For the past few days I’ve been testing an impressive open-weight AI coding model called Flint. It suggests that fully recursive self-improvement, or RSI—an exciting and controversial approach that uses AI to build AI—is getting closer.

Flint is the first offering from Recursive, a company that aims to use RSI to advance both AI itself and other areas of science using AI. “Nearly every aspect of the entire model pipeline was actually done and improved upon by AI,” Recursive CEO and cofounder Richard Socher told me.

Socher says his company used its recursive AI system, dubbed the Eureka Machine, to automate most elements of the model’s development. This enabled a company with a handful of researchers to build a world-class model in just a few months, he says.

Socher told me that the Eureka Machine devised a number of innovations, including a new way of training the base model, new model architectures, and code optimized for specific silicon chips. Recursive shared benchmark scores that suggest Flint outperforms other models of similar size and architecture on coding tasks.

AI is already widely used to help build, test, and improve AI thanks to its amazing coding skills. This can speed up the process of refining a model and testing new ideas without the need for as much input from humans.

It is also possible to create a loop through which AI decides what improvements to make and then begins another round of improvement. For the moment this technique is limited, but if AI can contribute to every aspect of model development, then the process could theoretically continue indefinitely.

Some experts worry that letting AI improve itself in this manner could ultimately lead to disaster. These doomsayers claim that AI capable of improving indefinitely could escape human control and might decide that humans are an impediment to its goals.

Those concerns gained wider notice this September when a researcher quit Anthropic and warned that advances in AI, including recursive self-improvement, could lead to human extinction within a few years—a concern then echoed by a number of other experts. The idea of RSI has become so controversial that several bills in Congress propose to ban it.

Recursive sees these fears as more sci-fi than reality and believes legislation to prevent RSI is an unnecessary impediment to progress.

Socher says Flint was built using a system incapable of escaping human control. He says the goal is for the Eureka Machine itself to achieve full recursive self-improvement, but this would still be overseen by humans who could stop the process if it goes awry. “We hope that this shows people that indeed we have all the steps in place” to build such a system, he says.

Recursive has raised $650 million at a $4.65 billion valuation from Google Ventures, Greycroft, Nvidia, and AMD Ventures. Socher was previously the chief scientist at Salesforce, and his cofounders include Jeff Clune, a professor at the University of British Columbia who researches ways of building AI systems capable of learning indefinitely.

I must say I am impressed with Flint. I tested it on a Mac with 48 GB of RAM—a healthy amount but only enough to run a modest-size AI model like Flint, which has 40 billion parameters. The biggest models out there have several trillions of parameters, and they can only run on racks of industrial servers.

I used Flint to build a bunch of programs and found it to be both fast and very capable. In my experience, models of this size can be annoyingly error-prone or slow compared to Claude or Codex, but Flint nailed my coding requests every time. I used it to create a command line to-do list, then tried having it build a program that scours the web for interesting research related to recursive self-improvement. I also had Flint whip up a simple website that explains how RSI works. You can see that here.

It’s unclear how much progress other US labs have made chasing recursive self-improvement, but some published work suggests fully recursive systems are already feasible. A team from Microsoft, Carnegie Mellon University, and the University of Virginia recently published work, for instance, showing how an AI coding agent can recursively improve itself by modifying the software harness that turns reasoning into useful action and its own model’s weights.

Perhaps there are risks to full RSI if you are close to building a godlike intelligence, but for the time being it seems like a very promising way to improve your own AI models. This is something I’ve dabbled with in previous editions of AI Lab.

Indeed, Socher says the approach used to build Flint could make cutting-edge AI more accessible. “It's the only way this very small team can produce a model that's actually better than any other model in its class,” he told me. “This will hopefully show people that [recursive self-improvement] is actually quite nice and will benefit a lot of people.”This is an edition of Will Knight’s AI Lab newsletter.