80,000 Hours Podcast

This is an (unpaid) cross-post of an episode from the podcast NonZero, which Rob Wiblin thought you might be interested in. The originals are on the NonZero website: Part 1 and Part 2. If you like it and want more similar stuff in the future, consider subscribing to NonZero on YouTube, Spotify, Apple, or wherever you get podcasts.
What will it take for Washington to slow down AI? Not better arguments, says Jeremie Harris, CEO of Gladstone AI and coauthor of a 2024 State Department–commissioned action plan on AI risk. He argues it will take an “AI Chernobyl”: a disaster so severe the president fears for their own life, and Washington’s official stance on AGI risk flips.
Jeremie has spent six years betting that AI’s scaling curve won’t bend. His biggest miss ran the other way: “Frankly, I thought we ought to be dead by now.” Capabilities keep improving almost automatically, but safety doesn’t, because “no one knows how to make a curve of control or a curve of alignment.”
He was making that bet before it was respectable. Jeremie recounts his years trying to get people in government to take AI risk more seriously, including his role in the battle to get “AGI” into a Biden-era State Department report.
He also takes aim at Open Philanthropy, now Coefficient Giving (80,000 Hours’ largest funder), which was then the dominant funder in AI safety. Jeremie claims it discouraged outsiders from briefing Washington, for fear officials would hear “powerful weapon” and launch a Manhattan Project for superintelligence. He criticises its long timelines and a grantee culture of telling funders what they wanted to hear, which he says left the field slow to engage government. Still, he credits Coefficient Giving with getting the problem “much more right than almost anyone.”
His assessment of the AI companies is worse. Jeremie thinks OpenAI has gone through “evaporative cooling,” shedding its most safety-minded staff round after round. He also doubts Sam Altman ever fully believed his own warnings.
We’re sharing this because Jeremie’s takes on warning shots, government response, and past mistakes in AI governance were new to us. Not everyone will remember the history the way he does, but it’s provocative in a useful way.
Chapters:
Rob’s intro (00:00:00)
Jeremie’s podcast and prescient AI report (00:01:31)
Is scale still (mostly) all you need? (00:07:32)
Jeremie: We’re way past “stochastic parrots” (00:13:45)
Inside the early AI safety ecosystem (00:22:35)
What Yudkowsky got less wrong than most (00:34:24)
Why capability is outpacing alignment (00:42:18)
Were Altman’s fears ever sincere? (00:49:49)
Would pausing AI crash the economy? (00:56:04)
Overtime preview: Debating China (01:01:45)
Bob’s beef with “kumbaya” (01:10:22)
Will it take an AI Chernobyl to convince the skeptics? (01:12:19)
Is China-hawkism the safest option? (01:22:55)
The case for “organic transparency” with China (01:50:43)
How alignment progress could backfire (01:58:02)
Has China infiltrated US tech? (02:03:18)
Breaking down Dario’s democracy bloc idea (02:12:38)

The big 3 AI companies all had serious scandals last month. Anthropic pushed a federal AI bill that would make it harder to stop dangerous model training. OpenAI fired three safety researchers based on evidence that’s somewhere between weak and nonexistent. And it came out that Google DeepMind gets staff to sign contracts barring them from criticising anything Google does for the rest of their lives.
But these companies are not beyond influence. The track record of holding them to account — staff objecting internally, journalists digging for dirt, campaigners making a fuss — is impressive for how little effort has gone into it.
Many staff want to do the right thing and can be persuaded on the merits. And the fierce competition for talent forces companies to try to seem like the good guys.
That’s why being a pain in the ass for the leading AI companies is a legitimate career path, and an underrated one.
Inside the companies we need even more staff to speak up clearly and visibly with the substantive case for their firms to raise their game.
And externally we need, among other things:
Investigators uncovering company misbehaviour and failures
Subject matter experts laying out the facts for journalists and politicians
Campaign organisers and social media experts focusing public attention where it’s needed
Today I lay out what each AI company got wrong this time, why the ability to bring down the immediate hammer of fierce public and expert outrage matters more as events speed up, and the many ways you can contribute to that effort.
If you want to work on holding AI companies to account, our career review of AI safety advocacy is a good place to start, our 1–1 advising team can help you think it through, and our job board lists AI safety and policy roles.

This episode was recorded on October 29, 2026.

Links to learn more, video, and full transcript: https://80k.info/AI-accountability
Chapters:
We need a stronger effort to push AI companies to do better (00:00:00)
Anthropic: who gets to pull the brake? (00:01:14)
OpenAI fires three safety researchers (00:07:14)
Google DeepMind’s lifelong gag clause (00:12:44)
Credit where it’s due (00:15:39)
How to make AI companies behave better (00:16:34)
Insiders: if you want to stay (00:17:31)
Insiders: if you’re willing to leave (00:18:40)
So… should you leave? (00:19:37)
Outsiders: what’s already worked (00:20:39)
Outsiders: digging up and explaining (00:22:51)
Outsiders: reaching the public and politicians (00:25:12)
Keep the pressure on (00:27:52)

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 and Lou Moran
Camera operator: Dominic Armstrong

The US government spends 25 times more on military marching bands than on its AI safety institute. And that’s just one of the many institutions we’re missing as AI becomes more powerful and dangerous.
Aviation has the FAA to regulate safety and the NTSB to investigate crashes. Nuclear power has international inspectors and industry watchdogs. But for advanced AI, as Luke Muehlhauser puts it, “most of the institutions that need to exist don’t exist.”
Luke has worked on AI risk since 2011, and now leads AI governance and international policy grantmaking at Coefficient Giving. He thinks we urgently need organisations that can independently evaluate frontier models, investigate AI incidents, verify AI companies’ safety claims, and hold them accountable.
He also wants people to build more specialised organisations: one that tests whether AI systems are prone to lying or pursuing hidden goals; another that tracks whether we can still understand their reasoning; and others that probe how easily AI could enable biological attacks.
The encouraging news is that many of these projects could attract substantial philanthropic backing. What’s missing are people willing to build them.
Luke and host Luisa Rodriguez discuss which opportunities are most neglected, how Coefficient’s new Project Tailwind is helping new organisations get started, how recent AI warning shots are transforming politics, and why Luke thinks we may have much less time than we thought.
Learn more, video, and full transcript: https://80k.info/lm
This episode was recorded August 16–17, 2026.
Chapters:
Cold open + a note from Luisa (00:00:00)
Who’s Luke Muehlhauser? (00:02:27)
The state of AI safety right now (00:03:07)
The nonprofits Luke most wants to see founded (00:09:58)
Mid-career pivots that paid off (00:18:58)
How much did Mythos shift the Overton window? (00:30:38)
Preparing for the next warning shot (00:37:33)
How nonprofits shape policy (00:43:52)
Are chip export controls good for safety? (00:50:17)
The three worst-case AI scenarios (00:54:29)
How Luke’s AI timelines have changed in the last year (01:00:13)
Why alignment is not on track (01:14:31)
How realistic is a deal with China? (01:20:58)
Triaging AI risk (01:23:56)
When AI safety was 10 people (01:30:36)
AI safety’s mistakes, then and now (01:39:36)
What is it like to do grantmaking at Coefficient Giving? (02:02:42)
More projects Luke would like to see founded (02:20:45)
Staying sane in a scary field (02:26:07)
“The glorious, cyberpunk future” (02:34:07)

Our production team includes:
Video editors: Josh Alward, Dominic Armstrong, Andrés Escobar, Milo McGuire, Luke Monsour, and Simon Monsour
Producers: Elizabeth Cox and Nick Stockton
Coordination and support: Katy Moore and Lou Moran
Camera operator: Milo McGuire
Music: CORBIT

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