{
  "schema_version": "1.0.0",
  "title": "AI Productivity and Time-Savings Benchmarks",
  "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/",
  "published": "2026-09-12",
  "modified": "2026-09-12",
  "accessed": "2026-09-12",
  "language": "en",
  "scope": "Selected professional-writing, consulting and developer experiments; no pooled workforce estimate",
  "geography": "Study-specific recruitment; not a representative global worker sample",
  "observation_period": "2022–2025 experiments; institutional updates checked through September 2026",
  "editorial_owner": "Workspace369 editorial team",
  "review_cadence": "Review quarterly and when a cited producer releases a replacement study. Next editorial review: December 2026. Retain historical model versions and observation dates.",
  "attribution": "Reported observations belong to the original producers. Workspace369 is responsible for the compilation and explicitly labeled calculations, not an original survey.",
  "methodology": [
    "This is a selected evidence comparison, not a systematic review or meta-analysis. Studies were included for identifiable tasks, original-producer evidence and clearly stated outcome definitions.",
    "The writing and consulting figures use their research institutions’ public summaries of published studies. The coding paper and METR report provide original experimental detail. Working-paper and published versions are not mixed.",
    "Measured outcomes and perceived speedups carry separate evidence labels. The time-index calculation uses only measured completion-time changes; survey beliefs are excluded from it.",
    "Model versions and study dates remain attached to the findings. We checked the METR follow-up and retained its warning about the reliability of newer estimates."
  ],
  "limitations": [
    "Selected experiments cannot establish a universal AI return on investment. Different tasks, control conditions, participants and quality tests prevent a defensible pooled percentage.",
    "The sample sizes describe study scope, not independent replications. Vendor involvement in the Copilot study is relevant to interpretation.",
    "Historical results do not establish the capabilities of models available today. The normalization does not include implementation costs or demonstrate sustained annual savings."
  ],
  "sources": [
    {
      "id": "mit-writing",
      "publisher": "MIT / Noy and Zhang",
      "title": "MIT research announcement for the published Science writing experiment",
      "url": "https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714",
      "publicationDate": "2023-07-14",
      "accessed": "2026-09-12",
      "primarySource": true,
      "license": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer."
    },
    {
      "id": "hbs",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "publicationDate": "2026-04-09",
      "accessed": "2026-09-12",
      "primarySource": true,
      "license": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer."
    },
    {
      "id": "copilot",
      "publisher": "Peng, Kalliamvakou, Cihon and Demirer",
      "title": "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot, arXiv v1",
      "url": "https://arxiv.org/pdf/2302.06590",
      "publicationDate": "2023-02-13",
      "accessed": "2026-09-12",
      "primarySource": true,
      "license": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer."
    },
    {
      "id": "metr",
      "publisher": "METR",
      "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity",
      "url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
      "publicationDate": "2025-07-10",
      "accessed": "2026-09-12",
      "primarySource": true,
      "license": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer."
    },
    {
      "id": "metr-update",
      "publisher": "METR",
      "title": "We are Changing our Developer Productivity Experiment Design",
      "url": "https://metr.org/blog/2026-02-24-uplift-update/",
      "publicationDate": "2026-02-24",
      "accessed": "2026-09-12",
      "primarySource": true,
      "license": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer."
    }
  ],
  "statistics": [
    {
      "stat_id": "writing-time",
      "claim": "Writing experiment: lower task time with AI assistance: 40% (2023 experiment; published July 2023).",
      "value": 40,
      "unit": "percent",
      "population": "453 college-educated professionals assigned short occupational writing tasks",
      "geography": "Online recruited professionals; not nationally representative",
      "observation_period": "2023 experiment; published July 2023",
      "publisher": "MIT / Noy and Zhang",
      "source_title": "MIT research announcement for the published Science writing experiment",
      "source_url": "https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714",
      "source_locator": "Opening findings; Simulating work for chatbots",
      "publication_date": "2023-07-14",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Randomized access to ChatGPT-3.5-era assistance on the second task. Independent blinded evaluators judged quality. Uses the published-study figures in MIT’s announcement, not the March working-paper version.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#writing-time",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
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    {
      "stat_id": "writing-quality",
      "claim": "Writing experiment: higher evaluated output quality: 18% (2023 experiment; published July 2023).",
      "value": 18,
      "unit": "percent",
      "population": "453 college-educated professionals assigned short occupational writing tasks",
      "geography": "Online recruited professionals; not nationally representative",
      "observation_period": "2023 experiment; published July 2023",
      "publisher": "MIT / Noy and Zhang",
      "source_title": "MIT research announcement for the published Science writing experiment",
      "source_url": "https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714",
      "source_locator": "Opening findings",
      "publication_date": "2023-07-14",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Randomized access to ChatGPT-3.5-era assistance on the second task. Independent blinded evaluators judged quality. Uses the published-study figures in MIT’s announcement, not the March working-paper version.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#writing-quality",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
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    },
    {
      "stat_id": "consulting-completion",
      "claim": "Consulting experiment: increase in subtasks completed: 12.2% (Study first released in 2023; published-study summary April 2026).",
      "value": 12.2,
      "unit": "percent",
      "population": "758 BCG consultants in the overall randomized experiment; assignment-specific bases differ",
      "geography": "BCG participants; not a national workforce sample",
      "observation_period": "Study first released in 2023; published-study summary April 2026",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "source_title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "source_url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "source_locator": "Key Insight: AI as a Booster and Disruptor",
      "publication_date": "2026-04-09",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "GPT-4 access versus control, with a separate AI-plus-prompt-overview condition. Figures follow HBS’s 2026 institutional summary of the published study, not older working-paper quality estimates.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#consulting-completion",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
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    },
    {
      "stat_id": "consulting-quality",
      "claim": "Consulting experiment: approximately higher quality on the innovation exercise: 32% (Study first released in 2023; published-study summary April 2026).",
      "value": 32,
      "unit": "percent",
      "population": "758 BCG consultants in the overall randomized experiment; assignment-specific bases differ",
      "geography": "BCG participants; not a national workforce sample",
      "observation_period": "Study first released in 2023; published-study summary April 2026",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "source_title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "source_url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "source_locator": "Key Insight: AI as a Booster and Disruptor",
      "publication_date": "2026-04-09",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "GPT-4 access versus control, with a separate AI-plus-prompt-overview condition. Figures follow HBS’s 2026 institutional summary of the published study, not older working-paper quality estimates.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
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      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#consulting-quality",
      "evidence_type": "Randomized experiment",
      "qualifier": "approximately",
      "significance": null
    },
    {
      "stat_id": "strategy-control",
      "claim": "Strategy task: correct answers without AI: 84.5% (Study first released in 2023; published-study summary April 2026).",
      "value": 84.5,
      "unit": "percent",
      "population": "758 BCG consultants in the overall randomized experiment; assignment-specific bases differ",
      "geography": "BCG participants; not a national workforce sample",
      "observation_period": "Study first released in 2023; published-study summary April 2026",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "source_title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "source_url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "source_locator": "Key Insight: AI as a Booster and Disruptor, control condition",
      "publication_date": "2026-04-09",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "GPT-4 access versus control, with a separate AI-plus-prompt-overview condition. Figures follow HBS’s 2026 institutional summary of the published study, not older working-paper quality estimates.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
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      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#strategy-control",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "strategy-ai",
      "claim": "Strategy task: correct answers with GPT-4 access: 70.6% (Study first released in 2023; published-study summary April 2026).",
      "value": 70.6,
      "unit": "percent",
      "population": "758 BCG consultants in the overall randomized experiment; assignment-specific bases differ",
      "geography": "BCG participants; not a national workforce sample",
      "observation_period": "Study first released in 2023; published-study summary April 2026",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "source_title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "source_url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "source_locator": "Key Insight: AI as a Booster and Disruptor, GPT access condition",
      "publication_date": "2026-04-09",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "GPT-4 access versus control, with a separate AI-plus-prompt-overview condition. Figures follow HBS’s 2026 institutional summary of the published study, not older working-paper quality estimates.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
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      "cohort": null,
      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#strategy-ai",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "strategy-prompt",
      "claim": "Strategy task: correct answers with GPT-4 and prompt overview: 60% (Study first released in 2023; published-study summary April 2026).",
      "value": 60,
      "unit": "percent",
      "population": "758 BCG consultants in the overall randomized experiment; assignment-specific bases differ",
      "geography": "BCG participants; not a national workforce sample",
      "observation_period": "Study first released in 2023; published-study summary April 2026",
      "publisher": "Harvard Business School AI Institute / Dell’Acqua and colleagues",
      "source_title": "Back to the Beginnings of AI at Work: institutional review of the published consulting experiment",
      "source_url": "https://aiinstitute.hbs.edu/back-to-the-beginnings-of-ai-at-work/",
      "source_locator": "Key Insight: AI as a Booster and Disruptor, prompt-engineering condition",
      "publication_date": "2026-04-09",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "GPT-4 access versus control, with a separate AI-plus-prompt-overview condition. Figures follow HBS’s 2026 institutional summary of the published study, not older working-paper quality estimates.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
      "standard_error_locator": null,
      "cohort": null,
      "age_years": null,
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#strategy-prompt",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "copilot-time",
      "claim": "Copilot experiment: reduced completion time on one coding assignment: 55.8% (May 15–June 20, 2022).",
      "value": 55.8,
      "unit": "percent",
      "population": "95 professional programmers recruited through Upwork; 35 in each arm completed the task and survey",
      "geography": "International recruitment, mostly India and Pakistan",
      "observation_period": "May 15–June 20, 2022",
      "publisher": "Peng, Kalliamvakou, Cihon and Demirer",
      "source_title": "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot, arXiv v1",
      "source_url": "https://arxiv.org/pdf/2302.06590",
      "source_locator": "Pages 1–2, abstract and main estimate; pages 3–5, design",
      "publication_date": "2023-02-13",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Randomized GitHub Copilot access for a JavaScript HTTP-server assignment. Completion is test-based. Published 95% confidence interval for the time reduction: 21%–89%. Researchers include Microsoft and GitHub employees; task and attrition limit generalization.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
      "standard_error_locator": null,
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      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#copilot-time",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "metr-time",
      "claim": "METR early-2025 experiment: longer completion time with AI allowed: 19% (February–June 2025).",
      "value": 19,
      "unit": "percent",
      "population": "16 experienced open-source developers working on 246 real issues in familiar repositories",
      "geography": "Selected open-source developers; not geographically representative",
      "observation_period": "February–June 2025",
      "publisher": "METR",
      "source_title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity",
      "source_url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
      "source_locator": "Core Result",
      "publication_date": "2025-07-10",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Issues randomized to AI allowed or disallowed. Mainly Cursor Pro with Claude 3.5/3.7 Sonnet. Measured time effect differs from participant forecasts; 95% confidence interval for slowdown is 2%–39%, as restated in METR’s 2026 update.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
      "standard_error_percentage_points": null,
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      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#metr-time",
      "evidence_type": "Randomized experiment",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "metr-expected",
      "claim": "METR participants: anticipated AI speedup before the experiment: 24% (February–June 2025).",
      "value": 24,
      "unit": "percent",
      "population": "16 experienced open-source developers working on 246 real issues in familiar repositories",
      "geography": "Selected open-source developers; not geographically representative",
      "observation_period": "February–June 2025",
      "publisher": "METR",
      "source_title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity",
      "source_url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
      "source_locator": "Core Result, developer expectations",
      "publication_date": "2025-07-10",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Issues randomized to AI allowed or disallowed. Mainly Cursor Pro with Claude 3.5/3.7 Sonnet. Measured time effect differs from participant forecasts; 95% confidence interval for slowdown is 2%–39%, as restated in METR’s 2026 update.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
      "input_source_urls": [],
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#metr-expected",
      "evidence_type": "Participant perception",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "metr-perceived",
      "claim": "METR participants: perceived AI speedup after the experiment: 20% (February–June 2025).",
      "value": 20,
      "unit": "percent",
      "population": "16 experienced open-source developers working on 246 real issues in familiar repositories",
      "geography": "Selected open-source developers; not geographically representative",
      "observation_period": "February–June 2025",
      "publisher": "METR",
      "source_title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity",
      "source_url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
      "source_locator": "Core Result, after-study beliefs",
      "publication_date": "2025-07-10",
      "accessed_date": "2026-09-12",
      "primary_source": true,
      "method_notes": "Issues randomized to AI allowed or disallowed. Mainly Cursor Pro with Claude 3.5/3.7 Sonnet. Measured time effect differs from participant forecasts; 95% confidence interval for slowdown is 2%–39%, as restated in METR’s 2026 update.",
      "license_notes": "Selected factual observations paraphrased with attribution. No source report, chart, participant data or proprietary database is redistributed; source rights remain with its producer.",
      "status": "verified",
      "kind": "reported",
      "formula": null,
      "input_stat_ids": [],
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#metr-perceived",
      "evidence_type": "Participant perception",
      "qualifier": null,
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    },
    {
      "stat_id": "index-writing-time",
      "claim": "Writing experiment: calculated assisted-time index, control = 100: 60 (2023 experiment; published July 2023).",
      "value": 60,
      "unit": "index points",
      "population": "Index for Writing experiment; not a combined study population",
      "geography": "Study-specific recruitment; not a representative global worker sample",
      "observation_period": "2023 experiment; published July 2023",
      "publisher": "Workspace369 (calculation from cited primary inputs)",
      "source_title": "AI Productivity and Time-Savings Benchmarks: Workspace369 model",
      "source_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/",
      "source_locator": "Workspace369 calculation; see formula and input IDs in the download",
      "publication_date": "2026-09-12",
      "accessed_date": "2026-09-12",
      "primary_source": false,
      "method_notes": "100 × (1 − reported percentage reduction ÷ 100)",
      "license_notes": "Attribute Workspace369 calculations; original input source rights remain with their producers.",
      "status": "verified",
      "kind": "calculated",
      "formula": "100 × (1 − reported percentage reduction ÷ 100)",
      "input_stat_ids": [
        "writing-time"
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      "input_source_urls": [
        "https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714"
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      "standard_error_percentage_points": null,
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      "age_years": null,
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      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#index-writing-time",
      "evidence_type": "Normalized calculation",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "index-copilot-time",
      "claim": "Copilot assignment: calculated assisted-time index, control = 100: 44.2 (May 15–June 20, 2022).",
      "value": 44.2,
      "unit": "index points",
      "population": "Index for Copilot assignment; not a combined study population",
      "geography": "Study-specific recruitment; not a representative global worker sample",
      "observation_period": "May 15–June 20, 2022",
      "publisher": "Workspace369 (calculation from cited primary inputs)",
      "source_title": "AI Productivity and Time-Savings Benchmarks: Workspace369 model",
      "source_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/",
      "source_locator": "Workspace369 calculation; see formula and input IDs in the download",
      "publication_date": "2026-09-12",
      "accessed_date": "2026-09-12",
      "primary_source": false,
      "method_notes": "100 × (1 − reported percentage reduction ÷ 100)",
      "license_notes": "Attribute Workspace369 calculations; original input source rights remain with their producers.",
      "status": "verified",
      "kind": "calculated",
      "formula": "100 × (1 − reported percentage reduction ÷ 100)",
      "input_stat_ids": [
        "copilot-time"
      ],
      "input_source_urls": [
        "https://arxiv.org/pdf/2302.06590"
      ],
      "standard_error_percentage_points": null,
      "standard_error_locator": null,
      "cohort": null,
      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#index-copilot-time",
      "evidence_type": "Normalized calculation",
      "qualifier": null,
      "significance": null
    },
    {
      "stat_id": "index-metr-time",
      "claim": "METR repository tasks: calculated assisted-time index, control = 100: 119 (February–June 2025).",
      "value": 119,
      "unit": "index points",
      "population": "Index for METR repository tasks; not a combined study population",
      "geography": "Study-specific recruitment; not a representative global worker sample",
      "observation_period": "February–June 2025",
      "publisher": "Workspace369 (calculation from cited primary inputs)",
      "source_title": "AI Productivity and Time-Savings Benchmarks: Workspace369 model",
      "source_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/",
      "source_locator": "Workspace369 calculation; see formula and input IDs in the download",
      "publication_date": "2026-09-12",
      "accessed_date": "2026-09-12",
      "primary_source": false,
      "method_notes": "100 × (1 + reported percentage increase ÷ 100)",
      "license_notes": "Attribute Workspace369 calculations; original input source rights remain with their producers.",
      "status": "verified",
      "kind": "calculated",
      "formula": "100 × (1 + reported percentage increase ÷ 100)",
      "input_stat_ids": [
        "metr-time"
      ],
      "input_source_urls": [
        "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/"
      ],
      "standard_error_percentage_points": null,
      "standard_error_locator": null,
      "cohort": null,
      "age_years": null,
      "surviving_establishments": null,
      "canonical_url": "https://workspace369.com/research/ai-productivity-time-savings-benchmarks/#index-metr-time",
      "evidence_type": "Normalized calculation",
      "qualifier": null,
      "significance": null
    }
  ]
}