<p>Dario Amodei thinks we are just a few years away from AGI — or as he puts it, from having “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, why task-specific RL might lead to generalization, and how AI will diffuse throughout the economy. We also dive into Anthropic’s revenue projections, compute commitments, path to profitability, and more.</p><p>Watch on <a target="_blank" href="https://youtu.be/n1E9IZfvGMA">YouTube</a>; read the <a target="_blank" href="https://www.dwarkesh.com/p/dario-amodei-2">transcript</a>.</p><p>Sponsors</p><p>* <a target="_blank" href="https://labelbox.com/dwarkesh">Labelbox</a> can get you the RL tasks and environments you need. Their massive network of subject-matter experts ensures realism across domains, and their in-house tooling lets them continuously tweak task difficulty to optimize learning. Reach out at <a target="_blank" href="https://labelbox.com/dwarkesh">labelbox.com/dwarkesh</a>.</p><p>* <a target="_blank" href="https://janestreet.com/dwarkesh">Jane Street</a> sent me another puzzle… this time, they’ve trained backdoors into 3 different language models — they want you to find the triggers. Jane Street isn’t even sure this is possible, but they’ve set aside $50,000 for the best attempts and write-ups. They’re accepting submissions until April 1st at <a target="_blank" href="https://janestreet.com/dwarkesh">janestreet.com/dwarkesh</a>.</p><p>* <a target="_blank" href="https://mercury.com/personal-banking">Mercury</a>’s personal accounts make it easy to share finances with a partner, a roommate… or OpenClaw. Last week, I wanted to try OpenClaw for myself, so I used Mercury to spin up a virtual debit card with a small spend limit, and then I let my agent loose. No matter your use case, apply at <a target="_blank" href="https://mercury.com/personal-banking">mercury.com/personal-banking</a>.</p><p><strong>Timestamps</strong></p><p>(00:00:00) - What exactly are we scaling?</p><p>(00:12:36) - Is diffusion cope?</p><p>(00:29:42) - Is continual learning necessary?</p><p>(00:46:20) - If AGI is imminent, why not buy more compute?</p><p>(00:58:49) - How will AI labs actually make profit?</p><p>(01:31:19) - Will regulations destroy the boons of AGI?</p><p>(01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter?</p> <br/><br/>Get full access to Dwarkesh Podcast at <a href="https://www.dwarkesh.com/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_4">www.dwarkesh.com/subscribe</a>