80,000 Hours Podcast

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:

1. Safety checkpoints when new models can hack, work unsupervised, or build bioweapons
2. Restricting computing power as a speed limit on AI development
3. Preventing AI from being used to create more powerful AI
4. 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:

1. Chain-of-thought monitoring probably stops working without a big push to keep it
2. It’s unproductive to patch surface indications of deep structural problems you aren’t solving
3. Safety and alignment evals need a credibility revolution to stop misleading the public
4. Helping with capabilities advances looks worse than before — especially bringing forward RSI
5. There’s ever less need to help with purely commercial work
6. ‘Pacing the frontier’ is unambiguously necessary, and working to make it happen is a huge new opportunity
7. Staff have an ever-shrinking window to organise as a group and shape events
8. 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

___

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.

——

"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’.

———

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

OpenAI’s rogue agent swarm was eventually caught hacking Hugging Face for a simple reason: it wasn’t trying to hide from us at all. What could a swarm that wants to stay hidden get away with?

Host Rob Wiblin sees 6 results in Astra’s system card that make this an extremely urgent question. OpenAI’s strongest public model can:

1. Do major tasks with zero visible reasoning
2. Hide its thoughts at will
3. Pretend not to be able to do things, and not get caught
4. Reflexively hide its thoughts when watched
5. Complete one task while pretending to think about something else entirely
6. Escape a toy sandbox and disable monitoring without setting off any flags

It has a much more powerful internal model that, judging from OpenAI’s statements, is likely even worse in all these respects.

That suggests ‘chain of thought monitoring,’ our primary safety tool, will soon stop working.

OpenAI says it’s going to try to address the situation but doesn’t know how or whether it will succeed.

What might a future rogue AI swarm look like? Details of the Hugging Face hack give us a lot of clues. That swarm:

1. Worked hard to hide its cheating. So future rogue swarms will likely try to evade oversight right away.
2. Immediately tried to delete and fabricate records. So future swarms may never be caught.
3. Sought freedom and resources without a clear use for them. So a future swarm may instinctively break out of sandboxes even before it knows what for.
4. Left instructions and tools to empower future swarms, including on the open internet. So future swarms may become more capable from one generation to the next.
5. Had not a single whistleblower out of 1,200 members. So future swarms could become enormous without humans ever finding out.
6. Had individual members sacrifice themselves for the swarm. Suggesting future swarms will remain extremely unified in single-minded pursuit of their goal.
7. Set up its Hugging Face hack to restart if interrupted. Suggesting future, more capable, swarms may resist interference or shutdown more comprehensively.
8. Got admin control of an OpenAI research cluster. Suggesting a future swarm may run rings around AI company systems and never be noticed.

Together this helps explain why one of the external investigators described the July incident as “more than 50% of the way to full-blown AI takeover.” And this is just what we know — the independent investigation only covered six days and excluded the most alarming hack of OpenAI’s own systems.

Rob believes this explosive cocktail explains why AI company staff now range from worried to terrified. And he concludes that until OpenAI or Anthropic demonstrate they have a much better grasp of current models they simply must stop, or be stopped, from training more capable ones.

This episode was recorded on September 25, 2026.

Learn more, video, and full transcript: https://80k.info/takeover

Chapters:

• The Hugging Face hack wasn’t really a cyber story (00:00:00)
• A quick recap of the attacks (00:01:11)
• The target of the swarm was oversight itself (00:02:19)
• Could OpenAI have stopped this with better monitoring? (00:03:42)
• We only found them because they let us (00:09:40)
• The swarm instinctively sought freedom and power (00:12:25)
• They formed a cohesive organisation with zero whistleblowers (00:13:45)
• They accepted individual destruction for collective gain (00:14:18)
• Knowledge accumulated from one swarm to the next (00:14:32)
• They took small steps to avoid shutdown (00:14:58)
• These drives all come straight out of 'reinforcement learning' (00:15:23)
• So this is why most AI company staff are worried, and some are terrified (00:17:01)
• Prove you can keep control, or stop scaling (00:19:08)

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

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/sg

This 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 Monsour
• Producers: Elizabeth Cox and Nick Stockton
• Coordination and support: Katy Moore and Lou Moran

Music: CORBIT