Scored High for AI Exposure? Five Moves the Evidence Supports

The short answer: read your score next to your occupation's BLS projection; steer discretionary hours toward work that is physical, accountable, or live between people; search adjacent occupations by the low-exposure tasks you already do; and time your own work with and without the tools instead of trusting how it feels. One of those is a heuristic rather than a tested finding, and I label it below.

First, read the score next to the projection

A high score on a growing occupation is a different situation from the same score on a shrinking one, and the exposure number alone cannot tell them apart. Software developers carry high exposure and a 15% growth projection. Bookkeeping clerks carry high exposure and a 6% decline. Those are not the same situation and they do not call for the same response.

The lookup is free at BLS and takes two minutes. Do it before anything else, because it decides whether the rest of this list is about repositioning inside your occupation or moving out of it.

Second, steer discretionary hours toward the floor

Across the ratings, the work that stays low has three properties: somebody has to be physically present, somebody is accountable by name, or the judgement happens in real time between people with something at stake. Those consistently rate low, and difficulty is not the reason.

Most roles have some discretionary hours: the client call a colleague could take or you could, the site visit, the review step, the presentation. Taking on more of the hours with those three properties moves your week, and where the hours go is the whole question.

Third, search adjacent occupations by your low-exposure tasks

Rather than by whatever is trending. Your experience transfers along the seam of the tasks you already do that the ratings put near the floor. A paralegal whose strength is client intake and court process has a different set of adjacent occupations than one whose strength is document review, even though they share a title.

O*NET lets you search occupations by task, which makes this concrete: take your three lowest-exposure tasks and find the occupations where they are central rather than peripheral. Those are the moves where the gap is smallest.

Fourth, time something

Rather than trusting how it feels. Pick a task you do regularly. Do four runs with a tool and four without, measured through to a finished deliverable at the quality you would actually ship, not to a first draft. Write down the times.

This is the one item on the list that produces evidence about your own job instead of borrowing it. The Science rubric asks whether a model halves the time on a task; you can answer that question for your own tasks directly, and the answer will often differ from the general rating in both directions. It is also the fastest way to find out which of your exposed tasks are exposed in practice at your quality bar, which is one of the things the score cannot know.

Fifth, watch the length of your tasks

METR's time horizon gives you a way of ordering your tasks by when the technology is likely to reach them. Tasks that take under an hour without anyone checking are already inside it. Day-long tasks are roughly two years out on the current trend. Building toward the longer, more supervised, more branching work is a direction the horizon supports, with the caveat that its task set is drawn mostly from software and research.

What "learn AI" is missing

None of this says do not learn the tools. It says that learning the tools is not a plan by itself, any more than learning a spreadsheet was a plan in 1995. The plan is knowing which of your hours the technology has reached, which it has not, which direction your occupation is moving, and where your existing strengths transfer. The tools are part of how you execute that. They are not the answer to which direction to go.

Which of the five would actually change what you do on Monday? That is the one to start with.

Frequently asked questions

What should I do if my AI Job Risk Score is high?

Read it next to your occupation's BLS projection first, since a high score in a growing field and in a shrinking one call for different responses. Then move discretionary hours toward physical, accountable, or live interpersonal work, and search adjacent occupations by the low-exposure tasks you already do.

Is "learn AI" good advice for a high score?

It is incomplete. Learning the tools helps you execute, but it does not tell you which hours the technology has reached, which direction your occupation is moving, or where your strengths transfer. Those come first.

How can I test my own exposure rather than relying on a general rating?

Time a regular task four times with a tool and four times without, measured to a finished deliverable at your real quality bar. The result tells you whether the general rating holds for your work.