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1/ One productivity question stuck in my head: when AI can draft, explain, and propose solutions on demand (across a huge terrain!), what still has to live in your head for that to turn into real work, rather than a pile of plausible slop? 2/ Now you can wander into a field you barely know and still coax out ideas that feel specific, original, and coherent. But the moment the idea appears, you run into the tough bit: is it true, is it actually new, what’s it assuming, is it meaningful? 3/ Most obvious in software. You can vibe code a LOT (until state, edge cases, security, performance, integration, or debugging show up). And then there's a big difference between understanding the system and having just generated fluent patches. 4/ The "grind” doing some jobs gives you repeated contact with reality: seeing failures, learning the shape of typical mistakes, developing a sense for what breaks first, and internalizing what a decisive test looks like. 5/ It’s not just "I was once an old man walking uphill both ways" hazing, it’s how a generation of developers built error-sensors and system-wide intuition. 6/ Which is why the unease keeps coming up in other roles too. Banking, law, science, medicine: each has tacit competence you don’t get from surface fluency. 7/ None of this cancels the upside. The cognitive leverage is going to be enormous: fast map-making, translating between subfields, drafting hypotheses / plans, shrinking the search space, iterating faster. It widens the front door to a lot of work that used to be gated by time. 8/ But “epistemic posture” is now easy to manufacture: confidence + coherence + detail no longer implies contact with the underlying thing. So I expect practice to adapt. More provenance, clearer uncertainty, quicker checks...how else? 9/9 This is not as a moral stance, but a question on how to get leverage from this strange "knowledge" instead of a lot of self-confident, plausible noise. Epistemology feels like a very practical question. Who's studying it in the age of AI?