Recursive Self-Improvement and the Future of AI

a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise. Timestamps: 00:00 - Intro 00:58 - What actually counts as RSI 05:10 - Capability as a numerator and denominator 06:58 - Closed loop vs takeoff 08:32 - Humans as agents in the system 13:39 - Why most RSI papers don't scale 17:41 - Why they chose inference 24:00 - "It's not a belief, it's happening" 25:42 - The Viceroy of Variance 29:39 - The garden hose company in Wisconsin Resources: Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Dhaval AdjodahguestOwen HeguestMartin Casadohost

Oct 12, 202632mWatch on YouTube ↗

EPISODE INFO

Released: October 12, 2026
Duration: 32m
Channel: a16z
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise. Timestamps: 00:00 - Intro 00:58 - What actually counts as RSI 05:10 - Capability as a numerator and denominator 06:58 - Closed loop vs takeoff 08:32 - Humans as agents in the system 13:39 - Why most RSI papers don't scale 17:41 - Why they chose inference 24:00 - "It's not a belief, it's happening" 25:42 - The Viceroy of Variance 29:39 - The garden hose company in Wisconsin Resources: Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

SPEAKERS

Dhaval Adjodah
guest
AI researcher/engineer with a physics background; did a PhD in reinforcement learning at the MIT Media Lab and is building the MakerMaker startup.
Owen He
guest
AI researcher focused on continual/meta-learning; former DeepMind research scientist working on recursive self-improvement (RSI).
Martin Casado
host
a16z investor/host with a networking background who moderates the discussion and interviews the guests.

EPISODE SUMMARY

In this episode of a16z, featuring Dhaval Adjodah and Owen He, Recursive Self-Improvement and the Future of AI explores defining “real” RSI: closed-loop AI improvement, measured not imagined The speakers define “real” recursive self-improvement (RSI) as a closed loop where the AI system is both the optimizer and the object being optimized, distinct from ordinary autocatalysis where humans remain the primary source of improvements.

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