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.

Watch on YouTube; read the transcript.

Sponsors

* Labelbox 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 labelbox.com/dwarkesh.

* Jane Street 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 janestreet.com/dwarkesh.

* Mercury’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 mercury.com/personal-banking.

Timestamps

(00:00:00) - What exactly are we scaling?

(00:12:36) - Is diffusion cope?

(00:29:42) - Is continual learning necessary?

(00:46:20) - If AGI is imminent, why not buy more compute?

(00:58:49) - How will AI labs actually make profit?

(01:31:19) - Will regulations destroy the boons of AGI?

(01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter?

In this episode, John and I got to do a real deep-dive with Elon. We discuss the economics of orbital data centers, the difficulties of scaling power on Earth, what it would take to manufacture humanoids at high-volume in America, xAI’s business and alignment plans, DOGE, and much more.

Watch on YouTube; read the transcript.

Sponsors

* Mercury just started offering personal banking! I’m already banking with Mercury for business purposes, so getting to bank with them for my personal life makes everything so much simpler. Apply now at mercury.com/personal-banking

* Jane Street sent me a new puzzle last week: they trained a neural net, shuffled all 96 layers, and asked me to put them back in order. I tried but… I didn’t quite nail it. If you’re curious, or if you think you can do better, you should take a stab at janestreet.com/dwarkesh

* Labelbox can get you robotics and RL data at scale. Labelbox starts by helping you define your ideal data distribution, and then their massive Alignerr network collects frontier-grade data that you can use to train your models. Learn more at labelbox.com/dwarkesh

Timestamps

(00:00:00) - Orbital data centers

(00:36:46) - Grok and alignment

(00:59:56) - xAI’s business plan

(01:17:21) - Optimus and humanoid manufacturing

(01:30:22) - Does China win by default?

(01:44:16) - Lessons from running SpaceX

(02:20:08) - DOGE

(02:38:28) - TeraFab