Atlas of the Present Atlas · v0.1

As of 12 September 2026

Dossier 11

Work and Education

v1.2-draft · unchecked

1. As of

Date
Version
v1.2-draft
Author / model
Atlas generator
Reviewer
unchecked (Josef)

2. In one sentence

Systems that write, see, and plan have changed task structure in measured settings — not the employment statistics; Noy & Zhang Science 381 (E1): writing RCT n=453, time −40%, quality +18% (MIT WP remains E1 for WP figures); exposure scores (Frey 47%, Eloundou 80%) run ahead of firm adoption (Census 17–20%); education: Bastani exam −17% GPT Base / Tutor ≈ control; Dell’Acqua jagged frontier — no universal “AI harms education”; PISA/UNESCO still unopened.

Established now · E1 / E2

3. What works today

Writing, code, customer service: tasks, not jobs

  1. E1

    Noy & Zhang MIT WP 2023 (source 1, still E1 for WP figures): 444 college-educated professionals, two writing tasks, GPT-3.5. Time −10 minutes / 37% vs. control mean 27 min. Grades +0.45 SD. 68% submit ChatGPT first output unedited. Inequality within the task compresses. Limits: short self-contained tasks; control leakage 10–20%; employment unobserved. Science figures (n=453 / −40% / +18%) now source 14 — not inferred from the WP.

    Noy & Zhang. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf. As of MIT WP 2023-03-02. Checked 2026-08-28. Type: Working paper.
  2. E1

    Noy & Zhang Science 381:187–192 (2023-07-13; DOI 10.1126/science.adh2586; author PDF opened 2026-09-11): preregistered online RCT, n=453 college-educated professionals, occupation-specific writing tasks, ChatGPT (GPT-3.5 era). Abstract: average time −40%; output quality +18%. Body: time −11 minutes (0.75 SD) vs. control mean 27 min (p<0.001); grades +0.45 SD (p<0.001). Almost everyone submitted lightly edited or unedited ChatGPT output; 33% without editing, 53% editing (editors only ~3.3 min active).

    Noy & Zhang Science. Experimental evidence on the productivity effects of generative artificial intelligence. https://doi.org/10.1126/science.adh2586. As of Science 381:187–192 (2023-07-13); author PDF research/pdfs/Noy_Zhang_Science_adh2586.pdf. Checked 2026-09-11. Type: Paper (Science; author PDF opened).
  3. E1

    Noy & Zhang Science follow-up: treatment ~2× as likely to report job use at 2 weeks (34% vs 18%); ~1.6× at 2 months (42% vs 27%). Control leakage ~10–20% ChatGPT use. Employment/wages unobserved. WP figures 444 / −37% / 68% unedited remain warrants of source 1 — do not restate as Science warrant.

    Noy & Zhang Science. Experimental evidence on the productivity effects of generative artificial intelligence. https://doi.org/10.1126/science.adh2586. As of Science 381:187–192 (2023-07-13); author PDF research/pdfs/Noy_Zhang_Science_adh2586.pdf. Checked 2026-09-11. Type: Paper (Science; author PDF opened).
  4. E1

    Peng et al.: RCT May–Jun 2022, 95 Upwork developers, 35 completers per arm. HTTP server in JavaScript. Completers: 71.17 vs. 160.89 min → −55.8% time. Success rate +7 pp, n.s. Code quality unmeasured. n small; authors Microsoft/GitHub. Extrapolation to US computer/math occupations in the discussion is speculation, not fact.

    Peng et al. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://arxiv.org/html/2302.06590. As of arXiv:2302.06590, experiment May–Jun 2022. Checked 2026-08-28. Type: Paper.
  5. E1

    Brynjolfsson, Li, Raymond NBER WP, rev. Nov 2023: 5,179 customer-support agents, one Fortune-500 software firm. Resolutions/hour +0.30 / +13.8%. Novices +34%; top skill ~0, quality partly −. Tenure <1 month +46%; >1 year ~0. Adherence ~38%. No evidence on wages, headcount, skill mix. QJE 2025 (15%, 5,172) not opened.

    Brynjolfsson et al. Generative AI at Work. https://www.nber.org/system/files/working_papers/w31161/w31161.pdf. As of NBER WP 31161, revised Nov 2023. Checked 2026-08-28. Type: Working paper.

Education: GPT Tutor design vs. unguarded GPT-4 (one field RCT)

  1. E1

    Bastani et al. PNAS 2025 (author PDF opened): field RCT at one Turkish high school, grades 9–11, mathematics, GPT-4-0613, four 90-min sessions; arms Control / GPT Base / GPT Tutor. Practice with tool: GPT Base +48%, GPT Tutor +127% vs control (Table 1: +0.137 and +0.361 on control mean 0.284; 2,848 observations). Subsequent unassisted exam: GPT Base −17% (−0.054; control mean 0.321); GPT Tutor ≈ control (−0.004, n.s.) — harm largely mitigated, no positive learning effect. Main sample n=839 (honors/non-survey excluded). Pre-reg: aspredicted.org/4DL_Q3J.

    Bastani et al. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. As of PNAS 122(26) e2422633122, published online 2025-06-25; DOI 10.1073/pnas.2422633122; experiment Fall 2023–2024. Checked 2026-08-30. Type: Field RCT (PNAS; author PDF opened).
  2. E1

    Bastani §4.1: on 57 practice problems (10 “What is the answer?” queries each), GPT Base is correct only 51% of the time on average (42% logical, 8% arithmetic errors). Authors’ mechanism: students often use GPT Base as a “crutch” (copy answers); logical-error spillover onto the exam not significant — not a universal hallucination warrant.

    Bastani et al. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. As of PNAS 122(26) e2422633122, published online 2025-06-25; DOI 10.1073/pnas.2422633122; experiment Fall 2023–2024. Checked 2026-08-30. Type: Field RCT (PNAS; author PDF opened).

Jagged frontier: tasks inside vs. outside (BCG, GPT-4 2023)

  1. E1

    Dell’Acqua et al., Organization Science 37(2):403–423 (March–April 2026; online 11 March 2026). Preregistered lab-in-the-field, 758 BCG consultants (~7% of the individual-contributor workforce), GPT-4 as of end of April 2023. Inside the frontier (n=385, 18 realistic product tasks): AI arms complete 12.2% more tasks (control ~82%, GPT+overview ~93%, GPT only ~91%) and are 25.1% faster on average (body: overview 22.5% / only 27.6% on the first 17 questions). Human quality, control mean 4.38: overview +1.48 / +33.9%, only +1.31 / +29.9%; text: quality ~+32% on average.

    Dell’Acqua et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. As of Organization Science 37(2):403–423, March–April 2026; published online 2026-03-11; DOI 10.1287/orsc.2025.21838; GPT-4 end of April 2023; first WP 2023-09-18 / HBS 24-013. Checked 2026-08-30. Type: Paper (field RCT, journal).
  2. E1

    Outside the frontier (n=373, one strategy case with spreadsheet + interviews): control correct ~84.5%; AI arms 60% and 70.6% — body: −19 percentage points averaging the AI arms (overview −24.5 pp, only −13.9 pp). Abstract says “19% less likely”; Atlas follows the body (percentage points). Coherence/persuasiveness still rises (overview +25.1% on the coherence scale — not the same 25.1% as inside speed).

    Dell’Acqua et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. As of Organization Science 37(2):403–423, March–April 2026; published online 2026-03-11; DOI 10.1287/orsc.2025.21838; GPT-4 end of April 2023; first WP 2023-09-18 / HBS 24-013. Checked 2026-08-30. Type: Paper (field RCT, journal).

Automation substitutes and complements; aggregates still invisible

  1. E1

    Autor 2015: automation substitutes routine tasks and complements non-routine. Employment-to-population in the 20th century did not collapse. US agriculture 41% (1900) → 2% (2000) — occupations vanish, work does not. ATMs 100k→400k (1995–2010), bank tellers 500k→~550k (1980–2010). Polarization 1979–2012: middle-skill 60% → 46%. Forecast “middle-skill jobs will persist” = Autor conjecture, E3.

    Autor 2015. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. https://economics.mit.edu/sites/default/files/publications/why%20are%20there%20still%20jobs%202014.pdf. As of JEP 29(3), 2015. Checked 2026-08-28. Type: Review/essay (JEP).
  2. E1

    Acemoglu, Autor, Hazell, Restrepo JOLE 2022: Burning Glass 2010–Oct 2018, pre-ChatGPT. AI vacancies rise in exposed establishments; non-AI hiring there falls (Felten −13.8%). Aggregate employment/wages in exposed occupations/industries not detectable. NAICS 51/54 excluded. Vacancies ≠ employment stock.

    Acemoglu et al. Artificial Intelligence and Jobs: Evidence from Online Vacancies. https://economics.mit.edu/sites/default/files/publications/AI%20and%20Jobs%20-%20Evidence%20from%20Online%20Vacancies.pdf. As of JOLE 40(S1), 2022. Checked 2026-08-28. Type: Paper.
  3. E1

    OECD Employment Outlook 2023 Ch. 3: “little evidence of significant negative employment effects due to AI.” High-skill occupations most AI-exposed and with relative employment gains. Occupations at highest general automation risk = 27% of employment (≠ AI displacement). Evidence without generative AI; adoption low.

    OECD EO 2023. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. As of 2023-07-11. Checked 2026-08-28. Type: International report.

Adoption 2026 real, not universal; the macro turning point is demography

  1. E1

    US Census BTOS, 14 Dec 2025 – 3 May 2026: firm AI use 17–20% nationally; expected next 6 months 20–23%. ≥250 employees: 37%; 100–249: 32%; <4 employees <20%. Information 39.7%, finance 33.9% vs. national 19.8% (3 May 2026); retail ~14%. Firm-count, not employment-weighted (CES WP not opened).

    Census BTOS. Large Firms With at Least 20 Employees Biggest AI Users. https://www.census.gov/library/stories/2026/05/ai-use-businesses.html. As of 2026-05-26 (BTOS 14 Dec 2025 – 3 May 2026). Checked 2026-08-28. Type: Official statistics story.
  2. E1

    OECD Employment Outlook 2025: working-age population (20–64) in the OECD aggregate stops growing in 2025 and starts to fall. Old-age dependency 2060 52%, almost 3× 1980. Title/focus: demographic crunch / labour shortage — not AI mass unemployment. E1 for demography as an OECD statement; E3 as “AI has no 2025 employment effects” — that is not what it says.

    OECD EO 2025. OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/07/oecd-employment-outlook-2025_5345f034/194a947b-en.pdf. As of 2025. Checked 2026-08-28. Type: International report.

Claimed · E3

4. What is claimed, not shown

Exposure scores and scenarios, not displacement 2026

  1. E2

    Frey & Osborne 2013: 47% of US employment (BLS 2010) in high-risk (p>0.7). Paper’s own qualification: “potentially automatable over some unspecified number of years, perhaps a decade or two.” E2 as a 2013 occupation score; E4 as observed displacement 2013–2026 (contradicts OECD 2023, Acemoglu 2022, Census adoption).

    Frey & Osborne. The Future of Employment: How Susceptible are Jobs to Computerisation?. https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf. As of Oxford Martin WP 2013-09-17. Checked 2026-08-28. Type: Working paper.
  2. E2

    Eloundou et al. 2023: ~80% of the US workforce with ≥10% of tasks affected (β); ~19% with >50%. LLM alone ~15% of tasks “significantly faster”; with software 47–56%. “We do not make predictions about the development or adoption timeline.” Exposure without an augmentation/displacement distinction. OpenAI authors; rubric subjective.

    Eloundou et al. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://arxiv.org/html/2303.10130. As of arXiv:2303.10130 (rev. Aug 2023). Checked 2026-08-28. Type: Paper (OpenAI).
  3. E2

    IAB research report 23/2025: QuBe scenario, assumptions. GDP +0.8 pp/year, cumulative 4.5 trillion €. Employment after 15 years similar to the reference. Turnover ~1.6 million. IT +110,000 / business services −120,000. Without new business models “clearly more negative.” Explicitly: not for creating accurate future answers. E3 as a world forecast.

    IAB FB 23/2025. Künstliche Intelligenz: Potenzielle Effekte für den deutschen Arbeitsmarkt. https://doku.iab.de/forschungsbericht/2025/fb2325.pdf. As of IAB-Forschungsbericht 23/2025. Checked 2026-08-28. Type: Scenario report.
  4. E2

    OECD EO 2023 survey (finance+manufacturing, 7 countries, 2022): 63% of AI-using workers report more enjoyment; 60% worried (infographic). Two sectors, not all occupations.

    OECD EO 2023. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. As of 2023-07-11. Checked 2026-08-28. Type: International report.

Constrained · Limit

5. Bottleneck and limit

What has not shifted

  1. E1

    Pattern through BTOS 2026: writing/seeing/planning speeds executable tasks in RCT/field; education shows the Bastani pattern (practice ↑, unaided exam Base ↓); Dell’Acqua jagged frontier. Employment statistics and firm adoption remain far behind Frey-47% and Eloundou-80%.

    What has moved, what has not. Opened measurements only.
    MeasurementMovedNot moved
    Noy/Zhang WPTime, grade, draft→edit (444 / −37% / 68%)Employment, wages
    Noy/Zhang ScienceTime −40% / quality +18%; follow-up job useEmployment, wages
    Peng CopilotJS-task timeCode quality, headcount
    BrynjolfssonRPH, novice onboardingFirm headcount, wages
    Bastani PNAS 2025Exam −17% Base; Tutor ≈ controlUniversal education, other subjects, long run
    Dell’Acqua OrgSci 2026Inside: completion +12.2%, speed ~25%, quality ~+32%; outside: correctness −19 pp, coherence +Employment, education, 2026 models, other firms
    Acemoglu 2022AI vacancies, non-AI hiring −Occup./industry employment
    OECD 2023Exposure scores, surveyAggregate employment levels
    Census BTOS 2026Firm adoption 17–20%“Every other job”
    OECD 2025Working-age population turnsAI mass unemployment
    Noy & Zhang. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf. As of MIT WP 2023-03-02. Checked 2026-08-28. Type: Working paper.Noy & Zhang Science. Experimental evidence on the productivity effects of generative artificial intelligence. https://doi.org/10.1126/science.adh2586. As of Science 381:187–192 (2023-07-13); author PDF research/pdfs/Noy_Zhang_Science_adh2586.pdf. Checked 2026-09-11. Type: Paper (Science; author PDF opened).Peng et al. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://arxiv.org/html/2302.06590. As of arXiv:2302.06590, experiment May–Jun 2022. Checked 2026-08-28. Type: Paper.Brynjolfsson et al. Generative AI at Work. https://www.nber.org/system/files/working_papers/w31161/w31161.pdf. As of NBER WP 31161, revised Nov 2023. Checked 2026-08-28. Type: Working paper.Bastani et al. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. As of PNAS 122(26) e2422633122, published online 2025-06-25; DOI 10.1073/pnas.2422633122; experiment Fall 2023–2024. Checked 2026-08-30. Type: Field RCT (PNAS; author PDF opened).Dell’Acqua et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. As of Organization Science 37(2):403–423, March–April 2026; published online 2026-03-11; DOI 10.1287/orsc.2025.21838; GPT-4 end of April 2023; first WP 2023-09-18 / HBS 24-013. Checked 2026-08-30. Type: Paper (field RCT, journal).Acemoglu et al. Artificial Intelligence and Jobs: Evidence from Online Vacancies. https://economics.mit.edu/sites/default/files/publications/AI%20and%20Jobs%20-%20Evidence%20from%20Online%20Vacancies.pdf. As of JOLE 40(S1), 2022. Checked 2026-08-28. Type: Paper.OECD EO 2023. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. As of 2023-07-11. Checked 2026-08-28. Type: International report.Census BTOS. Large Firms With at Least 20 Employees Biggest AI Users. https://www.census.gov/library/stories/2026/05/ai-use-businesses.html. As of 2026-05-26 (BTOS 14 Dec 2025 – 3 May 2026). Checked 2026-08-28. Type: Official statistics story.OECD EO 2025. OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/07/oecd-employment-outlook-2025_5345f034/194a947b-en.pdf. As of 2025. Checked 2026-08-28. Type: International report.
  2. E2

    Eloundou: human α only 14% of occupation tasks directly; ζ 46% needs software co-invention. Only 3% of US workers have >50% of tasks exposed without extra software. Frey: 2013+13 years = 2026, and OECD/Census/Acemoglu show no 47% event.

    Eloundou et al. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://arxiv.org/html/2303.10130. As of arXiv:2303.10130 (rev. Aug 2023). Checked 2026-08-28. Type: Paper (OpenAI).Frey & Osborne. The Future of Employment: How Susceptible are Jobs to Computerisation?. https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf. As of Oxford Martin WP 2013-09-17. Checked 2026-08-28. Type: Working paper.Autor 2015. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. https://economics.mit.edu/sites/default/files/publications/why%20are%20there%20still%20jobs%202014.pdf. As of JEP 29(3), 2015. Checked 2026-08-28. Type: Review/essay (JEP).
  3. E1

    Productivity gains compress task inequality: Noy low-baseline benefits more; 68% unedited can cut labour demand (authors, E3). Brynjolfsson: novices +34%, experts 0 — reversal of skill-biased IT (Autor 2015).

    Noy & Zhang. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf. As of MIT WP 2023-03-02. Checked 2026-08-28. Type: Working paper.Brynjolfsson et al. Generative AI at Work. https://www.nber.org/system/files/working_papers/w31161/w31161.pdf. As of NBER WP 31161, revised Nov 2023. Checked 2026-08-28. Type: Working paper.
  4. E1

    Limits (authors §5): one school, Turkey, mathematics only, GPT-4 (Fall 2023), short-term same-session exams — not employment, not long-run learning, not other subjects, not 2026 SOTA tutors. No warrant for universal “AI harms education” or “AI helps education.” Productivity RCTs (Noy/Peng) measure output with the tool, not unaided learning. PISA/UNESCO/OECD Education still unopened.

    Bastani et al. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. As of PNAS 122(26) e2422633122, published online 2025-06-25; DOI 10.1073/pnas.2422633122; experiment Fall 2023–2024. Checked 2026-08-30. Type: Field RCT (PNAS; author PDF opened).
  5. E1

    Dell’Acqua does not measure jobs, wages, headcount, occupations, or learning. The finding is task quality/speed/correctness in a BCG lab-in-the-field with GPT-4 (April 2023). One firm; early-career consultants; one outside task; subjective grades. The frontier is, per the paper, not fixed; 2026 models are unmeasured. “Writing/planning always better” is contradicted by the outside arm in this setting.

    Dell’Acqua et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. As of Organization Science 37(2):403–423, March–April 2026; published online 2026-03-11; DOI 10.1287/orsc.2025.21838; GPT-4 end of April 2023; first WP 2023-09-18 / HBS 24-013. Checked 2026-08-30. Type: Paper (field RCT, journal).
  6. E0

    Not in the dossier: BLS EP 2024–34 (bot block); Cui Copilot field; QJE-15% (Brynjolfsson); ifo 27% DE (not opened). Noy Science is source 14.

6. Actors and incentives

Who measures tasks, who forecasts, who measures adoption

  1. E1

    Noy/Zhang (MIT WP + Science 381), Peng (Microsoft/GitHub/MIT), Brynjolfsson/Li/Raymond: RCT and one firm set. Human-in-the-loop by design.

    Noy & Zhang. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf. As of MIT WP 2023-03-02. Checked 2026-08-28. Type: Working paper.Noy & Zhang Science. Experimental evidence on the productivity effects of generative artificial intelligence. https://doi.org/10.1126/science.adh2586. As of Science 381:187–192 (2023-07-13); author PDF research/pdfs/Noy_Zhang_Science_adh2586.pdf. Checked 2026-09-11. Type: Paper (Science; author PDF opened).Peng et al. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://arxiv.org/html/2302.06590. As of arXiv:2302.06590, experiment May–Jun 2022. Checked 2026-08-28. Type: Paper.Brynjolfsson et al. Generative AI at Work. https://www.nber.org/system/files/working_papers/w31161/w31161.pdf. As of NBER WP 31161, revised Nov 2023. Checked 2026-08-28. Type: Working paper.
  2. E1

    Bastani et al.: education field RCT (high-school mathematics, GPT-4). Dell’Acqua/BCG/HBS: task RCT (jagged frontier, GPT-4 2023).

    Bastani et al. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. As of PNAS 122(26) e2422633122, published online 2025-06-25; DOI 10.1073/pnas.2422633122; experiment Fall 2023–2024. Checked 2026-08-30. Type: Field RCT (PNAS; author PDF opened).Dell’Acqua et al. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. As of Organization Science 37(2):403–423, March–April 2026; published online 2026-03-11; DOI 10.1287/orsc.2025.21838; GPT-4 end of April 2023; first WP 2023-09-18 / HBS 24-013. Checked 2026-08-30. Type: Paper (field RCT, journal).
  3. E1

    OECD and US Census measure adoption and aggregates; the 2025 headline is labour shortage, not a jobless future.

    OECD EO 2023. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. As of 2023-07-11. Checked 2026-08-28. Type: International report.OECD EO 2025. OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/07/oecd-employment-outlook-2025_5345f034/194a947b-en.pdf. As of 2025. Checked 2026-08-28. Type: International report.Census BTOS. Large Firms With at Least 20 Employees Biggest AI Users. https://www.census.gov/library/stories/2026/05/ai-use-businesses.html. As of 2026-05-26 (BTOS 14 Dec 2025 – 3 May 2026). Checked 2026-08-28. Type: Official statistics story.
  4. E2

    Eloundou (OpenAI), Frey/Osborne, IAB/BIBB/GWS: exposure and scenarios, not 2026 employment actuals.

    Eloundou et al. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://arxiv.org/html/2303.10130. As of arXiv:2303.10130 (rev. Aug 2023). Checked 2026-08-28. Type: Paper (OpenAI).Frey & Osborne. The Future of Employment: How Susceptible are Jobs to Computerisation?. https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf. As of Oxford Martin WP 2013-09-17. Checked 2026-08-28. Type: Working paper.IAB FB 23/2025. Künstliche Intelligenz: Potenzielle Effekte für den deutschen Arbeitsmarkt. https://doku.iab.de/forschungsbericht/2025/fb2325.pdf. As of IAB-Forschungsbericht 23/2025. Checked 2026-08-28. Type: Scenario report.

7. State of the dispute

47% at risk vs. no aggregate effects vs. 17–20% adoption

  1. E4

    Frey 47% “at risk” (2013, technical feasibility) vs. OECD 2023 “no signs of slowing labour demand (yet)” vs. Census 2026 adoption 17–20% vs. Acemoglu establishment hiring − under exposure, aggregate 0. Autor 2015 cites exactly this Frey passage as overstated ML optimism.

    Frey & Osborne. The Future of Employment: How Susceptible are Jobs to Computerisation?. https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf. As of Oxford Martin WP 2013-09-17. Checked 2026-08-28. Type: Working paper.OECD EO 2023. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. As of 2023-07-11. Checked 2026-08-28. Type: International report.Acemoglu et al. Artificial Intelligence and Jobs: Evidence from Online Vacancies. https://economics.mit.edu/sites/default/files/publications/AI%20and%20Jobs%20-%20Evidence%20from%20Online%20Vacancies.pdf. As of JOLE 40(S1), 2022. Checked 2026-08-28. Type: Paper.Census BTOS. Large Firms With at Least 20 Employees Biggest AI Users. https://www.census.gov/library/stories/2026/05/ai-use-businesses.html. As of 2026-05-26 (BTOS 14 Dec 2025 – 3 May 2026). Checked 2026-08-28. Type: Official statistics story.Autor 2015. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. https://economics.mit.edu/sites/default/files/publications/why%20are%20there%20still%20jobs%202014.pdf. As of JEP 29(3), 2015. Checked 2026-08-28. Type: Review/essay (JEP).

8. Open questions

  1. Brynjolfsson QJE 2025 full text.
  2. BLS Employment Projections 2024–34 (bot block).
  3. Census CES-WP-26-25 employment-weighted adoption.
  4. Cui et al. Copilot field RCTs.
  5. IAB short report on substitutability 2024 + DiWaBe 2.0.
  6. OECD Education / PISA / UNESCO — without them half the title stays empty.

9. Changes

  • v1.2-draft2026-09-11: Noy & Zhang Science 381 (source 14, DOI 10.1126/science.adh2586, author PDF) opened — n=453, time −40% / quality +18%, body −11 min / +0.45 SD; follow-up job use 2×/1.6×. WP (source 1) remains E1 for WP figures 444/−37%/68%.
  • v1.1-draft2026-08-30: Bastani PNAS education RCT (source 12) and Dell’Acqua OrgSci jagged frontier (source 13) opened — education no longer E0; jagged frontier established. PISA/UNESCO still unopened.
  • v1.0-draftFirst version from the 2026-08-28 verification log. Education remains a gap (no opened education RCT).

10. Sources

No. Source As of Checked Grade
1 Noy, S.; Zhang, W.. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. https://economics.mit.edu/sites/default/files/inline-files/Noy_Zhang_1.pdf. Type: Working paper. E1
2 Peng, S.; Kalliamvakou, E.; Cihon, P.; Demirer, M.. The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://arxiv.org/html/2302.06590. Type: Paper. E1
3 Brynjolfsson, E.; Li, D.; Raymond, L. R.. Generative AI at Work. https://www.nber.org/system/files/working_papers/w31161/w31161.pdf. Type: Working paper. E1
4 Autor, D. H.. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. https://economics.mit.edu/sites/default/files/publications/why%20are%20there%20still%20jobs%202014.pdf. Type: Review/essay (JEP). E1
5 Eloundou, T.; Manning, S.; Mishkin, P.; Rock, D.. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://arxiv.org/html/2303.10130. Type: Paper (OpenAI). E2
6 Acemoglu, D.; Autor, D.; Hazell, J.; Restrepo, P.. Artificial Intelligence and Jobs: Evidence from Online Vacancies. https://economics.mit.edu/sites/default/files/publications/AI%20and%20Jobs%20-%20Evidence%20from%20Online%20Vacancies.pdf. Type: Paper. E1
7 OECD. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/07/oecd-employment-outlook-2023_904bcef3/08785bba-en.pdf. Type: International report. E1
8 OECD. OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/07/oecd-employment-outlook-2025_5345f034/194a947b-en.pdf. Type: International report. E1
9 Grundy, A.; Breaux, C.; Khatiwoda, D.. Large Firms With at Least 20 Employees Biggest AI Users. https://www.census.gov/library/stories/2026/05/ai-use-businesses.html. Type: Official statistics story. E1
10 Frey, C. B.; Osborne, M. A.. The Future of Employment: How Susceptible are Jobs to Computerisation?. https://oms-www.files.svdcdn.com/production/downloads/academic/The_Future_of_Employment.pdf. Type: Working paper. E2
11 Zika, G.; Hassemer, T.-M.; Hummel, M.; Krebs, B.; Maier, T.; Mönnig, A.; Schneemann, C.; Weber, E.; Zenk, J.. Künstliche Intelligenz: Potenzielle Effekte für den deutschen Arbeitsmarkt. https://doku.iab.de/forschungsbericht/2025/fb2325.pdf. Type: Scenario report. E2
12 Bastani, H.; Bastani, O.; Sungu, A.; Ge, H.; Kabakcı, Ö.; Mariman, R.. Generative AI without guardrails can harm learning: Evidence from high school mathematics. https://hamsabastani.github.io/education_llm.pdf. Type: Field RCT (PNAS; author PDF opened). E1
13 Dell’Acqua, F.; McFowland III, E.; Mollick, E.; Lifshitz, H.; Kellogg, K. C.; Rajendran, S.; Krayer, L.; Candelon, F.; Lakhani, K. R.. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. https://pubsonline.informs.org/doi/pdf/10.1287/orsc.2025.21838. Type: Paper (field RCT, journal). E1
14 Noy, S.; Zhang, W.. Experimental evidence on the productivity effects of generative artificial intelligence. https://doi.org/10.1126/science.adh2586. Type: Paper (Science; author PDF opened). E1

11. Uncertainty log

Overall uncertainty of this entry, bound to the verification log of 2026-08-28 plus openings 12+13 (2026-08-30) and Noy Science source 14 (2026-09-11). 14 full-text openings. Education is no longer E0; Noy Science opened. Not used as warrant: QJE snippets, BLS bot-block, WEF, McKinsey, LinkedIn Work Trend. Keep WP figures and Science figures separate.

  • Established (layer 1): Noy Science −40% time / +18% quality (n=453); Noy WP −37% / +0.45 SD / 68% unedited; Peng −55.8%; Brynjolfsson +14% RPH, novices +34%; Bastani exam −17% Base / Tutor ≈ control; Dell’Acqua inside +12.2% completion / outside −19 pp; Autor task framework; Acemoglu aggregate 0; OECD 2023 little evidence; Census 17–20%; OECD 2025 demography.
  • Claimed (layer 2): Frey 47% high-risk; Eloundou 80% / 19% exposure; IAB scenario net jobs ≈ 0 under assumptions.
  • Constrained (layer 3): exposure ≠ displacement; productivity RCTs ≠ learning; jagged frontier (one firm, GPT-4 2023); PISA/UNESCO still unopened; Frey 47% as a 2026 event E4.

Not opened (not a warrant)

  • Brynjolfsson QJE 2025 — NBER WP 5,179 / 14% is the warrant.
  • BLS EP 2024–34 / MLR / TED bot block.
  • Census CES-WP-26-25 HTTP 500; HTOS Aug 2026 JS-empty.
  • Cui Copilot field; Autor New Frontiers; Arntz/Gregory/Zierahn; DiWaBe 2.0; IAB-KB 2024.
  • WEF Future of Jobs; McKinsey/Goldman; PISA/UNESCO/OECD Education.