Most people do not understand at all how the replacement of people by AI works (or how it DOES NOT work). Even a ten-fold acceleration of everything a specialist does doesn’t automatically erase the job itself—it just rewrites the economics around it. When the effective price of a deliverable plummets, latent demand that used to sit on the shelf suddenly becomes viable. I’ve never met a product owner who thinks their engineers are shipping more features than the roadmap needs; the wish-list is always longer than the headcount allows. Make each feature ten times cheaper to build and you don’t cut teams by a factor of ten—you light up every “nice-to-have” that once looked unaffordable, plus entire green-field products no one bothered to scope. A recent @Microsoft Research study on real-world Copilot usage underlines the same point. Users come for help drafting code or gathering facts, but the model ends up coaching, advising and teaching—folding brand-new kinds of labour into a single session. Professions aren’t monoliths; they’re bundles of subprocesses, each only partially (and imperfectly) covered by today’s models. As AI tools evolve, the scope of the role evolves with them, often expanding rather than shrinking. Even in an AI smart contract auditor that we've built at @NethermindEth, despite its name, we target a very specific narrow part of the process: finding potential vulnerabilities. Meanwhile, security specialists use this as a tool and do much more complex and multifaceted work—formulating strategies, validating findings, correcting the AI, adding implicit context, communicating with developers, discovering hidden intentions and managing expectations. So instead of tallying which jobs will “disappear,” it’s more useful to ask what problems become worth solving once the marginal cost of solving them falls off a cliff. History suggests the answer is “far more than we can staff for,” and that argues for a future where talent is redeployed and multiplied, not rendered obsolete.