A medical transcriptionist in Phoenix got her contract cut in November. The hospital cited "operational efficiency." She'd been in the role for eleven years. Her AI exposure score: 10 out of 10. The platform that employs her competitor, a speech-to-text model, cost $15 a month.
She had checked one of the popular AI job risk tools two years earlier. It told her she was "moderately exposed." She stayed put. That was the wrong tool.
This is a comparison of the major approaches to measuring AI displacement risk, what they get right, what they miss, and which methodology actually predicts the outcome. Not in theory. In practice.
What Most Tools Actually Measure
Most AI exposure tools do the same thing: they take an O*NET job category, run it through a rubric of task types, and return a percentage or a color. Green, yellow, red. Safe, watch out, danger.
The problem is the level of analysis. They measure the job title. Not the work.
"Software developer" sounds high-risk. And by some measures it is. The AI Displacement Score puts software developers at 8-9 out of 10. But job growth in that category is +25%. So the score captures real disruption without predicting elimination. Most tools stop at the score and let you draw the wrong conclusion.
The gap most tools ignore
Only 3% of jobs score 9-10. The bulk land at 7-8, which means restructured, not eliminated. Tools that don't distinguish these are predicting the wrong thing.
This is where the AI displacement score review gets uncomfortable. Simplistic tools flatten the curve. A 6 and a 9 both get called "high risk." But a 6 means you have five or more years. A 9 means the timeline is now. Treating them the same is malpractice.
The Education Trap Other Tools Don't Warn You About
Here is the finding that makes people angry. You think your degree protects you. It doesn't. It points you toward the work that AI is coming for first.
Jobs paying $100K+ average an AI exposure score of 6.7. Jobs paying under $35K average 3.4. Bachelor's degree holders average 6.7. No degree: 4.1. The more credentialed and compensated the role, the more of it involves the language, analysis, and synthesis tasks that large language models handle well.
High earners, high exposure
42% of US jobs score 7 or above, representing 59.9 million jobs and $3.7 trillion in wages. The pay grade is not a shield. In many cases it's a target.
Plumbers score 1. HVAC technicians score 0-2. The physical, dexterous, judgment-in-the-moment work that no college program credentialed stays human. That is why 42% of Gen Z is pursuing trades. Not because the pay is bad. Because they ran the math.
Nurses score 2. Electricians score 1. Physical therapists score 3. These are not low-skill jobs. They are low-AI-exposure jobs. The difference matters enormously when you are deciding where to spend the next five years of your career.
The credential got you the role. The role put you in the path. That is the trap most AI exposure score comparisons never show you.
Where the AI Displacement Score Methodology Diverges
The key differentiator in any AI exposure score comparison is granularity. Not just task-level analysis, but the distinction between three types of exposure that most tools collapse into one number.
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Score 9-10: Disruption is active. Not approaching. Not projected. Happening now. Medical transcriptionists. Data entry processors. Basic paralegal research. These roles are contracting in real time.
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Score 7-8: Two to three years. Restructured, not eliminated. The role changes shape. Tasks get carved away. New skills required. Software developers live here. So do most financial analysts and marketing managers.
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Score 5-6: Five-plus years. Meaningful exposure, but long runway. Enough time to adapt, layer skills, and reposition deliberately.
The healthcare split illustrates this better than any abstract framework. Surgeons score 3. Radiologists score 7. Same hospital. Same floor. Opposite trajectories. The surgeon's work is physical, improvisational, and tactile in ways current AI cannot replicate. The radiologist reads scans. Pattern recognition. That is AI's native language.
Andrej Karpathy's 342-occupation analysis published March 15, 2026 reinforced the same finding: task composition predicts exposure better than job category every time. Broad categories are noise. Specific tasks are signal.
The Second-Order Problem Every Other Tool Ignores
Here is where most AI displacement score reviews miss the story entirely. Your score matters. Your boss's score might matter more.
A VP of Sales scores 6. Moderate exposure. Reasonable runway. Fine. But the SDRs who report to that VP score 8. The VP's budget depends on the output those SDRs produce. As AI tools replace the prospecting and outreach work SDRs currently do, the VP's headcount shrinks, the tools get cheaper, and the VP's own role gets redefined around metrics that used to require a ten-person team.
The score didn't capture that. No title-level tool does.
Where do you actually land?
500+ occupations scored 0-10. Task-level methodology. Free. Takes 60 seconds.
Second-order effects are the real displacement mechanism for mid-to-senior roles. The direct work doesn't get automated. The justification for the headcount beneath them does. Understanding this requires reading your team's scores alongside your own.
What an Accurate Score Actually Tells You
An accurate AI exposure score comparison doesn't just rank tools. It changes what you do on Monday morning.
The 81% of physicians now using AI daily, up from 38% in 2023, aren't waiting for their exposure score to hit 9 before adapting. They're ahead of it. And the 56% salary premium that AI-skilled workers command is real, measurable, and widening. The people capturing that premium didn't panic. They planned.
The premium window is open. For now.
AI-skilled workers command a 56% salary premium today. That gap narrows as supply catches up. The timing of the move is as important as making it.
What a task-level score gives you that a title-level score does not:
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Specific task vulnerability. Which parts of your day are most exposed, not just whether your industry is at risk.
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Timing signal. A 9 is different from a 7. The score bands map to actual deployment timelines, not just relative risk.
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Adjacent opportunity. Knowing your score reveals which adjacent roles have lower exposure and higher demand. Radiologist at 7. Radiologist-AI specialist at 4, and growing.
High score. Booming demand. The paradox resolves when you understand the difference between tasks getting automated and roles getting eliminated.
The global average is 5.3 out of 10. Most people sit in the middle of the distribution, with time and options. The medical transcriptionist in Phoenix didn't. If she had understood the difference between a 6 and a 10, she would have moved two years earlier. The data was available. The interpretation wasn't.
The full analysis, covering specific adaptation strategies by score band, AI-safe adjacent pivots, and the 12-action survival playbook, goes deeper than what fits in a single article. But knowing your number is where it starts.
Bottom Line
The best AI job risk tool is the one that tells you what to do differently tomorrow. Title-level scores tell you where you are on a map. Task-level scores tell you the road is about to end. The difference between the two is the difference between an uncomfortable conversation and an avoidable career crisis.
The score isn't the destination. It's the data that clears the fog.
Find out where you stand
500+ occupations scored 0-10 on AI displacement risk. Free.