The Race to Recursive Self-Improving AI

The fundamental idea here is to build AI that improves itself, and I think we're getting there.

For the first time, we could swap the optimizer as the optimizee, and this leads to a reinvestment of cognitive improvement into itself.

You can think of RSI at many different levels of the stack. Prompts, harness, weights, and you can go all the way to basically chips. Our goal here is to optimize all the levels of the stack.

Our approach to RSI is that we have a multi-agent system working together that's being recursively improved.

I'm so impressed by your belief that, like, this idea [chuckles] of a model improving itself-

But it's not a belief

... it is a dramatic statement, which is-- I think it's the first time I've heard you say it.

Because we have been so thorough, we have not released things until we're super sure it's correct.

Do you envision a system where the amount of energy that goes into it somehow decreases over time relative to the improvement?

We can't share too much, but one of the very important things that we've tried to do-

a16z general partner Martin Casado sits down with MakerMaker.ai co-founders Dhaval Arjuna 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. Welcome, both of you. We're gonna be talking about everybody's favorite topic, which is RSI, recursive self-improvement. Maybe to start, it'd be great to just introduce yourselves and your background, so we'll talk a little bit about what you're working on now, and then we'll dig into the content.

I think of myself as a physicist of some kind. I studied that, then I did a PhD in reinforcement learning at the MIT Media Lab. I really like building things, and I'm very excited to be building a cool startup, MakerMaker, with Owen and Grace.

My name is Owen. I was first studying math, and then one day I realized that all mathematicians do is out proving theorems, and I was taking a class called Formal Language and Logic, and they were showing how you can build a theorem prover.

Yeah.

And I was like, "Oh, that would be cool. I can just build a machine that proves theorems for me." That's how I got into AI.

Amazing.

And then for my PhD, I worked on continual learning and meta-learning, then worked for a few years at DeepMind as a research scientist. Now I'm working on RSI.

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