One person works across a design file, a product roadmap, and a code editor at a single desk, with their badge face down beside the keyboard. AI assisted, steampunk style.

Exposure to artificial intelligence runs through the tasks you do each week, not the role on your badge. Here’s how to measure yours and lower the risk.

My mom doesn’t understand my job, and it isn’t her fault. The closest I’ve ever come to explaining it is “I design how it works,” which is true and tells her almost nothing about my day.

It also works best when I’m wearing a black turtleneck and holding an iPhone, channeling my inner Steve Jobs.

That’s the curse of knowledge, and I’ve never directly defined my role in many of the places I’ve worked. Over decades I’ve managed design, product management, and engineering, and more than once I started without a job description. It wasn’t about my job title, it was about judgement applied through my experience building software.

Artificial intelligence doesn’t care about your job title or mine. It’s probabilistically predicting an outcome without looking at your LinkedIn.

In GPTs are GPTs, published in 2023, researchers from OpenAI and the University of Pennsylvania scored occupations task by task. They estimated that 80% of U.S. workers could see at least a tenth of their tasks affected, and about 19% could see at least half.

Those averages hide what matters for one person. Your exposure depends on how much of your week can be specified briefly and checked quickly, and seniority protects you only when it changes that mix.

For her work, she’s not exposed even though she’s in the models. From Claude:

The main trace of Jean Neeman in the art world is a single, well-documented piece: a teapot titled “Tea on the Go,” made in 1994 by Jean Neeman (American, b. 1948) from silver, copper, brass, and cocobolo wood, measuring about 8.4 x 7.75 x 4.5 inches. That puts her work alongside some serious company.

She doesn’t have to measure her risk because she’s an artist. I measure mine all the time so I can add value that AI can’t add, and that’s judgement.

The five steps below show how to measure yours, each with a prompt to run in Claude.

List Your Actual Tasks

Start with a week, not a job description.

List everything you spent more than half an hour on: the usability readout, the stakeholder review, the status update nobody read. Note the hours, how often each task repeats, and who reviews the result.

Also leave room for the white space between tasks.

Unblocking a teammate, the quick answer that saves someone a day, the thinking before a hard review: none of it shows up in a job description or on a calendar. It also goes unseen when people judge what you do, whether that’s a manager at review time or a researcher scoring tasks for exposure.

It’s often where you absorb complexity that, as Tesler’s Law puts it, can be moved but never removed.

Write it down anyway.

For a checklist, O*NET OnLine, the Labor Department’s occupational database, lists 31 tasks for Web and Digital Interface Designers. In Putting Tasks to the Test: Human Capital, Job Tasks and Wages, a 2009 paper, economists David Autor and Michael Handel found that tasks vary substantially within occupations, enough to predict wage gaps among people who hold the same job.

ATMs didn’t kill the teller, iPhones did.

In Toil and Technology, written for the International Monetary Fund in 2015, economist James Bessen showed how automated teller machines took over dispensing cash and taking deposits. The tellers needed to run an average urban branch fell from 20 to 13 between 1988 and 2004, but banks opened 43% more urban branches, so the jobs held.

The title stayed the same while the job underneath it was rebuilt.

My own title changed in March, from director to senior principal designer. Neither title would tell you which of my tasks a model can do, but my calendar would.

Try It in Claude

Run these prompts in one Claude conversation. The first one drafts your week from your role, so you correct a list instead of writing one, and you can check its tasks against O*NET OnLine.

To map my exposure to artificial intelligence, don’t read my calendar or connected accounts, or ask me to describe my week. If you don’t know my title, ask once. Name the O*NET occupation you matched, and say if you’re recalling its tasks from memory. Draft 15 to 20 tasks from it, plus the undocumented work between them, for a normal week. Give hours, frequency, and reviewer for each, marked as guesses. Then have me correct it, including whether I manage anyone.

Score Each Task’s Exposure

Difficulty is a poor guide to exposure. In a 2023 experiment for Navigating the Jagged Technological Frontier, a team led by Fabrizio Dell’Acqua gave 758 Boston Consulting Group consultants realistic work.

With the model, they did far better on 18 tasks inside its abilities but were 19 percentage points less likely to get the right answer on one task outside them. Coauthor Ethan Mollick calls this the jagged frontier in Centaurs and Cyborgs on the Jagged Frontier: tasks that look equally hard can sit on opposite sides of an invisible wall.

Score each task with two questions instead. I call it the spec-and-check test.

Specify the result. Ask whether you could describe a finished version in two sentences that someone else could follow.
Check the output. Ask whether someone could confirm it’s right in less time than it took to make.

A task that’s quick to specify and quick to check is exposed, whatever your title says.

Twenty variations of an empty state pass both questions. Deciding which of three problems to fund this quarter fails both, and for now that decision stays yours.

Then weight the list by hours, because the Pareto Principle usually holds and a few tasks fill most of the week. A task that passes both and fills two days a week is where your risk sits.

When leadership reviews a pie chart of where a team’s hours go, you don’t want to be on the wrong side of it.

Passing the test means a model could do the task, not that one already does. In Labor market impacts of AI: A new measure and early evidence, from its own March 2026 usage data, Anthropic found Claude covering about 33% of computer and math tasks, against 94% rated feasible in GPTs are GPTs.

So the prompt below has Claude try one.

Try It in Claude

Paste this after the task table. It ends with Claude attempting your most exposed task, so you judge real output instead of a forecast.

Score each task in my table: could a finished version be described in two sentences, and checked faster than it took to make? Answer yes, partly, or no for each. Then say honestly if you could do each task today, fully, partly, or not yet, and why. Mark tasks with two yeses as exposed, sort by hours, and tell me what share of my week is exposed. Offer to try my top exposed task so I can judge it.

Check Your Career Stage

In the August 2026 revision of Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence, Stanford’s Digital Economy Lab found no economy-wide job displacement in payroll data. Employment for workers ages 22 to 25 in exposed occupations, though, sits 19% below where it would be had it kept pace with less-exposed peers, mostly because companies are hiring fewer young people.

Experienced workers show no comparable gap, and the authors call these early indicators, not proof of cause.

Entry-level work is often the most specified on a team, handed down with clear instructions and reviewed by someone senior, which is where a model does best. Experience protects through judgment and context that rarely fit in a two-sentence spec.

A senior title over a week of status decks is junior exposure with a bigger paycheck.

If you’re early in your career, ask to own one small decision from start to finish. If you’re senior, hand your juniors judgment calls along with tasks.

Management will also change because there will be smaller teams.

Melvin Conway observed in 1968 that organizations build systems that copy their communication structures, which I cover in Conway’s Law. My extension is that much of management exists to carry that communication, so a smaller team needs less of it. Four people share six possible pairs, while 10 people share 45.

The flattening predates these tools. For The Manager Mass Exodus: How SMBs Are Flattening the Org Chart, Gusto studied payroll at about 8,500 small and midsize businesses. The share of their workers who manage anyone fell from just under 25% in 2019 to about 15% by late 2024. If you manage, move your hours from relaying information toward the decisions and coaching a smaller team still needs.

Try It in Claude

This prompt compares your task list against your experience. Correct it if it gets your background wrong.

Based on my title and experience, split my tasks into entry-level work (well specified and reviewed by others) and judgment work only I can do. If you don’t know my years of experience, ask once. Give the share of hours in each and, if I manage, how much goes to relaying information. Name the knowledge I bring that you could supply without my judgment. Tell me plainly if my week looks junior for my title, and suggest one decision I could own this quarter.

Deepen Your Domain Expertise

Knowing your field pays off more than knowing your tools. For Agentic coding and persistent returns to expertise, published in June 2026, Anthropic studied about 400,000 Claude Code sessions.

People made about 70% of the planning decisions — that’s human agency — and the agent made about 80% of the execution decisions.

The researchers rated expertise task by task and said it differs from job title: a senior engineer asking a first question about the Rust language counts as a beginner at Rust. Expert sessions succeeded about twice as often as novice ones, and a working grasp of the domain captured most of that gain, the same early-steep curve as the Power Law of Practice.

The person who understands the problem and not the model gets more out than the person who understands the model and not the problem.

Knowledge on its own doesn’t protect you anymore: If what you know can be pulled from a large language model without judgment to apply it, you’re at risk.

Companies don’t need you to tell them something they already know, they need the person who knows which answer fits this customer and this quarter, and the person who acts as connective tissue between people, building trust and getting decisions made rather than relaying status.

If you’re neither, your expertise is a search ot an AI Assistant result.

For designers, the domain is the user’s world and the business around it. Get closer to your users’ domain than your product manager is: read the regulations your customers follow, sit with support for a day, and learn how the money moves.

Try It in Claude

Answer the way you would with a new manager, without polishing. Short, unsure answers show the gaps fastest.

If you don’t know what my company sells, ask once. Tell me in one sentence who my product serves, and let me correct it. Ask me eight to 10 questions, one at a time, about their regulations, money, metrics, and workflow; “not sure” is a fine answer. Rank my knowledge gaps by how much they’d change my design decisions, suggest one way to close the top gap in 30 days, and revise the decision you suggested earlier.

Rebuild Your Portfolio

Employers expect 39% of workers’ core skills to change by 2030, according to the World Economic Forum’s The Future of Jobs Report 2025. A portfolio of polished final screens puts the fastest-changing part of your job up front and hides the part that holds its value.

The Future of Jobs Report 2025 — much of what employers value can’t be done by an agent.

Rebuild it around decisions: the problem you chose and the ones you turned down, the tradeoff and why, and what happened after launch. Show one case where you directed a model, caught its mistake, and shipped the fix.

Show the work a model couldn’t have done without you.

Look sideways too.

O*NET lists 10 related occupations for the same designer category, from software developers to graphic designers, and jobs overlap where their tasks do. As I wrote in We’ve Seen This Movie Before: Desktop Publishing Already Showed Us How AI Prototyping Plays Out, typesetting effectively ended as a profession while graphic design grew into web, motion, and product design. They all adjusted, and we’re seeing this move now too.

Whole economies have also made this move before.

The 20th Century Transformation of U.S. Agriculture and Farm Policy

The agricultural revolution let fewer people grow more food, and the industrial revolution absorbed the workers it freed. In 1900, 41% of American workers farmed, and by 2000 fewer than 2% did, according to The 20th Century Transformation of U.S. Agriculture and Farm Policy from the USDA’s Economic Research Service.

The adjustment wasn’t gentle, though.

In Engels’ Pause, economic historian Robert Allen found that British wages stalled for roughly half a century in the early industrial era while output per worker kept climbing.

People adjusted, but it took a generation, and the ones who fared best moved toward the new work instead of waiting for it to reach them.

The good news is that employers now have software to map those overlaps, something Henry Ford never had. When Ford ended the Model T in 1927, about 60,000 workers were laid off, according to Last Day of Model T Production at Ford from HISTORY. Skills platforms such as Skills Cloud, from my employer Workday, now relate the skills across a workforce.

In the report’s workforce strategies chapter, 85% of employers plan to upskill and 51% plan to move staff into growing roles. Organizations are looking to reinvest and reinvent.

Intentions aren’t programs, though, as Dan Maccarone argues in What the AI era owes you for UX Collective Editors. You own your career, not your company.

It’s up to you to figure out where the hockey puck is going, the advice Wayne Gretzky got from his father, Walter, and your task list shows which way it’s moving.

Try It in Claude

Run this on your strongest case study first. Then reuse it on the rest, and keep the list of adjacent jobs it produces.

Ask me for my strongest case study. Rewrite it around my decisions: the problem, the options I turned down, the tradeoff, how I used and corrected artificial intelligence tools (or that I didn’t), and the outcome. Ask for missing facts one at a time instead of inventing them. Then list four or five jobs whose tasks overlap most with mine, what I’d need to add for each, and which carry more exposure than mine, using the earlier test.

Conclusion

The tellers’ story has a second chapter, and the branch boom that saved their jobs didn’t last. The Bureau of Labor Statistics outlook for Tellers projects employment falling 13% from 2025 to 2035, as mobile deposits and online banking keep customers away from the window. The first wave of automation changed what tellers did, and the second is taking away where they did it.That’s why the task list is a recurring exercise. Models improve unevenly, and a task that failed the spec-and-check test last spring can pass it next spring. Redo the list every six months and move a few more hours toward the work a model can’t finish without you.

There will always need to be a human in the loop, the person who commits to what the system proposes, as my Human-in-the-Loop entry puts it. Beyond that, there has to be human agency: someone who decides rather than someone who clicks approve.

My mom won’t be able to explain my job, whatever the next title is. A model won’t need the title to find my exposed work, and it can point out where it is.

Run the first prompt next Monday, before the next reorganization or the next model release decides the mix for you and how you can explain it to your mom.

Action items

Draft your task list. Run the first prompt and correct the table it builds from your role, or log every task longer than 30 minutes for five days by hand, using the O*NET task list for your occupation to catch what you missed.
Score each task. Mark whether a finished version can be specified in two sentences and checked faster than it was made, and treat tasks that pass both as your exposure.
Trade one hour. Each week, move an hour from your most exposed task to a planning decision, a stakeholder conversation, or time with users.
Rewrite one case study. Rebuild your strongest portfolio piece around the decisions you made, including any evaluation criteria you wrote and what happened after launch, and note which adjacent job its tasks would also qualify you for.

AI doesn’t care about your job title was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

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AI doesn’t care about your job title