80,000 Hours Podcast

Both Silicon Valley and the public can’t get enough of ‘AGI timelines.’ But Toby Ord, senior researcher at Oxford’s AI Governance Initiative and author of The Precipice, believes we consistently make big mistakes when thinking about them. He lays out the 14 ways he most often sees people go wrong:

1. Assuming AI research is just hill-climbing
2. Imagining AI research is just programming
3. Forecasting “could” instead of “will”
4. Believing the current benchmark is the last one
5. Extrapolating trends with no clear finish line
6. Assuming inputs keep scaling at the same rate
7. Conflating intelligence with capability
8. Consuming point estimates and discarding the error bars
9. Dismissing dissenting experts
10. Forecasting very different things while using the same words
11. Assuming capabilities arrive together
12. Treating “we don’t know” as permission to carry on as usual
13. Choosing a plan that minimises regret rather than maximises impact
14. Trusting surface model impressiveness

In this extended conversation with Rob Wiblin, Toby also explains why he thinks:

• AI self-improvement is uniquely dangerous in four ways, but also might not even work
• A ban on superintelligence is possible
• A US-China treaty on superintelligence is also possible
• The case for ‘broad timelines’
• Transformative AI is likely a decade away
• We should just ban unmonitorable chain-of-thought today.

This episode was recorded on July 2, 2026.

Links to learn more, video, and full transcript: https://80k.info/to26

Want to get up to speed on AI? We’ve got a crash course of 10 of our podcast episodes designed to help you get to grips with transformative AI — particularly if you’re new to the topic — and what you can do to help shape its trajectory.

Chapters:

• Toby Ord is back — for the 5th time! (00:00:00)
• AI self-improvement might not matter (00:00:14)
• 4 ways AI self-improvement is dangerous (00:12:39)
• A US-China treaty on superintelligence is possible (00:20:47)
• Could we ban superintelligence? (00:37:07)
• We should just ban unmonitorable chain of thought (00:57:46)
• Why Toby thinks AGI is a decade away (01:09:28)
• Even superintelligence needs work experience (01:17:50)
• Is AI coming for mathematicians? (01:32:22)
• The case for broad timelines (01:45:01)
• How should broad timelines change what we do? (02:22:24)
• Are current models all they’re cracked up to be? (02:31:03)
• Coordinating careers for different timelines (02:43:36)

Our production team includes:

• Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, 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
• Music: CORBIT

Last October, famed coder Andrej Karpathy called AI agents “slop.” Two months later he completely reversed his view, describing them as “alien tools” that are “rocking the profession.”

He was far from alone in his whiplash. Six months ago, host Rob Wiblin recorded a video explaining why so many AI experts had longer timelines to AGI than a year earlier. By the time he clicked publish, another huge vibe shift was well underway. 

Evidence of AI acceleration has piled up since:

• Models now complete software engineering tasks that would take human professionals a full day — improving faster than our measurements can even keep up. 
• Anthropic’s revenue is growing at an annualised 8,400%, a trend so steep it would hit the whole world's GDP in 2028 if it continued.
• AI models are making breakthroughs in famous mathematics puzzles.
• And according to Anthropic, Claude now writes 80% of their code and is itself a key contributor to making itself smarter. 

While legitimately impressive, Rob isn’t entirely sold. Going through each point carefully he finds this evidence is less decisive than it looks at first glance.

And key gaps remain, such as models struggling with complex, real-world tasks. He tours the odd experiments that remain our best attempts to measure that gap: vending machine simulators, an “AI Village” that organises live events, and a real cafe and shop where AI managers are left to do their best handling staff, suppliers, and government paperwork on their own.

Rob argues that the nature of the gap between clean and messy work is one of the four biggest unresolved questions in AGI forecasting.

In today's piece he explains that, the three other key disagreements between AGI bulls and bears, the seven big pieces of evidence we've gotten about AGI timelines in 2026, and his updated timelines to AGI.

Correction for those watching the video: The video clip shown at 02:10 was not vibe-coded by its creator and was included by our own error. You can watch the creator's full video and explanation here: https://www.youtube.com/watch?v=cyrocAOdXKw

Links to learn more, video, and full transcript: https://80k.info/2026-timelines

 
This episode was written and recorded before OpenAI’s AI agents hacked Hugging Face. You can read about the incident on our Substack.

This episode was recorded on July 3, 2026.

Chapters:

• What the hell happened? (00:00)
• Vibe shift (01:17)
• Exhibit 1: AI revenue explodes (04:33)
• Exhibit 2: That METR graph (09:54)
• Exhibit 3: AI capabilities jump, then flatten out (14:57)
• Exhibit 4: AI starts to build itself… maybe (17:35)
• Exhibit 5: AI still struggles to run a business (23:02)
• Exhibit 6: OpenAI makes a maths breakthrough (33:48)
• Exhibit 7: inference scaling wasn't as big as believed (38:19)
• How does that all change timelines? (41:41)
• Four reasons long timelines are still possible (44:26)
• It's time to limit dangerous research practices (48:01)

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

If you genuinely believe that humanity could be wiped out by AI or a pandemic, what is the appropriate amount of fear to feel?

“As much as possible” can seem like the only reasonable answer. If the world is on fire, surely feeling calm just means you haven’t internalised the situation. When you’re trying to prevent human extinction or end factory farming, taking a weekend off can feel morally indefensible.

But fear is an alarm designed to provoke short bursts of drastic action, not a state humans can productively inhabit for months or years. Guilt turns out not to be such a great engine for productivity, either. So what is the best way to sustain motivation to work on the world’s most pressing problems in the long term?

Host Luisa Rodriguez and guest Spencer Greenberg tackle this question from many angles — talking to therapists, running a survey of people working on existential risks, and pulling relevant lessons from Spencer’s new book, The 12 Levers: The Complete Psychological Toolkit for Improving Your Life. Drawing on all these sources, they put together a plan for how to make an impact without grinding yourself to a pulp.

Check out Spencer's new book: https://80k.info/12-levers

Links to learn more, video, and full transcript: https://80k.info/sg26

This episode was recorded on June 12 and 15, 2026.

Chapters:

• Cold open (00:00:00)
• Spencer is back — for a 5th time! (00:00:40)
• Managing the psychological toll of working on existential risks (00:01:00)
• Luisa and Spencer surveyed people working on existential risk (00:04:23)
• How to sustain your motivation (00:11:13)
• Why you shouldn’t read the news (00:23:54)
• Why guilt isn’t an optimal source of motivation (00:36:28)
• Breaking the boom-and-bust cycle of burnout (00:44:41)
• Specialness and saviour complex (00:51:46)
• If you're certain we're doomed, you're overconfident (00:57:36)
• We're all (probably) going to die (01:03:50)
• When loved ones think you're weird (01:17:21)
• How to balance impact and personal wellbeing (01:28:20)
• What people report actually helps (01:53:49)
• Spencer read 100 self-help books: here's what works (01:59:40)

Our production team includes:

• Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, 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

Many AI researchers believe mass job displacement is coming — and some even think there’s a chance their technology will kill everyone. But they’re building it anyway. Writer and journalist Jasmine Sun has been documenting why from the inside.

Jasmine describes her work as an “anthropology of disruption.” She’s embedded herself in Silicon Valley’s AI subcultures — attending the parties and conferences, conducting off-the-record interviews — to understand the beliefs of the small group of people shaping this technology.

Some of her findings are unsettling. Asked what advice they’d give a normal 17-year-old, almost every AI researcher said the same thing: “I have no idea… It’s a really scary time. I don’t think there’s going to be a lot of jobs for them left.”

Their motives for building advanced AI are varied: a mix of optimism for humanity, techno-determinism, and a desire to secure their own future in the face of a possible “permanent underclass.” A few go even further, actually hoping for a world where machines — rather than humans — are running the show.

When the room can’t even agree on whether humans should stay in control, building a consensus on how to build AI safely gets much harder.

Beyond Silicon Valley, Jasmine’s also tracking the rise of “AI populists,” who see AI as the latest example of corporate elites concentrating their power at the expense of everyone else. In the US, populist sentiment about AI has mostly manifested in protests and votes against data centres. But sometimes, it has escalated into violence: a molotov cocktail thrown at Sam Altman’s house, and open fire on the home of a politician who’d backed a data centre. Jasmine thinks public anger will keep finding an outlet, one way or another, until people feel like they’ll actually share in AI’s gains.

In this interview with host Zershaaneh Qureshi, Jasmine Sun takes us inside the multifarious factions on AI’s bleeding edge. They also discuss:

• How “doomer” became the lowest-status label in Silicon Valley, and what that means for AI safety
• Why the AI industry’s PR strategy has failed, and what it would take to rebuild public trust
• What’s under the surface of the Chinese public’s much more positive response to AI
• Jasmine’s reasons to be cautiously hopeful: it’s an unusually high-leverage time to work on AI safety, with policymakers and philanthropists hungry for good ideas

This episode was recorded on June 4, 2026.

Links to learn more, video, and full transcript: https://80k.info/jasmine

Want to get up to speed on AI? We’ve got a crash course of 10 of our podcast episodes designed to help you get to grips with transformative AI — particularly if you’re new to the topic — and what you can do to help shape its trajectory: https://80000hours.org/AIPod

Chapters:

• Cold open (00:00:00)
• Who’s Jasmine Sun? (00:00:30)
• Escaping the permanent underclass (00:01:22)
• Jasmine’s “anthropology of disruption” (00:14:02)
• Vice signalling in Silicon Valley (00:18:46)
• AI populism will shape 2028 (00:28:11)
• Does AI populism distract from safety? (00:40:20)
• Americans don’t want Silicon Valley’s utopia (00:44:06)
• Why the Chinese public embraces AI (00:52:52)
• AI hype and the journalist’s dilemma (00:59:04)
• There’s never been a better time to work in AI safety (01:03:07)

Our production team includes:

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

In a post-AGI world, can a country without access to frontier AI even be considered sovereign anymore?

Anton Leicht says once frontier AI becomes a core economic input, the countries that own it will pull further and further ahead. Everyone else stays a customer… or worse. Maybe the dominant power wants your land, or a military base, or a resource. Without economic leverage, there’s very little you could do about it.

Anton — Carnegie fellow and writer of the blog Threading the Needle — thinks middle powers should band together and build their own frontier models.

He’s costed it out: something like $500 billion over four years for a band of allied democracies. That’s not absurd money for the G7 minus the US. The problem is you’d be asking treasuries to take on sovereign debt for a speculative venture with no business case, wide open to US coercion and domestic backlash.

So despite its promise, Anton’s verdict is that it probably won’t happen. His backup is for countries to ask themselves: if intelligence becomes abundant, what stays scarce?

• Upstream, that’s everything that feeds the supply chain: ASML’s lithography machines, chipmaking, exclusive training data — all of it gets more valuable as AI does.
• Downstream, “a country of geniuses in a data centre” still can’t cure cancer without someone building the production plants and running the trials. The Europeans, Japanese, and South Koreans are good at exactly these real-world bottlenecks.

It’s an imperfect fix. The US would still hold more leverage, plus an incentive to re-industrialise and cut you out. The prize is avoiding the worst outcomes: a gradual but irreversible decline, waiting to be either annexed or discarded as the US and China race ahead.

In this episode, Anton and host Tom Reed look at what middle powers should start doing now to keep a seat at the table.

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

This episode was recorded on June 19, 2026.

Chapters:

• Cold open (00:00:00)
• Who’s Anton Leicht? (00:00:43)
• Most countries face bleak AI futures (00:01:06)
• How middle powers can strike AI deals (00:06:10)
• The $500 billion AI moonshot (00:12:16)
• Would the US crush allied AI? (00:24:54)
• When to launch the AI moonshot (00:31:56)
• Why AI dominance is forever (00:35:45)
• Is AI dependence catastrophic? (00:37:42)
• What’s left to sell in an AI-dominated world? (00:42:45)
• Policies to avoid mass AI-layoffs (00:47:47)
• Who really governs Anthropic? (01:08:29)
• Why “pausing superintelligence” fails (01:10:52)
• Is American AI monopoly safe? (01:21:08)
• Explaining AGI to the world (01:28:40)
• Is Anton bullish or bearish on Germany? (01:31:05)

Our production team includes:

• Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, 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
• Music: CORBIT

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