Out of distribution humans

This thought provoking article from Ahmed on the current state of AI’s onslaught on the job market.

This is where I keep coming back to a phrase that has been rattling around my brain for the past month: out of distribution humans.

Most work lives in the fat middle of a bell curve. Tasks repeat with small variations. Most graduate schemes are built around that fact. You take reasonably bright people, give them a handbook and a mentor, and let them climb a well mapped gradient. Shared service centres, call centres, warehouses, junior consulting rotations, entry level software roles, even a lot of legal and accounting work, all sit in that comfortable hunk of the curve where yesterday’s data is a very good guide to tomorrow’s tasks.

Models feast on that part of the curve. That is what they are trained on: logs, emails, historical cases, recordings of someone else doing the job, code repositories, scanned documents. If your work looks a lot like a large pile of past episodes, it is a short hop from playing them back to imitating them. The central question for future labour markets is not whether you are clever or diligent in some absolute sense. It is whether what you do is ordinary enough for a model to learn or strange enough to fall through the gaps.

An out of distribution human, in my head, is someone whose job sits far enough in the tail of that curve that it does not currently compress into training data. Maybe they work with genuinely novel problems. Maybe they operate at small scales or in messy physical situations where we do not yet have enough sensors. Maybe they have taste that is not easily reduced to click logs. They are not safe; nothing is. They are simply late on the automation curve. The system needs them until it can watch them for long enough and in enough detail that it can flatten what they do into data.

This reminds me of Zara Zhang’s observation.