An aircraft passing overhead led me to a strange thought about distance. Someone can be physically close yet inhabit a completely different world, while a point only ten feet from an airline passenger can be effectively unreachable without life-supporting technology. That observation suggests a broader way of looking at reality: as countless overlapping conceptual landscapes containing gradients of value, knowledge, trust, accessibility, scarcity, risk and possibility. Some of those gradients are unusually steep, where a small change in state produces a disproportionate change in outcome. Those places often resemble what we call opportunity. But steepness alone is not enough. The more useful challenge is finding the consequential gradients: the ones that actually govern outcomes, can be traversed economically, and perhaps have only recently become accessible. AI may not only change those gradients; it may eventually help us discover ones we have never thought to look for.
Writing
Essays, papers, notes, and references.
A place for longer pieces, formal work, and useful written records.
An aircraft passing overhead led me to a strange thought about distance. Someone can be physically close yet inhabit a completely different world, while a point only ten feet from an airline passenger can be effectively unreachable without life-supporting technology. That observation suggests a broader way of looking at reality: as countless overlapping conceptual landscapes containing gradients of value, knowledge, trust, accessibility, scarcity, risk and possibility. Some of those gradients are unusually steep, where a small change in state produces a disproportionate change in outcome. Those places often resemble what we call opportunity. But steepness alone is not enough. The more useful challenge is finding the consequential gradients: the ones that actually govern outcomes, can be traversed economically, and perhaps have only recently become accessible. AI may not only change those gradients; it may eventually help us discover ones we have never thought to look for.
“Make money while you sleep” is an old dream, and AI has made it newly plausible: a machine can research, draft, sort, and prepare while you rest. But the phrase confuses production with revenue. A draft is not a sale; output overnight is not income. AI is neither employee, oracle, nor entrepreneur. It is a layer of executable cognition wrapped around repeatable work, and in the hands of a fantasist it produces fantasy at scale, in the hands of an operator, leverage. The machine does not make money while you sleep. It performs the labour that used to stop you building the thing that makes money, clearing the path between judgement and output. The old world rewarded those who worked hardest for longest. The next rewards those who can tell labour from judgement, and automate only the first.
An under-16 social media ban need not become a national identity layer for the internet. The fear that age checks are digital ID through the back door is well founded, but only if we ask the wrong question. The right one is not who this person is, but whether a platform can know only that they are over 16. Privacy-preserving credentials and zero-knowledge proofs answer it: a narrow, unlinkable, session-bound proof that reveals no name, no face, no document. The standard should be brutal and simple: prove the attribute, not the person.
Every argument about where AI goes next is, underneath, an argument about the climb: recursion, self-improvement, compute bending the curve back on itself and accelerating. The progress is real and the excitement is earned. But a climber is only as good as the hill. Optimisation power, however vast, is worthless without something faithful to climb toward, and that target, the gradient that tells the system which way is up, is the thing nobody is pricing. We have built an extraordinary engine for going up, and said almost nothing about who decides where up is. This piece argues that the unpriced variable in the whole debate is not capability but direction.
An ant colony solves a transport problem without any individual understanding the whole terrain. Studying natural and artificial intelligence made me wonder what that could teach us about network routing. From OSPF’s map of the network to AntNet’s learning through journeys, I follow the idea of intelligence emerging from local interactions, and explore what it would take to make that intelligence useful, stable and trustworthy in a decentralised web.