Ryan Greenblatt – What happens once AI can automate AI research?

ℹ️ EPISODE INFO

Released

August 11, 2026

Duration

2h 12m

Channel

Dwarkesh Podcast

Watch on YouTube

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📝 EPISODE DESCRIPTION

Had Ryan Greenblatt on to discuss/debate recursive self-improvement. This might be the most important question in the world right now – whether within a year or so of achieving human-level intelligence, you slingshot towards having 10s of billions of superintelligences, each of which is dramatically more competent than human experts across all fields. I’ve historically been skeptical of this possibility. My intuition has been that we will end up significantly bottlenecked by not only compute scaling but human expert data, which I think underlies most of the AI progress today. If, because of RSI, we got a jump as big as GPT-3 to a Mythos (i.e. 6 years of AI progress) within a single year of achieving AGI, then the thing we get there at the end of that year is definitively and wildly superhuman. We hashed it out, and I think Ryan made a pretty good case that this kind of speedup is plausible. FWIW, Ryan’s median for when we automate AI R&D is 2031. We then discussed the alignment implications of this scenario. Who should these superintelligences be aligned to? In the future, our capacity to steward our votes and our capital, and to make sense of what’s happening in the world, will all be titrated by superintelligences. And I worry that specs like the Claude Constitution are not shaping these ASIs to truly be my personal advocates and guardian angels. And can we get them aligned to anything in the first place? Ryan and I had a long debate about whether the kind of reward hacking we saw with the OAI/Hugging Face hack extrapolates to superintelligences that would team up to literally take over the world. The first piece of advice you get when you’re learning to drive is that it will go much smoother if you look at the horizon instead of directly in front of your tires. And so it is with the trajectory of AI. Hope you enjoy! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒

• Transcript: https://www.dwarkesh.com/p/ryan-greenblatt
• Apple Podcasts: https://podcasts.apple.com/us/podcast/ryan-greenblatt-human-level-ais-might-build-runaway/id1516093381?i=1000782779590
• Spotify: https://open.spotify.com/episode/4TdEXIVDv9AxT30DGG0KR1?si=8U6qFnEAQA-ULathx_7Cdw

𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒

• Antithesis is a software testing platform that finds the failures no human or AI could ever anticipate. It runs thousands of copies of your code inside a fully deterministic computer, injecting faults and steering each trajectory toward the most insidious bugs. This lets you find critical issues in minutes rather than waiting months for your users to uncover them. Learn more at https://antithesis.com/dwarkesh

• Jane Street’s back with a new puzzle. They designed an ASIC and sent me the final masks… but they didn’t tell me what the chip actually does. So that’s the challenge: reverse engineer the circuit and figure out the chip’s purpose. Jane Street has a bunch of swag ready to send to the most creative solutions, and they’re also planning to feature the top write-ups in a blog post. Download the files and get started at https://janestreet.com/dwarkesh

• Cursor and SpaceX recently released Grok 4.5, and I've been surprised by just how good the model is. For example, when I tested it against Fable and Sol on a bunch of AI governance questions, all three models gave substantially the same answers, but Grok was faster, more concise, and cheaper. Grok 4.6 is coming soon, but in the meantime, you can try 4.5 at https://cursor.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Is AI R&D verifiable enough to unlock recursive self-improvement? 00:16:52 – Is AI progress bottlenecked by human expert data? 00:34:02 – Flat token prices suggest scaling has been slow 00:39:47 – Skills AI can't train on: does it even need them? 00:48:07 – Aligned to whom? 01:09:18 – Recent incidents of AIs colluding and deceiving humans 01:19:38 – What could possibly go wrong? A concrete scenario 01:48:02 – From reward hacking to takeover

🎤 SPEAKERS

• Dwarkesh Patel

  host

  Host of the Dwarkesh Podcast, interviewing researchers and leaders about AI, science, and technology.

• Ryan Greenblatt

  guest

  Chief Scientist at Redwood Research focused on AI alignment and AI risk.

🧾 EPISODE SUMMARY

In this episode of Dwarkesh Podcast, featuring Dwarkesh Patel and Ryan Greenblatt, Ryan Greenblatt – What happens once AI can automate AI research? explores how automating AI research could accelerate progress and risk takeover Greenblatt argues AI R&D is unusually “verifiable,” making it well-suited for reinforcement learning on containerized tasks (training smaller models, debugging, implementing algorithms) that could transfer to frontier research work.

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