80,000 Hours Podcast

China’s best models are only six months behind America’s, despite having far less computing power. How is that possible?
Luisa Rodriguez investigates, and finds that much of China’s progress is made by using American chips and copying American models:
US rules stop NVIDIA selling its best chips to China. But Chinese companies can still rent those same chips in data centres abroad.
Smugglers go to extreme lengths to sneak chips into China.
Chinese AI companies copy America’s best models on an industrial scale.
Today’s most powerful technological secrets are protected by alarmingly inadequate security.
The US could tackle these gaps with powers it already has. So the current situation, with China only months behind, is largely a policy choice.
If the main argument against slowing down is that China might catch up, none of this quite adds up.
Four days after recording this, NVIDIA’s CEO Jensen Huang — who features in the video as one of the loudest voices against restricting China’s access to chips — admitted to having an affair with an alleged Chinese spy. This shows just how badly China wants American chips.
Closing these gaps in US AI policy could buy time to test new models and hold back those that aren’t safe. Before we accept that the US has to race ahead, it’s worth asking who benefits from racing, and what’s currently being given away.
This episode was recorded on November 4, 2026.
Links to learn more, video, and full transcript: https://80k.info/china-chips
Chapters:
Update: since we recorded (00:00:00)
Everyone said China had caught up (00:00:30)
Who’s building China’s AI? (00:01:50)
How far behind is China? (00:03:20)
Why can’t China make its own chips? (00:04:40)
So where do China’s chips come from? (00:06:24)
How do you copy an AI? (00:10:35)
China is running a different race (00:12:34)
Why a bigger lead matters (00:15:30)
Could the US widen the gap? (00:16:51)
What if China just steals the models? (00:21:12)
So can the US slow down? (00:24:53)

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

We make a lot of episodes about AI these days. We recently picked out the best 10 episodes of the show to really help deepen your understanding of AI and AGI and superintelligence and recursive self-improvement — and the impact all of those things might have.

We aimed for episodes that were entertaining as well as highly informative, and tried to choose 10 that didn't overlap too much, so between them could cover most of the core issues. 

You can find them all now in a feed called The 80,000 Hours Podcast on Artificial Intelligence, available wherever you get podcasts.

And at https://80000hours.org/AIpod, you can sign up to get one of them sent to your email each week for 10 weeks — plus one week with our bonus episodes of article readings and how to pivot your career into AI.

Across the 10 core episodes, you will learn:
How AI could compress a century of intellectual progress into a decade
Why today's training techniques actively encourage models to lie to us
How the economy and geopolitics will shift in response to superhuman AI, and indeed are shifting already in response to superhuman AI
How superintelligent AI is in serious tension with maintaining democracy
the real nature of the AI arms race between the US and China
How AI shifts the risks of pandemics and bioweapons over time
how to think about AI consciousness and welfare and whether it matters, and the scramble to understand what AI models are really thinking about deep down.
If you're also looking to change your career to work in AI, we close out with an episode that lays out dozens of valuable concrete projects that need people to work on them right now, and an interview with the founder of 80,000 Hours explaining how to redirect your career towards this kind of work.

Learn more about what you can do to help: https://80000hours.org/ai/

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