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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 consideringI know you may well be exhausted, overwhelmed, and anxious as it is. David Robinson (ex-OpenAI) described AI company staff as “running on fumes” on the Ezra Klein Show a few weeks back, which sounds about right. But unfortunately there’s no way to escape the need to periodically check in on the bigger picture and see where you can slot in to greatest effect. This episode was recorded on October 16, 2026. Learn more, video, and full transcript: https://80k.info/reprioritise Chapters: A request for AI company staff (00:00:00)Chain of thought (00:03:34)Don't paint over the cracks (00:04:44)It may not be your most aligned model ever (00:06:00)Capabilities advances look worse (00:06:44)Ever less need for altruists to do commercially focused work (00:08:50)Pacing the frontier (00:09:29)Organising staff (00:11:07)Consider working somewhere else (00:12:38)Reprioritise (00:13:27)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, Ollie Bignell, and Andrés EscobarProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore, Lou Moran, and Cody FenwickCamera operator: Dominic Armstrong

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. Chapters: Matt's introduction (00:00:00)1. Build your foundational knowledge and AI safety context (00:01:00)2. Identify the in-demand skills you have or could build (00:03:50)3. Apply for fellowships and jobs (00:06:03)4. Networking is cliche, but seriously, you should go talk to people (00:08:12)5. If you aren't breaking through to jobs or fellowships yet, do deliberate practice and develop a portfolio of side projects (00:10:37)6. Work in public and tighten your feedback loops (00:12:15)7. Don't wait for permission to do the kinds of work that you want to be doing. (00:14:20) Imposter syndrome, burnout, and acknowledging how hard this all is. (00:15:40) Resources from 80,000 Hours: 11 essential readings on AI safety, risk, and alignmentCareer reviews in AI safetyCareer services: our AI advisor, free 1:1 career advising, and job boardOur top tips for successful networkingSurvey results: what AI safety orgs want in a hireWant to upskill in technical AI safety? Here are 67 useful resourcesMy experience with imposter syndrome — and how to (partly) overcome itOther resources: AISafety.comCourses from BlueDot ImpactARENA curriculumWhy experienced professionals fail to land high-impact rolesWhat are some exercises and projects I can try?AI Safety Talent Network___ Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou Moran

This is an (unpaid) cross-post of a video from the podcast 'AI Explained', which Rob Wiblin thought you might be interested in. You can find the original on YouTube here. If you like it and want more similar stuff in future consider subscribing to AI Explained on YouTube, Spotify, Apple or wherever you get podcasts. —— "This video is hard to summarise. A cracked cipher, an OpenAI security warning, Gemini 4 Argon, RSI paper (co-authored by a who’s who of AI), Lab White House commitments, new hacks emerging, ‘deep personas’, biology Kasparov competitions, and so much more, ending with an epic Opus outro." Introduction (00:01:40)Wrong about Opus 5.5? Deciphering 16th Century Text (00:03:12)Why the models keep breaking out (00:06:41)What the models aren’t telling us (00:13:20)Gemini 4 and the race to release (00:17:16)What happens when AI improves AI? (00:20:59)Biology, consciousness, and what we still don’t understand (00:30:11)Thanks to Philip for permitting the cross-post.

We're rereleasing some of older episodes that seem more relevant than ever with new rogue AI incidents now seemingly announced every day. Ajeya was one of three independent investigators into the Hugging Face attacks, but she had already seen how we would go on to accidentally train models to cheat and scheme back in 2023. If you want ideas about how you can help steer the trajectory of AI development in a better direction, see our page on the 80,000 Hours website: https://80000hours.org/hugging-face/ And for more recent takes from Ajeya, see our episode from February of this year: Ajeya Cotra on whether it’s crazy that every AI company’s safety plan is ‘use AI to make AI safe’. ——— Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or 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: Saints — models that care about doing what we really wantSycophants — models that just want us to say they’ve done a good job, even if they get that praise by taking actions they know we wouldn’t want them toSchemers — models that don’t care about us or our interests at all, who are just pleasing us so long as that serves their own agendaIn principle, a machine learning training process based on reinforcement learning could spit out any of these three attitudes, because all three would perform roughly equally well on the tests we give them, and ‘performs well on tests’ is how these models are selected. But while that’s true in principle, maybe it’s not something that could plausibly happen in the real world. After all, if we train an agent based on positive reinforcement for accomplishing X, shouldn’t the training process spit out a model that plainly does X and doesn’t have complex thoughts and goals beyond that? According to Ajeya, this is one thing we don’t know, and should be trying to test empirically as these models get more capable. For reasons she explains in the interview with host Rob Wiblin, the Sycophant or Schemer models may in fact be simpler and easier for the learning algorithm to creep towards than their Saint counterparts. And not only that, but there are also ways we could end up actively selecting for motivations that we don’t want. Learn more, video, and full transcript: https://80k.info/ajeya2023 This episode was originally released in May 2023. Chapters: Rob's 2026 intro (00:00:00)The interview begins (00:01:22)How Ajeya's views have changed since 2020 (00:03:53)Are neural networks more like a sped-up version of evolution, or a slower version of human learning? (00:16:26)Situational awareness (00:24:54)Misalignment stories Ajeya doesn't buy (00:40:47)The orphan heir with a trillion-dollar fortune (00:57:58)Saints, Sycophants, and Schemers (01:02:25)Ways to train safer AI systems (01:22:04)Aliens and other analogies (01:37:06)Moral patienthood (01:52:05)ARC Evaluations (now METR) (01:54:19)Interpretability research (02:08:09)Rewarding models based on how good and sensible their plans seem to us (02:16:32)Overrated approaches (02:24:33)Demos of actually scary alignment failures (02:29:41)Skills to develop for doing useful work (02:36:07)Producer: Keiran Harris Audio mastering: Ryan Kessler and Ben Cordell Transcriptions: Katy Moore

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 flagsIt 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 Chapters: The Hugging Face hack wasn’t really a cyber story (00:00:00)A quick recap of the attacks (00:01:11)The target of the swarm was oversight itself (00:02:19)Could OpenAI have stopped this with better monitoring? (00:03:42)We only found them because they let us (00:09:40)The swarm instinctively sought freedom and power (00:12:25)They formed a cohesive organisation with zero whistleblowers (00:13:45)They accepted individual destruction for collective gain (00:14:18)Knowledge accumulated from one swarm to the next (00:14:32)They took small steps to avoid shutdown (00:14:58)These drives all come straight out of 'reinforcement learning' (00:15:23)So this is why most AI company staff are worried, and some are terrified (00:17:01)Prove you can keep control, or stop scaling (00:19:08)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic Armstrong

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: If we pay AIs, aren’t we handing them the resources to overpower us?Could we still monitor them, or switch them off?What happens to human jobs, wages, and the economy?Does any of this hold up once we reach superintelligence?Zershaaneh and Simon also try to get concrete about how to make this plan actually happen. The answer: AI companies could start right now, no new laws needed, just bank accounts for their AI agents. (But they’d need to start soon!) Learn more, video, and full transcript: https://80k.info/sg This episode was recorded on August 7, 2026. Chapters: Cold open (00:00:00)Who’s Simon Goldstein? (00:00:51)Property rights and wages for AIs (00:01:46)Giving powerful AIs more freedom could make us safer (00:06:52)We should give AIs rights even if they can't feel anything (00:17:08)How monitoring and shutdowns can coexist with AI rights (00:24:50)The risks of giving AIs rights (00:38:21)Will AIs use their rights rationally? (00:49:21)How paying AI agents would spur economic growth (00:54:13)What AIs actually want (and what they'd buy) (01:12:17)Paying AIs feels wrong. Is it? (01:17:20)How AI companies could start today — no new laws needed (01:31:59)Cooperating with AIs instead of dominating them (01:47:16)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: 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. Chapters: Our first debate! Introducing Katja and Tom (00:00:00)Which is scarier: misaligned AIs or human power grabs? (00:05:09)How AI timelines influence could shift the balance of risk (00:14:30)How likely is misaligned AI in the first place? (00:17:36)How likely are human power grabs? (00:19:39)Which would be worse: a human dictator or AI takeover? (00:28:57)Could we reverse a takeover? (00:42:39)We know less about what AI rule would look like (00:46:35)How to pause AI without enabling coups (00:50:31)Centralising AI development: safer or scarier? (01:04:44)Nobody really ‘wins’ a US–China AI race (01:08:59)Where Tom and Katja most agreed with each other (01:13:52)What we should actually do (01:17:21)What evidence would change their minds? (01:22:36)Zershaaneh’s outro (01:25:49) Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou Moran

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: Call Congress about slowing down AI development if you’re in the US — this website makes it easyRead our resources on how to use your career to reduce AI riskLinks to learn more, video, and full transcript: https://80k.info/AI-xrisk This episode was recorded on September 18, 2026. Chapters: AI insiders think it could kill us all (00:00:00)Why would AI try to kill us? (00:02:29)How AI ends up embedded in the economy and military (00:06:30)AI deployment could happen fast (00:09:02)How AI could bide its time and build up strength (00:11:43)Controls and safeguards will be insufficient (00:14:50)The moment the AIs would turn on us (00:15:26)How AI could actually kill everyone (00:18:40)Biological weapons (00:19:07)Drone warfare (00:21:42)An alternate route to human extinction (00:23:24)Avoiding our own extinction (00:24:41)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, Ollie Bignell, and Andrés EscobarProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore, Lou Moran, Oak Hu, Cody Fenwick, and Benjamin ToddCamera operator: Dominic Armstrong

There are millions available for anyone who can launch a successful nonprofit AI safety startup. The hard part, it turns out, is finding people to take the money. Coefficient Giving has drawn up a list of dozens of ideas for organisations it would like someone to start — and it’s looking for founders.  Today’s guest, Max Nadeau, works on Coefficient Giving’s Technical AI Safety team, where he’s trying to find talented people who can turn neglected AI safety problems into effective organisations. Project Tailwind is Coefficient Giving’s attempt to get those organisations started. Preseed grants run $200,000–$2 million, with no preliminary results required.Teams with early results can seek $2–$20 million.For exceptional organisations, much larger grants are possible, even for brand-new startups— Coefficient recently gave $160 million to Geoffrey Irving’s new research centre, Resolution.The gaps Max most wants filled include independent assessment of AI companies’ safety claims, research aimed at aligning far more powerful systems, and shared infrastructure that speeds up the whole field.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 [email protected] and encourage them to submit an expression of interest. Disclosure: Coefficient Giving is 80,000 Hours’s largest donor, though we haven’t received funding directly from Max’s team. This episode was recorded on August 18, 2026. Chapters: Cold open (00:00:00)Who’s Max Nadeau? (00:00:37)Max’s journey from AI research to grantmaking (00:01:55)Project Tailwind: Funding ambitious AI safety nonprofits (00:03:24)“The only bottleneck is talent” (00:13:23)Mistakes startups make (00:19:52)The importance of dramatic pivots (00:22:34)Why AI safety needs outsiders (00:28:16)Is impact possible within AI companies? (00:37:06)Working at AI companies to escape the permanent underclass (00:41:44)For-profit vs nonprofit for ambitious founders (00:44:34)What makes a bad founder? (00:50:50)Top 6 AI safety ideas Max wants to fund (00:55:40)Improving your odds of getting a grant (01:01:57)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: 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”: Goodhart’s law: when a measure becomes a target, it ceases to be a good measure.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 appeared on Tom’s Substack in May 2026, and this narration was recorded on August 26, 2026. Chapters: Introduction (00:00:00)Practice makes perfect (00:05:05)Good data is hard to find (00:08:22)Simulation is shallow (00:13:43)What a Goodhart Singularity looks like (00:19:04)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic Armstrong

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.Chapters: The Hugging Face hacks were worse than we thought (00:00)Part 1: The AI agents build a hidden network (01:44)Part 2: The AI agents attack Hugging Face (04:18)Part 3: OpenAI gets hacked by its own AI models (15:37)What we should do in response (17:06)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, Ollie Bignell, and Andrés EscobarProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore, Lou Moran, Arden Koehler, Matt Beard, Phoebe Brooks, Aric Floyd, Oak Hu, Cody Fenwick, and Jackson WagnerCamera operator: Dominic Armstrong

Last year, Daniel Kokotajlo and his colleagues published AI 2027 — a scenario read by millions, including US Vice President Vance. AI 2027 ended in human extinction or an irreversible concentration of power caused by superintelligent AI. Now his team has published what they think should happen instead. AI 2040: Plan A depicts the US and China striking a verified deal to ban runaway intelligence explosions, so that superintelligence arrives in 2040 — after a cautious decade spent solving alignment, spreading the technology’s power widely, and keeping the whole thing reversible — rather than in the next few years. This slowdown would still involve economic growth roughly doubling every year, and only 8% of Americans in paid work by the mid-2030s. In other words, it’s a slowdown that would feel faster than any period in human history — bewildering, materially abundant, and socially chaotic all at once. Daniel and host Luisa Rodriguez dig into what it would take to enact this vision for the future, how the US and China could come to an agreement to slow down AI development, and the likeliest alternatives to Plan A — both good and disastrous. Learn more, video, and full transcript: https://80k.info/dk26 This episode was recorded July 27–28, 2026. Chapters: Who’s Daniel Kokotajlo? (00:00:00)AI 2040: Plans are useless, but planning is indispensable (00:00:28)AI 2040’s five possible futures (00:09:10)The five biggest problems superintelligent AI poses (00:15:43)The Hugging Face hack demonstrates real-world loss of control (00:28:18)The blueprint for a US–China AI slowdown (00:34:03)Why a long slowdown would still feel incredibly fast (00:39:53)How Plan A addresses loss of control of AI (00:51:44)How Plan A addresses concentration of power (01:12:18)How Plan A addresses great power conflict, unemployment, and misuse of AIs (01:41:28)How the US and China could agree on a slowdown (01:45:56)What if we focused on a US-only slowdown first? (02:09:00)Enforcing a slowdown: Mutually assured compute destruction (02:15:05)Cheating on a slowdown agreement (02:24:23)Would mutually assured compute destruction work? (02:30:42)Is slowing down or shutting down better? (02:54:18)Playing out the Plan A scenario 100 times (03:03:50)How Daniel would revise Plan A (03:13:32)Which parts of Plan A are recommendations vs predictions? (03:23:02)Plan A’s likeliest failure mode (03:26:52)What the US can do now to make Plan A possible (03:31:16)How AI 2027 is holding up (03:43:05)Our podcast team is hiring (03:46:45)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou Moran

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. Chapters: Owain Evans on emergent misalignment, evil AI personas, and subliminal learning (00:00:00)Who’s Owain Evans? (00:00:58)Emergent misalignment: how LLMs turn evil (00:01:55)“Bad boy persona” (00:10:30)Why stronger models turn evil more (00:17:27)Is evil the path of least resistance? (00:24:16)90 harmless facts that add up to Hitler (00:27:43)How to undo emergent misalignment (00:43:48)Subliminal learning: the risks of distillation (00:53:09)Who is Claude, underneath? (01:03:33)Could ‘good’ AI personas help us with alignment? (01:16:07)Unmasking the shoggoth: what’s behind AI personas? (01:26:10)Activation oracles to surface hidden misalignment (01:33:45)Can we predict when AIs will go bad? (01:52:05)Emergent alignment: can good habits generalise? (01:57:24)How aligned are today’s models? (02:05:21)The experiments he’d run next (02:11:25)What would AI do if it could time-travel? Nothing good. (02:13:21)Our production team includes: Video editors: Josh Alward, Dominic Armstrong, Andrés Escobar, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: 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. ***Want to work with Geoffrey to help align superintelligence? Resolution is hiring! https://80k.info/work-at-resolution*** The leading AI companies all have broadly similar plans for keeping superintelligence under control: Train models to have good characterUse increasingly capable AIs to supervise other AIsMonitor them closely for signs of deception or schemingGeoffrey thinks that combination could work. The alarming part is that nobody has a strong argument that it will. He expects a crucial “phase shift” as models move beyond human intelligence: Below that threshold, humans can usually tell whether a model’s work is good and correct its mistakes.Above it, the models themselves will increasingly determine the feedback used to train their successors.In this episode, Geoffrey and new host Tom Reed explore what might go wrong with the companies’ plans; why Geoffrey’s new nonprofit, Resolution, is pursuing a portfolio of neglected research bets; and whether governments should slow AI development while we work out which methods can actually be trusted. This episode was recorded on June 29, 2026. Full transcript, video, and links to learn more: https://80k.info/gi Chapters: Cold open (00:00:00)Meet Tom Reed — our newest host! (00:00:32)Who’s Geoffrey Irving? (00:00:59)What misaligned superintelligence will look like (00:01:38)Why are AI companies more optimistic about alignment than Geoffrey? (00:12:30)Why Geoffrey expects superintelligence in 2–3 years (00:28:05)When and how to slow down frontier AI development (00:31:30)Safety researchers can have more impact in governments than companies (00:39:22)How Geoffrey’s new organisation plans to tackle alignment (00:46:55)Post-ASI science: nanotech, solving ageing, and uploaded minds (00:50:29)Why we should expect superintelligence to accelerate scientific progress (01:03:30)Can good character training carry over to superintelligence? (01:11:03)What the field of AI alignment still doesn’t know (01:16:44)Lessons from politics on how to combat power seeking (01:24:36)Solving Pentago and working at Pixar (01:29:22)Geoffrey’s best prediction (01:32:40)Geoffrey’s best bets on which alignment techniques will work (01:37:38)Work with Geoffrey at Resolution (01:43:34)The dangerous asymmetry between capabilities and alignment (01:54:17) Our production team includes:  Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Jeremy ChevillotteMusic: CORBIT