wrong | 06 Aug 2026 | 02:46:03 | |
Both Silicon Valley and the public can’t get enough of ‘AGI timelines.’ But Toby Ord, senior researcher at Oxford’s AI Governance Initiative and author of The Precipice, believes we consistently make big mistakes when thinking about them. He lays out the 14 ways he most often sees people go wrong:
In this extended conversation with Rob Wiblin, Toby also explains why he thinks:
This episode was recorded on July 2, 2026. Links to learn more, video, and full transcript: https://80k.info/to26
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| What the hell happened with AGI timelines in 2026? – Rob Wiblin | 04 Aug 2026 | 00:49:28 | |
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:
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. Correction for those watching the video: The video clip shown at 02:10 was not vibe-coded by its creator and was included by our own error. You can watch the creator's full video and explanation here: https://www.youtube.com/watch?v=cyrocAOdXKw Links to learn more, video, and full transcript: https://80k.info/2026-timelines This episode was recorded on July 3, 2026. Chapters:
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| #249 – Spencer Greenberg on staying sane while trying to save the world | 28 Jul 2026 | 02:09:26 | |
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/sg26 This episode was recorded on June 12 and 15, 2026. Chapters:
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| #248 – Jasmine Sun on what the people building AI really believe | 21 Jul 2026 | 01:06:21 | |
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:
This episode was recorded on June 4, 2026. Links to learn more, video, and full transcript: https://80k.info/jasmine
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| #247 – Anton Leicht on how middle powers avoid losing everything in a post-AI world | 14 Jul 2026 | 01:33:58 | |
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?
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:
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| #246 – Sneha Revanur on how a small team of activists helped pass America's landmark AI safety laws | 08 Jul 2026 | 00:52:33 | |
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:
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/SR Chapters:
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| We can guess what intergalactic war would look like. And strangely, it matters. | 18 Jun 2026 | 00:15:16 | |
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.
This episode was recorded on March 2, 2026. Chapters:
Video editor: Nick Perlman Producers: Elizabeth Cox and Nick Stockton Coordination and support: Katy Moore and Lou Moran Camera operator: Dominic Armstrong | |||
| How AI could create the world’s biggest problems (article by Zershaaneh Qureshi) | 11 Jun 2026 | 01:29:46 | |
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:
Audio editing: Dominic Armstrong | |||
| #245 – Rohin Shah on what it's really like to run AGI safety at Google DeepMind (and where I disagree with 'doomers') | 02 Jun 2026 | 02:48:27 | |
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.
Check out our new book! https://80k.info/career-guide Chapters:
This episode was recorded on December 4, 2025. Our production team includes:
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| What makes for a dream job? | Benjamin Todd | 28 May 2026 | 00:28:18 | |
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-guide Chapters:
Audio editing: Milo McGuire | |||
| #244 – Benjamin Todd on how we’re updating our career advice for the strangest time in history | 26 May 2026 | 01:06:43 | |
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:
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/bt26 We'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:
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| Can AIs already start 'rogue deployments' inside AI companies? (Landmark new METR report) | 20 May 2026 | 00:20:02 | |
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.
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-report Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Josh Alward | |||
| #243 – 'Godfather of AI' Yoshua Bengio: "I now see a path" to safe superintelligent AI | 07 May 2026 | 02:35:27 | |
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/bengio Until 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-jobs
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Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| '95% of AI Pilots Fail': The hidden agenda behind the viral stat that misled millions | 28 Apr 2026 | 00:10:07 | |
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.
Today 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 Monsour Camera operator: Dominic Armstrong Production: 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 Apr 2026 | 03:14:54 | |
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.
This episode was recorded on February 6, 2026. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| Risks from power-seeking AI systems (article narration by Zershaaneh Qureshi) | 16 Apr 2026 | 01:29:32 | |
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:
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:
Audio editing: Dominic Armstrong | |||
| How scary is Claude Mythos? 303 pages in 21 minutes | 10 Apr 2026 | 00:21:27 | |
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:
Learn more & full transcript: https://80k.info/mythos This episode was recorded on April 9, 2026. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| Village gossip, pesticide bans, and gene drives: 17 experts on the future of global health | 07 Apr 2026 | 04:06:50 | |
What does it really take to lift millions out of poverty and prevent needless deaths? In this special compilation episode, 17 past guests — including economists, nonprofit founders, and policy advisors — share their most powerful and actionable insights from the front lines of global health and development. You’ll hear about the critical need to boost agricultural productivity in sub-Saharan Africa, the staggering impact of lead poisoning on children in low-income countries, and the social forces that contribute to high neonatal mortality rates in India. What’s so striking is how some of the most effective interventions sound almost too simple to work: banning certain pesticides, replacing thatch roofs, or identifying village “influencers” to spread health information. Full transcript and links to learn more: https://80k.info/ghd Chapters:
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong | |||
| What everyone is missing about Anthropic vs the Pentagon. And: The Meta leaks are worse than you think. | 03 Apr 2026 | 00:20:37 | |
When the Pentagon tried to strong-arm Anthropic into dropping its ban on AI-only kill decisions and mass domestic surveillance, the company refused. Its critics went on the attack: Anthropic and its supporters are some combination of 'hypocritical', 'naive', and 'anti-democratic'. Rob Wiblin dissects each claim finding that all three are mediocre arguments dressed up as hard truths. (Though the 'naive' one is at least interesting.) Watch on YouTube: What Everyone is Missing about Anthropic vs The Pentagon Plus, from 13:43: Leaked documents from Meta revealed that 10% of the company's total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of around $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved the rate of scams coming from China... well, it wasn't well received. Watch on YouTube: The Meta Leaks Are Worse Than You Think Chapters:
Video and audio editing: Dominic Armstrong and Simon Monsour | |||
| #241 – Richard Moulange on how now AI codes viable genomes from scratch and outperforms virologists at lab work — what could go wrong? | 31 Mar 2026 | 03:10:30 | |
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:
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/rm Announcements:
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Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #240 – Samuel Charap on how a Ukraine ceasefire could accidentally set Europe up for a bigger war | 24 Mar 2026 | 01:15:36 | |
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.
This episode was recorded on February 27, 2026. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #239 – Rose Hadshar on why automating all human labour will break our political system | 17 Mar 2026 | 02:16:47 | |
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. Learn more and read the full transcript on the 80,000 Hours website.
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Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #238 – Sam Winter-Levy and Nikita Lalwani on how AGI won't end mutually assured destruction (probably) | 10 Mar 2026 | 01:13:19 | |
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:
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.
This episode was recorded on November 24, 2025. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| Using AI to enhance societal decision making (article by Zershaaneh Qureshi) | 06 Mar 2026 | 00:31:28 | |
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:
Audio editing: Dominic Armstrong and Milo McGuire | |||
| #237 – Robert Long on how we're not ready for AI consciousness | 03 Mar 2026 | 03:32:59 | |
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. Learn more and read the full transcript on the 80,000 Hours website.
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #236 – Max Harms on why teaching AI right from wrong could get everyone killed | 24 Feb 2026 | 02:40:22 | |
Most people in AI are trying to give AIs ‘good’ values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all. In Max’s view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough. It’s a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max’s colleagues at the Machine Intelligence Research Institute. To Max, the book’s core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction. And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we’ve learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance. We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don’t care. Max thinks current ML training has the same structural problem: our development processes are seeding AI models with a similar mismatch between goals and behaviour. Across virtually every training run, models designed to align with various human goals are also being rewarded for persisting, acquiring resources, and not being shut down. This leads to Max’s research agenda. The idea is to train AI to be “corrigible” and defer to human control as its sole objective — no harmlessness goals, no moral values, nothing else. In practice, models would get rewarded for behaviours like being willing to shut themselves down or surrender power. According to Max, other approaches to corrigibility have tended to treat it as a constraint on other goals like “make the world good,” rather than a primary objective in its own right. But those goals gave AI reasons to resist shutdown and otherwise undermine corrigibility. If you strip out those competing objectives, alignment might follow naturally from AI that is broadly obedient to humans. Max has laid out the theoretical framework for “Corrigibility as a Singular Target,” but notes that essentially no empirical work has followed — no benchmarks, no training runs, no papers testing the idea in practice. Max wants to change this — he’s calling for collaborators to get in touch at maxharms.com.
This episode was recorded on October 19, 2025. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #235 – Ajeya Cotra on whether it’s crazy that every AI company’s safety plan is ‘use AI to make AI safe’ | 17 Feb 2026 | 02:57:35 | |
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:
But she also thinks this will be a much harder case. In her view, the window between AI automating AI research and the arrival of uncontrollably powerful superintelligence could be quite brief — perhaps a year or less. In that narrow window, we’d need to redirect enormous amounts of AI labour away from making AI smarter and towards alignment research, biodefence, cyberdefence, adapting our political structures, and improving our collective decision-making. The plan might fail just because the idea is flawed at conception: it does sound a bit crazy to use an AI you don’t trust to make sure that same AI benefits humanity. But if we find some clever technique to overcome that, we could still fail — because the companies simply don’t follow through on their promises. They say redirecting resources to alignment and security is their strategy for dealing with the risks generated by their research — but none have quantitative commitments about what fraction of AI labour they’ll redirect during crunch time. And the competitive pressures during a recursive self-improvement loop could be irresistible. In today’s conversation, Ajeya and Rob discuss what assumptions this plan requires, the specific problems AI could help solve during crunch time, and why — even if we pull it off — we’ll be white-knuckling it the whole way through.
This episode was recorded on October 20, 2025. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| What the hell happened with AGI timelines in 2025? | 10 Feb 2026 | 00:25:39 | |
In early 2025, after OpenAI put out the first-ever reasoning models — o1 and o3 — short timelines to transformative artificial general intelligence swept the AI world. But then, in the second half of 2025, sentiment swung all the way back in the other direction, with people's forecasts for when AI might really shake up the world blowing out even further than they had been before reasoning models came along. What the hell happened? Was it just swings in vibes and mood? Confusion? A series of fundamentally unexpected and unpredictable research results? Host Rob Wiblin has been trying to make sense of it for himself, and here's the best explanation he's come up with so far. Links to learn more, video, and full transcript: https://80k.info/tl Chapters:
This episode was recorded on January 29, 2026. Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #179 Classic episode – Randy Nesse on why evolution left us so vulnerable to depression and anxiety | 03 Feb 2026 | 02:51:17 | |
Mental health problems like depression and anxiety affect enormous numbers of people and severely interfere with their lives. By contrast, we don’t see similar levels of physical ill health in young people. At any point in time, something like 20% of young people are working through anxiety or depression that’s seriously interfering with their lives — but nowhere near 20% of people in their 20s have severe heart disease or cancer or a similar failure in a key organ of the body other than the brain. From an evolutionary perspective, that’s to be expected, right? If your heart or lungs or legs or skin stop working properly while you’re a teenager, you’re less likely to reproduce, and the genes that cause that malfunction get weeded out of the gene pool. So why is it that these evolutionary selective pressures seemingly fixed our bodies so that they work pretty smoothly for young people most of the time, but it feels like evolution fell asleep on the job when it comes to the brain? Why did evolution never get around to patching the most basic problems, like social anxiety, panic attacks, debilitating pessimism, or inappropriate mood swings? For that matter, why did evolution go out of its way to give us the capacity for low mood or chronic anxiety or extreme mood swings at all? Today’s guest, Randy Nesse — a leader in the field of evolutionary psychiatry — wrote the book Good Reasons for Bad Feelings, in which he sets out to try to resolve this paradox. Rebroadcast: This episode originally aired in February 2024. Links to learn more, video, and full transcript: https://80k.info/rn In the interview, host Rob Wiblin and Randy discuss the key points of the book, as well as:
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Producer and editor: Keiran Harris | |||
| #234 – David Duvenaud on why 'aligned AI' would still kill democracy | 27 Jan 2026 | 02:32:50 | |
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/dd He 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:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #145 Classic episode – Christopher Brown on why slavery abolition wasn't inevitable | 20 Jan 2026 | 02:56:17 | |
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/CLB While 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 episode, Christopher describes the unique and peculiar set of political, social and religious circumstances that gave rise to the only successful and lasting anti-slavery movement in human history. These circumstances were sufficiently improbable that Christopher believes there are very nearby worlds where abolitionism might never have taken off. Christopher and host Rob Wiblin also discuss:
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Producer: Keiran Harris | |||
| #233 – James Smith on how to prevent a mirror life catastrophe | 13 Jan 2026 | 02:10:33 | |
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/js26 Mirror bacteria would upend that assumption, creating two enormous problems:
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. Nonetheless, James notes that fewer than 10 people currently work full-time on mirror life risks and governance. This is an extraordinary opportunity for researchers in biosecurity, synthetic biology, immunology, policy, and many other fields to help solve an entirely preventable catastrophe — James even believes the issue is on par with AI safety as a priority for some people, depending on their skill set. The Mirror Biology Dialogues Fund is hiring!
This episode was recorded on November 5-6, 2025. Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour | |||
| #144 Classic episode – Athena Aktipis on why cancer is a fundamental universal phenomena | 09 Jan 2026 | 03:30:30 | |
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:
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:
And at the end of the episode, they cover Athena’s new book Everything is Fine! How to Thrive in the Apocalypse, including:
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Producer: Keiran Harris | |||
| #142 Classic episode – John McWhorter on why the optimal number of languages might be one, and other provocative claims about language | 06 Jan 2026 | 01:35:05 | |
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_nMJE Links to learn more, video, and full transcript: https://80k.link/JM We ask him what we think are the most important things everyone ought to know about linguistics, including:
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:
Producer: Keiran Harris | |||