by 80,000 Hours
10 experts, 10 episodes: a crash course on transformative AI and what you can do to help shape its trajectory. This compilation features 10 key episodes of The 80,000 Hours Podcast to help listeners — particularly those new to the topic — get to grips with the potential upsides and downsides of powerful, transformative AI.
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Publishing Since
9/1/2023
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June 5, 2026
<p>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.</p><p>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.</p><p>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 <a href="https://80k.info/career-guide">new book</a>, that makes thinking carefully about your career more important than ever.</p><p>Fortunately, there are lots of ways to use your career to make the AI transition go well.</p><p>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.</p><p>Ben also addresses the obvious anxieties:</p><ul><li>Will AI come for all the jobs he’s recommending?</li><li>What’s the point in following his advice if the job market is about to collapse?</li><li>Which skills are actually worth building right now?</li></ul><p>His new book, <a href="https://80k.info/career-guide">80,000 Hours: How to Have a Fulfilling Career That Does Good</a>, provides a surprisingly concrete framework for making career decisions in these radically uncertain times.</p><p>This episode was recorded on May 7, 2026.</p><p>Chapters:<br>• Cold open (<a href="https://www.youtube.com/watch?v=YrEMun5nenU">00:00:00</a>)<br>• Benjamin Todd on AI-era career advice (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=94s">00:01:34</a>)<br>• A deadline for your career plan? (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=142s">00:02:22</a>)<br>• Three timelines, one career (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=529s">00:08:49</a>)<br>• What if you’re not an ‘AI person’? (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=836s">00:13:56</a>)<br>• Ben’s own AI wake-up call (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=1283s">00:21:23</a>)<br>• How to break into AI safety in 3 months (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=1542s">00:25:42</a>)<br>• Is mass unemployment coming? (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=2028s">00:33:48</a>)<br>• 99% automation vs 100% automation (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=2409s">00:40:09</a>)<br>• Don’t become a plumber to dodge AI (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=3164s">00:52:44</a>)<br>• Is it already too late? (<a href="https://www.youtube.com/watch?v=YrEMun5nenU&t=3664s">01:01:04</a>)</p><p>Our production team includes:<br>• Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour<br>• Producers: Elizabeth Cox and Nick Stockton<br>• Coordination and support: Katy Moore and Lou Moran<br>• Camera operator: Jeremy Chevillotte<br>• Music: <a href="https://open.spotify.com/artist/4lWobp6IUcSZ7w5mhnU1c9">CORBIT</a><br><br></p>
September 2, 2023
<p>A short introduction to what you'll get out of these episodes!</p>
September 2, 2023
<p><strong>Originally released in March 2021. </strong></p><p>Brian Christian is a bestselling author with a particular knack for accurately communicating difficult or technical ideas from both mathematics and computer science. </p><p>Listeners loved <a href="https://80000hours.org/podcast/episodes/brian-christian-algorithms-to-live-by/"><strong>our episode</strong></a> about his book <a href="https://brianchristian.org/algorithms-to-live-by/"><strong>Algorithms to Live By</strong></a> — so when the team read his new book, <a href="https://brianchristian.org/the-alignment-problem/"><strong>The Alignment Problem</strong></a>, and found it to be an insightful and comprehensive review of the state of the research into making advanced AI useful and reliably safe, getting him back on the show was a no-brainer. </p><p>Brian has so much of substance to say this episode will likely be of interest to people who know a lot about AI as well as those who know a little, and of interest to people who are nervous about where AI is going as well as those who aren't nervous at all. </p><p><a href="https://80000hours.org/podcast/episodes/brian-christian-the-alignment-problem/?utm_campaign=podcast__brian-christian-2&utm_source=80000+Hours+Podcast&utm_medium=podcast"><strong>Links to learn more, summary and full transcript.</strong></a><strong> </strong></p><p>Here’s a tease of 10 Hollywood-worthy stories from the episode: </p><p>• The Riddle of Dopamine: The development of reinforcement learning solves a long-standing mystery of how humans are able to learn from their experience.<br>• ALVINN: A student teaches a military vehicle to drive between Pittsburgh and Lake Erie, without intervention, in the early 1990s, using a computer with a tenth the processing capacity of an Apple Watch.<br>• Couch Potato: An agent trained to be curious is stopped in its quest to navigate a maze by a paralysing TV screen.<br>• Pitts & McCulloch: A homeless teenager and his foster father figure invent the idea of the neural net.<br>• Tree Senility: Agents become so good at living in trees to escape predators that they forget how to leave, starve, and die.<br>• The Danish Bicycle: A reinforcement learning agent figures out that it can better achieve its goal by riding in circles as quickly as possible than reaching its purported destination.<br>• Montezuma's Revenge: By 2015 a reinforcement learner can play 60 different Atari games — the majority impossibly well — but can’t score a single point on one game humans find tediously simple.<br>• Curious Pong: Two novelty-seeking agents, forced to play Pong against one another, create increasingly extreme rallies.<br>• AlphaGo Zero: A computer program becomes superhuman at Chess and Go in under a day by attempting to imitate itself.<br>• Robot Gymnasts: Over the course of an hour, humans teach robots to do perfect backflips just by telling them which of 2 random actions look more like a backflip.</p><p>We also cover:</p><p>• How reinforcement learning actually works, and some of its key achievements and failures<br>• How a lack of curiosity can cause AIs to fail to be able to do basic things<br>• The pitfalls of getting AI to imitate how we ourselves behave<br>• The benefits of getting AI to infer what we must be trying to achieve<br>• Why it’s good for agents to be uncertain about what they're doing<br>• Why Brian isn’t that worried about explicit deception<br>• The interviewees Brian most agrees with, and most disagrees with<br>• Developments since Brian finished the manuscript<br>• The effective altruism and AI safety communities<br>• And much more</p><p>Producer: Keiran Harris.<br>Audio mastering: Ben Cordell.<br>Transcriptions: Sofia Davis-Fogel.</p>
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10 experts, 10 episodes: a crash course on transformative AI and what you can do to help shape its trajectory.
This compilation features 10 key episodes of The 80,000 Hours Podcast to help listeners — particularly those new to the topic — get to grips with the potential upsides and downsides of powerful, transformative AI.
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