--- title: "Which Jobs AI Is Actually Hitting" slug: ai-jobs-exposure type: data-index sector: ai-models canonical_url: https://deepstoryresearch.com/data/ai-jobs-exposure series: [exposure, role_growth] series_count: 2 data_point_count: 5 data_as_of: 2026-06 source_quality: curated_snapshot tier: free generated_at: 2026-08-11 --- # Which Jobs AI Is Actually Hitting _Which jobs AI is actually hitting — exposure vs role growth_ > Deepstory Research context file · **Free tier** · > Self-contained AI briefing. Drop into an LLM window or RAG pipeline. ## AI research use contract - Treat this file as source-grounded context, not a live database. Quote `source_ref` and `data_as_of` when using numbers. - Separate `FACT`, `ESTIMATE`, `FORECAST`, `TARGET`, and `INFERENCE`. If a row basis is unclear, say it is unclear. - Prefer T1/T2 sources for hard facts; use T3/T4 sources as context that should be checked before money, legal, medical, operational, or policy decisions. - Keep answer structure clean: first say what the data says, then what you infer, then what would change the conclusion. ## Research upgrade checklist - Distinguish task exposure from job loss, hiring growth, wage pressure, and productivity gains. - Add occupation-code granularity where possible so users can map broad categories to real roles. - Pair exposure data with adoption data by employer size and sector. - Add a prompt that forces the AI to split replacement risk, augmentation upside, and transition-training demand. ## TL;DR — index summary AI touches ~40% of jobs globally (IMF), but the labor-market signal is two-sided: AI/ML engineer postings grew ~143% year-over-year even as exposure spreads. The two series separate the threat (broad task exposure) from the pull (specific roles booming) — AI is reshaping the job mix, not just cutting it. ## What this index covers Two series: AI exposure by scope (global and segment), and the fastest-growing AI-related roles. **Series in this index:** - `exposure` — Share of jobs exposed to AI, by scope. - `role_growth` — Year-over-year growth in AI-related roles. **Entities tracked:** IMF, PwC, BLS, National University (data); AI/ML engineering roles. ## Key findings - IMF: AI affects ~40% of jobs globally (imf-ai-jobs). - AI/ML engineer postings grew ~143.2% YoY (nu-ai-stats). - PwC and BLS provide the barometer and occupational detail (pwc-barometer / bls-oes). ## Evidence basis map The free tables above show `source_ref` but not the row-level `value_basis`. This section mirrors the visible rows only, so AI tools can separate observed values from estimates, forecasts, targets, and derived values. | series | row | source_ref | value_basis | | --- | --- | --- | --- | | exposure | label=Global (all jobs); pct=40 | imf-ai-jobs | IMF: AI affects ~40% of jobs globally | | exposure | label=Advanced economies; pct=60 | imf-ai-jobs | IMF: ~60% of jobs in advanced economies exposed to AI | | role_growth | label=AI/ML engineer postings (YoY); pct=143.2 | nu-ai-stats | AI/Machine Learning Engineer positions +143.2% year-over-year | | role_growth | label=AI skills in job postings (decade); pct=297 | nu-ai-stats | AI skills requested in 2.5% of US job postings — a 297% increase over the past decade | | role_growth | label=Data scientist roles (2024–34, BLS); pct=34 | bls-oes | BLS: data-scientist employment projected +34% over 2024–2034 | ## The series (per-chart briefings) ### AI exposure — bar **Direct answer:** Share of jobs exposed to AI, by scope. **Time bracket:** snapshot | label | pct | source_ref | | --- | --- | --- | | Global (all jobs) | 40 | imf-ai-jobs | | Advanced economies | 60 | imf-ai-jobs | **Read:** ~40% globally — advanced economies higher. ### Role growth — bar **Direct answer:** Year-over-year growth in AI-related roles. **Time bracket:** annual | label | pct | source_ref | | --- | --- | --- | | AI/ML engineer postings (YoY) | 143.2 | nu-ai-stats | | AI skills in job postings (decade) | 297 | nu-ai-stats | | Data scientist roles (2024–34, BLS) | 34 | bls-oes | **Read:** AI/ML roles are booming even as exposure rises. ## Cross-series synthesis — why this matters Read the two series against each other: broad exposure (the automation threat) coexists with explosive growth in AI-building roles (the creation effect). The net labor story in 2026 is reallocation — the same technology that exposes ~40% of tasks is generating triple-digit growth in the roles that build it. Which effect dominates for a given worker depends on whether their tasks are AI-exposed or AI-complementary. ## Entities & relationships | Entity | What they do | Key stat | Relationship | | --- | --- | --- | --- | | IMF | Global exposure estimate | ~40% of jobs | The macro exposure anchor | | PwC | AI Jobs Barometer | Wage/productivity signal | Corroborates the reallocation story | | BLS | Occupational data | Data-scientist outlook | Ground-truth on specific roles | ## Timeline of key events & decisions - **2024–2025** — AI/ML postings +143% YoY → the creation-side surge - **2026** — IMF ~40% exposure estimate → the exposure baseline the index is built on ## Cross-industry ripple - **Education / reskilling:** Exposure vs role-growth split raises the premium on AI-complementary skills. _(inference)_ - **Wages:** Booming AI roles pull up pay for scarce skills while exposed roles face pressure. _(inference)_ - **Macro:** Cheaper frontier capability (frontier-ai-race) widens the exposed set over time. _(inference)_ ## Non-obvious reads (interpretation) _This section is interpretation, not sourced fact — each item names the observation it is built on and a confidence level._ - **Observation:** ~40% exposure coexists with +143% growth in AI-building roles. **Read:** The 2026 labor effect is reallocation, not net destruction — but the distribution is uneven, so aggregate "AI and jobs" takes hide who wins and who loses. _(confidence: medium)_ ## Glossary / key terms - **Exposure** — The share of a job's tasks that AI can perform or assist. - **Complementary** — Tasks where AI raises a worker's productivity rather than replacing them. - **Reallocation** — Shifting workers across roles rather than net job loss. ## How to use this with AI Paste this file into an LLM context window (or a RAG store) and ask cross-series questions. The tables carry a `source_ref` per row; the source registry below maps each ref to a named source and a trust tier. ### Suggested prompts (multi-series) ```text Using exposure and role_growth, argue whether 2026 AI is net job-destroying or job-reallocating. ``` ```text Connect this index to frontier-ai-race: how does falling capability-per-dollar change the exposed set? ``` ## Sources & trust | ref | source | trust tier | url | | --- | --- | --- | --- | | imf-ai-jobs | IMF — AI Will Transform the Global Economy (jobs exposure) | T1 · Government / regulator / central bank | https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity | | pwc-barometer | PwC — 2026 Global AI Jobs Barometer | T3 · Reputable press / research org / OWID | https://www.pwc.com/gx/en/services/ai/ai-jobs-barometer.html | | nu-ai-stats | National University — 137 AI Statistics and Trends for 2026 | T3 · Reputable press / research org / OWID | https://www.nu.edu/blog/ai-statistics-trends/ | | bls-oes | US BLS — Occupational Outlook Handbook: Data Scientists | T1 · Government / regulator / central bank | https://www.bls.gov/ooh/math/data-scientists.htm | ## Caveats & what this index cannot answer - Net employment outcomes (exposure ≠ displacement). - Individual-role fate (aggregates only). - Estimates and forward targets are labelled in the working dataset; never read a labelled estimate or forecast as a settled figure. - This is an observational data index, not investment, legal, or medical advice. ## Data freshness & methodology - **Last updated:** 2026-08-11 - **Data as of:** 2026-06 - **What changed most recently:** AI/ML role postings ~+143% YoY against a ~40% global exposure backdrop. **Methodology (as seeded):** > Two source-backed charts: share of jobs exposed to AI (%, IMF) and growth in > AI-related roles and skills demand (%). > > Exposure figures (40% globally, 60% in advanced economies) trace to the IMF. > Growth figures — AI/ML engineer postings +143.2% YoY, AI skills in postings > +297% over the decade, and BLS’s +34% data-scientist projection for 2024–2034 — > trace to the cited statistics compilations and BLS. > > CAVEAT: the growth bars use different time horizons (year-over-year, decade, and > a 2024–2034 projection), noted in each value_basis; they should not be read as > like-for-like. Exposure ≠ elimination. Re-verified 2026-07-17. --- _Free tier. The full row-level dataset and per-source detail live on the live page and in the working file. Canonical: . Deepstory Research · https://deepstoryresearch.com_