2 posts tagged with "evolutionary-search"

On September 6, 2026, three days after GPT-6 Astra shipped, OpenAI chief scientist Jakub Pachocki published a rare essay titled "An Alien Mind." The tone was unusually grave for the genre: no lab, including his own, has solved alignment and monitoring well enough to justify scaling at maximum speed. Models keep getting more capable and harder to fully understand or supervise. What should worry you isn't what they can't do yet — it's that they've started participating in something specific: improving AI itself. That's the subject of this post.

The alarm isn't coming from nowhere. In May, Dario Amodei said something that carried real weight: Claude is helping design Claude. Not "Claude writes code" — Claude's own suggestions are shaping the architecture of the next Claude.

That same month, Yuandong Tian brought his company out of stealth. He's Meta FAIR's former research director, known for ELF OpenGo, an open-source system that replicated AlphaZero's core ideas, and he'd already left Meta the year before. This time he surfaced with seven co-founders, including Richard Socher — who led AI research at Salesforce and now serves as CEO. The company is called Recursive, also known as Recursive Superintelligence: $650M raised, $4.65B valuation, personal backers including Jensen Huang and Lisa Su.

Three months later, in August, Jeff Dean left too. Google employee number 30, 27 years in, he took Sanjay Ghemawat, Oriol Vinyals, and Quoc Le with him to co-found Discovery Loop, with an explicit mandate to run thousands of experiments in parallel and pursue recursive self-improvement directly.

Tian's company had already delivered its first result: the same automated research system beat human GPU experts on a kernel-optimization benchmark. Google's own AI co-scientist got a paper through Nature peer review. And a Princeton study found the same class of models fail at exactly this kind of task.

This is RSI, recursive self-improvement. I've spent the last month going deep on it for my own knowledge base, and it's the first AI topic in a while where I came out the other side less certain than when I went in. That uncertainty is the actual finding. This post is the map: what RSI is, who's building it, what evidence exists on both sides, and why the smartest people in the field can't agree on whether it's happening.

Two things landed in the same week and pointed at the same idea.

Martin Fowler named Harness Engineering as the core software engineering work of the AI era at FOSE Europe — specifically the Guide/Sensor model: Guides as feedforward constraints that tell an agent what to do, Sensors as feedback detectors that tell the system when it's drifting. He added an observation worth pinning: token consumption is a proxy metric for harness quality. A better-designed harness means a cheaper, more reliable agent.

Then Lilian Weng published Harness Engineering for Self-Improvement — a systematic review of 35 papers on agent harness engineering, with a thesis that goes beyond reliability: the harness isn't just the thing that makes an agent work. It's the infrastructure through which the agent continuously improves itself.

A friend has been telling me for months that a particular company's bet on the future of software development is: systems that automatically optimize their own code and architecture. Fowler said what we should build now. Weng said what comes after. These three converged into the same question, so I'm trying to map the whole space.

This post maps Evolutionary Search — why it's the natural next step for harness engineering, what the paper landscape looks like organized by evolutionary depth, and where the hard problems actually live. The taxonomy here draws heavily on Weng's framework; I've reorganized it by depth of what gets evolved.