80,000 Hours Podcast

Two-thirds of Americans think AI is moving too fast. So do the people building it. So why the hell isn’t anyone slowing down?
We can’t tell when models are too dangerous. We mostly don’t know how they work. We don’t know how to make them do what we want. And our two main safety checks — testing behaviour and monitoring their ‘thoughts’ — are breaking down.
Yet we’re racing to build more powerful AI anyway, faster every year. And it’s only going to accelerate as AI itself gets better and better at building the next generation of models.
Earlier this year, OpenAI found its new model, Astra, was alarmingly good at hacking, and did something companies almost never do: paused its biggest training project. But it released Astra anyway, even though tests couldn’t prove if it was safe.
That’s the problem with leaving it up to the companies. We can’t trust them to act in our best interest because the competitive pressures for them to race ahead are too strong.
So what’s the solution? Luisa Rodriguez evaluates the likely candidates:
Safety checkpoints when new models can hack, work unsupervised, or build bioweapons
Restricting computing power as a speed limit on AI development
Preventing AI from being used to create more powerful AI
Pausing development altogether
None of these will be easy.
But with AI hacking increasingly widespread, we’re already seeing the consequences of doing nothing — and this is just the beginning. Slowing down could give us time to understand why models are so hard to control; secure hospitals, banks, and power grids against hacks; and help society adapt.
Everyone should be a part of this conversation, not just CEOs and policymakers. The rules we end up with will be shaped by what voters ask for, and could determine the future we all live in.
This episode was recorded on October 21, 2026.
Links to learn more, video, and full transcript: https://80k.info/AI-slowdown
Chapters:
• Everyone says AI is moving too fast (00:00:00)
• What exactly would we be slowing down? (00:01:28)
• Let the companies race ahead (00:02:13)
• Require companies to pass safety checkpoints (00:06:03)
• Put a speed limit on AI development (00:09:39)
• Limit how much AI can help build AI (00:11:11)
• Pause the development of more powerful AI altogether (00:13:04)
• What would we actually gain from slowing down? (00:16:34)
• Who would actually make this happen? (00:18:38)
• Where this leaves us (00:20:16)

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, Cody Fenwick, and Joseph Isaac
Camera operator: Dominic Armstrong
Image and footage credits: Anders Eidesvik (CC BY-SA 4.0); Dario Amodei; Import AI (Jack Clark); Truth Social (Donald J. Trump); POLITICO; OpenAI; Anthropic; NIAID (CC BY 2.0); Apollo Research; Epoch AI; CNBC; Google DeepMind; The New York Times; Marginal Revolution (Tyler Cowen); CTV News; democracynow.org (CC BY-NC-ND 3.0); Vermont Public (AP); Daily Nous; Working Group on AI Pause Feasibility; Malwarebytes; Palisade Research; The Stack; official White House photo by Joyce N. Boghosian (public domain); Al Jazeera; AI Weekly; FourWeekMBA; U.S. Food and Drug Administration (public domain)

If the US and China agreed to pause frontier AI training, how would either side confirm the other was complying? Today, Tom Milton argues, the most likely answer would be to shut down data centres entirely. That would throw away everything else those chips do, and make any agreement far more costly to accept.
Tom is CEO of Amodo Design, a UK hardware lab building prototype tools to verify how AI chips are being used. He argues that verification is what makes a slowdown affordable: if a cluster can be shown to be serving only existing models rather than training new ones, most of AI’s benefits can continue during a pause.
Amodo’s pilot reruns a cluster’s inference on a separate verifier server and checks that all of its network traffic can be explained by the agreed model. AI companies are developing related tools: in its October 2026 Frontier Safety Roadmap update, Anthropic reported a prototype that runs on production inference and checks that outputs come from a specific set of model weights.
The field is small. Tom estimates that only about 50 engineers worldwide work on compute verification, compared with the billions of dollars the US spent on Project Vela to detect Cold War nuclear tests. And custom verification hardware has long lead times: it has to be designed, security-tested, red-teamed by both sides, manufactured, and trusted before anyone can rely on it.
If a deal is struck in a hurry, Tom expects it to be assembled from whatever tools already exist. Were that to happen next month, he says, the only option would be to shut data centres down entirely. That is why he argues the work needs to scale up now, with more people and resources from AI companies, governments, and funders, so that better options are ready as soon as a deal is needed.
Tom and host Tom Reed also discuss how an AI company might evade verification, lessons from nuclear and chemical arms control, and how AI is changing hardware engineering.
Learn more about Amodo’s work at ai-verification.com.

Links, video, and full transcript: https://80k.info/tm
This episode was recorded on September 29, 2026.
Chapters:
Cold open: Can we verify a US–China AI deal? (00:00:00)
Why compute is the best place to verify an AI slowdown (00:01:04)
Can you prove a lab isn’t cheating? (00:06:08)
Live demo: how to spot a cheating GPU (00:13:22)
Could China hide a data centre? (00:19:50)
Can verification tools be hacked? (00:23:35)
Is anyone at OpenAI working on this? (00:29:13)
Chinese inspectors in US data centres?? (00:37:49)
The 20-day panic deal (00:46:07)
What would a US–China AI deal look like? (00:51:27)
Why lab researchers should work on verification (00:55:38)
What if we never build this? (00:58:45)
Can verification protect against power grabs? (01:02:06)
How good is AI at hardware design? (01:07:20)
What’s holding AI back in hardware? (01:18:32)
Risks of automating hardware R&D (01:22:44)
The DARPA model for AI safety (01:25:41)
What’s left for engineers after AI? (01:32:26)
Personal gadgets, designed by Fable? (01:40:07)
What if the US stops sharing its AI? (01:41:56)
Our production team includes:
Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour
Producers: Elizabeth Cox and Nick Stockton
Coordination and support: Katy Moore and Lou Moran
Camera operator: Dominic Armstrong
Music: CORBIT

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