Research

TL;DR Recursive self-improvement (RSI) is the idea of an AI that improves its own ability to improve - each round producing a smarter system that does the next round better. It is the engine behind every “intelligence explosion” story since I.J. Good described it in 1965 The narrow version is already real. Systems like AlphaEvolve and the AI Scientist measurably improve algorithms, code, and even research output - including, in AlphaEvolve’s case, the infrastructure that trains the models themselves The leap people fear is different: improving an algorithm is not the same as improving general intelligence. Nothing in 2026 has crossed that line, and the gap is structural, not just a matter of scale Four bottlenecks decide whether RSI runs away or fizzles: compute, data, verification, and diminishing returns. Each is a hard physical or informational limit, not a temporary engineering nuisance The realistic picture is steady, human-paced acceleration - AI assisting AI research - not an overnight takeoff. METR’s time-horizon data shows fast but smooth exponential progress, which is exactly what a bottlenecked process looks like It still deserves serious safety attention, because a slow takeoff is the one we can actually govern There is a particular shape of argument that has haunted artificial intelligence since before the field had a settled name. It goes like this: build a machine slightly better than humans at designing machines, and it will design a machine better than itself. That machine designs a better one. The loop tightens, each turn faster than the last, and intelligence runs away from us in an afternoon. ...

A common dismissal of modern AI goes like this: “It is just a fancy autocomplete. It memorises text and stitches it back together. There is no real understanding, only retrieval.” It is a comforting story, and it has the shape of a critique that ought to be true. But spend enough time with frontier systems and a different picture starts to form. The thing that large models actually seem to be good at is not memorisation. It is something stranger and arguably more important: the formation of analogies, the combination of distant concepts, and the generation of conceptual relationships that were not explicitly present in any one place in the training data. ...

AlphaFold’s release in 2021 was the AI-for-science moment that broke through to the general public. A computational solution to a 50-year-old problem in biology - predicting protein structure from sequence - that produced a tool used by hundreds of thousands of researchers. The narrative around AI-for-science crystallised: deep learning would produce a series of similar breakthroughs across scientific domains. The 2026 reality is more interesting and less clean. AlphaFold-class breakthroughs have been rarer than the early narrative suggested. But AI has spread across scientific practice in subtler ways that, in aggregate, have done more to change how science is actually done than the few headline breakthroughs. ...

Most people writing about AI risk in 2026 are recent arrivals. Roman Yampolskiy is not. He has been making the same argument - that advanced AI systems may be fundamentally uncontrollable - since before the field of AI safety had a settled name, which is partly because he is the one who gave it that name. Whether you find his conclusions alarmist, prescient, or somewhere in between depends mostly on how you read the gap between current systems and the ones he writes about. This post is an attempt to lay out the man, the argument, and the reasons it deserves more than a dismissal. ...