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 it
It’s unproductive to patch surface indications of deep structural problems you aren’t solving
Safety and alignment evals need a credibility revolution to stop misleading the public
Helping with capabilities advances looks worse than before — especially bringing forward RSI
There’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 opportunity
Staff have an ever-shrinking window to organise as a group and shape events
There 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
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 Escobar
Producers: Elizabeth Cox and Nick Stockton
Coordination and support: Katy Moore, Lou Moran, and Cody Fenwick
Camera 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 alignment
Career reviews in AI safety
Career services: our AI advisor, free 1:1 career advising, and job board
Our top tips for successful networking
Survey results: what AI safety orgs want in a hire
Want to upskill in technical AI safety? Here are 67 useful resources
My experience with imposter syndrome — and how to (partly) overcome it
Other resources:
AISafety.com
Courses from BlueDot Impact
ARENA curriculum
Why experienced professionals fail to land high-impact roles
What are some exercises and projects I can try?
AI Safety Talent Network
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Our production team includes:
Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour
Producers: Elizabeth Cox and Nick Stockton
Coordination 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.
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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."
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’.
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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:
Saints — models that care about doing what we really want
Sycophants — 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 to
Schemers — models that don’t care about us or our interests at all, who are just pleasing us so long as that serves their own agenda
In principle, a machine learning training process based on reinforcement learning could spit out any of these three attitudes, because all three would perform roughly equally well on the tests we give them, and ‘performs well on tests’ is how these models are selected.
But while that’s true in principle, maybe it’s not something that could plausibly happen in the real world. After all, if we train an agent based on positive reinforcement for accomplishing X, shouldn’t the training process spit out a model that plainly does X and doesn’t have complex thoughts and goals beyond that?
According to Ajeya, this is one thing we don’t know, and should be trying to test empirically as these models get more capable. For reasons she explains in the interview with host Rob Wiblin, the Sycophant or Schemer models may in fact be simpler and easier for the learning algorithm to creep towards than their Saint counterparts. And not only that, but there are also ways we could end up actively selecting for motivations that we don’t want.
Learn more, video, and full transcript: https://80k.info/ajeya2023
This episode was originally released in May 2023.
Chapters:
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