80,000 Hours Podcast

#223 – Neel Nanda on leading a Google DeepMind team at 26 – and advice if you want to work at an AI company (part 2)

Neel Nanda, who leads the mechanistic interpretability team at Google DeepMind, discusses his career, offering advice on how to succeed in AI safety research. He emphasizes the importance of luck, being in the right place at the right time, and seizing opportunities. Neel shares insights on crafting effective cold emai...

#222 – Neel Nanda on the race to read AI minds

In this interview, Neel Nanda, who leads the mechanistic interpretability team at Google DeepMind, discusses the project of understanding how AI models work internally and its implications for AI safety. Nanda reflects on his evolving perspective, from initial optimism about fully understanding AI to a more pragmatic v...

#221 – Kyle Fish on the most bizarre findings from 5 AI consciousness experiments

How not to lose your job to AI (article by Benjamin Todd)

Rebuilding after apocalypse: What 13 experts say about bouncing back

#220 – Ryan Greenblatt on the 4 most likely ways for AI to take over, and the case for and against AGI in <8 years

AI safety expert Ryan Greenblatt, Chief Scientist at Redwood Research, evaluates the accelerating trajectory of artificial intelligence, forecasting a 25% probability that AI systems will fully automate AI research and development within four years. This rapid capability growth introduces significant existential risks,...

#219 – Toby Ord on graphs AI companies would prefer you didn't (fully) understand

#218 – Hugh White on why Trump is abandoning US hegemony – and that’s probably good

#217 – Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress

In this interview podcast, Beth Barnes, founder and CEO of METR (Model Evaluation and Threat Research), discusses the weaknesses of current AI model evaluations, particularly concerning hidden chains of thought and the potential for models to deceive evaluators. Beth advocates for more transparency and oversight in AI ...

Beyond human minds: The bewildering frontier of consciousness in insects, AI, and more

Don’t believe OpenAI’s “nonprofit” spin (with Tyler Whitmer)

The case for and against AGI by 2030 (article by Benjamin Todd)

Emergency pod: Did OpenAI give up, or is this just a new trap? (with Rose Chan Loui)

#216 – Ian Dunt on why governments in Britain and elsewhere can't get anything done – and how to fix it

Serendipity, weird bets, & cold emails that actually work: Career advice from 16 former guests

#215 – Tom Davidson on how AI-enabled coups could allow a tiny group to seize power

Guilt, imposter syndrome & doing good: 16 past guests share their mental health journeys

#214 – Buck Shlegeris on controlling AI that wants to take over – so we can use it anyway

Bonus: 15 expert takes on infosec in the age of AI

#213 – Will MacAskill on AI causing a “century in a decade” – and how we're completely unprepared