{
  "schemaVersion": 1,
  "reviewedAt": "2026-10-23",
  "methodologyVersion": "impact-curated-facts-v1",
  "publicationStatus": "approved",
  "coverage": "Selected primary-source factual summaries. Non-exhaustive; studies are not pooled or ranked against each other.",
  "studies": [
    {
      "id": "writing",
      "sector": "Productivity",
      "title": "Writing faster is not the same as replacing a job",
      "sourceUrl": "https://doi.org/10.1126/science.adh2586",
      "publishedAt": "2023-07-13",
      "observationPeriod": "Writing experiment; collection dates not specified in the reviewed abstract",
      "sample": "453 college-educated professionals",
      "design": "Preregistered randomized experiment",
      "finding": "ChatGPT access reduced task time by 40%; independently rated output quality rose by 18%.",
      "limitations": "Short writing assignments, not complete jobs or economy-wide output. Experimental tasks required less factual and organizational context than many real assignments.",
      "rights": "Limited independently written factual summary; Science default licence. No source figure, full table or participant data reproduced.",
      "updates": [],
      "metrics": [
        {
          "id": "writing:time",
          "label": "Change in time per writing task",
          "unit": "% relative to control",
          "direction": "lower",
          "mode": "relative"
        },
        {
          "id": "writing:quality",
          "label": "Change in rated writing quality",
          "unit": "% relative to control",
          "direction": "higher",
          "mode": "relative"
        }
      ]
    },
    {
      "id": "support",
      "sector": "Productivity",
      "title": "Customer support saw a measurable productivity gain",
      "sourceUrl": "https://doi.org/10.1093/qje/qjae044",
      "publishedAt": "2025-02-04",
      "observationPeriod": "Staggered workplace rollout studied in the 2025 journal edition; not a 2025 annual statistic",
      "sample": "5,172 customer-support agents",
      "design": "Workplace staggered rollout; quasi-experimental analysis",
      "finding": "The published journal study reports 15% more issues resolved per hour with AI assistance.",
      "limitations": "One company and one workflow; not a randomized global productivity estimate. Use the 2025 journal result, not the 14% estimate from the earlier working-paper edition.",
      "rights": "Limited factual summary with attribution to Brynjolfsson, Li and Raymond; no source figure or microdata reproduced.",
      "updates": [],
      "metrics": [
        {
          "id": "support:throughput",
          "label": "Change in issues resolved per hour",
          "unit": "% relative to control",
          "direction": "higher",
          "mode": "relative"
        }
      ]
    },
    {
      "id": "coding",
      "sector": "Software",
      "title": "Perceived speed and measured speed diverged",
      "sourceUrl": "https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf",
      "publishedAt": "2025-07-10",
      "observationPeriod": "February–June 2025",
      "sample": "16 experienced developers; 246 tasks",
      "design": "Randomized field experiment on familiar open-source repositories",
      "finding": "Tasks took 19% longer with the tools tested, despite developers expecting time savings.",
      "limitations": "Historical tools and a narrow sample; the reported 95% confidence interval is +2% to +39% task time. METR's February 2026 update warns that later experiments suffer from selection and time-measurement problems; this is not an estimate of current tool performance.",
      "rights": "Limited factual summary credited to Becker, Rush, Barnes and Rein / METR; no figure or task data copied.",
      "updates": [
        {
          "date": "2026-02-24",
          "url": "https://metr.org/blog/2026-02-24-uplift-update/",
          "note": "Follow-up estimates are affected by selection and measurement bias; no reliable current effect size established."
        }
      ],
      "metrics": [
        {
          "id": "coding:time",
          "label": "Expected versus measured task-time change",
          "unit": "% change in completion time",
          "direction": "lower",
          "mode": "change"
        }
      ]
    },
    {
      "id": "education",
      "sector": "Education",
      "title": "A purpose-built tutor improved immediate test results",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/",
      "publishedAt": "2025-06-03",
      "observationPeriod": "Fall 2023 semester; weeks 9–10; two Harvard undergraduate physics lessons",
      "sample": "194 eligible students; repeated lesson observations, not 316 unique students",
      "design": "Randomized crossover experiment",
      "finding": "Median post-test scores were 4.5 with the tutor and 3.5 in active learning.",
      "limitations": "Immediate outcomes in two lessons, not long-term retention or all education. Post-test observations: tutor n=142, class n=174. Classroom learning time is an assumed 60 minutes; tutor time is a measured median.",
      "rights": "Limited factual summary of Kestin et al. Article: CC BY-NC-ND 4.0; no source artwork, text, full table or raw dataset is redistributed.",
      "updates": [],
      "metrics": [
        {
          "id": "education:score",
          "label": "Median immediate post-test score",
          "unit": "score points",
          "direction": "higher",
          "mode": "comparison"
        },
        {
          "id": "education:time",
          "label": "Learning time: classroom allocation versus tutor median",
          "unit": "minutes",
          "direction": "lower",
          "mode": "comparison"
        }
      ]
    },
    {
      "id": "health",
      "sector": "Health",
      "title": "A strong model did not establish a physician benefit",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11519755/",
      "publishedAt": "2024-10-28",
      "observationPeriod": "2023-11-29 → 2023-12-29",
      "sample": "50 physicians; 244 completed vignette cases",
      "design": "Randomized trial; standalone GPT-4 arm was exploratory",
      "finding": "Physician scores were 74% with conventional resources and 76% with GPT-4; the adjusted difference was not statistically significant.",
      "limitations": "Rubric scores on written diagnostic cases, not patient outcomes or real-world diagnostic accuracy. The exploratory standalone score is not a randomized physician group. Adjusted difference: 2 pp, 95% CI −4 to 8; P=.60.",
      "rights": "Goh et al., JAMA Network Open (2024), CC BY; adapted numeric summary, no patient data.",
      "updates": [],
      "metrics": [
        {
          "id": "health:reasoning",
          "label": "Median diagnostic reasoning rubric score",
          "unit": "% of available rubric points",
          "direction": "higher",
          "mode": "comparison"
        }
      ]
    },
    {
      "id": "energy",
      "sector": "Energy",
      "title": "Data-centre demand has a growing physical footprint",
      "sourceUrl": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary",
      "publishedAt": "2026-04-16",
      "observationPeriod": "IEA April 2026 edition; 2025 estimate and 2030 projection",
      "sample": "Global data centres; AI-focused facilities are a subset",
      "design": "Energy-system estimates and forward projection",
      "finding": "IEA estimates 485 TWh in 2025 and projects about 950 TWh in 2030 for all data centres.",
      "limitations": "Total data-centre consumption is not AI-only consumption. The 2030 value is a projection, not a measurement; no annual path is interpolated. Growth rates compare overlapping populations and are not additive.",
      "rights": "IEA (2026), Key Questions on Energy and AI, IEA, Paris; CC BY 4.0. Adapted factual chart.",
      "updates": [],
      "metrics": [
        {
          "id": "energy:electricity",
          "label": "All data-centre electricity consumption",
          "unit": "TWh per year",
          "direction": "context",
          "mode": "comparison"
        },
        {
          "id": "energy:growth",
          "label": "Estimated electricity consumption growth in 2025",
          "unit": "% year-on-year",
          "direction": "context",
          "mode": "comparison"
        }
      ]
    }
  ],
  "observations": [
    {
      "id": "writing:time:0",
      "studyId": "writing",
      "entityId": "writing:ChatGPT access",
      "label": "ChatGPT access",
      "metricId": "writing:time",
      "value": -40,
      "unit": "% relative to control",
      "observationPeriod": "Writing experiment; collection dates not specified in the reviewed abstract",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://doi.org/10.1126/science.adh2586"
    },
    {
      "id": "writing:quality:0",
      "studyId": "writing",
      "entityId": "writing:ChatGPT access",
      "label": "ChatGPT access",
      "metricId": "writing:quality",
      "value": 18,
      "unit": "% relative to control",
      "observationPeriod": "Writing experiment; collection dates not specified in the reviewed abstract",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://doi.org/10.1126/science.adh2586"
    },
    {
      "id": "support:throughput:0",
      "studyId": "support",
      "entityId": "support:AI assistance",
      "label": "AI assistance",
      "metricId": "support:throughput",
      "value": 15,
      "unit": "% relative to control",
      "observationPeriod": "Staggered workplace rollout studied in the 2025 journal edition; not a 2025 annual statistic",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "quasi-experiment",
      "sourceUrl": "https://doi.org/10.1093/qje/qjae044"
    },
    {
      "id": "coding:time:0",
      "studyId": "coding",
      "entityId": "coding:Before: expected",
      "label": "Before: expected",
      "metricId": "coding:time",
      "value": -24,
      "unit": "% change in completion time",
      "observationPeriod": "February–June 2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "expectation",
      "sourceUrl": "https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf"
    },
    {
      "id": "coding:time:1",
      "studyId": "coding",
      "entityId": "coding:After: believed",
      "label": "After: believed",
      "metricId": "coding:time",
      "value": -20,
      "unit": "% change in completion time",
      "observationPeriod": "February–June 2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "expectation",
      "sourceUrl": "https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf"
    },
    {
      "id": "coding:time:2",
      "studyId": "coding",
      "entityId": "coding:Measured in trial",
      "label": "Measured in trial",
      "metricId": "coding:time",
      "value": 19,
      "unit": "% change in completion time",
      "observationPeriod": "February–June 2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf",
      "intervalLow": 2,
      "intervalHigh": 39,
      "intervalType": "95% confidence interval; reported in METR 2026 update"
    },
    {
      "id": "education:score:0",
      "studyId": "education",
      "entityId": "education:Active-learning class",
      "label": "Active-learning class",
      "metricId": "education:score",
      "value": 3.5,
      "unit": "score points",
      "observationPeriod": "Fall 2023 semester; weeks 9–10; two Harvard undergraduate physics lessons",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/"
    },
    {
      "id": "education:score:1",
      "studyId": "education",
      "entityId": "education:Purpose-built AI tutor",
      "label": "Purpose-built AI tutor",
      "metricId": "education:score",
      "value": 4.5,
      "unit": "score points",
      "observationPeriod": "Fall 2023 semester; weeks 9–10; two Harvard undergraduate physics lessons",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/"
    },
    {
      "id": "education:time:0",
      "studyId": "education",
      "entityId": "education:Class: assumed learning time",
      "label": "Class: assumed learning time",
      "metricId": "education:time",
      "value": 60,
      "unit": "minutes",
      "observationPeriod": "Fall 2023 semester; weeks 9–10; two Harvard undergraduate physics lessons",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "assumption",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/"
    },
    {
      "id": "education:time:1",
      "studyId": "education",
      "entityId": "education:AI tutor: measured median",
      "label": "AI tutor: measured median",
      "metricId": "education:time",
      "value": 49,
      "unit": "minutes",
      "observationPeriod": "Fall 2023 semester; weeks 9–10; two Harvard undergraduate physics lessons",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/"
    },
    {
      "id": "health:reasoning:0",
      "studyId": "health",
      "entityId": "health:Physicians + conventional resources",
      "label": "Physicians + conventional resources",
      "metricId": "health:reasoning",
      "value": 74,
      "unit": "% of available rubric points",
      "observationPeriod": "2023-11-29 → 2023-12-29",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11519755/"
    },
    {
      "id": "health:reasoning:1",
      "studyId": "health",
      "entityId": "health:Physicians + GPT-4",
      "label": "Physicians + GPT-4",
      "metricId": "health:reasoning",
      "value": 76,
      "unit": "% of available rubric points",
      "observationPeriod": "2023-11-29 → 2023-12-29",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "experiment",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11519755/"
    },
    {
      "id": "health:reasoning:2",
      "studyId": "health",
      "entityId": "health:GPT-4 alone: exploratory",
      "label": "GPT-4 alone: exploratory",
      "metricId": "health:reasoning",
      "value": 92,
      "unit": "% of available rubric points",
      "observationPeriod": "2023-11-29 → 2023-12-29",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "exploratory",
      "sourceUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11519755/"
    },
    {
      "id": "energy:electricity:0",
      "studyId": "energy",
      "entityId": "energy:2025: estimate",
      "label": "2025: estimate",
      "metricId": "energy:electricity",
      "value": 485,
      "unit": "TWh per year",
      "observationPeriod": "2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "estimate",
      "sourceUrl": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary"
    },
    {
      "id": "energy:electricity:1",
      "studyId": "energy",
      "entityId": "energy:2030: projection",
      "label": "2030: projection",
      "metricId": "energy:electricity",
      "value": 950,
      "unit": "TWh per year",
      "observationPeriod": "2030",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "projection",
      "sourceUrl": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary"
    },
    {
      "id": "energy:growth:0",
      "studyId": "energy",
      "entityId": "energy:All data centres",
      "label": "All data centres",
      "metricId": "energy:growth",
      "value": 17,
      "unit": "% year-on-year",
      "observationPeriod": "2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "estimate",
      "sourceUrl": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary"
    },
    {
      "id": "energy:growth:1",
      "studyId": "energy",
      "entityId": "energy:AI-focused data centres",
      "label": "AI-focused data centres",
      "metricId": "energy:growth",
      "value": 50,
      "unit": "% year-on-year",
      "observationPeriod": "2025",
      "updatedAt": "2026-10-23",
      "methodologyVersion": "impact-curated-facts-v1",
      "publicationStatus": "approved",
      "evidenceType": "estimate",
      "sourceUrl": "https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary"
    }
  ],
  "datasetVersion": "46b3944a14eb685b9a5537187e3a67eb2f8e4706b53e2593e4ec9843b7e9699b"
}