Where do the role exposure scores come from?

Synthesis of 2024-2025 published research: McKinsey GenAI workforce survey (occupation-level exposure), Goldman Sachs 2023 productivity report (task-decomposition method), OECD Future of Work AI risk index, Brookings AI Index 2024-2025. Where sources disagree we use the median exposure score; for roles not directly studied we map to the closest occupational analogue. The result is a defensible exposure index — not a peer-reviewed precise forecast.

Why does the calculator cap the score at 95%, not 100%?

Because 100% would imply certainty, and the academic literature is unanimous that AI replacement timing is uncertain. Even the highest-risk roles (data entry, telemarketing, basic transcription) have 5-15% residual demand for human judgment, error correction, and edge-case handling. Capping at 95% acknowledges that uncertainty honestly rather than producing alarmist headlines.

How is the trajectory multiplier calibrated?

OECD / Brookings consensus is the reference (×1.0): roughly the AI-capability-frontier roadmap implied by aggregating 2024-2025 forecasts. 'Slower' (×0.72) reflects regulatory headwinds, technical plateaus, and adoption lag scenarios — roughly the bottom-quartile forecast band. 'Faster' (×1.32) reflects capability-surprise scenarios where models meaningfully exceed expectations on agentic / multi-step task completion within the horizon. Pick the scenario whose underlying logic you find most credible.

Is the seniority dampener really only 15 points?

Yes — and the cap is empirically supported. Senior people don't get automated faster; they get reorganized into smaller teams + more managerial-style work. The 15-point dampener (peaking at 25+ years experience) reflects the empirical pattern that experienced workers transition into hybrid roles where AI augments rather than replaces. Below the seniority cap, the dampener scales at ~0.6 pp per year.

Why does reskilling only reduce risk by up to 18 points?

Because reskilling captures part — not all — of the adaptation gap. The remaining ~80% comes from the role itself, the broader sector dynamics, and capability frontier movement. 9+ months of focused upskilling reliably moves workers from 'AI-naive' to 'AI-fluent' on McKinsey's adoption framework; beyond that, marginal returns flatten because the next gain comes from on-the-job application, not more study.

What if my role isn't in the dropdown?

Pick 'Other / not listed' (base 50, the median exposure across roles studied). Then bump routine-task share to its honest level for your work. The result will land in the right tier most of the time — Moderate to Elevated. If you want a more precise number, find the closest analogue in the 25 listed roles and use that.

How does this compare to other 'will AI take my job' tools?

Most online 'AI risk' tools either (a) plug into the BLS occupation database without horizon or trajectory inputs (binary risk classification) or (b) score every role at 70%+ for clickbait. This calculator surfaces the underlying multipliers (base × routine adjustment × horizon × trajectory + reskill dampener) so you can defend the answer in a career conversation. The score is also bounded — never 0%, never 100% — to reflect honest uncertainty.

Is the score sensitive to the trajectory I pick?

Yes — meaningfully. A junior developer at 10y horizon, 50% routine, 5y experience scores 76% on the consensus trajectory but 55% on slower and 95% on faster. The trajectory multiplier is the largest single lever. If you find the consensus credible, use it; otherwise pick the band you actually believe.

Should I switch careers based on this score?

Probably not on its own — but the score IS a useful input to a broader career conversation. Score above 75% with limited reskill capacity AND a long horizon suggests the math is against you; consider adjacent roles with lower base exposure. Score between 50-75% suggests upskilling rather than pivoting. Below 50%, the right move is usually 'use AI well' rather than 'pivot away'.

Is the calculator vendor-neutral?

Yes — the underlying research synthesis is academic / consulting-firm output, not vendor-sponsored. CalcBold's owner runs Anthropic and OpenAI products in production at semisoftwares.com but the calculator's exposure scores are NOT calibrated to favor any specific frontier model's roadmap. The trajectory multiplier is the right place to layer your own bias if you have one.

What about creative work?

Creative roles split. Mass-production creative (junior copywriting, SEO content, junior graphic design, transcription) is high exposure (60-75%). High-craft creative (creative direction, art direction, narrative-driven editorial) is medium-low (25-40%). The break is roughly: routine-and-template-driven creative is exposed; vision-and-judgment-driven creative is dampened. Pick the role that fits your actual workload mix.

AI & TechFree · No signup · 350K+/month

AI Job Replacement Risk Calculator — How Exposed Is Your Role?

Pick your role, set the routine-task share, your seniority, your horizon, and the AI capability scenario you find most credible. The calculator returns a 0-95% task-replacement risk score with a concrete next action — calibrated against published 2024-2025 research, framed honestly: the score is a signal to adapt, not a forecast about you.

Instant result
Private — nothing saved
Works on any device
AI insight included

Reviewed by CalcBold EditorialLast verified May 4, 2026Methodology

Role

Pick the closest fit. Each role carries a research-backed exposure score (0-100) drawn from McKinsey, Goldman Sachs, OECD, and Brookings 2024-2025 reports.

Routine-task share

What % of your work is repetitive / predictable / template-driven (vs novel / strategic / interpersonal)? 50% is the reference baseline; adjustments scale ±18 points.

Years of experience

Seniority dampener: every 5 years past 5 reduces exposure by ~3 pp (cap −15 at 25+ years). Reflects faster role-pivot capacity at senior level.

Horizon

Planning window. 5 = baseline. 10 = +30%. 15 = +60%. Capped at +75% for 25y.

AI capability trajectory

Sensitivity scenario. 'Slower' models regulatory / technical headwinds; 'faster' models capability-surprise releases.

Reskill budget

Months of focused upskilling you can commit. Each month dampens risk by ~2 pp (cap −18 at 9+ months). AI-fluent workers consistently outperform AI-naive peers in role transitions.

Embed builderDrop the AI Job Risk on your site →Free widget · 3 sizes · custom theme · auto-resizes · no signupGet embed code

What This Calculator Does

The AI Job Replacement Risk Calculator returns a 0-95% task-replacement risk score for your current role over a chosen horizon. It synthesises the published 2024-2025 research — McKinsey GenAI workforce survey, Goldman Sachs 2023 productivity report, OECD Future of Work AI risk index, Brookings AI Index — and modulates a role's base exposure with your inputs: routine-task share, seniority, horizon, AI capability trajectory, and reskill budget.

Read it as a signal to adapt, not a forecast about you specifically. A “75% task-replacement risk” doesn't mean a 75% chance you lose your job; it means roughly 75% of the tasks in your role today are at substantial AI replacement risk within your horizon. Most workers in “high-risk” roles transition into AI-augmented versions of the same role — often with a temporary productivity bump.

The Math

Role base is a 0-100 exposure score per published research synthesis (data entry 85, transcriptionist 90, junior developer 58, mid developer 45, senior developer 28, physician 10, therapist 12). Routine delta scales ±18 points off a 50% reference. Seniority dampener caps at −15 at 25+ years. Horizon scaling caps at +75% at 25y. Trajectory multiplier is 0.72 / 1.00 / 1.32 (slower / consensus / faster). Reskill dampener caps at −18 at 9+ months.

A Worked Example

A junior software developer with 5 years experience, 50% routine-task share, 10-year horizon, OECD/Brookings consensus trajectory, 3 months of reskill budget:

Role base — 58 (junior developer)
Routine delta — 0 (50% = reference baseline)
Seniority dampener — 0 (5 years = baseline)
Horizon scaling — × 1.30 (10-year window) → 75.4
Trajectory — × 1.00 (consensus) → 75.4
Reskill dampener — −6 (3 months × 2pp) → ~69
Final score — ~69% (Elevated tier)

Flip the trajectory to faster (×1.32) and the score climbs to ~95% (capped). Flip to slower (×0.72) and it drops to ~48% (Moderate tier). The trajectory multiplier is the single largest lever; pick the band whose underlying logic you find most credible.

When This Is Useful

Use this calculator at career inflection points: deciding whether to specialise deeper or pivot adjacent, sizing a reskill investment, or pressure-testing a long-term financial plan against an AI-disruption scenario. It's also useful as a conversation anchor with a manager or coach — the decomposed inputs (base × adjustments) let you defend the score in a way that “ChatGPT will take my job” clickbait can't.

The calculator is bounded at 0% and 95% on purpose. Even the highest-exposure roles (data entry, telemarketing, basic transcription) retain 5-15% residual demand for human judgment, error correction, and edge-case handling. Capping at 95% acknowledges honest uncertainty — academic literature is unanimous that timing is uncertain.

Common Mistakes

Reading the score as a forecast about you. The score measures your role's task exposure, not your personal employment outcome. Adjacent role transitions and AI-augmented versions of the same role absorb most of the high-risk population. The right reaction to a 75% score is “reskill toward the AI-fluent version of my work,” not “quit and go back to school.”
Inflating routine-task share. Most workers underestimate the strategic / interpersonal / novel share of their week and overestimate the routine share. Walk through a typical week and tag actual tasks before setting the number; honest junior-developer routine share is usually 50-65%, not 80%+.
Picking the “faster” trajectory by default.The trajectory multiplier is a sensitivity knob, not a doom dial. The OECD / Brookings consensus (×1.00) is the median professional forecast across academic and consulting research. Use “faster” only if you have specific reasons to believe capability surprise is plausible within your horizon.
Ignoring the seniority dampener cap.The dampener tops out at −15 points at 25+ years experience — and that's deliberate. Senior people don't become immune to AI; they get reorganised into smaller teams with more managerial / hybrid work. The cap reflects empirical patterns, not seniority privilege.
Treating “Other / not listed” as a neutral fallback.“Other” defaults to base 50 (the median across 25 studied roles). If your role is closer to a high-exposure analogue (paralegal, financial analyst, content writer) you should use that analogue's base instead — picking “Other” will systematically underestimate your real exposure.
Reading reskill as a guarantee. The −18pp cap reflects the empirical finding that AI-fluent workers out-perform AI-naive peers in role transitions, NOT that 9+ months of study makes you immune. Reskilling captures part — not all — of the adaptation gap.

Related Calculators

For the financial side of a career pivot, run the Freelance Rate Calculator and the API Token Cost Calculator (if your reskill path is into AI-tooling work). To value the time you currently save with personal AI tools, the Personal AI Stack ROI Calculator converts hours-saved into a productivity ROI vs subscription spend. And for break-even thinking on a reskill investment over time, the Break-even Calculator helps you size when the investment pays back.

Frequently Asked Questions

The most common questions we get about this calculator — each answer is kept under 60 words so you can scan.

What does the score actually measure?
The percentage of your CURRENT role's tasks that are at substantial AI replacement risk within your horizon — NOT the probability that you personally lose your job. Most workers in 'high-risk' roles transition into AI-augmented versions of the same role, often with a temporary productivity bump before competition catches up. The score is a signal to adapt, not a forecast about you specifically.
Where do the role exposure scores come from?
Synthesis of 2024-2025 published research: McKinsey GenAI workforce survey (occupation-level exposure), Goldman Sachs 2023 productivity report (task-decomposition method), OECD Future of Work AI risk index, Brookings AI Index 2024-2025. Where sources disagree we use the median exposure score; for roles not directly studied we map to the closest occupational analogue. The result is a defensible exposure index — not a peer-reviewed precise forecast.
Why does the calculator cap the score at 95%, not 100%?
Because 100% would imply certainty, and the academic literature is unanimous that AI replacement timing is uncertain. Even the highest-risk roles (data entry, telemarketing, basic transcription) have 5-15% residual demand for human judgment, error correction, and edge-case handling. Capping at 95% acknowledges that uncertainty honestly rather than producing alarmist headlines.
How is the trajectory multiplier calibrated?
OECD / Brookings consensus is the reference (×1.0): roughly the AI-capability-frontier roadmap implied by aggregating 2024-2025 forecasts. 'Slower' (×0.72) reflects regulatory headwinds, technical plateaus, and adoption lag scenarios — roughly the bottom-quartile forecast band. 'Faster' (×1.32) reflects capability-surprise scenarios where models meaningfully exceed expectations on agentic / multi-step task completion within the horizon. Pick the scenario whose underlying logic you find most credible.
Is the seniority dampener really only 15 points?
Yes — and the cap is empirically supported. Senior people don't get automated faster; they get reorganized into smaller teams + more managerial-style work. The 15-point dampener (peaking at 25+ years experience) reflects the empirical pattern that experienced workers transition into hybrid roles where AI augments rather than replaces. Below the seniority cap, the dampener scales at ~0.6 pp per year.
Why does reskilling only reduce risk by up to 18 points?
Because reskilling captures part — not all — of the adaptation gap. The remaining ~80% comes from the role itself, the broader sector dynamics, and capability frontier movement. 9+ months of focused upskilling reliably moves workers from 'AI-naive' to 'AI-fluent' on McKinsey's adoption framework; beyond that, marginal returns flatten because the next gain comes from on-the-job application, not more study.
What if my role isn't in the dropdown?
Pick 'Other / not listed' (base 50, the median exposure across roles studied). Then bump routine-task share to its honest level for your work. The result will land in the right tier most of the time — Moderate to Elevated. If you want a more precise number, find the closest analogue in the 25 listed roles and use that.
How does this compare to other 'will AI take my job' tools?
Most online 'AI risk' tools either (a) plug into the BLS occupation database without horizon or trajectory inputs (binary risk classification) or (b) score every role at 70%+ for clickbait. This calculator surfaces the underlying multipliers (base × routine adjustment × horizon × trajectory + reskill dampener) so you can defend the answer in a career conversation. The score is also bounded — never 0%, never 100% — to reflect honest uncertainty.
Is the score sensitive to the trajectory I pick?
Yes — meaningfully. A junior developer at 10y horizon, 50% routine, 5y experience scores 76% on the consensus trajectory but 55% on slower and 95% on faster. The trajectory multiplier is the largest single lever. If you find the consensus credible, use it; otherwise pick the band you actually believe.
Should I switch careers based on this score?
Probably not on its own — but the score IS a useful input to a broader career conversation. Score above 75% with limited reskill capacity AND a long horizon suggests the math is against you; consider adjacent roles with lower base exposure. Score between 50-75% suggests upskilling rather than pivoting. Below 50%, the right move is usually 'use AI well' rather than 'pivot away'.
Is the calculator vendor-neutral?
Yes — the underlying research synthesis is academic / consulting-firm output, not vendor-sponsored. CalcBold's owner runs Anthropic and OpenAI products in production at semisoftwares.com but the calculator's exposure scores are NOT calibrated to favor any specific frontier model's roadmap. The trajectory multiplier is the right place to layer your own bias if you have one.
What about creative work?
Creative roles split. Mass-production creative (junior copywriting, SEO content, junior graphic design, transcription) is high exposure (60-75%). High-craft creative (creative direction, art direction, narrative-driven editorial) is medium-low (25-40%). The break is roughly: routine-and-template-driven creative is exposed; vision-and-judgment-driven creative is dampened. Pick the role that fits your actual workload mix.