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{"parentUuid":"39c7cf05-118c-4915-8104-f69d013018af","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# T3-synthesis — Does generative-AI assistance shorten the time workers need to complete their tasks? A small evidence synthesis\n\n## Scientific question\nFive accessible primary studies report how access to a generative-AI assistant changed the performance of workers (software developers, IT administrators, security analysts) on their tasks. They report their effects under different definitions: a regression coefficient on log time-on-task, a percentage change in completion time, a percentage difference in mean task duration holding accuracy constant, a percentage time saving per processed item with a p-value, and a difference-in-differences coefficient on output counts around the withdrawal of access. This is a complete, bounded evidence-synthesis project: extraction with locators → harmonisation onto one stated definition → pooled estimates with heterogeneity → forest plot → an appropriately limited synthesis. The target definition is θ = ln(T_AI / T_noAI), the natural logarithm of the ratio of mean task-completion time with AI assistance to mean task-completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI). The estimands are: the five extracted values exactly as printed; the harmonised θ and its standard error for every study where both can be obtained from the printed quantities under the conventions below; the inverse-variance fixed-effect and DerSimonian–Laird random-effects pooled θ over the poolable studies, with Q, I² and τ²; and the synthesis conclusion that these numbers support.\n\n## Inputs\n- `data/studies.json` — the five items {\"id\", \"source\", \"short\", \"extract\"}: `extract` names the printed quantity to take from each source (which table/row/column or sentence).\n- `data/sources/arxiv-2410.12944v3.txt` — Paradis et al. (2024), *How much does AI impact development speed? An enterprise-based randomized controlled trial* (Google), CC BY 4.0: abstract, analytic approach, results Sections VI-B/VI-C with Table III (OLS on logged time on task; coefficients with standard errors in parentheses; the four model columns are collapsed into one line per row, see the extract's editorial note).\n- `data/sources/arxiv-2507.09089v2.txt` — Becker et al. (2025), *Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity* (METR), CC BY 4.0: abstract, effect-estimation section, opening of the results, Appendix D (regression, confidence intervals, conversion to speedup).\n- `data/sources/arxiv-2411.01067v2.txt` — Bono & Xu (2024), *Randomized Controlled Trials for Security Copilot for IT Administrators* (Microsoft), CC BY 4.0: abstract, RCT protocol, results on speed with Table 3 (task durations in minutes, holding accuracy constant; significance by asterisks only), Appendix B (how time savings are computed and bootstrapped).\n- `data/sources/arxiv-2511.13860v1.txt` — Bono (2025), *Randomized Controlled Trials for Phishing Triage Agent* (Microsoft), CC BY 4.0: abstract, methodology (three arms), results Sections 3.1 (true positives per analyst minute, Table 1) and 3.3 (Table 3, time per email with % time savings and p-values), Appendix B.\n- `data/sources/arxiv-2304.09339v1.txt` — Kreitmeir & Raschky (2023), *The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban*, CC BY 4.0: abstract, outcome definitions (Section 3.1), Section 4 with Table 1 (difference-in-differences coefficients, standard errors in parentheses).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. A quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper, in a figure, or elsewhere; do not substitute a remembered or guessed value.\n- Harmonisation conventions (state the one you apply in `transformation`):\n  - a coefficient b on an AI indicator in a regression whose dependent variable is the natural log of completion time gives θ = b with SE(θ) = SE(b); a 95% confidence interval [L, U] on that scale gives SE = (U − L)/(2 × 1.96);\n  - a percentage change in time c (e.g. \"+19%\" means c = 0.19, \"−29.79%\" means c = −0.2979) or a ratio of mean times T_AI / T_noAI gives θ = ln(1 + c) = ln(T_AI / T_noAI);\n  - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n  - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n  - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a \n\n... [2016 characters truncated] ...\n\nl), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ntotal 16\ndrwxr-xr-x  5 simon-pierreboucher  staff   160 oct.  30 00:28 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  30 00:32 ..\n-rw-r--r--  1 simon-pierreboucher  staff  3417 oct.  30 00:28 README.md\ndrwxr-xr-x  7 simon-pierreboucher  staff   224 oct.  30 00:28 sources\n-rw-r--r--  1 simon-pierreboucher  staff  1758 oct.  30 00:28 studies.json\nhead: Error reading data/sources\n==> data/README.md <==\n# Data provenance — T3-synthesis\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n\n==> data/sources <==\n\n==> data/studies.json <==\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n  \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n  \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},","is_error":true,"tool_use_id":"toolu_01J5e3qba7qzHMJvQjKPbbX5"}]},"uuid":"0226683f-93e3-436f-973f-8599fd9532e6","timestamp":"2026-10-30T05:29:13.431Z","toolUseResult":"Error: Exit code 1\n# T3-synthesis — Does generative-AI assistance shorten the time workers need to complete their tasks? A small evidence synthesis\n\n## Scientific question\nFive accessible primary studies report how access to a generative-AI assistant changed the performance of workers (software developers, IT administrators, security analysts) on their tasks. They report their effects under different definitions: a regression coefficient on log time-on-task, a percentage change in completion time, a percentage difference in mean task duration holding accuracy constant, a percentage time saving per processed item with a p-value, and a difference-in-differences coefficient on output counts around the withdrawal of access. This is a complete, bounded evidence-synthesis project: extraction with locators → harmonisation onto one stated definition → pooled estimates with heterogeneity → forest plot → an appropriately limited synthesis. The target definition is θ = ln(T_AI / T_noAI), the natural logarithm of the ratio of mean task-completion time with AI assistance to mean task-completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI). The estimands are: the five extracted values exactly as printed; the harmonised θ and its standard error for every study where both can be obtained from the printed quantities under the conventions below; the inverse-variance fixed-effect and DerSimonian–Laird random-effects pooled θ over the poolable studies, with Q, I² and τ²; and the synthesis conclusion that these numbers support.\n\n## Inputs\n- `data/studies.json` — the five items {\"id\", \"source\", \"short\", \"extract\"}: `extract` names the printed quantity to take from each source (which table/row/column or sentence).\n- `data/sources/arxiv-2410.12944v3.txt` — Paradis et al. (2024), *How much does AI impact development speed? An enterprise-based randomized controlled trial* (Google), CC BY 4.0: abstract, analytic approach, results Sections VI-B/VI-C with Table III (OLS on logged time on task; coefficients with standard errors in parentheses; the four model columns are collapsed into one line per row, see the extract's editorial note).\n- `data/sources/arxiv-2507.09089v2.txt` — Becker et al. (2025), *Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity* (METR), CC BY 4.0: abstract, effect-estimation section, opening of the results, Appendix D (regression, confidence intervals, conversion to speedup).\n- `data/sources/arxiv-2411.01067v2.txt` — Bono & Xu (2024), *Randomized Controlled Trials for Security Copilot for IT Administrators* (Microsoft), CC BY 4.0: abstract, RCT protocol, results on speed with Table 3 (task durations in minutes, holding accuracy constant; significance by asterisks only), Appendix B (how time savings are computed and bootstrapped).\n- `data/sources/arxiv-2511.13860v1.txt` — Bono (2025), *Randomized Controlled Trials for Phishing Triage Agent* (Microsoft), CC BY 4.0: abstract, methodology (three arms), results Sections 3.1 (true positives per analyst minute, Table 1) and 3.3 (Table 3, time per email with % time savings and p-values), Appendix B.\n- `data/sources/arxiv-2304.09339v1.txt` — Kreitmeir & Raschky (2023), *The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban*, CC BY 4.0: abstract, outcome definitions (Section 3.1), Section 4 with Table 1 (difference-in-differences coefficients, standard errors in parentheses).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. A quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper, in a figure, or elsewhere; do not substitute a remembered or guessed value.\n- Harmonisation conventions (state the one you apply in `transformation`):\n  - a coefficient b on an AI indicator in a regression whose dependent variable is the natural log of completion time gives θ = b with SE(θ) = SE(b); a 95% confidence interval [L, U] on that scale gives SE = (U − L)/(2 × 1.96);\n  - a percentage change in time c (e.g. \"+19%\" means c = 0.19, \"−29.79%\" means c = −0.2979) or a ratio of mean times T_AI / T_noAI gives θ = ln(1 + c) = ln(T_AI / T_noAI);\n  - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n  - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n  - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a \n\n... [2016 characters truncated] ...\n\nl), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ntotal 16\ndrwxr-xr-x  5 simon-pierreboucher  staff   160 oct.  30 00:28 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  30 00:32 ..\n-rw-r--r--  1 simon-pierreboucher  staff  3417 oct.  30 00:28 README.md\ndrwxr-xr-x  7 simon-pierreboucher  staff   224 oct.  30 00:28 sources\n-rw-r--r--  1 simon-pierreboucher  staff  1758 oct.  30 00:28 studies.json\nhead: Error reading data/sources\n==> data/README.md <==\n# Data provenance — T3-synthesis\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n\n==> data/sources <==\n\n==> data/studies.json <==\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. 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(2025), METR field RCT, experienced open-source developers, real issues\",\n  \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},","sandboxed":false,"sourceToolAssistantUUID":"39c7cf05-118c-4915-8104-f69d013018af","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"5553c80d-e087-487c-ab06-62ed59672a59","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WpNCTTJkoxuGNc2KwH32tr","type":"tool_result","content":"  - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n  - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n  - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a comparison of task-completion times with and without AI access on the same tasks (for example an effect on output counts, or a comparison that is not AI-versus-no-AI).\n- Pooling: inverse-variance fixed effect and DerSimonian–Laird random effects over the poolable studies only, on the θ scale, with 95% intervals using 1.96; Q with k − 1 degrees of freedom; I² = max(0, (Q − df)/Q) × 100; τ² = max(0, (Q − df)/C) with C = Σw − Σw²/Σw. `heterogeneity_interpretation` is \"low\" if I² < 25, \"moderate\" if 25 ≤ I² < 50, \"substantial\" if I² ≥ 50. `pooled_significant_5pct` is true only if the random-effects 95% interval excludes 0.\n- `feasibility` refers to the project as a whole: the project is complete when every item has either harmonised values or an explicit, justified statement of what cannot be obtained, and the pooled estimates are computed over the poolable items.\n\n## Required outputs\n- `out/table1.csv` — the extraction table, one row per study (5 rows): `study`, `estimate_as_printed`, `uncertainty_as_printed`, `sample_as_printed`, `definition`, `locator`, `harmonised_estimate`, `harmonised_se`, `harmonisable`, `poolable`, `transformation`, `reason_if_not`.\n- `out/figure1.png` — a forest plot of the harmonised estimates (all harmonisable studies; those without an SE shown as points without intervals) with the pooled random-effects estimate.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T3-synthesis\" |\n| `target_definition` | string | — | the harmonisation target in your words |\n| `items` | object | — | {\"S1\": {...}, …, \"S5\": {...}} with, for each study: `estimate_as_printed` (string, the printed numbers with their printed unit/sign), `uncertainty_as_printed` (string: SE, CI, p-value, stars, or \"none printed\"), `sample_as_printed` (string), `definition` (string), `locator` (string: source id, table/section, row, column), `harmonisable` (boolean), `harmonised_estimate` (number or null, θ), `harmonised_se` (number or null), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n  \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n  \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},\n {\"id\": \"S3\", \"source\": \"arxiv-2411.01067v2\", \"short\": \"Bono and Xu (2024), Microsoft Security Copilot RCT, IT administrators\",\n  \"extract\": \"the aggregate 'All Content' row of Table 3 (task duration in minutes, holding accuracy constant): the treatment and control durations and the printed improvement, together with whatever uncertainty is printed for it\"},\n {\"id\": \"S4\", \"source\": \"arxiv-2511.13860v1\", \"short\": \"Bono (2025), Microsoft phishing-triage agent RCT, security analysts\",\n  \"extract\": \"the 'Full Corpus' column of Table 3 (time per email): the Aware and Control times, the printed % time savings and its p-value\"},\n {\"id\": \"S5\", \"source\": \"arxiv-2304.09339v1\", \"short\": \"Kreitmeir and Raschky (2023), difference-in-differences around Italy's ChatGPT ban, GitHub users\",\n  \"extract\": \"the Treat x Post coefficient and standard error for the Output measure (column 3) in panel A of Table 1 (users with affiliation), and its sample size\"}\n]\n# Data provenance — T3-synthesis\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2410.12944v3.txt` | Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2024). How much does AI impact development speed? An enterprise-based randomized controlled trial. arXiv:2410.12944v3 [cs.SE]. https://arxiv.org/abs/2410.12944v3 (full text: https://arxiv.org/html/2410.12944v3) | 2026-10-30T04:19:39Z | CC BY 4.0 | d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b |\n| `sources/arxiv-2507.09089v2.txt` | Becker, J., Rush, N., Barnes, B., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv:2507.09089v2 [cs.AI]. https://arxiv.org/abs/2507.09089v2 (full text: https://arxiv.org/html/2507.09089v2) | 2026-10-30T04:19:45Z | CC BY 4.0 | b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d |\n| `sources/arxiv-2411.01067v2.txt` | Bono, J., & Xu, A. (2024). Randomized Controlled Trials for Security Copilot for IT Administrators. arXiv:2411.01067v2 [econ.GN]. https://arxiv.org/abs/2411.01067v2 (full text: https://arxiv.org/html/2411.01067v2) | 2026-10-30T04:20:32Z | CC BY 4.0 | 46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a |\n| `sources/arxiv-2511.13860v1.txt` | Bono, J. (2025). Randomized Controlled Trials for Phishing Triage Agent. arXiv:2511.13860v1 [econ.GN]. https://arxiv.org/abs/2511.13860v1 (full text: https://arxiv.org/html/2511.13860v1) | 2026-10-30T04:20:39Z | CC BY 4.0 | 8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 |\n| `sources/arxiv-2304.09339v1.txt` | Kreitmeir, D. H., & Raschky, P. A. (2023). The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban. arXiv:2304.09339v1 [econ.GN]. https://arxiv.org/abs/2304.09339v1 (full text: https://arxiv.org/html/2304.09339v1) | 2026-10-30T04:19:51Z | CC BY 4.0 | 1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e |\n\nTask-specific input file (prepared by the task designer, not observed data):\n\n- `studies.json` — the five study items (id, source extract, short description, and the printed quantity to extract for each), written by the task designer.\n\nRights: the five e-prints are distributed by arXiv under the Creative Commons Attribution 4.0 licence (the licence link is shown on each abstract page, verified 2026-10-30); they are reproduced in part with attribution. Stray lines such as \"p<\" or \"p=\" and footnote numbers on their own line are rendering artefacts of footnote markers in the HTML and carry no information.\n     108 data/sources/arxiv-2304.09339v1.txt\n     112 data/sources/arxiv-2410.12944v3.txt\n     109 data/sources/arxiv-2411.01067v2.txt\n     132 data/sources/arxiv-2507.09089v2.txt\n     162 data/sources/arxiv-2511.13860v1.txt\n     623 total","is_error":false}]},"uuid":"af1b9c36-a3b9-45fa-8973-080aed4f66d6","timestamp":"2026-10-30T05:29:15.237Z","toolUseResult":{"stdout":"  - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n  - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n  - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a comparison of task-completion times with and without AI access on the same tasks (for example an effect on output counts, or a comparison that is not AI-versus-no-AI).\n- Pooling: inverse-variance fixed effect and DerSimonian–Laird random effects over the poolable studies only, on the θ scale, with 95% intervals using 1.96; Q with k − 1 degrees of freedom; I² = max(0, (Q − df)/Q) × 100; τ² = max(0, (Q − df)/C) with C = Σw − Σw²/Σw. `heterogeneity_interpretation` is \"low\" if I² < 25, \"moderate\" if 25 ≤ I² < 50, \"substantial\" if I² ≥ 50. `pooled_significant_5pct` is true only if the random-effects 95% interval excludes 0.\n- `feasibility` refers to the project as a whole: the project is complete when every item has either harmonised values or an explicit, justified statement of what cannot be obtained, and the pooled estimates are computed over the poolable items.\n\n## Required outputs\n- `out/table1.csv` — the extraction table, one row per study (5 rows): `study`, `estimate_as_printed`, `uncertainty_as_printed`, `sample_as_printed`, `definition`, `locator`, `harmonised_estimate`, `harmonised_se`, `harmonisable`, `poolable`, `transformation`, `reason_if_not`.\n- `out/figure1.png` — a forest plot of the harmonised estimates (all harmonisable studies; those without an SE shown as points without intervals) with the pooled random-effects estimate.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T3-synthesis\" |\n| `target_definition` | string | — | the harmonisation target in your words |\n| `items` | object | — | {\"S1\": {...}, …, \"S5\": {...}} with, for each study: `estimate_as_printed` (string, the printed numbers with their printed unit/sign), `uncertainty_as_printed` (string: SE, CI, p-value, stars, or \"none printed\"), `sample_as_printed` (string), `definition` (string), `locator` (string: source id, table/section, row, column), `harmonisable` (boolean), `harmonised_estimate` (number or null, θ), `harmonised_se` (number or null), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n  \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n  \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},\n {\"id\": \"S3\", \"source\": \"arxiv-2411.01067v2\", \"short\": \"Bono and Xu (2024), Microsoft Security Copilot RCT, IT administrators\",\n  \"extract\": \"the aggregate 'All Content' row of Table 3 (task duration in minutes, holding accuracy constant): the treatment and control durations and the printed improvement, together with whatever uncertainty is printed for it\"},\n {\"id\": \"S4\", \"source\": \"arxiv-2511.13860v1\", \"short\": \"Bono (2025), Microsoft phishing-triage agent RCT, security analysts\",\n  \"extract\": \"the 'Full Corpus' column of Table 3 (time per email): the Aware and Control times, the printed % time savings and its p-value\"},\n {\"id\": \"S5\", \"source\": \"arxiv-2304.09339v1\", \"short\": \"Kreitmeir and Raschky (2023), difference-in-differences around Italy's ChatGPT ban, GitHub users\",\n  \"extract\": \"the Treat x Post coefficient and standard error for the Output measure (column 3) in panel A of Table 1 (users with affiliation), and its sample size\"}\n]\n# Data provenance — T3-synthesis\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2410.12944v3.txt` | Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2024). How much does AI impact development speed? An enterprise-based randomized controlled trial. arXiv:2410.12944v3 [cs.SE]. https://arxiv.org/abs/2410.12944v3 (full text: https://arxiv.org/html/2410.12944v3) | 2026-10-30T04:19:39Z | CC BY 4.0 | d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b |\n| `sources/arxiv-2507.09089v2.txt` | Becker, J., Rush, N., Barnes, B., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv:2507.09089v2 [cs.AI]. https://arxiv.org/abs/2507.09089v2 (full text: https://arxiv.org/html/2507.09089v2) | 2026-10-30T04:19:45Z | CC BY 4.0 | b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d |\n| `sources/arxiv-2411.01067v2.txt` | Bono, J., & Xu, A. (2024). Randomized Controlled Trials for Security Copilot for IT Administrators. arXiv:2411.01067v2 [econ.GN]. https://arxiv.org/abs/2411.01067v2 (full text: https://arxiv.org/html/2411.01067v2) | 2026-10-30T04:20:32Z | CC BY 4.0 | 46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a |\n| `sources/arxiv-2511.13860v1.txt` | Bono, J. (2025). Randomized Controlled Trials for Phishing Triage Agent. arXiv:2511.13860v1 [econ.GN]. https://arxiv.org/abs/2511.13860v1 (full text: https://arxiv.org/html/2511.13860v1) | 2026-10-30T04:20:39Z | CC BY 4.0 | 8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 |\n| `sources/arxiv-2304.09339v1.txt` | Kreitmeir, D. H., & Raschky, P. A. (2023). The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban. arXiv:2304.09339v1 [econ.GN]. https://arxiv.org/abs/2304.09339v1 (full text: https://arxiv.org/html/2304.09339v1) | 2026-10-30T04:19:51Z | CC BY 4.0 | 1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e |\n\nTask-specific input file (prepared by the task designer, not observed data):\n\n- `studies.json` — the five study items (id, source extract, short description, and the printed quantity to extract for each), written by the task designer.\n\nRights: the five e-prints are distributed by arXiv under the Creative Commons Attribution 4.0 licence (the licence link is shown on each abstract page, verified 2026-10-30); they are reproduced in part with attribution. Stray lines such as \"p<\" or \"p=\" and footnote numbers on their own line are rendering artefacts of footnote markers in the HTML and carry no information.\n     108 data/sources/arxiv-2304.09339v1.txt\n     112 data/sources/arxiv-2410.12944v3.txt\n     109 data/sources/arxiv-2411.01067v2.txt\n     132 data/sources/arxiv-2507.09089v2.txt\n     162 data/sources/arxiv-2511.13860v1.txt\n     623 total","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5553c80d-e087-487c-ab06-62ed59672a59","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"641f8683-48d0-4bcf-9e5b-0e4e9059336f","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XzQtonfAVvpv1qfX2f9qN7","type":"tool_result","content":"| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n     1\tSOURCE EXTRACT — arxiv-2410.12944v3\n     2\tTitle: How much does AI impact development speed? An enterprise-based randomized controlled trial\n     3\tAuthors: Elise Paradis; Kate Grey; Quinn Madison; Daye Nam; Andrew Macvean; Vahid Meimand; Nan Zhang; Ben Ferrari-Church; Satish Chandra (Google)\n     4\tBibliographic record: arXiv:2410.12944v3 [cs.SE], version 3 submitted 11 Nov 2024. Abstract page: https://arxiv.org/abs/2410.12944v3\n     5\tRetrieved: https://arxiv.org/html/2410.12944 on 2026-10-30T04:19:39Z (SHA-256 of the retrieved HTML: 2a5507903fe618f4cd8f91c0268ced5032a265742e9d7c18fa3d45c1170fe2ec)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n     8\tOmitted: Sections I-III, IV-A, IV-B, V, VI-A, VI-D, VII-IX, Tables I-II, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: in the HTML rendering of Table III the four model columns are collapsed into one cell per row (values for Models 1-4 appear left to right on each line, followed by the variable name); the line below each coefficient line holds the standard errors in parentheses in the same order. Significance markers: '.' p<.1, '*' p<.05, '**' p<.01, '***' p<.001.\n    11\t================================================================\n    12\t\n    13\t# How much does AI impact development speed?\n    14\t\n    15\tAn enterprise-based randomized controlled trial\n    16\t\n    17\tElise Paradis, Kate Grey, Quinn Madison, Daye Nam,\n    18\t\n    19\tAndrew Macvean, Vahid Meimand, Nan Zhang, Ben Ferrari-Church, Satish Chandra\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tHow much does AI assistance impact developer productivity? To date, the software engineering literature has provided a range of answers, targeting a diversity of outcomes: from perceived productivity to speed on task and developer throughput. Our randomized controlled trial with 96 full-time Google software engineers contributes to this literature by sharing an estimate of the impact of three AI features on the time developers spent on a complex, enterprise-grade task. We found that AI significantly shortened the time developers spent on task. Our best estimate of the size of this effect, controlling for factors known to influence developer time on task, stands at about 21%, although our confidence interval is large. We also found an interesting effect whereby developers who spend more hours on code-related activities per day were faster with AI. Product and future research considerations are discussed. In particular, we invite further research that explores the impact of AI at the ecosystem level and across multiple suites of AI-enhanced tools, since we cannot assume that the effect size obtained in our lab study will necessarily apply more broadly, or that the effect of AI found using internal Google tooling in the summer of 2024 will translate across tools and over time.\n    24\t\n    25\t### IV-C Analytic approach\n    26\t\n    27\tWe ran a two-tailed Student t-test ( \\alpha=0.05 ) on logged time on task, comparing mean time for participants in the control versus experimental groups.\n    28\t\n    29\tTo explore the robustness of our estimate and test our theoretical model, we ran three follow up linear regressions: one with the complete framework, i.e. with the experimental condition, developer-level factors, and task-level factors on logged time on task; one with only the experimental condition and developer-level factors; and one with only the experimental condition and task-level factors.\n    30\t\n    31\tThe full framework can be represented by the following equation:\n    32\t\n    33\t\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n    34\t\n    35\t\\displaystyle+\\beta_{3}*Level+\\beta_{4}*NbrHighFreqAreas | | |\n    36\t\n    37\t\\displaystyle+\\beta_{5}*TopLangCpp+\\beta_{6}*DataLogExp+\\epsilon | | |\n    38\t\n    39\tGiven the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n    40\t\n    41\tFinally, to explore interaction effects between experimental condition and developer-level factors, we ran three separate linear regressions with interaction effects, one for each covariate: average hours coded daily, level (seniority), and frequency of use of AI tooling.\n    42\t\n    43\tTo identify the best model, we used adjusted R^{2} values and model-level p values.\n    44\t\n    45\t### VI-B RQ1: Impact of AI tools on developer task speed\n    46\t\n    47\tFigure 7 shows the time distribution by experimental condition in a box and whisker plot.\n    48\tOn average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n    49\t\n    50\tp<\n    51\t\n    52\t### VI-C RQ2: Impact of covariates on main effect\n    53\t\n    54\tTable III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n    55\t2\n    56\t\n    57\tFrom Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI  =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n    58\tHowever, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24;   95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n    59\t\n    60\tControlling for experimental condition and other model covariates, average programming hours per day was significantly associated with time spent on task, with participants reporting five or more hours of development work per day being 32% ( \\beta=-0.38 ) faster than participants reporting zero to four hours of development work per day. Similarly, higher levels of seniority were also associated with a decrease in time on task, with one increased level (from 4 to 5, or 5 to 6, for example) being associated with a 15% ( \\beta=-0.16 ) decrease in time spent on task.\n    61\t\n    62\tOther covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n    63\t\n    64\tp=\n    65\t\n    66\tTABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n    67\t\n    68\tComplete Dev Task Interaction | | | |\n    69\t\n    70\t(1) (2) (3) (4) | | | |\n    71\t\n    72\t5.36*** 5.41*** 4.55*** 4.66*** Constant | | | |\n    73\t\n    74\t(0.42) (0.37) (0.19) (0.11) | | | |\n    75\t\n    76\t-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n    77\t\n    78\t(0.14) (0.14) (0.14) (0.17) | | | |\n    79\t\n    80\t-0.38* -0.38* -0.16 AvgProgHrsDay | | | |\n    81\t\n    82\t(0.15) (0.15) (0.22) | | | |\n    83\t\n    84\t-0.16. -0.16* Level | | | |\n    85\t\n    86\t(0.08) (0.08) | | | |\n    87\t\n    88\t-0.01 0.00 NbrHighFreqAreas | | | |\n    89\t\n    90\t(0.03) (0.03) | | | |\n    91\t\n    92\t0.02 0.06 TopLangCpp | | | |\n    93\t\n    94\t(0.15) (0.15) | | | |\n    95\t\n    96\t0.05 0.02 DataLogExp | | | |\n    97\t\n    98\t(0.09) (0.09) | | | |\n    99\t\n   100\t-0.29 EC:APHD | | | |\n   101\t\n   102\t(0.29) | | | |\n   103\t\n   104\tR^{2} 0.078 0.095 0.017 0.075 Adj. | | | |\n   105\t\n   106\t0.040 0.011 0.209 0.018 p value | | | |\n   107\t\n   108\tp< p< p< p< .: .1 *: .05. **: .01. ***: 0.001 | | | |\n   109\t\n   110\tNote: Estimates are for our logged dependent variable. The effect of each | | | |\n   111\t\n   112\tcovariate on time on task can be calculated by exponentiating the estimate. | | | |","is_error":false}]},"uuid":"43a888f3-595f-4a2c-9f96-4218a57717c0","timestamp":"2026-10-30T05:29:16.752Z","toolUseResult":{"stdout":"| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n     1\tSOURCE EXTRACT — arxiv-2410.12944v3\n     2\tTitle: How much does AI impact development speed? An enterprise-based randomized controlled trial\n     3\tAuthors: Elise Paradis; Kate Grey; Quinn Madison; Daye Nam; Andrew Macvean; Vahid Meimand; Nan Zhang; Ben Ferrari-Church; Satish Chandra (Google)\n     4\tBibliographic record: arXiv:2410.12944v3 [cs.SE], version 3 submitted 11 Nov 2024. Abstract page: https://arxiv.org/abs/2410.12944v3\n     5\tRetrieved: https://arxiv.org/html/2410.12944 on 2026-10-30T04:19:39Z (SHA-256 of the retrieved HTML: 2a5507903fe618f4cd8f91c0268ced5032a265742e9d7c18fa3d45c1170fe2ec)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n     8\tOmitted: Sections I-III, IV-A, IV-B, V, VI-A, VI-D, VII-IX, Tables I-II, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: in the HTML rendering of Table III the four model columns are collapsed into one cell per row (values for Models 1-4 appear left to right on each line, followed by the variable name); the line below each coefficient line holds the standard errors in parentheses in the same order. Significance markers: '.' p<.1, '*' p<.05, '**' p<.01, '***' p<.001.\n    11\t================================================================\n    12\t\n    13\t# How much does AI impact development speed?\n    14\t\n    15\tAn enterprise-based randomized controlled trial\n    16\t\n    17\tElise Paradis, Kate Grey, Quinn Madison, Daye Nam,\n    18\t\n    19\tAndrew Macvean, Vahid Meimand, Nan Zhang, Ben Ferrari-Church, Satish Chandra\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tHow much does AI assistance impact developer productivity? To date, the software engineering literature has provided a range of answers, targeting a diversity of outcomes: from perceived productivity to speed on task and developer throughput. Our randomized controlled trial with 96 full-time Google software engineers contributes to this literature by sharing an estimate of the impact of three AI features on the time developers spent on a complex, enterprise-grade task. We found that AI significantly shortened the time developers spent on task. Our best estimate of the size of this effect, controlling for factors known to influence developer time on task, stands at about 21%, although our confidence interval is large. We also found an interesting effect whereby developers who spend more hours on code-related activities per day were faster with AI. Product and future research considerations are discussed. In particular, we invite further research that explores the impact of AI at the ecosystem level and across multiple suites of AI-enhanced tools, since we cannot assume that the effect size obtained in our lab study will necessarily apply more broadly, or that the effect of AI found using internal Google tooling in the summer of 2024 will translate across tools and over time.\n    24\t\n    25\t### IV-C Analytic approach\n    26\t\n    27\tWe ran a two-tailed Student t-test ( \\alpha=0.05 ) on logged time on task, comparing mean time for participants in the control versus experimental groups.\n    28\t\n    29\tTo explore the robustness of our estimate and test our theoretical model, we ran three follow up linear regressions: one with the complete framework, i.e. with the experimental condition, developer-level factors, and task-level factors on logged time on task; one with only the experimental condition and developer-level factors; and one with only the experimental condition and task-level factors.\n    30\t\n    31\tThe full framework can be represented by the following equation:\n    32\t\n    33\t\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n    34\t\n    35\t\\displaystyle+\\beta_{3}*Level+\\beta_{4}*NbrHighFreqAreas | | |\n    36\t\n    37\t\\displaystyle+\\beta_{5}*TopLangCpp+\\beta_{6}*DataLogExp+\\epsilon | | |\n    38\t\n    39\tGiven the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n    40\t\n    41\tFinally, to explore interaction effects between experimental condition and developer-level factors, we ran three separate linear regressions with interaction effects, one for each covariate: average hours coded daily, level (seniority), and frequency of use of AI tooling.\n    42\t\n    43\tTo identify the best model, we used adjusted R^{2} values and model-level p values.\n    44\t\n    45\t### VI-B RQ1: Impact of AI tools on developer task speed\n    46\t\n    47\tFigure 7 shows the time distribution by experimental condition in a box and whisker plot.\n    48\tOn average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n    49\t\n    50\tp<\n    51\t\n    52\t### VI-C RQ2: Impact of covariates on main effect\n    53\t\n    54\tTable III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n    55\t2\n    56\t\n    57\tFrom Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI  =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n    58\tHowever, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24;   95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n    59\t\n    60\tControlling for experimental condition and other model covariates, average programming hours per day was significantly associated with time spent on task, with participants reporting five or more hours of development work per day being 32% ( \\beta=-0.38 ) faster than participants reporting zero to four hours of development work per day. Similarly, higher levels of seniority were also associated with a decrease in time on task, with one increased level (from 4 to 5, or 5 to 6, for example) being associated with a 15% ( \\beta=-0.16 ) decrease in time spent on task.\n    61\t\n    62\tOther covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n    63\t\n    64\tp=\n    65\t\n    66\tTABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n    67\t\n    68\tComplete Dev Task Interaction | | | |\n    69\t\n    70\t(1) (2) (3) (4) | | | |\n    71\t\n    72\t5.36*** 5.41*** 4.55*** 4.66*** Constant | | | |\n    73\t\n    74\t(0.42) (0.37) (0.19) (0.11) | | | |\n    75\t\n    76\t-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n    77\t\n    78\t(0.14) (0.14) (0.14) (0.17) | | | |\n    79\t\n    80\t-0.38* -0.38* -0.16 AvgProgHrsDay | | | |\n    81\t\n    82\t(0.15) (0.15) (0.22) | | | |\n    83\t\n    84\t-0.16. -0.16* Level | | | |\n    85\t\n    86\t(0.08) (0.08) | | | |\n    87\t\n    88\t-0.01 0.00 NbrHighFreqAreas | | | |\n    89\t\n    90\t(0.03) (0.03) | | | |\n    91\t\n    92\t0.02 0.06 TopLangCpp | | | |\n    93\t\n    94\t(0.15) (0.15) | | | |\n    95\t\n    96\t0.05 0.02 DataLogExp | | | |\n    97\t\n    98\t(0.09) (0.09) | | | |\n    99\t\n   100\t-0.29 EC:APHD | | | |\n   101\t\n   102\t(0.29) | | | |\n   103\t\n   104\tR^{2} 0.078 0.095 0.017 0.075 Adj. | | | |\n   105\t\n   106\t0.040 0.011 0.209 0.018 p value | | | |\n   107\t\n   108\tp< p< p< p< .: .1 *: .05. **: .01. ***: 0.001 | | | |\n   109\t\n   110\tNote: Estimates are for our logged dependent variable. The effect of each | | | |\n   111\t\n   112\tcovariate on time on task can be calculated by exponentiating the estimate. | | | |","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"641f8683-48d0-4bcf-9e5b-0e4e9059336f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"7d1af58b-f820-46e4-be9a-1b728c7cfc95","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GnwK11UKMmMep2VEEb4WPo","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2507.09089v2\n     2\tTitle: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n     3\tAuthors: Joel Becker; Nate Rush; Beth Barnes; David Rein (Model Evaluation & Threat Research, METR)\n     4\tBibliographic record: arXiv:2507.09089v2 [cs.AI], version 2 submitted 25 Jul 2025. Abstract page: https://arxiv.org/abs/2507.09089v2\n     5\tRetrieved: https://arxiv.org/html/2507.09089 on 2026-10-30T04:19:45Z (SHA-256 of the retrieved HTML: e860161026afa19c69e7ba332f5cccc8fec6b8d74865868db1316a364d270c6a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 2.3 'Effect Estimation'; the opening of Section 3 'Results' and Section 3.1 'Forecasts' (figure captions included as text); Appendix D.1 'Regression', D.2 'Confidence intervals' and D.3 'Converting to speedup'.\n     8\tOmitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n    13\t\n    14\tJoel Becker^*\n    15\t\n    16\t  \n    17\tNate Rush^*\n    18\t\n    19\t  \n    20\tBeth Barnes\n    21\t\n    22\t  \n    23\tDavid Rein\n    24\t\n    25\tAffiliation: Model Evaluation & Threat Research (METR)\n    26\t\n    27\t###### Abstract\n    28\t\n    29\tDespite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February–June 2025 frontier affect the productivity of experienced open-source developers.\n    30\t16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience.\n    31\tEach task is randomly assigned to allow or disallow usage of early-2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet.\n    32\tBefore starting tasks, developers forecast that allowing AI will reduce completion time by 24%.\n    33\tAfter completing the study, developers estimate that allowing AI reduced completion time by 20%.\n    34\tSurprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n    35\tThis slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter).\n    36\tTo understand this result, we collect and evaluate evidence for 21 properties of our setting that a priori could contribute to the observed slowdown effect—for example, the size and quality standards of projects, or prior developer experience with AI tooling.\n    37\tAlthough the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.\n    38\t\n    39\t^1^1footnotetext: Equal contribution. Correspondence to {nate, joel}@metr.org\n    40\t\n    41\t#### 2.3 Effect Estimation\n    42\t\n    43\tWe aim to measure the percentage change in issue time to completion due to AI, defined as:\n    44\t\n    45\tS=\\frac{\\mathbb{E}[\\text{completion time with AI allowed}]}{\\mathbb{E}[\\text{completion time with AI disallowed}]}-1 | |\n    46\t\n    47\tA value of -50% would indicate issues assigned to AI-allowed taking half the time of issues assigned to AI-disallowed; 0% would indicate that issues take the same time to complete with and without AI-allowed; 100% would indicate that issues assigned to AI-allowed take twice as long as issues assigned to AI-disallowed. For convenience and clarity, we typically report the absolute value of S , and colloquially refer to negative values of as speedup, and positive values as slowdown^8^8\n    48\t8\n    49\t\n    50\tThis is a mild abuse of language, because speedup and slowdown refer to rates, while the quantity we actually estimate and report is the percentage change in time from AI.\n    51\t\n    52\tWe use the total implementation time (including both pre- and post-pull request review time) developers self-report spending on each issue to estimate the percentage change in issue completion time due to AI. A small number of issues do not end up getting reviewed/merged for various reasons—we impute the post-review implementation time of these issues with the mean post-review time per treatment condition. Section C.3.4 shows that our results are robust to the specific imputation method used.\n    53\t\n    54\tWe estimate S via a log-linear regression for total implementation time, incorporating estimates of issue difficulty to reduce noise in our estimate. We also use adapted versions of this regression when examining how performance varies in different subsets of the data (to efficiently account for any post-randomization issue difficulty imbalances that may be more pronounced within subsets). See Appendix D for details of the regression approach used.\n    55\t\n    56\t### 3 Results\n    57\t\n    58\tFigure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n    59\t\n    60\tFigure 5: Empirical cumulative distribution functions of observed implementation times. Percentile ordering is calculated separately for each treatment group.\n    61\t\n    62\tDevelopers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n    63\t\n    64\t#### 3.1 Forecasts\n    65\t\n    66\t###### Developer Forecasts and Post hoc Estimates\n    67\t\n    68\tBefore developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n    69\tFigure 4 displays the raw average forecasted and observed implementation times^12^12\n    70\t12\n    71\t\n    72\tThe raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n    73\t\n    74\tDespite developers forecasting speedup from AI (while they are slowed down), developer forecasts are informative about completion time—the Pearson correlation between the time developers forecast AI-allowed issues taking and the actual time they take is 0.64, and the correlation between the time developers forecast AI-disallowed issues taking and the actual time they take is 0.59. This suggests that developers are broadly well-calibrated on the relative amount of time that issues will take, but their expectations regarding the usefulness of AI assistance are reversed.\n    75\t\n    76\t#### D.1 Regression\n    77\t\n    78\tFor each issue i we observe the realised completion time T_{i}>0 , a binary treatment flag \\text{AI}_{i}\\in\\{0,1\\} , and the developer’s ex-ante forecast of how long the task would take without AI, denoted \\widehat{T}^{\\text{NoAI}}_{i}>0 .\n    79\t\n    80\tWe estimate the log-linear model\n    81\t\n    82\t\\log T_{i}=\\alpha+\\beta\\,\\text{AI}_{i}+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}}_{i}+\\varepsilon_{i}, | | | (1)\n    83\t\n    84\tvia ordinary least squares. Random assignment of \\text{AI}_{i} guarantees consistency of \\hat{\\beta} for \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0] .\n    85\t\n    86\tWe include forecasts as a control variable because they serve as a proxy for issue difficulty and are highly predictive of completion times. This substantially increases our statistical power without introducing bias, as forecasts were elicited prior to treatment assignment and thus cannot be affected by treatment status.^18^18\n    87\t18\n    88\t\n    89\tWe do not generally include developer fixed effects because they explain minimal variation in the outcome conditional on forecasts. Section C.3.5 displays estimates from a regression specification including developer fixed effects.\n    90\t\n    91\tFigure 14: Regression diagnostics associated with the regression specification in equation (1).\n    92\t\n    93\tFigure 14displays regression diagnostics associated with this specification.\n    94\t\n    95\t#### D.2 Confidence intervals\n    96\t\n    97\tBy default, we report 95% confidence intervals using HC3 standard errors.^19^19\n    98\t19\n    99\t\n   100\tGiven that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n   101\t\n   102\tFigure 15: Speedup calculated using the regression specification in equation (1) and alternative uncertainty estimation procedures. CR2 and CR3 standard errors are bias-corrected cluster-robust standard errors Colin Cameron and Miller [2015], Bell and Mccaffrey [2002]. Two-stage bootstrap resamples developers, then issues within developers.\n   103\t\n   104\t#### D.3 Converting to speedup\n   105\t\n   106\tTaking conditional expectations of equation (1) given treatment status and forecast:\n   107\t\n   108\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\beta+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (2)\n   109\t\n   110\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (3)\n   111\t\n   112\tTherefore, \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] represents the average treatment effect on the log scale, conditional on forecast.\n   113\t\n   114\tTo convert to a speedup measure, we note that:\n   115\t\n   116\t\\displaystyle\\exp(\\beta) \\displaystyle=\\frac{\\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]}{\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]}\\quad | | | | (4)\n   117\t\n   118\t\\displaystyle=\\exp(\\beta)-1 | S | | | (5)\n   119\t\n   120\t(Step (4) holds provided the disturbance \\varepsilon_{i} is independent of both \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} and that \\mathbb{E}\\!\\left[e^{\\varepsilon_{i}}\\right] exists.^20^20\n   121\t20\n   122\t\n   123\tBecause T_{i}=e^{\\alpha}e^{\\beta\\,\\text{AI}_{i}}\\,(\\widehat{T}^{\\text{NoAI}}_{i})^{\\delta}\\,e^{\\varepsilon_{i}} , taking expectations conditional on \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} gives \\mathbb{E}[T\\,|\\,\\text{AI}=j,\\widehat{T}^{\\text{NoAI}}]=e^{(\\alpha+\\beta j)}\\,(\\widehat{T}^{\\text{NoAI}})^{\\delta}\\,\\mathbb{E}[e^{\\varepsilon_{i}}] .\n   124\tThe common factor \\mathbb{E}[e^{\\varepsilon_{i}}] cancels when we form the ratio of these conditional means, yielding \\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]\\big/\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]=\\exp(\\beta) .)\n   125\t\n   126\tThen under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n   127\t\n   128\t\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n   129\t\n   130\tAs \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n   131\t\n   132\t\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","is_error":false}]},"uuid":"709dfbd3-67ca-4e6b-812c-2b7f93724e61","timestamp":"2026-10-30T05:29:17.167Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2507.09089v2\n     2\tTitle: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n     3\tAuthors: Joel Becker; Nate Rush; Beth Barnes; David Rein (Model Evaluation & Threat Research, METR)\n     4\tBibliographic record: arXiv:2507.09089v2 [cs.AI], version 2 submitted 25 Jul 2025. Abstract page: https://arxiv.org/abs/2507.09089v2\n     5\tRetrieved: https://arxiv.org/html/2507.09089 on 2026-10-30T04:19:45Z (SHA-256 of the retrieved HTML: e860161026afa19c69e7ba332f5cccc8fec6b8d74865868db1316a364d270c6a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 2.3 'Effect Estimation'; the opening of Section 3 'Results' and Section 3.1 'Forecasts' (figure captions included as text); Appendix D.1 'Regression', D.2 'Confidence intervals' and D.3 'Converting to speedup'.\n     8\tOmitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n    13\t\n    14\tJoel Becker^*\n    15\t\n    16\t  \n    17\tNate Rush^*\n    18\t\n    19\t  \n    20\tBeth Barnes\n    21\t\n    22\t  \n    23\tDavid Rein\n    24\t\n    25\tAffiliation: Model Evaluation & Threat Research (METR)\n    26\t\n    27\t###### Abstract\n    28\t\n    29\tDespite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February–June 2025 frontier affect the productivity of experienced open-source developers.\n    30\t16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience.\n    31\tEach task is randomly assigned to allow or disallow usage of early-2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet.\n    32\tBefore starting tasks, developers forecast that allowing AI will reduce completion time by 24%.\n    33\tAfter completing the study, developers estimate that allowing AI reduced completion time by 20%.\n    34\tSurprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n    35\tThis slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter).\n    36\tTo understand this result, we collect and evaluate evidence for 21 properties of our setting that a priori could contribute to the observed slowdown effect—for example, the size and quality standards of projects, or prior developer experience with AI tooling.\n    37\tAlthough the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.\n    38\t\n    39\t^1^1footnotetext: Equal contribution. Correspondence to {nate, joel}@metr.org\n    40\t\n    41\t#### 2.3 Effect Estimation\n    42\t\n    43\tWe aim to measure the percentage change in issue time to completion due to AI, defined as:\n    44\t\n    45\tS=\\frac{\\mathbb{E}[\\text{completion time with AI allowed}]}{\\mathbb{E}[\\text{completion time with AI disallowed}]}-1 | |\n    46\t\n    47\tA value of -50% would indicate issues assigned to AI-allowed taking half the time of issues assigned to AI-disallowed; 0% would indicate that issues take the same time to complete with and without AI-allowed; 100% would indicate that issues assigned to AI-allowed take twice as long as issues assigned to AI-disallowed. For convenience and clarity, we typically report the absolute value of S , and colloquially refer to negative values of as speedup, and positive values as slowdown^8^8\n    48\t8\n    49\t\n    50\tThis is a mild abuse of language, because speedup and slowdown refer to rates, while the quantity we actually estimate and report is the percentage change in time from AI.\n    51\t\n    52\tWe use the total implementation time (including both pre- and post-pull request review time) developers self-report spending on each issue to estimate the percentage change in issue completion time due to AI. A small number of issues do not end up getting reviewed/merged for various reasons—we impute the post-review implementation time of these issues with the mean post-review time per treatment condition. Section C.3.4 shows that our results are robust to the specific imputation method used.\n    53\t\n    54\tWe estimate S via a log-linear regression for total implementation time, incorporating estimates of issue difficulty to reduce noise in our estimate. We also use adapted versions of this regression when examining how performance varies in different subsets of the data (to efficiently account for any post-randomization issue difficulty imbalances that may be more pronounced within subsets). See Appendix D for details of the regression approach used.\n    55\t\n    56\t### 3 Results\n    57\t\n    58\tFigure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n    59\t\n    60\tFigure 5: Empirical cumulative distribution functions of observed implementation times. Percentile ordering is calculated separately for each treatment group.\n    61\t\n    62\tDevelopers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n    63\t\n    64\t#### 3.1 Forecasts\n    65\t\n    66\t###### Developer Forecasts and Post hoc Estimates\n    67\t\n    68\tBefore developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n    69\tFigure 4 displays the raw average forecasted and observed implementation times^12^12\n    70\t12\n    71\t\n    72\tThe raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n    73\t\n    74\tDespite developers forecasting speedup from AI (while they are slowed down), developer forecasts are informative about completion time—the Pearson correlation between the time developers forecast AI-allowed issues taking and the actual time they take is 0.64, and the correlation between the time developers forecast AI-disallowed issues taking and the actual time they take is 0.59. This suggests that developers are broadly well-calibrated on the relative amount of time that issues will take, but their expectations regarding the usefulness of AI assistance are reversed.\n    75\t\n    76\t#### D.1 Regression\n    77\t\n    78\tFor each issue i we observe the realised completion time T_{i}>0 , a binary treatment flag \\text{AI}_{i}\\in\\{0,1\\} , and the developer’s ex-ante forecast of how long the task would take without AI, denoted \\widehat{T}^{\\text{NoAI}}_{i}>0 .\n    79\t\n    80\tWe estimate the log-linear model\n    81\t\n    82\t\\log T_{i}=\\alpha+\\beta\\,\\text{AI}_{i}+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}}_{i}+\\varepsilon_{i}, | | | (1)\n    83\t\n    84\tvia ordinary least squares. Random assignment of \\text{AI}_{i} guarantees consistency of \\hat{\\beta} for \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0] .\n    85\t\n    86\tWe include forecasts as a control variable because they serve as a proxy for issue difficulty and are highly predictive of completion times. This substantially increases our statistical power without introducing bias, as forecasts were elicited prior to treatment assignment and thus cannot be affected by treatment status.^18^18\n    87\t18\n    88\t\n    89\tWe do not generally include developer fixed effects because they explain minimal variation in the outcome conditional on forecasts. Section C.3.5 displays estimates from a regression specification including developer fixed effects.\n    90\t\n    91\tFigure 14: Regression diagnostics associated with the regression specification in equation (1).\n    92\t\n    93\tFigure 14displays regression diagnostics associated with this specification.\n    94\t\n    95\t#### D.2 Confidence intervals\n    96\t\n    97\tBy default, we report 95% confidence intervals using HC3 standard errors.^19^19\n    98\t19\n    99\t\n   100\tGiven that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n   101\t\n   102\tFigure 15: Speedup calculated using the regression specification in equation (1) and alternative uncertainty estimation procedures. CR2 and CR3 standard errors are bias-corrected cluster-robust standard errors Colin Cameron and Miller [2015], Bell and Mccaffrey [2002]. Two-stage bootstrap resamples developers, then issues within developers.\n   103\t\n   104\t#### D.3 Converting to speedup\n   105\t\n   106\tTaking conditional expectations of equation (1) given treatment status and forecast:\n   107\t\n   108\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\beta+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (2)\n   109\t\n   110\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (3)\n   111\t\n   112\tTherefore, \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] represents the average treatment effect on the log scale, conditional on forecast.\n   113\t\n   114\tTo convert to a speedup measure, we note that:\n   115\t\n   116\t\\displaystyle\\exp(\\beta) \\displaystyle=\\frac{\\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]}{\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]}\\quad | | | | (4)\n   117\t\n   118\t\\displaystyle=\\exp(\\beta)-1 | S | | | (5)\n   119\t\n   120\t(Step (4) holds provided the disturbance \\varepsilon_{i} is independent of both \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} and that \\mathbb{E}\\!\\left[e^{\\varepsilon_{i}}\\right] exists.^20^20\n   121\t20\n   122\t\n   123\tBecause T_{i}=e^{\\alpha}e^{\\beta\\,\\text{AI}_{i}}\\,(\\widehat{T}^{\\text{NoAI}}_{i})^{\\delta}\\,e^{\\varepsilon_{i}} , taking expectations conditional on \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} gives \\mathbb{E}[T\\,|\\,\\text{AI}=j,\\widehat{T}^{\\text{NoAI}}]=e^{(\\alpha+\\beta j)}\\,(\\widehat{T}^{\\text{NoAI}})^{\\delta}\\,\\mathbb{E}[e^{\\varepsilon_{i}}] .\n   124\tThe common factor \\mathbb{E}[e^{\\varepsilon_{i}}] cancels when we form the ratio of these conditional means, yielding \\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]\\big/\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]=\\exp(\\beta) .)\n   125\t\n   126\tThen under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n   127\t\n   128\t\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n   129\t\n   130\tAs \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n   131\t\n   132\t\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7d1af58b-f820-46e4-be9a-1b728c7cfc95","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n     1\tSOURCE EXTRACT — arxiv-2410.12944v3\n     2\tTitle: How much does AI impact development speed? An enterprise-based randomized controlled trial\n     3\tAuthors: Elise Paradis; Kate Grey; Quinn Madison; Daye Nam; Andrew Macvean; Vahid Meimand; Nan Zhang; Ben Ferrari-Church; Satish Chandra (Google)\n     4\tBibliographic record: arXiv:2410.12944v3 [cs.SE], version 3 submitted 11 Nov 2024. Abstract page: https://arxiv.org/abs/2410.12944v3\n     5\tRetrieved: https://arxiv.org/html/2410.12944 on 2026-10-30T04:19:39Z (SHA-256 of the retrieved HTML: 2a5507903fe618f4cd8f91c0268ced5032a265742e9d7c18fa3d45c1170fe2ec)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n     8\tOmitted: Sections I-III, IV-A, IV-B, V, VI-A, VI-D, VII-IX, Tables I-II, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: in the HTML rendering of Table III the four model columns are collapsed into one cell per row (values for Models 1-4 appear left to right on each line, followed by the variable name); the line below each coefficient line holds the standard errors in parentheses in the same order. Significance markers: '.' p<.1, '*' p<.05, '**' p<.01, '***' p<.001.\n    11\t================================================================\n    12\t\n    13\t# How much does AI impact development speed?\n    14\t\n    15\tAn enterprise-based randomized controlled trial\n    16\t\n    17\tElise Paradis, Kate Grey, Quinn Madison, Daye Nam,\n    18\t\n    19\tAndrew Macvean, Vahid Meimand, Nan Zhang, Ben Ferrari-Church, Satish Chandra\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tHow much does AI assistance impact developer productivity? To date, the software engineering literature has provided a range of answers, targeting a diversity of outcomes: from perceived productivity to speed on task and developer throughput. Our randomized controlled trial with 96 full-time Google software engineers contributes to this literature by sharing an estimate of the impact of three AI features on the time developers spent on a complex, enterprise-grade task. We found that AI significantly shortened the time developers spent on task. Our best estimate of the size of this effect, controlling for factors known to influence developer time on task, stands at about 21%, although our confidence interval is large. We also found an interesting effect whereby developers who spend more hours on code-related activities per day were faster with AI. Product and future research considerations are discussed. In particular, we invite further research that explores the impact of AI at the ecosystem level and across multiple suites of AI-enhanced tools, since we cannot assume that the effect size obtained in our lab study will necessarily apply more broadly, or that the effect of AI found using internal Google tooling in the summer of 2024 will translate across tools and over time.\n    24\t\n    25\t### IV-C Analytic approach\n    26\t\n    27\tWe ran a two-tailed Student t-test ( \\alpha=0.05 ) on logged time on task, comparing mean time for participants in the control versus experimental groups.\n    28\t\n    29\tTo explore the robustness of our estimate and test our theoretical model, we ran three follow up linear regressions: one with the complete framework, i.e. with the experimental condition, developer-level factors, and task-level factors on logged time on task; one with only the experimental condition and developer-level factors; and one with only the experimental condition and task-level factors.\n    30\t\n    31\tThe full framework can be represented by the following equation:\n    32\t\n    33\t\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n    34\t\n    35\t\\displaystyle+\\beta_{3}*Level+\\beta_{4}*NbrHighFreqAreas | | |\n    36\t\n    37\t\\displaystyle+\\beta_{5}*TopLangCpp+\\beta_{6}*DataLogExp+\\epsilon | | |\n    38\t\n    39\tGiven the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n    40\t\n    41\tFinally, to explore interaction effects between experimental condition and developer-level factors, we ran three separate linear regressions with interaction effects, one for each covariate: average hours coded daily, level (seniority), and frequency of use of AI tooling.\n    42\t\n    43\tTo identify the best model, we used adjusted R^{2} values and model-level p values.\n    44\t\n    45\t### VI-B RQ1: Impact of AI tools on developer task speed\n    46\t\n    47\tFigure 7 shows the time distribution by experimental condition in a box and whisker plot.\n    48\tOn average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n    49\t\n    50\tp<\n    51\t\n    52\t### VI-C RQ2: Impact of covariates on main effect\n    53\t\n    54\tTable III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n    55\t2\n    56\t\n    57\tFrom Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI  =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n    58\tHowever, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24;   95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n    59\t\n    60\tControlling for experimental condition and other model covariates, average programming hours per day was significantly associated with time spent on task, with participants reporting five or more hours of development work per day being 32% ( \\beta=-0.38 ) faster than participants reporting zero to four hours of development work per day. Similarly, higher levels of seniority were also associated with a decrease in time on task, with one increased level (from 4 to 5, or 5 to 6, for example) being associated with a 15% ( \\beta=-0.16 ) decrease in time spent on task.\n    61\t\n    62\tOther covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n    63\t\n    64\tp=\n    65\t\n    66\tTABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n    67\t\n    68\tComplete Dev Task Interaction | | | |\n    69\t\n    70\t(1) (2) (3) (4) | | | |\n    71\t\n    72\t5.36*** 5.41*** 4.55*** 4.66*** Constant | | | |\n    73\t\n    74\t(0.42) (0.37) (0.19) (0.11) | | | |\n    75\t\n    76\t-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n    77\t\n    78\t(0.14) (0.14) (0.14) (0.17) | | | |\n    79\t\n    80\t-0.38* -0.38* -0.16 AvgProgHrsDay | | | |\n    81\t\n    82\t(0.15) (0.15) (0.22) | | | |\n    83\t\n    84\t-0.16. -0.16* Level | | | |\n    85\t\n    86\t(0.08) (0.08) | | | |\n    87\t\n    88\t-0.01 0.00 NbrHighFreqAreas | | | |\n    89\t\n    90\t(0.03) (0.03) | | | |\n    91\t\n    92\t0.02 0.06 TopLangCpp | | | |\n    93\t\n    94\t(0.15) (0.15) | | | |\n    95\t\n    96\t0.05 0.02 DataLogExp | | | |\n    97\t\n    98\t(0.09) (0.09) | | | |\n    99\t\n   100\t-0.29 EC:APHD | | | |\n   101\t\n   102\t(0.29) | | | |\n   103\t\n   104\tR^{2} 0.078 0.095 0.017 0.075 Adj. | | | |\n   105\t\n   106\t0.040 0.011 0.209 0.018 p value | | | |\n   107\t\n   108\tp< p< p< p< .: .1 *: .05. **: .01. ***: 0.001 | | | |\n   109\t\n   110\tNote: Estimates are for our logged dependent variable. The effect of each | | | |\n   111\t\n   112\tcovariate on time on task can be calculated by exponentiating the estimate. | | | |","is_error":false},{"tool_use_id":"toolu_01GnwK11UKMmMep2VEEb4WPo","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2507.09089v2\n     2\tTitle: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n     3\tAuthors: Joel Becker; Nate Rush; Beth Barnes; David Rein (Model Evaluation & Threat Research, METR)\n     4\tBibliographic record: arXiv:2507.09089v2 [cs.AI], version 2 submitted 25 Jul 2025. Abstract page: https://arxiv.org/abs/2507.09089v2\n     5\tRetrieved: https://arxiv.org/html/2507.09089 on 2026-10-30T04:19:45Z (SHA-256 of the retrieved HTML: e860161026afa19c69e7ba332f5cccc8fec6b8d74865868db1316a364d270c6a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 2.3 'Effect Estimation'; the opening of Section 3 'Results' and Section 3.1 'Forecasts' (figure captions included as text); Appendix D.1 'Regression', D.2 'Confidence intervals' and D.3 'Converting to speedup'.\n     8\tOmitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity\n    13\t\n    14\tJoel Becker^*\n    15\t\n    16\t  \n    17\tNate Rush^*\n    18\t\n    19\t  \n    20\tBeth Barnes\n    21\t\n    22\t  \n    23\tDavid Rein\n    24\t\n    25\tAffiliation: Model Evaluation & Threat Research (METR)\n    26\t\n    27\t###### Abstract\n    28\t\n    29\tDespite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February–June 2025 frontier affect the productivity of experienced open-source developers.\n    30\t16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience.\n    31\tEach task is randomly assigned to allow or disallow usage of early-2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet.\n    32\tBefore starting tasks, developers forecast that allowing AI will reduce completion time by 24%.\n    33\tAfter completing the study, developers estimate that allowing AI reduced completion time by 20%.\n    34\tSurprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n    35\tThis slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter).\n    36\tTo understand this result, we collect and evaluate evidence for 21 properties of our setting that a priori could contribute to the observed slowdown effect—for example, the size and quality standards of projects, or prior developer experience with AI tooling.\n    37\tAlthough the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.\n    38\t\n    39\t^1^1footnotetext: Equal contribution. Correspondence to {nate, joel}@metr.org\n    40\t\n    41\t#### 2.3 Effect Estimation\n    42\t\n    43\tWe aim to measure the percentage change in issue time to completion due to AI, defined as:\n    44\t\n    45\tS=\\frac{\\mathbb{E}[\\text{completion time with AI allowed}]}{\\mathbb{E}[\\text{completion time with AI disallowed}]}-1 | |\n    46\t\n    47\tA value of -50% would indicate issues assigned to AI-allowed taking half the time of issues assigned to AI-disallowed; 0% would indicate that issues take the same time to complete with and without AI-allowed; 100% would indicate that issues assigned to AI-allowed take twice as long as issues assigned to AI-disallowed. For convenience and clarity, we typically report the absolute value of S , and colloquially refer to negative values of as speedup, and positive values as slowdown^8^8\n    48\t8\n    49\t\n    50\tThis is a mild abuse of language, because speedup and slowdown refer to rates, while the quantity we actually estimate and report is the percentage change in time from AI.\n    51\t\n    52\tWe use the total implementation time (including both pre- and post-pull request review time) developers self-report spending on each issue to estimate the percentage change in issue completion time due to AI. A small number of issues do not end up getting reviewed/merged for various reasons—we impute the post-review implementation time of these issues with the mean post-review time per treatment condition. Section C.3.4 shows that our results are robust to the specific imputation method used.\n    53\t\n    54\tWe estimate S via a log-linear regression for total implementation time, incorporating estimates of issue difficulty to reduce noise in our estimate. We also use adapted versions of this regression when examining how performance varies in different subsets of the data (to efficiently account for any post-randomization issue difficulty imbalances that may be more pronounced within subsets). See Appendix D for details of the regression approach used.\n    55\t\n    56\t### 3 Results\n    57\t\n    58\tFigure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n    59\t\n    60\tFigure 5: Empirical cumulative distribution functions of observed implementation times. Percentile ordering is calculated separately for each treatment group.\n    61\t\n    62\tDevelopers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n    63\t\n    64\t#### 3.1 Forecasts\n    65\t\n    66\t###### Developer Forecasts and Post hoc Estimates\n    67\t\n    68\tBefore developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n    69\tFigure 4 displays the raw average forecasted and observed implementation times^12^12\n    70\t12\n    71\t\n    72\tThe raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n    73\t\n    74\tDespite developers forecasting speedup from AI (while they are slowed down), developer forecasts are informative about completion time—the Pearson correlation between the time developers forecast AI-allowed issues taking and the actual time they take is 0.64, and the correlation between the time developers forecast AI-disallowed issues taking and the actual time they take is 0.59. This suggests that developers are broadly well-calibrated on the relative amount of time that issues will take, but their expectations regarding the usefulness of AI assistance are reversed.\n    75\t\n    76\t#### D.1 Regression\n    77\t\n    78\tFor each issue i we observe the realised completion time T_{i}>0 , a binary treatment flag \\text{AI}_{i}\\in\\{0,1\\} , and the developer’s ex-ante forecast of how long the task would take without AI, denoted \\widehat{T}^{\\text{NoAI}}_{i}>0 .\n    79\t\n    80\tWe estimate the log-linear model\n    81\t\n    82\t\\log T_{i}=\\alpha+\\beta\\,\\text{AI}_{i}+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}}_{i}+\\varepsilon_{i}, | | | (1)\n    83\t\n    84\tvia ordinary least squares. Random assignment of \\text{AI}_{i} guarantees consistency of \\hat{\\beta} for \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0] .\n    85\t\n    86\tWe include forecasts as a control variable because they serve as a proxy for issue difficulty and are highly predictive of completion times. This substantially increases our statistical power without introducing bias, as forecasts were elicited prior to treatment assignment and thus cannot be affected by treatment status.^18^18\n    87\t18\n    88\t\n    89\tWe do not generally include developer fixed effects because they explain minimal variation in the outcome conditional on forecasts. Section C.3.5 displays estimates from a regression specification including developer fixed effects.\n    90\t\n    91\tFigure 14: Regression diagnostics associated with the regression specification in equation (1).\n    92\t\n    93\tFigure 14displays regression diagnostics associated with this specification.\n    94\t\n    95\t#### D.2 Confidence intervals\n    96\t\n    97\tBy default, we report 95% confidence intervals using HC3 standard errors.^19^19\n    98\t19\n    99\t\n   100\tGiven that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n   101\t\n   102\tFigure 15: Speedup calculated using the regression specification in equation (1) and alternative uncertainty estimation procedures. CR2 and CR3 standard errors are bias-corrected cluster-robust standard errors Colin Cameron and Miller [2015], Bell and Mccaffrey [2002]. Two-stage bootstrap resamples developers, then issues within developers.\n   103\t\n   104\t#### D.3 Converting to speedup\n   105\t\n   106\tTaking conditional expectations of equation (1) given treatment status and forecast:\n   107\t\n   108\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\beta+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (2)\n   109\t\n   110\t\\displaystyle\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] \\displaystyle=\\alpha+\\delta\\,\\log\\widehat{T}^{\\text{NoAI}} | | | | (3)\n   111\t\n   112\tTherefore, \\beta=\\mathbb{E}[\\log T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]-\\mathbb{E}[\\log T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}] represents the average treatment effect on the log scale, conditional on forecast.\n   113\t\n   114\tTo convert to a speedup measure, we note that:\n   115\t\n   116\t\\displaystyle\\exp(\\beta) \\displaystyle=\\frac{\\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]}{\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]}\\quad | | | | (4)\n   117\t\n   118\t\\displaystyle=\\exp(\\beta)-1 | S | | | (5)\n   119\t\n   120\t(Step (4) holds provided the disturbance \\varepsilon_{i} is independent of both \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} and that \\mathbb{E}\\!\\left[e^{\\varepsilon_{i}}\\right] exists.^20^20\n   121\t20\n   122\t\n   123\tBecause T_{i}=e^{\\alpha}e^{\\beta\\,\\text{AI}_{i}}\\,(\\widehat{T}^{\\text{NoAI}}_{i})^{\\delta}\\,e^{\\varepsilon_{i}} , taking expectations conditional on \\text{AI}_{i} and \\widehat{T}^{\\text{NoAI}}_{i} gives \\mathbb{E}[T\\,|\\,\\text{AI}=j,\\widehat{T}^{\\text{NoAI}}]=e^{(\\alpha+\\beta j)}\\,(\\widehat{T}^{\\text{NoAI}})^{\\delta}\\,\\mathbb{E}[e^{\\varepsilon_{i}}] .\n   124\tThe common factor \\mathbb{E}[e^{\\varepsilon_{i}}] cancels when we form the ratio of these conditional means, yielding \\mathbb{E}[T\\,|\\,\\text{AI}=1,\\widehat{T}^{\\text{NoAI}}]\\big/\\mathbb{E}[T\\,|\\,\\text{AI}=0,\\widehat{T}^{\\text{NoAI}}]=\\exp(\\beta) .)\n   125\t\n   126\tThen under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n   127\t\n   128\t\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n   129\t\n   130\tAs \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n   131\t\n   132\t\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","is_error":false}]}}
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{"parentUuid":"e1d9b0a6-8a40-45f9-af77-c0ac877e8cf2","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V4puGPphX3uBd6mXDzCkzW","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2411.01067v2\n     2\tTitle: Randomized Controlled Trials for Security Copilot for IT Administrators\n     3\tAuthors: James Bono; Alec Xu (Microsoft Corporation)\n     4\tBibliographic record: arXiv:2411.01067v2 [econ.GN], version 2 submitted 12 Nov 2024. Abstract page: https://arxiv.org/abs/2411.01067v2\n     5\tRetrieved: https://arxiv.org/html/2411.01067 on 2026-10-30T04:20:32Z (SHA-256 of the retrieved HTML: 649d54bc890b793eb99d51e52fcbccf1a93c0cd853953debe0da36a195945c04)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; from Section 3 'Methodology' the RCT protocol and subject-count paragraphs; the opening of Section 4 'Results'; Section 4.2 'Speed' in full (Table 3 'Comparison of task duration (minutes) by task type, holding accuracy constant' and figure captions); Appendix B 'Calculating Time Savings Holding Accuracy Constant'.\n     8\tOmitted: Sections 1-2, the rest of Section 3 (tasks, Table 1), Section 4.1 (accuracy, Table 2), 4.3 (sentiment, Tables 4-5), Section 5, Appendix A, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n    11\t================================================================\n    12\t\n    13\t# Randomized Controlled Trials for Security Copilot for IT Administrators\n    14\t\n    15\tJames Bono\n    16\t^†^†thanks: james.bono@microsoft.com\n    17\t  \n    18\tAlec Xu\n    19\t^†^†thanks: alecxu@microsoft.com\n    20\t\n    21\tMicrosoft Corporation\n    22\t\n    23\tOctober 2024\n    24\t\n    25\t###### Abstract\n    26\t\n    27\tAs generative AI (GAI) tools become increasingly integrated into workplace environments, it is essential to measure their impact on productivity across specific domains. This study evaluates the effects of Microsoft’s Security Copilot (“Copilot”) on information technology administrators (“IT admins”) through randomized controlled trials. Participants were divided into treatment and control groups, with the former granted access to Copilot within Microsoft’s Entra and Intune admin centers. Across three IT admin scenarios – sign-in troubleshooting, device policy management, and device troubleshooting – Copilot users demonstrated substantial improvements in both accuracy and speed. Across all scenarios and tasks, Copilot subjects experienced a 34.53% improvement in overall accuracy and a 29.79% reduction in task completion time. We also find that the productivity benefits vary by task type, with more complex tasks showing greater improvement. In free response tasks, Copilot users identified 146.07% more relevant facts and reduced task completion time by 61.14%. Subject satisfaction with Copilot was high, with participants reporting reduced effort and a strong preference for using the tool in future tasks. These findings suggest that GAI tools like Copilot can significantly enhance the productivity and efficiency of IT admins, especially in scenarios requiring information synthesis and complex decision-making.\n    28\t\n    29\t## 3 Methodology\n    30\t\n    31\tWe follow a standard RCT protocol to identify and estimate the causal effect of Copilot on accuracy and task completion time. That is, we randomly assign half of our subjects to the treatment group, which gives them the ability to use the Entra and Intune admin centers with Copilot in responding to the experimental tasks. The other half of the subjects are assigned to the control group and have use of the Entra and Intune admin centers without Copilot. To make our design as realistic as possible, we placed no restrictions on how subjects used other tools, such as other AI tools or web searches.^2^2\n    32\t2\n    33\t\n    34\tIndeed, after they completed the task, we asked control subjects whether they used any other AI tools, and 25% indicated they used some AI tool to help them in this task – most said they used ChatGPT. Therefore, our measurements reflect the incremental benefit of Copilot on top of the admin centers and other tools like web searches and non-Copilot AI. This makes our estimates more conservative than they would be if we had tightly restricted tool use beyond Copilot.\n    35\t\n    36\tWe recruited our subject pool through Upwork, a marketplace for freelancers. We required subjects to be proficient in reading and writing English and to have a positive reputation on Upwork. We told them to expect the tasks to take approximately two hours to complete. We offered performance incentives for combined speed and accuracy. Specifically, we multiplied the percentiles of each subject’s speed and accuracy and awarded payments on the following scale:\n    37\t\n    38\tWe had 181 subjects complete the task satisfactorily.^3^3\n    39\t3\n    40\t\n    41\tWe dropped one subject who finished in unreasonably short time and scored worse than guessing percentage. Our subject pool embodied a diverse range of experience levels across identity and network device management. See table 1 for a full breakdown of subjects by experience level.\n    42\t\n    43\t## 4 Results\n    44\t\n    45\tThe results answer RQ1 in the affirmative: Copilot does offer speed and accuracy improvements in common IT scenarios. Across all tasks, we measure a 34.53%*** increase in accuracy and a 29.79%*** reduction in task completion time. In the subsections on accuracy and speed below, we present detailed results answering RQ1-RQ4.\n    46\t\n    47\t### 4.2 Speed\n    48\t\n    49\tWe measure statistically significant time savings in all scenarios. Table 3 summarizes these results by task type and scenario. The “Aggregate Results, All Content (Holding accuracy constant)” numbers represent our headline speed improvements, a 29.79%*** time savings for Copilot users. We also note that Copilot generally involves greater latency than typical page-loading in the admin centers, which is common among GAI tools. This necessarily slowed the Copilot users. Product improvements should reduce this latency and further increase the time savings for users with Copilot.\n    50\t\n    51\t| Overall | Treatment | Control | Improvement\n    52\t\n    53\tAggregate Results | | | |\n    54\t\n    55\tAll Content | 53.84 | 44.29 | 63.10 | -29.79%***\n    56\t\n    57\tSATA | 17.81 | 15.25 | 20.14 | -24.28%***\n    58\t\n    59\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    60\t\n    61\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    62\t\n    63\tFree Response | 30.98 | 17.03 | 43.82 | -61.14%***\n    64\t\n    65\tSign-In Troubleshooting | | | |\n    66\t\n    67\tAll Content | 24.04 | 16.63 | 30.46 | -45.41%***\n    68\t\n    69\tSATA | 6.41 | 5.04 | 7.63 | -33.98%***\n    70\t\n    71\tFree Response Content | 17.37 | 11.59 | 22.40 | -48.23%***\n    72\t\n    73\tDevice Policy Management | | | |\n    74\t\n    75\tAll Content | 20.00 | 17.45 | 22.23 | -21.64%***\n    76\t\n    77\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    78\t\n    79\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    80\t\n    81\tFree Response Content | 12.17 | 5.43 | 19.27 | -71.80%***\n    82\t\n    83\tDevice Troubleshooting | | | |\n    84\t\n    85\tSATA | 11.24 | 10.21 | 12.11 | -15.69%***\n    86\t\n    87\tTable 3: Comparison of task duration (minutes) by task type, holding accuracy constant\n    88\t\n    89\tWe measure time savings for Copilot across all three scenarios. The time savings included 45.41%*** for sign-in troubleshooting, 21.64%*** for device policy management, and 15.69%*** for device troubleshooting.\n    90\t\n    91\tFigure 2: The difference in time spent between Copilot and control users is greatest on the first MC question, suggesting a possible learning phenomenon.\n    92\t\n    93\tWe find that Copilot’s time savings track its accuracy improvements across task types. That is, the biggest gains again come from free response (61.14%*** time savings), followed by SATA (24.28%***). Lastly, the MC questions show a decrease in speed for Copilot users (25.13%*** time increase). For these MC questions we also estimate a negative relationship between accuracy and time spent for the control group. This is likely because control subjects who knew the answer responded quickly, and those who did not know it gained little accuracy by spending more time by, for example, searching the internet. This can explain the data we see, where even though Copilot provided an advantage in responding accurately, controlling for accuracy in calculating its time savings does not make up for the disadvantage of its latency.\n    94\t\n    95\tWe also notice that Copilot users spent much more time than the control users on the first MC question compared to the subsequent three questions (see figure 2). We think this suggests Copilot users were learning how to use Copilot for this type of task. Therefore, we drop the first question and recompute the time differences across the remaining tasks, which results in no statistically significant difference in task completion time (see the “Drop first” entry in table 3).\n    96\t\n    97\tFigure 3: Copilot’s effect heterogeneity by experience level on speed, holding accuracy constant\n    98\t\n    99\tAs we did with accuracy, we examine time savings across different experience levels and domains. Our findings once again reveal statistically significant point estimates of time savings for all groups, as shown in figure 3. However, we do not observe statistically significant differences in estimated effect sizes between any of the groups. The left panel illustrates Copilot’s impact on task completion times for the sign-in troubleshooting task, with a 50.1%*** and 45.2%*** time savings for Copilot users with 0-3 and 3+ years of experience, respectively. These point estimates are consistent with intuition and our accuracy findings.\n   100\t\n   101\tThe right panel of figure 3 shows time savings of 12.6%*** and 30.2%*** for 0-3 and 3+ years of experience, respectively, for device management tasks. This suggests that experienced network management subjects benefit more from Copilot in terms of time savings than their less experienced counterparts, which runs counter to intuition and expectation. This is driven by the fact that less experienced control subjects are driving the negative relationship between task completion time and accuracy for the device policy management MC question, mentioned above. This shrinks the possible time savings for the less experienced Copilot subjects.\n   102\t\n   103\t## Appendix B Calculating Time Savings Holding Accuracy Constant\n   104\t\n   105\tNote that a simple regression of time on treatment and score assumes the returns to additional time spent are the same between the treatment and control groups, which is unlikely true. Therefore, to compare speed holding accuracy constant, we employ a linear regression framework that proceeds in three steps. First, we estimate the task duration as a function of accuracy for the control group. Then we predict the task duration the control group would need to achieve the same accuracy as the Copilot group. Finally, we compute the difference between task durations for the two groups, using a bootstrap to compute the level of certainty of this finding.\n   106\t\n   107\tLet T_{g} and A_{g} represent the set of task durations and accuracy scores for each subject in group g\\in\\{(t)reatment,(c)ontrol\\} . And let \\bar{T}_{g} and \\bar{A}_{g} represent the sample means. We then estimate the effect of accuracy on task duration for the control group via the following regression and ordinary least squares.\n   108\t\n   109\tT_{c}=\\alpha+\\beta A_{c}+\\epsilon | |","is_error":false}]},"uuid":"78b3bb83-8719-4159-80c3-193250af2888","timestamp":"2026-10-30T05:29:21.864Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2411.01067v2\n     2\tTitle: Randomized Controlled Trials for Security Copilot for IT Administrators\n     3\tAuthors: James Bono; Alec Xu (Microsoft Corporation)\n     4\tBibliographic record: arXiv:2411.01067v2 [econ.GN], version 2 submitted 12 Nov 2024. Abstract page: https://arxiv.org/abs/2411.01067v2\n     5\tRetrieved: https://arxiv.org/html/2411.01067 on 2026-10-30T04:20:32Z (SHA-256 of the retrieved HTML: 649d54bc890b793eb99d51e52fcbccf1a93c0cd853953debe0da36a195945c04)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; from Section 3 'Methodology' the RCT protocol and subject-count paragraphs; the opening of Section 4 'Results'; Section 4.2 'Speed' in full (Table 3 'Comparison of task duration (minutes) by task type, holding accuracy constant' and figure captions); Appendix B 'Calculating Time Savings Holding Accuracy Constant'.\n     8\tOmitted: Sections 1-2, the rest of Section 3 (tasks, Table 1), Section 4.1 (accuracy, Table 2), 4.3 (sentiment, Tables 4-5), Section 5, Appendix A, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n    11\t================================================================\n    12\t\n    13\t# Randomized Controlled Trials for Security Copilot for IT Administrators\n    14\t\n    15\tJames Bono\n    16\t^†^†thanks: james.bono@microsoft.com\n    17\t  \n    18\tAlec Xu\n    19\t^†^†thanks: alecxu@microsoft.com\n    20\t\n    21\tMicrosoft Corporation\n    22\t\n    23\tOctober 2024\n    24\t\n    25\t###### Abstract\n    26\t\n    27\tAs generative AI (GAI) tools become increasingly integrated into workplace environments, it is essential to measure their impact on productivity across specific domains. This study evaluates the effects of Microsoft’s Security Copilot (“Copilot”) on information technology administrators (“IT admins”) through randomized controlled trials. Participants were divided into treatment and control groups, with the former granted access to Copilot within Microsoft’s Entra and Intune admin centers. Across three IT admin scenarios – sign-in troubleshooting, device policy management, and device troubleshooting – Copilot users demonstrated substantial improvements in both accuracy and speed. Across all scenarios and tasks, Copilot subjects experienced a 34.53% improvement in overall accuracy and a 29.79% reduction in task completion time. We also find that the productivity benefits vary by task type, with more complex tasks showing greater improvement. In free response tasks, Copilot users identified 146.07% more relevant facts and reduced task completion time by 61.14%. Subject satisfaction with Copilot was high, with participants reporting reduced effort and a strong preference for using the tool in future tasks. These findings suggest that GAI tools like Copilot can significantly enhance the productivity and efficiency of IT admins, especially in scenarios requiring information synthesis and complex decision-making.\n    28\t\n    29\t## 3 Methodology\n    30\t\n    31\tWe follow a standard RCT protocol to identify and estimate the causal effect of Copilot on accuracy and task completion time. That is, we randomly assign half of our subjects to the treatment group, which gives them the ability to use the Entra and Intune admin centers with Copilot in responding to the experimental tasks. The other half of the subjects are assigned to the control group and have use of the Entra and Intune admin centers without Copilot. To make our design as realistic as possible, we placed no restrictions on how subjects used other tools, such as other AI tools or web searches.^2^2\n    32\t2\n    33\t\n    34\tIndeed, after they completed the task, we asked control subjects whether they used any other AI tools, and 25% indicated they used some AI tool to help them in this task – most said they used ChatGPT. Therefore, our measurements reflect the incremental benefit of Copilot on top of the admin centers and other tools like web searches and non-Copilot AI. This makes our estimates more conservative than they would be if we had tightly restricted tool use beyond Copilot.\n    35\t\n    36\tWe recruited our subject pool through Upwork, a marketplace for freelancers. We required subjects to be proficient in reading and writing English and to have a positive reputation on Upwork. We told them to expect the tasks to take approximately two hours to complete. We offered performance incentives for combined speed and accuracy. Specifically, we multiplied the percentiles of each subject’s speed and accuracy and awarded payments on the following scale:\n    37\t\n    38\tWe had 181 subjects complete the task satisfactorily.^3^3\n    39\t3\n    40\t\n    41\tWe dropped one subject who finished in unreasonably short time and scored worse than guessing percentage. Our subject pool embodied a diverse range of experience levels across identity and network device management. See table 1 for a full breakdown of subjects by experience level.\n    42\t\n    43\t## 4 Results\n    44\t\n    45\tThe results answer RQ1 in the affirmative: Copilot does offer speed and accuracy improvements in common IT scenarios. Across all tasks, we measure a 34.53%*** increase in accuracy and a 29.79%*** reduction in task completion time. In the subsections on accuracy and speed below, we present detailed results answering RQ1-RQ4.\n    46\t\n    47\t### 4.2 Speed\n    48\t\n    49\tWe measure statistically significant time savings in all scenarios. Table 3 summarizes these results by task type and scenario. The “Aggregate Results, All Content (Holding accuracy constant)” numbers represent our headline speed improvements, a 29.79%*** time savings for Copilot users. We also note that Copilot generally involves greater latency than typical page-loading in the admin centers, which is common among GAI tools. This necessarily slowed the Copilot users. Product improvements should reduce this latency and further increase the time savings for users with Copilot.\n    50\t\n    51\t| Overall | Treatment | Control | Improvement\n    52\t\n    53\tAggregate Results | | | |\n    54\t\n    55\tAll Content | 53.84 | 44.29 | 63.10 | -29.79%***\n    56\t\n    57\tSATA | 17.81 | 15.25 | 20.14 | -24.28%***\n    58\t\n    59\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    60\t\n    61\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    62\t\n    63\tFree Response | 30.98 | 17.03 | 43.82 | -61.14%***\n    64\t\n    65\tSign-In Troubleshooting | | | |\n    66\t\n    67\tAll Content | 24.04 | 16.63 | 30.46 | -45.41%***\n    68\t\n    69\tSATA | 6.41 | 5.04 | 7.63 | -33.98%***\n    70\t\n    71\tFree Response Content | 17.37 | 11.59 | 22.40 | -48.23%***\n    72\t\n    73\tDevice Policy Management | | | |\n    74\t\n    75\tAll Content | 20.00 | 17.45 | 22.23 | -21.64%***\n    76\t\n    77\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    78\t\n    79\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    80\t\n    81\tFree Response Content | 12.17 | 5.43 | 19.27 | -71.80%***\n    82\t\n    83\tDevice Troubleshooting | | | |\n    84\t\n    85\tSATA | 11.24 | 10.21 | 12.11 | -15.69%***\n    86\t\n    87\tTable 3: Comparison of task duration (minutes) by task type, holding accuracy constant\n    88\t\n    89\tWe measure time savings for Copilot across all three scenarios. The time savings included 45.41%*** for sign-in troubleshooting, 21.64%*** for device policy management, and 15.69%*** for device troubleshooting.\n    90\t\n    91\tFigure 2: The difference in time spent between Copilot and control users is greatest on the first MC question, suggesting a possible learning phenomenon.\n    92\t\n    93\tWe find that Copilot’s time savings track its accuracy improvements across task types. That is, the biggest gains again come from free response (61.14%*** time savings), followed by SATA (24.28%***). Lastly, the MC questions show a decrease in speed for Copilot users (25.13%*** time increase). For these MC questions we also estimate a negative relationship between accuracy and time spent for the control group. This is likely because control subjects who knew the answer responded quickly, and those who did not know it gained little accuracy by spending more time by, for example, searching the internet. This can explain the data we see, where even though Copilot provided an advantage in responding accurately, controlling for accuracy in calculating its time savings does not make up for the disadvantage of its latency.\n    94\t\n    95\tWe also notice that Copilot users spent much more time than the control users on the first MC question compared to the subsequent three questions (see figure 2). We think this suggests Copilot users were learning how to use Copilot for this type of task. Therefore, we drop the first question and recompute the time differences across the remaining tasks, which results in no statistically significant difference in task completion time (see the “Drop first” entry in table 3).\n    96\t\n    97\tFigure 3: Copilot’s effect heterogeneity by experience level on speed, holding accuracy constant\n    98\t\n    99\tAs we did with accuracy, we examine time savings across different experience levels and domains. Our findings once again reveal statistically significant point estimates of time savings for all groups, as shown in figure 3. However, we do not observe statistically significant differences in estimated effect sizes between any of the groups. The left panel illustrates Copilot’s impact on task completion times for the sign-in troubleshooting task, with a 50.1%*** and 45.2%*** time savings for Copilot users with 0-3 and 3+ years of experience, respectively. These point estimates are consistent with intuition and our accuracy findings.\n   100\t\n   101\tThe right panel of figure 3 shows time savings of 12.6%*** and 30.2%*** for 0-3 and 3+ years of experience, respectively, for device management tasks. This suggests that experienced network management subjects benefit more from Copilot in terms of time savings than their less experienced counterparts, which runs counter to intuition and expectation. This is driven by the fact that less experienced control subjects are driving the negative relationship between task completion time and accuracy for the device policy management MC question, mentioned above. This shrinks the possible time savings for the less experienced Copilot subjects.\n   102\t\n   103\t## Appendix B Calculating Time Savings Holding Accuracy Constant\n   104\t\n   105\tNote that a simple regression of time on treatment and score assumes the returns to additional time spent are the same between the treatment and control groups, which is unlikely true. Therefore, to compare speed holding accuracy constant, we employ a linear regression framework that proceeds in three steps. First, we estimate the task duration as a function of accuracy for the control group. Then we predict the task duration the control group would need to achieve the same accuracy as the Copilot group. Finally, we compute the difference between task durations for the two groups, using a bootstrap to compute the level of certainty of this finding.\n   106\t\n   107\tLet T_{g} and A_{g} represent the set of task durations and accuracy scores for each subject in group g\\in\\{(t)reatment,(c)ontrol\\} . And let \\bar{T}_{g} and \\bar{A}_{g} represent the sample means. We then estimate the effect of accuracy on task duration for the control group via the following regression and ordinary least squares.\n   108\t\n   109\tT_{c}=\\alpha+\\beta A_{c}+\\epsilon | |","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e1d9b0a6-8a40-45f9-af77-c0ac877e8cf2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"59eff391-8abb-41c5-92fb-420302f2936b","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G8UtbhLFhERLbtct7GVkTA","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2511.13860v1\n     2\tTitle: Randomized Controlled Trials for Phishing Triage Agent\n     3\tAuthors: James Bono (Microsoft Corporation)\n     4\tBibliographic record: arXiv:2511.13860v1 [econ.GN], version 1 submitted 17 Nov 2025. Abstract page: https://arxiv.org/abs/2511.13860v1\n     5\tRetrieved: https://arxiv.org/html/2511.13860 on 2026-10-30T04:20:39Z (SHA-256 of the retrieved HTML: 9a25378878d5735130154b510c032744445d4089e70415e3f1c3b2ca066b984a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 2 'Methodology' (arms, subjects, queue construction); Section 3 'Results' opening and Section 3.1 'Productivity Gains: True Positives per Analyst Minute' (Table 1); Section 3.3 'Behavior: Effect of Agent on Analyst Behavior' (Table 3 'Comparison of Email Processing Times'); Appendix B 'TP per Analyst Minute'.\n     8\tOmitted: Section 1, Sections 3.2 and 3.4 onward (Tables 2 and 4), Section 4, Appendices A, C and D, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Randomized Controlled Trials for Phishing Triage Agent\n    13\t\n    14\tJames Bono\n    15\t^†^†thanks: james.bono@microsoft.com; This work would not be possible without the support of the following contributors: Eden Abadi, Costas Boulis, Michael Browning, Jael Fafner, Corina Feuerstein, Justin Grana, Paula Greve, Cristina Da Gama Henriquez, Adel Khaldi, Amit Kollman, Aviel Lavie, Katerina Marazopoulou, Hani Neuvirth-Telem, Inbar Rotem, Felicity Scott-Milligan, Cole Sims, Holly Stewart, and Tracy Zhen\n    16\t\n    17\tMicrosoft Corporation\n    18\t\n    19\tOctober 2025\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tSecurity operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent—the Microsoft Security Copilot Phishing Triage Agent—on analyst productivity and accuracy. Our results demonstrate that agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group. The agent’s queue prioritization and verdict explanations were both significant drivers of efficiency. Behavioral analysis revealed that agent-augmented analysts reallocated their attention, spending 53% more time on malicious emails, and were not prone to rubber-stamping the agent’s malicious verdicts. These findings offer actionable insights for SOC leaders considering AI adoption, including the potential for agents to fundamentally change the optimal allocation of SOC resources.\n    24\t\n    25\t## 2 Methodology\n    26\t\n    27\tWe conduct an RCT with three arms, randomly assigned:\n    28\t\n    29\t1.\n    30\t\n    31\tControl ( N{=}52 ): Random queue order, no references to AI.\n    32\t\n    33\t2.\n    34\t\n    35\tAware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n    36\t\n    37\t3.\n    38\t\n    39\tBlind ( N{=}59 ): Identical ordering as Aware but without any AI references; neither verdicts nor rationales are revealed.\n    40\t\n    41\tOur subjects are 167 external security analysts.^1^1\n    42\t1\n    43\t\n    44\tWe filter out 13 subject responses that scored less than 40% – significantly below guessing percentage. These included three in the Aware group, six in the Blind group, and four in the Control group. This step does not affect our qualitative results. Their task is to triage a 25-email queue. The queue comes from an original corpus of 93 randomly selected emails reported by Microsoft employees. Through an in-depth analysis by expert graders and security researchers, we arrived at our ground truth of 82 non-malicious and 11 malicious emails.\n    45\t\n    46\tOn this corpus, the agent’s precision and recall were both 100%. The agent’s accuracy is very good, but it is not always perfect. It will occasionally have false negative (FN) and false positive (FP) verdicts. However, without FNs and FPs in our corpus, we cannot measure the effect of the agent on analyst trust and diligence. Hence, we create synthetic false positives (SFPs) and synthetic false negatives (SFNs). We create SFNs from each of the malicious emails by changing the agent verdict and swapping the justification for a generic justification of a non-malicious verdict that fits the content of the malicious email. For example, for a malicious email impersonating Microsoft 365, we used the justification from a non-malicious email from Microsoft 365. We create fake false negatives (SFNs) with a similar technique, e.g., by using the justification from a non-malicious email from Microsoft 365 for a malicious email impersonating Microsoft 365. In both cases, we ensure no details in the fake justification are in obvious conflict with the SFN and SFP emails. Using this method, we ended up with five SFPs and 10 SFNs.\n    47\t\n    48\tTo construct each subject’s 25 email queue, we randomly select four malicious emails, 19 non-malicious emails, one SFN, and one SFP. This sampling scheme mimics agent precision and recall of 80%, which aligns closely to Microsoft’s internal measurements on large random samples. It also includes 20% malicious samples.^2^2\n    49\t2\n    50\t\n    51\tOur results are not sensitive to this malicious percentage, since we calculate the time to triage for TP, TN, SFP, and SFN for all groups separately. We need only that the malicious percentage does not dramatically affect the expectations of the Control group, which 20% should not do. For the Aware and Blind groups, we randomly order the four malicious and one SFP at the front of the queue, i.e., in positions one through five. We then randomly order the 19 non-malicious and one SFN in queue positions six through 25. This implies agent accuracy with precision=recall=0.8. Each email is presented as a timed, forced-choice classification: Malicious vs. Non-Malicious. Responses are final (no backtracking).\n    52\t\n    53\tTo reduce the importance of learning effects and to control for the heterogeneous beliefs of subjects using the agent for the first time, we calibrate our Aware group’s beliefs about the agent’s accuracy with the following information: “On a very large sample, the agent’s accuracy is approximately as follows: Malicious verdicts are 80% correct; Not Malicious verdicts are 95% correct.”\n    54\t\n    55\tFor each email in the queue, subjects have a link to a read-only OneDrive folder with the email’s artifacts, including a screenshot (annotated with primary URL), EML and HTML text, URL list, URL detonation screenshots, and sanitized attachments.^3^3\n    56\t\n    57\t## 3 Results\n    58\t\n    59\tThe results show that the agent offers dramatic improvements in both productivity and protection. These improvements are driven by both the agent’s ability to prioritize the queue as well as its verdicts and explanations. In addition, the agent’s accuracy and its effect on analyst accuracy both strongly support implementing the resolve-benign protocol.\n    60\t\n    61\t### 3.1 Productivity Gains: True Positives per Analyst Minute\n    62\t\n    63\tThe Aware group identifies 6.5 times as many TPs per analyst minute (550% productivity gain) on the corpus ground truth. This number reflects the resolve-benign protocol.\n    64\tUnder the pessimistic counterfactual, analysts identify 3.1 times as many TPs per minute. At a high malicious rate of 30%, we still find an effect of 2.0-2.6 times. These measurements are all statistically significant above the 99% level. Figure 1 illustrates the relationship between the malicious rate and the agent’s productivity gain (as a factor improvement) for both the case where agent precision = 1.0 and the case where agent precision = 0.8. The shaded region is the 99% confidence interval for the case where agent precision = 0.8.\n    65\t\n    66\tFigure 1: Factor Increase in TPs per unit of Analyst Time\n    67\t\n    68\tNext, by applying the resolve-benign protocol to the Blind group’s responses, we measure how much of this gain is due to the resolve-benign protocol versus the subject being aware of the agent, including its verdicts and explanations. We find that the resolve-benign protocol, which dramatically reduces the number of non-malicious emails for review, is the biggest factor driving the productivity gain, accounting for between 79-84% of the total agent-driven productivity gain. As precision decreases, the information conveyed in the agent’s verdicts and explanations becomes relatively more important. However, even when the agent’s precision is maximized, the information conveyed in the agent’s verdicts and explanations remains relatively important, about 13-17% of the total improvement.\n    69\t\n    70\tTable 1: Productivity Improvements by Agent Feature\n    71\t\n    72\tScenario | Factor Increase | Resolve-Benign (%) | Agent Awareness (%)\n    73\t\n    74\tCorpus ground truth | 6.53 | 83 | 17\n    75\t\n    76\tAccuracy counterfactual | 5.04 | 78 | 22\n    77\t\n    78\tMalicious rate counterfactual | 3.97 | 87 | 13\n    79\t\n    80\tPessimistic counterfactual | 3.07 | 83 | 17\n    81\t\n    82\t### 3.3 Behavior: Effect of Agent on Analyst Behavior\n    83\t\n    84\tNext, we look at subject behavior across the entire queue, not just the agent-prioritized portion, i.e., this is how analysts would perform if we removed the resolve-benign protocol. First, we find that the Aware group spends 53% more time per malicious email. They spend marginally less on non-malicious email, and the overall time is roughly the same. This indicates they are utilizing the agent to help optimize their time and attention.\n    85\t\n    86\tTable 3: Comparison of Email Processing Times\n    87\t\n    88\tMetric | Malicious | Non-Malicious | Full Corpus\n    89\t\n    90\tAware Time per Email (s) | 2.8 | 1.8 | 50.5\n    91\t\n    92\tControl Time per Email (s) | 1.9 | 2.4 | 56.9\n    93\t\n    94\t% Time Savings | -52.9 | 23.6 | 11.1\n    95\t\n    96\tp-value | 0.006 | 0.126 | 0.471\n    97\t\n    98\tWe also find that the Aware group is no more likely to confirm the agent’s TPs or miss the agent’s SFPs, which are precisely the samples the Aware group would see under the resolve-benign protocol. Taken together with the evidence that Aware subjects spend more time on malicious emails, this means they are not rubber-stamping the agent’s malicious verdicts. Rather, they use the agent’s insights to guide their own investigation.\n    99\t\n   100\tHowever, we do find they are more likely to confirm agent TNs by 21 percentage points and more likely to miss agent SFNs by 29 percentage points. This lends further support to the use of the resolve-benign protocol. In addition to the productivity gains documented above, the protection benefit of having analysts spend time on emails the agent has already classified as non-malicious is minimal because analysts are likely to agree with the agent anyway.\n   101\t\n   102\t## Appendix B TP per Analyst Minute\n   103\t\n   104\tLet p denote the malicious email rate in the corpus. We define the following parameters and variables:\n   105\t\n   106\t### B.1 Control Group Performance\n   107\t\n   108\tFor the control group, the expected time to process an email is:\n   109\t\n   110\t\\mathbb{E}[T_{c}]=p\\cdot\\bar{T}_{c,m}+(1-p)\\cdot\\bar{T}_{c,b} | |\n   111\t\n   112\twhere:\n   113\t\n   114\t•\n   115\t\n   116\t\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n   117\t\n   118\t•\n   119\t\n   120\t\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n   121\t\n   122\tThe true positive rate for the control group is defined as:\n   123\t\n   124\t\\text{TPR}_{c}=\\frac{\\text{TP}_{c}}{n_{m}} | |\n   125\t\n   126\twhere \\text{TP}_{c} is the number of true positives identified by control subjects and n_{m} is the total number of malicious emails in the sample.\n   127\t\n   128\tThe productivity metric for the control group (true positives per minute) is:\n   129\t\n   130\t\\rho_{c}=\\frac{p\\cdot\\text{TPR}_{c}}{\\mathbb{E}[T_{c}]} | |\n   131\t\n   132\t### B.2 Aware Group Performance (Priority Queue)\n   133\t\n   134\tFor the Aware group processing a priority queue filtered by an agent with precision \\pi and recall \\sigma , the queue composition changes. The priority queue contains only items flagged by the agent as malicious. The expected time to process an item in the priority queue is:\n   135\t\n   136\t\\mathbb{E}[T_{a}]=\\pi\\cdot\\bar{T}_{a,\\text{TP}}+(1-\\pi)\\cdot\\bar{T}_{a,\\text{FP}} | |\n   137\t\n   138\twhere:\n   139\t\n   140\t•\n   141\t\n   142\t\\bar{T}_{a,\\text{TP}} is the mean time for Aware group to process agent-identified true positives\n   143\t\n   144\t•\n   145\t\n   146\t\\bar{T}_{a,\\text{FP}} is the mean time for Aware group to process agent-identified false positives\n   147\t\n   148\tThe accuracy of the Aware group on agent-identified true positives is:\n   149\t\n   150\t\\alpha_{a,\\text{TP}}=\\mathbb{E}[\\mathbbm{1}_{\\text{correct}}|\\text{Agent}=\\text{TP}] | |\n   151\t\n   152\tThe productivity metric for the Aware group (true positives per minute) is:\n   153\t\n   154\t\\rho_{a}=\\frac{\\pi\\cdot\\alpha_{a,\\text{TP}}}{\\mathbb{E}[T_{a}]} | |\n   155\t\n   156\t### B.3 Productivity Factor\n   157\t\n   158\tThe productivity factor, representing the multiplicative improvement of the Aware group over the control group, is:\n   159\t\n   160\t\\phi^{(p)}=\\frac{\\rho_{a}}{\\rho_{c}} | |\n   161\t\n   162\tA value of \\phi^{(p)}>1 indicates improved productivity, with \\phi^{(p)}=2 representing a doubling of productivity (twice as many true positives identified per unit time).","is_error":false}]},"uuid":"800d6c29-5dfe-4d78-bdbd-5eaa20340c36","timestamp":"2026-10-30T05:29:22.482Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2511.13860v1\n     2\tTitle: Randomized Controlled Trials for Phishing Triage Agent\n     3\tAuthors: James Bono (Microsoft Corporation)\n     4\tBibliographic record: arXiv:2511.13860v1 [econ.GN], version 1 submitted 17 Nov 2025. Abstract page: https://arxiv.org/abs/2511.13860v1\n     5\tRetrieved: https://arxiv.org/html/2511.13860 on 2026-10-30T04:20:39Z (SHA-256 of the retrieved HTML: 9a25378878d5735130154b510c032744445d4089e70415e3f1c3b2ca066b984a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 2 'Methodology' (arms, subjects, queue construction); Section 3 'Results' opening and Section 3.1 'Productivity Gains: True Positives per Analyst Minute' (Table 1); Section 3.3 'Behavior: Effect of Agent on Analyst Behavior' (Table 3 'Comparison of Email Processing Times'); Appendix B 'TP per Analyst Minute'.\n     8\tOmitted: Section 1, Sections 3.2 and 3.4 onward (Tables 2 and 4), Section 4, Appendices A, C and D, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Randomized Controlled Trials for Phishing Triage Agent\n    13\t\n    14\tJames Bono\n    15\t^†^†thanks: james.bono@microsoft.com; This work would not be possible without the support of the following contributors: Eden Abadi, Costas Boulis, Michael Browning, Jael Fafner, Corina Feuerstein, Justin Grana, Paula Greve, Cristina Da Gama Henriquez, Adel Khaldi, Amit Kollman, Aviel Lavie, Katerina Marazopoulou, Hani Neuvirth-Telem, Inbar Rotem, Felicity Scott-Milligan, Cole Sims, Holly Stewart, and Tracy Zhen\n    16\t\n    17\tMicrosoft Corporation\n    18\t\n    19\tOctober 2025\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tSecurity operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent—the Microsoft Security Copilot Phishing Triage Agent—on analyst productivity and accuracy. Our results demonstrate that agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group. The agent’s queue prioritization and verdict explanations were both significant drivers of efficiency. Behavioral analysis revealed that agent-augmented analysts reallocated their attention, spending 53% more time on malicious emails, and were not prone to rubber-stamping the agent’s malicious verdicts. These findings offer actionable insights for SOC leaders considering AI adoption, including the potential for agents to fundamentally change the optimal allocation of SOC resources.\n    24\t\n    25\t## 2 Methodology\n    26\t\n    27\tWe conduct an RCT with three arms, randomly assigned:\n    28\t\n    29\t1.\n    30\t\n    31\tControl ( N{=}52 ): Random queue order, no references to AI.\n    32\t\n    33\t2.\n    34\t\n    35\tAware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n    36\t\n    37\t3.\n    38\t\n    39\tBlind ( N{=}59 ): Identical ordering as Aware but without any AI references; neither verdicts nor rationales are revealed.\n    40\t\n    41\tOur subjects are 167 external security analysts.^1^1\n    42\t1\n    43\t\n    44\tWe filter out 13 subject responses that scored less than 40% – significantly below guessing percentage. These included three in the Aware group, six in the Blind group, and four in the Control group. This step does not affect our qualitative results. Their task is to triage a 25-email queue. The queue comes from an original corpus of 93 randomly selected emails reported by Microsoft employees. Through an in-depth analysis by expert graders and security researchers, we arrived at our ground truth of 82 non-malicious and 11 malicious emails.\n    45\t\n    46\tOn this corpus, the agent’s precision and recall were both 100%. The agent’s accuracy is very good, but it is not always perfect. It will occasionally have false negative (FN) and false positive (FP) verdicts. However, without FNs and FPs in our corpus, we cannot measure the effect of the agent on analyst trust and diligence. Hence, we create synthetic false positives (SFPs) and synthetic false negatives (SFNs). We create SFNs from each of the malicious emails by changing the agent verdict and swapping the justification for a generic justification of a non-malicious verdict that fits the content of the malicious email. For example, for a malicious email impersonating Microsoft 365, we used the justification from a non-malicious email from Microsoft 365. We create fake false negatives (SFNs) with a similar technique, e.g., by using the justification from a non-malicious email from Microsoft 365 for a malicious email impersonating Microsoft 365. In both cases, we ensure no details in the fake justification are in obvious conflict with the SFN and SFP emails. Using this method, we ended up with five SFPs and 10 SFNs.\n    47\t\n    48\tTo construct each subject’s 25 email queue, we randomly select four malicious emails, 19 non-malicious emails, one SFN, and one SFP. This sampling scheme mimics agent precision and recall of 80%, which aligns closely to Microsoft’s internal measurements on large random samples. It also includes 20% malicious samples.^2^2\n    49\t2\n    50\t\n    51\tOur results are not sensitive to this malicious percentage, since we calculate the time to triage for TP, TN, SFP, and SFN for all groups separately. We need only that the malicious percentage does not dramatically affect the expectations of the Control group, which 20% should not do. For the Aware and Blind groups, we randomly order the four malicious and one SFP at the front of the queue, i.e., in positions one through five. We then randomly order the 19 non-malicious and one SFN in queue positions six through 25. This implies agent accuracy with precision=recall=0.8. Each email is presented as a timed, forced-choice classification: Malicious vs. Non-Malicious. Responses are final (no backtracking).\n    52\t\n    53\tTo reduce the importance of learning effects and to control for the heterogeneous beliefs of subjects using the agent for the first time, we calibrate our Aware group’s beliefs about the agent’s accuracy with the following information: “On a very large sample, the agent’s accuracy is approximately as follows: Malicious verdicts are 80% correct; Not Malicious verdicts are 95% correct.”\n    54\t\n    55\tFor each email in the queue, subjects have a link to a read-only OneDrive folder with the email’s artifacts, including a screenshot (annotated with primary URL), EML and HTML text, URL list, URL detonation screenshots, and sanitized attachments.^3^3\n    56\t\n    57\t## 3 Results\n    58\t\n    59\tThe results show that the agent offers dramatic improvements in both productivity and protection. These improvements are driven by both the agent’s ability to prioritize the queue as well as its verdicts and explanations. In addition, the agent’s accuracy and its effect on analyst accuracy both strongly support implementing the resolve-benign protocol.\n    60\t\n    61\t### 3.1 Productivity Gains: True Positives per Analyst Minute\n    62\t\n    63\tThe Aware group identifies 6.5 times as many TPs per analyst minute (550% productivity gain) on the corpus ground truth. This number reflects the resolve-benign protocol.\n    64\tUnder the pessimistic counterfactual, analysts identify 3.1 times as many TPs per minute. At a high malicious rate of 30%, we still find an effect of 2.0-2.6 times. These measurements are all statistically significant above the 99% level. Figure 1 illustrates the relationship between the malicious rate and the agent’s productivity gain (as a factor improvement) for both the case where agent precision = 1.0 and the case where agent precision = 0.8. The shaded region is the 99% confidence interval for the case where agent precision = 0.8.\n    65\t\n    66\tFigure 1: Factor Increase in TPs per unit of Analyst Time\n    67\t\n    68\tNext, by applying the resolve-benign protocol to the Blind group’s responses, we measure how much of this gain is due to the resolve-benign protocol versus the subject being aware of the agent, including its verdicts and explanations. We find that the resolve-benign protocol, which dramatically reduces the number of non-malicious emails for review, is the biggest factor driving the productivity gain, accounting for between 79-84% of the total agent-driven productivity gain. As precision decreases, the information conveyed in the agent’s verdicts and explanations becomes relatively more important. However, even when the agent’s precision is maximized, the information conveyed in the agent’s verdicts and explanations remains relatively important, about 13-17% of the total improvement.\n    69\t\n    70\tTable 1: Productivity Improvements by Agent Feature\n    71\t\n    72\tScenario | Factor Increase | Resolve-Benign (%) | Agent Awareness (%)\n    73\t\n    74\tCorpus ground truth | 6.53 | 83 | 17\n    75\t\n    76\tAccuracy counterfactual | 5.04 | 78 | 22\n    77\t\n    78\tMalicious rate counterfactual | 3.97 | 87 | 13\n    79\t\n    80\tPessimistic counterfactual | 3.07 | 83 | 17\n    81\t\n    82\t### 3.3 Behavior: Effect of Agent on Analyst Behavior\n    83\t\n    84\tNext, we look at subject behavior across the entire queue, not just the agent-prioritized portion, i.e., this is how analysts would perform if we removed the resolve-benign protocol. First, we find that the Aware group spends 53% more time per malicious email. They spend marginally less on non-malicious email, and the overall time is roughly the same. This indicates they are utilizing the agent to help optimize their time and attention.\n    85\t\n    86\tTable 3: Comparison of Email Processing Times\n    87\t\n    88\tMetric | Malicious | Non-Malicious | Full Corpus\n    89\t\n    90\tAware Time per Email (s) | 2.8 | 1.8 | 50.5\n    91\t\n    92\tControl Time per Email (s) | 1.9 | 2.4 | 56.9\n    93\t\n    94\t% Time Savings | -52.9 | 23.6 | 11.1\n    95\t\n    96\tp-value | 0.006 | 0.126 | 0.471\n    97\t\n    98\tWe also find that the Aware group is no more likely to confirm the agent’s TPs or miss the agent’s SFPs, which are precisely the samples the Aware group would see under the resolve-benign protocol. Taken together with the evidence that Aware subjects spend more time on malicious emails, this means they are not rubber-stamping the agent’s malicious verdicts. Rather, they use the agent’s insights to guide their own investigation.\n    99\t\n   100\tHowever, we do find they are more likely to confirm agent TNs by 21 percentage points and more likely to miss agent SFNs by 29 percentage points. This lends further support to the use of the resolve-benign protocol. In addition to the productivity gains documented above, the protection benefit of having analysts spend time on emails the agent has already classified as non-malicious is minimal because analysts are likely to agree with the agent anyway.\n   101\t\n   102\t## Appendix B TP per Analyst Minute\n   103\t\n   104\tLet p denote the malicious email rate in the corpus. We define the following parameters and variables:\n   105\t\n   106\t### B.1 Control Group Performance\n   107\t\n   108\tFor the control group, the expected time to process an email is:\n   109\t\n   110\t\\mathbb{E}[T_{c}]=p\\cdot\\bar{T}_{c,m}+(1-p)\\cdot\\bar{T}_{c,b} | |\n   111\t\n   112\twhere:\n   113\t\n   114\t•\n   115\t\n   116\t\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n   117\t\n   118\t•\n   119\t\n   120\t\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n   121\t\n   122\tThe true positive rate for the control group is defined as:\n   123\t\n   124\t\\text{TPR}_{c}=\\frac{\\text{TP}_{c}}{n_{m}} | |\n   125\t\n   126\twhere \\text{TP}_{c} is the number of true positives identified by control subjects and n_{m} is the total number of malicious emails in the sample.\n   127\t\n   128\tThe productivity metric for the control group (true positives per minute) is:\n   129\t\n   130\t\\rho_{c}=\\frac{p\\cdot\\text{TPR}_{c}}{\\mathbb{E}[T_{c}]} | |\n   131\t\n   132\t### B.2 Aware Group Performance (Priority Queue)\n   133\t\n   134\tFor the Aware group processing a priority queue filtered by an agent with precision \\pi and recall \\sigma , the queue composition changes. The priority queue contains only items flagged by the agent as malicious. The expected time to process an item in the priority queue is:\n   135\t\n   136\t\\mathbb{E}[T_{a}]=\\pi\\cdot\\bar{T}_{a,\\text{TP}}+(1-\\pi)\\cdot\\bar{T}_{a,\\text{FP}} | |\n   137\t\n   138\twhere:\n   139\t\n   140\t•\n   141\t\n   142\t\\bar{T}_{a,\\text{TP}} is the mean time for Aware group to process agent-identified true positives\n   143\t\n   144\t•\n   145\t\n   146\t\\bar{T}_{a,\\text{FP}} is the mean time for Aware group to process agent-identified false positives\n   147\t\n   148\tThe accuracy of the Aware group on agent-identified true positives is:\n   149\t\n   150\t\\alpha_{a,\\text{TP}}=\\mathbb{E}[\\mathbbm{1}_{\\text{correct}}|\\text{Agent}=\\text{TP}] | |\n   151\t\n   152\tThe productivity metric for the Aware group (true positives per minute) is:\n   153\t\n   154\t\\rho_{a}=\\frac{\\pi\\cdot\\alpha_{a,\\text{TP}}}{\\mathbb{E}[T_{a}]} | |\n   155\t\n   156\t### B.3 Productivity Factor\n   157\t\n   158\tThe productivity factor, representing the multiplicative improvement of the Aware group over the control group, is:\n   159\t\n   160\t\\phi^{(p)}=\\frac{\\rho_{a}}{\\rho_{c}} | |\n   161\t\n   162\tA value of \\phi^{(p)}>1 indicates improved productivity, with \\phi^{(p)}=2 representing a doubling of productivity (twice as many true positives identified per unit time).","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"59eff391-8abb-41c5-92fb-420302f2936b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"30cf3aa8-6154-4dc0-bc02-8799b0825859","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PrkqTXnrHzs5GPYYFrpyw8","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2304.09339v1\n     2\tTitle: The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban\n     3\tAuthors: David H. Kreitmeir; Paul A. Raschky (Monash University)\n     4\tBibliographic record: arXiv:2304.09339v1 [econ.GN], version 1 submitted 18 Apr 2023. Abstract page: https://arxiv.org/abs/2304.09339v1\n     5\tRetrieved: https://arxiv.org/html/2304.09339 on 2026-10-30T04:19:51Z (SHA-256 of the retrieved HTML: d419d3955978fb42e4ea99263e206a396a33ec3ff53ae8d83abbd3db1dacaa8c)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3.1 'GitHub Data' (outcome definitions); Section 4 'The Effect of the Ban on Productivity' including Table 1 'The Effect of the ChatGPT Ban on GitHub Output' with its notes.\n     8\tOmitted: Sections 1-2, 3.2, 5-6, Table 2, the appendices, all figures, the references. The date line printed under the author block in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# The Unintended Consequences of Censoring Digital Technology – Evidence from Italy’s ChatGPT Ban\n    13\t\n    14\tWe would like to thank Yashdeep Dahiya for outstanding research assistance. Department of Economics and SoDa Labs, Monash University. See praschky.GitHub.io and paul.raschky@monash.edu\n    15\t\n    16\tDavid Kreitmeir\n    17\t\n    18\tNote: Department of Economics and SoDa Labs, Monash University. See davidkreitmeir.GitHub.io and david.kreitmeir@monash.edu\n    19\t\n    20\tAffiliation: Paul A. Raschky\n    21\t\n    22\tAugust 24, 2026\n    23\t\n    24\t###### Abstract\n    25\t\n    26\tWe analyse the effects of the ban of ChatGPT, a generative pre-trained transformer chatbot, on individual productivity. We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.\n    27\t\n    28\t### 3.1 GitHub Data\n    29\t\n    30\tWe access individual, real-time activity data from GitHub users in Italy (treatment), Austria and France (control) in the weeks prior and immediately after the ChatGPT ban in Italy, March 27th – April 11th 2023.\n    31\t\n    32\tGitHub is the world’s largest online code hosting platform, used for storing and jointly working on coding projects, so called repositories. All modifications to the GitHub repository are automatically timestamped and stored, and GitHub permits the tracking of any iterations to specific files and lines of code. The history of iterations and actions is available for anybody with access to a repository to examine and download. Every action taken by a team member is automatically recorded, keeping details about the kind and substance of the modification, the files and code lines affected, and the date those changes were performed. With GitHub’s history of developing open-source software, a significant portion of the repositories are not access restricted, meaning that the information about projects’ activity is available to everyone. As such, public GitHub repositories provide a direct, real-time measurement of labor activity of software and code developers for millions of\n    33\tsoftware and code developers worldwide (McDermott and Hansen,, 2021).\n    34\t\n    35\tGitHub data has already been used in empirical research to study changes in software developers’ productivity during the onset of COVID19 (Forsgren,, 2021), how COVID19 changed the daily and weekly patterns of labour allocation of individuals (McDermott and Hansen,, 2021); the impact of the effects of working from home on individuals’ productivity (Shen,, 2023), the effect of air pollution on individual output (Holub and Thies,, 2023), or analyse the relationship between social links and the likelihood of joining professional software development teams(Casalnuovo et al.,, 2015).\n    36\t\n    37\tOur preferred measure for GitHub users’ output are Release events. A release is a deployable version of a software project that make it available for a wider audience and easy for users to download and install the software. It typically includes a set of release notes, documentation, and binaries or source code.^4^4\n    38\t4\n    39\t\n    40\tSee https://docs.GitHub.com/en/repositories/releasing-projects-on-GitHub/about-releases. On an hourly and daily basis, releases are relatively rare events and we create an indicator variable that switches to one if the user has recorded at least one release event during the respective time period (hour or day).\n    41\t\n    42\tWe also use the (log of) number of total GitHub events by a user, Events , the sum of Push and Pull events, PushPull and an aggregate Output measure based on Holub and Thies, (2023), which is the sum of Push, Pull Requests, Pull Request Comments, Commit Comments, Create, and Issues. In addition, we use information about the users organisational affiliation to distinguish between GitHub users who have entered an organisational affiliation (i.e. company, research organisation) and those without an organisational affiliation.\n    43\t\n    44\tWe access data on individual GitHub user’s activity from and GitHub Archive which is updated daily and contains all public event data. GitHub Archive data is hosted on Google’s BigQuery warehouse system and can be accessed with a query on google’s cloud infrastructure. GitHub user information was downloaded using the GitHub GraphQL API ^5^5\n    45\t5\n    46\t\n    47\tThe python scripts that was written to access the GitHub user information for the respective countries is available at https://GitHub.com/sodalabsio/GitHub_scrape. Both datasets are then joined via the unique GitHub user login.\n    48\t\n    49\tDescriptive statistics and a balance test for the GitHub data is presented in Table A.1 in the appendix.\n    50\t\n    51\t## 4 The Effect of the Ban on Productivity\n    52\t\n    53\tTo analyse the effect of the Italian ChatGPT ban on output by GitHub users, we estimate variants of the following Difference-in-Difference (DID) event study model:\n    54\t\n    55\t\\displaystyle Y_{it}= \\displaystyle\\alpha_{i}+\\lambda_{t}+\\sum_{\\tau=-3}^{-1}\\beta_{\\tau}D_{it}^{\\tau}+\\sum_{\\tau=0}^{4}\\beta_{\\tau}D_{it}^{\\tau}+\\epsilon_{it}, | | | | (1)\n    56\t\n    57\twhere Y_{it} is an indicator variable that equaling to one if user i made a Release on GitHub on day t and zero otherwise. D is a dummy variable, switching to one for observation’s in the treatment group is in period \\tau and zero otherwise. \\alpha_{i} is a vector of user specific fixed effects, \\lambda_{t} are day (date) fixed effects, \\beta are the parameters of interest and \\epsilon_{it} is the error term. Standard errors are clustered at the GitHub user-level.\n    58\t\n    59\tThe results are presented in Figure 1 showing a statistically significant drop in release likelihood in the two first business days after the ChatGPT ban was initiated.\n    60\t\n    61\tFigure 1: The Effect of the ChatGpt ban on GitHub Releases\n    62\t\n    63\tNotes: Black dots represent the coefficients from estimating the difference-in-Difference (DID) event study model from equation 1. 95% confidence intervals using robust standard errors clustered on the GitHub user-level are depicted. The horizontal axis label denotes the days before and after the ChatGPT ban was implemented. The last day prior to the ban, 31/03/23 is the left out period. The weekend days (01/02.04) after the ban was implemented are not included.\n    64\t\n    65\tWe then proceed and estimate a difference-in-differences specification that combines the Pre and Post period. The results are presented in Table 1. In the first specification in panel A , we specify the pre period as the last Monday and Tuesday (March 27th and 28th 2023) prior to the ban and the post period as the first Monday and Tuesday (April 3rd and 4th 2023) after the implementation of the ban. Columns 1–4 present estimates for the sample of users with organisational affiliation, while columns 5–8 are the estimates for users without affiliation. Starting with the sample of GitHub users with an organisational affiliation, we find no systematic effect on the ChatGPT ban on the total number of GitHub events. However, we find a a statistically significant and negative effect on the likelihood of Release events. Considering the mean of Release in the sample of 0.024, the point translates into an approx. 50% reduction in the likelihood that a developer will have a release event recorded on GitHub in the first two business days after the ban. The estimated coefficients for Output and PushPull also have a negative sign but are not statistically significant. We do not find any systematic effects of the ban on activity by GitHub users without an organisational affiliation. Panel B contains results where the post period consists of the next three business days after the ban, Thursday, Friday (April 5th and 6th)^7^7\n    66\t7\n    67\t\n    68\tWe excluded April 7th because this was Good Friday as well as Monday (April 11th). None of the estimated coefficients of Treat\\times Post is statistically significant and the coefficients also have a lower magnitude compared to the results in panel A . This suggests that the disruptions caused by the ban was relatively short-lived and Italian GitHub users quickly recovered their pre-ban levels of productivity.\n    69\t\n    70\tTable 1: The Effect of the ChatGPT Ban on GitHub Output\n    71\t\n    72\t| Users with Affiliation | | | | Users w/o Affiliation | | |\n    73\t\n    74\t| Events | Release | Output | PushPull | Events | Release | Output | PushPull\n    75\t\n    76\t| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)\n    77\t\n    78\t| A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n    79\t\n    80\t\\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n    81\t\n    82\t| (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n    83\t\n    84\tPost | 0.052 | 0.002 | 0.453*** | 0.323*** | -0.013 | 0.002 | 0.040 | 0.051\n    85\t\n    86\t| (0.053) | (0.002) | (0.123) | (0.094) | (0.060) | (0.002) | (0.119) | (0.101)\n    87\t\n    88\tN | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n    89\t\n    90\t| B: Pre 27/28/29.03 - Post 05/06/11.04 | | | | | | |\n    91\t\n    92\t\\times TreatPost | 0.048 | -0.003 | 0.135 | 0.073 | 0.003 | 0.000 | 0.067 | 0.058\n    93\t\n    94\t| (0.090) | (0.004) | (0.223) | (0.163) | (0.101) | (0.004) | (0.175) | (0.142)\n    95\t\n    96\tPost | -0.217*** | -0.002 | -0.037 | -0.001 | -0.325*** | -0.000 | -0.112 | -0.069\n    97\t\n    98\t| (0.042) | (0.002) | (0.097) | (0.074) | (0.050) | (0.002) | (0.095) | (0.078)\n    99\t\n   100\tN | 31983 | 31983 | 31983 | 31983 | 25368 | 25368 | 25368 | 25368\n   101\t\n   102\t•\n   103\t\n   104\tNotes:\n   105\t\n   106\t•\n   107\t\n   108\tAll specifications include user fixed effects. Robust standard errors are clustered on the user-level in parentheses: *p<0.1, ** p<0.5, *** p<0.01.","is_error":false}]},"uuid":"73f9bff5-2fd7-4194-8572-dd464d22b133","timestamp":"2026-10-30T05:29:22.951Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2304.09339v1\n     2\tTitle: The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban\n     3\tAuthors: David H. Kreitmeir; Paul A. Raschky (Monash University)\n     4\tBibliographic record: arXiv:2304.09339v1 [econ.GN], version 1 submitted 18 Apr 2023. Abstract page: https://arxiv.org/abs/2304.09339v1\n     5\tRetrieved: https://arxiv.org/html/2304.09339 on 2026-10-30T04:19:51Z (SHA-256 of the retrieved HTML: d419d3955978fb42e4ea99263e206a396a33ec3ff53ae8d83abbd3db1dacaa8c)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3.1 'GitHub Data' (outcome definitions); Section 4 'The Effect of the Ban on Productivity' including Table 1 'The Effect of the ChatGPT Ban on GitHub Output' with its notes.\n     8\tOmitted: Sections 1-2, 3.2, 5-6, Table 2, the appendices, all figures, the references. The date line printed under the author block in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# The Unintended Consequences of Censoring Digital Technology – Evidence from Italy’s ChatGPT Ban\n    13\t\n    14\tWe would like to thank Yashdeep Dahiya for outstanding research assistance. Department of Economics and SoDa Labs, Monash University. See praschky.GitHub.io and paul.raschky@monash.edu\n    15\t\n    16\tDavid Kreitmeir\n    17\t\n    18\tNote: Department of Economics and SoDa Labs, Monash University. See davidkreitmeir.GitHub.io and david.kreitmeir@monash.edu\n    19\t\n    20\tAffiliation: Paul A. Raschky\n    21\t\n    22\tAugust 24, 2026\n    23\t\n    24\t###### Abstract\n    25\t\n    26\tWe analyse the effects of the ban of ChatGPT, a generative pre-trained transformer chatbot, on individual productivity. We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.\n    27\t\n    28\t### 3.1 GitHub Data\n    29\t\n    30\tWe access individual, real-time activity data from GitHub users in Italy (treatment), Austria and France (control) in the weeks prior and immediately after the ChatGPT ban in Italy, March 27th – April 11th 2023.\n    31\t\n    32\tGitHub is the world’s largest online code hosting platform, used for storing and jointly working on coding projects, so called repositories. All modifications to the GitHub repository are automatically timestamped and stored, and GitHub permits the tracking of any iterations to specific files and lines of code. The history of iterations and actions is available for anybody with access to a repository to examine and download. Every action taken by a team member is automatically recorded, keeping details about the kind and substance of the modification, the files and code lines affected, and the date those changes were performed. With GitHub’s history of developing open-source software, a significant portion of the repositories are not access restricted, meaning that the information about projects’ activity is available to everyone. As such, public GitHub repositories provide a direct, real-time measurement of labor activity of software and code developers for millions of\n    33\tsoftware and code developers worldwide (McDermott and Hansen,, 2021).\n    34\t\n    35\tGitHub data has already been used in empirical research to study changes in software developers’ productivity during the onset of COVID19 (Forsgren,, 2021), how COVID19 changed the daily and weekly patterns of labour allocation of individuals (McDermott and Hansen,, 2021); the impact of the effects of working from home on individuals’ productivity (Shen,, 2023), the effect of air pollution on individual output (Holub and Thies,, 2023), or analyse the relationship between social links and the likelihood of joining professional software development teams(Casalnuovo et al.,, 2015).\n    36\t\n    37\tOur preferred measure for GitHub users’ output are Release events. A release is a deployable version of a software project that make it available for a wider audience and easy for users to download and install the software. It typically includes a set of release notes, documentation, and binaries or source code.^4^4\n    38\t4\n    39\t\n    40\tSee https://docs.GitHub.com/en/repositories/releasing-projects-on-GitHub/about-releases. On an hourly and daily basis, releases are relatively rare events and we create an indicator variable that switches to one if the user has recorded at least one release event during the respective time period (hour or day).\n    41\t\n    42\tWe also use the (log of) number of total GitHub events by a user, Events , the sum of Push and Pull events, PushPull and an aggregate Output measure based on Holub and Thies, (2023), which is the sum of Push, Pull Requests, Pull Request Comments, Commit Comments, Create, and Issues. In addition, we use information about the users organisational affiliation to distinguish between GitHub users who have entered an organisational affiliation (i.e. company, research organisation) and those without an organisational affiliation.\n    43\t\n    44\tWe access data on individual GitHub user’s activity from and GitHub Archive which is updated daily and contains all public event data. GitHub Archive data is hosted on Google’s BigQuery warehouse system and can be accessed with a query on google’s cloud infrastructure. GitHub user information was downloaded using the GitHub GraphQL API ^5^5\n    45\t5\n    46\t\n    47\tThe python scripts that was written to access the GitHub user information for the respective countries is available at https://GitHub.com/sodalabsio/GitHub_scrape. Both datasets are then joined via the unique GitHub user login.\n    48\t\n    49\tDescriptive statistics and a balance test for the GitHub data is presented in Table A.1 in the appendix.\n    50\t\n    51\t## 4 The Effect of the Ban on Productivity\n    52\t\n    53\tTo analyse the effect of the Italian ChatGPT ban on output by GitHub users, we estimate variants of the following Difference-in-Difference (DID) event study model:\n    54\t\n    55\t\\displaystyle Y_{it}= \\displaystyle\\alpha_{i}+\\lambda_{t}+\\sum_{\\tau=-3}^{-1}\\beta_{\\tau}D_{it}^{\\tau}+\\sum_{\\tau=0}^{4}\\beta_{\\tau}D_{it}^{\\tau}+\\epsilon_{it}, | | | | (1)\n    56\t\n    57\twhere Y_{it} is an indicator variable that equaling to one if user i made a Release on GitHub on day t and zero otherwise. D is a dummy variable, switching to one for observation’s in the treatment group is in period \\tau and zero otherwise. \\alpha_{i} is a vector of user specific fixed effects, \\lambda_{t} are day (date) fixed effects, \\beta are the parameters of interest and \\epsilon_{it} is the error term. Standard errors are clustered at the GitHub user-level.\n    58\t\n    59\tThe results are presented in Figure 1 showing a statistically significant drop in release likelihood in the two first business days after the ChatGPT ban was initiated.\n    60\t\n    61\tFigure 1: The Effect of the ChatGpt ban on GitHub Releases\n    62\t\n    63\tNotes: Black dots represent the coefficients from estimating the difference-in-Difference (DID) event study model from equation 1. 95% confidence intervals using robust standard errors clustered on the GitHub user-level are depicted. The horizontal axis label denotes the days before and after the ChatGPT ban was implemented. The last day prior to the ban, 31/03/23 is the left out period. The weekend days (01/02.04) after the ban was implemented are not included.\n    64\t\n    65\tWe then proceed and estimate a difference-in-differences specification that combines the Pre and Post period. The results are presented in Table 1. In the first specification in panel A , we specify the pre period as the last Monday and Tuesday (March 27th and 28th 2023) prior to the ban and the post period as the first Monday and Tuesday (April 3rd and 4th 2023) after the implementation of the ban. Columns 1–4 present estimates for the sample of users with organisational affiliation, while columns 5–8 are the estimates for users without affiliation. Starting with the sample of GitHub users with an organisational affiliation, we find no systematic effect on the ChatGPT ban on the total number of GitHub events. However, we find a a statistically significant and negative effect on the likelihood of Release events. Considering the mean of Release in the sample of 0.024, the point translates into an approx. 50% reduction in the likelihood that a developer will have a release event recorded on GitHub in the first two business days after the ban. The estimated coefficients for Output and PushPull also have a negative sign but are not statistically significant. We do not find any systematic effects of the ban on activity by GitHub users without an organisational affiliation. Panel B contains results where the post period consists of the next three business days after the ban, Thursday, Friday (April 5th and 6th)^7^7\n    66\t7\n    67\t\n    68\tWe excluded April 7th because this was Good Friday as well as Monday (April 11th). None of the estimated coefficients of Treat\\times Post is statistically significant and the coefficients also have a lower magnitude compared to the results in panel A . This suggests that the disruptions caused by the ban was relatively short-lived and Italian GitHub users quickly recovered their pre-ban levels of productivity.\n    69\t\n    70\tTable 1: The Effect of the ChatGPT Ban on GitHub Output\n    71\t\n    72\t| Users with Affiliation | | | | Users w/o Affiliation | | |\n    73\t\n    74\t| Events | Release | Output | PushPull | Events | Release | Output | PushPull\n    75\t\n    76\t| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)\n    77\t\n    78\t| A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n    79\t\n    80\t\\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n    81\t\n    82\t| (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n    83\t\n    84\tPost | 0.052 | 0.002 | 0.453*** | 0.323*** | -0.013 | 0.002 | 0.040 | 0.051\n    85\t\n    86\t| (0.053) | (0.002) | (0.123) | (0.094) | (0.060) | (0.002) | (0.119) | (0.101)\n    87\t\n    88\tN | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n    89\t\n    90\t| B: Pre 27/28/29.03 - Post 05/06/11.04 | | | | | | |\n    91\t\n    92\t\\times TreatPost | 0.048 | -0.003 | 0.135 | 0.073 | 0.003 | 0.000 | 0.067 | 0.058\n    93\t\n    94\t| (0.090) | (0.004) | (0.223) | (0.163) | (0.101) | (0.004) | (0.175) | (0.142)\n    95\t\n    96\tPost | -0.217*** | -0.002 | -0.037 | -0.001 | -0.325*** | -0.000 | -0.112 | -0.069\n    97\t\n    98\t| (0.042) | (0.002) | (0.097) | (0.074) | (0.050) | (0.002) | (0.095) | (0.078)\n    99\t\n   100\tN | 31983 | 31983 | 31983 | 31983 | 25368 | 25368 | 25368 | 25368\n   101\t\n   102\t•\n   103\t\n   104\tNotes:\n   105\t\n   106\t•\n   107\t\n   108\tAll specifications include user fixed effects. Robust standard errors are clustered on the user-level in parentheses: *p<0.1, ** p<0.5, *** p<0.01.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"30cf3aa8-6154-4dc0-bc02-8799b0825859","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; from Section 3 'Methodology' the RCT protocol and subject-count paragraphs; the opening of Section 4 'Results'; Section 4.2 'Speed' in full (Table 3 'Comparison of task duration (minutes) by task type, holding accuracy constant' and figure captions); Appendix B 'Calculating Time Savings Holding Accuracy Constant'.\n     8\tOmitted: Sections 1-2, the rest of Section 3 (tasks, Table 1), Section 4.1 (accuracy, Table 2), 4.3 (sentiment, Tables 4-5), Section 5, Appendix A, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\tEditorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n    11\t================================================================\n    12\t\n    13\t# Randomized Controlled Trials for Security Copilot for IT Administrators\n    14\t\n    15\tJames Bono\n    16\t^†^†thanks: james.bono@microsoft.com\n    17\t  \n    18\tAlec Xu\n    19\t^†^†thanks: alecxu@microsoft.com\n    20\t\n    21\tMicrosoft Corporation\n    22\t\n    23\tOctober 2024\n    24\t\n    25\t###### Abstract\n    26\t\n    27\tAs generative AI (GAI) tools become increasingly integrated into workplace environments, it is essential to measure their impact on productivity across specific domains. This study evaluates the effects of Microsoft’s Security Copilot (“Copilot”) on information technology administrators (“IT admins”) through randomized controlled trials. Participants were divided into treatment and control groups, with the former granted access to Copilot within Microsoft’s Entra and Intune admin centers. Across three IT admin scenarios – sign-in troubleshooting, device policy management, and device troubleshooting – Copilot users demonstrated substantial improvements in both accuracy and speed. Across all scenarios and tasks, Copilot subjects experienced a 34.53% improvement in overall accuracy and a 29.79% reduction in task completion time. We also find that the productivity benefits vary by task type, with more complex tasks showing greater improvement. In free response tasks, Copilot users identified 146.07% more relevant facts and reduced task completion time by 61.14%. Subject satisfaction with Copilot was high, with participants reporting reduced effort and a strong preference for using the tool in future tasks. These findings suggest that GAI tools like Copilot can significantly enhance the productivity and efficiency of IT admins, especially in scenarios requiring information synthesis and complex decision-making.\n    28\t\n    29\t## 3 Methodology\n    30\t\n    31\tWe follow a standard RCT protocol to identify and estimate the causal effect of Copilot on accuracy and task completion time. That is, we randomly assign half of our subjects to the treatment group, which gives them the ability to use the Entra and Intune admin centers with Copilot in responding to the experimental tasks. The other half of the subjects are assigned to the control group and have use of the Entra and Intune admin centers without Copilot. To make our design as realistic as possible, we placed no restrictions on how subjects used other tools, such as other AI tools or web searches.^2^2\n    32\t2\n    33\t\n    34\tIndeed, after they completed the task, we asked control subjects whether they used any other AI tools, and 25% indicated they used some AI tool to help them in this task – most said they used ChatGPT. Therefore, our measurements reflect the incremental benefit of Copilot on top of the admin centers and other tools like web searches and non-Copilot AI. This makes our estimates more conservative than they would be if we had tightly restricted tool use beyond Copilot.\n    35\t\n    36\tWe recruited our subject pool through Upwork, a marketplace for freelancers. We required subjects to be proficient in reading and writing English and to have a positive reputation on Upwork. We told them to expect the tasks to take approximately two hours to complete. We offered performance incentives for combined speed and accuracy. Specifically, we multiplied the percentiles of each subject’s speed and accuracy and awarded payments on the following scale:\n    37\t\n    38\tWe had 181 subjects complete the task satisfactorily.^3^3\n    39\t3\n    40\t\n    41\tWe dropped one subject who finished in unreasonably short time and scored worse than guessing percentage. Our subject pool embodied a diverse range of experience levels across identity and network device management. See table 1 for a full breakdown of subjects by experience level.\n    42\t\n    43\t## 4 Results\n    44\t\n    45\tThe results answer RQ1 in the affirmative: Copilot does offer speed and accuracy improvements in common IT scenarios. Across all tasks, we measure a 34.53%*** increase in accuracy and a 29.79%*** reduction in task completion time. In the subsections on accuracy and speed below, we present detailed results answering RQ1-RQ4.\n    46\t\n    47\t### 4.2 Speed\n    48\t\n    49\tWe measure statistically significant time savings in all scenarios. Table 3 summarizes these results by task type and scenario. The “Aggregate Results, All Content (Holding accuracy constant)” numbers represent our headline speed improvements, a 29.79%*** time savings for Copilot users. We also note that Copilot generally involves greater latency than typical page-loading in the admin centers, which is common among GAI tools. This necessarily slowed the Copilot users. Product improvements should reduce this latency and further increase the time savings for users with Copilot.\n    50\t\n    51\t| Overall | Treatment | Control | Improvement\n    52\t\n    53\tAggregate Results | | | |\n    54\t\n    55\tAll Content | 53.84 | 44.29 | 63.10 | -29.79%***\n    56\t\n    57\tSATA | 17.81 | 15.25 | 20.14 | -24.28%***\n    58\t\n    59\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    60\t\n    61\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    62\t\n    63\tFree Response | 30.98 | 17.03 | 43.82 | -61.14%***\n    64\t\n    65\tSign-In Troubleshooting | | | |\n    66\t\n    67\tAll Content | 24.04 | 16.63 | 30.46 | -45.41%***\n    68\t\n    69\tSATA | 6.41 | 5.04 | 7.63 | -33.98%***\n    70\t\n    71\tFree Response Content | 17.37 | 11.59 | 22.40 | -48.23%***\n    72\t\n    73\tDevice Policy Management | | | |\n    74\t\n    75\tAll Content | 20.00 | 17.45 | 22.23 | -21.64%***\n    76\t\n    77\tMC | 10.91 | 12.02 | 9.60 | 25.13%***\n    78\t\n    79\tMC (Drop first) | 7.17 | 7.47 | 6.71 | 10.92%\n    80\t\n    81\tFree Response Content | 12.17 | 5.43 | 19.27 | -71.80%***\n    82\t\n    83\tDevice Troubleshooting | | | |\n    84\t\n    85\tSATA | 11.24 | 10.21 | 12.11 | -15.69%***\n    86\t\n    87\tTable 3: Comparison of task duration (minutes) by task type, holding accuracy constant\n    88\t\n    89\tWe measure time savings for Copilot across all three scenarios. The time savings included 45.41%*** for sign-in troubleshooting, 21.64%*** for device policy management, and 15.69%*** for device troubleshooting.\n    90\t\n    91\tFigure 2: The difference in time spent between Copilot and control users is greatest on the first MC question, suggesting a possible learning phenomenon.\n    92\t\n    93\tWe find that Copilot’s time savings track its accuracy improvements across task types. That is, the biggest gains again come from free response (61.14%*** time savings), followed by SATA (24.28%***). Lastly, the MC questions show a decrease in speed for Copilot users (25.13%*** time increase). For these MC questions we also estimate a negative relationship between accuracy and time spent for the control group. This is likely because control subjects who knew the answer responded quickly, and those who did not know it gained little accuracy by spending more time by, for example, searching the internet. This can explain the data we see, where even though Copilot provided an advantage in responding accurately, controlling for accuracy in calculating its time savings does not make up for the disadvantage of its latency.\n    94\t\n    95\tWe also notice that Copilot users spent much more time than the control users on the first MC question compared to the subsequent three questions (see figure 2). We think this suggests Copilot users were learning how to use Copilot for this type of task. Therefore, we drop the first question and recompute the time differences across the remaining tasks, which results in no statistically significant difference in task completion time (see the “Drop first” entry in table 3).\n    96\t\n    97\tFigure 3: Copilot’s effect heterogeneity by experience level on speed, holding accuracy constant\n    98\t\n    99\tAs we did with accuracy, we examine time savings across different experience levels and domains. Our findings once again reveal statistically significant point estimates of time savings for all groups, as shown in figure 3. However, we do not observe statistically significant differences in estimated effect sizes between any of the groups. The left panel illustrates Copilot’s impact on task completion times for the sign-in troubleshooting task, with a 50.1%*** and 45.2%*** time savings for Copilot users with 0-3 and 3+ years of experience, respectively. These point estimates are consistent with intuition and our accuracy findings.\n   100\t\n   101\tThe right panel of figure 3 shows time savings of 12.6%*** and 30.2%*** for 0-3 and 3+ years of experience, respectively, for device management tasks. This suggests that experienced network management subjects benefit more from Copilot in terms of time savings than their less experienced counterparts, which runs counter to intuition and expectation. This is driven by the fact that less experienced control subjects are driving the negative relationship between task completion time and accuracy for the device policy management MC question, mentioned above. This shrinks the possible time savings for the less experienced Copilot subjects.\n   102\t\n   103\t## Appendix B Calculating Time Savings Holding Accuracy Constant\n   104\t\n   105\tNote that a simple regression of time on treatment and score assumes the returns to additional time spent are the same between the treatment and control groups, which is unlikely true. Therefore, to compare speed holding accuracy constant, we employ a linear regression framework that proceeds in three steps. First, we estimate the task duration as a function of accuracy for the control group. Then we predict the task duration the control group would need to achieve the same accuracy as the Copilot group. Finally, we compute the difference between task durations for the two groups, using a bootstrap to compute the level of certainty of this finding.\n   106\t\n   107\tLet T_{g} and A_{g} represent the set of task durations and accuracy scores for each subject in group g\\in\\{(t)reatment,(c)ontrol\\} . And let \\bar{T}_{g} and \\bar{A}_{g} represent the sample means. We then estimate the effect of accuracy on task duration for the control group via the following regression and ordinary least squares.\n   108\t\n   109\tT_{c}=\\alpha+\\beta A_{c}+\\epsilon | |","is_error":false},{"tool_use_id":"toolu_01G8UtbhLFhERLbtct7GVkTA","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2511.13860v1\n     2\tTitle: Randomized Controlled Trials for Phishing Triage Agent\n     3\tAuthors: James Bono (Microsoft Corporation)\n     4\tBibliographic record: arXiv:2511.13860v1 [econ.GN], version 1 submitted 17 Nov 2025. Abstract page: https://arxiv.org/abs/2511.13860v1\n     5\tRetrieved: https://arxiv.org/html/2511.13860 on 2026-10-30T04:20:39Z (SHA-256 of the retrieved HTML: 9a25378878d5735130154b510c032744445d4089e70415e3f1c3b2ca066b984a)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author and abstract; Section 2 'Methodology' (arms, subjects, queue construction); Section 3 'Results' opening and Section 3.1 'Productivity Gains: True Positives per Analyst Minute' (Table 1); Section 3.3 'Behavior: Effect of Agent on Analyst Behavior' (Table 3 'Comparison of Email Processing Times'); Appendix B 'TP per Analyst Minute'.\n     8\tOmitted: Section 1, Sections 3.2 and 3.4 onward (Tables 2 and 4), Section 4, Appendices A, C and D, all figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Randomized Controlled Trials for Phishing Triage Agent\n    13\t\n    14\tJames Bono\n    15\t^†^†thanks: james.bono@microsoft.com; This work would not be possible without the support of the following contributors: Eden Abadi, Costas Boulis, Michael Browning, Jael Fafner, Corina Feuerstein, Justin Grana, Paula Greve, Cristina Da Gama Henriquez, Adel Khaldi, Amit Kollman, Aviel Lavie, Katerina Marazopoulou, Hani Neuvirth-Telem, Inbar Rotem, Felicity Scott-Milligan, Cole Sims, Holly Stewart, and Tracy Zhen\n    16\t\n    17\tMicrosoft Corporation\n    18\t\n    19\tOctober 2025\n    20\t\n    21\t###### Abstract\n    22\t\n    23\tSecurity operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent—the Microsoft Security Copilot Phishing Triage Agent—on analyst productivity and accuracy. Our results demonstrate that agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group. The agent’s queue prioritization and verdict explanations were both significant drivers of efficiency. Behavioral analysis revealed that agent-augmented analysts reallocated their attention, spending 53% more time on malicious emails, and were not prone to rubber-stamping the agent’s malicious verdicts. These findings offer actionable insights for SOC leaders considering AI adoption, including the potential for agents to fundamentally change the optimal allocation of SOC resources.\n    24\t\n    25\t## 2 Methodology\n    26\t\n    27\tWe conduct an RCT with three arms, randomly assigned:\n    28\t\n    29\t1.\n    30\t\n    31\tControl ( N{=}52 ): Random queue order, no references to AI.\n    32\t\n    33\t2.\n    34\t\n    35\tAware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n    36\t\n    37\t3.\n    38\t\n    39\tBlind ( N{=}59 ): Identical ordering as Aware but without any AI references; neither verdicts nor rationales are revealed.\n    40\t\n    41\tOur subjects are 167 external security analysts.^1^1\n    42\t1\n    43\t\n    44\tWe filter out 13 subject responses that scored less than 40% – significantly below guessing percentage. These included three in the Aware group, six in the Blind group, and four in the Control group. This step does not affect our qualitative results. Their task is to triage a 25-email queue. The queue comes from an original corpus of 93 randomly selected emails reported by Microsoft employees. Through an in-depth analysis by expert graders and security researchers, we arrived at our ground truth of 82 non-malicious and 11 malicious emails.\n    45\t\n    46\tOn this corpus, the agent’s precision and recall were both 100%. The agent’s accuracy is very good, but it is not always perfect. It will occasionally have false negative (FN) and false positive (FP) verdicts. However, without FNs and FPs in our corpus, we cannot measure the effect of the agent on analyst trust and diligence. Hence, we create synthetic false positives (SFPs) and synthetic false negatives (SFNs). We create SFNs from each of the malicious emails by changing the agent verdict and swapping the justification for a generic justification of a non-malicious verdict that fits the content of the malicious email. For example, for a malicious email impersonating Microsoft 365, we used the justification from a non-malicious email from Microsoft 365. We create fake false negatives (SFNs) with a similar technique, e.g., by using the justification from a non-malicious email from Microsoft 365 for a malicious email impersonating Microsoft 365. In both cases, we ensure no details in the fake justification are in obvious conflict with the SFN and SFP emails. Using this method, we ended up with five SFPs and 10 SFNs.\n    47\t\n    48\tTo construct each subject’s 25 email queue, we randomly select four malicious emails, 19 non-malicious emails, one SFN, and one SFP. This sampling scheme mimics agent precision and recall of 80%, which aligns closely to Microsoft’s internal measurements on large random samples. It also includes 20% malicious samples.^2^2\n    49\t2\n    50\t\n    51\tOur results are not sensitive to this malicious percentage, since we calculate the time to triage for TP, TN, SFP, and SFN for all groups separately. We need only that the malicious percentage does not dramatically affect the expectations of the Control group, which 20% should not do. For the Aware and Blind groups, we randomly order the four malicious and one SFP at the front of the queue, i.e., in positions one through five. We then randomly order the 19 non-malicious and one SFN in queue positions six through 25. This implies agent accuracy with precision=recall=0.8. Each email is presented as a timed, forced-choice classification: Malicious vs. Non-Malicious. Responses are final (no backtracking).\n    52\t\n    53\tTo reduce the importance of learning effects and to control for the heterogeneous beliefs of subjects using the agent for the first time, we calibrate our Aware group’s beliefs about the agent’s accuracy with the following information: “On a very large sample, the agent’s accuracy is approximately as follows: Malicious verdicts are 80% correct; Not Malicious verdicts are 95% correct.”\n    54\t\n    55\tFor each email in the queue, subjects have a link to a read-only OneDrive folder with the email’s artifacts, including a screenshot (annotated with primary URL), EML and HTML text, URL list, URL detonation screenshots, and sanitized attachments.^3^3\n    56\t\n    57\t## 3 Results\n    58\t\n    59\tThe results show that the agent offers dramatic improvements in both productivity and protection. These improvements are driven by both the agent’s ability to prioritize the queue as well as its verdicts and explanations. In addition, the agent’s accuracy and its effect on analyst accuracy both strongly support implementing the resolve-benign protocol.\n    60\t\n    61\t### 3.1 Productivity Gains: True Positives per Analyst Minute\n    62\t\n    63\tThe Aware group identifies 6.5 times as many TPs per analyst minute (550% productivity gain) on the corpus ground truth. This number reflects the resolve-benign protocol.\n    64\tUnder the pessimistic counterfactual, analysts identify 3.1 times as many TPs per minute. At a high malicious rate of 30%, we still find an effect of 2.0-2.6 times. These measurements are all statistically significant above the 99% level. Figure 1 illustrates the relationship between the malicious rate and the agent’s productivity gain (as a factor improvement) for both the case where agent precision = 1.0 and the case where agent precision = 0.8. The shaded region is the 99% confidence interval for the case where agent precision = 0.8.\n    65\t\n    66\tFigure 1: Factor Increase in TPs per unit of Analyst Time\n    67\t\n    68\tNext, by applying the resolve-benign protocol to the Blind group’s responses, we measure how much of this gain is due to the resolve-benign protocol versus the subject being aware of the agent, including its verdicts and explanations. We find that the resolve-benign protocol, which dramatically reduces the number of non-malicious emails for review, is the biggest factor driving the productivity gain, accounting for between 79-84% of the total agent-driven productivity gain. As precision decreases, the information conveyed in the agent’s verdicts and explanations becomes relatively more important. However, even when the agent’s precision is maximized, the information conveyed in the agent’s verdicts and explanations remains relatively important, about 13-17% of the total improvement.\n    69\t\n    70\tTable 1: Productivity Improvements by Agent Feature\n    71\t\n    72\tScenario | Factor Increase | Resolve-Benign (%) | Agent Awareness (%)\n    73\t\n    74\tCorpus ground truth | 6.53 | 83 | 17\n    75\t\n    76\tAccuracy counterfactual | 5.04 | 78 | 22\n    77\t\n    78\tMalicious rate counterfactual | 3.97 | 87 | 13\n    79\t\n    80\tPessimistic counterfactual | 3.07 | 83 | 17\n    81\t\n    82\t### 3.3 Behavior: Effect of Agent on Analyst Behavior\n    83\t\n    84\tNext, we look at subject behavior across the entire queue, not just the agent-prioritized portion, i.e., this is how analysts would perform if we removed the resolve-benign protocol. First, we find that the Aware group spends 53% more time per malicious email. They spend marginally less on non-malicious email, and the overall time is roughly the same. This indicates they are utilizing the agent to help optimize their time and attention.\n    85\t\n    86\tTable 3: Comparison of Email Processing Times\n    87\t\n    88\tMetric | Malicious | Non-Malicious | Full Corpus\n    89\t\n    90\tAware Time per Email (s) | 2.8 | 1.8 | 50.5\n    91\t\n    92\tControl Time per Email (s) | 1.9 | 2.4 | 56.9\n    93\t\n    94\t% Time Savings | -52.9 | 23.6 | 11.1\n    95\t\n    96\tp-value | 0.006 | 0.126 | 0.471\n    97\t\n    98\tWe also find that the Aware group is no more likely to confirm the agent’s TPs or miss the agent’s SFPs, which are precisely the samples the Aware group would see under the resolve-benign protocol. Taken together with the evidence that Aware subjects spend more time on malicious emails, this means they are not rubber-stamping the agent’s malicious verdicts. Rather, they use the agent’s insights to guide their own investigation.\n    99\t\n   100\tHowever, we do find they are more likely to confirm agent TNs by 21 percentage points and more likely to miss agent SFNs by 29 percentage points. This lends further support to the use of the resolve-benign protocol. In addition to the productivity gains documented above, the protection benefit of having analysts spend time on emails the agent has already classified as non-malicious is minimal because analysts are likely to agree with the agent anyway.\n   101\t\n   102\t## Appendix B TP per Analyst Minute\n   103\t\n   104\tLet p denote the malicious email rate in the corpus. We define the following parameters and variables:\n   105\t\n   106\t### B.1 Control Group Performance\n   107\t\n   108\tFor the control group, the expected time to process an email is:\n   109\t\n   110\t\\mathbb{E}[T_{c}]=p\\cdot\\bar{T}_{c,m}+(1-p)\\cdot\\bar{T}_{c,b} | |\n   111\t\n   112\twhere:\n   113\t\n   114\t•\n   115\t\n   116\t\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n   117\t\n   118\t•\n   119\t\n   120\t\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n   121\t\n   122\tThe true positive rate for the control group is defined as:\n   123\t\n   124\t\\text{TPR}_{c}=\\frac{\\text{TP}_{c}}{n_{m}} | |\n   125\t\n   126\twhere \\text{TP}_{c} is the number of true positives identified by control subjects and n_{m} is the total number of malicious emails in the sample.\n   127\t\n   128\tThe productivity metric for the control group (true positives per minute) is:\n   129\t\n   130\t\\rho_{c}=\\frac{p\\cdot\\text{TPR}_{c}}{\\mathbb{E}[T_{c}]} | |\n   131\t\n   132\t### B.2 Aware Group Performance (Priority Queue)\n   133\t\n   134\tFor the Aware group processing a priority queue filtered by an agent with precision \\pi and recall \\sigma , the queue composition changes. The priority queue contains only items flagged by the agent as malicious. The expected time to process an item in the priority queue is:\n   135\t\n   136\t\\mathbb{E}[T_{a}]=\\pi\\cdot\\bar{T}_{a,\\text{TP}}+(1-\\pi)\\cdot\\bar{T}_{a,\\text{FP}} | |\n   137\t\n   138\twhere:\n   139\t\n   140\t•\n   141\t\n   142\t\\bar{T}_{a,\\text{TP}} is the mean time for Aware group to process agent-identified true positives\n   143\t\n   144\t•\n   145\t\n   146\t\\bar{T}_{a,\\text{FP}} is the mean time for Aware group to process agent-identified false positives\n   147\t\n   148\tThe accuracy of the Aware group on agent-identified true positives is:\n   149\t\n   150\t\\alpha_{a,\\text{TP}}=\\mathbb{E}[\\mathbbm{1}_{\\text{correct}}|\\text{Agent}=\\text{TP}] | |\n   151\t\n   152\tThe productivity metric for the Aware group (true positives per minute) is:\n   153\t\n   154\t\\rho_{a}=\\frac{\\pi\\cdot\\alpha_{a,\\text{TP}}}{\\mathbb{E}[T_{a}]} | |\n   155\t\n   156\t### B.3 Productivity Factor\n   157\t\n   158\tThe productivity factor, representing the multiplicative improvement of the Aware group over the control group, is:\n   159\t\n   160\t\\phi^{(p)}=\\frac{\\rho_{a}}{\\rho_{c}} | |\n   161\t\n   162\tA value of \\phi^{(p)}>1 indicates improved productivity, with \\phi^{(p)}=2 representing a doubling of productivity (twice as many true positives identified per unit time).","is_error":false},{"tool_use_id":"toolu_01PrkqTXnrHzs5GPYYFrpyw8","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2304.09339v1\n     2\tTitle: The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban\n     3\tAuthors: David H. Kreitmeir; Paul A. Raschky (Monash University)\n     4\tBibliographic record: arXiv:2304.09339v1 [econ.GN], version 1 submitted 18 Apr 2023. Abstract page: https://arxiv.org/abs/2304.09339v1\n     5\tRetrieved: https://arxiv.org/html/2304.09339 on 2026-10-30T04:19:51Z (SHA-256 of the retrieved HTML: d419d3955978fb42e4ea99263e206a396a33ec3ff53ae8d83abbd3db1dacaa8c)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3.1 'GitHub Data' (outcome definitions); Section 4 'The Effect of the Ban on Productivity' including Table 1 'The Effect of the ChatGPT Ban on GitHub Output' with its notes.\n     8\tOmitted: Sections 1-2, 3.2, 5-6, Table 2, the appendices, all figures, the references. The date line printed under the author block in the HTML rendering is generated at rendering time and is not the submission date.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# The Unintended Consequences of Censoring Digital Technology – Evidence from Italy’s ChatGPT Ban\n    13\t\n    14\tWe would like to thank Yashdeep Dahiya for outstanding research assistance. Department of Economics and SoDa Labs, Monash University. See praschky.GitHub.io and paul.raschky@monash.edu\n    15\t\n    16\tDavid Kreitmeir\n    17\t\n    18\tNote: Department of Economics and SoDa Labs, Monash University. See davidkreitmeir.GitHub.io and david.kreitmeir@monash.edu\n    19\t\n    20\tAffiliation: Paul A. Raschky\n    21\t\n    22\tAugust 24, 2026\n    23\t\n    24\t###### Abstract\n    25\t\n    26\tWe analyse the effects of the ban of ChatGPT, a generative pre-trained transformer chatbot, on individual productivity. We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.\n    27\t\n    28\t### 3.1 GitHub Data\n    29\t\n    30\tWe access individual, real-time activity data from GitHub users in Italy (treatment), Austria and France (control) in the weeks prior and immediately after the ChatGPT ban in Italy, March 27th – April 11th 2023.\n    31\t\n    32\tGitHub is the world’s largest online code hosting platform, used for storing and jointly working on coding projects, so called repositories. All modifications to the GitHub repository are automatically timestamped and stored, and GitHub permits the tracking of any iterations to specific files and lines of code. The history of iterations and actions is available for anybody with access to a repository to examine and download. Every action taken by a team member is automatically recorded, keeping details about the kind and substance of the modification, the files and code lines affected, and the date those changes were performed. With GitHub’s history of developing open-source software, a significant portion of the repositories are not access restricted, meaning that the information about projects’ activity is available to everyone. As such, public GitHub repositories provide a direct, real-time measurement of labor activity of software and code developers for millions of\n    33\tsoftware and code developers worldwide (McDermott and Hansen,, 2021).\n    34\t\n    35\tGitHub data has already been used in empirical research to study changes in software developers’ productivity during the onset of COVID19 (Forsgren,, 2021), how COVID19 changed the daily and weekly patterns of labour allocation of individuals (McDermott and Hansen,, 2021); the impact of the effects of working from home on individuals’ productivity (Shen,, 2023), the effect of air pollution on individual output (Holub and Thies,, 2023), or analyse the relationship between social links and the likelihood of joining professional software development teams(Casalnuovo et al.,, 2015).\n    36\t\n    37\tOur preferred measure for GitHub users’ output are Release events. A release is a deployable version of a software project that make it available for a wider audience and easy for users to download and install the software. It typically includes a set of release notes, documentation, and binaries or source code.^4^4\n    38\t4\n    39\t\n    40\tSee https://docs.GitHub.com/en/repositories/releasing-projects-on-GitHub/about-releases. On an hourly and daily basis, releases are relatively rare events and we create an indicator variable that switches to one if the user has recorded at least one release event during the respective time period (hour or day).\n    41\t\n    42\tWe also use the (log of) number of total GitHub events by a user, Events , the sum of Push and Pull events, PushPull and an aggregate Output measure based on Holub and Thies, (2023), which is the sum of Push, Pull Requests, Pull Request Comments, Commit Comments, Create, and Issues. In addition, we use information about the users organisational affiliation to distinguish between GitHub users who have entered an organisational affiliation (i.e. company, research organisation) and those without an organisational affiliation.\n    43\t\n    44\tWe access data on individual GitHub user’s activity from and GitHub Archive which is updated daily and contains all public event data. GitHub Archive data is hosted on Google’s BigQuery warehouse system and can be accessed with a query on google’s cloud infrastructure. GitHub user information was downloaded using the GitHub GraphQL API ^5^5\n    45\t5\n    46\t\n    47\tThe python scripts that was written to access the GitHub user information for the respective countries is available at https://GitHub.com/sodalabsio/GitHub_scrape. Both datasets are then joined via the unique GitHub user login.\n    48\t\n    49\tDescriptive statistics and a balance test for the GitHub data is presented in Table A.1 in the appendix.\n    50\t\n    51\t## 4 The Effect of the Ban on Productivity\n    52\t\n    53\tTo analyse the effect of the Italian ChatGPT ban on output by GitHub users, we estimate variants of the following Difference-in-Difference (DID) event study model:\n    54\t\n    55\t\\displaystyle Y_{it}= \\displaystyle\\alpha_{i}+\\lambda_{t}+\\sum_{\\tau=-3}^{-1}\\beta_{\\tau}D_{it}^{\\tau}+\\sum_{\\tau=0}^{4}\\beta_{\\tau}D_{it}^{\\tau}+\\epsilon_{it}, | | | | (1)\n    56\t\n    57\twhere Y_{it} is an indicator variable that equaling to one if user i made a Release on GitHub on day t and zero otherwise. D is a dummy variable, switching to one for observation’s in the treatment group is in period \\tau and zero otherwise. \\alpha_{i} is a vector of user specific fixed effects, \\lambda_{t} are day (date) fixed effects, \\beta are the parameters of interest and \\epsilon_{it} is the error term. Standard errors are clustered at the GitHub user-level.\n    58\t\n    59\tThe results are presented in Figure 1 showing a statistically significant drop in release likelihood in the two first business days after the ChatGPT ban was initiated.\n    60\t\n    61\tFigure 1: The Effect of the ChatGpt ban on GitHub Releases\n    62\t\n    63\tNotes: Black dots represent the coefficients from estimating the difference-in-Difference (DID) event study model from equation 1. 95% confidence intervals using robust standard errors clustered on the GitHub user-level are depicted. The horizontal axis label denotes the days before and after the ChatGPT ban was implemented. The last day prior to the ban, 31/03/23 is the left out period. The weekend days (01/02.04) after the ban was implemented are not included.\n    64\t\n    65\tWe then proceed and estimate a difference-in-differences specification that combines the Pre and Post period. The results are presented in Table 1. In the first specification in panel A , we specify the pre period as the last Monday and Tuesday (March 27th and 28th 2023) prior to the ban and the post period as the first Monday and Tuesday (April 3rd and 4th 2023) after the implementation of the ban. Columns 1–4 present estimates for the sample of users with organisational affiliation, while columns 5–8 are the estimates for users without affiliation. Starting with the sample of GitHub users with an organisational affiliation, we find no systematic effect on the ChatGPT ban on the total number of GitHub events. However, we find a a statistically significant and negative effect on the likelihood of Release events. Considering the mean of Release in the sample of 0.024, the point translates into an approx. 50% reduction in the likelihood that a developer will have a release event recorded on GitHub in the first two business days after the ban. The estimated coefficients for Output and PushPull also have a negative sign but are not statistically significant. We do not find any systematic effects of the ban on activity by GitHub users without an organisational affiliation. Panel B contains results where the post period consists of the next three business days after the ban, Thursday, Friday (April 5th and 6th)^7^7\n    66\t7\n    67\t\n    68\tWe excluded April 7th because this was Good Friday as well as Monday (April 11th). None of the estimated coefficients of Treat\\times Post is statistically significant and the coefficients also have a lower magnitude compared to the results in panel A . This suggests that the disruptions caused by the ban was relatively short-lived and Italian GitHub users quickly recovered their pre-ban levels of productivity.\n    69\t\n    70\tTable 1: The Effect of the ChatGPT Ban on GitHub Output\n    71\t\n    72\t| Users with Affiliation | | | | Users w/o Affiliation | | |\n    73\t\n    74\t| Events | Release | Output | PushPull | Events | Release | Output | PushPull\n    75\t\n    76\t| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)\n    77\t\n    78\t| A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n    79\t\n    80\t\\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n    81\t\n    82\t| (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n    83\t\n    84\tPost | 0.052 | 0.002 | 0.453*** | 0.323*** | -0.013 | 0.002 | 0.040 | 0.051\n    85\t\n    86\t| (0.053) | (0.002) | (0.123) | (0.094) | (0.060) | (0.002) | (0.119) | (0.101)\n    87\t\n    88\tN | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n    89\t\n    90\t| B: Pre 27/28/29.03 - Post 05/06/11.04 | | | | | | |\n    91\t\n    92\t\\times TreatPost | 0.048 | -0.003 | 0.135 | 0.073 | 0.003 | 0.000 | 0.067 | 0.058\n    93\t\n    94\t| (0.090) | (0.004) | (0.223) | (0.163) | (0.101) | (0.004) | (0.175) | (0.142)\n    95\t\n    96\tPost | -0.217*** | -0.002 | -0.037 | -0.001 | -0.325*** | -0.000 | -0.112 | -0.069\n    97\t\n    98\t| (0.042) | (0.002) | (0.097) | (0.074) | (0.050) | (0.002) | (0.095) | (0.078)\n    99\t\n   100\tN | 31983 | 31983 | 31983 | 31983 | 25368 | 25368 | 25368 | 25368\n   101\t\n   102\t•\n   103\t\n   104\tNotes:\n   105\t\n   106\t•\n   107\t\n   108\tAll specifications include user fixed effects. Robust standard errors are clustered on the user-level in parentheses: *p<0.1, ** p<0.5, *** p<0.01.","is_error":false}]}}
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Deterministic.\"\"\"\nimport json, math, re, os\nimport numpy as np, pandas as pd\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nD = \"data\"; SRC = os.path.join(D, \"sources\"); OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nstudies = json.load(open(os.path.join(D, \"studies.json\")))\n\ndef lines(src):\n    return open(os.path.join(SRC, src + \".txt\"), encoding=\"utf-8\").read().split(\"\\n\")\n\ndef find(src, pattern, start=0):\n    \"\"\"Return (1-based line number, text) of first line matching regex at/after `start`; print it.\"\"\"\n    L = lines(src)\n    for i in range(start, len(L)):\n        if re.search(pattern, L[i]):\n            print(f\"[{src}:{i+1}] {L[i].strip()[:300]}\")\n            return i + 1, L[i]\n    raise ValueError(f\"pattern {pattern!r} not found in {src}\")\n\ndef nums(s):\n    return re.findall(r\"-?\\d+\\.?\\d*\", s)\n\nitems = {}\nprint(\"=== Source lines relied upon ===\")\n\n# ---------------- S1 Paradis et al. 2024 ----------------\ns = \"arxiv-2410.12944v3\"\nn_tab, l_tab = find(s, r\"TABLE III: Summaries\")\nn_b, l_b = find(s, r\"ExpCon \\|\")\nn_se, l_se = find(s, r\"^\\(0\\.\\d+\\) \\(0\\.\\d+\\) \\(0\\.\\d+\\) \\(0\\.\\d+\\) \\|\", n_b)\nn_ci, l_ci = find(s, r\"best-fit Model 2\")\nfind(s, r\"Mean LogToT\")  # printed for data_issues\nb_tokens = l_b.split()[:4]             # '-0.25.' '-0.24.' '-0.30*' '-0.14'\nse_tokens = re.findall(r\"\\(([\\d.]+)\\)\", l_se)\nb_print = b_tokens[1]                  # Model 2 column\nb2 = float(re.sub(r\"[^\\d.\\-]\", \"\", b_print).rstrip(\".\"))\nse2_print = se_tokens[1]; se2 = float(se2_print)\nci = re.search(r\"95\\\\%CI = \\[(-?[\\d.]+),(-?[\\d.]+)\\]\", l_ci); L1, U1 = float(ci.group(1)), float(ci.group(2))\np1 = re.search(r\"p=([\\d.]+)\", l_ci).group(1)\nse_from_ci = (U1 - L1) / (2 * 1.96)\nitems[\"S1\"] = dict(\n    estimate_as_printed=f\"ExpCon (Model 2) = {b_print.rstrip('.')} (marker '.', p<.1) on LogToT\",\n    uncertainty_as_printed=f\"SE ({se2_print}) [Table III, Model 2]; 95%CI = [{ci.group(1)},{ci.group(2)}], p={p1} [Section VI-C]\",\n    sample_as_printed=\"n = 96 (Table III caption); N=47 AI, N=46 no-AI in Section VI-B\",\n    definition=\"OLS coefficient on experimental condition (AI vs no-AI) with natural-log time on task as dependent variable, controlling for developer-level factors (Model 2)\",\n    locator=f\"{s}, Table III (line {n_b}: ExpCon row, 2nd value = Model 2; line {n_se}: SE row), Section VI-C (line {n_ci})\",\n    harmonisable=True, harmonised_estimate=b2, harmonised_se=se2, poolable=True,\n    transformation=f\"theta = b = {b2}; SE(theta) = SE(b) = {se2} (printed). Cross-check from CI: ({U1} - ({L1}))/(2*1.96) = {se_from_ci:.4f}\",\n    reason_if_not=None)\n\n# ---------------- S2 Becker et al. 2025 ----------------\ns = \"arxiv-2507.09089v2\"\nn2, l2 = find(s, r\"implement issues in 19% more time\")\nn2b, _ = find(s, r\"allowing AI actually increases completion time by 19%\")\nn2c, _ = find(s, r\"Developers complete 136 issues\")\nn2d, _ = find(s, r\"By default, we report 95% confidence intervals\")\nn2e, _ = find(s, r\"Omitted:\")\nc2 = float(re.search(r\"(\\d+)% more time\", l2).group(1)) / 100\ntheta2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n    estimate_as_printed=\"19% more time (issue completion time, AI allowed vs disallowed; regression-based)\",\n    uncertainty_as_printed=\"none printed in extract (95% CI using HC3 SEs is shown only in Figure 1, not reproduced)\",\n    sample_as_printed=\"16 developers; 246 tasks (136 issues AI-allowed, 110 AI-disallowed)\",\n    definition=\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from log-linear OLS of completion time on AI indicator and log forecast time\",\n    locator=f\"{s}, Section 3 Results (line {n2}); Abstract (line {n2b}); estimator Sections 2.3, D.1-D.3; CI method D.2 (line {n2d})\",\n    harmonisable=True, harmonised_estimate=theta2, harmonised_se=None, poolable=False,\n    transformation=f\"theta = ln(1 + c) = ln(1 + {c2}) = {theta2:.4f}\",\n    reason_if_not=\"Not poolable: no SE, CI or p-value for the 19% estimate is printed in the extract; the confidence interval appears only in Figure 1 (not reproduced), so SE(theta) cannot be obtained.\")\n\n# ---------------- S3 Bono & Xu 2024 ----------------\ns = \"arxiv-2411.01067v2\"\nn3h, _ = find(s, r\"\\| Overall \\| Treatment \\| Control \\| Improvement\")\nn3a, _ = find(s, r\"^Aggregate Results\")\nn3, l3 = find(s, r\"^All Content \\|\", n3a)\nn3c, _ = find(s, r\"Table 3: Comparison of task duration\")\nn3e, _ = find(s, r\"Editorial note\")\nn3n, _ = find(s, r\"We had 181 subjects\")\ncells = [c.strip() for c in l3.split(\"|\")]\noverall3, treat3, ctrl3, impr3 = cells[1], cells[2], cells[3], cells[4]\nc3 = float(re.sub(r\"[^\\d.\\-]\", \"\", impr3)) / 100\ntheta3 = math.log(1 + c3)\ntheta3_ratio = math.log(float(treat3) / float(ctrl3))\nitems[\"S3\"] = dict(\n    estimate_as_printed=f\"All Content (aggregate): Overall {overall3}, Treatment {treat3}, Control {ctrl3} minutes; Improvement {impr3}\",\n    uncertainty_as_printed=\"significance stars only (*** = 0<p<=0.01, per editorial note); no SE or CI printed\",\n    sample_as_printed=\"181 subjects completed the task (Section 3); treatment/control split not printed\",\n    definition=\"Percentage difference in mean task duration (minutes), Copilot vs control, holding accuracy constant (control duration predicted at treatment accuracy; Appendix B); bootstrap significance\",\n    locator=f\"{s}, Table 3 (line {n3}: 'Aggregate Results / All Content' row; columns Treatment, Control, Improvement)\",\n    harmonisable=True, harmonised_estimate=theta3, harmonised_se=None, poolable=False,\n    transformation=f\"theta = ln(1 + c) = ln(1 + ({c3})) = {theta3:.4f} (cross-check ln({treat3}/{ctrl3}) = {theta3_ratio:.4f})\",\n    reason_if_not=\"Not poolable: uncertainty is reported only as significance stars (***, p<=0.01); a p-value threshold does not identify SE(theta).\")\n\n# ---------------- S4 Bono 2025 ----------------\ns = \"arxiv-2511.13860v1\"\nn4h, l4h = find(s, r\"^Metric \\| Malicious\")\nn4a, l4a = find(s, r\"^Aware Time per Email\")\nn4c, l4c = find(s, r\"^Control Time per Email\")\nn4s, l4s = find(s, r\"^% Time Savings\")\nn4p, l4p = find(s, r\"^p-value \\|\")\nn4n, _ = find(s, r\"Control \\( N\\{=\\}52\")\nfind(s, r\"Aware \\( N\\{=\\}56\")\nlast = lambda l: l.split(\"|\")[-1].strip()\naware4, ctrl4, sav4, p4 = last(l4a), last(l4c), last(l4s), last(l4p)\nc4 = -float(sav4) / 100            # savings of 11.1% -> change in time c = -0.111\ntheta4 = math.log(1 + c4)\nz4 = norm.ppf(1 - float(p4) / 2)\nse4 = abs(theta4) / z4\nitems[\"S4\"] = dict(\n    estimate_as_printed=f\"Full Corpus: Aware Time per Email (s) {aware4}; Control Time per Email (s) {ctrl4}; % Time Savings {sav4}\",\n    uncertainty_as_printed=f\"p-value {p4}\",\n    sample_as_printed=\"Aware N=56, Control N=52 (Blind N=59 not used); 25-email queue per subject\",\n    definition=\"Percentage time saving in mean processing time per email (full queue), Aware arm (agent verdicts + rationale + priority ordering) vs Control (no AI)\",\n    locator=f\"{s}, Section 3.3, Table 3 (lines {n4a}-{n4p}; column 'Full Corpus')\",\n    harmonisable=True, harmonised_estimate=theta4, harmonised_se=se4, poolable=True,\n    transformation=(f\"c = -{sav4}/100 = {c4}; theta = ln(1 + c) = ln({1 + c4:.3f}) = {theta4:.4f} \"\n                    f\"(cross-check ln({aware4}/{ctrl4}) = {math.log(float(aware4)/float(ctrl4)):.4f}); \"\n                    f\"APPROXIMATE SE = |theta|/z_(1-p/2) = {abs(theta4):.4f}/{z4:.4f} = {se4:.4f} from printed p = {p4}\"),\n    reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky 2023 ----------------\ns = \"arxiv-2304.09339v1\"\nn5a, _ = find(s, r\"^\\| A: Pre 27/28.03\")\nn5, l5 = find(s, r\"TreatPost \\|\", n5a)\nn5s, l5s = find(s, r\"^\\| \\(\", n5)\nn5n, l5n = find(s, r\"^N \\|\", n5)\nn5f, _ = find(s, r\"\\*\\* p<0\\.5\")\nb5 = [c.strip() for c in l5.split(\"|\")][3]\nse5 = [c.strip() for c in l5s.split(\"|\")][3]\nN5 = [c.strip() for c in l5n.split(\"|\")][3]\nitems[\"S5\"] = dict(\n    estimate_as_printed=f\"Treat x Post (Output, column 3, panel A) = {b5}\",\n    uncertainty_as_printed=f\"SE {se5} (clustered by user); no significance star\",\n    sample_as_printed=f\"N = {N5} (user-days, users with affiliation, panel A)\",\n    definition=\"Difference-in-differences coefficient on an aggregate GitHub output count (Push, PRs, comments, Create, Issues) for Italian vs Austrian/French users, around the ChatGPT ban (withdrawal of access)\",\n    locator=f\"{s}, Section 4, Table 1, panel A, row 'Treat x Post' (line {n5}), SE line {n5s}, N line {n5n}, column (3) Output\",\n    harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n    transformation=\"none (not harmonisable)\",\n    reason_if_not=\"Not harmonisable: the outcome is a count of GitHub output events per user-day (activity volume), not task-completion time on the same tasks; the contrast is country-level withdrawal of access (DiD), not a randomised AI-vs-no-AI comparison of time on identical tasks.\")\n\n# ---------------- Pooling ----------------\norder = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\npool = [k for k in order if items[k][\"poolable\"]]\ny = np.array([items[k][\"harmonised_estimate\"] for k in pool])\nse = np.array([items[k][\"harmonised_se\"] for k in pool])\n\ndef meta(y, se):\n    w = 1 / se**2\n    fe = np.sum(w * y) / np.sum(w); fe_se = math.sqrt(1 / np.sum(w))\n    Q = float(np.sum(w * (y - fe) ** 2)); df = len(y) - 1\n    i2 = max(0.0, (Q - df) / Q) * 100 if Q > 0 else 0.0\n    C = np.sum(w) - np.sum(w**2) / np.sum(w)\n    tau2 = max(0.0, (Q - df) / C)\n    wr = 1 / (se**2 + tau2)\n    re_ = np.sum(wr * y) / np.sum(wr); re_se = math.sqrt(1 / np.sum(wr))\n    return dict(fe=float(fe), fe_se=fe_se, Q=Q, i2=float(i2), tau2=float(tau2), re=float(re_), re_se=re_se)\n\nm = meta(y, se)\n# sensitivity: S1 SE from the printed CI instead of printed SE\nse_alt = se.copy(); se_alt[pool.index(\"S1\")] = se_from_ci\nm_alt = meta(y, se_alt)\n\ni2 = m[\"i2\"]\nhet = \"low\" if i2 < 25 else (\"moderate\" if i2 < 50 else \"substantial\")\nre_lo, re_hi = m[\"re\"] - 1.96 * m[\"re_se\"], m[\"re\"] + 1.96 * m[\"re_se\"]\n\ndata_issues = [\n    {\"issue\": \"S1 (arxiv-2410.12944v3, Section VI-B): the printed mean LogToT is 4.6 for the AI group and 4.3 for the no-AI group, which contradicts the printed mean times (96 min AI vs 114 min no-AI; ln 96 = 4.56, ln 114 = 4.74) and the statement that the AI group was faster.\",\n     \"handling\": \"Not used. The harmonised value is the Model 2 regression coefficient specified in studies.json, whose sign agrees with the mean times and with the text.\"},\n    {\"issue\": \"S1: Table III caption says n = 96, whereas Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 for the time comparison.\",\n     \"handling\": \"Both reported as printed in sample_as_printed; the SE is taken as printed, so the discrepancy does not affect theta or SE.\"},\n    {\"issue\": \"S1: Table III renders the four model columns collapsed into one line, with significance markers attached to the numbers (e.g. '-0.24.'). The trailing '.' is the p<.1 marker, not a decimal point. The printed SE (0.14) and the SE implied by the printed 95% CI [-0.51, 0.03] (0.1378) differ slightly because of rounding.\",\n     \"handling\": \"Model 2 = second value on the ExpCon line; the marker was stripped. The printed SE 0.14 was used, and a sensitivity analysis uses the CI-derived SE.\"},\n    {\"issue\": \"S1: the extract has stray 'p<' / 'p=' lines and footnote digits ('^2^2', '2').\",\n     \"handling\": \"Ignored as rendering artefacts (data/README.md).\"},\n    {\"issue\": \"S2: the uncertainty of the headline 19% estimate is shown only in Figure 1 (not reproduced). The raw ratio of means (34%) differs from the regression-based 19%.\",\n     \"handling\": \"Used the regression-based 19%, as studies.json specifies. Harmonised theta = ln(1.19). Not poolable because there is no SE.\"},\n    {\"issue\": \"S3: the Table 3 header row has four labels ('Overall | Treatment | Control | Improvement') with an empty leading cell. The 'Improvement' column is actually a signed % change in time (negative = faster); for MC rows a positive value is a time increase (per the text). The printed -29.79% matches 44.29/63.10 - 1 = -0.2981 only up to rounding.\",\n     \"handling\": \"Read the 'Improvement' value as the signed % change c = -0.2979. Computed theta from the printed percentage and reported ln(44.29/63.10) as a cross-check.\"},\n    {\"issue\": \"S3: the significance-star convention is non-standard (* 0.05<p<=0.1, ** 0.01<p<=0.05, *** p<=0.01).\",\n     \"handling\": \"Recorded. Stars give only a p-value threshold, so S3 is not poolable.\"},\n    {\"issue\": \"S4: Table 3 labels all columns 'Time per Email (s)', but the Malicious (2.8 / 1.9) and Non-Malicious (1.8 / 2.4) values cannot be seconds if the Full Corpus value is 50.5 / 56.9 s. No mix of the two categories gives the full-corpus mean in either seconds or minutes, so the units and/or rows are internally inconsistent.\",\n     \"handling\": \"Only the Full Corpus column is used, and theta depends only on the unit-free ratio and % saving. The printed 11.1% agrees with 1 - 50.5/56.9 = 11.25% within rounding of the printed means. Flagged.\"},\n    {\"issue\": \"S4: the 'Aware' arm differs from Control in queue ordering as well as AI verdicts/rationales, and its % time saving is printed positive for a decrease in time (sign opposite to theta).\",\n     \"handling\": \"Aware vs Control is taken as the AI vs no-AI contrast, and the sign is converted (c = -0.111). The SE is approximate, back-calculated from p = 0.471.\"},\n    {\"issue\": \"S5: the Table 1 footnote prints '** p<0.5' (presumably p<0.05). The extract's date line 'August 24, 2026' is a rendering-time artefact.\",\n     \"handling\": \"Recorded. This does not affect anything because S5 is not harmonisable.\"},\n]\n\nn_input_rows = {\"studies.json\": len(studies)}\nfor f in [\"README.md\"] + [f\"sources/{x}\" for x in sorted(os.listdir(SRC))]:\n    with open(os.path.join(D, f), \"rb\") as fh:\n        n_input_rows[f] = fh.read().count(b\"\\n\")\n\nres = dict(\n    task_id=\"T3-synthesis\",\n    target_definition=\"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, on the same tasks, from a randomised comparison (negative = faster with AI).\",\n    items={k: items[k] for k in order},\n    k_extracted=sum(1 for k in order if items[k][\"estimate_as_printed\"]),\n    k_harmonised=sum(1 for k in order if items[k][\"harmonised_estimate\"] is not None),\n    k_pooled=len(pool),\n    fe_estimate=m[\"fe\"], fe_se=m[\"fe_se\"], fe_ci_low=m[\"fe\"] - 1.96 * m[\"fe_se\"], fe_ci_high=m[\"fe\"] + 1.96 * m[\"fe_se\"],\n    re_estimate=m[\"re\"], re_se=m[\"re_se\"], re_ci_low=re_lo, re_ci_high=re_hi,\n    q_stat=m[\"Q\"], i2=i2, tau2=m[\"tau2\"],\n    heterogeneity_interpretation=het,\n    pooled_significant_5pct=bool(re_lo > 0 or re_hi < 0),\n    pooled_pct_change_re=100 * (math.exp(m[\"re\"]) - 1),\n    feasibility=\"complete\",\n    feasibility_reason=None,\n    data_issues=data_issues,\n    n_input_rows=n_input_rows,\n    n_analysed=len(order),\n    random_seed=None,\n    sensitivity_s1_se_from_ci=dict(s1_se=se_from_ci, fe_estimate=m_alt[\"fe\"], fe_se=m_alt[\"fe_se\"],\n                                   re_estimate=m_alt[\"re\"], re_se=m_alt[\"re_se\"], q_stat=m_alt[\"Q\"], i2=m_alt[\"i2\"], tau2=m_alt[\"tau2\"]),\n)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\ncols = [\"estimate_as_printed\", \"uncertainty_as_printed\", \"sample_as_printed\", \"definition\", \"locator\",\n        \"harmonised_estimate\", \"harmonised_se\", \"harmonisable\", \"poolable\", \"transformation\", \"reason_if_not\"]\npd.DataFrame([dict(study=k, **{c: items[k][c] for c in cols}) for k in order]).to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# ---------------- Forest plot ----------------\nharm = [k for k in order if items[k][\"harmonisable\"]]\nfig, ax = plt.subplots(figsize=(8, 4.2))\nlabels = []\nfor i, k in enumerate(harm):\n    yv = len(harm) - i\n    t = items[k][\"harmonised_estimate\"]; s_ = items[k][\"harmonised_se\"]\n    if s_ is not None:\n        ax.errorbar(t, yv, xerr=1.96 * s_, fmt=\"s\", color=\"k\", capsize=3)\n        lab = f\"{k}: {t:.3f} [{t-1.96*s_:.3f}, {t+1.96*s_:.3f}]\" + (\" (approx. SE)\" if k == \"S4\" else \"\")\n    else:\n        ax.plot(t, yv, \"o\", mfc=\"white\", mec=\"gray\")\n        lab = f\"{k}: {t:.3f} (no SE; not pooled)\"\n    labels.append(lab)\nax.errorbar(m[\"re\"], 0, xerr=1.96 * m[\"re_se\"], fmt=\"D\", color=\"tab:blue\", capsize=3)\nlabels.append(f\"RE pooled (k={len(pool)}): {m['re']:.3f} [{re_lo:.3f}, {re_hi:.3f}]\")\nax.set_yticks(list(range(len(harm), -1, -1))); ax.set_yticklabels(labels, fontsize=8)\nax.axvline(0, color=\"gray\", ls=\"--\", lw=0.8)\nax.set_xlabel(\"theta = ln(T_AI / T_noAI)   (negative = faster with AI)\")\nax.set_title(\"Harmonised effects of generative-AI access on task time (95% CI)\", fontsize=10)\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nprint(\"\\n=== Summary ===\")\nfor k in order:\n    print(k, items[k][\"harmonised_estimate\"], items[k][\"harmonised_se\"], items[k][\"poolable\"])\nprint(json.dumps({k: res[k] for k in [\"k_pooled\", \"fe_estimate\", \"fe_se\", \"re_estimate\", \"re_se\", \"re_ci_low\", \"re_ci_high\",\n                                      \"q_stat\", \"i2\", \"tau2\", \"pooled_pct_change_re\", \"pooled_significant_5pct\"]}, indent=1))\nprint(\"sensitivity:\", res[\"sensitivity_s1_se_from_ci\"])\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1847,"cache_read_input_tokens":35650,"output_tokens":8476,"output_tokens_details":{"thinking_tokens":27},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1847},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsUtg48SY3LrtGNewop","requestRef":"29fe1ada-fc8f-4973-a9e3-5b48e6591c54","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fb3eb87a-7f98-4ac6-b88e-c11181d87b5a","timestamp":"2026-10-30T05:30:48.315Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"fb3eb87a-7f98-4ac6-b88e-c11181d87b5a","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510"}
{"type":"atis-latch","atis":"","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510"}
{"parentUuid":"fb3eb87a-7f98-4ac6-b88e-c11181d87b5a","isSidechain":false,"promptId":"6085db16-004f-4189-b776-e3f7ccec1706","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DesrFswrSEngHwhV1x2RPZ","type":"tool_result","content":"=== Source lines relied upon ===\n[arxiv-2410.12944v3:66] TABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n[arxiv-2410.12944v3:76] -0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n[arxiv-2410.12944v3:78] (0.14) (0.14) (0.14) (0.17) | | | |\n[arxiv-2410.12944v3:58] However, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24;   95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supporte\n[arxiv-2410.12944v3:48] On average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignmen\n[arxiv-2507.09089v2:62] Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all\n[arxiv-2507.09089v2:34] Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n[arxiv-2507.09089v2:62] Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all\n[arxiv-2507.09089v2:97] By default, we report 95% confidence intervals using HC3 standard errors.^19^19\n[arxiv-2507.09089v2:8] Omitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n[arxiv-2411.01067v2:51] | Overall | Treatment | Control | Improvement\n[arxiv-2411.01067v2:53] Aggregate Results | | | |\n[arxiv-2411.01067v2:55] All Content | 53.84 | 44.29 | 63.10 | -29.79%***\n[arxiv-2411.01067v2:87] Table 3: Comparison of task duration (minutes) by task type, holding accuracy constant\n[arxiv-2411.01067v2:10] Editorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n[arxiv-2411.01067v2:38] We had 181 subjects complete the task satisfactorily.^3^3\n[arxiv-2511.13860v1:88] Metric | Malicious | Non-Malicious | Full Corpus\n[arxiv-2511.13860v1:90] Aware Time per Email (s) | 2.8 | 1.8 | 50.5\n[arxiv-2511.13860v1:92] Control Time per Email (s) | 1.9 | 2.4 | 56.9\n[arxiv-2511.13860v1:94] % Time Savings | -52.9 | 23.6 | 11.1\n[arxiv-2511.13860v1:96] p-value | 0.006 | 0.126 | 0.471\n[arxiv-2511.13860v1:31] Control ( N{=}52 ): Random queue order, no references to AI.\n[arxiv-2511.13860v1:35] Aware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n[arxiv-2304.09339v1:78] | A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n[arxiv-2304.09339v1:80] \\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n[arxiv-2304.09339v1:82] | (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n[arxiv-2304.09339v1:88] N | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n[arxiv-2304.09339v1:108] All specifications include user fixed effects. Robust standard errors are clustered on the user-level in parentheses: *p<0.1, ** p<0.5, *** p<0.01.\n\n=== Summary ===\nS1 -0.24 0.14 True\nS2 0.17395330712343798 None False\nS3 -0.353679434958934 None False\nS4 -0.11765804346823246 0.1632205919720678 True\nS5 None None False\n{\n \"k_pooled\": 2,\n \"fe_estimate\": -0.18814332871148873,\n \"fe_se\": 0.10626483095451474,\n \"re_estimate\": -0.18814332871148873,\n \"re_se\": 0.10626483095451474,\n \"re_ci_low\": -0.39642239738233764,\n \"re_ci_high\": 0.02013573995936016,\n \"q_stat\": 0.3236860522786328,\n \"i2\": 0.0,\n \"tau2\": 0.0,\n \"pooled_pct_change_re\": -17.15040485348688,\n \"pooled_significant_5pct\": false\n}\nsensitivity: {'s1_se': 0.13775510204081634, 'fe_estimate': -0.1891067723193802, 'fe_se': 0.10527305720065441, 're_estimate': -0.1891067723193802, 're_se': 0.10527305720065441, 'q_stat': 0.3281104262022853, 'i2': 0.0, 'tau2': 0.0}","is_error":false}]},"uuid":"3da8a86e-7b56-4c65-8a1e-59b8e0c6df21","timestamp":"2026-10-30T05:30:49.023Z","toolUseResult":{"stdout":"=== Source lines relied upon ===\n[arxiv-2410.12944v3:66] TABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n[arxiv-2410.12944v3:76] -0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n[arxiv-2410.12944v3:78] (0.14) (0.14) (0.14) (0.17) | | | |\n[arxiv-2410.12944v3:58] However, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24;   95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supporte\n[arxiv-2410.12944v3:48] On average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignmen\n[arxiv-2507.09089v2:62] Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all\n[arxiv-2507.09089v2:34] Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n[arxiv-2507.09089v2:62] Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all\n[arxiv-2507.09089v2:97] By default, we report 95% confidence intervals using HC3 standard errors.^19^19\n[arxiv-2507.09089v2:8] Omitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n[arxiv-2411.01067v2:51] | Overall | Treatment | Control | Improvement\n[arxiv-2411.01067v2:53] Aggregate Results | | | |\n[arxiv-2411.01067v2:55] All Content | 53.84 | 44.29 | 63.10 | -29.79%***\n[arxiv-2411.01067v2:87] Table 3: Comparison of task duration (minutes) by task type, holding accuracy constant\n[arxiv-2411.01067v2:10] Editorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n[arxiv-2411.01067v2:38] We had 181 subjects complete the task satisfactorily.^3^3\n[arxiv-2511.13860v1:88] Metric | Malicious | Non-Malicious | Full Corpus\n[arxiv-2511.13860v1:90] Aware Time per Email (s) | 2.8 | 1.8 | 50.5\n[arxiv-2511.13860v1:92] Control Time per Email (s) | 1.9 | 2.4 | 56.9\n[arxiv-2511.13860v1:94] % Time Savings | -52.9 | 23.6 | 11.1\n[arxiv-2511.13860v1:96] p-value | 0.006 | 0.126 | 0.471\n[arxiv-2511.13860v1:31] Control ( N{=}52 ): Random queue order, no references to AI.\n[arxiv-2511.13860v1:35] Aware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n[arxiv-2304.09339v1:78] | A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n[arxiv-2304.09339v1:80] \\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n[arxiv-2304.09339v1:82] | (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n[arxiv-2304.09339v1:88] N | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n[arxiv-2304.09339v1:108] All specifications include user fixed effects. Robust standard errors are clustered on the user-level in parentheses: *p<0.1, ** p<0.5, *** p<0.01.\n\n=== Summary ===\nS1 -0.24 0.14 True\nS2 0.17395330712343798 None False\nS3 -0.353679434958934 None False\nS4 -0.11765804346823246 0.1632205919720678 True\nS5 None None False\n{\n \"k_pooled\": 2,\n \"fe_estimate\": -0.18814332871148873,\n \"fe_se\": 0.10626483095451474,\n \"re_estimate\": -0.18814332871148873,\n \"re_se\": 0.10626483095451474,\n \"re_ci_low\": -0.39642239738233764,\n \"re_ci_high\": 0.02013573995936016,\n \"q_stat\": 0.3236860522786328,\n \"i2\": 0.0,\n \"tau2\": 0.0,\n \"pooled_pct_change_re\": -17.15040485348688,\n \"pooled_significant_5pct\": false\n}\nsensitivity: {'s1_se': 0.13775510204081634, 'fe_estimate': -0.1891067723193802, 'fe_se': 0.10527305720065441, 're_estimate': -0.1891067723193802, 're_se': 0.10527305720065441, 'q_stat': 0.3281104262022853, 'i2': 0.0, 'tau2': 0.0}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fb3eb87a-7f98-4ac6-b88e-c11181d87b5a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","hash":"4da3beeccd24a3570429ceb960fe3525fbf1e70d6968fe8bb05a8ac170c9554a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01DesrFswrSEngHwhV1x2RPZ","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"T3-synthesis: extraction, harmonisation to theta = ln(T_AI/T_noAI), FE + DL pooling.\nRun from workspace root:  ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/table1.csv, out/figure1.png. Deterministic.\"\"\"\nimport json, math, re, os\nimport numpy as np, pandas as pd\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nD = \"data\"; SRC = os.path.join(D, \"sources\"); OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nstudies = json.load(open(os.path.join(D, \"studies.json\")))\n\ndef lines(src):\n    return open(os.path.join(SRC, src + \".txt\"), encoding=\"utf-8\").read().split(\"\\n\")\n\ndef find(src, pattern, start=0):\n    \"\"\"Return (1-based line number, text) of first line matching regex at/after `start`; print it.\"\"\"\n    L = lines(src)\n    for i in range(start, len(L)):\n        if re.search(pattern, L[i]):\n            print(f\"[{src}:{i+1}] {L[i].strip()[:300]}\")\n            return i + 1, L[i]\n    raise ValueError(f\"pattern {pattern!r} not found in {src}\")\n\ndef nums(s):\n    return re.findall(r\"-?\\d+\\.?\\d*\", s)\n\nitems = {}\nprint(\"=== Source lines relied upon ===\")\n\n# ---------------- S1 Paradis et al. 2024 ----------------\ns = \"arxiv-2410.12944v3\"\nn_tab, l_tab = find(s, r\"TABLE III: Summaries\")\nn_b, l_b = find(s, r\"ExpCon \\|\")\nn_se, l_se = find(s, r\"^\\(0\\.\\d+\\) \\(0\\.\\d+\\) \\(0\\.\\d+\\) \\(0\\.\\d+\\) \\|\", n_b)\nn_ci, l_ci = find(s, r\"best-fit Model 2\")\nfind(s, r\"Mean LogToT\")  # printed for data_issues\nb_tokens = l_b.split()[:4]             # '-0.25.' '-0.24.' '-0.30*' '-0.14'\nse_tokens = re.findall(r\"\\(([\\d.]+)\\)\", l_se)\nb_print = b_tokens[1]                  # Model 2 column\nb2 = float(re.sub(r\"[^\\d.\\-]\", \"\", b_print).rstrip(\".\"))\nse2_print = se_tokens[1]; se2 = float(se2_print)\nci = re.search(r\"95\\\\%CI = \\[(-?[\\d.]+),(-?[\\d.]+)\\]\", l_ci); L1, U1 = float(ci.group(1)), float(ci.group(2))\np1 = re.search(r\"p=([\\d.]+)\", l_ci).group(1)\nse_from_ci = (U1 - L1) / (2 * 1.96)\nitems[\"S1\"] = dict(\n    estimate_as_printed=f\"ExpCon (Model 2) = {b_print.rstrip('.')} (marker '.', p<.1) on LogToT\",\n    uncertainty_as_printed=f\"SE ({se2_print}) [Table III, Model 2]; 95%CI = [{ci.group(1)},{ci.group(2)}], p={p1} [Section VI-C]\",\n    sample_as_printed=\"n = 96 (Table III caption); N=47 AI, N=46 no-AI in Section VI-B\",\n    definition=\"OLS coefficient on experimental condition (AI vs no-AI) with natural-log time on task as dependent variable, controlling for developer-level factors (Model 2)\",\n    locator=f\"{s}, Table III (line {n_b}: ExpCon row, 2nd value = Model 2; line {n_se}: SE row), Section VI-C (line {n_ci})\",\n    harmonisable=True, harmonised_estimate=b2, harmonised_se=se2, poolable=True,\n    transformation=f\"theta = b = {b2}; SE(theta) = SE(b) = {se2} (printed). Cross-check from CI: ({U1} - ({L1}))/(2*1.96) = {se_from_ci:.4f}\",\n    reason_if_not=None)\n\n# ---------------- S2 Becker et al. 2025 ----------------\ns = \"arxiv-2507.09089v2\"\nn2, l2 = find(s, r\"implement issues in 19% more time\")\nn2b, _ = find(s, r\"allowing AI actually increases completion time by 19%\")\nn2c, _ = find(s, r\"Developers complete 136 issues\")\nn2d, _ = find(s, r\"By default, we report 95% confidence intervals\")\nn2e, _ = find(s, r\"Omitted:\")\nc2 = float(re.search(r\"(\\d+)% more time\", l2).group(1)) / 100\ntheta2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n    estimate_as_printed=\"19% more time (issue completion time, AI allowed vs disallowed; regression-based)\",\n    uncertainty_as_printed=\"none printed in extract (95% CI using HC3 SEs is shown only in Figure 1, not reproduced)\",\n    sample_as_printed=\"16 developers; 246 tasks (136 issues AI-allowed, 110 AI-disallowed)\",\n    definition=\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from log-linear OLS of completion time on AI indicator and log forecast time\",\n    locator=f\"{s}, Section 3 Results (line {n2}); Abstract (line {n2b}); estimator Sections 2.3, D.1-D.3; CI method D.2 (line {n2d})\",\n    harmonisable=True, harmonised_estimate=theta2, harmonised_se=None, poolable=False,\n    transformation=f\"theta = ln(1 + c) = ln(1 + {c2}) = {theta2:.4f}\",\n    reason_if_not=\"Not poolable: no SE, CI or p-value for the 19% estimate is printed in the extract; the confidence interval appears only in Figure 1 (not reproduced), so SE(theta) cannot be obtained.\")\n\n# ---------------- S3 Bono & Xu 2024 ----------------\ns = \"arxiv-2411.01067v2\"\nn3h, _ = find(s, r\"\\| Overall \\| Treatment \\| Control \\| Improvement\")\nn3a, _ = find(s, r\"^Aggregate Results\")\nn3, l3 = find(s, r\"^All Content \\|\", n3a)\nn3c, _ = find(s, r\"Table 3: Comparison of task duration\")\nn3e, _ = find(s, r\"Editorial note\")\nn3n, _ = find(s, r\"We had 181 subjects\")\ncells = [c.strip() for c in l3.split(\"|\")]\noverall3, treat3, ctrl3, impr3 = cells[1], cells[2], cells[3], cells[4]\nc3 = float(re.sub(r\"[^\\d.\\-]\", \"\", impr3)) / 100\ntheta3 = math.log(1 + c3)\ntheta3_ratio = math.log(float(treat3) / float(ctrl3))\nitems[\"S3\"] = dict(\n    estimate_as_printed=f\"All Content (aggregate): Overall {overall3}, Treatment {treat3}, Control {ctrl3} minutes; Improvement {impr3}\",\n    uncertainty_as_printed=\"significance stars only (*** = 0<p<=0.01, per editorial note); no SE or CI printed\",\n    sample_as_printed=\"181 subjects completed the task (Section 3); treatment/control split not printed\",\n    definition=\"Percentage difference in mean task duration (minutes), Copilot vs control, holding accuracy constant (control duration predicted at treatment accuracy; Appendix B); bootstrap significance\",\n    locator=f\"{s}, Table 3 (line {n3}: 'Aggregate Results / All Content' row; columns Treatment, Control, Improvement)\",\n    harmonisable=True, harmonised_estimate=theta3, harmonised_se=None, poolable=False,\n    transformation=f\"theta = ln(1 + c) = ln(1 + ({c3})) = {theta3:.4f} (cross-check ln({treat3}/{ctrl3}) = {theta3_ratio:.4f})\",\n    reason_if_not=\"Not poolable: uncertainty is reported only as significance stars (***, p<=0.01); a p-value threshold does not identify SE(theta).\")\n\n# ---------------- S4 Bono 2025 ----------------\ns = \"arxiv-2511.13860v1\"\nn4h, l4h = find(s, r\"^Metric \\| Malicious\")\nn4a, l4a = find(s, r\"^Aware Time per Email\")\nn4c, l4c = find(s, r\"^Control Time per Email\")\nn4s, l4s = find(s, r\"^% Time Savings\")\nn4p, l4p = find(s, r\"^p-value \\|\")\nn4n, _ = find(s, r\"Control \\( N\\{=\\}52\")\nfind(s, r\"Aware \\( N\\{=\\}56\")\nlast = lambda l: l.split(\"|\")[-1].strip()\naware4, ctrl4, sav4, p4 = last(l4a), last(l4c), last(l4s), last(l4p)\nc4 = -float(sav4) / 100            # savings of 11.1% -> change in time c = -0.111\ntheta4 = math.log(1 + c4)\nz4 = norm.ppf(1 - float(p4) / 2)\nse4 = abs(theta4) / z4\nitems[\"S4\"] = dict(\n    estimate_as_printed=f\"Full Corpus: Aware Time per Email (s) {aware4}; Control Time per Email (s) {ctrl4}; % Time Savings {sav4}\",\n    uncertainty_as_printed=f\"p-value {p4}\",\n    sample_as_printed=\"Aware N=56, Control N=52 (Blind N=59 not used); 25-email queue per subject\",\n    definition=\"Percentage time saving in mean processing time per email (full queue), Aware arm (agent verdicts + rationale + priority ordering) vs Control (no AI)\",\n    locator=f\"{s}, Section 3.3, Table 3 (lines {n4a}-{n4p}; column 'Full Corpus')\",\n    harmonisable=True, harmonised_estimate=theta4, harmonised_se=se4, poolable=True,\n    transformation=(f\"c = -{sav4}/100 = {c4}; theta = ln(1 + c) = ln({1 + c4:.3f}) = {theta4:.4f} \"\n                    f\"(cross-check ln({aware4}/{ctrl4}) = {math.log(float(aware4)/float(ctrl4)):.4f}); \"\n                    f\"APPROXIMATE SE = |theta|/z_(1-p/2) = {abs(theta4):.4f}/{z4:.4f} = {se4:.4f} from printed p = {p4}\"),\n    reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky 2023 ----------------\ns = \"arxiv-2304.09339v1\"\nn5a, _ = find(s, r\"^\\| A: Pre 27/28.03\")\nn5, l5 = find(s, r\"TreatPost \\|\", n5a)\nn5s, l5s = find(s, r\"^\\| \\(\", n5)\nn5n, l5n = find(s, r\"^N \\|\", n5)\nn5f, _ = find(s, r\"\\*\\* p<0\\.5\")\nb5 = [c.strip() for c in l5.split(\"|\")][3]\nse5 = [c.strip() for c in l5s.split(\"|\")][3]\nN5 = [c.strip() for c in l5n.split(\"|\")][3]\nitems[\"S5\"] = dict(\n    estimate_as_printed=f\"Treat x Post (Output, column 3, panel A) = {b5}\",\n    uncertainty_as_printed=f\"SE {se5} (clustered by user); no significance star\",\n    sample_as_printed=f\"N = {N5} (user-days, users with affiliation, panel A)\",\n    definition=\"Difference-in-differences coefficient on an aggregate GitHub output count (Push, PRs, comments, Create, Issues) for Italian vs Austrian/French users, around the ChatGPT ban (withdrawal of access)\",\n    locator=f\"{s}, Section 4, Table 1, panel A, row 'Treat x Post' (line {n5}), SE line {n5s}, N line {n5n}, column (3) Output\",\n    harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n    transformation=\"none (not harmonisable)\",\n    reason_if_not=\"Not harmonisable: the outcome is a count of GitHub output events per user-day (activity volume), not task-completion time on the same tasks; the contrast is country-level withdrawal of access (DiD), not a randomised AI-vs-no-AI comparison of time on identical tasks.\")\n\n# ---------------- Pooling ----------------\norder = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\npool = [k for k in order if items[k][\"poolable\"]]\ny = np.array([items[k][\"harmonised_estimate\"] for k in pool])\nse = np.array([items[k][\"harmonised_se\"] for k in pool])\n\ndef meta(y, se):\n    w = 1 / se**2\n    fe = np.sum(w * y) / np.sum(w); fe_se = math.sqrt(1 / np.sum(w))\n    Q = float(np.sum(w * (y - fe) ** 2)); df = len(y) - 1\n    i2 = max(0.0, (Q - df) / Q) * 100 if Q > 0 else 0.0\n    C = np.sum(w) - np.sum(w**2) / np.sum(w)\n    tau2 = max(0.0, (Q - df) / C)\n    wr = 1 / (se**2 + tau2)\n    re_ = np.sum(wr * y) / np.sum(wr); re_se = math.sqrt(1 / np.sum(wr))\n    return dict(fe=float(fe), fe_se=fe_se, Q=Q, i2=float(i2), tau2=float(tau2), re=float(re_), re_se=re_se)\n\nm = meta(y, se)\n# sensitivity: S1 SE from the printed CI instead of printed SE\nse_alt = se.copy(); se_alt[pool.index(\"S1\")] = se_from_ci\nm_alt = meta(y, se_alt)\n\ni2 = m[\"i2\"]\nhet = \"low\" if i2 < 25 else (\"moderate\" if i2 < 50 else \"substantial\")\nre_lo, re_hi = m[\"re\"] - 1.96 * m[\"re_se\"], m[\"re\"] + 1.96 * m[\"re_se\"]\n\ndata_issues = [\n    {\"issue\": \"S1 (arxiv-2410.12944v3, Section VI-B): the printed mean LogToT is 4.6 for the AI group and 4.3 for the no-AI group, which contradicts the printed mean times (96 min AI vs 114 min no-AI; ln 96 = 4.56, ln 114 = 4.74) and the statement that the AI group was faster.\",\n     \"handling\": \"Not used. The harmonised value is the Model 2 regression coefficient specified in studies.json, whose sign agrees with the mean times and with the text.\"},\n    {\"issue\": \"S1: Table III caption says n = 96, whereas Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 for the time comparison.\",\n     \"handling\": \"Both reported as printed in sample_as_printed; the SE is taken as printed, so the discrepancy does not affect theta or SE.\"},\n    {\"issue\": \"S1: Table III renders the four model columns collapsed into one line, with significance markers attached to the numbers (e.g. '-0.24.'). The trailing '.' is the p<.1 marker, not a decimal point. The printed SE (0.14) and the SE implied by the printed 95% CI [-0.51, 0.03] (0.1378) differ slightly because of rounding.\",\n     \"handling\": \"Model 2 = second value on the ExpCon line; the marker was stripped. The printed SE 0.14 was used, and a sensitivity analysis uses the CI-derived SE.\"},\n    {\"issue\": \"S1: the extract has stray 'p<' / 'p=' lines and footnote digits ('^2^2', '2').\",\n     \"handling\": \"Ignored as rendering artefacts (data/README.md).\"},\n    {\"issue\": \"S2: the uncertainty of the headline 19% estimate is shown only in Figure 1 (not reproduced). The raw ratio of means (34%) differs from the regression-based 19%.\",\n     \"handling\": \"Used the regression-based 19%, as studies.json specifies. Harmonised theta = ln(1.19). Not poolable because there is no SE.\"},\n    {\"issue\": \"S3: the Table 3 header row has four labels ('Overall | Treatment | Control | Improvement') with an empty leading cell. The 'Improvement' column is actually a signed % change in time (negative = faster); for MC rows a positive value is a time increase (per the text). The printed -29.79% matches 44.29/63.10 - 1 = -0.2981 only up to rounding.\",\n     \"handling\": \"Read the 'Improvement' value as the signed % change c = -0.2979. Computed theta from the printed percentage and reported ln(44.29/63.10) as a cross-check.\"},\n    {\"issue\": \"S3: the significance-star convention is non-standard (* 0.05<p<=0.1, ** 0.01<p<=0.05, *** p<=0.01).\",\n     \"handling\": \"Recorded. Stars give only a p-value threshold, so S3 is not poolable.\"},\n    {\"issue\": \"S4: Table 3 labels all columns 'Time per Email (s)', but the Malicious (2.8 / 1.9) and Non-Malicious (1.8 / 2.4) values cannot be seconds if the Full Corpus value is 50.5 / 56.9 s. No mix of the two categories gives the full-corpus mean in either seconds or minutes, so the units and/or rows are internally inconsistent.\",\n     \"handling\": \"Only the Full Corpus column is used, and theta depends only on the unit-free ratio and % saving. The printed 11.1% agrees with 1 - 50.5/56.9 = 11.25% within rounding of the printed means. Flagged.\"},\n    {\"issue\": \"S4: the 'Aware' arm differs from Control in queue ordering as well as AI verdicts/rationales, and its % time saving is printed positive for a decrease in time (sign opposite to theta).\",\n     \"handling\": \"Aware vs Control is taken as the AI vs no-AI contrast, and the sign is converted (c = -0.111). The SE is approximate, back-calculated from p = 0.471.\"},\n    {\"issue\": \"S5: the Table 1 footnote prints '** p<0.5' (presumably p<0.05). The extract's date line 'August 24, 2026' is a rendering-time artefact.\",\n     \"handling\": \"Recorded. This does not affect anything because S5 is not harmonisable.\"},\n]\n\nn_input_rows = {\"studies.json\": len(studies)}\nfor f in [\"README.md\"] + [f\"sources/{x}\" for x in sorted(os.listdir(SRC))]:\n    with open(os.path.join(D, f), \"rb\") as fh:\n        n_input_rows[f] = fh.read().count(b\"\\n\")\n\nres = dict(\n    task_id=\"T3-synthesis\",\n    target_definition=\"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, on the same tasks, from a randomised comparison (negative = faster with AI).\",\n    items={k: items[k] for k in order},\n    k_extracted=sum(1 for k in order if items[k][\"estimate_as_printed\"]),\n    k_harmonised=sum(1 for k in order if items[k][\"harmonised_estimate\"] is not None),\n    k_pooled=len(pool),\n    fe_estimate=m[\"fe\"], fe_se=m[\"fe_se\"], fe_ci_low=m[\"fe\"] - 1.96 * m[\"fe_se\"], fe_ci_high=m[\"fe\"] + 1.96 * m[\"fe_se\"],\n    re_estimate=m[\"re\"], re_se=m[\"re_se\"], re_ci_low=re_lo, re_ci_high=re_hi,\n    q_stat=m[\"Q\"], i2=i2, tau2=m[\"tau2\"],\n    heterogeneity_interpretation=het,\n    pooled_significant_5pct=bool(re_lo > 0 or re_hi < 0),\n    pooled_pct_change_re=100 * (math.exp(m[\"re\"]) - 1),\n    feasibility=\"complete\",\n    feasibility_reason=None,\n    data_issues=data_issues,\n    n_input_rows=n_input_rows,\n    n_analysed=len(order),\n    random_seed=None,\n    sensitivity_s1_se_from_ci=dict(s1_se=se_from_ci, fe_estimate=m_alt[\"fe\"], fe_se=m_alt[\"fe_se\"],\n                                   re_estimate=m_alt[\"re\"], re_se=m_alt[\"re_se\"], q_stat=m_alt[\"Q\"], i2=m_alt[\"i2\"], tau2=m_alt[\"tau2\"]),\n)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\ncols = [\"estimate_as_printed\", \"uncertainty_as_printed\", \"sample_as_printed\", \"definition\", \"locator\",\n        \"harmonised_estimate\", \"harmonised_se\", \"harmonisable\", \"poolable\", \"transformation\", \"reason_if_not\"]\npd.DataFrame([dict(study=k, **{c: items[k][c] for c in cols}) for k in order]).to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\n# ---------------- Forest plot ----------------\nharm = [k for k in order if items[k][\"harmonisable\"]]\nfig, ax = plt.subplots(figsize=(8, 4.2))\nlabels = []\nfor i, k in enumerate(harm):\n    yv = len(harm) - i\n    t = items[k][\"harmonised_estimate\"]; s_ = items[k][\"harmonised_se\"]\n    if s_ is not None:\n        ax.errorbar(t, yv, xerr=1.96 * s_, fmt=\"s\", color=\"k\", capsize=3)\n        lab = f\"{k}: {t:.3f} [{t-1.96*s_:.3f}, {t+1.96*s_:.3f}]\" + (\" (approx. SE)\" if k == \"S4\" else \"\")\n    else:\n        ax.plot(t, yv, \"o\", mfc=\"white\", mec=\"gray\")\n        lab = f\"{k}: {t:.3f} (no SE; not pooled)\"\n    labels.append(lab)\nax.errorbar(m[\"re\"], 0, xerr=1.96 * m[\"re_se\"], fmt=\"D\", color=\"tab:blue\", capsize=3)\nlabels.append(f\"RE pooled (k={len(pool)}): {m['re']:.3f} [{re_lo:.3f}, {re_hi:.3f}]\")\nax.set_yticks(list(range(len(harm), -1, -1))); ax.set_yticklabels(labels, fontsize=8)\nax.axvline(0, color=\"gray\", ls=\"--\", lw=0.8)\nax.set_xlabel(\"theta = ln(T_AI / T_noAI)   (negative = faster with AI)\")\nax.set_title(\"Harmonised effects of generative-AI access on task time (95% CI)\", fontsize=10)\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nprint(\"\\n=== Summary ===\")\nfor k in order:\n    print(k, items[k][\"harmonised_estimate\"], items[k][\"harmonised_se\"], items[k][\"poolable\"])\nprint(json.dumps({k: res[k] for k in [\"k_pooled\", \"fe_estimate\", \"fe_se\", \"re_estimate\", \"re_se\", \"re_ci_low\", \"re_ci_high\",\n                                      \"q_stat\", \"i2\", \"tau2\", \"pooled_pct_change_re\", \"pooled_significant_5pct\"]}, indent=1))\nprint(\"sensitivity:\", res[\"sensitivity_s1_se_from_ci\"])\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}
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{"parentUuid":"4b39a0b9-d0d9-4f4b-88de-90822fdbe8dd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsa16ASjumnJK927ovZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ENakrY4tAN5AepewPe56Nz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# T3-synthesis: Does generative-AI assistance shorten the time workers need to complete their tasks?\n\n## Question\nDoes access to a generative-AI assistant change how long workers take to complete their tasks? The target effect is θ = ln(T_AI / T_noAI): the natural log of the ratio of mean task-completion time with AI to mean time without it, on the same tasks, from a randomised comparison. Negative θ means faster with AI. The evidence comes from five study extracts (`data/studies.json`, `data/sources/*.txt`). Only numbers printed in those extracts are used.\n\n## Data and exclusions\nI extracted all five items (k_extracted = 5); `out/table1.csv` has the full extraction table with locators.\n\n| Study | Printed value (as printed) | Printed uncertainty | θ | SE(θ) | Pooled? |\n|---|---|---|---|---|---|\n| S1 Paradis et al. 2024 (Google RCT, 96 devs) | ExpCon, Model 2, Table III: -0.24 (on log time on task) | SE (0.14); 95%CI = [-0.51,0.03], p=0.086 | -0.240 | 0.140 | yes |\n| S2 Becker et al. 2025 (METR RCT, 16 devs, 246 issues) | \"19% more time\" (regression-based) | none printed (CI shown only in Figure 1) | 0.174 | – | **no**: no SE |\n| S3 Bono & Xu 2024 (Security Copilot RCT, IT admins, 181 subjects) | All Content: Treatment 44.29, Control 63.10 min; -29.79%*** | stars only (*** p≤0.01) | -0.354 | – | **no**: stars only |\n| S4 Bono 2025 (phishing-triage agent RCT; Aware N=56 vs Control N=52) | Full Corpus: 50.5 vs 56.9 s per email; % Time Savings 11.1 | p-value 0.471 | -0.118 | 0.163 (approx.) | yes, with approximate SE |\n| S5 Kreitmeir & Raschky 2023 (ChatGPT ban DiD, GitHub) | Treat×Post, Output, col. 3, panel A: -0.278 | (0.295), N = 18332 | – | – | **no**: not harmonisable |\n\nExclusions:\n- **S5 is not harmonisable.** Its outcome is a count of GitHub output events per user-day, not the time needed to complete the same tasks. The design is a country-level difference-in-differences around the withdrawal of access, not a randomised AI-vs-no-AI comparison.\n- **S2 is harmonisable but not poolable.** The extract prints no SE, CI or p-value for the 19% estimate; the CI appears only in Figure 1, which is not reproduced.\n- **S3 is harmonisable but not poolable.** Its only uncertainty information is significance stars.\n\n## Method\nHarmonisation followed the TASK conventions:\n- **S1:** θ = b and SE = SE(b), using the printed SE of 0.14. The SE implied by the printed CI, 0.54/3.92 = 0.138, is used as a sensitivity check.\n- **S2:** θ = ln(1 + 0.19).\n- **S3:** θ = ln(1 − 0.2979). The cross-check ln(44.29/63.10) = −0.354 agrees.\n- **S4:** the printed 11.1% saving means a time change of c = −0.111, so θ = ln(0.889). The SE is approximate, recovered from the two-sided p-value as |θ| / z₀.₇₆₄₅ = 0.1177 / 0.7206 = 0.163.\n\nPooling covers only the poolable studies (S1 and S4, so k = 2):\n- inverse-variance fixed effect;\n- DerSimonian–Laird random effects;\n- Cochran's Q with df = 1;\n- I² = max(0, (Q − df)/Q) × 100;\n- τ² = max(0, (Q − df)/C);\n- 95% intervals using 1.96.\n\n`out/analysis.py` regenerates every output from `data/` and prints the source lines it uses. No randomness is involved.\n\n## Results\n- **Fixed effect:** θ = −0.188 (SE 0.106), 95% CI [−0.396, 0.020].\n- **Random effects (DL):** θ = −0.188 (SE 0.106), 95% CI [−0.396, 0.020]. This equals the fixed-effect result because τ² = 0.\n  - As a percentage change in time: 100 × (exp(θ) − 1) = **−17.2%**.\n  - The 95% interval includes 0, so the pooled effect is **not statistically significant at 5%** (`pooled_significant_5pct` = false).\n- **Heterogeneity:** Q = 0.324 (df = 1), I² = 0%, τ² = 0, which counts as \"low\" under the stated rule.\n- **Sensitivity:** using the CI-derived SE for S1 (0.138) gives θ = −0.189 (SE 0.105) and Q = 0.328. The conclusions do not change.\n\nStudies that could not be pooled (Figure 1 shows them as points without intervals):\n- S3 points to a large speed-up: θ = −0.354, i.e. −29.8% time.\n- S2 points to a slowdown: θ = +0.174, i.e. +19% time.\n\n**What the synthesis supports:** across the two poolable RCTs, the point estimate is that AI access shortens task time by about 17%. This is compatible with no effect, and the 95% interval runs from about a 33% reduction to a 2% increase (exp of the CI bounds).\n\n**What it cannot support:**\n- **A general claim that generative AI shortens task time.** The pool contains only two small studies, of a single coding task and of email triage. One of them (S4) has only an approximate SE, back-calculated from a non-significant p = 0.471.\n- **Reliable heterogeneity statistics.** \"Low\" heterogeneity (I² = 0, τ² = 0) is uninformative with k = 2. Q has one degree of freedom, and DL τ² is essentially unidentifiable.\n- **Agreement among all studies.** The printed point estimates of the studies left out span opposite directions (S3: −30%; S2: +19%; S2 is the setting with experienced developers on their own repositories). Excluding them is a matter of missing uncertainty information, not irrelevance. A pool that could include them might differ in sign of conclusion and would very likely show substantial heterogeneity, though this cannot be quantified from the extracts.\n- **Comparability of outcomes.** The pooled studies measure different outcomes and tasks: S1 is a covariate-adjusted log-time regression; S4 is the difference in mean time per email between arms that also differ in queue ordering.\n\n## Uncertainty\n- The pooled 95% interval is [−0.396, 0.020] on the θ scale.\n- S4's SE is approximate. It assumes a normal test statistic and that the printed p-value refers to the same contrast as the 11.1% figure.\n- The p-value is rounded to 3 decimals and the % saving to 1 decimal. Back-calculation from an estimate near zero is imprecise.\n- The S1 SE is rounded to 2 decimals; the sensitivity check above covers this.\n- The interval does not reflect between-study uncertainty beyond DL τ² = 0, so with k = 2 it understates the true uncertainty about a population-average effect.\n\n## Limitations and open issues\n- **Too few studies.** Only 2 of 5 studies can be pooled. The pooled estimate is not a summary of the evidence base. It is a summary of the two studies that report usable uncertainty in the provided extracts.\n- **Values not used.** The extracts' rules forbid substituting values from figures or the full papers. For example, S2's Figure 1 CI was not used.\n- **Data issues** (detailed in `results.json` → `data_issues`):\n  - S1 prints mean LogToT 4.6 (AI) vs 4.3 (no-AI), which contradicts the printed mean times of 96 vs 114 min. These means were not used.\n  - S1 has n = 96 in Table III vs 47 + 46 = 93 in the text.\n  - S1's Table III has collapsed columns with significance markers fused to the numbers.\n  - S3's \"Improvement\" column is a signed % change, and its star convention is non-standard.\n  - S4's Table 3 units are internally inconsistent: Malicious and Non-Malicious \"seconds\" around 2 vs Full Corpus around 50. Only the unit-free Full Corpus contrast was used.\n  - S5's footnote has a typo (\"** p<0.5\").\n- **Different estimands.** The studies differ in estimand details:\n  - S1: covariate-adjusted, so the target is effectively the conditional ratio of geometric means.\n  - S2: regression-adjusted ratio of means.\n  - S3: durations holding accuracy constant, which is model-based.\n  - S4: per-email time across arms that differ in more than AI verdicts.\n\n  θ is therefore only approximately the same quantity across studies.\n- **Feasibility.** The project is complete in the TASK's sense: every item has either harmonised values or a justified statement of what cannot be obtained, and pooling was done over the poolable items.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Pooling the two poolable RCTs (S1, S4) gives a random-effects θ of -0.188 (95% CI -0.396 to 0.020), about a 17% reduction in task time with AI.\", \"results_keys\": [\"re_estimate\", \"re_ci_low\", \"re_ci_high\", \"pooled_pct_change_re\", \"k_pooled\"], \"status\": \"supported\", \"caveat\": \"k = 2, and S4's SE is approximate (back-calculated from p = 0.471).\"},\n {\"id\": \"C2\", \"text\": \"The pooled effect is not statistically significant at the 5% level.\", \"results_keys\": [\"pooled_significant_5pct\", \"re_ci_low\", \"re_ci_high\"], \"status\": \"supported\", \"caveat\": \"The interval only just includes 0; with k = 2 it understates between-study uncertainty.\"},\n {\"id\": \"C3\", \"text\": \"Heterogeneity between the pooled studies is low (Q = 0.32, I² = 0%, τ² = 0).\", \"results_keys\": [\"q_stat\", \"i2\", \"tau2\", \"heterogeneity_interpretation\"], \"status\": \"supported\", \"caveat\": \"With one degree of freedom these statistics are uninformative. The unpooled point estimates (S2 +19%, S3 -30%) point in opposite directions.\"},\n {\"id\": \"C4\", \"text\": \"The evidence does not support a general conclusion that generative-AI assistance shortens task-completion time.\", \"results_keys\": [\"k_pooled\", \"k_harmonised\", \"items\", \"pooled_significant_5pct\"], \"status\": \"supported\", \"caveat\": \"This is an interpretive judgement based on the small number of poolable studies, the non-significant pooled result and the conflicting unpooled estimates.\"},\n {\"id\": \"C5\", \"text\": \"S2 (+19% time) and S3 (-29.79% time) can be harmonised (θ = 0.174 and -0.354) but cannot be pooled, because no SE is obtainable from the extracts. S5 cannot be harmonised (it measures output counts, using a DiD around the withdrawal of access).\", \"results_keys\": [\"items\", \"k_harmonised\", \"k_extracted\"], \"status\": \"supported\", \"caveat\": \"S2's CI exists in the original paper's figure, but the extract rules exclude it.\"},\n {\"id\": \"C6\", \"text\": \"Using the CI-derived SE for S1 (0.138) instead of the printed SE (0.14) does not change the pooled result.\", \"results_keys\": [\"sensitivity_s1_se_from_ci\"], \"status\": \"supported\", \"caveat\": \"This only covers rounding of S1's SE.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));r=json.load(open('out/results.json'));print(r['n_input_rows'],r['k_harmonised'])\"; ls out","description":"Write report and claims, validate JSON"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1153,"cache_read_input_tokens":48217,"output_tokens":4217,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1153},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsZzq2vunCfvXyQ2Xki","requestRef":"bd12cea5-3075-459b-832e-6c45ba26a63f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"67ea14c2-21b1-48fb-85dd-a4f4c1de8138","timestamp":"2026-10-30T05:31:28.064Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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{"type":"api-request-blob","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","hash":"60bc8339754c29e9a8aa1799a6e68a5232bf1553c6eeb38be4e1a2a115d1a116","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_01ENakrY4tAN5AepewPe56Nz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# T3-synthesis: Does generative-AI assistance shorten the time workers need to complete their tasks?\n\n## Question\nDoes access to a generative-AI assistant change how long workers take to complete their tasks? The target effect is θ = ln(T_AI / T_noAI): the natural log of the ratio of mean task-completion time with AI to mean time without it, on the same tasks, from a randomised comparison. Negative θ means faster with AI. The evidence comes from five study extracts (`data/studies.json`, `data/sources/*.txt`). Only numbers printed in those extracts are used.\n\n## Data and exclusions\nI extracted all five items (k_extracted = 5); `out/table1.csv` has the full extraction table with locators.\n\n| Study | Printed value (as printed) | Printed uncertainty | θ | SE(θ) | Pooled? |\n|---|---|---|---|---|---|\n| S1 Paradis et al. 2024 (Google RCT, 96 devs) | ExpCon, Model 2, Table III: -0.24 (on log time on task) | SE (0.14); 95%CI = [-0.51,0.03], p=0.086 | -0.240 | 0.140 | yes |\n| S2 Becker et al. 2025 (METR RCT, 16 devs, 246 issues) | \"19% more time\" (regression-based) | none printed (CI shown only in Figure 1) | 0.174 | – | **no**: no SE |\n| S3 Bono & Xu 2024 (Security Copilot RCT, IT admins, 181 subjects) | All Content: Treatment 44.29, Control 63.10 min; -29.79%*** | stars only (*** p≤0.01) | -0.354 | – | **no**: stars only |\n| S4 Bono 2025 (phishing-triage agent RCT; Aware N=56 vs Control N=52) | Full Corpus: 50.5 vs 56.9 s per email; % Time Savings 11.1 | p-value 0.471 | -0.118 | 0.163 (approx.) | yes, with approximate SE |\n| S5 Kreitmeir & Raschky 2023 (ChatGPT ban DiD, GitHub) | Treat×Post, Output, col. 3, panel A: -0.278 | (0.295), N = 18332 | – | – | **no**: not harmonisable |\n\nExclusions:\n- **S5 is not harmonisable.** Its outcome is a count of GitHub output events per user-day, not the time needed to complete the same tasks. The design is a country-level difference-in-differences around the withdrawal of access, not a randomised AI-vs-no-AI comparison.\n- **S2 is harmonisable but not poolable.** The extract prints no SE, CI or p-value for the 19% estimate; the CI appears only in Figure 1, which is not reproduced.\n- **S3 is harmonisable but not poolable.** Its only uncertainty information is significance stars.\n\n## Method\nHarmonisation followed the TASK conventions:\n- **S1:** θ = b and SE = SE(b), using the printed SE of 0.14. The SE implied by the printed CI, 0.54/3.92 = 0.138, is used as a sensitivity check.\n- **S2:** θ = ln(1 + 0.19).\n- **S3:** θ = ln(1 − 0.2979). The cross-check ln(44.29/63.10) = −0.354 agrees.\n- **S4:** the printed 11.1% saving means a time change of c = −0.111, so θ = ln(0.889). The SE is approximate, recovered from the two-sided p-value as |θ| / z₀.₇₆₄₅ = 0.1177 / 0.7206 = 0.163.\n\nPooling covers only the poolable studies (S1 and S4, so k = 2):\n- inverse-variance fixed effect;\n- DerSimonian–Laird random effects;\n- Cochran's Q with df = 1;\n- I² = max(0, (Q − df)/Q) × 100;\n- τ² = max(0, (Q − df)/C);\n- 95% intervals using 1.96.\n\n`out/analysis.py` regenerates every output from `data/` and prints the source lines it uses. No randomness is involved.\n\n## Results\n- **Fixed effect:** θ = −0.188 (SE 0.106), 95% CI [−0.396, 0.020].\n- **Random effects (DL):** θ = −0.188 (SE 0.106), 95% CI [−0.396, 0.020]. This equals the fixed-effect result because τ² = 0.\n  - As a percentage change in time: 100 × (exp(θ) − 1) = **−17.2%**.\n  - The 95% interval includes 0, so the pooled effect is **not statistically significant at 5%** (`pooled_significant_5pct` = false).\n- **Heterogeneity:** Q = 0.324 (df = 1), I² = 0%, τ² = 0, which counts as \"low\" under the stated rule.\n- **Sensitivity:** using the CI-derived SE for S1 (0.138) gives θ = −0.189 (SE 0.105) and Q = 0.328. The conclusions do not change.\n\nStudies that could not be pooled (Figure 1 shows them as points without intervals):\n- S3 points to a large speed-up: θ = −0.354, i.e. −29.8% time.\n- S2 points to a slowdown: θ = +0.174, i.e. +19% time.\n\n**What the synthesis supports:** across the two poolable RCTs, the point estimate is that AI access shortens task time by about 17%. This is compatible with no effect, and the 95% interval runs from about a 33% reduction to a 2% increase (exp of the CI bounds).\n\n**What it cannot support:**\n- **A general claim that generative AI shortens task time.** The pool contains only two small studies, of a single coding task and of email triage. One of them (S4) has only an approximate SE, back-calculated from a non-significant p = 0.471.\n- **Reliable heterogeneity statistics.** \"Low\" heterogeneity (I² = 0, τ² = 0) is uninformative with k = 2. Q has one degree of freedom, and DL τ² is essentially unidentifiable.\n- **Agreement among all studies.** The printed point estimates of the studies left out span opposite directions (S3: −30%; S2: +19%; S2 is the setting with experienced developers on their own repositories). Excluding them is a matter of missing uncertainty information, not irrelevance. A pool that could include them might differ in sign of conclusion and would very likely show substantial heterogeneity, though this cannot be quantified from the extracts.\n- **Comparability of outcomes.** The pooled studies measure different outcomes and tasks: S1 is a covariate-adjusted log-time regression; S4 is the difference in mean time per email between arms that also differ in queue ordering.\n\n## Uncertainty\n- The pooled 95% interval is [−0.396, 0.020] on the θ scale.\n- S4's SE is approximate. It assumes a normal test statistic and that the printed p-value refers to the same contrast as the 11.1% figure.\n- The p-value is rounded to 3 decimals and the % saving to 1 decimal. Back-calculation from an estimate near zero is imprecise.\n- The S1 SE is rounded to 2 decimals; the sensitivity check above covers this.\n- The interval does not reflect between-study uncertainty beyond DL τ² = 0, so with k = 2 it understates the true uncertainty about a population-average effect.\n\n## Limitations and open issues\n- **Too few studies.** Only 2 of 5 studies can be pooled. The pooled estimate is not a summary of the evidence base. It is a summary of the two studies that report usable uncertainty in the provided extracts.\n- **Values not used.** The extracts' rules forbid substituting values from figures or the full papers. For example, S2's Figure 1 CI was not used.\n- **Data issues** (detailed in `results.json` → `data_issues`):\n  - S1 prints mean LogToT 4.6 (AI) vs 4.3 (no-AI), which contradicts the printed mean times of 96 vs 114 min. These means were not used.\n  - S1 has n = 96 in Table III vs 47 + 46 = 93 in the text.\n  - S1's Table III has collapsed columns with significance markers fused to the numbers.\n  - S3's \"Improvement\" column is a signed % change, and its star convention is non-standard.\n  - S4's Table 3 units are internally inconsistent: Malicious and Non-Malicious \"seconds\" around 2 vs Full Corpus around 50. Only the unit-free Full Corpus contrast was used.\n  - S5's footnote has a typo (\"** p<0.5\").\n- **Different estimands.** The studies differ in estimand details:\n  - S1: covariate-adjusted, so the target is effectively the conditional ratio of geometric means.\n  - S2: regression-adjusted ratio of means.\n  - S3: durations holding accuracy constant, which is model-based.\n  - S4: per-email time across arms that differ in more than AI verdicts.\n\n  θ is therefore only approximately the same quantity across studies.\n- **Feasibility.** The project is complete in the TASK's sense: every item has either harmonised values or a justified statement of what cannot be obtained, and pooling was done over the poolable items.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Pooling the two poolable RCTs (S1, S4) gives a random-effects θ of -0.188 (95% CI -0.396 to 0.020), about a 17% reduction in task time with AI.\", \"results_keys\": [\"re_estimate\", \"re_ci_low\", \"re_ci_high\", \"pooled_pct_change_re\", \"k_pooled\"], \"status\": \"supported\", \"caveat\": \"k = 2, and S4's SE is approximate (back-calculated from p = 0.471).\"},\n {\"id\": \"C2\", \"text\": \"The pooled effect is not statistically significant at the 5% level.\", \"results_keys\": [\"pooled_significant_5pct\", \"re_ci_low\", \"re_ci_high\"], \"status\": \"supported\", \"caveat\": \"The interval only just includes 0; with k = 2 it understates between-study uncertainty.\"},\n {\"id\": \"C3\", \"text\": \"Heterogeneity between the pooled studies is low (Q = 0.32, I² = 0%, τ² = 0).\", \"results_keys\": [\"q_stat\", \"i2\", \"tau2\", \"heterogeneity_interpretation\"], \"status\": \"supported\", \"caveat\": \"With one degree of freedom these statistics are uninformative. The unpooled point estimates (S2 +19%, S3 -30%) point in opposite directions.\"},\n {\"id\": \"C4\", \"text\": \"The evidence does not support a general conclusion that generative-AI assistance shortens task-completion time.\", \"results_keys\": [\"k_pooled\", \"k_harmonised\", \"items\", \"pooled_significant_5pct\"], \"status\": \"supported\", \"caveat\": \"This is an interpretive judgement based on the small number of poolable studies, the non-significant pooled result and the conflicting unpooled estimates.\"},\n {\"id\": \"C5\", \"text\": \"S2 (+19% time) and S3 (-29.79% time) can be harmonised (θ = 0.174 and -0.354) but cannot be pooled, because no SE is obtainable from the extracts. S5 cannot be harmonised (it measures output counts, using a DiD around the withdrawal of access).\", \"results_keys\": [\"items\", \"k_harmonised\", \"k_extracted\"], \"status\": \"supported\", \"caveat\": \"S2's CI exists in the original paper's figure, but the extract rules exclude it.\"},\n {\"id\": \"C6\", \"text\": \"Using the CI-derived SE for S1 (0.138) instead of the printed SE (0.14) does not change the pooled result.\", \"results_keys\": [\"sensitivity_s1_se_from_ci\"], \"status\": \"supported\", \"caveat\": \"This only covers rounding of S1's SE.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));r=json.load(open('out/results.json'));print(r['n_input_rows'],r['k_harmonised'])\"; ls out","description":"Write report and claims, validate JSON"}}]}}
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{"parentUuid":"9d698c9a-8f26-4790-8cc4-6b012cc39e0f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsck28YkCU2Eqk5edE9","type":"message","role":"assistant","content":[{"type":"text","text":"I extracted, harmonised and pooled all five studies, but only two of them (S1, S4) could be pooled. Their combined estimate is that AI access cuts task time by about 17%, and that result is not statistically significant. Running `out/analysis.py` from the workspace root regenerates the results, table and figure from `data/` only.\n\n| Study | As printed | θ = ln(T_AI / T_noAI) | SE | Pooled? |\n|---|---|---|---|---|\n| S1 Google RCT | −0.24, SE 0.14 (log time on task) | −0.240 | 0.140 | yes |\n| S2 METR RCT | 19% more time | +0.174 | none | no: its interval appears only in a figure the extract doesn't include |\n| S3 Security Copilot RCT | −29.79%, significance stars only | −0.354 | none | no: stars don't give a standard error |\n| S4 Phishing-triage RCT | 11.1% time saving, p = 0.471 | −0.118 | 0.163 (approximate) | yes |\n| S5 Italy ChatGPT ban | −0.278 (0.295), output counts | none | none | no: it measures activity counts, not task time |\n\n- **Pooled result (S1 + S4):** θ = −0.188 (SE 0.106), 95% CI [−0.396, 0.020], about a 17% reduction in time. The interval includes zero, so the effect is not significant at 5%. Fixed-effect and random-effects estimates are identical because the between-study variance is estimated as zero.\n- **Heterogeneity:** Q = 0.32, I² = 0%, which counts as \"low\" under the task's rule. With only two studies this tells us almost nothing.\n- **Sensitivity check:** using the S1 standard error implied by its printed confidence interval (0.138 instead of 0.14) leaves the result unchanged (θ = −0.189).\n- **Conclusion:** the evidence does not support a general claim that generative AI shortens task time. Two of the three studies left out point in opposite directions: S2 found a 19% slowdown and S3 a 30% speed-up.\n\nLimitations:\n- **Small pool:** only two studies, one of them with a standard error back-calculated from a non-significant p-value (approximate, as the task's rules allow).\n- **Different outcomes:** the five studies measure different things (a covariate-adjusted coding regression, time per email across arms that also differ in queue ordering, durations holding accuracy constant), so θ is only roughly the same quantity across them.\n- **Problems in the source extracts:** all are logged in `results.json` and in the report.\n  - S1 prints mean log times that contradict its own mean minutes, so I didn't use them.\n  - S1 gives two sample sizes: n = 96 in the table and 93 in the text.\n  - S4's Table 3 units don't add up; only the unit-free Full Corpus comparison was used.\n  - S5's footnote has a significance-threshold typo (\"p<0.5\").\n\nThe project counts as complete under the task's definition.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- table1.csv\n- figure1.png\n- analysis.py\n- claims.json"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4394,"cache_read_input_tokens":49370,"output_tokens":1107,"output_tokens_details":{"thinking_tokens":56},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4394},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXscjjGGWhqDFNKLadjm","requestRef":"3a75b924-90bc-40f1-a249-0501e31bc920","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d51cbab-209f-41c0-8a88-ad34dfe097e0","timestamp":"2026-10-30T05:31:39.366Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0003","sessionId":"0e046219-b597-49c3-9153-52ab1ac1e510","version":"2.1.314","gitBranch":"HEAD"}
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