10 essays7 browser toolssince 2019 writing online
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Executable Cognition
“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.
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Engineer working where operational technology, industrial networks, and AI-enabled automation meet, and writing about what it all means.
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The writing
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1
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.
2
A one-time pad is unbreakable, provided it is true, and that second half, the quiet condition, is where the whole claim lives. A true pad is a very specific object: genuinely random, at least as long as the message, used once, kept secret, never copied, logged, inferred, generated from a seed, or reused by accident. The word “true” is doing all the work. This piece pulls that condition into the light, showing how a guarantee that is flawless in theory turns fragile the moment it meets real machines and real people. The proof is easy; the discipline is not. And the lesson generalises far beyond cryptography: a word carries a claim only when the thing it names actually satisfies the conditions that make the claim hold. Until then it is language with ambitions.
3
AI is not just another tool in the cybersecurity stack. It is becoming part of the system being defended, part of the system doing the defending, and increasingly part of the system being attacked. This piece separates cybersecurity with AI, models that detect threats, triage alerts, and accelerate response, from cybersecurity of AI, where the model itself, its data, prompts, outputs, permissions, and training pipeline become the attack surface. It walks through adversarial manipulation, poisoned training data, inference and privacy leaks, and the model as a weapon, then argues for governance without theatre: discipline across the whole chain rather than one framework or control. As models move from tool to participant, the old security boundary does not disappear, part of it moves inside the model.
Recent
We've convinced ourselves that clarity is something you can acquire, read the right list, name the right bias, and step clear of the mess everyone else is stuck in. This is about why that doesn't work, and why the failure is worse than it looks. The frameworks that explain how your judgement fails are mostly correct, and that's precisely the problem: a false map you escape by noticing it's false, but a true one you move into and furnish. Detachment doesn't help, the certainty that you're the exception is itself one of the oldest seats in the room. What's left isn't another framework to collect but a single unglamorous habit: turning, deliberately, toward the evidence that costs you something. It won't make you immune. It just shifts the odds, late and unreliably, for anyone willing to stop wanting it cheap.
The popular fear of AI is trapped inside the wrong film. The real threat is not Terminator rupture but Matrix replacement: not that machines kill us, but that they break the scarcity mechanisms that give people status, and once status breaks, reality itself becomes negotiable. AI commoditises the middle layer of human competence, hollowing out the professional class's claim to be necessary. Capitalism answers abundance with enclosure; value migrates from things to scarce worlds; and people retreat into personalised synthetic realities warmer than the world outside. The end need not arrive as apocalypse. It can arrive as comfort, as a bespoke interface, as abundance so complete that human beings forget which scarcities made them real.
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.
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