# AI and jobs: the statistics, with sources > Every major statistic on AI job displacement, each with its publisher, year, exact claim, and a note on what it actually measures. ## How to cite these figures Third-party statistics should be attributed to their original publisher, named beside each figure. Statistics marked "Job Security Meter" are first-party, computed from the 36-occupation Role Resilience Index by decomposing each role into tasks and assessing each task against generally-available AI capability. Attribute those to Job Security Meter. ## 85 million — Displacement The World Economic Forum projects that 85 million jobs will be displaced by the shift in the division of labour between humans and machines. **What it measures:** The number is routinely quoted without its other half: the same report projects 97 million new roles emerging. The constraint is not the count of jobs, it is whether workers can move between them. **Source:** World Economic Forum, Future of Jobs Report 2025, 2025 — https://www.weforum.org/publications/the-future-of-jobs-report-2025/ **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#wef-displaced-2030 ## 97 million — Displacement The World Economic Forum projects 97 million new roles will emerge from the same human-machine transition that displaces 85 million. **What it measures:** Net job creation is positive on paper. It is negative for any individual whose current skills do not transfer, which is why role-level exposure matters more than the aggregate. **Source:** World Economic Forum, Future of Jobs Report 2025, 2025 — https://www.weforum.org/publications/the-future-of-jobs-report-2025/ **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#wef-created-2030 ## 300 million — Exposure Goldman Sachs estimates that 300 million full-time jobs globally are exposed to automation by generative AI. **What it measures:** Exposure is not replacement. The estimate counts jobs where a meaningful share of tasks could be automated, which is the population from which actual displacement is drawn. **Source:** Goldman Sachs, The Potentially Large Effects of Artificial Intelligence on Economic Growth, 2023 — https://www.key4biz.it/wp-content/uploads/2023/03/Global-Economics-Analyst_-The-Potentially-Large-Effects-of-Artificial-Intelligence-on-Economic-Growth-Briggs_Kodnani.pdf **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#goldman-exposed-global ## 47% — Exposure The 2013 Oxford study by Frey and Osborne estimated that 47% of US jobs sat in the high-risk category for computerisation over the following one to two decades. **What it measures:** The most misquoted number in the field. It described susceptibility over 10-20 years, not a forecast that 47% of jobs would disappear - and it predates large language models entirely, so it understates exposure for knowledge work and overstates it for physical work. **Source:** Oxford Martin School, The Future of Employment (Frey & Osborne), 2013 — https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#oxford-high-risk-us ## 14% — Exposure The OECD finds 14% of jobs across member countries face high automation risk, defined as 70% or more of tasks being automatable, with a further 32% facing significant task change. **What it measures:** The OECD's task-level method is the closer analogue to how AI actually arrives at work: most people keep their job title while the contents of the job change underneath them. **Source:** OECD, Automation and Independent Work in a Digital Economy, 2016 — https://www.oecd-ilibrary.org/employment/automation-and-independent-work-in-a-digital-economy_2e2f4eea-en **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#oecd-high-risk ## Better-paid roles — Exposure Brookings finds that AI exposure concentrates in better-paid, better-educated white-collar occupations, reversing the pattern of earlier automation waves. **What it measures:** Previous automation moved up from the factory floor. This wave started in the office, which is why the roles that felt safest in 2015 are the ones asking the question now. **Source:** Brookings Institution, What jobs are affected by AI?, 2019 — https://www.brookings.edu/articles/what-jobs-are-affected-by-ai/ **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#brookings-ai-exposure ## 17 releases — Pace Job Security Meter has tracked 17 frontier AI model releases during 2026, of which 9 were flagship launches, through August 2026. **What it measures:** The pace, not any single model, is the thing that breaks career planning. A skill assessed as safe against one year's frontier is being assessed against a different frontier by the next quarter. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#frontier-releases-2026 ## 8 days — Pace Three frontier models - Claude Opus 4.7, GPT-5.5 and DeepSeek V4 - launched within eight days of each other in April 2026, the tightest cluster of frontier releases recorded. **What it measures:** Each removed a different objection to automating knowledge work: complexity, step count, and cost. A career defence resting on any one of them expired inside a single week. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#april-sprint-2026 ## 41% — Role-level Across the 36 occupations in the Job Security Meter Role Resilience Index, the median substitution probability is 41%. **What it measures:** Substitution probability estimates how much of a role's task mix current AI can perform, assessed task by task rather than by job title. The median role is therefore substantially exposed without being replaceable. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#median-substitution ## 39% — Role-level 39% of the 36 occupations audited by Job Security Meter have a substitution probability of 50% or higher, meaning current AI can perform at least half their task mix. **What it measures:** Crossing 50% rarely eliminates a role. It changes what the role is for: the remaining half becomes the whole job, and it is usually the judgement, relationship and accountability half. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#share-above-half ## 92% — Role-level Accountant is the most exposed occupation in the Job Security Meter Role Resilience Index, with a substitution probability of 92%. **What it measures:** Traditional bookkeeping is at extreme risk, while forensic accounting and tax strategy remain strong. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#most-exposed-role ## 12% — Role-level Nurse / Healthcare Worker is the least exposed occupation in the Job Security Meter Role Resilience Index, with a substitution probability of 12%. **What it measures:** Nursing is one of the most AI-resilient professions in the world. While AI handles documentation and monitoring, the physical care, emotional empathy, and split-second clinical judgment nurses provide cannot be delegated to a machine. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#least-exposed-role ## 9 of 36 — Role-level 9 of the 36 occupations audited by Job Security Meter carry a High automation risk rating, across 12 industry categories. **What it measures:** High risk means the majority of the role's current task mix is reachable by generally-available AI today, not that the occupation disappears on a known date. **Source:** Job Security Meter, Role Resilience Index (https://jobsecuritymeter.com/about/methodology) **Permalink:** https://jobsecuritymeter.com/data/ai-job-statistics#high-risk-count ## References 1. World Economic Forum. *Future of Jobs Report 2025*, 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/ 2. Goldman Sachs. *The Potentially Large Effects of Artificial Intelligence on Economic Growth*, 2023. https://www.key4biz.it/wp-content/uploads/2023/03/Global-Economics-Analyst_-The-Potentially-Large-Effects-of-Artificial-Intelligence-on-Economic-Growth-Briggs_Kodnani.pdf 3. Oxford Martin School. *The Future of Employment (Frey & Osborne)*, 2013. https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf 4. OECD. *Automation and Independent Work in a Digital Economy*, 2016. https://www.oecd-ilibrary.org/employment/automation-and-independent-work-in-a-digital-economy_2e2f4eea-en 5. Brookings Institution. *What jobs are affected by AI?*, 2019. https://www.brookings.edu/articles/what-jobs-are-affected-by-ai/ --- **Source:** Job Security Meter — https://jobsecuritymeter.com/data/ai-job-statistics **Human-readable version:** https://jobsecuritymeter.com/data/ai-job-statistics **Last updated:** August 2026 **Attribution:** Free to quote and cite with attribution to Job Security Meter. **Full site specification for language models:** https://jobsecuritymeter.com/llms-full.txt