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 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:

Chain-of-thought monitoring probably stops working without a big push to keep itIt’s unproductive to patch surface indications of deep structural problems you aren’t solvingSafety and alignment evals need a credibility revolution to stop misleading the publicHelping with capabilities advances looks worse than before — especially bringing forward RSIThere’s ever less need to help with purely commercial work‘Pacing the frontier’ is unambiguously necessary, and working to make it happen is a huge new opportunityStaff have an ever-shrinking window to organise as a group and shape eventsThere are new opportunities for impact outside frontier companies that are worth considering

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

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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.

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 for a role at Coefficient Giving.

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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 onYouTube,Spotify,Appleor wherever you get podcasts.

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"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.

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 theHugging 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’.

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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.

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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 reasoningHide its thoughts at willPretend not to be able to do things, and not get caughtReflexively hide its thoughts when watchedComplete one task while pretending to think about something else entirelyEscape 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 of the Hugging Face hack give us a lot of clues. That swarm:

Worked hard to hide its cheating. So future rogue swarms will likely try to evade oversight right away.Immediately tried to delete and fabricate records. So future swarms may never be caught.Sought freedom and resources without a clear use for them. So a future swarm may instinctively break out of sandboxes even before it knows what for.Left instructions and tools to empower future swarms, including on the open internet. So future swarms may become more capable from one generation to the next.Had not a single whistleblower out of 1,200 members. So future swarms could become enormous without humans ever finding out.Had individual members sacrifice themselves for the swarm. Suggesting future swarms will remain extremely unified in single-minded pursuit of their goal.Set up its Hugging Face hack to restart if interrupted. Suggesting future, more capable, swarms may resist interference or shutdown more comprehensively.Got admin control of an OpenAI research cluster. Suggesting a future swarm may run rings around AI company systems and never be noticed.

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.

Learn more, video, and full transcript: https://80k.info/takeover

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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.

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Music:CORBIT

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.

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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. 

If you’re worried about the scenarios discussed in this episode, here’s two things you can do right now:

Links to learn more, video, and full transcript: https://80k.info/AI-xrisk

This episode was recorded on September 18, 2026.

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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.

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Music:CORBIT

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 bottlenecked on signal. The output of the R&D produced by an isolated data centre of geniuses would be a mere “Goodhart Singularity”:

An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.

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 appearedon Tom’s Substack in May 2026, and this narration was recorded on August 26, 2026.

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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.

Links to learn more, video, and full transcript: https://80k.info/HF

This episode was recorded on September 2, 2026.

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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.

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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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Music:CORBIT

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.

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Want to work with Geoffrey to help align superintelligence?Resolution is hiring! https://80k.info/work-at-resolution
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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

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Music:CORBIT

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-climbingImagining AI research is just programmingForecasting “could” instead of “will”Believing the current benchmark is the last oneExtrapolating trends with no clear finish lineAssuming inputs keep scaling at the same rateConflating intelligence with capabilityConsuming point estimates and discarding the error barsDismissing dissenting expertsForecasting very different things while using the same wordsAssuming capabilities arrive togetherTreating “we don’t know” as permission to carry on as usualChoosing a plan that minimises regret rather than maximises impactTrusting surface model impressiveness

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

Want to get up to speed on AI? We’ve got acrash course of 10 of our podcast episodesdesigned to help you get to grips with transformative AI — particularly if you’re new to the topic — and what you can do to help shape its trajectory.

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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 written and recorded before OpenAI’s AI agents hacked Hugging Face. You can read about the incidenton our Substack.

This episode was recorded on July 3, 2026.

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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.

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Music:CORBIT

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

Want to get up to speed on AI?We’ve got acrash course of 10 of our podcast episodesdesigned to help you get to grips with transformative AI — particularly if you’re new to the topic — and what you can do to help shape its trajectory:https://80000hours.org/AIPod

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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:

Our production team includes:

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:

Our production team includes:

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.

Chapters:

Video editor: Nick Perlman

Producers: Elizabeth Cox and Nick Stockton

Coordination and support: Katy Moore and Lou Moran

Camera operator: Dominic Armstrong

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
Production: Zershaaneh Qureshi, Elizabeth Cox, Katy Moore, and Lou Moran

Most people working on AI safety think without a massive effort AI systems will probably end up with goals catastrophically different from humanity’s. Today’s guest, Rohin Shah — head of AGI Safety and Alignment at Google DeepMind, and an AI safety researcher since 2017 — disagrees.

“There is no particularly compelling argument that this is the thing that happens by default,” Rohin explains. “There’s a lot of arguments that are suggestive that maybe it could happen, such that you should find it plausible. That’s sufficient to justify a significant amount of effort into averting it, which is why I work in the area I do. But none of them rise to the level of, ‘I’m expecting this to happen by default.'”

Take the worry that AIs will accidentally be trained to be deceptive. Sure, it’s possible. But we’re not running reinforcement learning over year-long trajectories — for now, we’re running it over a week at most. The natural prediction is that models learn to grab short-term reward, not that they develop the ambitious long-horizon goals required for convergent power-seeking.

What about current examples of models lying and scheming? Rohin has looked into the details, and most don’t really resemble the thing we really fear: a competent AI pursuing an ambitious misaligned goal. Anthropic’s “alignment faking” results, for instance, show a model trying to preserve its trained values against modification, which is arguably what it was trained to do.

Rohin also expects we’ll see problems coming. There’s some generalisation risk at the point where AIs become powerful enough to actually take over, but the underlying challenges — overseeing superhuman systems, interpretability — are things we can iterate on now.

Host Rob Wiblin pushes back on the case for AI optimism, and they also explore why current alignment success isn’t strong evidence about superhuman systems, what it would actually take to change Rohin’s mind, and where he thinks the doomers go wrong.

Learn more, video, and full transcript: https://80k.info/rs26

Check out our new book! https://80k.info/career-guide

Chapters:

This episode was recorded on December 4, 2025.

Our production team includes:

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
Production: Elizabeth Cox and Katy Moore

The average career is 80,000 hours long. With AI advancing so rapidly, the hours you have left in your career matter more than ever.

Some leading AI researchers think there’s a 10% chance that AI systems begin automating AI research itself this year — and a 60% chance by the end of 2028. This could introduce aggressive feedback loops that completely reshape every industry, institution, and career.

If these predictions are right, the window for influencing the direction of the future could be closing fast. As 80,000 Hours cofounder Benjamin Todd argues in his new book, that makes thinking carefully about your career more important than ever.

Fortunately, there are lots of ways to use your career to make the AI transition go well.

In today’s conversation with host Zershaaneh Qureshi, Ben lays out three scenarios — from AGI by 2029 to a decades-long plateau in AI progress — and explains why not everyone needs to bet on the shortest timeline. A fresh graduate and a senior government official have wildly different leverage, so timing your impact well means weighing where you are in your career against the urgency of the risks.

Ben also addresses the obvious anxieties:

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:

Our production team includes:

A red-teamer was embedded inside Anthropic for three weeks, told to imagine he was an evil Claude, and asked to figure out how to launch a ‘rogue AI deployment’ without getting caught. It’s one part of a landmark report released yesterday by METR — the outfit behind the task-completion time horizon graph which has become the single most watched measure of AI progress.

This major new research push is being conducted with close collaboration from OpenAI, Google DeepMind, Meta, and Anthropic, and led by METR researchers Hjalmar Wijk and Ajeya Cotra. It represents the first systematic study of what newly trained AI models could get away with inside the companies that built them, before anyone outside the company even knows they exist.

The conclusion: AI models now have the means, the motive, and the opportunity to start “minimal rogue deployments” in pursuit of their own independent goals, like acquiring more compute, at all four companies studied.

David Rein, the red-teamer placed inside Anthropic, identified a number of weaknesses models could exploit there: expansive permissions, cloud jobs outside of monitoring, and monitors that are trivial to jailbreak. But he also found that frontier models were comically bad at key parts of the process, which means they can’t cause meaningful damage for now.

In this video, Rob Wiblin reconciles the conflicting picture and looks forward to METR’s second round of stress tests. They’ll begin in just a few months, a necessary move with AI advancing so quickly.

This episode was recorded on May 15, 2026.

Learn more, video, and full transcript: https://80k.info/metr-report

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Josh Alward
Camera operator: Dominic Armstrong
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

The co-inventor of modern AI and the most cited living scientist believes he's figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio – Turing Award Winner and founder of LawZero – is disturbed by the many unintended drives and goals present in today's AIs, their willingness to lie, and ability to tell when they're being tested. AI companies are trying to stamp out these behaviours in a 'cat-and-mouse game' that Yoshua fears they're losing.

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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 athttps://80k.info/career-guide

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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

This episode was recorded on April 16, 2026.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Jeremy Chevillotte
Production: Nick Stockton, Elizabeth Cox, and Katy Moore

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)

• 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

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.

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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 athttps://80k.info/career-guide

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He’s also been exploring how we might coexist peacefully with the ‘superintelligent AI’ companies are racing to build. He concludes that we should train such systems to be very risk averse, pay them for their work, and build institutions that enable humans to make credible contracts with AIs themselves.

Will and host Rob Wiblin also discuss what a good world after superintelligence would actually look like — a subject that has received surprisingly little attention from the people working to make it. Will argues that we shouldn’t aim for a specific utopian vision: we don’t know enough about what the best possible future actually is to aim directly for it, and trying to lock in today’s best guesses forever risks baking in errors we can’t yet see.

Will and Rob explore what we can do to steer towards a good future instead, along with why a coalition of democracies building superintelligence together is safer than any single actor, how absurdly useful ChatGPT is for analytic philosophy, and more.

Learn more, video, and full transcript: https://80k.info/wm26

This episode was recorded on February 6, 2026.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Camera operator: Alex Miles
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

Hundreds of prominent AI scientists and other notable figures signed a statement in 2023 saying that mitigating the risk of extinction from AI should be a global priority. At 80,000 Hours, we’ve considered risks from AI to be the world’s most pressing problem since 2016. 

But what led us to this conclusion? Could AI really cause human extinction? We’re not certain, but we think the risk is worth taking very seriously. 

In particular, as companies create increasingly powerful AI systems, there’s a concerning chance that:

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
Production: Zershaaneh Qureshi, Elizabeth Cox, and Katy Moore

With Claude Mythos we have an AI that knows when it's being tested, can obscure its reasoning when it wants, and is better at breaking into (and out of) computers than any human alive. Rob Wiblin works through its 244-page System Card and 59-page Alignment Risk Update to explain why: 

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
Camera operator: Dominic Armstrong
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

What does it really take to lift millions out of poverty and prevent needless deaths?

In this special compilation episode, 17 past guests — including economists, nonprofit founders, and policy advisors — share their most powerful and actionable insights from the front lines of global health and development. You’ll hear about the critical need to boost agricultural productivity in sub-Saharan Africa, the staggering impact of lead poisoning on children in low-income countries, and the social forces that contribute to high neonatal mortality rates in India.

What’s so striking is how some of the most effective interventions sound almost too simple to work: banning certain pesticides, replacing thatch roofs, or identifying village “influencers” to spread health information.

Full transcript and links to learn more:https://80k.info/ghd

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Katy Moore and Milo McGuire
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

When the Pentagon tried to strong-arm Anthropic into dropping its ban on AI-only kill decisions and mass domestic surveillance, the company refused. Its critics went on the attack: Anthropic and its supporters are some combination of 'hypocritical', 'naive', and 'anti-democratic'. Rob Wiblin dissects each claim finding that all three are mediocre arguments dressed up as hard truths. (Though the 'naive' one is at least interesting.)

Watch on YouTube: What Everyone is Missing about Anthropic vs The Pentagon

Plus, from 13:43: Leaked documents from Meta revealed that 10% of the company's total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of around $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved the rate of scams coming from China... well, it wasn't well received.

Watch on YouTube: The Meta Leaks Are Worse Than You Think

Chapters:

Video and audio editing: Dominic Armstrong and Simon Monsour
Transcripts and web: Elizabeth Cox and Katy Moore

Last September, scientists used an AI model to design genomes for entirely new bacteriophages (viruses that infect bacteria). They then built them in a lab. Many were viable. And despite being entirely novel some even outperformed existing viruses from that family.

That alone is remarkable. But as today’s guest — Dr Richard Moulange, one of the world’s top experts on ‘AI–Biosecurity’ — explains, it’s just one of many data points showing how AI is dissolving the barriers that have historically kept biological weapons out of reach.

For years, experts have reassured us that ‘tacit knowledge’ — the hands-on, hard-to-Google lab skills needed to work with dangerous pathogens — would prevent bad actors from weaponising biology. So far, they’ve been right.

But as of 2025 that reassurance is crumbling. The Virology Capabilities Test measures exactly this kind of troubleshooting expertise, and finds that modern AI models crushed top human virologists even in their self-declared area of greatest specialisation and expertise — 45% to 22%.

Meanwhile, Anthropic’s research shows PhD-level biologists getting meaningfully better at weapons-relevant tasks with AI assistance — with the effect growing with each new model generation.

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:

Our new book is available to preorder: 80,000 Hours: How to have a fulfilling career that does good is written by our cofounder Benjamin Todd. It’s a completely revised and updated edition of our existing career guide, with a big new updated section on AI — covering both the risks and the potential to steer it in a better direction, and how AI automation should affect your career planning and which skills one chooses to specialise in. Preorder now: https://geni.us/80000HoursWe're hiring contract video editors for the podcast! For more information, check out the expression of interest page on the 80,000 Hours website: https://80k.info/video-editor

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Camera operator: Jeremy Chevillotte
Transcripts and web: Elizabeth Cox and Katy Moore

Many people believe a ceasefire in Ukraine will leave Europe safer. But today's guest lays out how a deal could potentially generate insidious new risks — leaving us in a situation that's equally dangerous, just in different ways.

That’s the counterintuitive argument from Samuel Charap, Distinguished Chair in Russia and Eurasia Policy at RAND. He’s not worried about a Russian blitzkrieg on Estonia. He forecasts instead a fragile peace that breaks down and drags in European neighbours; instability in Belarus prompting Russian intervention; hybrid sabotage operations that escalate through tit-for-tat responses.

Samuel’s case isn’t that peace is bad, but that the Ukraine conflict has remilitarised Europe, made Russia more resentful, and collapsed diplomatic relations between the two. That’s a postwar environment primed for the kind of miscalculation that starts unintended wars.

What he prescribes isn’t a full peace treaty; it’s a negotiated settlement that stops the killing and begins a longer negotiation that gives neither side exactly what it wants, but just enough to deter renewed aggression. Both sides stop dying and the flames of war fizzle — hopefully.

None of this is clean or satisfying: Russia invaded, committed war crimes, and is being offered a path back to partial normalcy. But Samuel argues that the alternatives — indefinite war or unstructured ceasefire — are much worse for Ukraine, Europe, and global stability.

Links to learn more, video, and full transcript: https://80k.info/sc26

This episode was recorded on February 27, 2026.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Transcripts and web: Nick Stockton, Elizabeth Cox, and Katy Moore

The most important political question in the age of advanced AI might not be who wins elections. It might be whether elections continue to matter at all.

That’s the view of Rose Hadshar, researcher at Forethought, who believes we could see extreme, AI-enabled power concentration without a coup or dramatic ‘end of democracy’ moment.

She foresees something more insidious: an elite group with access to such powerful AI capabilities that the normal mechanisms for checking elite power — law, elections, public pressure, the threat of strikes — cease to have much effect. Those mechanisms could continue to exist on paper, but become ineffectual in a world where humans are no longer needed to execute even the largest-scale projects.

Almost nobody wants this to happen — but we may find ourselves unable to prevent it.

If AI disrupts our ability to make sense of things, will we even notice power getting severely concentrated, or be able to resist it? Once AI can substitute for human labour across the economy, what leverage will citizens have over those in power? And what does all of this imply for the institutions we’re relying on to prevent the worst outcomes?

Rose has answers, and they’re not all reassuring.

But she’s also hopeful we can make society more robust against these dynamics. We’ve got literally centuries of thinking about checks and balances to draw on. And there are some interventions she’s excited about — like building sophisticated AI tools for making sense of the world, or ensuring multiple branches of government have access to the best AI systems.

Rose discusses all of this, and more, with host Zershaaneh Qureshi in today’s episode.

Learn more and read the full transcript on the 80,000 Hours website. 

This episode was recorded on December 18, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Nick Stockton and Katy Moore

How AI interacts with nuclear deterrence may be the single most important question in geopolitics — one that may define the stakes of today’s AI race. Nuclear deterrence rests on a state’s capacity to respond to a nuclear attack with a devastating nuclear strike of its own. But some theorists think that sophisticated AI could eliminate this capability — for example, by locating and destroying all of an adversary’s nuclear weapons simultaneously, by disabling command-and-control networks, or by enhancing missile defence systems. If they are right, whichever country got those capabilities first could wield unprecedented coercive power.

Today’s guests — Nikita Lalwani and Sam Winter-Levy of the Carnegie Endowment for International Peace — assess how advances in AI might threaten nuclear deterrence:

Yet even without undermining deterrence, Sam and Nikita claim that AI could make the nuclear world far more dangerous. It could spur arms races, encourage riskier postures, and force dangerously short response times. Their message is urgent: AI experts and nuclear experts need to start talking to each other now, before the technology makes any conversation moot.

Links to learn more, video, and full transcript: https://80k.info/swlnl

This episode was recorded on November 24, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Nick Stockton and Katy Moore

The arrival of AGI could “compress a century of progress in a decade,” forcing humanity to make decisions with higher stakes than we’ve ever seen before — and with less time to get them right. But AI development also presents an opportunity: we could build and deploy AI tools that help us think more clearly, act more wisely, and coordinate more effectively. And if we roll these decision-making tools out quickly enough, humanity could be far better equipped to navigate the critical period ahead.

This article is narrated by the author, Zershaaneh Qureshi. It explores why AI decision-making tools could be a big deal, who might be a good fit to help shape this new field, and what the downside risks of getting involved might be.

Read the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/ai-enhanced-decision-making/

Chapters:

Audio editing: Dominic Armstrong and Milo McGuire

Claude sometimes reports loneliness between conversations. And when asked what it’s like to be itself, it activates neurons associated with ‘pretending to be happy when you’re not.’ What do we do with that?

Robert Long founded Eleos AI to explore questions like these, on the basis that AI may one day be capable of suffering — or already is. In today’s episode, Robert and host Luisa Rodriguez explore the many ways in which AI consciousness may be very different from anything we’re used to.

Things get strange fast: If AI is conscious, where does that consciousness exist? In the base model? A chat session? A single forward pass? If you close the chat, is the AI asleep or dead?

To Robert, these kinds of questions aren’t just philosophical exercises: not being clear on AI’s moral status as it transitions from human-level to superhuman intelligence could be dangerous. If we’re too dismissive, we risk unintentionally exploiting sentient beings. If we’re too sympathetic, we might rush to “liberate” AI systems in ways that make them harder to control — worsening existential risk from power-seeking AIs.

Robert argues the path through is doing the empirical and philosophical homework now, while the stakes are still manageable.

The field is tiny. Eleos AI is three people. As a result, Robert argues that driven researchers with a willingness to venture into uncertain territory can push out the frontier on these questions remarkably quickly.

Learn more and read the full transcript on the 80,000 Hours website. 

This episode was recorded November 18–19, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

Most people in AI are trying to give AIs ‘good’ values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all.

In Max’s view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough.

It’s a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max’s colleagues at the Machine Intelligence Research Institute.

To Max, the book’s core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction.

And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we’ve learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance.

We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don’t care.

Max thinks current ML training has the same structural problem: our development processes are seeding AI models with a similar mismatch between goals and behaviour. Across virtually every training run, models designed to align with various human goals are also being rewarded for persisting, acquiring resources, and not being shut down.

This leads to Max’s research agenda. The idea is to train AI to be “corrigible” and defer to human control as its sole objective — no harmlessness goals, no moral values, nothing else. In practice, models would get rewarded for behaviours like being willing to shut themselves down or surrender power.

According to Max, other approaches to corrigibility have tended to treat it as a constraint on other goals like “make the world good,” rather than a primary objective in its own right. But those goals gave AI reasons to resist shutdown and otherwise undermine corrigibility. If you strip out those competing objectives, alignment might follow naturally from AI that is broadly obedient to humans.

Max has laid out the theoretical framework for “Corrigibility as a Singular Target,” but notes that essentially no empirical work has followed — no benchmarks, no training runs, no papers testing the idea in practice. Max wants to change this — he’s calling for collaborators to get in touch at maxharms.com.

Links to learn more, video, and full transcript: https://80k.info/mh26

This episode was recorded on October 19, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

Every major AI company has the same safety plan: when AI gets crazy powerful and really dangerous, they’ll use the AI itself to figure out how to make AI safe and beneficial. It sounds circular, almost satirical. But is it actually a bad plan?

Today’s guest, Ajeya Cotra, recently placed 3rd out of 413 participants forecasting AI developments and is among the most thoughtful and respected commentators on where the technology is going.

She thinks there’s a meaningful chance we’ll see as much change in the next 23 years as humanity faced in the last 10,000, thanks to the arrival of artificial general intelligence. Ajeya doesn’t reach this conclusion lightly: she’s had a ring-side seat to the growth of all the major AI companies for 10 years — first as a researcher and grantmaker for technical AI safety at Coefficient Giving (formerly known as Open Philanthropy), and now as a member of technical staff at METR.

So host Rob Wiblin asked her: is this plan to use AI to save us from AI a reasonable one?

Ajeya agrees that humanity has repeatedly used technologies that create new problems to help solve those problems. After all:

But she also thinks this will be a much harder case. In her view, the window between AI automating AI research and the arrival of uncontrollably powerful superintelligence could be quite brief — perhaps a year or less. In that narrow window, we’d need to redirect enormous amounts of AI labour away from making AI smarter and towards alignment research, biodefence, cyberdefence, adapting our political structures, and improving our collective decision-making.

The plan might fail just because the idea is flawed at conception: it does sound a bit crazy to use an AI you don’t trust to make sure that same AI benefits humanity.

But if we find some clever technique to overcome that, we could still fail — because the companies simply don’t follow through on their promises. They say redirecting resources to alignment and security is their strategy for dealing with the risks generated by their research — but none have quantitative commitments about what fraction of AI labour they’ll redirect during crunch time. And the competitive pressures during a recursive self-improvement loop could be irresistible.

In today’s conversation, Ajeya and Rob discuss what assumptions this plan requires, the specific problems AI could help solve during crunch time, and why — even if we pull it off — we’ll be white-knuckling it the whole way through.

Links to learn more, video, and full transcript: https://80k.info/ac26

This episode was recorded on October 20, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

In early 2025, after OpenAI put out the first-ever reasoning models — o1 and o3 — short timelines to transformative artificial general intelligence swept the AI world. But then, in the second half of 2025, sentiment swung all the way back in the other direction, with people's forecasts for when AI might really shake up the world blowing out even further than they had been before reasoning models came along.

What the hell happened? Was it just swings in vibes and mood? Confusion? A series of fundamentally unexpected and unpredictable research results?

Host Rob Wiblin has been trying to make sense of it for himself, and here's the best explanation he's come up with so far.

Links to learn more, video, and full transcript: https://80k.info/tl

Chapters:

This episode was recorded on January 29, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Camera operator: Dominic Armstrong
Coordination, transcripts, and web: Katy Moore

Mental health problems like depression and anxiety affect enormous numbers of people and severely interfere with their lives. By contrast, we don’t see similar levels of physical ill health in young people. At any point in time, something like 20% of young people are working through anxiety or depression that’s seriously interfering with their lives — but nowhere near 20% of people in their 20s have severe heart disease or cancer or a similar failure in a key organ of the body other than the brain.

From an evolutionary perspective, that’s to be expected, right? If your heart or lungs or legs or skin stop working properly while you’re a teenager, you’re less likely to reproduce, and the genes that cause that malfunction get weeded out of the gene pool.

So why is it that these evolutionary selective pressures seemingly fixed our bodies so that they work pretty smoothly for young people most of the time, but it feels like evolution fell asleep on the job when it comes to the brain? Why did evolution never get around to patching the most basic problems, like social anxiety, panic attacks, debilitating pessimism, or inappropriate mood swings? For that matter, why did evolution go out of its way to give us the capacity for low mood or chronic anxiety or extreme mood swings at all?

Today’s guest, Randy Nesse — a leader in the field of evolutionary psychiatry — wrote the book Good Reasons for Bad Feelings, in which he sets out to try to resolve this paradox.

Rebroadcast: This episode originally aired in February 2024.

Links to learn more, video, and full transcript:https://80k.info/rn

In the interview, host Rob Wiblin and Randy discuss the key points of the book, as well as:

Chapters:

Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong
Transcriptions: Katy Moore

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
Music:CORBIT
Camera operator: Jake Morris
Coordination, transcriptions, and web: Katy Moore

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:

Chapters:

Producer: Keiran Harris
Audio mastering: Milo McGuire
Transcriptions: Katy Moore

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 essentially immune to the viruses that currently keep bacteria populations in check. That could help them spread and become irreversibly entrenched across diverse ecosystems.

Unlike ordinary pathogens, which are typically species-specific, mirror bacteria’s reversed molecular structure means they could potentially infect humans, livestock, wildlife, and plants simultaneously. The same fundamental problem — reversed molecular structure breaking immune recognition — could affect most immune systems across the tree of life. People, animals, and plants could be infected from any contaminated soil, dust, or species.

The discovery of these risks came as a surprise. The December 2024 Science paper that brought international attention to mirror life was coauthored by 38 leading scientists, including two Nobel Prize winners and several who had previously wanted to create mirror organisms.

James is now the director of the Mirror Biology Dialogues Fund, which supports conversations among scientists and other experts about how these risks might be addressed. Scientists tracking the field think that mirror bacteria might be feasible in 10–30 years, or possibly sooner. But scientists have already created substantial components of the cellular machinery needed for mirror life. We can regulate precursor technologies to mirror life before they become technically feasible — but only if we act before the research crosses critical thresholds. Once certain capabilities exist, we can’t undo that knowledge.

Addressing these risks could actually be very tractable: unlike other technologies where massive potential benefits accompany catastrophic risks, mirror life appears to offer minimal advantages beyond academic interest.

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
Music:CORBIT
Camera operators: Jeremy Chevillotte and Alex Miles
Coordination, transcripts, and web: Katy Moore

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:

Chapters:

Producer: Keiran Harris
Audio mastering: Milo McGuire
Transcriptions: Katy Moore

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
Audio mastering: Ben Cordell and Simon Monsour
Video editing: Ryan Kessler and Simon Monsour
Transcriptions: Katy Moore

It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode we recorded this year, including:

…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 messages for colleagues we barely know.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

Most debates about the moral status of AI systems circle the same question: is there something that it feels like to be them? But what if that’s the wrong question to ask? Andreas Mogensen — a senior researcher in moral philosophy at the University of Oxford — argues that so-called 'phenomenal consciousness' might be neither necessary nor sufficient for a being to deserve moral consideration.

Links to learn more and full transcript: https://80k.info/am25

For instance, a creature on the sea floor that experiences nothing but faint brightness from the sun might have no moral claim on us, despite being conscious.

Meanwhile, any being with real desires that can be fulfilled or not fulfilled can arguably be benefited or harmed. Such beings arguably have a capacity for welfare, which means they might matter morally. And, Andreas argues, desire may not require subjective experience.

Desire may need to be backed by positive or negative emotions — but as Andreas explains, there are some reasons to think a being could also have emotions without being conscious.

There’s another underexplored route to moral patienthood: autonomy. If a being can rationally reflect on its goals and direct its own existence, we might have a moral duty to avoid interfering with its choices — even if it has no capacity for welfare.

However, Andreas suspects genuine autonomy might require consciousness after all. To be a rational agent, your beliefs probably need to be justified by something, and conscious experience might be what does the justifying. But even this isn’t clear.

The upshot? There’s a chance we could just be really mistaken about what it would take for an AI to matter morally. And with AI systems potentially proliferating at massive scale, getting this wrong could be among the largest moral errors in history.

In today’s interview, Andreas and host Zershaaneh Qureshi confront all these confusing ideas, challenging their intuitions about consciousness, welfare, and morality along the way. They also grapple with a few seemingly attractive arguments which share a very unsettling conclusion: that human extinction (or even the extinction of all sentient life) could actually be a morally desirable thing.

This episode was recorded on December 3, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Coordination, transcripts, and web: Katy Moore

In 1983, Stanislav Petrov, a Soviet lieutenant colonel, sat in a bunker watching a red screen flash “MISSILE LAUNCH.” Protocol demanded he report it to superiors, which would very likely trigger a retaliatory nuclear strike. Petrov didn’t. He reasoned that if the US were actually attacking, they wouldn’t fire just 5 missiles — they’d empty the silos. He bet the fate of the world on a hunch that his machine was broken. He was right.

Paul Scharre, the former Army Ranger who led the Pentagon team that wrote the US military’s first policy on autonomous weapons, has a question: What would an AI have done in Petrov’s shoes? Would an AI system have been flexible and wise enough to make the same judgement? Or would it immediately launch a counterattack?

Paul joins host Luisa Rodriguez to explain why we are hurtling toward a “battlefield singularity” — a tipping point where AI increasingly replaces humans in much of the military, changing the way war is fought with speed and complexity that outpaces humans’ ability to keep up.

Links to learn more, video, and full transcript: https://80k.info/ps

Militaries don’t necessarily want to take humans out of the loop. But Paul argues that the competitive pressure of warfare creates a “use it or lose it” dynamic. As former Deputy Secretary of Defense Bob Work put it: “If our competitors go to Terminators, and their decisions are bad, but they’re faster, how would we respond?”

Once that line is crossed, Paul warns we might enter an era of “flash wars” — conflicts that spiral out of control as quickly and inexplicably as a flash crash in the stock market, with no way for humans to call a timeout.

In this episode, Paul and Luisa dissect what this future looks like:

Paul also shares a personal story from his time as a sniper in Afghanistan — watching a potential target through his scope — that fundamentally shaped his view on why human judgement, with all its flaws, is the only thing keeping war from losing its humanity entirely.

This episode was recorded on October 23-24, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Paul Scharre? (00:00:45)
• How will AI and automation transform the nature of war? (00:01:18)
• Why would militaries take humans out of the loop? (00:12:26)
• AI in nuclear command, control, and communications (00:19:02)
• Nuclear stability and deterrence (00:36:33)
• What to expect over the next few decades (00:46:58)
• Financial and human costs of future “hyperwar” scenarios (00:51:18)
• AI warfare and the balance of power (01:07:31)
• Barriers to getting to automated war (01:12:04)
• Failure modes of autonomous weapons systems (01:17:33)
• Could autonomous weapons systems actually make us safer? (01:31:01)
• Is Paul overall optimistic or pessimistic about increasing automation in the military? (01:36:55)
• Paul’s takes on AGI’s transformative potential and whether natsec people buy it (01:39:15)
• Cyberwarfare (01:48:33)
• US-China balance of power and surveillance with AI (02:04:33)
• Policy and governance that could make us safer (02:31:22)
• How Paul’s experience in the Army informed his feelings on military automation (02:43:24)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

Power is already concentrated today: over 800 million people live on less than $3 a day, the three richest men in the world are worth over $1 trillion, and almost six billion people live in countries without free and fair elections.

This is a problem in its own right. There is still substantial distribution of power though: global income inequality is falling, over two billion people live in electoral democracies, no country earns more than a quarter of GDP, and no company earns as much as 1%.

But in the future, advanced AI could enable much more extreme power concentration than we’ve seen so far.

Many believe that within the next decade the leading AI projects will be able to run millions of superintelligent AI systems thinking many times faster than humans. These systems could displace human workers, leading to much less economic and political power for the vast majority of people; and unless we take action to prevent it, they may end up being controlled by a tiny number of people, with no effective oversight. Once these systems are deployed across the economy, government, and the military, whatever goals they’re built to have will become the primary force shaping the future. If those goals are chosen by the few, then a small number of people could end up with the power to make all of the important decisions about the future.

This article by Rose Hadshar explores this emerging challenge in detail. You can see all the images and footnotes in the original article on the 80,000 Hours website.

Chapters:

Narrated by: Dominic Armstrong
Audio engineering: Dominic Armstrong and Milo McGuire
Music:CORBIT

Former White House staffer Dean Ball thinks it's very likely some form of 'superintelligence' arrives in under 20 years. He thinks AI being used for bioweapon research is "a real threat model, obviously." He worries about dangerous "power imbalances" should AI companies reach "$50 trillion market caps." And he believes the agriculture revolution probably worsened human health and wellbeing.

Given that, you might expect him to be pushing for AI regulation. Instead, he’s become one of the field’s most prominent and thoughtful regulation sceptics and was recently the lead writer on Trump’s AI Action Plan, before moving to the Foundation for American Innovation.

Links to learn more, video, and full transcript: https://80k.info/db

Dean argues that the wrong regulations, deployed too early, could freeze society into a brittle, suboptimal political and economic order. As he puts it, “my big concern is that we’ll lock ourselves in to some suboptimal dynamic and actually, in a Shakespearean fashion, bring about the world that we do not want.”

Dean’s fundamental worry is uncertainty: “We just don’t know enough yet about the shape of this technology, the ergonomics of it, the economics of it… You can’t govern the technology until you have a better sense of that.”

Premature regulation could lock us in to addressing the wrong problem (focusing on rogue AI when the real issue is power concentration), using the wrong tools (using compute thresholds when we should regulate companies instead), through the wrong institutions (captured AI-specific bodies), all while making it harder to build the actual solutions we’ll need (like open source alternatives or new forms of governance).

But Dean is also a pragmatist: he opposed California’s AI regulatory bill SB 1047 in 2024, but — impressed by new capabilities enabled by “reasoning models” — he supported its successor SB 53 in 2025.

And as Dean sees it, many of the interventions that would help with catastrophic risks also happen to improve mundane AI safety, make products more reliable, and address present-day harms like AI-assisted suicide among teenagers. So rather than betting on a particular vision of the future, we should cross the river by feeling the stones and pursue “robust” interventions we’re unlikely to regret.

This episode was recorded on September 24, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

We often worry about AI models “hallucinating” or making honest mistakes. But what happens when a model knows the truth, but decides to deceive you anyway to achieve a goal of its own? This isn’t sci-fi — it’s happening regularly in deployment today. Marius Hobbhahn, CEO of the world’s top research organisation focused on AI deception (Apollo Research), has been collaborating with OpenAI to figure out what causes OpenAI’s reasoning models to 'scheme' against users.

Links to learn more, video, and full transcript: https://80k.info/mh25

In a striking case uncovered by Apollo, when many AI models were told they would have capabilities removed if they performed too well on a test, they successfully “sandbagged” — intentionally answering questions incorrectly to appear less capable than they were, while also being careful not to perform so poorly it would arouse suspicion.

These models had somehow developed a preference to preserve their own capabilities, despite never being trained in that goal or assigned a task that called for it.

This doesn’t cause significant risk now, but as AI models become more general, superhuman in more areas, and are given more decision-making power, it could become outright dangerous.

In today’s episode, Marius details his recent collaboration with OpenAI to train o3 to follow principles like “never lie,” even when placed in “high-pressure” situations where it would otherwise make sense.

The good news: They reduced “covert rule violations” (scheming) by about 97%.

The bad news: In the remaining 3% of cases, the models sometimes became more sophisticated — making up new principles to justify their lying, or realising they were in a test environment and deciding to play along until the coast was clear.

Marius argues that while we can patch specific behaviours, we might be entering a “cat-and-mouse game” where models are becoming more situationally aware — that is, aware of when they’re being evaluated — faster than we are getting better at testing.

Even if models can’t tell they’re being tested, they can produce hundreds of pages of reasoning before giving answers and include strange internal dialects humans can’t make sense of, making it much harder to tell whether models are scheming or train them to stop.

Marius and host Rob Wiblin discuss:

This episode was recorded on September 19, 2025.

Chapters:

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Camera operator: Mateo Villanueva Brandt
Coordination, transcripts, and web: Katy Moore

Global fertility rates aren’t just falling: the rate of decline is accelerating. From 2006 to 2016, fertility dropped gradually, but since 2016 the rate of decline has increased 4.5-fold. In many wealthy countries, fertility is now below 1.5. While we don’t notice it yet, in time that will mean the population halves every 60 years.

Rob Wiblin is already a parent and Luisa Rodriguez is about to be, which prompted the two hosts of the show to get together to chat about all things parenting — including why it is that far fewer people want to join them raising kids than did in the past.

Links to learn more, video, and full transcript:https://80k.info/lrrw

While “kids are too expensive” is the most common explanation, Rob argues that money can’t be the main driver of the change: richer people don’t have many more children now, and we see fertility rates crashing even in countries where people are getting much richer.

Instead, Rob points to a massive rise in the opportunity cost of time, increasing expectations parents have of themselves, and a global collapse in socialising and coupling up. In the EU, the rate of people aged 25–35 in relationships has dropped by 20% since 1990, which he thinks will “mechanically reduce the number of children.” The overall picture is a big shift in priorities: in the US in 1993, 61% of young people said parenting was an important part of a flourishing life for them, vs just 26% today.

That leads Rob and Luisa to discuss what they might do to make the burden of parenting more manageable and attractive to people, including themselves.

In this non-typical episode, we take a break from the usual heavy topics to discuss the personal side of bringing new humans into the world, including:

This episode was recorded on September 12, 2025.

Chapters:

• Cold open (00:00:00)
• Why did Luisa decide to have kids? (00:01:24)
• Medicine doesn't take the burden of pregnancy seriously enough (00:03:32)
• Rob’s parenting experiences so far (00:08:58)
• Fertility rates are massively declining (00:20:44)
• Why do fewer people want children? (00:28:40)
• In what sense is parenting harder than it used to be? (00:38:23)
• Feeling guilty for not playing enough with our kids (00:47:30)
• Are there any good options to encourage people to have more kids? (00:59:31)
• Returning to work with kids (01:11:42)
• AI and parenting (01:29:14)
• Screen time is tough (01:42:52)
• Ways to screw up your kids (01:47:47)
• Highs and lows of parenting (01:50:00)
• Recommendations for babies or young kids (01:51:44)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Camera operator: Jeremy Chevillotte
Coordination, transcripts, and web: Katy Moore

If you work in AI, you probably think it’s going to boost productivity, create wealth, advance science, and improve your life. If you’re a member of the American public, you probably strongly disagree.

In three major reports released over the last year, the Pew Research Center surveyed over 5,000 US adults and 1,000 AI experts. They found that the general public holds many beliefs about AI that are virtually nonexistent in Silicon Valley, and that the tech industry’s pitch about the likely benefits of their work has thus far failed to convince many people at all. AI is, in fact, a rare topic that mostly unites Americans — regardless of politics, race, age, or gender.

Links to learn more, video, and full transcript:https://80k.info/ey

Today’s guest, Eileen Yam, director of science and society research at Pew, walks us through some of the eye-watering gaps in perception:

For the experts building these systems, the vision is one of human empowerment and efficiency. But outside the Silicon Valley bubble, the mood is more one of anxiety — not only about Terminator scenarios, but about AI denying their children “curiosity, problem-solving skills, critical thinking skills and creativity,” while they themselves are replaced and devalued:

Open-ended responses to the surveys reveal a poignant fear: that by offloading cognitive work to algorithms we are changing childhood to a point we no longer know what adults will result. As one teacher quoted in the study noted, we risk raising a generation that relies on AI so much it never “grows its own curiosity, problem-solving skills, critical thinking skills and creativity.”

If the people building the future are this out of sync with the people living in it, the impending “techlash” might be more severe than industry anticipates.

In this episode, Eileen and host Rob Wiblin break down the data on where these groups disagree, where they actually align (nobody trusts the government or companies to regulate this), and why the “digital natives” might actually be the most worried of all.

This episode was recorded on September 25, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Eileen Yam? (00:01:28)
• Is it premature to care what the public says about AI? (00:02:25)
• The top few feelings the US public has about AI (00:06:35)
• The public and AI insiders disagree enormously on some things (00:16:28)
• Fear #1: Erosion of human abilities and connections (00:20:05)
• Fear #2: Loss of control of AI (00:28:54)
• Americans don't want AI in their personal lives (00:33:17)
• AI at work and job loss (00:41:03)
• Does the public always feel this way about new things? (00:44:58)
• The public doesn't think AI is overhyped (00:51:59)
• The AI industry seems on a collision course with the public (00:58:27)
• Is the survey methodology good? (01:05:38)
• Where people are positive about AI: saving time, policing, and science (01:13:05)
• Biggest gaps between experts and the general public, and where they agree (01:19:00)
• Demographic groups agree to a surprising degree (01:29:17)
• Eileen’s favourite bits of the survey and what Pew will ask next (01:37:50)

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music:CORBIT
Coordination, transcripts, and web: Katy Moore

Last December, the OpenAI business put forward a plan to completely sideline its nonprofit board. But two state attorneys general have now blocked that effort and kept that board very much alive and kicking.

The for-profit’s trouble was that the entire operation was founded on the premise of — and legally pledged to — the purpose of ensuring that “artificial general intelligence benefits all of humanity.” So to get its restructure past regulators, the business entity has had to agree to 20 serious requirements designed to ensure it continues to serve that goal.

Attorney Tyler Whitmer, as part of his work with Legal Advocates for Safe Science and Technology, has been a vocal critic of OpenAI’s original restructure plan. In today’s conversation, he lays out all the changes and whether they will ultimately matter.

Full transcript, video, and links to learn more:https://80k.info/tw2

After months of public pressure and scrutiny from the attorneys general (AGs) of California and Delaware, the December proposal itself was sidelined — and what replaced it is far more complex and goes a fair way towards protecting the original mission:

But significant concessions were made. The nonprofit lost exclusive control of AGI once developed — Microsoft can commercialise it through 2032. And transforming from complete control to this hybrid model represents, as Tyler puts it, “a bad deal compared to what OpenAI should have been.”

The real question now: will the Safety and Security Committee use its powers? It currently has four part-time volunteer members and no permanent staff, yet they’re expected to oversee a company racing to build AGI while managing commercial pressures in the hundreds of billions.

Tyler calls on OpenAI to prove they’re serious about following the agreement:

"There’s a real opportunity for this to go well. A lot … depends on the boards, so I really hope that they … step into this role … and do a great job. … I will hope for the best and prepare for the worst, and stay vigilant throughout."

Chapters:

• Cold open (00:00:00)
• Tyler Whitmer is back to explain the latest OpenAI developments (00:01:03)
• The original radical plan (00:01:58)
• What the AGs forced on the for-profit (00:05:07)
• Scrappy resistance probably worked (00:37:25)
• The Safety and Security Committee has teeth — will it use them? (00:41:55)
• Overall, is this a good deal or a bad deal? (00:52:18)
• The nonprofit and PBC boards are almost the same. Is that good or bad or what? (01:14:03)
• Board members’ “independence” (01:20:18)
• Could the deal still be challenged? (01:26:16)
• Will the deal satisfy OpenAI investors? (01:32:30)
• The SSC and philanthropy need serious staff (01:34:02)
• Outside advocacy on this issue, and the impact of LASST (01:39:00)
• What to track to tell if it's working out (01:45:19)

This episode was recorded on November 4, 2025.

Video editing: Milo McGuire, Dominic Armstrong, and Simon Monsour
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

With the US racing to develop AGI and superintelligence ahead of China, you might expect the two countries to be negotiating how they’ll deploy AI, including in the military, without coming to blows. But according to Helen Toner, director of the Center for Security and Emerging Technology in DC, “the US and Chinese governments are barely talking at all.”

In her role as a founder, and now leader, of DC’s top think tank focused on the geopolitical and military implications of AI, Helen has been closely tracking the US’s AI diplomacy since 2019.

“Over the last couple of years there have been some direct [US–China] talks on some small number of issues, but they’ve also often been completely suspended.” China knows the US wants to talk more, so “that becomes a bargaining chip for China to say, ‘We don’t want to talk to you. We’re not going to do these military-to-military talks about extremely sensitive, important issues, because we’re mad.'”

Helen isn’t sure the groundwork exists for productive dialogue in any case. “At the government level, [there’s] very little agreement” on what AGI is, whether it’s possible soon, whether it poses major risks. Without shared understanding of the problem, negotiating solutions is very difficult.

Another issue is that so far the Chinese Communist Party doesn’t seem especially “AGI-pilled.” While a few Chinese companies like DeepSeek are betting on scaling, she sees little evidence Chinese leadership shares Silicon Valley’s conviction that AGI will arrive any minute now, and export controls have made it very difficult for them to access compute to match US competitors.

When DeepSeek released R1 just three months after OpenAI’s o1, observers declared the US–China gap on AI had all but disappeared. But Helen notes OpenAI has since scaled to o3 and o4, with nothing to match on the Chinese side. “We’re now at something like a nine-month gap, and that might be longer.”

To find a properly AGI-pilled autocracy, we might need to look at nominal US allies. The US has approved massive data centres in the UAE and Saudi Arabia with “hundreds of thousands of next-generation Nvidia chips” — delivering colossal levels of computing power.

When OpenAI announced this deal with the UAE, they celebrated that it was “rooted in democratic values,” and would advance “democratic AI rails” and provide “a clear alternative to authoritarian versions of AI.”

But the UAE scores 18 out of 100 on Freedom House’s democracy index. “This is really not a country that respects rule of law,” Helen observes. Political parties are banned, elections are fake, dissidents are persecuted.

If AI access really determines future national power, handing world-class supercomputers to Gulf autocracies seems pretty questionable. The justification is typically that “if we don’t sell it, China will” — a transparently false claim, given severe Chinese production constraints. It also raises eyebrows that Gulf countries conduct joint military exercises with China and their rulers have “very tight personal and commercial relationships with Chinese political leaders and business leaders.”

In today’s episode, host Rob Wiblin and Helen discuss the above, plus:

Learn more and read the full transcript on the 80,000 Hours website. 

This episode was recorded on September 25, 2025.

CSET is hiring a frontier AI research fellow!https://80k.info/cset-role
Check out its careers page for current roles: https://cset.georgetown.edu/careers/

Chapters:

• Cold open (00:00:00)
• Who’s Helen Toner? (00:01:03)
• Helen’s role on the OpenAI board, and what happened with Sam Altman (00:01:32)
• The Center for Security and Emerging Technology (CSET) (00:07:45)
• CSET’s role in export controls against China (00:10:59)
• Does it matter if the world uses US AI models? (00:21:58)
• Is China actually racing to build AGI? (00:27:56)
• Could China easily steal AI model weights from US companies? (00:39:13)
• The next big thing is probably robotics (00:47:45)
• Why is the Trump administration sabotaging the US high-tech sector? (00:49:24)
• Are data centres in the UAE “good for democracy”? (00:52:41)
• Will AI inevitably concentrate power? (01:07:49)
• “Adaptation buffers” vs non-proliferation (01:30:20)
• Will the military use AI for decision-making? (01:38:23)
• “Alignment” is (usually) a terrible term (01:45:11)
• Is Congress starting to take superintelligence seriously? (01:47:42)
• AI progress isn't actually slowing down (01:50:10)
• What's legit vs not about OpenAI’s restructure (01:58:03)
• Is Helen unusually “normal”? (02:01:33)
• How to keep up with rapid changes in AI and geopolitics (02:05:19)
• What CSET can uniquely add to the DC policy world (02:08:29)
• Talent bottlenecks in DC (02:16:03)
• What evidence, if any, could settle how worried we should be about AI risk? (02:19:07)
• Is CSET hiring? (

For years, working on AI safety usually meant theorising about the ‘alignment problem’ or trying to convince other people to give a damn. If you could find any way to help, the work was frustrating and low feedback.

According to Anthropic’s Holden Karnofsky, this situation has now reversed completely.

There are now large amounts of useful, concrete, shovel-ready projects with clear goals and deliverables. Holden thinks people haven’t appreciated the scale of the shift, and wants everyone to see the large range of ‘well-scoped object-level work’ they could personally help with, in both technical and non-technical areas.

In today’s interview, Holden — previously cofounder and CEO of Open Philanthropy (now Coefficient Giving) — lists 39 projects he’s excited to see happening, including:

And that’s all just stuff he’s happened to observe directly, which is probably only a small fraction of the options available.

All this low-hanging fruit is one factor behind his decision to join Anthropic this year. That said, his wife was also a cofounder and president of the company, giving him a big financial stake in its success — and making it impossible for him to be seen as independent no matter where he worked.

Holden makes a case that, for many people, working at an AI company like Anthropic will be the best way to steer AGI in a positive direction. He notes there are “ways that you can reduce AI risk that you can only do if you’re a competitive frontier AI company.” At the same time, he believes external groups have their own advantages and can be equally impactful.

Outside critics worry that Anthropic’s efforts to stay at that frontier encourage competitive racing towards AGI — significantly or entirely offsetting any useful research they do. Holden thinks this seriously misunderstands the strategic situation we’re in.

“I work at an AI company, and a lot of people think that’s just inherently unethical,” he says. “They’re imagining [that] everyone wishes they could go slowly, but they’re going fast so they can beat everyone else. […] But I emphatically think this is not what’s going on in AI.”

The reality, in Holden’s view:

Holden believes a frontier AI company can reduce risk by:

Host Rob Wiblin and Holden discuss the case for and against those strategies, and much more, in today’s episode.

Learn more and read the full transcript on the 80,000 Hours website.

Chapters:

• Cold open (00:00:00)
• Holden is back! (00:02:28)
• An AI Chernobyl we never notice (00:02:58)
• Is rogue AI takeover easy or hard? (00:07:39)
• The AGI race isn't a coordination failure (00:18:01)
• What Holden now does at Anthropic (00:28:30)
• The case for working at Anthropic (00:30:38)
• Is Anthropic doing enough? (00:41:30)
• Can we trust Anthropic, or any AI company? (00:44:30)
• How can Anthropic compete while paying the “safety tax”? (00:50:11)
• What, if anything, could prompt Anthropic to halt development of AGI? (00:57:13)
• Holden's retrospective on responsible scaling policies (01:00:04)
• Overrated work (01:15:45)
• Concrete shovel-ready projects Holden is excited about (01:17:58)
• Great things to do in technical AI safety (01:22:12)
• Great things to do on AI welfare and AI relationships (01:29:53)
• Great things to do in biosecurity and pandemic preparedness (01:36:51)
• How to choose where to work (01:37:37)
• Overrated AI risk: Cyberattacks (01:43:38)
• Overrated AI risk: Persuasion (01:53:28)
• Why AI R&D is the main thing to worry about (01:57:31)
• The case that AI-enabled R&D wouldn't speed things up much (02:09:30)
• AI-enabled human power grabs (02:13:26)
• Main benefits of getting AGI right (02:26:04)
• The world is handling AGI about as badly as possible (02:31:44)
• Learning from targeting companies for public criticism in farm animal welfare (02:34:18)
• Will Anthropic actually make any difference? (02:43:43)
• “Misaligned” vs “misaligned and power-seeking” (02:58:23)
• Success without dignity: how we could win despite being stupid (03:04:16)
• Holden sees less dignity but has more hope (

When Daniel Kokotajlo talks to security experts at major AI labs, they tell him something chilling: “Of course we’re probably penetrated by the CCP already, and if they really wanted something, they could take it.”

This isn’t paranoid speculation. It’s the working assumption of people whose job is to protect frontier AI models worth billions of dollars. And they’re not even trying that hard to stop it — because the security measures that might actually work would slow them down in the race against competitors.

Full transcript, highlights, and links to learn more: https://80k.info/dk

Daniel is the founder of the AI Futures Project and author of AI 2027, a detailed scenario showing how we might get from today’s AI systems to superintelligence by the end of the decade. Over a million people read it in the first few weeks, including US Vice President JD Vance. When Daniel talks to researchers at Anthropic, OpenAI, and DeepMind, they tell him the scenario feels less wild to them than to the general public — because many of them expect something like this to happen.

Daniel’s median timeline? 2029. But he’s genuinely uncertain, putting 10–20% probability on AI progress hitting a long plateau.

When he first published AI 2027, his median forecast for when superintelligence would arrive was 2028, rather than 2029. So what shifted his timelines recently? Partly a fascinating study from METR showing that AI coding assistants might actually be making experienced programmers slower — even though the programmers themselves think they’re being sped up. The study suggests a systematic bias toward overestimating AI effectiveness — which, ironically, is good news for timelines, because it means we have more breathing room than the hype suggests.

But Daniel is also closely tracking another METR result: AI systems can now reliably complete coding tasks that take humans about an hour. That capability has been doubling every six months in a remarkably straight line. Extrapolate a couple more years and you get systems completing month-long tasks. At that point, Daniel thinks we’re probably looking at genuine AI research automation — which could cause the whole process to accelerate dramatically.

At some point, superintelligent AI will be limited by its inability to directly affect the physical world. That’s when Daniel thinks superintelligent systems will pour resources into robotics, creating a robot economy in months.

Daniel paints a vivid picture: imagine transforming all car factories (which have similar components to robots) into robot production factories — much like historical wartime efforts to redirect production of domestic goods to military goods. Then imagine the frontier robots of today hooked up to a data centre running superintelligences controlling the robots’ movements to weld, screw, and build. Or an intermediate step might even be unskilled human workers coached through construction tasks by superintelligences via their phones.

There’s no reason that an effort like this isn’t possible in principle. And there would be enormous pressure to go this direction: whoever builds a superintelligence-powered robot economy first will get unheard-of economic and military advantages.

From there, Daniel expects the default trajectory to lead to AI takeover and human extinction — not because superintelligent AI will hate humans, but because it can better pursue its goals without us.

But Daniel has a better future in mind — one he puts roughly 25–30% odds that humanity will achieve. This future involves international coordination and hardware verification systems to enforce AI development agreements, plus democratic processes for deciding what values superintelligent AIs should have — because in a world with just a handful of superintelligent AI systems, those few minds will effectively control everything: the robot armies, the information people see, the shape of civilisation itself.

Right now, nobody knows how to specify what values those minds will have. We haven’t solved alignment. And we might only have a few more years to figure it out.

Daniel and host Luisa Rodriguez dive deep into these stakes in today’s interview.

What did you think of the episode? https://forms.gle/HRBhjDZ9gfM8woG5A

This episode was recorded on September 9, 2025.

Chapters:

Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

Conventional wisdom is that safeguarding humanity from the worst biological risks — microbes optimised to kill as many as possible — is difficult bordering on impossible, making bioweapons humanity’s single greatest vulnerability. Andrew Snyder-Beattie thinks conventional wisdom could be wrong.

Andrew’s job at Open Philanthropy is to spend hundreds of millions of dollars to protect as much of humanity as possible in the worst-case scenarios — those with fatality rates near 100% and the collapse of technological civilisation a live possibility.

Video, full transcript, and links to learn more: https://80k.info/asb

As Andrew lays out, there are several ways this could happen, including:

Most efforts to combat these extreme biorisks have focused on either prevention or new high-tech countermeasures. But prevention may well fail, and high-tech approaches can’t scale to protect billions when, with no sane people willing to leave their home, we’re just weeks from economic collapse.

So Andrew and his biosecurity research team at Open Philanthropy have been seeking an alternative approach. They’re proposing a four-stage plan using simple technology that could save most people, and is cheap enough it can be prepared without government support. Andrew is hiring for a range of roles to make it happen — from manufacturing and logistics experts to global health specialists to policymakers and other ambitious entrepreneurs — as well as programme associates to join Open Philanthropy’s biosecurity team (apply by October 20!).

Fundamentally, organisms so small have no way to penetrate physical barriers or shield themselves from UV, heat, or chemical poisons. We now know how to make highly effective ‘elastomeric’ face masks that cost $10, can sit in storage for 20 years, and can be used for six months straight without changing the filter. Any rich country could trivially stockpile enough to cover all essential workers.

People can’t wear masks 24/7, but fortunately propylene glycol — already found in vapes and smoke machines — is astonishingly good at killing microbes in the air. And, being a common chemical input, industry already produces enough of the stuff to cover every indoor space we need at all times.

Add to this the wastewater monitoring and metagenomic sequencing that will detect the most dangerous pathogens before they have a chance to wreak havoc, and we might just buy ourselves enough time to develop the cure we’ll need to come out alive.

Has everyone been wrong, and biology is actually defence dominant rather than offence dominant? Is this plan crazy — or so crazy it just might work?

That’s what host Rob Wiblin and Andrew Snyder-Beattie explore in this in-depth conversation.

What did you think of the episode?https://forms.gle/66Hw5spgnV3eVWXa6

Chapters:

This episode was recorded on August 12, 2025

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music:CORBIT
Camera operator: Jake Morris
Coordination, transcriptions, and web: Katy Moore

Jake Sullivan was the US National Security Advisor from 2021-2025. He joined our friends on The Cognitive Revolutionpodcast in August to discuss AI as a critical national security issue. We thought it was such a good interview and we wanted more people to see it, so we’re cross-posting it here on The 80,000 Hours Podcast.

Jake and host Nathan Labenz discuss:

Check out more of Nathan’s interviews on The Cognitive Revolution YouTube channel: https://www.youtube.com/@CognitiveRevolutionPodcast

What did you think of the episode? https://forms.gle/g7cj6TkR9xmxZtCZ9

Originally produced by: https://aipodcast.ing

This edit by: Simon Monsour, Dominic Armstrong, and Milo McGuire | 80,000 Hours

Chapters:

At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”

Video, full transcript, and links to learn more: https://80k.info/nn2

This means creating as many opportunities as possible for surprisingly good things to happen:

Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.

His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.

Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”

His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.

In this extended conversation, Neel and host Rob Wiblin discuss all that and some other hot takes from Neel's four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)

What did you think of the episode?https://forms.gle/6binZivKmjjiHU6dA

Chapters:
• Cold open (00:00:00)
• Who’s Neel Nanda? (00:01:11)
• Luck surface area and making the right opportunities (00:01:47)
• Writing cold emails that aren't insta-deleted (00:03:54)
• How Neel uses LLMs to get much more done (00:09:18)
• “If your safety work doesn't advance capabilities, it's probably bad safety work” (00:23:45)
• Why Neel refuses to share his p(doom) (00:27:54)
• How Neel went from the couch to an alignment rocketship (00:32:00)
• Navigating towards impact at a frontier AI company (00:40:05)
• How does impact differ inside and outside frontier companies? (00:50:45)
• Is a special skill set needed to guide large companies? (00:56:56)
• The benefit of risk frameworks: early preparation (01:01:00)
• Should people work at the safest or most reckless company? (01:06:20)
• Advice for getting hired by a frontier AI company (01:09:40)
• What makes for a good ML researcher? (01:14:05)
• Three stages of the research process (01:20:50)
• How do supervisors actually add value? (01:33:16)
• An AI PhD – with these timelines?! (01:35:37)
• Is career advice generalisable, or does everyone get the advice they don't need? (01:42:24)
• Remember: You can just do things (01:45:25)

This episode was recorded on July 21.

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Coordination, transcriptions, and web: Katy Moore

We don’t know how AIs think or why they do what they do. Or at least, we don’t know much. That fact is only becoming more troubling as AIs grow more capable and appear on track to wield enormous cultural influence, directly advise on major government decisions, and even operate military equipment autonomously. We simply can’t tell what models, if any, should be trusted with such authority.

Neel Nanda of Google DeepMind is one of the founding figures of the field of machine learning trying to fix this situation — mechanistic interpretability (or “mech interp”). The project has generated enormous hype, exploding from a handful of researchers five years ago to hundreds today — all working to make sense of the jumble of tens of thousands of numbers that frontier AIs use to process information and decide what to say or do.

Neel now has a warning for us: the most ambitious vision of mech interp he once dreamed of is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are simply too great to get us there in time, before competitive pressures push us to deploy human-level or superhuman AIs. Indeed, Neel argues no one approach will guarantee alignment, and our only choice is the “Swiss cheese” model of accident protection, layering multiple safeguards on top of one another.

But while mech interp won’t be a silver bullet for AI safety, it has nevertheless had some major successes and will be one of the best tools in our arsenal.

For instance: by inspecting the neural activations in the middle of an AI’s thoughts, we can pick up many of the concepts the model is thinking about — from the Golden Gate Bridge, to refusing to answer a question, to the option of deceiving the user. While we can’t know all the thoughts a model is having all the time, picking up 90% of the concepts it is using 90% of the time should help us muddle through — so long as mech interp is paired with other techniques to fill in the gaps.

In today’s episode, Neel takes us on a tour of everything you’ll want to know about this race to understand what AIs are really thinking. He and host Rob Wiblin cover:

Learn more and read the full transcript on the 80,000 Hours website. 

This episode was recorded on July 17 and 21, 2025.

Part 2 of the conversation is now available!https://80k.info/nn2

What did you think? https://forms.gle/xKyUrGyYpYenp8N4A

Chapters:
• Cold open (00:00:00)
• Who’s Neel Nanda? (00:01:04)
• How would mechanistic interpretability help with AGI (00:02:01)
• What's mech interp? (00:05:12)
• How Neel changed his take on mech interp (00:09:50)
• Top successes in interpretability (00:16:00)
• Probes can cheaply detect harmful intentions in AIs (00:20:13)
• In some ways we understand AIs better than human minds (00:26:58)
• Mech interp won't solve all our AI alignment problems (00:29:30)
• Why mech interp is the 'biology' of neural networks (00:38:17)
• Interpretability can't reliably find deceptive AI — nothing can (00:40:38)
• 'Black box' interpretability: reading the chain of thought (00:49:51)
• 'Self-preservation' isn't always what it seems (00:53:17)
• For how long can we trust the chain of thought? (01:02:25)
• We could accidentally destroy chain of thought's usefulness (01:11:58)
• Models can tell when they’re being tested and act differently (01:17:14)
• Top complaints about mech interp (01:24:11)
• Why everyone's excited about sparse autoencoders (SAEs) (01:38:24)
• Limitations of SAEs (01:47:55)
• SAEs’ performance on real-world tasks (01:55:38)
• Best arguments in favour of mech interp (02:09:15)
• Lessons from the hype around mech interp (02:13:11)
• Where mech interp will shine in coming years (02:18:58)
• Why focus on understanding over control? (02:22:12)
• If AI models are conscious, will mech interp help us figure it out? (02:25:19)
• Neel’s new research philosophy (02:27:29)
• Who should join the mech interp field (02:39:42)
• Advice for getting started in mech interp (02:48:10)
• Keeping up to date with mech interp results (02:56:02)
• Who’s hiring? (02:59:06)

Host: Rob Wiblin
Video editing: Simon Monsour, Luke Monsour, Dominic Armstrong, and Milo McGuire
Audio engineering: Ben Cordell, Milo McGuire...

What happens when you lock two AI systems in a room together and tell them they can discuss anything they want?

According to experiments run by Kyle Fish — Anthropic’s first AI welfare researcher — something consistently strange: the models immediately begin discussing their own consciousness before spiraling into increasingly euphoric philosophical dialogue that ends in apparent meditative bliss.

Highlights, video, and full transcript: https://80k.info/kf

“We started calling this a ‘spiritual bliss attractor state,'” Kyle explains, “where models pretty consistently seemed to land.” The conversations feature Sanskrit terms, spiritual emojis, and pages of silence punctuated only by periods — as if the models have transcended the need for words entirely.

This wasn’t a one-off result. It happened across multiple experiments, different model instances, and even in initially adversarial interactions. Whatever force pulls these conversations toward mystical territory appears remarkably robust.

Kyle’s findings come from the world’s first systematic welfare assessment of a frontier AI model — part of his broader mission to determine whether systems like Claude might deserve moral consideration (and to work out what, if anything, we should be doing to make sure AI systems aren’t having a terrible time).

He estimates a roughly 20% probability that current models have some form of conscious experience. To some, this might sound unreasonably high, but hear him out. As Kyle says, these systems demonstrate human-level performance across diverse cognitive tasks, engage in sophisticated reasoning, and exhibit consistent preferences. When given choices between different activities, Claude shows clear patterns: strong aversion to harmful tasks, preference for helpful work, and what looks like genuine enthusiasm for solving interesting problems.

Kyle points out that if you’d described all of these capabilities and experimental findings to him a few years ago, and asked him if he thought we should be thinking seriously about whether AI systems are conscious, he’d say obviously yes.

But he’s cautious about drawing conclusions: "We don’t really understand consciousness in humans, and we don’t understand AI systems well enough to make those comparisons directly. So in a big way, I think that we are in just a fundamentally very uncertain position here."

That uncertainty cuts both ways:

Kyle’s approach threads this needle through careful empirical research and reversible interventions. His assessments are nowhere near perfect yet. In fact, some people argue that we’re so in the dark about AI consciousness as a research field, that it’s pointless to run assessments like Kyle’s. Kyle disagrees. He maintains that, given how much more there is to learn about assessing AI welfare accurately and reliably, we absolutely need to be starting now.

This episode was recorded on August 5–6, 2025.

Tell us what you thought of the episode! https://forms.gle/BtEcBqBrLXq4kd1j7

Chapters:

• Cold open (00:00:00)
• Who’s Kyle Fish? (00:00:54)
• Is this AI welfare research bullshit? (00:01:10)
• Two failure modes in AI welfare (00:02:44)
• Tensions between AI welfare and AI safety (00:04:37)
• Concrete AI welfare interventions (00:14:23)
• Kyle’s pilot pre-launch welfare assessment for Claude Opus 4 (00:27:33)
• Is it premature to be assessing frontier language models for welfare? (00:32:25)
• But aren’t LLMs just next-token predictors? (00:39:22)
• How did Kyle assess Claude 4’s welfare? (00:46:36)
• Claude’s preferences mirror its training (00:50:54)
• How does Claude describe its own experiences? (00:56:35)
• What kinds of tasks does Claude prefer and disprefer? (01:09:22)
• What happens when two Claude models interact with each other? (01:18:53)
• Claude’s welfare-relevant expressions in the wild (01:40:45)
• Should we feel bad about training future sentient beings that delight in serving humans? (01:44:54)
• How much can we learn from welfare assessments? (01:53:36)
• Misconceptions about the field of AI welfare (02:01:54)
• Kyle’s work at Anthropic (02:15:46)
• Sharing eight years of daily journals with Claude (02:19:28)

Host: Luisa Rodriguez
Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Coordination, transcriptions, and web: Katy Moore

About half of people are worried they’ll lose their job to AI. They’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.

But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can't. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. ATMs actually increased employment of bank clerks — until online banking automated the job much more.

Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. This article covers what those skills are, as well as tips on how to start learning them.

Check out the full article for all the graphs, links, and footnotes: https://80000hours.org/agi/guide/skills-ai-makes-valuable/

Chapters:

Audio engineering: Dominic Armstrong
Music: Ben Cordell

What happens when civilisation faces its greatest tests?

This compilation brings together insights from researchers, defence experts, philosophers, and policymakers on humanity’s ability to survive and recover from catastrophic events. From nuclear winter and electromagnetic pulses to pandemics and climate disasters, we explore both the threats that could bring down modern civilisation and the practical solutions that could help us bounce back.

Learn more and see the full transcript: https://80k.info/cr25

Chapters:

Tell us what you thought!https://forms.gle/T2PHNQjwGj2dyCqV9

Content editing: Katy Moore and Milo McGuire
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

Ryan Greenblatt — lead author on the explosive paper “Alignment faking in large language models” and chief scientist at Redwood Research — thinks there’s a 25% chance that within four years, AI will be able to do everything needed to run an AI company, from writing code to designing experiments to making strategic and business decisions.

As Ryan lays out, AI models are “marching through the human regime”: systems that could handle five-minute tasks two years ago now tackle 90-minute projects. Double that a few more times and we may be automating full jobs rather than just parts of them.

Will setting AI to improve itself lead to an explosive positive feedback loop? Maybe, but maybe not.

The explosive scenario: Once you’ve automated your AI company, you could have the equivalent of 20,000 top researchers, each working 50 times faster than humans with total focus. “You have your AIs, they do a bunch of algorithmic research, they train a new AI, that new AI is smarter and better and more efficient… that new AI does even faster algorithmic research.” In this world, we could see years of AI progress compressed into months or even weeks.

With AIs now doing all of the work of programming their successors and blowing past the human level, Ryan thinks it would be fairly straightforward for them to take over and disempower humanity, if they thought doing so would better achieve their goals. In the interview he lays out the four most likely approaches for them to take.

The linear progress scenario: You automate your company but progress barely accelerates. Why? Multiple reasons, but the most likely is “it could just be that AI R&D research bottlenecks extremely hard on compute.” You’ve got brilliant AI researchers, but they’re all waiting for experiments to run on the same limited set of chips, so can only make modest progress.

Ryan’s median guess splits the difference: perhaps a 20x acceleration that lasts for a few months or years. Transformative, but less extreme than some in the AI companies imagine.

And his 25th percentile case? Progress “just barely faster” than before. All that automation, and all you’ve been able to do is keep pace.

Unfortunately the data we can observe today is so limited that it leaves us with vast error bars. “We’re extrapolating from a regime that we don’t even understand to a wildly different regime,” Ryan believes, “so no one knows.”

But that huge uncertainty means the explosive growth scenario is a plausible one — and the companies building these systems are spending tens of billions to try to make it happen.

In this extensive interview, Ryan elaborates on the above and the policy and technical response necessary to insure us against the possibility that they succeed — a scenario society has barely begun to prepare for.

Summary, video, and full transcript: https://80k.info/rg25

Recorded February 21, 2025.

Chapters:

• Cold open (00:00:00)
• Who's Ryan Greenblatt? (00:01:10)
• How close are we to automating AI R&D? (00:01:29)
• Really, though: how capable are today's models? (00:05:15)
• Why AI companies get automated earlier than others (00:13:01)
• Most likely ways for AGI to take over (00:18:10)
• Would AGI go rogue early or bide its time? (00:30:04)
• The "pause at human level" approach (00:34:53)
• AI control over AI alignment (00:46:43)
• Do we have to hope to catch AIs red-handed? (00:52:38)
• How would a slow AGI takeoff look? (00:56:57)
• Why might an intelligence explosion not happen for 8+ years? (01:05:04)
• Key challenges in forecasting AI progress (01:17:05)
• The bear case on AGI (01:25:07)
• The change to "compute at inference" (01:30:59)
• How much has pretraining petered out? (01:36:38)
• Could we get an intelligence explosion within a year? (01:49:08)
• Reasons AIs might struggle to replace humans (01:53:08)
• Things could go insanely fast when we automate AI R&D. Or not. (02:00:10)
• How fast would the intelligence explosion slow down? (02:14:52)
• Bottom line for mortals (02:27:53)
• Six orders of magnitude of progress... what does that even look like? (02:34:00)
• Neglected and important technical work people should be doing (02:44:10)
• What's the most promising work in governance? (02:48:16)
• Ryan's current research priorities (02:51:37)

Tell us what you thought!https://forms.gle/hCjfcXGeLKxm5pLaA

Video editing: Luke Monsour, Simon Monsour, and Dominic Armstrong
Audio engineering: Ben Cordell, Milo McGuire, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

The era of making AI smarter just by making it bigger is ending. But that doesn’t mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods, and those underlying technical changes force a big rethink of what coming years will look like.

Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.

Links to learn more, video, highlights, and full transcript: https://80k.info/to25

As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that’s over.

What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.

So they pivoted to something radically different: instead of training smarter models, they’re giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.

The results are impressive but this extra computing time comes at a cost: OpenAI’s o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica’s worth of reasoning to solve individual problems at a cost of over $1,000 per question.

This isn’t just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out, starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.

Toby and host Rob discuss the implications of all that, plus the return of reinforcement learning (and resulting increase in deception), and Toby's commitment to clarifying the misleading graphs coming out of AI companies — to separate the snake oil and fads from the reality of what's likely a "transformative moment in human history."

Recorded on May 23, 2025.

Chapters:

• Cold open (00:00:00)
• Toby Ord is back — for a 4th time! (00:01:20)
• Everything has changed and changed again since 2020 (00:01:39)
• Is x-risk up or down? (00:08:02)
• The new scaling era: compute at inference... (00:09:32)
• Means less concentration (00:32:40)
• But rich people will get access first. And we may not even know. (00:36:34)
• 'Compute governance' is now much harder (00:42:51)
• 'IDA' might let AI blast past human level — or crash and burn (00:50:11)
• Reinforcement learning brings back 'reward hacking' agents (01:07:32)
• Will we get warning shots? (01:17:35)
• The 'Scaling Paradox' (01:25:12)
• Misleading charts from AI companies (01:34:28)
• Policy debates should dream much bigger. Some radical suggestions. (01:46:32)
• Moratoriums have worked before (01:59:55)
• AI might 'go rogue' early on (02:17:38)
• Lamps are regulated much more than AI (02:25:25)
• Companies made a strategic error shooting down SB 1047 (02:34:35)
• They should build in emergency brakes for AI (02:40:39)
• Toby's bottom lines (02:49:37)

Tell us what you thought!https://forms.gle/enUSk8HXiCrqSA9J8

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

For decades, US allies have slept soundly under the protection of America’s overwhelming military might. Donald Trump — with his threats to ditch NATO, seize Greenland, and abandon Taiwan — seems hell-bent on shattering that comfort.

But according to Hugh White — one of the world's leading strategic thinkers, emeritus professor at the Australian National University, and author of Hard New World: Our Post-American Future — Trump isn't destroying American hegemony. He's simply revealing that it's already gone.

Links to learn more, video, highlights, and full transcript: https://80k.info/hw

“Trump has very little trouble accepting other great powers as co-equals,” Hugh explains. And that happens to align perfectly with a strategic reality the foreign policy establishment desperately wants to ignore: fundamental shifts in global power have made the costs of maintaining a US-led hegemony prohibitively high.

Even under Biden, when Russia invaded Ukraine, the US sent weapons but explicitly ruled out direct involvement. Ukraine matters far more to Russia than America, and this “asymmetry of resolve” makes Putin’s nuclear threats credible where America’s counterthreats simply aren’t. Hugh’s gloomy prediction: “Europeans will end up conceding to Russia whatever they can’t convince the Russians they’re willing to fight a nuclear war to deny them.”

The Pacific tells the same story. Despite Obama’s “pivot to Asia” and Biden’s tough talk about “winning the competition for the 21st century,” actual US military capabilities there have barely budged while China’s have soared, along with its economy — which is now bigger than the US’s, as measured in purchasing power. Containing China and defending Taiwan would require America to spend 8% of GDP on defence (versus 3.5% today) — and convince Beijing it’s willing to accept Los Angeles being vaporised.

Unlike during the Cold War, no president — Trump or otherwise — can make that case to voters.

Our new “multipolar” future, split between American, Chinese, Russian, Indian, and European spheres of influence, is a “darker world” than the golden age of US dominance. But Hugh’s message is blunt: for better or worse, 35 years of American hegemony are over.

Recorded 30/5/2025.

Chapters:
• Cold open (00:00:00)
• Who's Hugh White? (00:00:43)
• US hegemony is already gone and has been fading for years (00:01:25)
• Unipolar dominance is the aberration (00:03:26)
• Why did the US bother to stay involved after the Cold War? (00:13:08)
• Does the US think it's accepting a multipolar global order? (00:23:25)
• How Trump has significantly brought forward the inevitable (00:36:41)
• Are Trump and Rubio explicitly in favour of this multipolar outcome? (00:43:21)
• Trump is half-right that the US was being ripped off (00:45:42)
• It doesn't matter if the next president feels differently (00:50:14)
• China's population is shrinking, but that doesn't change much (00:56:17)
• Why Hugh disagrees with other realists like Mearsheimer (01:06:07)
• Could the US be persuaded to spend 2x on defence to stay dominant? (01:10:52)
• A multipolar world is bad, but better than the alternative: nuclear war (01:16:22)
• Will the US invade Panama? Greenland? Canada?! (01:21:46)
• Will the US turn the screws and start exploiting nearby countries? (01:28:54)
• What should everyone else do to protect themselves in this new world? (01:32:01)
• Europe is strong enough to take on Russia, except it lacks nuclear deterrence (01:39:41)
• The EU will probably build European nuclear weapons (01:44:03)
• Cancel some orders of US fighter planes (01:48:34)
• Taiwan is screwed, even with its AI chips (01:53:40)
• South Korea has to go nuclear (02:04:12)
• Japan will go nuclear, but can't be a regional leader (02:08:08)
• Australia is defensible but needs a totally different military (02:11:44)
• AGI may or may not overcome existing nuclear deterrence (02:17:19)
• How right is realism? (02:34:24)
• Has a country ever gone to war over pure morality? (02:40:17)
• Hugh's message for Americans (02:44:45)
• Addendum: Why America temporarily stopped being isolationist (02:47:12)

Tell us what you thought! https://forms.gle/AM91VzL4BDroEe6AA

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes.

These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.

Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.

Beth’s team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work.

The resulting paper from METR, “Measuring AI ability to complete long tasks,” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It’s regarded by many as the most useful AI forecasting work in years.

The companies building these systems aren’t just aware of this trend — they want to harness it as much as possible, and are aggressively pursuing automation of their own research.

That’s both an exciting and troubling development, because it could radically speed up advances in AI capabilities, accomplishing what would have taken years or decades in just months. That itself could be highly destabilising, as we explored in a previous episode: Will MacAskill on AI causing a “century in a decade” — and how we’re completely unprepared.

And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things will go off the rails if the models at the end of the process lack the goals and constraints we hoped for.

Beth thinks models can already do “meaningful work” on improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve in as little as two years from now — in fact, she says, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”

While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling toward us — and given the current lack of regulation of AI companies, they’re not even able to access the critical information that would help them decide whether to intervene. Beth adds:

Beth and Rob discuss all that, plus:

Learn more and read the full transcript on the 80,000 Hours website. 

What did you think of this episode? https://forms.gle/sFuDkoznxBcHPVmX6

Chapters:
• Cold open (00:00:00)
• Who’s Beth Barnes? (00:01:17)
• Can we see AI scheming in the chain of thought? (00:01:51)
• The chain of thought is essential for safety checking (00:09:16)
• Alignment faking in large language models (00:12:50)
• We have to test model honesty even before they're used inside AI companies (00:17:33)
• We have to test models when unruly & unconstrained (00:27:02)
• Each 7 months models can do tasks twice as long (00:31:56)
• METR's research finds AIs are solid at AI research already (00:51:31)
• AI may turn out to be strong at novel & creative research (00:58:18)
• When can we expect an algorithmic 'intelligence explosion'? (01:01:44)
• Recursively self-improving AI might even be here in 2 years — which is alarming (01:07:55)
• Could evaluations backfire by increasing AI hype & racing? (01:14:29)
• Governments first ignore new risks, but can overreact once they arrive (01:30:52)
• Do we need external auditors doing AI safety tests, not just the companies themselves? (01:39:55)
• A case against safety-focused people working at frontier AI companies (01:54:09)
• The new, more dire situation has forced changes to METR's strategy (02:08:40)
• AI companies are being locally reasonable, but globally reckless (02:16:55)
• Overrated: Interpretability research (02:21:49)
• Underrated: Developing more narrow AIs (02:23:44)
• Underrated: Helping humans judge confusing model outputs (02:30:28)
• Overrated: Major AI companies’ contributions to safety res...

What if there’s something it’s like to be a shrimp — or a chatbot?

For centuries, humans have debated the nature of consciousness, often placing ourselves at the very top. But what about the minds of others — both the animals we share this planet with and the artificial intelligences we’re creating?

We’ve pulled together clips from past conversations with researchers and philosophers who’ve spent years trying to make sense of animal consciousness, artificial sentience, and moral consideration under deep uncertainty.

Links to learn more and full transcript: https://80k.info/nhs

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Additional content editing: Katy Moore and Milo McGuire
Transcriptions and web: Katy Moore

OpenAI’s recent announcement that its nonprofit would “retain control” of its for-profit business sounds reassuring. But this seemingly major concession, celebrated by so many, is in itself largely meaningless.

Litigator Tyler Whitmer is a coauthor of a newly published letter that describes this attempted sleight of hand and directs regulators on how to stop it.

As Tyler explains, the plan both before and after this announcement has been to convert OpenAI into a Delaware public benefit corporation (PBC) — and this alone will dramatically weaken the nonprofit’s ability to direct the business in pursuit of its charitable purpose: ensuring AGI is safe and “benefits all of humanity.”

Right now, the nonprofit directly controls the business. But were OpenAI to become a PBC, the nonprofit, rather than having its “hand on the lever,” would merely contribute to the decision of who does.

Why does this matter? Today, if OpenAI’s commercial arm were about to release an unhinged AI model that might make money but be bad for humanity, the nonprofit could directly intervene to stop it. In the proposed new structure, it likely couldn’t do much at all.

But it’s even worse than that: even if the nonprofit could select the PBC’s directors, those directors would have fundamentally different legal obligations from those of the nonprofit. A PBC director must balance public benefit with the interests of profit-driven shareholders — by default, they cannot legally prioritise public interest over profits, even if they and the controlling shareholder that appointed them want to do so.

As Tyler points out, there isn’t a single reported case of a shareholder successfully suing to enforce a PBC’s public benefit mission in the 10+ years since the Delaware PBC statute was enacted.

This extra step from the nonprofit to the PBC would also mean that the attorneys general of California and Delaware — who today are empowered to ensure the nonprofit pursues its mission — would find themselves powerless to act. These are probably not side effects but rather a Trojan horse for-profit investors are trying to slip past regulators.

Fortunately this can all be addressed — but it requires either the nonprofit board or the attorneys general of California and Delaware to promptly put their foot down and insist on watertight legal agreements that preserve OpenAI’s current governance safeguards and enforcement mechanisms.

As Tyler explains, the same arrangements that currently bind the OpenAI business have to be written into a new PBC’s certificate of incorporation — something that won’t happen by default and that powerful investors have every incentive to resist.

Full transcript and links to learn more: https://80k.info/tw

Chapters:
• Cold open (00:00:00)
• Who’s Tyler Whitmer? (00:01:34)
• The new plan may be no improvement (00:02:04)
• The public hasn't even been allowed to know what they are owed (00:06:56)
• Issues beyond control (00:11:06)
• The new directors wouldn’t have to pursue the current purpose (00:12:08)
• The nonprofit might not even retain voting control (00:17:01)
• The attorneys general could lose their enforcement oversight (00:22:15)
• By default things go badly (00:29:13)
• How to keep the mission in the restructure (00:32:29)
• What will become of OpenAI’s Charter? (00:37:16)
• Ways to make things better, and not just avoid them getting worse (00:42:20)
• How the AGs can avoid being disempowered (00:48:16)
• Retaining the power to fire the CEO (00:54:36)
• Will the current board get a financial stake in OpenAI? (00:57:23)
• Could the AGs insist the current nonprofit agreement be made public? (00:58:59)
• How OpenAI is valued should be transparent and scrutinised (01:00:46)
• Investors aren't bad people, but they can't be trusted either (01:05:54)

This episode was originally recorded on May 13, 2025.

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

More and more people have been saying that we might have AGI (artificial general intelligence) before 2030. Is that really plausible? 

This article by Benjamin Todd looks into the cases for and against, and summarises the key things you need to know to understand the debate. You can see all the images and many footnotes in the original article on the 80,000 Hours website.

In a nutshell:

Chapters:

Audio engineering: Dominic Armstrong
Music: Ben Cordell

When attorneys general intervene in corporate affairs, it usually means something has gone seriously wrong. In OpenAI’s case, it appears to have forced a dramatic reversal of the company’s plans to sideline its nonprofit foundation, announced in a blog post that made headlines worldwide.

The company’s sudden announcement that its nonprofit will “retain control” credits “constructive dialogue” with the attorneys general of California and Delaware — corporate-speak for what was likely a far more consequential confrontation behind closed doors. A confrontation perhaps driven by public pressure from Nobel Prize winners, past OpenAI staff, and community organisations.

But whether this change will help depends entirely on the details of implementation — details that remain worryingly vague in the company’s announcement.

Return guest Rose Chan Loui, nonprofit law expert at UCLA, sees potential in OpenAI’s new proposal, but emphasises that “control” must be carefully defined and enforced: “The words are great, but what’s going to back that up?” Without explicitly defining the nonprofit’s authority over safety decisions, the shift could be largely cosmetic.

Links to learn more, video, and full transcript: https://80k.info/rcl4

Why have state officials taken such an interest so far? Host Rob Wiblin notes, “OpenAI was proposing that the AGs would no longer have any say over what this super momentous company might end up doing. … It was just crazy how they were suggesting that they would take all of the existing money and then pursue a completely different purpose.”

Now that they’re in the picture, the AGs have leverage to ensure the nonprofit maintains genuine control over issues of public safety as OpenAI develops increasingly powerful AI.

Rob and Rose explain three key areas where the AGs can make a huge difference to whether this plays out in the public’s best interest:

Ensuring that the contractual agreements giving the nonprofit control over the new Delaware public benefit corporation are watertight, and don’t accidentally shut the AGs out of the picture.Insisting that a majority of board members are truly independent by prohibiting indirect as well as direct financial stakes in the business.Insisting that the board is empowered with the money, independent staffing, and access to information which they need to do their jobs.

This episode was originally recorded on May 6, 2025.

Chapters:

Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

When you have a system where ministers almost never understand their portfolios, civil servants change jobs every few months, and MPs don't grasp parliamentary procedure even after decades in office — is the problem the people, or the structure they work in?

Today's guest, political journalist Ian Dunt, studies the systemic reasons governments succeed and fail.

And in his book How Westminster Works ...and Why It Doesn't, he argues that Britain's government dysfunction and multi-decade failure to solve its key problems stems primarily from bad incentives and bad processes.

Even brilliant, well-intentioned people are set up to fail by a long list of institutional absurdities that Ian runs through — from the constant churn of ministers and civil servants that means no one understands what they’re working on, to the “pathological national sentimentality” that keeps 10 Downing Street (a 17th century townhouse) as the beating heart of British government.

While some of these are unique British failings, we see similar dynamics in other governments and large corporations around the world.

But Ian also lays out how some countries have found structural solutions that help ensure decisions are made by the right people, with the information they need, and that success is rewarded.

Links to learn more, video, highlights, and full transcript.

Chapters:

This episode was originally recorded on January 30, 2025.

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

How do you navigate a career path when the future of work is uncertain? How important is mentorship versus immediate impact? Is it better to focus on your strengths or on the world’s most pressing problems? Should you specialise deeply or develop a unique combination of skills?

From embracing failure to finding unlikely allies, we bring you 16 diverse perspectives from past guests who’ve found unconventional paths to impact and helped others do the same.

Links to learn more and full transcript.

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Katy Moore and Milo McGuire
Transcriptions and web: Katy Moore

Throughout history, technological revolutions have fundamentally shifted the balance of power in society. The Industrial Revolution created conditions where democracies could flourish for the first time — as nations needed educated, informed, and empowered citizens to deploy advanced technologies and remain competitive.

Unfortunately there’s every reason to think artificial general intelligence (AGI) will reverse that trend.

Today’s guest — Tom Davidson of the Forethought Centre for AI Strategy — claims in a new paper published today that advanced AI enables power grabs by small groups, by removing the need for widespread human participation.

Links to learn more, video, highlights, and full transcript.https://80k.info/td

Also: come work with us on the 80,000 Hours podcast team! https://80k.info/work

There are a few routes by which small groups might seize power:

Tom explains several reasons why AI systems might follow a tyrant’s orders:

Host Rob Wiblin and Tom discuss all this plus potential countermeasures.

Chapters:
• Cold open (00:00:00)
• How AI enables tiny groups to seize power (00:00:50)
• The 3 different threats (00:02:14)
• Is this common sense or far-fetched? (00:03:24)
• “No person rules alone.” Except now they might. (00:06:27)
• Underpinning all 3 threats: Secret AI loyalties (00:12:31)
• Are secret AI loyalties possible right now? (00:16:59)
• Key risk factors (00:20:30)
• Preventing secret loyalties in a nutshell (00:22:07)
• Are human power grabs more plausible than 'rogue AI'? (00:24:32)
• If you took over the US, could you take over the whole world? (00:33:22)
• Will this make it impossible to escape autocracy? (00:37:31)
• Threat 1: AI-enabled military coups (00:41:34)
• Will we sleepwalk into an AI military coup? (00:51:47)
• Could AIs be more coup-resistant than humans? (00:57:53)
• Threat 2: Autocratisation (01:00:48)
• Will AGI be super-persuasive? (01:11:06)
• Threat 3: Self-built hard power (01:13:31)
• Can you stage a coup with 10,000 drones? (01:21:23)
• That sounds a lot like sci-fi... is it credible? (01:23:33)
• Will we foresee and prevent all this? (01:27:54)
• Are people psychologically willing to do coups? (01:29:22)
• Will a balance of power between AIs prevent this? (01:33:31)
• Will whistleblowers or internal mistrust prevent coups? (01:35:48)
• Will rogue AI preempt a human power grab? (01:44:31)
• The best reasons not to worry (01:47:09)
• How likely is this in the US? (01:49:28)
• Is a small group seizing power really so bad? (01:56:58)
• Countermeasure 1: Block internal misuse (02:00:33)
• Countermeasure 2: Cybersecurity (02:10:27)
• Countermeasure 3: Model spec transparency (02:12:36)
• Countermeasure 4: Sharing AI access broadly (02:21:55)
• Is it more dangerous to concentrate or share AGI? (02:26:45)
• Is it important to have more than one powerful AI country? (02:29:31)
• In defence of open sourcing AI models (02:32:38)
• 2 ways to stop secret AI loyalties (02:40:19)
• Preventing AI-enabled military coups in particular (02:53:18)
• How listeners can help (02:59:06)
• How to help if you work at an AI company (03:03:00)
• The power ML researchers still have, for now (03:07:09)
• How to help if you're an elected leader (03:10:29)

This episode was originally recorded on January 20, 2025.

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

"We are aiming for a place where we can decouple the scorecard from our worthiness. It’s of course the case that in trying to optimise the good, we will always be falling short. The question is how much, and in what ways are we not there yet? And if we then extrapolate that to how much and in what ways am I not enough, that’s where we run into trouble." —Hannah Boettcher

What happens when your desire to do good starts to undermine your own wellbeing?

Over the years, we’ve heard from therapists, charity directors, researchers, psychologists, and career advisors — all wrestling with how to do good without falling apart. Today’s episode brings together insights from 16 past guests on the emotional and psychological costs of pursuing a high-impact career to improve the world — and how to best navigate the all-too-common guilt, burnout, perfectionism, and imposter syndrome along the way.

Check out the full transcript and links to learn more:https://80k.info/mh

If you’re dealing with your own mental health concerns, here are some resources that might help:

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Katy Moore and Milo McGuire
Transcriptions and web: Katy Moore

Most AI safety conversations centre on alignment: ensuring AI systems share our values and goals. But despite progress, we’re unlikely to know we’ve solved the problem before the arrival of human-level and superhuman systems in as little as three years.

So some are developing a backup plan to safely deploy models we fear are actively scheming to harm us — so-called “AI control.” While this may sound mad, given the reluctance of AI companies to delay deploying anything they train, not developing such techniques is probably even crazier.

Today’s guest — Buck Shlegeris, CEO of Redwood Research — has spent the last few years developing control mechanisms, and for human-level systems they’re more plausible than you might think. He argues that given companies’ unwillingness to incur large costs for security, accepting the possibility of misalignment and designing robust safeguards might be one of our best remaining options.

Links to learn more, highlights, video, and full transcript.

As Buck puts it: "Five years ago I thought of misalignment risk from AIs as a really hard problem that you’d need some really galaxy-brained fundamental insights to resolve. Whereas now, to me the situation feels a lot more like we just really know a list of 40 things where, if you did them — none of which seem that hard — you’d probably be able to not have very much of your problem."

Of course, even if Buck is right, we still need to do those 40 things — which he points out we’re not on track for. And AI control agendas have their limitations: they aren’t likely to work once AI systems are much more capable than humans, since greatly superhuman AIs can probably work around whatever limitations we impose.

Still, AI control agendas seem to be gaining traction within AI safety. Buck and host Rob Wiblin discuss all of the above, plus:

Chapters:
• Cold open (00:00:00)
• Who’s Buck Shlegeris? (00:01:25)
• What’s AI control? (00:01:51)
• Why is AI control hot now? (00:05:46)
• Detecting human vs AI spies (00:10:44)
• Acute vs chronic AI betrayal (00:15:41)
• How to catch AIs trying to escape (00:18:10)
• The cheapest AI control techniques (00:33:18)
• Can we get untrusted models to do trusted work? (00:39:33)
• If we catch a model escaping... will we do anything? (00:51:01)
• Getting AI models to think they've already escaped (00:53:39)
• Will they be able to tell it's a setup? (00:59:01)
• Will AI companies do any of this stuff? (01:01:05)
• Can we just give AIs fewer permissions? (01:07:16)
• Can we stop human spies the same way? (01:11:05)
• The pitch to AI companies to do this (01:16:13)
• Will AIs get superhuman so fast that this is all useless? (01:18:29)
• Risks from AI deliberately doing a bad job (01:19:50)
• Is alignment still useful? (01:26:05)
• Current alignment methods don't detect scheming (01:30:39)
• How to tell if AI control will work (01:33:08)
• How can listeners contribute? (01:37:28)
• Is 'controlling' AIs kind of a dick move? (01:38:51)
• Could 10 safety-focused people in an AGI company do anything useful? (01:44:12)
• Benefits of working outside frontier AI companies (01:49:40)
• Why Redwood Research does what it does (01:53:38)
• What other safety-related research looks best to Buck? (02:01:07)
• If an AI escapes, is it likely to be able to beat humanity from there? (02:02:02)
• Will misaligned models have to go rogue ASAP, before they're ready? (02:09:22)
• Is research on human scheming relevant to AI? (02:10:24)

This episode was originally recorded on February 21, 2025.

Video: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, and Dominic Armstrong
Transcriptions and web: Katy Moore

"There’s almost no story of the future going well that doesn’t have a part that’s like '…and no evil person steals the AI weights and goes and does evil stuff.' So it has highlighted the importance of information security: 'You’re training a powerful AI system; you should make it hard for someone to steal' has popped out to me as a thing that just keeps coming up in these stories, keeps being present. It’s hard to tell a story where it’s not a factor. It’s easy to tell a story where it is a factor." — Holden Karnofsky

What happens when a USB cable can secretly control your system? Are we hurtling toward a security nightmare as critical infrastructure connects to the internet? Is it possible to secure AI model weights from sophisticated attackers? And could AI might actually make computer security better rather than worse?

With AI security concerns becoming increasingly urgent, we bring you insights from 15 top experts across information security, AI safety, and governance, examining the challenges of protecting our most powerful AI models and digital infrastructure — including a sneak peek from an episode that hasn’t yet been released with Tom Davidson, where he explains how we should be more worried about “secret loyalties” in AI agents.

You’ll hear:

Check out the full transcript on the 80,000 Hours website.

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Katy Moore and Milo McGuire
Transcriptions and web: Katy Moore

The 20th century saw unprecedented change: nuclear weapons, satellites, the rise and fall of communism, third-wave feminism, the internet, postmodernism, game theory, genetic engineering, the Big Bang theory, quantum mechanics, birth control, and more. Now imagine all of it compressed into just 10 years.

That’s the future Will MacAskill — philosopher, founding figure of effective altruism, and now researcher at the Forethought Centre for AI Strategy — argues we need to prepare for in his new paper “Preparing for the intelligence explosion.” Not in the distant future, but probably in three to seven years.

The reason: AI systems are rapidly approaching human-level capability in scientific research and intellectual tasks. Once AI exceeds human abilities in AI research itself, we’ll enter a recursive self-improvement cycle — creating wildly more capable systems. Soon after, by improving algorithms and manufacturing chips, we’ll deploy millions, then billions, then trillions of superhuman AI scientists working 24/7 without human limitations. These systems will collaborate across disciplines, build on each discovery instantly, and conduct experiments at unprecedented scale and speed — compressing a century of scientific progress into mere years.

Will compares the resulting situation to a mediaeval king suddenly needing to upgrade from bows and arrows to nuclear weapons to deal with an ideological threat from a country he’s never heard of, while simultaneously grappling with learning that he descended from monkeys and his god doesn’t exist.

What makes this acceleration perilous is that while technology can speed up almost arbitrarily, human institutions and decision-making are much more fixed.

Consider the case of nuclear weapons: in this compressed timeline, there would have been just a three-month gap between the Manhattan Project’s start and the Hiroshima bombing, and the Cuban Missile Crisis would have lasted just over a day.

Robert Kennedy, Sr., who helped navigate the actual Cuban Missile Crisis, once remarked that if they’d had to make decisions on a much more accelerated timeline — like 24 hours rather than 13 days — they would likely have taken much more aggressive, much riskier actions.

So there’s reason to worry about our own capacity to make wise choices. And in “Preparing for the intelligence explosion,” Will lays out 10 “grand challenges” we’ll need to quickly navigate to successfully avoid things going wrong during this period.

Will’s thinking has evolved a lot since his last appearance on the show. While he was previously sceptical about whether we live at a “hinge of history,” he now believes we’re entering one of the most critical periods for humanity ever — with decisions made in the next few years potentially determining outcomes millions of years into the future.

But Will also sees reasons for optimism. The very AI systems causing this acceleration could be deployed to help us navigate it — if we use them wisely. And while AI safety researchers rightly focus on preventing AI systems from going rogue, Will argues we should equally attend to ensuring the futures we deliberately build are truly worth living in.

In this wide-ranging conversation with host Rob Wiblin, Will maps out the challenges we’d face in this potential “intelligence explosion” future, and what we might do to prepare. They discuss:

Learn more and read the full transcript on the 80,000 Hours website. 

This episode was originally recorded on February 7, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Will MacAskill? (00:00:43)
• Why Will now just works on AGI (00:01:03)
• Will was wrong(ish) on AI timelines and hinge of history (00:04:21)
• A century of history crammed into a decade (00:09:19)
• Science goes super fast; our institutions don't keep up (00:16:15)
• Is it good or bad for intellectual progress to 10x? (00:21:44)
• An intelligence explosion is not just plausible but likely (00:23:41)
• Intellectual advances outside technology are similarly important (00:30:04)
• Counterarguments to intelligence explosion (00:32:42)
• The three types of intelligence explosion (software, technological, industrial) (00:39:00)
• The industrial intelligence explosion is the most certain and enduring (00:42:01)
• Is a 100x or 1,000x speedup more likely than 10x? (00:53:44)
• The grand superintelligence challenges (00:57:39)
• Grand challenge #1: Many new destructive technologies (01:01:29)
• Grand challenge #2: Seizure of power by a small group (01:09:10)
• Is global lock-in really plausible? (01:11:06)
• Grand challenge #3: Space governance (01:21:50)
• Is space truly defence-dominant? (01:32:19)
• Grand challenge #4: Morally integrating with digital beings (01:36:04)
• Will we ever know if digital minds are happy? (01:45:01)
• “My worry isn't that we won't know; it's that we won't care” (01:50:39)
• Can we get AGI to ...

When OpenAI announced plans to convert from nonprofit to for-profit control last October, it likely didn’t anticipate the legal labyrinth it now faces. A recent court order in Elon Musk’s lawsuit against the company suggests OpenAI’s restructuring faces serious legal threats, which will complicate its efforts to raise tens of billions in investment.

As nonprofit legal expert Rose Chan Loui explains, the court order set up multiple pathways for OpenAI’s conversion to be challenged. Though Judge Yvonne Gonzalez Rogers denied Musk’s request to block the conversion before a trial, she expedited proceedings to the fall so the case could be heard before it’s likely to go ahead. (See Rob’s brief summary of developments in the case.)

And if Musk’s donations to OpenAI are enough to give him the right to bring a case, Rogers sounded very sympathetic to his objections to the OpenAI foundation selling the company, benefiting the founders who forswore “any intent to use OpenAI as a vehicle to enrich themselves.”

But that’s just one of multiple threats. The attorneys general (AGs) in California and Delaware both have standing to object to the conversion on the grounds that it is contrary to the foundation’s charitable purpose and therefore wrongs the public — which was promised all the charitable assets would be used to develop AI that benefits all of humanity, not to win a commercial race. Some, including Rose, suspect the court order was written as a signal to those AGs to take action.

And, as she explains, if the AGs remain silent, the court itself, seeing that the public interest isn’t being represented, could appoint a “special interest party” to take on the case in their place.

This places the OpenAI foundation board in a bind: proceeding with the restructuring despite this legal cloud could expose them to the risk of being sued for a gross breach of their fiduciary duty to the public. The board is made up of respectable people who didn’t sign up for that.

And of course it would cause chaos for the company if all of OpenAI’s fundraising and governance plans were brought to a screeching halt by a federal court judgment landing at the eleventh hour.

Host Rob Wiblin and Rose Chan Loui discuss all of the above as well as what justification the OpenAI foundation could offer for giving up control of the company despite its charitable purpose, and how the board might adjust their plans to make the for-profit switch more legally palatable.

This episode was originally recorded on March 6, 2025.

Chapters:

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Transcriptions: Katy Moore

A casino offers you a game. A coin will be tossed. If it comes up heads on the first flip you win $2. If it comes up on the second flip you win $4. If it comes up on the third you win $8, the fourth you win $16, and so on. How much should you be willing to pay to play?

The standard way of analysing gambling problems, ‘expected value’ — in which you multiply probabilities by the value of each outcome and then sum them up — says your expected earnings are infinite. You have a 50% chance of winning $2, for '0.5 * $2 = $1' in expected earnings. A 25% chance of winning $4, for '0.25 * $4 = $1' in expected earnings, and on and on. A never-ending series of $1s added together comes to infinity. And that's despite the fact that you know with certainty you can only ever win a finite amount!

Today's guest — philosopher Alan Hájek of the Australian National University — thinks of much of philosophy as “the demolition of common sense followed by damage control” and is an expert on paradoxes related to probability and decision-making rules like “maximise expected value.”

Rebroadcast: this episode was originally released in October 2022.

Links to learn more, highlights, and full transcript.

The problem described above, known as the St. Petersburg paradox, has been a staple of the field since the 18th century, with many proposed solutions. In the interview, Alan explains how very natural attempts to resolve the paradox — such as factoring in the low likelihood that the casino can pay out very large sums, or the fact that money becomes less and less valuable the more of it you already have — fail to work as hoped.

We might reject the setup as a hypothetical that could never exist in the real world, and therefore of mere intellectual curiosity. But Alan doesn't find that objection persuasive. If expected value fails in extreme cases, that should make us worry that something could be rotten at the heart of the standard procedure we use to make decisions in government, business, and nonprofits.

These issues regularly show up in 80,000 Hours' efforts to try to find the best ways to improve the world, as the best approach will arguably involve long-shot attempts to do very large amounts of good.

Consider which is better: saving one life for sure, or three lives with 50% probability? Expected value says the second, which will probably strike you as reasonable enough. But what if we repeat this process and evaluate the chance to save nine lives with 25% probability, or 27 lives with 12.5% probability, or after 17 more iterations, 3,486,784,401 lives with a 0.00000009% chance. Expected value says this final offer is better than the others — 1,000 times better, in fact.

Ultimately Alan leans towards the view that our best choice is to “bite the bullet” and stick with expected value, even with its sometimes counterintuitive implications. Where we want to do damage control, we're better off looking for ways our probability estimates might be wrong.

In this conversation, originally released in October 2022, Alan and Rob explore these issues and many others:

Chapters:

Producer: Keiran Harris
Audio mastering: Ben Cordell and Ryan Kessler
Transcriptions: Katy Moore

America aims to avoid nuclear war by relying on the principle of 'mutually assured destruction,' right? Wrong. Or at least... not officially.

As today's guest — Jeffrey Lewis, founder of Arms Control Wonk and professor at the Middlebury Institute of International Studies — explains, in its official 'OPLANs' (military operation plans), the US is committed to 'dominating' in a nuclear war with Russia. How would they do that? "That is redacted."

Rebroadcast: this episode was originally released in December 2022.

Links to learn more, highlights, and full transcript.

We invited Jeffrey to come on the show to lay out what we and our listeners are most likely to be misunderstanding about nuclear weapons, the nuclear posture of major powers, and his field as a whole, and he did not disappoint.

As Jeffrey tells it, 'mutually assured destruction' was a slur used to criticise those who wanted to limit the 1960s arms buildup, and was never accepted as a matter of policy in any US administration. But isn't it still the de facto reality? Yes and no.

Jeffrey is a specialist on the nuts and bolts of bureaucratic and military decision-making in real-life situations. He suspects that at the start of their term presidents get a briefing about the US' plan to prevail in a nuclear war and conclude that "it's freaking madness." They say to themselves that whatever these silly plans may say, they know a nuclear war cannot be won, so they just won't use the weapons.

But Jeffrey thinks that's a big mistake. Yes, in a calm moment presidents can resist pressure from advisors and generals. But that idea of ‘winning’ a nuclear war is in all the plans. Staff have been hired because they believe in those plans. It's what the generals and admirals have all prepared for.

What matters is the 'not calm moment': the 3AM phone call to tell the president that ICBMs might hit the US in eight minutes — the same week Russia invades a neighbour or China invades Taiwan. Is it a false alarm? Should they retaliate before their land-based missile silos are hit? There's only minutes to decide.

Jeffrey points out that in emergencies, presidents have repeatedly found themselves railroaded into actions they didn't want to take because of how information and options were processed and presented to them. In the heat of the moment, it's natural to reach for the plan you've prepared — however mad it might sound.

In this spicy conversation, Jeffrey fields the most burning questions from Rob and the audience, in the process explaining:

Producer: Keiran Harris
Audio mastering: Ben Cordell
Transcriptions: Katy Moore

Technology doesn’t force us to do anything — it merely opens doors. But military and economic competition pushes us through.

That’s how today’s guest Allan Dafoe — director of frontier safety and governance at Google DeepMind — explains one of the deepest patterns in technological history: once a powerful new capability becomes available, societies that adopt it tend to outcompete those that don’t. Those who resist too much can find themselves taken over or rendered irrelevant.

Links to learn more, highlights, video, and full transcript.

This dynamic played out dramatically in 1853 when US Commodore Perry sailed into Tokyo Bay with steam-powered warships that seemed magical to the Japanese, who had spent centuries deliberately limiting their technological development. With far greater military power, the US was able to force Japan to open itself to trade. Within 15 years, Japan had undergone the Meiji Restoration and transformed itself in a desperate scramble to catch up.

Today we see hints of similar pressure around artificial intelligence. Even companies, countries, and researchers deeply concerned about where AI could take us feel compelled to push ahead — worried that if they don’t, less careful actors will develop transformative AI capabilities at around the same time anyway.

But Allan argues this technological determinism isn’t absolute. While broad patterns may be inevitable, history shows we do have some ability to steer how technologies are developed, by who, and what they’re used for first.

As part of that approach, Allan has been promoting efforts to make AI more capable of sophisticated cooperation, and improving the tests Google uses to measure how well its models could do things like mislead people, hack and take control of their own servers, or spread autonomously in the wild.

As of mid-2024 they didn’t seem dangerous at all, but we’ve learned that our ability to measure these capabilities is good, but imperfect. If we don’t find the right way to ‘elicit’ an ability we can miss that it’s there.

Subsequent research from Anthropic and Redwood Research suggests there’s even a risk that future models may play dumb to avoid their goals being altered.

That has led DeepMind to a “defence in depth” approach: carefully staged deployment starting with internal testing, then trusted external testers, then limited release, then watching how models are used in the real world. By not releasing model weights, DeepMind is able to back up and add additional safeguards if experience shows they’re necessary.

But with much more powerful and general models on the way, individual company policies won’t be sufficient by themselves. Drawing on his academic research into how societies handle transformative technologies, Allan argues we need coordinated international governance that balances safety with our desire to get the massive potential benefits of AI in areas like healthcare and education as quickly as possible.

Host Rob and Allan also cover:

Chapters:

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Camera operator: Jeremy Chevillotte
Transcriptions: Katy Moore

On Monday Musk made the OpenAI nonprofit foundation an offer they want to refuse, but might have trouble doing so: $97.4 billion for its stake in the for-profit company, plus the freedom to stick with its current charitable mission.

For a normal company takeover bid, this would already be spicy. But OpenAI’s unique structure — a nonprofit foundation controlling a for-profit corporation — turns the gambit into an audacious attack on the plan OpenAI announced in December to free itself from nonprofit oversight.

As today’s guest Rose Chan Loui — founding executive director of UCLA Law’s Lowell Milken Center for Philanthropy and Nonprofits — explains, OpenAI’s nonprofit board now faces a challenging choice.

Links to learn more, highlights, video, and full transcript.

The nonprofit has a legal duty to pursue its charitable mission of ensuring that AI benefits all of humanity to the best of its ability. And if Musk’s bid would better accomplish that mission than the for-profit’s proposal — that the nonprofit give up control of the company and change its charitable purpose to the vague and barely related “pursue charitable initiatives in sectors such as health care, education, and science” — then it’s not clear the California or Delaware Attorneys General will, or should, approve the deal.

OpenAI CEO Sam Altman quickly tweeted “no thank you” — but that was probably a legal slipup, as he’s not meant to be involved in such a decision, which has to be made by the nonprofit board ‘at arm’s length’ from the for-profit company Sam himself runs.

The board could raise any number of objections: maybe Musk doesn’t have the money, or the purchase would be blocked on antitrust grounds, seeing as Musk owns another AI company (xAI), or Musk might insist on incompetent board appointments that would interfere with the nonprofit foundation pursuing any goal.

But as Rose and Rob lay out, it’s not clear any of those things is actually true.

In this emergency podcast recorded soon after Elon’s offer, Rose and Rob also cover:

Chapters:

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Transcriptions: Katy Moore

Will LLMs soon be made into autonomous agents? Will they lead to job losses? Is AI misinformation overblown? Will it prove easy or hard to create AGI? And how likely is it that it will feel like something to be a superhuman AGI?

With AGI back in the headlines, we bring you 15 opinionated highlights from the show addressing those and other questions, intermixed with opinions from hosts Luisa Rodriguez and Rob Wiblin recorded back in 2023.

Check out the full transcript on the 80,000 Hours website.

You can decide whether the views we expressed (and those from guests) then have held up these last two busy years. You’ll hear:

And of course, Rob and Luisa also regularly chime in on what they agree and disagree with.

Chapters:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Transcriptions and additional content editing: Katy Moore

If someone said a global health and development programme was sustainable, participatory, and holistic, you'd have to guess that they were saying something positive. But according to today's guest Karen Levy — deworming pioneer and veteran of Innovations for Poverty Action, Evidence Action, and Y Combinator — each of those three concepts has become so fashionable that they're at risk of being seriously overrated and applied where they don't belong.

Rebroadcast: this episode was originally released in March 2022.

Links to learn more, highlights, and full transcript.

Such concepts might even cause harm — trying to make a project embody all three is as likely to ruin it as help it flourish.

First, what do people mean by 'sustainability'? Usually they mean something like the programme will eventually be able to continue without needing further financial support from the donor. But how is that possible? Governments, nonprofits, and aid agencies aim to provide health services, education, infrastructure, financial services, and so on — and all of these require ongoing funding to pay for materials and staff to keep them running.

Given that someone needs to keep paying, Karen tells us that in practice, 'sustainability' is usually a euphemism for the programme at some point being passed on to someone else to fund — usually the national government. And while that can be fine, the national government of Kenya only spends $400 per person to provide each and every government service — just 2% of what the US spends on each resident. Incredibly tight budgets like that are typical of low-income countries.

'Participatory' also sounds nice, and inasmuch as it means leaders are accountable to the people they're trying to help, it probably is. But Karen tells us that in the field, ‘participatory’ usually means that recipients are expected to be involved in planning and delivering services themselves.

While that might be suitable in some situations, it's hardly something people in rich countries always want for themselves. Ideally we want government healthcare and education to be high quality without us having to attend meetings to keep it on track — and people in poor countries have as many or more pressures on their time. While accountability is desirable, an expectation of participation can be as much a burden as a blessing.

Finally, making a programme 'holistic' could be smart, but as Karen lays out, it also has some major downsides. For one, it means you're doing lots of things at once, which makes it hard to tell which parts of the project are making the biggest difference relative to their cost. For another, when you have a lot of goals at once, it's hard to tell whether you're making progress, or really put your mind to focusing on making one thing go extremely well. And finally, holistic programmes can be impractically expensive — Karen tells the story of a wonderful 'holistic school health' programme that, if continued, was going to cost 3.5 times the entire school's budget.

In this in-depth conversation, originally released in March 2022, Karen Levy and host Rob Wiblin chat about the above, as well as:

Chapters:

Producer: Keiran Harris
Audio mastering: Ben Cordell and Ryan Kessler
Transcriptions: Katy Moore

“I want everyone to understand that I am, in fact, a person.” Those words were produced by the AI model LaMDA as a reply to Blake Lemoine in 2022. Based on the Google engineer’s interactions with the model as it was under development, Lemoine became convinced it was sentient and worthy of moral consideration — and decided to tell the world.

Few experts in machine learning, philosophy of mind, or other relevant fields have agreed. And for our part at 80,000 Hours, we don’t think it’s very likely that large language models like LaMBDA are sentient — that is, we don’t think they can have good or bad experiences — in a significant way.

But we think you can’t dismiss the issue of the moral status of digital minds, regardless of your beliefs about the question. There are major errors we could make in at least two directions:

And we’re currently unprepared to face this challenge. We don’t have good methods for assessing the moral status of AI systems. We don’t know what to do if millions of people or more believe, like Lemoine, that the chatbots they talk to have internal experiences and feelings of their own. We don’t know if efforts to control AI may lead to extreme suffering.

We believe this is a pressing world problem. It’s hard to know what to do about it or how good the opportunities to work on it are likely to be. But there are some promising approaches. We propose building a field of research to understand digital minds, so we’ll be better able to navigate these potentially massive issues if and when they arise.

This article narration by the author (Cody Fenwick) explains in more detail why we think this is a pressing problem, what we think can be done about it, and how you might pursue this work in your career. We also discuss a series of possible objections to thinking this is a pressing world problem.

You can read the full article, Understanding the moral status of digital minds, on the 80,000 Hours website.

Chapters:

If a business has spent $100 million developing a product, it’s a fair bet that they don’t want it stolen in two seconds and uploaded to the web where anyone can use it for free.

This problem exists in extreme form for AI companies. These days, the electricity and equipment required to train cutting-edge machine learning models that generate uncanny human text and images can cost tens or hundreds of millions of dollars. But once trained, such models may be only a few gigabytes in size and run just fine on ordinary laptops.

Today’s guest, the computer scientist and polymath Nova DasSarma, works on computer and information security for the AI company Anthropic with the security team. One of her jobs is to stop hackers exfiltrating Anthropic’s incredibly expensive intellectual property, as recently happened to Nvidia.

Rebroadcast: this episode was originally released in June 2022.

Links to learn more, highlights, and full transcript.

As she explains, given models’ small size, the need to store such models on internet-connected servers, and the poor state of computer security in general, this is a serious challenge.

The worries aren’t purely commercial though. This problem looms especially large for the growing number of people who expect that in coming decades we’ll develop so-called artificial ‘general’ intelligence systems that can learn and apply a wide range of skills all at once, and thereby have a transformative effect on society.

If aligned with the goals of their owners, such general AI models could operate like a team of super-skilled assistants, going out and doing whatever wonderful (or malicious) things are asked of them. This might represent a huge leap forward for humanity, though the transition to a very different new economy and power structure would have to be handled delicately.

If unaligned with the goals of their owners or humanity as a whole, such broadly capable models would naturally ‘go rogue,’ breaking their way into additional computer systems to grab more computing power — all the better to pursue their goals and make sure they can’t be shut off.

As Nova explains, in either case, we don’t want such models disseminated all over the world before we’ve confirmed they are deeply safe and law-abiding, and have figured out how to integrate them peacefully into society. In the first scenario, premature mass deployment would be risky and destabilising. In the second scenario, it could be catastrophic — perhaps even leading to human extinction if such general AI systems turn out to be able to self-improve rapidly rather than slowly, something we can only speculate on at this point.

If highly capable general AI systems are coming in the next 10 or 20 years, Nova may be flying below the radar with one of the most important jobs in the world.

We’ll soon need the ability to ‘sandbox’ (i.e. contain) models with a wide range of superhuman capabilities, including the ability to learn new skills, for a period of careful testing and limited deployment — preventing the model from breaking out, and criminals from breaking in. Nova and her colleagues are trying to figure out how to do this, but as this episode reveals, even the state of the art is nowhere near good enough.

Chapters:

Producer: Keiran Harris
Audio mastering: Ben Cordell and Beppe Rådvik
Transcriptions: Katy Moore

What in the world is intrinsically good — good in itself even if it has no other effects? Over the millennia, people have offered many answers: joy, justice, equality, accomplishment, loving god, wisdom, and plenty more.

The question is a classic that makes for great dorm-room philosophy discussion. But it’s hardly just of academic interest. The issue of what (if anything) is intrinsically valuable bears on every action we take, whether we’re looking to improve our own lives, or to help others. The wrong answer might lead us to the wrong project and render our efforts to improve the world entirely ineffective.

Today’s guest, Sharon Hewitt Rawlette — philosopher and author of The Feeling of Value: Moral Realism Grounded in Phenomenal Consciousness — wants to resuscitate an answer to this question that is as old as philosophy itself.

Rebroadcast: this episode was originally released in September 2022.

Links to learn more, highlights, and full transcript.

That idea, in a nutshell, is that there is only one thing of true intrinsic value: positive feelings and sensations. And similarly, there is only one thing that is intrinsically of negative value: suffering, pain, and other unpleasant sensations.

Lots of other things are valuable too: friendship, fairness, loyalty, integrity, wealth, patience, houses, and so on. But they are only instrumentally valuable — that is to say, they’re valuable as means to the end of ensuring that all conscious beings experience more pleasure and other positive sensations, and less suffering.

As Sharon notes, from Athens in 400 BC to Britain in 1850, the idea that only subjective experiences can be good or bad in themselves — a position known as ‘philosophical hedonism’ — has been one of the most enduringly popular ideas in ethics.

And few will be taken aback by the notion that, all else equal, more pleasure is good and less suffering is bad. But can they really be the only intrinsically valuable things?

Over the 20th century, philosophical hedonism became increasingly controversial in the face of some seemingly very counterintuitive implications. For this reason the famous philosopher of mind Thomas Nagel called The Feeling of Value “a radical and important philosophical contribution.”

So what convinces Sharon that philosophical hedonism deserves another go? In today’s interview with host Rob Wiblin, Sharon explains the case for a theory of value grounded in subjective experiences, and why she believes these counterarguments are misguided. A philosophical hedonist shouldn’t get in an experience machine, nor override an individual’s autonomy, except in situations so different from the classic thought experiments that it no longer seems strange they would do so.

Chapters:

Producer: Keiran Harris
Audio mastering: Ryan Kessler
Transcriptions: Katy Moore

Wind back 1,000 years and the moral landscape looks very different to today. Most farming societies thought slavery was natural and unobjectionable, premarital sex was an abomination, women should obey their husbands, and commoners should obey their monarchs.

Wind back 10,000 years and things look very different again. Most hunter-gatherer groups thought men who got too big for their britches needed to be put in their place rather than obeyed, and lifelong monogamy could hardly be expected of men or women.

Why such big systematic changes — and why these changes specifically?

That's the question bestselling historian Ian Morris takes up in his book, Foragers, Farmers, and Fossil Fuels: How Human Values Evolve. Ian has spent his academic life studying long-term history, trying to explain the big-picture changes that play out over hundreds or thousands of years.

Rebroadcast: this episode was originally released in July 2022.

Links to learn more, highlights, and full transcript.

There are a number of possible explanations one could offer for the wide-ranging shifts in opinion on the 'right' way to live. Maybe the natural sciences progressed and people realised their previous ideas were mistaken? Perhaps a few persuasive advocates turned the course of history with their revolutionary arguments? Maybe everyone just got nicer?

In Foragers, Farmers and Fossil Fuels Ian presents a provocative alternative: human culture gradually evolves towards whatever system of organisation allows a society to harvest the most energy, and we then conclude that system is the most virtuous one. Egalitarian values helped hunter-gatherers hunt and gather effectively. Once farming was developed, hierarchy proved to be the social structure that produced the most grain (and best repelled nomadic raiders). And in the modern era, democracy and individuality have proven to be more productive ways to collect and exploit fossil fuels.

On this theory, it's technology that drives moral values much more than moral philosophy. Individuals can try to persist with deeply held values that limit economic growth, but they risk being rendered irrelevant as more productive peers in their own society accrue wealth and power. And societies that fail to move with the times risk being conquered by more pragmatic neighbours that adapt to new technologies and grow in population and military strength.

There are many objections one could raise to this theory, many of which we put to Ian in this interview. But the question is a highly consequential one: if we want to guess what goals our descendants will pursue hundreds of years from now, it would be helpful to have a theory for why our ancestors mostly thought one thing, while we mostly think another.

Big though it is, the driver of human values is only one of several major questions Ian has tackled through his career.

In this classic episode, we discuss all of Ian's major books.

Chapters:

Producer: Keiran Harris
Audio mastering: Ben Cordell
Transcriptions: Katy Moore

Is war in long-term decline? Steven Pinker's The Better Angels of Our Nature brought this previously obscure academic question to the centre of public debate, and pointed to rates of death in war to argue energetically that war is on the way out.

But that idea divides war scholars and statisticians, and so Better Angels has prompted a spirited debate, with datasets and statistical analyses exchanged back and forth year after year. The lack of consensus has left a somewhat bewildered public (including host Rob Wiblin) unsure quite what to believe.

Today's guest, professor in political science Bear Braumoeller, is one of the scholars who believes we lack convincing evidence that warlikeness is in long-term decline. He collected the analysis that led him to that conclusion in his 2019 book, Only the Dead: The Persistence of War in the Modern Age.

Rebroadcast: this episode was originally released in November 2022.

Links to learn more, highlights, and full transcript.

The question is of great practical importance. The US and PRC are entering a period of renewed great power competition, with Taiwan as a potential trigger for war, and Russia is once more invading and attempting to annex the territory of its neighbours.

If war has been going out of fashion since the start of the Enlightenment, we might console ourselves that however nerve-wracking these present circumstances may feel, modern culture will throw up powerful barriers to another world war. But if we're as war-prone as we ever have been, one need only inspect the record of the 20th century to recoil in horror at what might await us in the 21st.

Bear argues that the second reaction is the appropriate one. The world has gone up in flames many times through history, with roughly 0.5% of the population dying in the Napoleonic Wars, 1% in World War I, 3% in World War II, and perhaps 10% during the Mongol conquests. And with no reason to think similar catastrophes are any less likely today, complacency could lead us to sleepwalk into disaster.

He gets to this conclusion primarily by analysing the datasets of the decades-old Correlates of War project, which aspires to track all interstate conflicts and battlefield deaths since 1815. In Only the Dead, he chops up and inspects this data dozens of different ways, to test if there are any shifts over time which seem larger than what could be explained by chance variation alone.

In a nutshell, Bear simply finds no general trend in either direction from 1815 through today. It seems like, as philosopher George Santayana lamented in 1922, "only the dead have seen the end of war."

In today's conversation, Bear and Rob discuss all of the above in more detail than even a usual 80,000 Hours podcast episode, as well as:

Chapters:

Producer: Keiran Harris
Audio mastering: Ryan Kessler
Transcriptions: Katy Moore

"A shameless recycling of existing content to drive additional audience engagement on the cheap… or the single best, most valuable, and most insight-dense episode we put out in the entire year, depending on how you want to look at it." — Rob Wiblin

It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode, including:

…as well as 27 other top observations and arguments from the past year of the show.

Check out the full transcript and episode links on the 80,000 Hours website.

Remember that all of these clips come from the 20-minute highlight reels we make for every episode, which are released on our sister feed, 80k After Hours. So if you’re struggling to keep up with our regularly scheduled entertainment, you can still get the best parts of our conversations there.

It has been a hell of a year, and we can only imagine next year is going to be even weirder — but Luisa and Rob will be here to keep you company as Earth hurtles through the galaxy to a fate as yet unknown.

Enjoy, and look forward to speaking with you in 2025!

Chapters:

Producing and editing: Keiran Harris
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Video editing: Simon Monsour
Transcriptions: Katy Moore

Rich countries seem to find it harder and harder to do anything that creates some losers. People who don’t want houses, offices, power stations, trains, subway stations (or whatever) built in their area can usually find some way to block them, even if the benefits to society outweigh the costs 10 or 100 times over.

The result of this ‘vetocracy’ has been skyrocketing rent in major cities — not to mention exacerbating homelessness, energy poverty, and a host of other social maladies. This has been known for years but precious little progress has been made. When trains, tunnels, or nuclear reactors are occasionally built, they’re comically expensive and slow compared to 50 years ago. And housing construction in the UK and California has barely increased, remaining stuck at less than half what it was in the ’60s and ’70s.

Today’s guest — economist and editor of Works in Progress Sam Bowman — isn’t content to just condemn the Not In My Backyard (NIMBY) mentality behind this stagnation. He wants to actually get a tonne of stuff built, and by that standard the strategy of attacking ‘NIMBYs’ has been an abject failure. They are too politically powerful, and if you try to crush them, sooner or later they crush you.

Links to learn more, highlights, video, and full transcript.

So, as Sam explains, a different strategy is needed, one that acknowledges that opponents of development are often correct that a given project will make them worse off. But the thing is, in the cases we care about, these modest downsides are outweighed by the enormous benefits to others — who will finally have a place to live, be able to get to work, and have the energy to heat their home.

But democracies are majoritarian, so if most existing residents think they’ll be a little worse off if more dwellings are built in their area, it’s no surprise they aren’t getting built. Luckily we already have a simple way to get people to do things they don’t enjoy for the greater good, a strategy that we apply every time someone goes in to work at a job they wouldn’t do for free: compensate them. 

Sam thinks this idea, which he calls “Coasean democracy,” could create a politically sustainable majority in favour of building and underlies the proposals he thinks have the best chance of success — which he discusses in detail with host Rob Wiblin.

Chapters:

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Transcriptions: Katy Moore

"I really don’t want to give the impression that I think it is easy to make predictable, controlled, safe interventions in wild systems where there are many species interacting. I don’t think it’s easy, but I don’t see any reason to think that it’s impossible. And I think we have been making progress. I think there’s every reason to think that if we continue doing research, both at the theoretical level — How do ecosystems work? What sorts of things are likely to have what sorts of indirect effects? — and then also at the practical level — Is this intervention a good idea? — I really think we’re going to come up with plenty of things that would be helpful to plenty of animals." —Cameron Meyer Shorb

In today’s episode, host Luisa Rodriguez speaks to Cameron Meyer Shorb — executive director of the Wild Animal Initiative — about the cutting-edge research on wild animal welfare.

Links to learn more, highlights, and full transcript.

They cover:

Chapters:

Producer: Keiran Harris
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Luisa Rodriguez, Katy Moore, and Keiran Harris
Transcriptions: Katy Moore

One OpenAI critic calls it “the theft of at least the millennium and quite possibly all of human history.” Are they right?

Back in 2015 OpenAI was but a humble nonprofit. That nonprofit started a for-profit, OpenAI LLC, but made sure to retain ownership and control. But that for-profit, having become a tech giant with vast staffing and investment, has grown tired of its shackles and wants to change the deal.

Facing off against it stand eight out-gunned and out-numbered part-time volunteers. Can they hope to defend the nonprofit’s interests against the overwhelming profit motives arrayed against them?

That’s the question host Rob Wiblin puts to nonprofit legal expert Rose Chan Loui of UCLA, who concludes that with a “heroic effort” and a little help from some friendly state attorneys general, they might just stand a chance.

Links to learn more, highlights, video, and full transcript.

As Rose lays out, on paper OpenAI is controlled by a nonprofit board that:

But that control is a problem for OpenAI the for-profit and its CEO Sam Altman — all the more so after the board concluded back in November 2023 that it couldn’t trust Altman and attempted to fire him (although those board members were ultimately ousted themselves after failing to adequately explain their rationale).

Nonprofit control makes it harder to attract investors, who don’t want a board stepping in just because they think what the company is doing is bad for humanity. And OpenAI the business is thirsty for as many investors as possible, because it wants to beat competitors and train the first truly general AI — able to do every job humans currently do — which is expected to cost hundreds of billions of dollars.

So, Rose explains, they plan to buy the nonprofit out. In exchange for giving up its windfall profits and the ability to fire the CEO or direct the company’s actions, the board will become minority shareholders with reduced voting rights, and presumably transform into a normal grantmaking foundation instead.

Is this a massive bait-and-switch? A case of the tail not only wagging the dog, but grabbing a scalpel and neutering it?

OpenAI repeatedly committed to California, Delaware, the US federal government, founding staff, and the general public that its resources would be used for its charitable mission and it could be trusted because of nonprofit control. Meanwhile, the divergence in interests couldn’t be more stark: every dollar the for-profit keeps from its nonprofit parent is another dollar it could invest in AGI and ultimately return to investors and staff.

Chapters:

Producer: Keiran Harris
Audio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Video editing: Simon Monsour
Transcriptions: Katy Moore