How much of your work an agent can take depends on how completely you can specify it, and you are paying for the gap on every single run.
TL;DR: Everyone has the same models, and nobody has the same space around them, which is where the real variable sits. The framework for measuring that gap ran in a management journal back in 2005, written for an entirely different purpose. Two questions decide it: can you codify the work, can you measure the result. What ruled work out then, untransferable team knowledge, is exactly what you can now supply. A colleague is onboarded once; an agent without memory is onboarded every time. Specify once, where the agent reads by default; unclear thinking now runs a meter.
The work AI is absorbing now is the work we offshored twenty years ago, for the same reason. The advantage won’t go to whoever swaps a human for a machine. It goes to whoever redesigns the work.
AI had an alibi for the vanishing entry-level job until 2025. It won’t have one for what comes next. The work most exposed to it in the short term is the one we spent the 2000s offshoring, from call centers to entry-level coding to the back office, because both waves seek the same thing: how much of a job is written down and how little of it is judgment. That work already left once. The mistake now would be to automate it in place, swapping a machine for a person and changing nothing else. The larger prize, including for the economies that first took this work, belongs to whoever is willing to rethink how it gets done at all.
