Good morning. No one is made redundant by a job title. No one is protected by one either.
AI Fluency.
Tasks, Not Jobs
Take the heaviest thing on this week's calendar and break it into the six activities you actually do inside it: which of those six could a model do to a standard you'd sign, and which have you never once tested? Whatever you currently believe about your own job is resting on that second list.
In 2023 OpenAI's Tyna Eloundou, with Daniel Rock of the Wharton School and two colleagues, published GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. Its contribution wasn't a prediction. It was a change of unit. Instead of ranking occupations, the paper broke work into tasks — the individual activities catalogued for every American occupation — and asked of each one whether access to a language model would cut the time to complete it by at least half without loss of quality. On that measure around eighty per cent of US workers had at least a tenth of their tasks exposed, and roughly one in five had at least half. Read exposed carefully, because the authors do: it marks a task the technology can plausibly help with, not a job that disappears. These are proxy estimates rather than observations, and the labour evidence remains genuinely unsettled. The trap is the job title itself: it is the unit the headlines use, the unit your worry attaches to, and the one unit inside which every useful difference has already been averaged out.
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