80,000 Hours Podcast

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…

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…

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. Read Matt’s original article on Substack: https://80000hours.substack.com/p/how-to-get-into-ai-safety-in-3-months Note: Since writing the article, Matt has left 80,000 Hours…

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." Introduction (00:01:40) Wrong about Opus 5.5? Deciphering 16th Century Text (00:03:12) Why the models keep breaking out (00:06:41) What the models aren’t telling us (00:13:20) Gemini 4 and the race to release (00:17:16) What happens when AI improves AI? (00:20:59) Biology, consciousness, and what we still don’t understand (00:30:11) Thanks to Philip for permitting the cross-post.

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…

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: Do major tasks with zero visible reasoning Hide its thoughts at will Pretend not to be able to do things, and not get caught Reflexively hide its thoughts when watched Complete one task while pretending to think about something else entirely Escape a toy sandbox and disable monitoring without setting off any flags 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…

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…

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…

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…

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. Preseed grants run $200,000–$2 million, with no preliminary results required. Teams with early results can seek $2–$20 million. For exceptional organisations, much larger grants are possible, even for brand-new startups— Coefficient recently gave $160 million to Geoffrey Irving’s new research centre, Resolution. The gaps Max most wants filled include independent assessment of AI companies’ safety claims, research aimed at aligning far…

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: It’s impossible to get good at most things without practice. AI companies lack the data their models would need to practice most things. This can’t be fixed with “sample efficiency.” In most cases, the relevant data doesn’t exist at all. This also can’t be fixed with simulations or synthetic data. This means that the relevant data for superintelligence in most non-coding domains will only become available through deployment of AI models throughout the economy. The singularity, therefore, will be…

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…

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…

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…

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. *** Want to work with Geoffrey to help align superintelligence? Resolution is hiring! https://80k.info/work-at-resolution *** The leading AI companies all have broadly similar plans for keeping superintelligence under control: Train models to have good character Use increasingly capable AIs to supervise other AIs Monitor them closely for signs of deception or scheming 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: Below that threshold, humans can usually tell whether a model’s work is good and correct its mistakes.…

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: Assuming AI research is just hill-climbing Imagining AI research is just programming Forecasting “could” instead of “will” Believing the current benchmark is the last one Extrapolating trends with no clear finish line Assuming inputs keep scaling at the same rate Conflating intelligence with capability Consuming point estimates and discarding the error bars Dismissing dissenting experts Forecasting very different things while using the same words Assuming capabilities arrive together Treating “we don’t know” as permission to carry on as usual Choosing a plan that minimises regret rather than maximises impact Trusting surface model impressiveness In this extended conversation with Rob Wiblin, Toby also explains why…

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…

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…

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…

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…

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…

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-space This episode was recorded on March 2, 2026.…

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…

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…

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: 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…

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…

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…

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…

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-study 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)…

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…

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…

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 security It has arrived far ahead of schedule It might be great news for alignment and safety But 3 key problems mean we can’t take its alignment results at face value Mythos isn’t building its replacement yet, probably Anthropic staff are, for the first time, kinda scared of Claude He's losing sleep Learn more & full transcript: https://80k.info/mythos This episode was recorded on April 9, 2026. Chapters: Why people are panicking about computer security (01:05) Mythos could break out of containment (04:23) Anthropic is losing billions in revenue by not releasing Mythos (06:21) Mythos is actually the most aligned model to date, except… (07:48) Mythos knows when it’s being tested (09:52) Mythos…

What does it really take to lift millions out of poverty and prevent needless deaths? In this special compilation episode, 17 past guests — including economists, nonprofit founders, and policy advisors — share their most powerful and actionable insights from the front lines of global health and development. You’ll hear about the critical need to boost agricultural productivity in sub-Saharan Africa, the staggering impact of lead poisoning on children in low-income countries, and the social forces that contribute to high neonatal mortality rates in India. What’s so striking is how some of the most effective interventions sound almost too simple to work: banning certain pesticides, replacing thatch roofs, or identifying village “influencers” to spread health information. Full transcript and links to learn more: https://80k.info/ghd Chapters: Cold open (00:00:00) Luisa’s intro (00:00:58) Development consultant Karen Levy on why pushing for “sustainable” programmes isn’t as good as it sounds (00:02:15) Economist…

When the Pentagon tried to strong-arm Anthropic into dropping its ban on AI-only kill decisions and mass domestic surveillance, the company refused. Its critics went on the attack: Anthropic and its supporters are some combination of 'hypocritical', 'naive', and 'anti-democratic'. Rob Wiblin dissects each claim finding that all three are mediocre arguments dressed up as hard truths. (Though the 'naive' one is at least interesting.) Watch on YouTube: What Everyone is Missing about Anthropic vs The Pentagon Plus, from 13:43: Leaked documents from Meta revealed that 10% of the company's total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of around $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved the rate of scams coming from China... well, it wasn't well received. Watch on YouTube: The Meta Leaks Are Worse Than You Think Chapters: Introduction (00:00:00)…

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…

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…

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…

How AI interacts with nuclear deterrence may be the single most important question in geopolitics — one that may define the stakes of today’s AI race. Nuclear deterrence rests on a state’s capacity to respond to a nuclear attack with a devastating nuclear strike of its own. But some theorists think that sophisticated AI could eliminate this capability — for example, by locating and destroying all of an adversary’s nuclear weapons simultaneously, by disabling command-and-control networks, or by enhancing missile defence systems. If they are right, whichever country got those capabilities first could wield unprecedented coercive power. Today’s guests — Nikita Lalwani and Sam Winter-Levy of the Carnegie Endowment for International Peace — assess how advances in AI might threaten nuclear deterrence: Would AI be able to locate nuclear submarines hiding in a vast, opaque ocean? Would road-mobile launchers still be able to hide in tunnels and under netting? Would missile defence become so accurate that the United…

The arrival of AGI could “compress a century of progress in a decade,” forcing humanity to make decisions with higher stakes than we’ve ever seen before — and with less time to get them right. But AI development also presents an opportunity: we could build and deploy AI tools that help us think more clearly, act more wisely, and coordinate more effectively. And if we roll these decision-making tools out quickly enough, humanity could be far better equipped to navigate the critical period ahead. This article is narrated by the author, Zershaaneh Qureshi. It explores why AI decision-making tools could be a big deal, who might be a good fit to help shape this new field, and what the downside risks of getting involved might be. Read the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/ai-enhanced-decision-making/ Chapters: Check out our new narrations feed (00:00:00) Summary (00:01:21) Section 1: Why advancing AI decision making tools might matter a lot (00:02:52) AI tools…

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…

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…

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…

In early 2025, after OpenAI put out the first-ever reasoning models — o1 and o3 — short timelines to transformative artificial general intelligence swept the AI world. But then, in the second half of 2025, sentiment swung all the way back in the other direction, with people's forecasts for when AI might really shake up the world blowing out even further than they had been before reasoning models came along. What the hell happened? Was it just swings in vibes and mood? Confusion? A series of fundamentally unexpected and unpredictable research results? Host Rob Wiblin has been trying to make sense of it for himself, and here's the best explanation he's come up with so far. Links to learn more, video, and full transcript: https://80k.info/tl Chapters: Making sense of the timelines madness in 2025 (00:00:00) The great timelines contraction (00:00:46) Why timelines went back out again (00:02:10) Other longstanding reasons AGI could take a good while (00:11:13) So what's the upshot of all of these updates?…

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…

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…

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,…

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: Many critical immune pathways would likely fail to activate, creating risks of fatal infection across many species. Mirror bacteria could have substantial resistance to natural predators: for example, they would be…

What’s the opposite of cancer? If you answered “cure,” “antidote,” or “antivenom” — you’ve obviously been reading the antonym section at www.merriam-webster.com/thesaurus/cancer. But today’s guest Athena Aktipis says that the opposite of cancer is us: it's having a functional multicellular body that’s cooperating effectively in order to make that multicellular body function. If, like us, you found her answer far more satisfying than the dictionary, maybe you could consider closing your dozens of merriam-webster.com tabs, and start listening to this podcast instead. Rebroadcast: this episode was originally released in January 2023. Links to learn more, video, and full transcript: https://80k.link/AA As Athena explains in her book The Cheating Cell, what we see with cancer is a breakdown in each of the foundations of cooperation that allowed multicellularity to arise: Cells will proliferate when they shouldn't. Cells won't die when they should. Cells won't engage in the kind of division of labour that they…

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: Can you communicate faster in some languages than others, or is there some constraint that prevents that? Does learning a second or third language make you smarter or not? Can a language decay and get worse at communicating what people want to say? If children aren't taught a language, how many generations does it take them to…

It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode we recorded this year, including: Kyle Fish explaining how Anthropic’s AI Claude descends into spiritual woo when left to talk to itself Ian Dunt on why the unelected House of Lords is by far the best part of the British government Sam Bowman’s strategy to get NIMBYs to love it when things get built next to their houses Buck Shlegeris on how to get an AI model that wants to seize control to accidentally help you foil its plans …as well as 18 other top observations and arguments from the past year of the show. Links to learn more, video, and full transcript: https://80k.info/best25 It's been another year of living through history, whether we asked for it or not. Luisa and Rob will be back in 2026 to help you make sense of whatever comes next — as Earth continues its indifferent journey through the cosmos, now accompanied by AI systems that can summarise our meetings and generate adequate birthday…