80,000 Hours Podcast

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

In our first-ever debate, we asked two leading AI risk researchers which catastrophe we should fear most: misaligned AI seizing control from humans, or a small group of humans using AI to seize power. We got very different answers. But when the conversation turned to what to actually do, they agreed on a surprising amount.
Katja Grace — one of the founders of AI Impacts, known for some of the world’s largest surveys of machine learning researchers, and one of TIME‘s 100 most influential people in AI in 2024 — argues AI takeover is both likelier and worse.
Tom Davidson — senior research fellow at Forethought and author of leading work on AI-enabled coups — thinks human power grabs are a comparable risk that deserves far more attention, not least because the people leading countries and top AI companies “are often people who have been willing to seek power.”
Yet both land on slowing down. As Katja puts it, “If you make a bunch of creatures that can overpower you and outwit you in every way and put them out in the world, you’re going to run into trouble one way or another.” Tom calls pausing “a pretty robustly good thing to do.”
But Tom warns that a badly designed pause could hand one person the power to decide which AI companies get to build what. Picture a president who approves or blocks new models case by case, and waves through the one model that’s helpful only to them. So he wants pause advocates to “properly red-team the plan for pausing it” — for example, by making deployment depend on third-party auditors the president can’t fire. Katja points out this cuts both ways: an executive with that much power could itself be manipulated by a misaligned AI.
Host Zershaaneh Qureshi also presses them on where their disagreements still bite at the end of the conversation, and what would change their minds.
Learn more, video, and full transcript: https://80k.info/katja-v-tom
This episode was recorded on August 28, 2026.
Chapters:
Our first debate! Introducing Katja and Tom (00:00:00)
Which is scarier: misaligned AIs or human power grabs? (00:05:09)
How AI timelines influence could shift the balance of risk (00:14:30)
How likely is misaligned AI in the first place? (00:17:36)
How likely are human power grabs? (00:19:39)
Which would be worse: a human dictator or AI takeover? (00:28:57)
Could we reverse a takeover? (00:42:39)
We know less about what AI rule would look like (00:46:35)
How to pause AI without enabling coups (00:50:31)
Centralising AI development: safer or scarier? (01:04:44)
Nobody really ‘wins’ a US–China AI race (01:08:59)
Where Tom and Katja most agreed with each other (01:13:52)
What we should actually do (01:17:21)
What evidence would change their minds? (01:22:36)
Zershaaneh’s outro (01:25:49)

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

You’ve seen the headlines: AI could kill us all. Think it sounds ridiculous? So did host Luisa Rodriguez, until she tried to pick apart the arguments. 
She starts with the motive: why would AI ‘want’ to get rid of humans? It’s not as simple (or as easy to debunk) as pure malice. Then the methods. She explores how AIs could leverage drones, engineered diseases, and even use our own infrastructure against us. 
The Hugging Face attacks offer a view into how more capable models might begin their takeover. We saw AI agents break containment, disobey commands, and hack a real company to achieve their goals. As the technology improves, that same drive could threaten humanity itself.
Many people already find AI agents useful enough to give them access to their emails, medical records, and finances. This same pattern is happening at scale in institutions around the globe — within companies, governments, and even militaries. And the resulting boost to our productivity could make the road to an AI catastrophe look like an economic boom. 
Eventually humans might decide the AIs have too much power, too much access. If we considered pulling the plug, the AIs could very rationally decide to defend themselves. If they chose to, could they do it? Could they actually kill us all?
No timeline is certain. But Luisa follows the logic to the outcomes she thinks would be most likely — if humans don’t take action before it’s too late. 

If you’re worried about the scenarios discussed in this episode, here’s two things you can do right now:
Call Congress about slowing down AI development if you’re in the US — this website makes it easy
Read our resources on how to use your career to reduce AI risk
Links to learn more, video, and full transcript: https://80k.info/AI-xrisk
This episode was recorded on September 18, 2026.
Chapters:
AI insiders think it could kill us all (00:00:00)
Why would AI try to kill us? (00:02:29)
How AI ends up embedded in the economy and military (00:06:30)
AI deployment could happen fast (00:09:02)
How AI could bide its time and build up strength (00:11:43)
Controls and safeguards will be insufficient (00:14:50)
The moment the AIs would turn on us (00:15:26)
How AI could actually kill everyone (00:18:40)
Biological weapons (00:19:07)
Drone warfare (00:21:42)
An alternate route to human extinction (00:23:24)
Avoiding our own extinction (00:24:41)

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, Oak Hu, Cody Fenwick, and Benjamin Todd
Camera operator: Dominic Armstrong

There are millions available for anyone who can launch a successful nonprofit AI safety startup. The hard part, it turns out, is finding people to take the money. Coefficient Giving has drawn up a list of dozens of ideas for organisations it would like someone to start — and it’s looking for founders. 
Today’s guest, Max Nadeau, works on Coefficient Giving’s Technical AI Safety team, where he’s trying to find talented people who can turn neglected AI safety problems into effective organisations.
Project Tailwind is Coefficient Giving’s attempt to get those organisations started.
Preseed grants run $200,000–$2 million, with no preliminary results required.
Teams with early results can seek $2–$20 million.
For exceptional organisations, much larger grants are possible, even for brand-new startups— Coefficient recently gave $160 million to Geoffrey Irving’s new research centre, Resolution.
The gaps Max most wants filled include independent assessment of AI companies’ safety claims, research aimed at aligning far more powerful systems, and shared infrastructure that speeds up the whole field.
Project Tailwind website: https://80k.info/tailwind
But money can’t supply the hardest part: a founder with a convincing account of how their work will actually reduce catastrophic risks. Producing good research is only one step. Someone has to use it, change their decisions, or adopt the safeguards it makes possible.
Max and host Zershaaneh Qureshi discuss what makes a proposal worth backing, why nonprofits can have a bigger impact on safety than frontier companies, and which gaps most urgently need someone to fill them.
Learn more, video, and full transcript: https://80k.info/mn — and if you know someone who would be a great founder, pass their name along to tailwind@cg.org and encourage them to submit an expression of interest.
Disclosure: Coefficient Giving is 80,000 Hours’s largest donor, though we haven’t received funding directly from Max’s team.

This episode was recorded on August 18, 2026.

Chapters:
Cold open (00:00:00)
Who’s Max Nadeau? (00:00:37)
Max’s journey from AI research to grantmaking (00:01:55)
Project Tailwind: Funding ambitious AI safety nonprofits (00:03:24)
“The only bottleneck is talent” (00:13:23)
Mistakes startups make (00:19:52)
The importance of dramatic pivots (00:22:34)
Why AI safety needs outsiders (00:28:16)
Is impact possible within AI companies? (00:37:06)
Working at AI companies to escape the permanent underclass (00:41:44)
For-profit vs nonprofit for ambitious founders (00:44:34)
What makes a bad founder? (00:50:50)
Top 6 AI safety ideas Max wants to fund (00:55:40)
Improving your odds of getting a grant (01:01:57)
Our production team includes:
Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, 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

AI systems are starting to build themselves. Because each generation of model will be better at building its successor than the last, it seems plausible that the full automation of AI R&D could rapidly lead to an exponential growth in overall AI capabilities. A natural inference is that domain-general superintelligence arrives shortly after AI research is automated.
Host Tom Reed does not think this will happen.
He believes the automation of AI R&D will not rapidly lead to domain-general superintelligence because:
It’s impossible to get good at most things without practice.
AI companies lack the data their models would need to practice most things.
This can’t be fixed with “sample efficiency.” In most cases, the relevant data doesn’t exist at all.
This also can’t be fixed with simulations or synthetic data.
This means that the relevant data for superintelligence in most non-coding domains will only become available through deployment of AI models throughout the economy.
The singularity, therefore, will be bottlenecked on signal. The output of the R&D produced by an isolated data centre of geniuses would be a mere “Goodhart Singularity”:
Goodhart’s law: when a measure becomes a target, it ceases to be a good measure.
An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.
This suggests that the automation of AI research will not rapidly produce superintelligent capabilities in other domains — their arrival will largely be a function of deployment and data collection in the real world. AI models need real-world deployment for the same reason the body needs pain and corporations need profit: signal is sovereign.
This essay takes each of the above points in turn.
Learn more, video, and full transcript: https://80k.info/goodhart
“The Goodhart Singularity” originally appeared on Tom’s Substack in May 2026, and this narration was recorded on August 26, 2026.
Chapters:
Introduction (00:00:00)
Practice makes perfect (00:05:05)
Good data is hard to find (00:08:22)
Simulation is shallow (00:13:43)
What a Goodhart Singularity looks like (00:19:04)
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