AI

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. ...

Few people have done more to build modern AI, and fewer still have turned around to warn the world about it as loudly. Geoffrey Hinton spent half a century making neural networks work when most of the field thought they never would, and then - at the point of maximum credibility - left his job at Google to say he was worried about where the technology is heading. This page is a growing, chronological index of his interviews, talks, and public appearances, with enough context around each to know what you are clicking into. ...

TL;DR The Trust series is my answer to one question: what has to be true before you can hand a non-deterministic system a real job and walk away? Read in this order: research map → evals → security → world models → trajectory evaluation Supporting posts cover reliability, context engineering, and safety foundations Full series index: /series/trust/ Start here What I’m Researching in AI Right Now — the research map and trust through-line AI Evals Are Broken — why public benchmarks stopped measuring real capability Securing AI Agents — MCP hardening, confused deputy, and what I run on my home stack World Models: What Comes After the Language-Only Era — when text-only agents hit their ceiling Evaluating Agents in Production: Trajectory Metrics — step-level scoring, not just final answers Supporting reading AI Agents That Actually Work — patterns from real projects The Agent Reliability Problem — debugging non-deterministic systems Context Engineering — curating the window across a whole agent run AI Reliability Is Weird — why testing LLMs breaks familiar QA AI Safety From First Principles — engineering safety vs speculative scenarios Related paths Home Agent Stack — build the stack these defenses protect AI Dev Tooling — the coding-agent side of the same problem

For most of the last decade, “ethical data use” was something that happened in a different building. The lawyers wrote the privacy policy, the data protection officer ran the impact assessment, and the engineers built whatever the ticket said. The ethics lived in a PDF, and the pipeline lived in the warehouse, and the two rarely met. In 2026 that separation has quietly collapsed. The reason is not that engineers suddenly became more principled - it is that the decisions which determine whether data is used ethically are now made at the schema, the table, and the access-control layer, and those are the engineer’s decisions. Consent, deletion, minimisation, provenance, bias: every one of them is now something you either build into the pipeline or fail to. This is a practical look at what that means. ...

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. ...

I have written about one of Max Tegmark’s ideas already - the Mathematical Universe Hypothesis - and in doing so I admitted he sits at the top of my favourite physicists list. That post was about a single claim. This one is about the man, and about the thing I find more interesting than any individual theory of his: the through-line. Tegmark has spent a career moving steadily inward, from measurable cosmology toward the deepest possible questions about what reality is, and the move never feels like a physicist losing the plot and drifting into metaphysics. It feels like someone following the maths until it runs out of floor. ...

When I wrote about Donald Hoffman, I was working through one half of a question I keep saying I have not settled: whether consciousness is fundamental, there from the start as part of the floor of reality, or computational, something that switches on once a physical process organises information in the right way. Hoffman is the most serious case I have found for the fundamental side, and I gave it a fair hearing because I genuinely find it compelling. ...

When I wrote about Yampolskiy’s Personal Universes recently, I left a thread hanging. The question underneath that whole post - the one I said I genuinely had not settled - was whether consciousness is fundamental, there first with the universe as something it experiences, or whether it is computational, something that switches on once a process gets complex enough. I said I had only recently started reading my way into the fundamental side, mostly through Donald Hoffman. This is me pulling on that thread properly. ...

First, the thing this is all in service of. The AI alignment problem is the challenge of making a powerful AI system reliably pursue what we actually want it to pursue - getting its goals, values, and behaviour to line up with human intentions, and to stay lined up even as the system becomes more capable than the people supervising it. It sounds simple and is not: we struggle to state our own values precisely, those values conflict between people, and an AI optimising hard for a slightly-wrong objective can produce outcomes nobody asked for. The multi-agent version - aligning one system with all of humanity at once, rather than a single person - is harder still, and it is the specific version Personal Universes is trying to dodge. ...

The “is the software engineer dead” genre has been running long enough that you can predict most of the takes before you read them. The interesting interviews are the ones where the person being interviewed is in a position to know something the rest of us do not. Boris Cherny, the creator of Claude Code at Anthropic, is one of those people. Sky News got him in front of three charts and asked him to react. ...