Research

TL;DR I treat my own learning like a research agenda - a small set of questions I am actively chasing, not a reading list I feel guilty about The work I have been deep in clusters into four areas: agent reliability and non-determinism, context engineering and memory, the economics of intelligence, and the open-weight and small-model frontier The areas I have decided to move into next are the ones where I keep hitting questions I cannot answer well: securing agents that hold real tool access, evaluating agents on their trajectory rather than their final answer, world models beyond the language-only era, and the machine-to-machine agent economy I treat AGI timelines less as a forecast to win and more as a planning input - what changes for an engineer if capable autonomous systems arrive in three years rather than fifteen I am deliberately not chasing every frontier. Quantum machine learning and neuromorphic hardware sit on my watch list, not my work list, and being honest about that line is the whole point Most people consume AI news. I used to do the same - a feed of model releases, benchmark claims, and launch threads that left me feeling informed and changed nothing about what I could actually build. ...

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 In May 2026 Anthropic put numbers on this from inside a frontier lab. Its essay When AI Builds Itself reports that over 80% of the code it merges is now written by Claude, that task horizons are doubling every roughly four months rather than seven, and lays out a candid three-way bet on where this ends. None of it overturns the bottlenecked-flywheel picture - but it sharpens it 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. ...