80,000 Hours Podcast

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

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/ajeya2023This 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 HarrisAudio mastering: Ryan Kessler and Ben CordellTranscriptions: Katy Moore

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/sgThis 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

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-xriskThis 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