80,000 Hours Podcast

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

The average career is 80,000 hours long. With AI advancing so rapidly, the hours you have left in your career matter more than ever.
Some leading AI researchers think there’s a 10% chance that AI systems begin automating AI research itself this year — and a 60% chance by the end of 2028. This could introduce aggressive feedback loops that completely reshape every industry, institution, and career.
If these predictions are right, the window for influencing the direction of the future could be closing fast. As 80,000 Hours cofounder Benjamin Todd argues in his new book, that makes thinking carefully about your career more important than ever.
Fortunately, there are lots of ways to use your career to make the AI transition go well.
In today’s conversation with host Zershaaneh Qureshi, Ben lays out three scenarios — from AGI by 2029 to a decades-long plateau in AI progress — and explains why not everyone needs to bet on the shortest timeline. A fresh graduate and a senior government official have wildly different leverage, so timing your impact well means weighing where you are in your career against the urgency of the risks.
Ben also addresses the obvious anxieties:
Will AI come for all the jobs he’s recommending?
What’s the point in following his advice if the job market is about to collapse?
Which skills are actually worth building right now?
His new book, 80,000 Hours: How to Have a Fulfilling Career That Does Good, provides a surprisingly concrete framework for making career decisions in these radically uncertain times.
This episode was recorded on May 7, 2026.
Learn more and read the full transcript: https://80k.info/bt26
We're hiring: we have lots of open roles at 80,000 Hours — across advising, web, video, and ops — check them out and apply on our website.
Chapters:
Cold open (00:00:00)
Benjamin Todd on AI-era career advice (00:01:34)
A deadline for your career plan? (00:02:21)
Three timelines, one career (00:08:48)
What if you’re not an ‘AI person’? (00:13:55)
Ben’s own AI wake-up call (00:21:23)
How to break into AI safety in 3 months (00:25:42)
Is mass unemployment coming? (00:33:48)
99% automation vs 100% automation (00:40:09)
Don’t become a plumber to dodge AI (00:52:43)
Is it already too late? (01:01:03)
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
Music: CORBIT

A red-teamer was embedded inside Anthropic for three weeks, told to imagine he was an evil Claude, and asked to figure out how to launch a ‘rogue AI deployment’ without getting caught. It’s one part of a landmark report released yesterday by METR — the outfit behind the task-completion time horizon graph which has become the single most watched measure of AI progress.

This major new research push is being conducted with close collaboration from OpenAI, Google DeepMind, Meta, and Anthropic, and led by METR researchers Hjalmar Wijk and Ajeya Cotra. It represents the first systematic study of what newly trained AI models could get away with inside the companies that built them, before anyone outside the company even knows they exist.
The conclusion: AI models now have the means, the motive, and the opportunity to start “minimal rogue deployments” in pursuit of their own independent goals, like acquiring more compute, at all four companies studied.
David Rein, the red-teamer placed inside Anthropic, identified a number of weaknesses models could exploit there: expansive permissions, cloud jobs outside of monitoring, and monitors that are trivial to jailbreak. But he also found that frontier models were comically bad at key parts of the process, which means they can’t cause meaningful damage for now.
In this video, Rob Wiblin reconciles the conflicting picture and looks forward to METR’s second round of stress tests. They’ll begin in just a few months, a necessary move with AI advancing so quickly.
This episode was recorded on May 15, 2026.
Learn more, video, and full transcript: https://80k.info/metr-report
Chapters:
What could an unreleased AI get away with? – the new METR report (00:00:00)
Motive: Why grab more compute? (00:01:54)
Opportunity: YOLO mode and jailbreaks (00:05:46)
Means: Brilliant idiots in data centres (00:11:02)
We have to test unreleased models (00:15:45)
Especially if AI R&D is coming in 2028 (00:18:30)
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Josh Alward
Camera operator: Dominic Armstrong
Production: Elizabeth Cox, Nick Stockton, and Katy Moore