[ { "id": "2026-04-cbp-uk-labour-market-evidence", "date": "2026-04-22", "title": "Centre for British Progress: \"AI and the UK labour market: the evidence so far\"", "source": { "name": "Centre for British Progress (Dr Pedro Serôdio)", "url": "https://www.britishprogress.org/", "type": "Evidence review (UK)", "url_pdf": "/research/2026-04-cbp-uk-labour-market-evidence.pdf", "url_markdown": "/vault/research/2026-04-cbp-uk-labour-market-evidence.md" }, "summary": "Three years after ChatGPT, UK Annual Population Survey data covering 412 occupations shows no aggregate displacement signal. Wages in high-exposure occupations have grown more slowly since 2019 — but the trend predates ChatGPT. Programmers and finance analysts continue to grow; admin and clerical roles have contracted. Adoption is concentrated: ~20% of tasks account for the vast majority of usage.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "Reframe narrative copy to acknowledge no aggregate UK displacement signal yet; admin/clerical contraction is the visible change." }, { "area": "Industry Insights — Tech & Software", "url": "/industries/tech", "change": "Update prose to note total programmer roles continue to grow in UK; entry-level hiring is the divergence vector." }, { "area": "Industry Insights — Finance", "url": "/industries/finance", "change": "Note that finance analyst roles continue to grow in aggregate." }, { "area": "Industry Insights — Admin & Customer Support", "url": "/industries/admin-support", "change": "Cite UK PAYE data showing admin/clerical contraction since ChatGPT." }, { "area": "How to Adapt — AI Tools", "url": "/adapt/ai-tools", "change": "Cite collaboration patterns (29% iteration / 28% directive / 24% learning) to support 'use AI as a tool' framing." } ] }, { "id": "2026-04-gla-london-workforce-genai-exposure", "date": "2026-04-15", "title": "GLA Economics Working Paper 103: London's workforce exposure to generative AI", "source": { "name": "GLA Economics — Greater London Authority (Jeff Dwan-O'Reilly)", "url": "https://www.london.gov.uk/", "type": "Regional government research (UK / London)", "url_pdf": "/research/2026-04-gla-london-workforce-genai-exposure.pdf", "url_markdown": "/vault/research/2026-04-gla-london-workforce-genai-exposure.md" }, "summary": "46% of London's workforce (~2.4M people) are in roles where GenAI could automate a share of their tasks — substantially higher than the UK average of 38%. Over 300,000 are in highest-exposure routine administrative roles. Worker- and business-reported AI adoption has roughly doubled in two years to 26–35%. Mayor Khan announces a London AI and Jobs Taskforce.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "Refresh regionalData for London & SE — supports raising the highExposureJobs figure beyond the current 42 to reflect the 46% London-only exposure." }, { "area": "Industry Insights — Public Sector", "url": "/industries/public-sector", "change": "Public administration named as one of four highest-exposure sectors — strong rationale to add chart data (currently empty)." }, { "area": "Industry Insights — Professional Services", "url": "/industries/professional-services", "change": "Confirmed as Taskforce priority sector; refresh prose." }, { "area": "Industry Insights — Creative", "url": "/industries/creative", "change": "Taskforce priority sector; refresh prose to note both commercial and culturally creative impacts." }, { "area": "How to Adapt — Repetitive Tasks", "url": "/adapt/repetitive-tasks", "change": "Cite 300,000 Londoners in highest-exposure admin roles." }, { "area": "Job Impact Calculator", "url": "/calculator", "change": "Public Administration industry modifier (+4) may be too low given GLA's identification as a top-4 exposure sector." } ] }, { "id": "2026-03-arxiv-ai-layoff-trap", "date": "2026-03-21", "title": "Falk & Tsoukalas: \"The AI Layoff Trap\" — over-automation as a market failure", "source": { "name": "Brett Hemenway Falk & Gerry Tsoukalas (UPenn / BU)", "url": "https://arxiv.org/abs/2603.20617", "type": "Theoretical economic model (arXiv)", "url_pdf": "/research/2026-03-arxiv-ai-layoff-trap.pdf", "url_markdown": "/vault/research/2026-03-arxiv-ai-layoff-trap.md" }, "summary": "Theoretical paper showing that competitive demand externalities trap rational firms in an automation arms race that exceeds the collectively optimal level — even when firms can foresee the demand cliff. Capital income taxes, worker equity participation, UBI, upskilling, and Coasian bargaining all fail to fix it; only a Pigouvian automation tax restores the cooperative optimum.", "impact": [ { "area": "How to Adapt — Rights & Protections", "url": "/adapt/rights-protections", "change": "Theoretical backing for policy-level intervention (automation tax + retraining) over individual market remedies." }, { "area": "How to Adapt — Upskilling", "url": "/adapt/upskilling", "change": "Important caveat: paper finds upskilling alone cannot eliminate the demand externality. Useful corrective to over-optimistic upskilling narratives." }, { "area": "Big Picture", "url": "/big-picture", "change": "Add 'Red Queen effect' framing — better AI doesn't slow displacement absent policy intervention." } ] }, { "id": "2026-03-orebro-sweden-age-gradient", "date": "2026-03-16", "title": "Lodefalk et al.: \"Same Storm, Different Boats\" — age-gradient hiring evidence from Sweden", "source": { "name": "Magnus Lodefalk, Lydia Löthman, Michael Koch, Erik Engberg (Örebro University)", "url": "https://www.ai-econlab.com/papers", "type": "Working paper (Sweden, population register data)", "url_pdf": "/research/2026-03-orebro-sweden-age-gradient.pdf", "url_markdown": "/vault/research/2026-03-orebro-sweden-age-gradient.md" }, "summary": "Uses Sweden's natural experiment (Riksbank rate hike preceded ChatGPT by seven months) to disentangle monetary tightening from AI. The aggregate posting decline aligns with monetary policy, not AI. But within employers: 22–25 year olds in high-AI-exposure occupations saw employment fall 5.5% by early 2025, while 50+ rose 1.3%. First population-register evidence of the 'canaries' effect outside the US.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "unemploymentByAge — refresh youth16to24 trajectory; note 50+ gain pattern in narrative copy." }, { "area": "How to Adapt — Career Transitions", "url": "/adapt/career-transitions", "change": "Load-bearing source for advice to early-career workers (22–25) in AI-exposed roles." }, { "area": "Job Impact Calculator", "url": "/calculator", "change": "Strong case for an age-aware insight (or optional age input) since the squeeze is strongly age-conditional." }, { "area": "Industry Insights — Tech & Software", "url": "/industries/tech", "change": "Software/IT roles are the clearest 'canaries' in parallel US data; refresh entry-level framing." } ] }, { "id": "2026-03-anthropic-labor-market-impacts", "date": "2026-03-05", "title": "Anthropic: \"Labor market impacts of AI\" — introducing Observed Exposure", "source": { "name": "Maxim Massenkoff & Peter McCrory (Anthropic)", "url": "https://www.anthropic.com/research/labor-market-impacts", "type": "Empirical research note (US)", "url_pdf": "/research/2026-03-anthropic-labor-market-impacts.pdf", "url_markdown": "/vault/research/2026-03-anthropic-labor-market-impacts.md" }, "summary": "Introduces a new measure (Observed Exposure) combining theoretical LLM capability with real-world Claude usage. Computer Programmers 75% covered, Customer Service Reps next, Data Entry Keyers 67%. No systematic unemployment rise for highly exposed since late 2022, but suggestive ~14% drop in job-finding rate for 22–25 year olds in exposed occupations.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "industryExposure.aiExposure — Observed Exposure validates higher exposure for Admin & Customer Support, Tech & IT, Finance." }, { "area": "Industry Insights — Tech & Software", "url": "/industries/tech", "change": "Refresh prose with 75% coverage stat for Computer Programmers." }, { "area": "Industry Insights — Admin & Customer Support", "url": "/industries/admin-support", "change": "Strong supporting citations: Customer Service Reps and Data Entry Keyers in top 10 most exposed." }, { "area": "Job Impact Calculator", "url": "/calculator", "change": "Cross-check jobTitlesWeighted entries against the top-10 exposed occupations list." } ] }, { "id": "2026-03-fedres-atlanta-ai-productivity-workforce-cfos", "date": "2026-03-01", "title": "Atlanta Fed Working Paper 2026-4: AI productivity & workforce — evidence from ~750 CFOs", "source": { "name": "Baslandze, Edwards, Graham, McClure, Sparks, Meyer, Waddell, Weitz (Federal Reserve Bank of Atlanta + Duke)", "url": "https://doi.org/10.29338/wp2026-04", "type": "Survey-based working paper (US)", "url_pdf": "/research/2026-03-fedres-atlanta-ai-productivity-workforce-cfos.pdf", "url_markdown": "/vault/research/2026-03-fedres-atlanta-ai-productivity-workforce-cfos.md" }, "summary": "Survey of ~750 US CFOs: more than half of firms have invested in AI, with productivity gains concentrated in high-skill services and finance (≈0.8% labor productivity growth in 2025, expected >2% in 2026). Aggregate employment expected to fall <0.4% in 2026 — but composition shifts: routine clerical down >2pp over three years, with offsetting growth in skilled-technical roles.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "skillsDemand.declining — supports Routine Analysis, Standard Reporting trajectories. skillsDemand.growing — AI/ML Engineering, Complex Problem Solving." }, { "area": "Industry Insights — Finance", "url": "/industries/finance", "change": "Refresh prose to cite 2025 ≈ 0.8% / 2026 > 2% labor productivity gains." }, { "area": "Industry Insights — Admin & Customer Support", "url": "/industries/admin-support", "change": "Supports the >2pp clerical decline framing." }, { "area": "Job Impact Calculator", "url": "/calculator", "change": "Negative Exposure Index validates pushing office/admin support weights upward, professional/technical/sales weights downward." } ] }, { "id": "2026-01-dsit-uk-labour-market-assessment", "date": "2026-01-28", "title": "DSIT GOV.UK: Assessment of AI capabilities and the impact on the UK labour market", "source": { "name": "UK Department for Science, Innovation & Technology + AI Security Institute", "url": "https://www.gov.uk/government/publications/assessment-of-ai-capabilities-and-the-impact-on-the-uk-labour-market", "type": "Government research (UK, Crown Copyright)", "url_pdf": "/research/2026-01-dsit-uk-labour-market-assessment.pdf", "url_markdown": "/vault/research/2026-01-dsit-uk-labour-market-assessment.md" }, "summary": "UK Government's authoritative assessment. Five judgements: AI capabilities improving rapidly (task time horizon doubling every 7 months); ~70% of UK workers in AI-exposed occupations (vs ~60% advanced-economy avg); UK productivity gains 0.4–1.2pp annually possible; hiring falling faster in exposed occupations (UK ads -38% high-exposure vs -21% low-exposure 2022–2025); 16–24 year olds in computer programming -44% in a single year.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "keyStats — UK at 70% AI-exposed framing. unemploymentByAge.youth16to24 — 44% drop in 16–24 programmers is a load-bearing data point." }, { "area": "Industry Insights — Tech & Software", "url": "/industries/tech", "change": "Software development +56% productivity, programmer 16–24 employment -44%, UK digital sector total employment fell first time in a decade." }, { "area": "Industry Insights — Professional Services", "url": "/industries/professional-services", "change": "Direct citations: Legal +34% productivity, Consulting +25%." }, { "area": "Job Impact Calculator", "url": "/calculator", "change": "Per-task productivity figures (writing 59%, software 56%, IT support 44%, legal 34%, consulting 25%, R&D IT 20%) map to automatedNow lists for matching jobTitlesWeighted entries." }, { "area": "How to Adapt — AI Tools", "url": "/adapt/ai-tools", "change": "Productivity gains by task type are exactly the right evidence to cite." } ] }, { "id": "2026-01-bis-eu-firms-ai-adoption", "date": "2026-01-15", "title": "BIS Working Paper 1325: AI adoption, productivity and employment — European firms", "source": { "name": "Aldasoro, Gambacorta, Pal, Revoltella, Weiss, Wolski (BIS + EIB)", "url": "https://www.bis.org/publ/work1325.htm", "type": "Central-bank working paper (EU + US, causal)", "url_pdf": "/research/2026-01-bis-eu-firms-ai-adoption.pdf", "url_markdown": "/vault/research/2026-01-bis-eu-firms-ai-adoption.md" }, "summary": "Causal study of 12,000+ EU firms + 800 US firms. AI adoption increases labor productivity by 4%, with gains driven by capital deepening rather than employment displacement — AI augments worker output, does not replace labour in the short run. Adoption stratified: large firms 45% vs small firms 24%; Sweden 52%, US 34%, Romania 22%. AI-adopting firms pay higher wages.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "aiAdoptionSpeed — UK should sit between Sweden 52% and US 34% based on benchmarks. Counterweight to current displacement-heavy framing in narrative copy." }, { "area": "Industry Insights — Tech & Software", "url": "/industries/tech", "change": "Add 'fusion skills' (prompt engineering, data stewardship, human-in-the-loop) to skills sections." }, { "area": "How to Adapt — Upskilling", "url": "/adapt/upskilling", "change": "Direct policy recommendation: re-skilling should prioritise fusion skills." }, { "area": "How to Adapt — AI Tools", "url": "/adapt/ai-tools", "change": "Supports framing that AI tools augment workers; add citations." } ] }, { "id": "2026-01-imf-skill-gaps-new-jobs", "date": "2026-01-10", "title": "IMF Staff Discussion Note 2026/001: Bridging skill gaps for the future — new jobs in the AI age", "source": { "name": "Jaumotte, Kim, Koll, Li, Li, Melina, Song, Tavares (International Monetary Fund)", "url": "https://www.imf.org/en/Publications/staff-discussion-notes", "type": "Multilateral staff discussion note (cross-country)", "url_pdf": "/research/2026-01-imf-skill-gaps-new-jobs.pdf", "url_markdown": "/vault/research/2026-01-imf-skill-gaps-new-jobs.md" }, "summary": "Cross-country analysis using Lightcast vacancy data (Brazil, Denmark, Germany, South Africa, UK, US). About 1 in 10 advanced-economy postings demand at least one new skill (3–3.4% wage premium in US/UK). AI-related skills boost wages but do NOT raise overall employment — and for high-AI-exposure low-complementarity occupations, employment levels are 3.6% lower in regions with higher AI-skills demand 5 years on. Reinforces job polarisation, potentially shrinking the middle class.", "impact": [ { "area": "Big Picture", "url": "/big-picture", "change": "skillsDemand.growing — AI/ML Engineering, Prompt Engineering match the new-skill categories. Add polarisation framing to narrative copy." }, { "area": "How to Adapt — Career Transitions", "url": "/adapt/career-transitions", "change": "Middle-skilled workers face 'no significant benefits' finding — supports advising 'move up' or 'sideways into low-skill services' over staying mid-skill." }, { "area": "How to Adapt — Upskilling", "url": "/adapt/upskilling", "change": "Counter-balance: AI-skills training boosts individual wages but not aggregate employment." }, { "area": "How to Adapt — Human Advantage", "url": "/adapt/human-advantage", "change": "Middle-skill cognitive work is the squeeze zone — emphasise hard-to-replicate human skills." } ] } ]