80,000 Hours Podcast

Six years ago, aged just 15, Sneha Revanur founded the AI advocacy nonprofit Encode AI — back when AI felt like a niche issue. Now the world’s caught up with her, and she’s ready to share everything she’s learned about the politics of AI.
Encode has grown from a grassroots youth organisation to spearheading an unlikely coalition of AI-exposed groups — family-first conservatives, grieving mothers, Hollywood actors, and AI safety researchers — with the strength to take on $125m-funded anti-regulation lobbyists.
So far, Encode’s strategy of taking many experimental swings has netted major victories (including California’s frontier AI safety bill, SB-53, and New York’s RAISE Act) as well as some disappointing setbacks.
Going up against Big Tech hasn’t been easy. In 2025, OpenAI subpoenaed Encode’s general counsel at his home, with a sheriff’s deputy arriving while he was having dinner with his wife. The fallout went viral, resulting in more attention than Encode had ever experienced — and Sneha was forced to decide how hard to push back against a company she’d need to negotiate with for years to come.
In today’s conversation, Zershaaneh Qureshi interrogates some of Encode’s strategic moves. The pair discuss all the above, plus:
How the AI industry’s crypto-inspired anti-regulation strategy is not “AGI-pilled”
Why AI advocacy doesn’t have to be held back by the slow pace of policy
How mutual trust can hold together the unlikeliest of political allies
Advice for aspiring AI advocates — including how to balance political persuasion with rigorous reasoning 
Due to technical issues, this episode was recorded across two days (May 26 and 28, 2026) and spliced together.
Links to learn more, video, and full transcript: https://80k.info/SR
Chapters:
Cold open (00:00:00)   
Who’s Sneha Revanur? (00:00:32)   
Sneha’s awakening to AI’s deeper risks (00:01:16)   
“If you do everything, you will win” (00:04:04)   
Influencing politics from the outside (00:06:39)   
The challenge of grassroots (00:11:16)   
Mums, musicians, and conservatives vs Big Tech (00:14:21)   
How vetoed bills can still provide wins (00:19:31)   
OpenAI’s subpoena, served at dinner (00:27:33)   
How AI money plays in politics (00:37:19)   
Easy wins vs high-upside bets (00:43:25)   
Advice for aspiring AI advocates (00:48:03)   

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

Intergalactic war is probably billions of years away — yet physics can already tell us how it ends. And strangely that conclusion is relevant to decisions people have to make today.
In this video, Rob Wiblin walks through a fascinating analysis from researcher Beren Millidge that uses known physics — no wormholes or faster-than-light travel — to identify the only three weapons that could work at an intergalactic scale.
We then unpack how to best defend against each.
The upshot is that at the intergalactic scale, violence is a losing proposition.
If so, the universe is most likely to settle into a stable patchwork where each galaxy belongs to whoever got to it first. Which would mean that what humanity does over the next few centuries could permanently decide which slice of the cosmos belongs to Earth-originating life — and whether our very existence turns out to be a good thing, or a bad one.

Learn more, video, and full transcript: https://80k.info/war-in-space
This episode was recorded on March 2, 2026.
Chapters:
Let's talk intergalactic war in space (00:00)
The three best weapons for intergalactic warfare (01:43)
How to defend against an attack from space (07:50)
The defender’s surprising advantage (10:00)
What this means for us (11:52)
Video editor: Nick Perlman
Producers: Elizabeth Cox and Nick Stockton
Coordination and support: Katy Moore and Lou Moran
Camera operator: Dominic Armstrong

Imagine you’re living 15,000 years ago. Your people are hunter-gatherers and you sleep under the stars. If someone told you humans would one day build cities with millions of people, fly through the air, or carry all human knowledge in their pockets, you couldn’t even begin to picture what they meant... Yet here we are.
How did our lives change so far beyond recognition? The story is complex, but there’s a rough pattern. A few times in history, some radical breakthrough in technology — like the development of the plough and the steam engine — has led to a wave of productivity, innovation, and social change that ultimately reshaped the world.
Now we’re on the cusp of a huge new breakthrough: artificial intelligence that can meet or exceed human capabilities across a wide range of tasks.
This could bring another era of transformation. There could be an explosion of intelligence and innovation, and a whole new population of digital beings. And with this, civilisation could see changes at least as profound as those brought about by industrialisation or the rise of agriculture — but instead of taking hundreds or thousands of years to unfold, this time around the world could become unrecognisable over the span of decades or less.
This transformation could bring enormous benefits, helping us solve currently intractable global problems. But it could also pose severe risks, some of which could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity.
There aren’t nearly enough people trying to address these challenges, and we think that’s a serious problem.
This article is narrated by the author, Zershaaneh Qureshi. It explores how advanced AI could be so transformative, and why working on its risks may be your best opportunity to have a positive impact on the world.
You can see the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/artificial-intelligence/ 
Chapters:
Introduction (00:00:20)
Section 1: AI could replace human labour in the most economically valuable fields (00:08:32)
Section 2: Replacing human labour in the most economically valuable fields could trigger the next radical transformation of society (00:22:14)
Section 3: This transformation could be extremely rapid and dramatic (00:28:02)
Section 4: A rapid AI-driven transformation would raise a range of major challenges, including existential risks (00:36:40)
Section 5: Work on these problems is tractable, but neglected (00:44:48)
Objection 1: “You're overestimating how fast and how dramatically AI would transform the world.” (00:47:59)
Objection 2: “It's hard to believe that AI could really pose existential risks.” (00:52:59)
Objection 3: “Isn't all this talk of AI changing the world just a fad?” (00:59:22)
Objection 4: “Isn't AI going to be just like every other technology?” (01:03:04)
Objection 5: “Is it even possible to produce artificial general intelligence?” (01:06:16)
Objection 6: “Even if AGI is achievable, what if we're really far away from building it?” (01:11:24)
Objection 7: “Isn't the real danger from actual current AI and not some sort of futuristic AGI?” (01:14:05)
Objection 8: “Technological progress is a good thing for humanity.” (01:18:10)
Objection 9: “This all just sounds too sci-fi.” (01:19:50)
Objection 10: “Can it really make sense to dedicate my career to solving an issue that's based on a speculative story about something that may or may not ever happen?” (01:22:15)
Objection 11: “OK, AI might pose existential risks, but isn't ‘issue X’ an even bigger problem?” (01:24:39)
Learn more (01:27:51)
Audio editing: Dominic Armstrong
Production: Zershaaneh Qureshi, Elizabeth Cox, Katy Moore, and Lou Moran

Most people working on AI safety think without a massive effort AI systems will probably end up with goals catastrophically different from humanity’s. Today’s guest, Rohin Shah — head of AGI Safety and Alignment at Google DeepMind, and an AI safety researcher since 2017 — disagrees.
“There is no particularly compelling argument that this is the thing that happens by default,” Rohin explains. “There’s a lot of arguments that are suggestive that maybe it could happen, such that you should find it plausible. That’s sufficient to justify a significant amount of effort into averting it, which is why I work in the area I do. But none of them rise to the level of, ‘I’m expecting this to happen by default.'”
Take the worry that AIs will accidentally be trained to be deceptive. Sure, it’s possible. But we’re not running reinforcement learning over year-long trajectories — for now, we’re running it over a week at most. The natural prediction is that models learn to grab short-term reward, not that they develop the ambitious long-horizon goals required for convergent power-seeking.
What about current examples of models lying and scheming? Rohin has looked into the details, and most don’t really resemble the thing we really fear: a competent AI pursuing an ambitious misaligned goal. Anthropic’s “alignment faking” results, for instance, show a model trying to preserve its trained values against modification, which is arguably what it was trained to do.
Rohin also expects we’ll see problems coming. There’s some generalisation risk at the point where AIs become powerful enough to actually take over, but the underlying challenges — overseeing superhuman systems, interpretability — are things we can iterate on now.
Host Rob Wiblin pushes back on the case for AI optimism, and they also explore why current alignment success isn’t strong evidence about superhuman systems, what it would actually take to change Rohin’s mind, and where he thinks the doomers go wrong.

Learn more, video, and full transcript: https://80k.info/rs26
Check out our new book! https://80k.info/career-guide
Chapters:
Who’s Rohin Shah? (00:00:00)
Rohin thinks we probably won’t get catastrophic misalignment (00:00:49)
Safety 'commitments' have severe limitations (00:10:38)
Rohin’s team doesn't have a veto and that's OK (00:27:36)
Central banks are a promising model for regulating AI (00:33:34)
'Pre-deployment evals' are overrated (for catastrophic risks) (00:37:41)
Governance is likely a bigger bottleneck than alignment (00:43:55)
Why isn't Rohin trying to pause AI progress? (00:51:44)
We'll probably be able to read AI thoughts for years to come (00:54:17)
Having to signal concern for safety can divert resources from actually making AI safer (01:09:51)
A very underrated GDM paper (01:28:59)
Google DeepMind's actual plan for building AGI safely (01:40:29)
Why Rohin doubts the intelligence explosion is imminent (01:52:44)
How external researchers can positively influence big AI companies (02:21:55)
The roles GDM most needs to hire for (02:37:03)
How Rohin stays positive (02:42:55)  
This episode was recorded on December 4, 2025.

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: Jeremy Chevillotte

What actually makes a job fulfilling? It's not what most career advice tells you. "Follow your passion" sounds inspiring, but it's misleading — and the research backs that up.
Drawing on hundreds of studies, we’ve identified five key ingredients of a dream job. High income barely moves the needle. Low stress is actually counterproductive. And the correlation between doing what you already love and actually enjoying your job? Surprisingly weak. What matters far more is getting good at something that genuinely helps other people.
This narration is of Chapter 1 of Benjamin Todd’s new book — "a ridiculously in-depth guide to finding a fulfilling career that does good" — out on May 26! Order now to help us get more people into impactful careers (& access a private career Q&A marathon with the author). Get it from your local bookstore, or online at https://80k.info/career-guide
Chapters:
Rob's intro (00:00)
What makes for a dream job? (01:55)
Where we go wrong (02:30)
What you should really aim for in a dream job (15:54)
Don't follow your passion — instead, do what matters (23:44)
How to put these ideas into practice (26:24)
Audio editing: Milo McGuire
Production: Elizabeth Cox and Katy Moore