80,000 Hours Podcast

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

The co-inventor of modern AI and the most cited living scientist believes he's figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio – Turing Award Winner and founder of LawZero – is disturbed by the many unintended drives and goals present in today's AIs, their willingness to lie, and ability to tell when they're being tested. AI companies are trying to stamp out these behaviours in a 'cat-and-mouse game' that Yoshua fears they're losing.
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Our new book is "a ridiculously in-depth guide to finding a fulfilling career that does good" and is out now! Order from your local bookstore, or online at https://80k.info/career-guide
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But Yoshua is optimistic: he believes the companies can win this battle decisively with a single rearrangement to how AI models are trained, and has been developing mathematical proofs to back up the claim. The core idea is that instead of training AI to predict what a human would say, or to produce responses we'd rate highly, we should train it to model what's actually true.
Yoshua argues this new architecture, which he calls 'Scientist AI,' is a small enough change that we could keep almost all the techniques and data we use to train frontier AIs like Claude and ChatGPT. And that the new architecture need not cost more, could be built iteratively, and might be more capable as well as more honest.
Links to learn more, video, and full transcript: https://80k.info/bengio
Until recently, the biggest practical objection to Scientist AI was simple: the world wants agents, and Scientist AI isn’t one. But in new research, Yoshua has extended the design and believes the same honest predictor can be turned into a capable agent without losing its "safety guarantees."
With the Scientist AI proposal on the table, Yoshua argues that it's absurd to race to get current untrustworthy AI models to design their successors, which the leading companies are attempting to do as soon as possible.
But critics argue the approach wouldn't be so technically solid in practice, and that frontier capabilities are advancing so fast, and cost so much to match, that Scientist AI risks arriving too late to matter.
Host Rob Wiblin and AI pioneer Yoshua Bengio cover all this and more in today's conversation.
LawZero is hiring! https://80k.info/lawzero-jobs

This episode was recorded on April 16, 2026.
Chapters:
Yoshua Bengio on making AI honest and safe (00:00:00)
The Scientist AI in plain English (00:02:27)
Yoshua on how Scientist AI differs from LLMs (00:06:32)
How the training data works (00:14:02)
Can this become an agent? (00:21:02)
Why Yoshua is more optimistic on alignment now (00:32:11)
Why companies can’t stop racing (00:36:35)
How close to a working prototype? (00:49:15)
Honest models might be more capable (00:53:34)
“Reinforcement learning is evil” (01:01:27)
Scientist AI from guardrail to agent (01:08:37)
Can safe AI still be competent? (01:12:38)
How much will this cost? (01:19:29)
Can it generalise beyond maths and science? (01:23:26)
A UN for superintelligence (01:39:19)
Want to work with Yoshua Bengio? (01:51:16)
Why smart people ignore AI risk (01:54:45)
Don’t let AI build the next AI (02:01:33)
Why the public doesn’t get the real risk (02:12:28)
Why Yoshua changed his mind about AI risk (02:21:27)
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Jeremy Chevillotte
Production: Nick Stockton, Elizabeth Cox, and Katy Moore