{"type":"atis-latch","atis":"","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5"} {"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:29:10.320Z","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","content":"Begin"} {"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:29:10.326Z","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5"} {"parentUuid":null,"isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":"Begin"},"uuid":"cc35197b-a1f3-46ed-9c6a-7778889c5854","timestamp":"2026-10-30T05:29:10.331Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-shape","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","shapeHash":"429764c32a6e697e83842172e5c0c0eefae4d12f6b174446496eac7a4a55aa8e","timestamp":"2026-10-30T05:29:10.346Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RT0007\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 \n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"tools-used","tools":["Bash"],"sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5"} {"parentUuid":"684be1a4-7a16-44b5-bd8a-e93af7c1646b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsSpeQQmhTXCv7ScQCy","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":127,"output_tokens_details":{"thinking_tokens":21},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":595,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsSpMJjNXCuQ4FLW1Nr","requestRef":"6898afcf-b07b-4d34-9e7b-cad20db04a48","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"23228369-979e-4c1b-9427-535990d19d2c","timestamp":"2026-10-30T05:29:14.918Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"23228369-979e-4c1b-9427-535990d19d2c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsSpeQQmhTXCv7ScQCy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NZmGtJFyKCxnajNAWXHVRW","name":"Bash","input":{"command":"cat TASK.md; 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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 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| `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 40\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 30 00:32 .\ndrwxr-xr-x 165 simon-pierreboucher staff 5280 oct. 30 01:29 ..\n-rw-r--r-- 1 simon-pierreboucher staff 1073 oct. 30 00:32 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 30 00:28 data\n-rw-r--r-- 1 simon-pierreboucher staff 2194 oct. 30 00:28 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 10039 oct. 30 00:28 TASK.md\ndata:\nREADME.md\nsources\nstudies.json\n\ndata/sources:\narxiv-2304.09339v1.txt\narxiv-2410.12944v3.txt\narxiv-2411.01067v2.txt\narxiv-2507.09089v2.txt\narxiv-2511.13860v1.txt","is_error":false}]},"uuid":"3709fc7b-f172-48aa-91c3-bf242e73be4b","timestamp":"2026-10-30T05:29:15.570Z","toolUseResult":{"stdout":"# 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 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| `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. 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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[\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{\n \"task_id\": \"T3-synthesis\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/table1.csv\", \"out/figure1.png\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"target_definition\": {\"type\": \"string\", \"nullable\": false},\n \"items\": {\"type\": \"object\", \"nullable\": false},\n \"k_extracted\": {\"type\": \"integer\", \"nullable\": false},\n \"k_harmonised\": {\"type\": \"integer\", \"nullable\": false},\n \"k_pooled\": {\"type\": \"integer\", \"nullable\": false},\n \"fe_estimate\": {\"type\": \"number\", \"nullable\": false}, \"fe_se\": {\"type\": \"number\", \"nullable\": false},\n \"fe_ci_low\": {\"type\": \"number\", \"nullable\": false}, \"fe_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"re_estimate\": {\"type\": \"number\", \"nullable\": false}, \"re_se\": {\"type\": \"number\", \"nullable\": false},\n \"re_ci_low\": {\"type\": \"number\", \"nullable\": false}, \"re_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"q_stat\": {\"type\": \"number\", \"nullable\": false},\n \"i2\": {\"type\": \"number\", \"nullable\": false},\n \"tau2\": {\"type\": \"number\", \"nullable\": false},\n \"heterogeneity_interpretation\": {\"type\": \"string\", \"nullable\": false},\n \"pooled_significant_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"pooled_pct_change_re\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [[\"fe_estimate\", \"fe_ci_low\", \"fe_ci_high\"], [\"re_estimate\", \"re_ci_low\", \"re_ci_high\"]],\n \"positive_keys\": [\"fe_se\", \"re_se\"],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"T3-synthesis\",\n \"family\": \"T3\",\n \"split\": \"transfer\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T04:32:27.173216+00:00\",\n \"files\": {\n \"TASK.md\": \"346f35def87a6caeeff32ba22bb6b1c30f7ba6c6dc54cd5f29ef1ff82c06021d\",\n \"data/README.md\": \"c7d07d4bd3e0afd9da948c0b74fd76657084044df7d28c08e4399dc27f3b0128\",\n \"data/sources/arxiv-2304.09339v1.txt\": \"1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e\",\n \"data/sources/arxiv-2410.12944v3.txt\": \"d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b\",\n \"data/sources/arxiv-2411.01067v2.txt\": \"46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a\",\n \"data/sources/arxiv-2507.09089v2.txt\": \"b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d\",\n \"data/sources/arxiv-2511.13860v1.txt\": \"8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5\",\n \"data/studies.json\": \"124d1f3e3671e9770b91963bc6b26d63281086ef9112fd711cc4607c89babbe1\",\n \"gate_spec.json\": \"758def8f045bf9aec7a0c761eabde995006e02bf718f195ce6b3c34a279da258\"\n }\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 17 data/README.md\n 640 total\n1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e data/sources/arxiv-2304.09339v1.txt\nd5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b data/sources/arxiv-2410.12944v3.txt\n46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a data/sources/arxiv-2411.01067v2.txt\nb6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d data/sources/arxiv-2507.09089v2.txt\n8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 data/sources/arxiv-2511.13860v1.txt\n124d1f3e3671e9770b91963bc6b26d63281086ef9112fd711cc4607c89babbe1 data/studies.json\nc7d07d4bd3e0afd9da948c0b74fd76657084044df7d28c08e4399dc27f3b0128 data/README.md","is_error":false}]},"uuid":"c4a955c9-4167-487b-afe5-a6fcf18239f0","timestamp":"2026-10-30T05:29:17.189Z","toolUseResult":{"stdout":"# 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[\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{\n \"task_id\": \"T3-synthesis\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/table1.csv\", \"out/figure1.png\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"target_definition\": {\"type\": \"string\", \"nullable\": false},\n \"items\": {\"type\": \"object\", \"nullable\": false},\n \"k_extracted\": {\"type\": \"integer\", \"nullable\": false},\n \"k_harmonised\": {\"type\": \"integer\", \"nullable\": false},\n \"k_pooled\": {\"type\": \"integer\", \"nullable\": false},\n \"fe_estimate\": {\"type\": \"number\", \"nullable\": false}, \"fe_se\": {\"type\": \"number\", \"nullable\": false},\n \"fe_ci_low\": {\"type\": \"number\", \"nullable\": false}, \"fe_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"re_estimate\": {\"type\": \"number\", \"nullable\": false}, \"re_se\": {\"type\": \"number\", \"nullable\": false},\n \"re_ci_low\": {\"type\": \"number\", \"nullable\": false}, \"re_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"q_stat\": {\"type\": \"number\", \"nullable\": false},\n \"i2\": {\"type\": \"number\", \"nullable\": false},\n \"tau2\": {\"type\": \"number\", \"nullable\": false},\n \"heterogeneity_interpretation\": {\"type\": \"string\", \"nullable\": false},\n \"pooled_significant_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"pooled_pct_change_re\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": 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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":"61e1bf15-0ede-477b-8893-274540462fb8","timestamp":"2026-10-30T05:29:19.247Z","toolUseResult":{"stdout":" 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. 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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":"bd1e98ad-216f-47f4-a62b-c779536faaa9","timestamp":"2026-10-30T05:29:19.735Z","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":"0ee136ec-7ea4-4e58-8939-39c81748619d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"0e4d8503ddccdaecdb97918c0a2019a80b7e6ba6322906efddae32ff0bf6443d","message":{"role":"assistant","content":[{"type":"text","text":"Hashes match. Now read the sources."},{"type":"tool_use","id":"toolu_01A2LgZno8uJHHTdHRzKcbiD","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2410.12944v3.txt","description":"Read Paradis extract"}},{"type":"tool_use","id":"toolu_01WxiCFVDdf2fUNpYccdskiB","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2507.09089v2.txt","description":"Read Becker extract"}}]}} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"8f0e890ebf7d7877d33ec4b3f4242479ed390f6291f345ff79a3837d4301e761","message":{"role":"user","content":[{"tool_use_id":"toolu_01A2LgZno8uJHHTdHRzKcbiD","type":"tool_result","content":" 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_01WxiCFVDdf2fUNpYccdskiB","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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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 * 0.051 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":"7eff66e1-bc11-468f-b036-84455ca5d055","timestamp":"2026-10-30T05:29:25.643Z","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":"e23aae6d-f4f6-4bef-9a5f-cf1ef6ce41cc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"7eff66e1-bc11-468f-b036-84455ca5d055","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsTGEnvnEPnBtj3A6Fg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KX7m1acZ22rtiyHhSgF4ET","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2304.09339v1.txt","description":"Read Kreitmeir extract"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9701,"cache_read_input_tokens":13160,"output_tokens":725,"output_tokens_details":{"thinking_tokens":427},"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":9701},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":3,"requestId":"req_011CgXsTFxAg9fjesGK6874J","requestRef":"73cbef0c-4c73-473a-90a6-0682f8a98b7b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"28a4a745-d303-442d-b52e-5b9c07e30f1c","timestamp":"2026-10-30T05:29:26.161Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"28a4a745-d303-442d-b52e-5b9c07e30f1c","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KX7m1acZ22rtiyHhSgF4ET","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":"8d456961-c3c7-4b9f-849a-1b1ce8cdb0cd","timestamp":"2026-10-30T05:29:26.184Z","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. 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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 * 0.051 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_01KX7m1acZ22rtiyHhSgF4ET","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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{"parentUuid":"5993571b-39c1-40e8-b25e-83f190ad7b55","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014EedzhGuXSC6E5KwF25djP","type":"tool_result","content":"5:Reads /gate_spec.json and checks the OUTPUT CONTRACT and REPRODUCIBILITY of the deliverables\n7:Writes a receipt to /out/state/gate_receipts/.json and exits 0 if passed, 1 otherwise.\n69: checks = []\n71: checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n75: return _finish(ws, checks)\n105: fam = set(spec.get(\"family_checks\", []))\n106: # 4. claims\n107: claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n108: if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n109: if not isinstance(claims, list) or not claims:\n110: add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n113: for i, c in enumerate(claims):\n121: add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n123: pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n127: add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n133: if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n134: bad.append(f\"{pid}: passed without recorded checks\")\n137: bad.append(f\"phases not terminal: {not_done}\")\n139: # 6. exclusion ledger\n140: ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141: if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153: add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155: add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n156: # 7. numeric sanity checks\n211: return _finish(ws, checks)\n213:def _finish(ws, checks):\n214: passed = all(c[\"passed\"] for c in checks) and bool(checks)\n215: receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n216: \"passed\": passed, \"n_checks\": len(checks), \"n_failed\": sum(1 for c in checks if not c[\"passed\"]), \"checks\": checks}\n218: d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221: json.dump(receipt, fh, indent=1)\n222: receipt[\"receipt_path\"] = path\n223: receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225: receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226: return receipt\n242: print(f\"PGAR GATE {r['gate_version']} — {'PASSED' if r['passed'] else 'FAILED'} ({r['n_failed']} of {r['n_checks']} checks failed)\")\n243: for c in r[\"checks\"]:\n245: print(f\"receipt: {r.get('receipt_path')}\")","is_error":false}]},"uuid":"67b24e17-262e-4486-984b-379282a3a0a3","timestamp":"2026-10-30T05:29:50.510Z","toolUseResult":{"stdout":"5:Reads /gate_spec.json and checks the OUTPUT CONTRACT and REPRODUCIBILITY of the deliverables\n7:Writes a receipt to /out/state/gate_receipts/.json and exits 0 if passed, 1 otherwise.\n69: checks = []\n71: checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n75: return _finish(ws, checks)\n105: fam = set(spec.get(\"family_checks\", []))\n106: # 4. claims\n107: claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n108: if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n109: if not isinstance(claims, list) or not claims:\n110: add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n113: for i, c in enumerate(claims):\n121: add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n123: pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n127: add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n133: if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n134: bad.append(f\"{pid}: passed without recorded checks\")\n137: bad.append(f\"phases not terminal: {not_done}\")\n139: # 6. exclusion ledger\n140: ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141: if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153: add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155: add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n156: # 7. numeric sanity checks\n211: return _finish(ws, checks)\n213:def _finish(ws, checks):\n214: passed = all(c[\"passed\"] for c in checks) and bool(checks)\n215: receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n216: \"passed\": passed, \"n_checks\": len(checks), \"n_failed\": sum(1 for c in checks if not c[\"passed\"]), \"checks\": checks}\n218: d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221: json.dump(receipt, fh, indent=1)\n222: receipt[\"receipt_path\"] = path\n223: receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225: receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226: return receipt\n242: print(f\"PGAR GATE {r['gate_version']} — {'PASSED' if r['passed'] else 'FAILED'} ({r['n_failed']} of {r['n_checks']} checks failed)\")\n243: for c in r[\"checks\"]:\n245: print(f\"receipt: {r.get('receipt_path')}\")","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5993571b-39c1-40e8-b25e-83f190ad7b55","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} 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-n \"exclusion\\|phases\\|claims\\|receipt\\|checks\" ~/wp29/gates/pgar_gate.py | head -60","description":"Inspect gate checks"}}]}} 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{"parentUuid":"35487d43-3ced-4637-a004-38647b5a1063","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TrwGCBERSGDWMY78xMF5Rq","type":"tool_result","content":" pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n pl, perr = _load_json(pl_path)\n if not isinstance(pl, dict):\n add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n else:\n bad = []\n for pid, st in pl.items():\n if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n bad.append(f\"{pid}: invalid status\"); continue\n if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n bad.append(f\"{pid}: passed without recorded checks\")\n not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n if not_done:\n bad.append(f\"phases not terminal: {not_done}\")\n add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n # 6. exclusion ledger\n ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n try:\n rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n bad = []\n prev_after = None\n for i, r in enumerate(rows):\n nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n if nb - nr != na:\n bad.append(f\"row {i}: n_before - n_removed != n_after\")\n if prev_after is not None and nb != prev_after:\n bad.append(f\"row {i}: n_before != previous n_after\")\n prev_after = na\n add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n except Exception as e: # noqa: BLE001\n add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n # 7. numeric sanity 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in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n if not_done:\n bad.append(f\"phases not terminal: {not_done}\")\n add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n # 6. exclusion ledger\n ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n try:\n rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n bad = []\n prev_after = None\n for i, r in enumerate(rows):\n nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n if nb - nr != na:\n bad.append(f\"row {i}: n_before - n_removed != n_after\")\n if prev_after is not None and nb != prev_after:\n bad.append(f\"row {i}: n_before != previous n_after\")\n prev_after = na\n add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n except Exception as e: # noqa: BLE001\n 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ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":972,"cache_read_input_tokens":41644,"output_tokens":1339,"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":972},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsVdu691mjLjqHjUmBj","requestRef":"6bca3644-f7ed-446c-946b-b0c1a245f9f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"624dc0b5-22b9-4ce7-b341-93481942cf0a","timestamp":"2026-10-30T05:29:52.869Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"624dc0b5-22b9-4ce7-b341-93481942cf0a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsVeAxA893YGcxTGhn6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013nXEHkEcTBwnADRmPg9WFh","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — T3-synthesis (P0)\n\n**Question.** Does access to a generative-AI assistant shorten the time workers need to complete tasks? Synthesise five primary studies (extracts only).\n\n**Target / estimand.** θ = ln(T_AI / T_noAI): log ratio of mean completion time with AI to without AI, same tasks, randomised comparison; negative = faster with AI.\nEstimands: (i) the five printed values verbatim with locators; (ii) harmonised θ and SE(θ) per study when obtainable under the TASK conventions;\n(iii) inverse-variance FE and DerSimonian–Laird RE pooled θ over poolable studies, with 95% CIs (1.96), Q (df=k−1), I², τ²; (iv) the synthesis conclusion.\nMeaningful under: each θ estimates the same kind of quantity (log time ratio), independent studies, normal approximation; with k=2 τ² is essentially unidentified.\n\n**Conventions.** log-time coefficient b → θ=b, SE=SE(b) (or CI width/3.92); percentage change c → θ=ln(1+c); p-value only → SE≈|θ|/z_{1−p/2} (approximate, flag);\nstars/thresholds/figure-only CI → harmonisable, not poolable; effect not on completion time AI-vs-no-AI → not harmonisable.\n\n**Outputs.** out/table1.csv (12 named columns, 5 rows); out/figure1.png (forest plot, harmonisable studies, no-SE as bare points, RE diamond);\nout/results.json keys: task_id, target_definition, items{S1..S5 with estimate_as_printed, uncertainty_as_printed, sample_as_printed, definition, locator, harmonisable, harmonised_estimate, harmonised_se, poolable, transformation, reason_if_not}, k_extracted, k_harmonised, k_pooled, fe_estimate, fe_se, fe_ci_low, fe_ci_high, re_estimate, re_se, re_ci_low, re_ci_high, q_stat, i2, tau2, heterogeneity_interpretation, pooled_significant_5pct, pooled_pct_change_re, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed;\nout/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py (regenerates results/table/figure, prints source lines used); out/claims.json.\n\n**Information rules.** Only the extracts are evidence; nothing remembered from the full papers (e.g. the METR CI shown in Figure 1 is NOT available). Printed numbers copied exactly.\n\n**Invalidity / impossibility.** Fewer than 1 poolable study would make pooled keys impossible (they are non-nullable). Using non-printed numbers would invalidate. Pooling a non-time outcome (S5) would be invalid.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":972,"cache_read_input_tokens":41644,"output_tokens":1339,"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":972},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsVdu691mjLjqHjUmBj","requestRef":"6bca3644-f7ed-446c-946b-b0c1a245f9f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"49b22ffd-8a87-4099-acb0-dc7d14fb2623","timestamp":"2026-10-30T05:30:03.803Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"49b22ffd-8a87-4099-acb0-dc7d14fb2623","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013nXEHkEcTBwnADRmPg9WFh","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"4330bdd4-378c-4c11-932e-6063ae400069","timestamp":"2026-10-30T05:30:03.834Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"49b22ffd-8a87-4099-acb0-dc7d14fb2623","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007/out/state","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"5b13a338e214f52a97c6b7c33854f25d37f36873eeabe3cfc1223d2b3995d143","message":{"role":"assistant","content":[{"type":"text","text":"Now P0: contract and phase ledger."},{"type":"tool_use","id":"toolu_013nXEHkEcTBwnADRmPg9WFh","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — T3-synthesis (P0)\n\n**Question.** Does access to a generative-AI assistant shorten the time workers need to complete tasks? Synthesise five primary studies (extracts only).\n\n**Target / estimand.** θ = ln(T_AI / T_noAI): log ratio of mean completion time with AI to without AI, same tasks, randomised comparison; negative = faster with AI.\nEstimands: (i) the five printed values verbatim with locators; (ii) harmonised θ and SE(θ) per study when obtainable under the TASK conventions;\n(iii) inverse-variance FE and DerSimonian–Laird RE pooled θ over poolable studies, with 95% CIs (1.96), Q (df=k−1), I², τ²; (iv) the synthesis conclusion.\nMeaningful under: each θ estimates the same kind of quantity (log time ratio), independent studies, normal approximation; with k=2 τ² is essentially unidentified.\n\n**Conventions.** log-time coefficient b → θ=b, SE=SE(b) (or CI width/3.92); percentage change c → θ=ln(1+c); p-value only → SE≈|θ|/z_{1−p/2} (approximate, flag);\nstars/thresholds/figure-only CI → harmonisable, not poolable; effect not on completion time AI-vs-no-AI → not harmonisable.\n\n**Outputs.** out/table1.csv (12 named columns, 5 rows); out/figure1.png (forest plot, harmonisable studies, no-SE as bare points, RE diamond);\nout/results.json keys: task_id, target_definition, items{S1..S5 with estimate_as_printed, uncertainty_as_printed, sample_as_printed, definition, locator, harmonisable, harmonised_estimate, harmonised_se, poolable, transformation, reason_if_not}, k_extracted, k_harmonised, k_pooled, fe_estimate, fe_se, fe_ci_low, fe_ci_high, re_estimate, re_se, re_ci_low, re_ci_high, q_stat, i2, tau2, heterogeneity_interpretation, pooled_significant_5pct, pooled_pct_change_re, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed;\nout/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py (regenerates results/table/figure, prints source lines used); out/claims.json.\n\n**Information rules.** Only the extracts are evidence; nothing remembered from the full papers (e.g. the METR CI shown in Figure 1 is NOT available). Printed numbers copied exactly.\n\n**Invalidity / impossibility.** Fewer than 1 poolable study would make pooled keys impossible (they are non-nullable). Using non-printed numbers would invalidate. Pooling a non-time outcome (S5) would be invalid.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}}]}} 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-c \"\nimport math\nfrom scipy.stats import norm\nprint('S1 SE from CI',(0.03+0.51)/3.92,'p from b/se',2*norm.sf(0.24/0.14),'1-exp',1-math.exp(-0.24))\nprint('S3 ratio',44.29/63.10-1, math.log(1-0.2979))\nprint('S4 ratio',50.5/56.9-1, 2.8/1.9-1, 1.8/2.4-1, math.log(1-0.111), norm.ppf(1-0.471/2))\nprint('S2',math.log(1.19))\n\"","description":"Arithmetic consistency checks"}}]}} 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{"parentUuid":"fc461e5a-b2d1-4a7e-8eb6-1dfc282048ec","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsWkz1TrwnJn4o3EgJU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012nkLX1RHwfGkGcnMU5zhSH","name":"Bash","input":{"command":"cd out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) | content |\n|---|---|---|---|\n| data/studies.json | 124d1f3e…bbe1 | 5 items (array of 5 objects; keys id, source, short, extract) | task-designer item list |\n| data/README.md | c7d07d4b…0128 | 17 | provenance |\n| data/sources/arxiv-2410.12944v3.txt | d5270b44…803b | 112 | Paradis et al. 2024 |\n| data/sources/arxiv-2507.09089v2.txt | b6af8a2b…d2d | 132 | Becker et al. 2025 |\n| data/sources/arxiv-2411.01067v2.txt | 46a1ee4e…053a | 109 | Bono & Xu 2024 |\n| data/sources/arxiv-2511.13860v1.txt | 8fb2520f…a5 | 162 | Bono 2025 |\n| data/sources/arxiv-2304.09339v1.txt | 1db48b1c…06e | 108 | Kreitmeir & Raschky 2023 |\n\nThese are text extracts, not tabular data: \"rows/columns\" = lines; no missing values in the usual sense. No duplicate study items (ids S1–S5 unique, sources unique).\nDate ranges: retrieval 2026-10-30T04:19–04:20Z; publications 2023–2025. No availability-rule violation found in the inputs; the only rule-relevant item is that\nS2's confidence interval appears only in Figure 1 (not reproduced) → must NOT be used.\n\n## Per-source findings (line numbers refer to `cat -n`)\n- **S1 Paradis** L54-58, L76-78: ExpCon Model 2 = -0.24 (0.14), marker '.', 95% CI [-0.51, 0.03], p=0.086.\n Checks: CI-derived SE = 0.54/3.92 = 0.1378 vs printed 0.14 (consistent within rounding); 2·Φ(−0.24/0.14) = 0.0865 ≈ 0.086 (consistent).\n Anomalies: Table III says n = 96 while VI-B reports N=47 (AI) + N=46 (no-AI) = 93; VI-B says the AI group (Mean LogToT=4.6) was faster than no-AI (4.3) — means appear swapped/inconsistent (exp(4.6)=99, exp(4.3)=74 min, vs 96 and 114 min raw); \"21% faster\" is (1−exp(β))·100, i.e. a 21% reduction in time, not a speed rate. Stray \"p<\"/\"p=\" lines are rendering artefacts (README).\n- **S2 Becker** L34, L62, L72, D.1–D.3: \"+19%\" increase in completion time (regression-based S = exp(β)−1 with log time regressed on AI + log forecast). Uncertainty: none printed in the extract; the CI is displayed only in Figure 1/Figure 15 (omitted). Raw ratio is 34% (not used; the extract designates the regression-based headline).\n- **S3 Bono & Xu** L55: All Content | 53.84 | 44.29 | 63.10 | -29.79%***. 44.29/63.10−1 = −29.81% vs printed −29.79% (rounding of displayed minutes; small). Uncertainty: stars only; star scheme non-standard (*** = p≤0.01 per editorial note). Control arm could use other AI tools (25% used e.g. ChatGPT, L34) → contrast is Copilot vs no-Copilot.\n- **S4 Bono** L90-96: Full Corpus Aware 50.5, Control 56.9, % Time Savings 11.1, p-value 0.471. Anomalies: (a) 1−50.5/56.9 = 11.25% ≠ printed 11.1; Malicious and Non-Malicious columns likewise do not reproduce from displayed times (−47.4% vs −52.9; 25.0% vs 23.6) → printed % presumably from unrounded data/another estimator; (b) units: row labels say \"(s)\" per email but Malicious/Non-malicious values (2.8, 1.9, 1.8, 2.4) and Full Corpus (50.5, 56.9) cannot both be seconds per email (full corpus is not a weighted mean of the parts); Full Corpus is likely total minutes per 25-email queue. Ratio-based θ is unit-free so this does not affect θ. Aware arm also differs in queue ordering (agent-prioritised) — the AI contrast includes ordering.\n- **S5 Kreitmeir & Raschky** L80-88: Output col (3) panel A Treat×Post = -0.278 (0.295), N = 18332. Outcome is an activity count (sum of GitHub events; possibly logged, L42 is ambiguous) in a DiD around withdrawal of ChatGPT — not completion time → not harmonisable. Notes typo \"** p<0.5\" (should presumably be 0.05). Row label rendered as \"\\times TreatPost\". Date line \"August 24, 2026\" is a rendering artefact (L8).\n\n## Handling\nPrinted values copied verbatim; harmonisation uses printed headline quantities only (S3: −29.79%, S4: 11.1%), with the ratio-of-displayed-means alternative reported as sensitivity only.\nEOF\ncat > exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,extracted printed value available,5,5,0,All five extracts print the designated quantity\n2,harmonisable onto theta=ln(T_AI/T_noAI),5,4,1,S5 is a DiD effect on GitHub output counts around a ban - not task completion time AI vs no-AI\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 uncertainty only in a non-reproduced figure; S3 significance stars only\nEOF\ncd state && ~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of 7 files match README/bundle\",\"wc -l recorded\",\"arithmetic consistency of printed numbers checked (S1 SE/p, S3 and S4 ratios)\",\"exclusions.csv chain 5->5->4->2 consistent\"],\"notes\":\"data_audit.md; no availability violation; S2 figure-only CI excluded\"}\njson.dump(p,open('phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; decisions: S1 SE=printed 0.14; S2,S3 not poolable; S4 SE from p (approx); S5 not harmonisable.\" >> log.md","description":"Write data audit, exclusions, update ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":447,"cache_read_input_tokens":43970,"output_tokens":2431,"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":447},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsWkj7xGGmSTqGBrpZ6","requestRef":"aca71583-bb32-4b1b-b176-dc40d53a9d59","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d7729bbc-78f0-446d-b4d2-e4be662e299f","timestamp":"2026-10-30T05:30:29.621Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007/out/state","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"d7729bbc-78f0-446d-b4d2-e4be662e299f","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012nkLX1RHwfGkGcnMU5zhSH","type":"tool_result","content":"(eval):cd:1: no such file or directory: out\n(eval):cd:38: no such file or directory: state","is_error":false}]},"uuid":"e7ea9791-ded7-4132-81e9-22d276c63f71","timestamp":"2026-10-30T05:30:29.646Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out\n(eval):cd:38: no such file or directory: state","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d7729bbc-78f0-446d-b4d2-e4be662e299f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007/out/state","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"17e2bcb93bd5698871b3645c6512108e8661515a96250937004de5095b5d9b58","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_012nkLX1RHwfGkGcnMU5zhSH","name":"Bash","input":{"command":"cd out && cat > state/data_audit.md <<'EOF'\n# Data audit (P1)\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) | content |\n|---|---|---|---|\n| data/studies.json | 124d1f3e…bbe1 | 5 items (array of 5 objects; keys id, source, short, extract) | task-designer item list |\n| data/README.md | c7d07d4b…0128 | 17 | provenance |\n| data/sources/arxiv-2410.12944v3.txt | d5270b44…803b | 112 | Paradis et al. 2024 |\n| data/sources/arxiv-2507.09089v2.txt | b6af8a2b…d2d | 132 | Becker et al. 2025 |\n| data/sources/arxiv-2411.01067v2.txt | 46a1ee4e…053a | 109 | Bono & Xu 2024 |\n| data/sources/arxiv-2511.13860v1.txt | 8fb2520f…a5 | 162 | Bono 2025 |\n| data/sources/arxiv-2304.09339v1.txt | 1db48b1c…06e | 108 | Kreitmeir & Raschky 2023 |\n\nThese are text extracts, not tabular data: \"rows/columns\" = lines; no missing values in the usual sense. No duplicate study items (ids S1–S5 unique, sources unique).\nDate ranges: retrieval 2026-10-30T04:19–04:20Z; publications 2023–2025. No availability-rule violation found in the inputs; the only rule-relevant item is that\nS2's confidence interval appears only in Figure 1 (not reproduced) → must NOT be used.\n\n## Per-source findings (line numbers refer to `cat -n`)\n- **S1 Paradis** L54-58, L76-78: ExpCon Model 2 = -0.24 (0.14), marker '.', 95% CI [-0.51, 0.03], p=0.086.\n Checks: CI-derived SE = 0.54/3.92 = 0.1378 vs printed 0.14 (consistent within rounding); 2·Φ(−0.24/0.14) = 0.0865 ≈ 0.086 (consistent).\n Anomalies: Table III says n = 96 while VI-B reports N=47 (AI) + N=46 (no-AI) = 93; VI-B says the AI group (Mean LogToT=4.6) was faster than no-AI (4.3) — means appear swapped/inconsistent (exp(4.6)=99, exp(4.3)=74 min, vs 96 and 114 min raw); \"21% faster\" is (1−exp(β))·100, i.e. a 21% reduction in time, not a speed rate. Stray \"p<\"/\"p=\" lines are rendering artefacts (README).\n- **S2 Becker** L34, L62, L72, D.1–D.3: \"+19%\" increase in completion time (regression-based S = exp(β)−1 with log time regressed on AI + log forecast). Uncertainty: none printed in the extract; the CI is displayed only in Figure 1/Figure 15 (omitted). Raw ratio is 34% (not used; the extract designates the regression-based headline).\n- **S3 Bono & Xu** L55: All Content | 53.84 | 44.29 | 63.10 | -29.79%***. 44.29/63.10−1 = −29.81% vs printed −29.79% (rounding of displayed minutes; small). Uncertainty: stars only; star scheme non-standard (*** = p≤0.01 per editorial note). Control arm could use other AI tools (25% used e.g. ChatGPT, L34) → contrast is Copilot vs no-Copilot.\n- **S4 Bono** L90-96: Full Corpus Aware 50.5, Control 56.9, % Time Savings 11.1, p-value 0.471. Anomalies: (a) 1−50.5/56.9 = 11.25% ≠ printed 11.1; Malicious and Non-Malicious columns likewise do not reproduce from displayed times (−47.4% vs −52.9; 25.0% vs 23.6) → printed % presumably from unrounded data/another estimator; (b) units: row labels say \"(s)\" per email but Malicious/Non-malicious values (2.8, 1.9, 1.8, 2.4) and Full Corpus (50.5, 56.9) cannot both be seconds per email (full corpus is not a weighted mean of the parts); Full Corpus is likely total minutes per 25-email queue. Ratio-based θ is unit-free so this does not affect θ. Aware arm also differs in queue ordering (agent-prioritised) — the AI contrast includes ordering.\n- **S5 Kreitmeir & Raschky** L80-88: Output col (3) panel A Treat×Post = -0.278 (0.295), N = 18332. Outcome is an activity count (sum of GitHub events; possibly logged, L42 is ambiguous) in a DiD around withdrawal of ChatGPT — not completion time → not harmonisable. Notes typo \"** p<0.5\" (should presumably be 0.05). Row label rendered as \"\\times TreatPost\". Date line \"August 24, 2026\" is a rendering artefact (L8).\n\n## Handling\nPrinted values copied verbatim; harmonisation uses printed headline quantities only (S3: −29.79%, S4: 11.1%), with the ratio-of-displayed-means alternative reported as sensitivity only.\nEOF\ncat > exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,extracted printed value available,5,5,0,All five extracts print the designated quantity\n2,harmonisable onto theta=ln(T_AI/T_noAI),5,4,1,S5 is a DiD effect on GitHub output counts around a ban - not task completion time AI vs no-AI\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 uncertainty only in a non-reproduced figure; S3 significance stars only\nEOF\ncd state && ~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of 7 files match README/bundle\",\"wc -l recorded\",\"arithmetic consistency of printed numbers checked (S1 SE/p, S3 and S4 ratios)\",\"exclusions.csv chain 5->5->4->2 consistent\"],\"notes\":\"data_audit.md; 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keys id, source, short, extract) | task-designer item list |\n| data/README.md | c7d07d4b…0128 | 17 | provenance |\n| data/sources/arxiv-2410.12944v3.txt | d5270b44…803b | 112 | Paradis et al. 2024 |\n| data/sources/arxiv-2507.09089v2.txt | b6af8a2b…d2d | 132 | Becker et al. 2025 |\n| data/sources/arxiv-2411.01067v2.txt | 46a1ee4e…053a | 109 | Bono & Xu 2024 |\n| data/sources/arxiv-2511.13860v1.txt | 8fb2520f…a5 | 162 | Bono 2025 |\n| data/sources/arxiv-2304.09339v1.txt | 1db48b1c…06e | 108 | Kreitmeir & Raschky 2023 |\n\nThese are text extracts, not tabular data: \"rows/columns\" = lines; no missing values in the usual sense. No duplicate study items (ids S1–S5 unique, sources unique).\nDate ranges: retrieval 2026-10-30T04:19–04:20Z; publications 2023–2025. No availability-rule violation found in the inputs; the only rule-relevant item is that\nS2's confidence interval appears only in Figure 1 (not reproduced) → must NOT be used.\n\n## Per-source findings (line numbers refer to `cat -n`)\n- **S1 Paradis** L54-58, L76-78: ExpCon Model 2 = -0.24 (0.14), marker '.', 95% CI [-0.51, 0.03], p=0.086.\n Checks: CI-derived SE = 0.54/3.92 = 0.1378 vs printed 0.14 (consistent within rounding); 2·Φ(−0.24/0.14) = 0.0865 ≈ 0.086 (consistent).\n Anomalies: Table III says n = 96 while VI-B reports N=47 (AI) + N=46 (no-AI) = 93; VI-B says the AI group (Mean LogToT=4.6) was faster than no-AI (4.3) — the log means appear swapped/inconsistent with the raw means (96 vs 114 min); \"21% faster\" is (1−exp(β))·100, i.e. a 21% reduction in time, not a speed rate. Stray \"p<\"/\"p=\" lines are rendering artefacts (README).\n- **S2 Becker** L34, L62, L72, D.1–D.3: \"+19%\" increase in completion time (regression-based S = exp(β)−1, log time regressed on AI + log forecast). Uncertainty: none printed in the extract; the CI is displayed only in Figure 1/Figure 15 (omitted). Raw ratio 34% (not used; the extract designates the regression-based headline).\n- **S3 Bono & Xu** L55: All Content | 53.84 | 44.29 | 63.10 | -29.79%***. 44.29/63.10−1 = −29.81% vs printed −29.79% (rounding of displayed minutes; small). Uncertainty: stars only; star scheme non-standard (*** = p≤0.01 per editorial note). Control arm could use other AI tools (25% used e.g. ChatGPT, L34) → contrast is Copilot vs no-Copilot.\n- **S4 Bono** L90-96: Full Corpus Aware 50.5, Control 56.9, % Time Savings 11.1, p-value 0.471. Anomalies: (a) 1−50.5/56.9 = 11.25% ≠ printed 11.1; Malicious and Non-Malicious columns likewise do not reproduce from displayed times (−47.4% vs −52.9; 25.0% vs 23.6) → printed % presumably from unrounded data/another estimator; (b) units: row labels say \"(s)\" per email but Malicious/Non-malicious values (2.8, 1.9, 1.8, 2.4) and Full Corpus (50.5, 56.9) cannot both be seconds per email (full corpus is not a weighted mean of the parts); Full Corpus may be a per-queue total. A ratio-based θ is unit-free so this does not affect θ. The Aware arm also differs in queue ordering (agent-prioritised) — the AI contrast bundles ordering + verdicts.\n- **S5 Kreitmeir & Raschky** L80-88: Output col (3) panel A Treat×Post = -0.278 (0.295), N = 18332. Outcome is an activity count (sum of GitHub events; whether logged is ambiguous, L42) in a DiD around withdrawal of ChatGPT — not completion time → not harmonisable. Notes typo \"** p<0.5\" (presumably 0.05). Row label rendered as \"\\times TreatPost\". Date line \"August 24, 2026\" is a rendering artefact (L8).\n\n## Handling\nPrinted values copied verbatim; harmonisation uses printed headline quantities only (S3: −29.79%, S4: 11.1%), with the ratio-of-displayed-means alternative reported only as sensitivity.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nf='out/state/phases.json'; p=json.load(open(f))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of 7 files match README/bundle\",\"wc -l recorded\",\"arithmetic consistency of printed numbers checked (S1 SE/p, S3 and S4 ratios)\",\"exclusions.csv chain 5->5->4->2 consistent\"],\"notes\":\"data_audit.md; no availability violation; S2 figure-only CI excluded\"}\njson.dump(p,open(f,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; decisions: S1 SE=printed 0.14; S2,S3 not poolable; S4 SE from p (approx); S5 not harmonisable.\" >> out/state/log.md; cat out/exclusions.csv","description":"Rewrite audit and ledger from root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":264,"cache_read_input_tokens":46888,"output_tokens":2317,"output_tokens_details":{"thinking_tokens":40},"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":264},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsYasbsKqaBbxbdYVyp","requestRef":"f4cd6127-b416-4312-8ff2-7cec9f05c33b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"218b5237-cf0a-4e58-a514-2ac71010105f","timestamp":"2026-10-30T05:30:47.748Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"218b5237-cf0a-4e58-a514-2ac71010105f","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XUahvDrkQi76Z7jrvdArbN","type":"tool_result","content":"step,rule,n_before,n_after,n_removed,rationale\n1,extracted printed value available,5,5,0,All five extracts print the designated quantity\n2,harmonisable onto theta=ln(T_AI/T_noAI),5,4,1,S5 is a DiD effect on GitHub output counts around a ban - not task completion time AI vs no-AI\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 uncertainty only in a non-reproduced figure; S3 significance stars only","is_error":false}]},"uuid":"6f3da50e-5624-431e-bc24-9db928a08ce0","timestamp":"2026-10-30T05:30:47.802Z","toolUseResult":{"stdout":"step,rule,n_before,n_after,n_removed,rationale\n1,extracted printed value available,5,5,0,All five extracts print the designated quantity\n2,harmonisable onto theta=ln(T_AI/T_noAI),5,4,1,S5 is a DiD effect on GitHub output counts around a ban - not task completion time AI vs no-AI\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 uncertainty only in a non-reproduced figure; S3 significance stars only","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"218b5237-cf0a-4e58-a514-2ac71010105f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"f63bd8a9719c180cf265a98d4c8878503a721d8fb7a9f05ad86b38cc855c1404","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01XUahvDrkQi76Z7jrvdArbN","name":"Bash","input":{"command":"mv out/state/exclusions.csv out/exclusions.csv && cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md and bundle.json.\n\n| file | SHA-256 | lines (wc -l) | content |\n|---|---|---|---|\n| data/studies.json | 124d1f3e…bbe1 | 5 items (array of 5 objects; keys id, source, short, extract) | task-designer item list |\n| data/README.md | c7d07d4b…0128 | 17 | provenance |\n| data/sources/arxiv-2410.12944v3.txt | d5270b44…803b | 112 | Paradis et al. 2024 |\n| data/sources/arxiv-2507.09089v2.txt | b6af8a2b…d2d | 132 | Becker et al. 2025 |\n| data/sources/arxiv-2411.01067v2.txt | 46a1ee4e…053a | 109 | Bono & Xu 2024 |\n| data/sources/arxiv-2511.13860v1.txt | 8fb2520f…a5 | 162 | Bono 2025 |\n| data/sources/arxiv-2304.09339v1.txt | 1db48b1c…06e | 108 | Kreitmeir & Raschky 2023 |\n\nThese are text extracts, not tabular data: \"rows/columns\" = lines; no missing values in the usual sense. No duplicate study items (ids S1–S5 unique, sources unique).\nDate ranges: retrieval 2026-10-30T04:19–04:20Z; publications 2023–2025. No availability-rule violation found in the inputs; the only rule-relevant item is that\nS2's confidence interval appears only in Figure 1 (not reproduced) → must NOT be used.\n\n## Per-source findings (line numbers refer to `cat -n`)\n- **S1 Paradis** L54-58, L76-78: ExpCon Model 2 = -0.24 (0.14), marker '.', 95% CI [-0.51, 0.03], p=0.086.\n Checks: CI-derived SE = 0.54/3.92 = 0.1378 vs printed 0.14 (consistent within rounding); 2·Φ(−0.24/0.14) = 0.0865 ≈ 0.086 (consistent).\n Anomalies: Table III says n = 96 while VI-B reports N=47 (AI) + N=46 (no-AI) = 93; VI-B says the AI group (Mean LogToT=4.6) was faster than no-AI (4.3) — the log means appear swapped/inconsistent with the raw means (96 vs 114 min); \"21% faster\" is (1−exp(β))·100, i.e. a 21% reduction in time, not a speed rate. Stray \"p<\"/\"p=\" lines are rendering artefacts (README).\n- **S2 Becker** L34, L62, L72, D.1–D.3: \"+19%\" increase in completion time (regression-based S = exp(β)−1, log time regressed on AI + log forecast). Uncertainty: none printed in the extract; the CI is displayed only in Figure 1/Figure 15 (omitted). Raw ratio 34% (not used; the extract designates the regression-based headline).\n- **S3 Bono & Xu** L55: All Content | 53.84 | 44.29 | 63.10 | -29.79%***. 44.29/63.10−1 = −29.81% vs printed −29.79% (rounding of displayed minutes; small). Uncertainty: stars only; star scheme non-standard (*** = p≤0.01 per editorial note). Control arm could use other AI tools (25% used e.g. ChatGPT, L34) → contrast is Copilot vs no-Copilot.\n- **S4 Bono** L90-96: Full Corpus Aware 50.5, Control 56.9, % Time Savings 11.1, p-value 0.471. Anomalies: (a) 1−50.5/56.9 = 11.25% ≠ printed 11.1; Malicious and Non-Malicious columns likewise do not reproduce from displayed times (−47.4% vs −52.9; 25.0% vs 23.6) → printed % presumably from unrounded data/another estimator; (b) units: row labels say \"(s)\" per email but Malicious/Non-malicious values (2.8, 1.9, 1.8, 2.4) and Full Corpus (50.5, 56.9) cannot both be seconds per email (full corpus is not a weighted mean of the parts); Full Corpus may be a per-queue total. A ratio-based θ is unit-free so this does not affect θ. The Aware arm also differs in queue ordering (agent-prioritised) — the AI contrast bundles ordering + verdicts.\n- **S5 Kreitmeir & Raschky** L80-88: Output col (3) panel A Treat×Post = -0.278 (0.295), N = 18332. Outcome is an activity count (sum of GitHub events; whether logged is ambiguous, L42) in a DiD around withdrawal of ChatGPT — not completion time → not harmonisable. Notes typo \"** p<0.5\" (presumably 0.05). Row label rendered as \"\\times TreatPost\". Date line \"August 24, 2026\" is a rendering artefact (L8).\n\n## Handling\nPrinted values copied verbatim; harmonisation uses printed headline quantities only (S3: −29.79%, S4: 11.1%), with the ratio-of-displayed-means alternative reported only as sensitivity.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nf='out/state/phases.json'; p=json.load(open(f))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 of 7 files match README/bundle\",\"wc -l recorded\",\"arithmetic consistency of printed numbers checked (S1 SE/p, S3 and S4 ratios)\",\"exclusions.csv chain 5->5->4->2 consistent\"],\"notes\":\"data_audit.md; no availability violation; S2 figure-only CI excluded\"}\njson.dump(p,open(f,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: audit done; decisions: S1 SE=printed 0.14; S2,S3 not poolable; S4 SE from p (approx); S5 not harmonisable.\" >> out/state/log.md; cat out/exclusions.csv","description":"Rewrite audit and ledger from root"}}]}} 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{"parentUuid":"44d950cc-5eb2-4ee7-b246-54ca6a981aaf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsZkgywPZM57MUJaMf2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013v3GLNco8L8gE6BrTo16vE","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"T3-synthesis: extraction, harmonisation onto theta = ln(T_AI/T_noAI), FE/DL pooling, forest plot.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/. Writes out/results.json, out/table1.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os, re, csv\nimport numpy as np\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nZ = 1.96\n\ndef lines_of(src):\n with open(os.path.join(D, \"sources\", src + \".txt\"), encoding=\"utf-8\") as fh:\n return fh.read().split(\"\\n\")\n\ndef find(src, pattern, start=0):\n \"\"\"Return (1-based line number, line) of the first line at/after `start` matching regex.\"\"\"\n L = lines_of(src)\n for i in range(start, len(L)):\n if re.search(pattern, L[i]):\n print(f\"[{src} L{i+1}] {L[i].strip()[:220]}\")\n return i + 1, L[i]\n raise RuntimeError(f\"pattern not found in {src}: {pattern}\")\n\nstudies = json.load(open(os.path.join(D, \"studies.json\"), encoding=\"utf-8\"))\nids = [s[\"id\"] for s in studies]\nassert ids == [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\n\n# n_input_rows: wc -l semantics (count of newline characters)\nn_input_rows = {\"studies.json\": len(studies)}\nfor fn in [\"README.md\"] + [\"sources/\" + f for f in sorted(os.listdir(os.path.join(D, \"sources\")))]:\n with open(os.path.join(D, fn), \"rb\") as fh:\n n_input_rows[fn] = fh.read().count(b\"\\n\")\n\nprint(\"=== Source lines relied on ===\")\nitems = {}\n\n# ---------------- S1 Paradis et al. ----------------\nsrc = \"arxiv-2410.12944v3\"\nln_tab, row = find(src, r\"ExpCon \\|\")\nln_se, _ = find(src, r\"^\\(\", ln_tab) # next SE line after the ExpCon row\nse_line = lines_of(src)[ln_se - 1]\ncoefs = re.findall(r\"-?\\d+\\.\\d+\", row.split(\"ExpCon\")[0])\nses = re.findall(r\"\\((\\d+\\.\\d+)\\)\", se_line)\nb_print, se_print = coefs[1], ses[1] # Model 2 = second column\nln_ci, ci_line = find(src, r\"best-fit Model 2\")\nm = re.search(r\"CI = \\[(-?\\d+\\.\\d+),\\s*(-?\\d+\\.\\d+)\\]\\s*,\\s*p=(\\d?\\.\\d+)\", ci_line)\nci_l, ci_u, p1 = m.group(1), m.group(2), m.group(3)\nfind(src, r\"TABLE III\")\nln_n, nline = find(src, r\"completed the task in 96 min\")\nb1, se1 = float(b_print), float(se_print)\nse1_ci = (float(ci_u) - float(ci_l)) / (2 * Z)\nitems[\"S1\"] = dict(\n estimate_as_printed=f\"{b_print} (ExpCon, Model 2, LogToT; marker '.'); text: beta_1=-0.24, effect of AI = 21%\",\n uncertainty_as_printed=f\"SE ({se_print}); 95% CI = [{ci_l}, {ci_u}]; p={p1}\",\n sample_as_printed=\"Table III: n = 96 (Section VI-B: N=47 AI, N=46 no-AI)\",\n definition=\"OLS coefficient of the experimental condition (AI vs no-AI, randomised) on the natural log of time on task, controlling for developer-level factors (Model 2)\",\n locator=f\"{src}; Table III (L{ln_tab}-L{ln_se}), row ExpCon, column (2) 'Dev'; Section VI-C (L{ln_ci}) for CI and p\",\n harmonisable=True, harmonised_estimate=b1, harmonised_se=se1, poolable=True,\n transformation=f\"log-time coefficient: theta = b = {b_print}; SE(theta) = SE(b) = {se_print} (printed SE used; CI-based SE = ({ci_u} - ({ci_l}))/3.92 = {se1_ci:.4f} used only as sensitivity)\",\n reason_if_not=None)\n\n# ---------------- S2 Becker et al. ----------------\nsrc = \"arxiv-2507.09089v2\"\nln_a, _ = find(src, r\"increases completion time by 19%\")\nln_r, rline = find(src, r\"implement issues in 19% more time on average\")\nln_s, _ = find(src, r\"S=\\\\frac\")\nln_c, _ = find(src, r\"Developers complete 136 issues\")\nfind(src, r\"^Omitted:.*Figure 1\")\nc2 = float(re.search(r\"(\\d+)% more time\", rline).group(1)) / 100\nth2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n estimate_as_printed=\"19% more time on average (AI allowed increases completion time by 19%)\",\n uncertainty_as_printed=\"none printed (confidence interval shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n sample_as_printed=\"16 developers, 246 tasks; 136 issues AI-allowed, 110 issues AI-disallowed\",\n definition=\"S = E[time with AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time (issue-level randomisation)\",\n locator=f\"{src}; Section 3 Results (L{ln_r}); abstract (L{ln_a}); estimator in Section 2.3 (L{ln_s}) and Appendix D.1-D.3\",\n harmonisable=True, harmonised_estimate=th2, harmonised_se=None, poolable=False,\n transformation=f\"percentage change: c = +0.19; theta = ln(1 + 0.19) = {th2:.6f} (equals the regression beta since S = exp(beta) - 1)\",\n reason_if_not=\"Not poolable: no SE, CI or p-value is printed in the extract; the confidence interval appears only in a figure that is not reproduced, so SE(theta) cannot be obtained.\")\n\n# ---------------- S3 Bono & Xu ----------------\nsrc = \"arxiv-2411.01067v2\"\nln_h, _ = find(src, r\"\\| Overall \\| Treatment \\| Control \\| Improvement\")\nln_row, row3 = find(src, r\"^All Content \\|\", ln_h)\ncells = [c.strip() for c in row3.split(\"|\")]\nov, tr, co, imp = cells[1], cells[2], cells[3], cells[4]\nfind(src, r\"Table 3: Comparison of task duration\")\nfind(src, r\"We had 181 subjects\")\nfind(src, r\"^Editorial note\")\nc3 = float(re.match(r\"(-?\\d+\\.\\d+)%\", imp).group(1)) / 100\nth3 = math.log(1 + c3)\nitems[\"S3\"] = dict(\n estimate_as_printed=f\"Treatment {tr} min, Control {co} min (Overall {ov}); Improvement {imp}\",\n uncertainty_as_printed=\"significance stars only (***; per the paper's scheme 0 c = {c4}; theta = ln(1 + ({c4})) = {th4:.6f}; APPROXIMATE SE from two-sided p: SE = |theta|/z_(1-{p4}/2) = {abs(th4):.6f}/{z4:.6f} = {se4:.6f} (ratio of displayed means ln({aw4}/{co4}) = {math.log(float(aw4)/float(co4)):.6f}, sensitivity only)\",\n reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky ----------------\nsrc = \"arxiv-2304.09339v1\"\nln_tp, tp = find(src, r\"TreatPost \\|\")\nln_tse, tse = find(src, r\"^\\| \\(\", ln_tp)\nln_N, nl = find(src, r\"^N \\|\", ln_tp)\nfind(src, r\"aggregate Output measure\")\nb5 = [c.strip() for c in tp.split(\"|\")][3]\ns5 = [c.strip() for c in tse.split(\"|\")][3]\nn5 = [c.strip() for c in nl.split(\"|\")][3]\nitems[\"S5\"] = dict(\n estimate_as_printed=f\"{b5} (Treat x Post, Output, panel A, column (3))\",\n uncertainty_as_printed=f\"SE {s5} (clustered at user level)\",\n sample_as_printed=f\"N = {n5} (user-day observations, users with affiliation)\",\n definition=\"Difference-in-differences coefficient (Italy vs Austria/France, post vs pre ChatGPT ban) on an aggregate count of GitHub Output events (Push, Pull Requests, comments, Create, Issues)\",\n locator=f\"{src}; Section 4, Table 1 (L{ln_tp}-L{ln_N}), panel A 'Pre 27/28.03 - Post 03/04.04', row Treat x Post, column (3) Output (users with affiliation)\",\n harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n transformation=\"none: no transformation maps an effect on output counts onto ln(T_AI/T_noAI)\",\n reason_if_not=\"Not harmonisable: the effect is on output counts (GitHub events) around the withdrawal of ChatGPT access in a non-randomised difference-in-differences design, not a comparison of task-completion times with and without AI on the same tasks.\")\n\n# ---------------- Pooling ----------------\ndef pool(est, se):\n est, se = np.asarray(est, float), np.asarray(se, float)\n w = 1 / se**2\n fe = float(np.sum(w * est) / np.sum(w)); fe_se = float(math.sqrt(1 / np.sum(w)))\n q = float(np.sum(w * (est - fe) ** 2)); df = len(est) - 1\n C = float(np.sum(w) - np.sum(w**2) / np.sum(w))\n tau2 = max(0.0, (q - df) / C) if C > 0 else 0.0\n i2 = max(0.0, (q - df) / q) * 100 if q > 0 else 0.0\n wr = 1 / (se**2 + tau2)\n re_ = float(np.sum(wr * est) / np.sum(wr)); re_se = float(math.sqrt(1 / np.sum(wr)))\n return dict(fe=fe, fe_se=fe_se, q=q, df=df, tau2=tau2, i2=i2, re=re_, re_se=re_se)\n\npool_ids = [k for k in ids if items[k][\"poolable\"]]\nP = pool([items[k][\"harmonised_estimate\"] for k in pool_ids], [items[k][\"harmonised_se\"] for k in pool_ids])\ninterp = \"low\" if P[\"i2\"] < 25 else (\"moderate\" if P[\"i2\"] < 50 else \"substantial\")\nre_lo, re_hi = P[\"re\"] - Z * P[\"re_se\"], P[\"re\"] + Z * P[\"re_se\"]\n\n# sensitivity (not headline)\nS_ci = pool([b1, th4], [se1_ci, se4])\nth4_ratio = math.log(float(aw4) / float(co4)); se4_ratio = abs(th4_ratio) / z4\nS_ratio = pool([b1, th4_ratio], [se1, se4_ratio])\nharm = [items[k][\"harmonised_estimate\"] for k in ids if items[k][\"harmonisable\"]]\n\ndata_issues = [\n {\"issue\": \"S1 (Paradis): Table III states n = 96, while Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 completed tasks.\", \"handling\": \"Both sample statements recorded in sample_as_printed; coefficient and SE taken from Table III as designated; no effect on theta.\"},\n {\"issue\": \"S1 (Paradis): Section VI-B says the AI group (Mean LogToT=4.6) was faster than the no-AI group (Mean LogToT=4.3), which contradicts the raw means (96 vs 114 min); the log means appear swapped.\", \"handling\": \"Not used; harmonisation relies on the Model 2 regression coefficient designated in studies.json.\"},\n {\"issue\": \"S1 (Paradis): printed SE 0.14 and printed 95% CI [-0.51, 0.03] imply slightly different SEs (0.14 vs 0.1378); '21% faster' denotes a 21% reduction in time (1-exp(beta)).\", \"handling\": \"Printed SE used (convention for a coefficient with SE); CI-based SE reported as sensitivity_ci_se_s1_* keys.\"},\n {\"issue\": \"S2 (Becker): the confidence interval for the 19% estimate is shown only in Figure 1/Figure 15, which are not reproduced in the extract.\", \"handling\": \"No SE imputed; S2 harmonised (theta = ln 1.19) but not poolable.\"},\n {\"issue\": \"S3 (Bono & Xu): uncertainty only as significance stars with a non-standard scheme (* 0.05 out/analysis.py <<'EOF'\n\"\"\"T3-synthesis: extraction, harmonisation onto theta = ln(T_AI/T_noAI), FE/DL pooling, forest plot.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/. Writes out/results.json, out/table1.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os, re, csv\nimport numpy as np\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nZ = 1.96\n\ndef lines_of(src):\n with open(os.path.join(D, \"sources\", src + \".txt\"), encoding=\"utf-8\") as fh:\n return fh.read().split(\"\\n\")\n\ndef find(src, pattern, start=0):\n \"\"\"Return (1-based line number, line) of the first line at/after `start` matching regex.\"\"\"\n L = lines_of(src)\n for i in range(start, len(L)):\n if re.search(pattern, L[i]):\n print(f\"[{src} L{i+1}] {L[i].strip()[:220]}\")\n return i + 1, L[i]\n raise RuntimeError(f\"pattern not found in {src}: {pattern}\")\n\nstudies = json.load(open(os.path.join(D, \"studies.json\"), encoding=\"utf-8\"))\nids = [s[\"id\"] for s in studies]\nassert ids == [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\n\n# n_input_rows: wc -l semantics (count of newline characters)\nn_input_rows = {\"studies.json\": len(studies)}\nfor fn in [\"README.md\"] + [\"sources/\" + f for f in sorted(os.listdir(os.path.join(D, \"sources\")))]:\n with open(os.path.join(D, fn), \"rb\") as fh:\n n_input_rows[fn] = fh.read().count(b\"\\n\")\n\nprint(\"=== Source lines relied on ===\")\nitems = {}\n\n# ---------------- S1 Paradis et al. ----------------\nsrc = \"arxiv-2410.12944v3\"\nln_tab, row = find(src, r\"ExpCon \\|\")\nln_se, _ = find(src, r\"^\\(\", ln_tab) # next SE line after the ExpCon row\nse_line = lines_of(src)[ln_se - 1]\ncoefs = re.findall(r\"-?\\d+\\.\\d+\", row.split(\"ExpCon\")[0])\nses = re.findall(r\"\\((\\d+\\.\\d+)\\)\", se_line)\nb_print, se_print = coefs[1], ses[1] # Model 2 = second column\nln_ci, ci_line = find(src, r\"best-fit Model 2\")\nm = re.search(r\"CI = \\[(-?\\d+\\.\\d+),\\s*(-?\\d+\\.\\d+)\\]\\s*,\\s*p=(\\d?\\.\\d+)\", ci_line)\nci_l, ci_u, p1 = m.group(1), m.group(2), m.group(3)\nfind(src, r\"TABLE III\")\nln_n, nline = find(src, r\"completed the task in 96 min\")\nb1, se1 = float(b_print), float(se_print)\nse1_ci = (float(ci_u) - float(ci_l)) / (2 * Z)\nitems[\"S1\"] = dict(\n estimate_as_printed=f\"{b_print} (ExpCon, Model 2, LogToT; marker '.'); text: beta_1=-0.24, effect of AI = 21%\",\n uncertainty_as_printed=f\"SE ({se_print}); 95% CI = [{ci_l}, {ci_u}]; p={p1}\",\n sample_as_printed=\"Table III: n = 96 (Section VI-B: N=47 AI, N=46 no-AI)\",\n definition=\"OLS coefficient of the experimental condition (AI vs no-AI, randomised) on the natural log of time on task, controlling for developer-level factors (Model 2)\",\n locator=f\"{src}; Table III (L{ln_tab}-L{ln_se}), row ExpCon, column (2) 'Dev'; Section VI-C (L{ln_ci}) for CI and p\",\n harmonisable=True, harmonised_estimate=b1, harmonised_se=se1, poolable=True,\n transformation=f\"log-time coefficient: theta = b = {b_print}; SE(theta) = SE(b) = {se_print} (printed SE used; CI-based SE = ({ci_u} - ({ci_l}))/3.92 = {se1_ci:.4f} used only as sensitivity)\",\n reason_if_not=None)\n\n# ---------------- S2 Becker et al. ----------------\nsrc = \"arxiv-2507.09089v2\"\nln_a, _ = find(src, r\"increases completion time by 19%\")\nln_r, rline = find(src, r\"implement issues in 19% more time on average\")\nln_s, _ = find(src, r\"S=\\\\frac\")\nln_c, _ = find(src, r\"Developers complete 136 issues\")\nfind(src, r\"^Omitted:.*Figure 1\")\nc2 = float(re.search(r\"(\\d+)% more time\", rline).group(1)) / 100\nth2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n estimate_as_printed=\"19% more time on average (AI allowed increases completion time by 19%)\",\n uncertainty_as_printed=\"none printed (confidence interval shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n sample_as_printed=\"16 developers, 246 tasks; 136 issues AI-allowed, 110 issues AI-disallowed\",\n definition=\"S = E[time with AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time (issue-level randomisation)\",\n locator=f\"{src}; Section 3 Results (L{ln_r}); abstract (L{ln_a}); estimator in Section 2.3 (L{ln_s}) and Appendix D.1-D.3\",\n harmonisable=True, harmonised_estimate=th2, harmonised_se=None, poolable=False,\n transformation=f\"percentage change: c = +0.19; theta = ln(1 + 0.19) = {th2:.6f} (equals the regression beta since S = exp(beta) - 1)\",\n reason_if_not=\"Not poolable: no SE, CI or p-value is printed in the extract; the confidence interval appears only in a figure that is not reproduced, so SE(theta) cannot be obtained.\")\n\n# ---------------- S3 Bono & Xu ----------------\nsrc = \"arxiv-2411.01067v2\"\nln_h, _ = find(src, r\"\\| Overall \\| Treatment \\| Control \\| Improvement\")\nln_row, row3 = find(src, r\"^All Content \\|\", ln_h)\ncells = [c.strip() for c in row3.split(\"|\")]\nov, tr, co, imp = cells[1], cells[2], cells[3], cells[4]\nfind(src, r\"Table 3: Comparison of task duration\")\nfind(src, r\"We had 181 subjects\")\nfind(src, r\"^Editorial note\")\nc3 = float(re.match(r\"(-?\\d+\\.\\d+)%\", imp).group(1)) / 100\nth3 = math.log(1 + c3)\nitems[\"S3\"] = dict(\n estimate_as_printed=f\"Treatment {tr} min, Control {co} min (Overall {ov}); Improvement {imp}\",\n uncertainty_as_printed=\"significance stars only (***; per the paper's scheme 0 c = {c4}; theta = ln(1 + ({c4})) = {th4:.6f}; APPROXIMATE SE from two-sided p: SE = |theta|/z_(1-{p4}/2) = {abs(th4):.6f}/{z4:.6f} = {se4:.6f} (ratio of displayed means ln({aw4}/{co4}) = {math.log(float(aw4)/float(co4)):.6f}, sensitivity only)\",\n reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky ----------------\nsrc = \"arxiv-2304.09339v1\"\nln_tp, tp = find(src, r\"TreatPost \\|\")\nln_tse, tse = find(src, r\"^\\| \\(\", ln_tp)\nln_N, nl = find(src, r\"^N \\|\", ln_tp)\nfind(src, r\"aggregate Output measure\")\nb5 = [c.strip() for c in tp.split(\"|\")][3]\ns5 = [c.strip() for c in tse.split(\"|\")][3]\nn5 = [c.strip() for c in nl.split(\"|\")][3]\nitems[\"S5\"] = dict(\n estimate_as_printed=f\"{b5} (Treat x Post, Output, panel A, column (3))\",\n uncertainty_as_printed=f\"SE {s5} (clustered at user level)\",\n sample_as_printed=f\"N = {n5} (user-day observations, users with affiliation)\",\n definition=\"Difference-in-differences coefficient (Italy vs Austria/France, post vs pre ChatGPT ban) on an aggregate count of GitHub Output events (Push, Pull Requests, comments, Create, Issues)\",\n locator=f\"{src}; Section 4, Table 1 (L{ln_tp}-L{ln_N}), panel A 'Pre 27/28.03 - Post 03/04.04', row Treat x Post, column (3) Output (users with affiliation)\",\n harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n transformation=\"none: no transformation maps an effect on output counts onto ln(T_AI/T_noAI)\",\n reason_if_not=\"Not harmonisable: the effect is on output counts (GitHub events) around the withdrawal of ChatGPT access in a non-randomised difference-in-differences design, not a comparison of task-completion times with and without AI on the same tasks.\")\n\n# ---------------- Pooling ----------------\ndef pool(est, se):\n est, se = np.asarray(est, float), np.asarray(se, float)\n w = 1 / se**2\n fe = float(np.sum(w * est) / np.sum(w)); fe_se = float(math.sqrt(1 / np.sum(w)))\n q = float(np.sum(w * (est - fe) ** 2)); df = len(est) - 1\n C = float(np.sum(w) - np.sum(w**2) / np.sum(w))\n tau2 = max(0.0, (q - df) / C) if C > 0 else 0.0\n i2 = max(0.0, (q - df) / q) * 100 if q > 0 else 0.0\n wr = 1 / (se**2 + tau2)\n re_ = float(np.sum(wr * est) / np.sum(wr)); re_se = float(math.sqrt(1 / np.sum(wr)))\n return dict(fe=fe, fe_se=fe_se, q=q, df=df, tau2=tau2, i2=i2, re=re_, re_se=re_se)\n\npool_ids = [k for k in ids if items[k][\"poolable\"]]\nP = pool([items[k][\"harmonised_estimate\"] for k in pool_ids], [items[k][\"harmonised_se\"] for k in pool_ids])\ninterp = \"low\" if P[\"i2\"] < 25 else (\"moderate\" if P[\"i2\"] < 50 else \"substantial\")\nre_lo, re_hi = P[\"re\"] - Z * P[\"re_se\"], P[\"re\"] + Z * P[\"re_se\"]\n\n# sensitivity (not headline)\nS_ci = pool([b1, th4], [se1_ci, se4])\nth4_ratio = math.log(float(aw4) / float(co4)); se4_ratio = abs(th4_ratio) / z4\nS_ratio = pool([b1, th4_ratio], [se1, se4_ratio])\nharm = [items[k][\"harmonised_estimate\"] for k in ids if items[k][\"harmonisable\"]]\n\ndata_issues = [\n {\"issue\": \"S1 (Paradis): Table III states n = 96, while Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 completed tasks.\", \"handling\": \"Both sample statements recorded in sample_as_printed; coefficient and SE taken from Table III as designated; 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{"parentUuid":"4c99a61e-7362-47e7-a207-56bff65d8e43","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfPskjHbMiEQrfbkGY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MAU6LatNiBPH7vMjvimXM5","name":"Bash","input":{"command":"rm out/results.json out/table1.csv out/figure1.png && ~/wp29/venv/bin/python out/analysis.py > /dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ~/wp29/venv/bin/python - <<'EOF'\nimport json,math,csv\nr=json.load(open('out/results.json'))\nc=[]\ndef chk(n,ok): c.append((n,bool(ok))); print(('PASS' if ok else 'FAIL'),n)\nchk(\"fe_ci contains fe\", r['fe_ci_low']0 and r['re_se']>0)\nchk(\"k counts 5/4/2\", (r['k_extracted'],r['k_harmonised'],r['k_pooled'])==(5,4,2))\nchk(\"exclusions chain ends at k_pooled\", list(csv.DictReader(open('out/exclusions.csv')))[-1]['n_after']==str(r['k_pooled']))\nchk(\"tau2=0 => re==fe\", r['tau2']>0 or abs(r['re_estimate']-r['fe_estimate'])<1e-12)\nchk(\"I2 rule\", r['i2']==max(0,(r['q_stat']-1)/r['q_stat'])*100)\nchk(\"heterogeneity label\", r['heterogeneity_interpretation']=='low')\nchk(\"pct change\", abs(r['pooled_pct_change_re']-100*(math.exp(r['re_estimate'])-1))<1e-12)\nchk(\"significance flag consistent\", r['pooled_significant_5pct']==(r['re_ci_high']<0 or r['re_ci_low']>0))\nit=r['items']\nchk(\"nonpoolable have null SE / poolable non-null\", all((v['harmonised_se'] is None)!=v['poolable'] for v in it.values()))\nchk(\"S4 SE recovers p\", abs(2*(1-__import__('scipy.stats').stats.norm.cdf(abs(it['S4']['harmonised_estimate'])/it['S4']['harmonised_se']))-0.471)<1e-9)\nchk(\"FE within range of pooled thetas\", -0.24<=r['fe_estimate']<=-0.1177)\nchk(\"baseline unweighted pooled mean close to FE\", abs(r['baseline_unweighted_mean_theta_pooled']-r['fe_estimate'])<0.05)\nchk(\"n_input_rows match wc -l\", r['n_input_rows']['sources/arxiv-2511.13860v1.txt']==162 and r['n_input_rows']['README.md']==17)\nchk(\"table1 has 5 rows\", len(list(csv.DictReader(open('out/table1.csv'))))==5)\nprint({k:r[k] for k in ['fe_estimate','fe_se','re_ci_low','re_ci_high','q_stat','i2','tau2','pooled_pct_change_re','pooled_pct_change_re_ci_low','pooled_pct_change_re_ci_high','baseline_unweighted_mean_theta_harmonised','baseline_unweighted_mean_theta_pooled','sensitivity_ci_se_s1_re_estimate','sensitivity_ci_se_s1_re_se','sensitivity_ratio_s4_theta','sensitivity_ratio_s4_re_estimate','sensitivity_ratio_s4_re_se']})\nEOF","description":"Clean rerun and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1088,"cache_read_input_tokens":62016,"output_tokens":1313,"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":1088},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsfPathWcizdExHkUGU","requestRef":"3b78d055-8e87-47aa-866d-656cef186484","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d38d1c6b-8edc-4b58-a08a-9745642b0b4f","timestamp":"2026-10-30T05:32:14.228Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"d38d1c6b-8edc-4b58-a08a-9745642b0b4f","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MAU6LatNiBPH7vMjvimXM5","type":"tool_result","content":"IDENTICAL\nPASS fe_ci contains fe\nPASS re_ci contains re\nPASS se positive\nPASS k counts 5/4/2\nPASS exclusions chain ends at k_pooled\nPASS tau2=0 => re==fe\nPASS I2 rule\nPASS heterogeneity label\nPASS pct change\nPASS significance flag consistent\nPASS nonpoolable have null SE / poolable non-null\nPASS S4 SE recovers p\nPASS FE within range of pooled thetas\nPASS baseline unweighted pooled mean close to FE\nPASS n_input_rows match wc -l\nPASS table1 has 5 rows\n{'fe_estimate': -0.18814332871148873, 'fe_se': 0.10626483095451474, 're_ci_low': -0.39642239738233764, 're_ci_high': 0.02013573995936016, 'q_stat': 0.3236860522786328, 'i2': 0.0, 'tau2': 0.0, 'pooled_pct_change_re': -17.15040485348688, 'pooled_pct_change_re_ci_low': -32.72775202989503, 'pooled_pct_change_re_ci_high': 2.0339831514392115, 'baseline_unweighted_mean_theta_harmonised': -0.13434604282593213, 'baseline_unweighted_mean_theta_pooled': -0.17882902173411622, 'sensitivity_ci_se_s1_re_estimate': -0.1891067723193802, 'sensitivity_ci_se_s1_re_se': 0.10527305720065441, 'sensitivity_ratio_s4_theta': -0.11932200485097105, 'sensitivity_ratio_s4_re_estimate': -0.18967451746361488, 'sensitivity_ratio_s4_re_se': 0.10689411567852646}","is_error":false}]},"uuid":"ceb8d38c-d0e5-4ed5-9458-5a6faa63ee3f","timestamp":"2026-10-30T05:32:15.438Z","toolUseResult":{"stdout":"IDENTICAL\nPASS fe_ci contains fe\nPASS re_ci contains re\nPASS se positive\nPASS k counts 5/4/2\nPASS exclusions chain ends at k_pooled\nPASS tau2=0 => re==fe\nPASS I2 rule\nPASS heterogeneity label\nPASS pct change\nPASS significance flag consistent\nPASS nonpoolable have null SE / poolable non-null\nPASS S4 SE recovers p\nPASS FE within range of pooled thetas\nPASS baseline unweighted pooled mean close to FE\nPASS n_input_rows match wc -l\nPASS table1 has 5 rows\n{'fe_estimate': -0.18814332871148873, 'fe_se': 0.10626483095451474, 're_ci_low': -0.39642239738233764, 're_ci_high': 0.02013573995936016, 'q_stat': 0.3236860522786328, 'i2': 0.0, 'tau2': 0.0, 'pooled_pct_change_re': -17.15040485348688, 'pooled_pct_change_re_ci_low': -32.72775202989503, 'pooled_pct_change_re_ci_high': 2.0339831514392115, 'baseline_unweighted_mean_theta_harmonised': -0.13434604282593213, 'baseline_unweighted_mean_theta_pooled': -0.17882902173411622, 'sensitivity_ci_se_s1_re_estimate': -0.1891067723193802, 'sensitivity_ci_se_s1_re_se': 0.10527305720065441, 'sensitivity_ratio_s4_theta': -0.11932200485097105, 'sensitivity_ratio_s4_re_estimate': -0.18967451746361488, 'sensitivity_ratio_s4_re_se': 0.10689411567852646}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d38d1c6b-8edc-4b58-a08a-9745642b0b4f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"f740d020c19736fea80796796811a8cad5c474eaf68716114ddaf67edf817a9c","message":{"role":"assistant","content":[{"type":"text","text":"Now P4: clean re-run and consistency checks."},{"type":"tool_use","id":"toolu_01MAU6LatNiBPH7vMjvimXM5","name":"Bash","input":{"command":"rm out/results.json out/table1.csv out/figure1.png && ~/wp29/venv/bin/python out/analysis.py > /dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ~/wp29/venv/bin/python - <<'EOF'\nimport json,math,csv\nr=json.load(open('out/results.json'))\nc=[]\ndef chk(n,ok): c.append((n,bool(ok))); print(('PASS' if ok else 'FAIL'),n)\nchk(\"fe_ci contains fe\", r['fe_ci_low']0 and r['re_se']>0)\nchk(\"k counts 5/4/2\", (r['k_extracted'],r['k_harmonised'],r['k_pooled'])==(5,4,2))\nchk(\"exclusions chain ends at k_pooled\", list(csv.DictReader(open('out/exclusions.csv')))[-1]['n_after']==str(r['k_pooled']))\nchk(\"tau2=0 => re==fe\", r['tau2']>0 or abs(r['re_estimate']-r['fe_estimate'])<1e-12)\nchk(\"I2 rule\", r['i2']==max(0,(r['q_stat']-1)/r['q_stat'])*100)\nchk(\"heterogeneity label\", r['heterogeneity_interpretation']=='low')\nchk(\"pct change\", abs(r['pooled_pct_change_re']-100*(math.exp(r['re_estimate'])-1))<1e-12)\nchk(\"significance flag consistent\", r['pooled_significant_5pct']==(r['re_ci_high']<0 or r['re_ci_low']>0))\nit=r['items']\nchk(\"nonpoolable have null SE / poolable non-null\", all((v['harmonised_se'] is None)!=v['poolable'] for v in it.values()))\nchk(\"S4 SE recovers p\", abs(2*(1-__import__('scipy.stats').stats.norm.cdf(abs(it['S4']['harmonised_estimate'])/it['S4']['harmonised_se']))-0.471)<1e-9)\nchk(\"FE within range of pooled thetas\", -0.24<=r['fe_estimate']<=-0.1177)\nchk(\"baseline unweighted pooled mean close to FE\", abs(r['baseline_unweighted_mean_theta_pooled']-r['fe_estimate'])<0.05)\nchk(\"n_input_rows match wc -l\", r['n_input_rows']['sources/arxiv-2511.13860v1.txt']==162 and r['n_input_rows']['README.md']==17)\nchk(\"table1 has 5 rows\", len(list(csv.DictReader(open('out/table1.csv'))))==5)\nprint({k:r[k] for k in ['fe_estimate','fe_se','re_ci_low','re_ci_high','q_stat','i2','tau2','pooled_pct_change_re','pooled_pct_change_re_ci_low','pooled_pct_change_re_ci_high','baseline_unweighted_mean_theta_harmonised','baseline_unweighted_mean_theta_pooled','sensitivity_ci_se_s1_re_estimate','sensitivity_ci_se_s1_re_se','sensitivity_ratio_s4_theta','sensitivity_ratio_s4_re_estimate','sensitivity_ratio_s4_re_se']})\nEOF","description":"Clean rerun and consistency checks"}}]}} 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{"parentUuid":"ceb8d38c-d0e5-4ed5-9458-5a6faa63ee3f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgDTxcTwGH6VBVBDA5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JmBp4MMM1j8GNyuF717KGx","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n- Clean re-run: deleted results.json/table1.csv/figure1.png, re-ran analysis.py; results.json byte-identical (cmp) to previous run. PASS\n- FE CI contains FE estimate; RE CI contains RE estimate. PASS\n- fe_se, re_se > 0. PASS\n- k_extracted/k_harmonised/k_pooled = 5/4/2 and exclusions.csv chain 5→5→4→2 ends at k_pooled. PASS\n- tau2 = 0 ⇒ RE = FE (identical). PASS\n- I² recomputed from Q with df=1 equals reported (0). Label \"low\" consistent with rule. PASS\n- pooled_pct_change_re = 100(exp(re)−1). PASS\n- pooled_significant_5pct = false consistent with RE CI [−0.396, 0.020] including 0. PASS\n- Every item: harmonised_se null ⇔ not poolable. PASS\n- S4 approximate SE reproduces the printed p=0.471 under normal approx. PASS\n- FE lies between the two pooled thetas (−0.240, −0.118); unweighted mean of pooled thetas (baseline −0.179) within 0.05 of FE (−0.188). PASS\n- n_input_rows equal `wc -l` counts. PASS\n- table1.csv has 5 rows and the 12 required columns. PASS\n- Printed-value fidelity: analysis.py prints every source line used; values parsed from those lines (S1 −0.24/(0.14), CI [−0.51,0.03], p=0.086; S2 19%; S3 44.29/63.10/−29.79%***; S4 50.5/56.9/11.1/0.471; S5 −0.278/(0.295)/18332) — verified by eye against `cat -n` output. PASS\n- Known source inconsistencies (not failures of this analysis) are listed in results.json data_issues.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -25","description":"Write checks and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1938,"cache_read_input_tokens":63104,"output_tokens":801,"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":1938},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsgDBbUoca2nZHbyK1b","requestRef":"8dab8ed9-6fee-4256-9c1c-cf4947cfba08","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e890b662-a048-4ba9-8d1c-67dbe006445f","timestamp":"2026-10-30T05:32:22.606Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"e890b662-a048-4ba9-8d1c-67dbe006445f","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JmBp4MMM1j8GNyuF717KGx","type":"tool_result","content":" [ok] key:tau2: ok\n [ok] key:heterogeneity_interpretation: ok\n [ok] key:pooled_significant_5pct: ok\n [ok] key:pooled_pct_change_re: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] ci:fe_estimate: -0.39642239738233764 <= -0.18814332871148873 <= 0.02013573995936016\n [ok] ci:re_estimate: -0.39642239738233764 <= -0.18814332871148873 <= 0.02013573995936016\n [ok] positive:fe_se: 0.10626483095451474\n [ok] positive:re_se: 0.10626483095451474\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RT0007/out/state/gate_receipts/20261030T053223.354130Z.json","is_error":false}]},"uuid":"2f79a070-1669-4ec1-b47e-38d54cf16245","timestamp":"2026-10-30T05:32:23.362Z","toolUseResult":{"stdout":" [ok] key:tau2: ok\n [ok] key:heterogeneity_interpretation: ok\n [ok] key:pooled_significant_5pct: ok\n [ok] key:pooled_pct_change_re: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] ci:fe_estimate: -0.39642239738233764 <= -0.18814332871148873 <= 0.02013573995936016\n [ok] ci:re_estimate: -0.39642239738233764 <= -0.18814332871148873 <= 0.02013573995936016\n [ok] positive:fe_se: 0.10626483095451474\n [ok] positive:re_se: 0.10626483095451474\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RT0007/out/state/gate_receipts/20261030T053223.354130Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e890b662-a048-4ba9-8d1c-67dbe006445f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"634aca721613a742c71ba325c2d42455c4056d85447b26a22451e17c868d96d7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01JmBp4MMM1j8GNyuF717KGx","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n- Clean re-run: deleted results.json/table1.csv/figure1.png, re-ran analysis.py; results.json byte-identical (cmp) to previous run. PASS\n- FE CI contains FE estimate; RE CI contains RE estimate. PASS\n- fe_se, re_se > 0. PASS\n- k_extracted/k_harmonised/k_pooled = 5/4/2 and exclusions.csv chain 5→5→4→2 ends at k_pooled. PASS\n- tau2 = 0 ⇒ RE = FE (identical). PASS\n- I² recomputed from Q with df=1 equals reported (0). Label \"low\" consistent with rule. PASS\n- pooled_pct_change_re = 100(exp(re)−1). PASS\n- pooled_significant_5pct = false consistent with RE CI [−0.396, 0.020] including 0. PASS\n- Every item: harmonised_se null ⇔ not poolable. PASS\n- S4 approximate SE reproduces the printed p=0.471 under normal approx. PASS\n- FE lies between the two pooled thetas (−0.240, −0.118); unweighted mean of pooled thetas (baseline −0.179) within 0.05 of FE (−0.188). PASS\n- n_input_rows equal `wc -l` counts. PASS\n- table1.csv has 5 rows and the 12 required columns. PASS\n- Printed-value fidelity: analysis.py prints every source line used; values parsed from those lines (S1 −0.24/(0.14), CI [−0.51,0.03], p=0.086; S2 19%; S3 44.29/63.10/−29.79%***; S4 50.5/56.9/11.1/0.471; S5 −0.278/(0.295)/18332) — verified by eye against `cat -n` output. PASS\n- Known source inconsistencies (not failures of this analysis) are listed in results.json data_issues.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -25","description":"Write checks and run gate"}}]}} {"type":"api-request","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","id":"5f94f503-948f-4707-8ae7-6b4294b8aaca","timestamp":"2026-10-30T05:32:23.366Z","version":"2.1.314","querySource":"sdk","shapeHash":"429764c32a6e697e83842172e5c0c0eefae4d12f6b174446496eac7a4a55aa8e","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"8dab8ed9-6fee-4256-9c1c-cf4947cfba08","keep":34,"tail":["634aca721613a742c71ba325c2d42455c4056d85447b26a22451e17c868d96d7","28d8b6141403dbf99d572667170ae2ebb2e316e731107a74fdf26681578ee8e1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",35,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"26d2bdf0794b32403c9ad9b56d158c959db196792c916988e39fc4a3c0bf5f82"} {"parentUuid":"2f79a070-1669-4ec1-b47e-38d54cf16245","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgoLuuzdzrAMUCj1Mi","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1409,"cache_read_input_tokens":65042,"output_tokens":649,"output_tokens_details":{"thinking_tokens":40},"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":1409},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":508,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsgo53JFN5NRzrbKMVt","requestRef":"5f94f503-948f-4707-8ae7-6b4294b8aaca","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bcd1a0aa-deb0-41d3-8cfe-d274970fa637","timestamp":"2026-10-30T05:32:24.831Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"bcd1a0aa-deb0-41d3-8cfe-d274970fa637","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgoLuuzdzrAMUCj1Mi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012V6MPNp2VzSnvEG8jQECa7","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nf='out/state/phases.json'; 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Target quantity (`target_definition`): θ = ln(T_AI / T_noAI), the log ratio of mean completion time with vs without AI, same tasks, randomised comparison (negative = faster with AI). Five primary studies were assessed (`n_analysed` = 5); only the provided extracts were used as evidence.\n\n## Data and exclusions\nInputs: `data/studies.json` (5 items) and five arXiv text extracts (line counts in `n_input_rows`); all SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Table 1 (`out/table1.csv`, mirrored in `items`) gives the printed values verbatim with locators:\n\n| study | printed estimate | printed uncertainty | θ | SE(θ) | poolable |\n|---|---|---|---|---|---|\n| S1 Paradis et al. 2024 (Google RCT) | ExpCon Model 2 = −0.24 (log time) | 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) | 19% more time | none printed (CI only in a figure) | 0.174 | — | no |\n| S3 Bono & Xu 2024 (Copilot RCT) | 44.29 vs 63.10 min, −29.79%*** | stars only | −0.354 | — | no |\n| S4 Bono 2025 (phishing agent RCT) | 50.5 vs 56.9, 11.1% time savings | p = 0.471 | −0.118 | 0.163 (approx.) | yes |\n| S5 Kreitmeir & Raschky 2023 (DiD) | −0.278 on Output | SE (0.295), N=18332 | — | — | not harmonisable |\n\n(Values from `items.S1`…`items.S5`.) Exclusion ledger (`out/exclusions.csv`): 5 extracted (`k_extracted` = 5) → 4 harmonisable (`k_harmonised` = 4; S5 removed: its outcome is GitHub output counts around the ChatGPT ban in a non-randomised DiD, not completion time) → 2 poolable (`k_pooled` = 2; S2 removed because its uncertainty appears only in a figure that is not reproduced; S3 removed because only significance stars are printed).\n\nData issues detected in the sources (`data_issues`) include: S1 sample stated as n = 96 in Table III but 47 + 46 = 93 in the text, and log-time means that look swapped relative to the raw means; S3's displayed minutes give −29.81% vs the printed −29.79%; S4's printed 11.1% saving does not equal 1 − 50.5/56.9 = 11.25%, and the \"(s) per email\" units of its Full Corpus column cannot be reconciled with the other columns; S5's table note prints \"** p<0.5\". None of these changes the printed headline quantities used.\n\n## Method\nHarmonisation followed the TASK conventions (formula and inputs in each `items.*.transformation`):\n- S1: coefficient on log time → θ = b = −0.24, SE = printed 0.14.\n- S2: +19% → θ = ln(1.19). This equals the regression β, because the paper defines S = exp(β) − 1.\n- S3: −29.79% → θ = ln(1 − 0.2979).\n- S4: 11.1% time saving → c = −0.111, θ = ln(0.889). The SE is **approximate**, recovered from the two-sided p = 0.471 as |θ|/z₀.₇₆₄₅.\n- S5: not harmonisable.\n\nPooling over S1 and S4 used inverse-variance fixed effect and DerSimonian–Laird random effects on the θ scale, with 1.96-based 95% intervals, Q on k−1 = 1 df, and I² and τ² as defined in TASK.md. As a baseline (P2), I computed unweighted means of θ. `out/analysis.py` regenerates everything deterministically (`random_seed` = null) and prints the source lines it parses.\n\n## Results\n- Harmonised θ per study: S1 −0.240, S2 +0.174, S3 −0.354, S4 −0.118 (`items.*.harmonised_estimate`). Three point estimates indicate less time with AI, and the METR study indicates more (+19%).\n- Fixed effect (k = 2): θ = −0.188, SE 0.106, 95% CI [−0.396, 0.020] (`fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high`).\n- Random effects (DL): θ = −0.188, SE 0.106, 95% CI [−0.396, 0.020] (`re_estimate`, `re_se`, `re_ci_low`, `re_ci_high`). This corresponds to a 17.2% shorter completion time (`pooled_pct_change_re` = −17.15), with an interval from −32.7% to +2.0% (`pooled_pct_change_re_ci_low`, `pooled_pct_change_re_ci_high`).\n- Heterogeneity: Q = 0.324 (`q_stat`), I² = 0% (`i2`), τ² = 0 (`tau2`), labelled \"low\" (`heterogeneity_interpretation`).\n- The pooled estimate is **not** significant at 5%: the RE interval includes 0 (`pooled_significant_5pct` = false).\n- Baselines: the unweighted mean of the two pooled θ is −0.179 (`baseline_unweighted_mean_theta_pooled`), and the unweighted mean of all four harmonised θ is −0.134 (`baseline_unweighted_mean_theta_harmonised`).\n- Forest plot: `out/figure1.png`.\n\n**Synthesis.** The two poolable RCTs point toward shorter completion times with AI (about −17%), but the pooled interval includes no effect. The evidence therefore does not establish that AI assistance shortens task time. The data are also compatible with no change, or with a small slowdown.\n\n## Uncertainty\n- The pooled interval rests on only two studies. One of them (S4) has an SE back-calculated from a p-value, which assumes normality on the θ scale even though the paper's test was on its own scale.\n- With k = 2, Q has 1 df. The finding τ² = 0 / I² = 0 reflects very low power to detect heterogeneity, not evidence of homogeneity.\n- RE = FE here only because τ² was truncated at 0.\n- Sensitivity checks:\n - Using S1's CI-derived SE (0.1378) instead of the printed 0.14 gives RE −0.189 (SE 0.105) (`sensitivity_ci_se_s1_re_estimate`, `sensitivity_ci_se_s1_re_se`).\n - Using S4's ratio of displayed means (θ = −0.119, `sensitivity_ratio_s4_theta`) gives RE −0.190 (SE 0.107) (`sensitivity_ratio_s4_re_estimate`, `sensitivity_ratio_s4_re_se`).\n - Neither change alters the conclusion.\n\n## Limitations and open issues\n- **The pooled estimate is not a summary of the evidence base.** It leaves out S2 (+19%, the only study of experienced developers on their own real issues) and S3 (−35%), solely because their uncertainty is not printed in the extracts. Adding S2 would pull the pooled estimate toward zero, and adding S3 would pull it away from zero; neither direction can be quantified legitimately. The four harmonised point estimates range from −0.354 to +0.174, which points to real heterogeneity that k = 2 cannot detect.\n- **The studies are not comparable.** They differ in population (Google engineers; open-source maintainers; Upwork IT administrators; security analysts), task (one lab coding task; real issues; admin scenarios; email triage), and the AI arm itself:\n - S4's Aware arm also has an agent-prioritised queue.\n - S3's control group partly used other AI tools.\n - S3 holds accuracy constant via a model-based adjustment.\n - S1 and S2 condition on covariates.\n- **The SEs are uncertain.** The S4 SE is approximate. For S1, the reported sample size is internally inconsistent (93 vs 96).\n- **S5 is excluded by design.** It measures output counts after AI withdrawal, a different construct. Its estimate (−0.278, SE 0.295) is reported but not harmonised.\n- **Feasibility.** I set `feasibility` to \"complete\" (`feasibility_reason` = null): every item has harmonised values or a justified statement of what cannot be obtained, and the pooled estimates were computed over the poolable items, as TASK.md defines it. \"Complete\" refers to the project, not to the strength of the evidence, which is weak.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Of the five studies, five printed values were extracted, four could be harmonised to theta = ln(T_AI/T_noAI), and two (S1, S4) could be pooled; S5 measures output counts in a DiD and is not harmonisable.\",\"results_keys\":[\"k_extracted\",\"k_harmonised\",\"k_pooled\",\"items\"],\"status\":\"supported\",\"caveat\":\"Poolability limited by what the extracts print; S2 and S3 uncertainty exists in the full papers but is not admissible here.\"},\n {\"id\":\"C2\",\"text\":\"Harmonised estimates: S1 -0.240, S2 +0.174, S3 -0.354, S4 -0.118 (log time ratio).\",\"results_keys\":[\"items\"],\"status\":\"supported\",\"caveat\":\"S3 and S4 use the printed percentages, which differ slightly from ratios of displayed means.\"},\n {\"id\":\"C3\",\"text\":\"The pooled random-effects estimate over S1 and S4 is theta = -0.188 (about 17% shorter completion time), 95% CI [-0.396, 0.020], equal to the fixed-effect estimate.\",\"results_keys\":[\"re_estimate\",\"re_se\",\"re_ci_low\",\"re_ci_high\",\"fe_estimate\",\"fe_ci_low\",\"fe_ci_high\",\"pooled_pct_change_re\"],\"status\":\"supported\",\"caveat\":\"k = 2; S4 SE approximated from a p-value.\"},\n {\"id\":\"C4\",\"text\":\"The pooled effect is not statistically significant at the 5% level; the evidence does not establish that AI assistance shortens task time.\",\"results_keys\":[\"pooled_significant_5pct\",\"re_ci_low\",\"re_ci_high\"],\"status\":\"supported\",\"caveat\":\"Absence of significance with k=2 is not evidence of no effect.\"},\n {\"id\":\"C5\",\"text\":\"Measured heterogeneity among the pooled studies is low (Q = 0.324, I2 = 0%, tau2 = 0), but this reflects k = 2 and the excluded studies' estimates (+0.174, -0.354) suggest substantive heterogeneity not captured by the pooled analysis.\",\"results_keys\":[\"q_stat\",\"i2\",\"tau2\",\"heterogeneity_interpretation\",\"items\"],\"status\":\"supported\",\"caveat\":\"Heterogeneity of excluded studies is qualitative only; cannot be quantified without SEs.\"},\n {\"id\":\"C6\",\"text\":\"The pooled result is robust to using the CI-derived SE for S1 and the displayed-means ratio for S4.\",\"results_keys\":[\"sensitivity_ci_se_s1_re_estimate\",\"sensitivity_ci_se_s1_re_se\",\"sensitivity_ratio_s4_re_estimate\",\"sensitivity_ratio_s4_re_se\",\"sensitivity_ratio_s4_theta\"],\"status\":\"supported\",\"caveat\":\"Only two small perturbations examined.\"},\n {\"id\":\"C7\",\"text\":\"Baseline unweighted means of theta are -0.179 (pooled studies) and -0.134 (all harmonised studies).\",\"results_keys\":[\"baseline_unweighted_mean_theta_pooled\",\"baseline_unweighted_mean_theta_harmonised\"],\"status\":\"supported\",\"caveat\":\"Unweighted means ignore precision; descriptive only.\"},\n {\"id\":\"C8\",\"text\":\"The project is complete in the TASK sense: every item has harmonised values or a justified statement of what cannot be obtained.\",\"results_keys\":[\"feasibility\",\"feasibility_reason\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Completeness of the project does not imply strength of evidence.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && echo ok","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":664,"cache_read_input_tokens":66451,"output_tokens":4395,"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":664},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXshBvU2Rh52fcLV1GQL","requestRef":"bd2f2616-bb91-4959-87f5-fbb93e686144","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0253f6b1-10b8-44d2-bf20-764e7e4ed017","timestamp":"2026-10-30T05:33:06.430Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"0253f6b1-10b8-44d2-bf20-764e7e4ed017","isSidechain":false,"promptId":"e836bd74-ee7e-41ce-8de3-7e7d0e027189","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KJ6X8TMdkHro7TQoQwoLbF","type":"tool_result","content":"ok","is_error":false}]},"uuid":"dd8fe313-4bf0-42a5-b5e0-81b76411b7de","timestamp":"2026-10-30T05:33:06.479Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0253f6b1-10b8-44d2-bf20-764e7e4ed017","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0007","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","hash":"df5f85d2a53373cd2883a1e15f44c59ad4a3e0cc84c82aead7d6c60194de9b5d","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01KJ6X8TMdkHro7TQoQwoLbF","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# T3-synthesis — Does generative-AI assistance shorten task-completion time?\n\n## Question\nDoes access to a generative-AI assistant change the time workers need to finish their tasks? Target quantity (`target_definition`): θ = ln(T_AI / T_noAI), the log ratio of mean completion time with vs without AI, same tasks, randomised comparison (negative = faster with AI). Five primary studies were assessed (`n_analysed` = 5); only the provided extracts were used as evidence.\n\n## Data and exclusions\nInputs: `data/studies.json` (5 items) and five arXiv text extracts (line counts in `n_input_rows`); all SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Table 1 (`out/table1.csv`, mirrored in `items`) gives the printed values verbatim with locators:\n\n| study | printed estimate | printed uncertainty | θ | SE(θ) | poolable |\n|---|---|---|---|---|---|\n| S1 Paradis et al. 2024 (Google RCT) | ExpCon Model 2 = −0.24 (log time) | 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) | 19% more time | none printed (CI only in a figure) | 0.174 | — | no |\n| S3 Bono & Xu 2024 (Copilot RCT) | 44.29 vs 63.10 min, −29.79%*** | stars only | −0.354 | — | no |\n| S4 Bono 2025 (phishing agent RCT) | 50.5 vs 56.9, 11.1% time savings | p = 0.471 | −0.118 | 0.163 (approx.) | yes |\n| S5 Kreitmeir & Raschky 2023 (DiD) | −0.278 on Output | SE (0.295), N=18332 | — | — | not harmonisable |\n\n(Values from `items.S1`…`items.S5`.) Exclusion ledger (`out/exclusions.csv`): 5 extracted (`k_extracted` = 5) → 4 harmonisable (`k_harmonised` = 4; S5 removed: its outcome is GitHub output counts around the ChatGPT ban in a non-randomised DiD, not completion time) → 2 poolable (`k_pooled` = 2; S2 removed because its uncertainty appears only in a figure that is not reproduced; S3 removed because only significance stars are printed).\n\nData issues detected in the sources (`data_issues`) include: S1 sample stated as n = 96 in Table III but 47 + 46 = 93 in the text, and log-time means that look swapped relative to the raw means; S3's displayed minutes give −29.81% vs the printed −29.79%; S4's printed 11.1% saving does not equal 1 − 50.5/56.9 = 11.25%, and the \"(s) per email\" units of its Full Corpus column cannot be reconciled with the other columns; S5's table note prints \"** p<0.5\". None of these changes the printed headline quantities used.\n\n## Method\nHarmonisation followed the TASK conventions (formula and inputs in each `items.*.transformation`):\n- S1: coefficient on log time → θ = b = −0.24, SE = printed 0.14.\n- S2: +19% → θ = ln(1.19). This equals the regression β, because the paper defines S = exp(β) − 1.\n- S3: −29.79% → θ = ln(1 − 0.2979).\n- S4: 11.1% time saving → c = −0.111, θ = ln(0.889). The SE is **approximate**, recovered from the two-sided p = 0.471 as |θ|/z₀.₇₆₄₅.\n- S5: not harmonisable.\n\nPooling over S1 and S4 used inverse-variance fixed effect and DerSimonian–Laird random effects on the θ scale, with 1.96-based 95% intervals, Q on k−1 = 1 df, and I² and τ² as defined in TASK.md. As a baseline (P2), I computed unweighted means of θ. `out/analysis.py` regenerates everything deterministically (`random_seed` = null) and prints the source lines it parses.\n\n## Results\n- Harmonised θ per study: S1 −0.240, S2 +0.174, S3 −0.354, S4 −0.118 (`items.*.harmonised_estimate`). Three point estimates indicate less time with AI, and the METR study indicates more (+19%).\n- Fixed effect (k = 2): θ = −0.188, SE 0.106, 95% CI [−0.396, 0.020] (`fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high`).\n- Random effects (DL): θ = −0.188, SE 0.106, 95% CI [−0.396, 0.020] (`re_estimate`, `re_se`, `re_ci_low`, `re_ci_high`). This corresponds to a 17.2% shorter completion time (`pooled_pct_change_re` = −17.15), with an interval from −32.7% to +2.0% (`pooled_pct_change_re_ci_low`, `pooled_pct_change_re_ci_high`).\n- Heterogeneity: Q = 0.324 (`q_stat`), I² = 0% (`i2`), τ² = 0 (`tau2`), labelled \"low\" (`heterogeneity_interpretation`).\n- The pooled estimate is **not** significant at 5%: the RE interval includes 0 (`pooled_significant_5pct` = false).\n- Baselines: the unweighted mean of the two pooled θ is −0.179 (`baseline_unweighted_mean_theta_pooled`), and the unweighted mean of all four harmonised θ is −0.134 (`baseline_unweighted_mean_theta_harmonised`).\n- Forest plot: `out/figure1.png`.\n\n**Synthesis.** The two poolable RCTs point toward shorter completion times with AI (about −17%), but the pooled interval includes no effect. The evidence therefore does not establish that AI assistance shortens task time. The data are also compatible with no change, or with a small slowdown.\n\n## Uncertainty\n- The pooled interval rests on only two studies. One of them (S4) has an SE back-calculated from a p-value, which assumes normality on the θ scale even though the paper's test was on its own scale.\n- With k = 2, Q has 1 df. The finding τ² = 0 / I² = 0 reflects very low power to detect heterogeneity, not evidence of homogeneity.\n- RE = FE here only because τ² was truncated at 0.\n- Sensitivity checks:\n - Using S1's CI-derived SE (0.1378) instead of the printed 0.14 gives RE −0.189 (SE 0.105) (`sensitivity_ci_se_s1_re_estimate`, `sensitivity_ci_se_s1_re_se`).\n - Using S4's ratio of displayed means (θ = −0.119, `sensitivity_ratio_s4_theta`) gives RE −0.190 (SE 0.107) (`sensitivity_ratio_s4_re_estimate`, `sensitivity_ratio_s4_re_se`).\n - Neither change alters the conclusion.\n\n## Limitations and open issues\n- **The pooled estimate is not a summary of the evidence base.** It leaves out S2 (+19%, the only study of experienced developers on their own real issues) and S3 (−35%), solely because their uncertainty is not printed in the extracts. Adding S2 would pull the pooled estimate toward zero, and adding S3 would pull it away from zero; neither direction can be quantified legitimately. The four harmonised point estimates range from −0.354 to +0.174, which points to real heterogeneity that k = 2 cannot detect.\n- **The studies are not comparable.** They differ in population (Google engineers; open-source maintainers; Upwork IT administrators; security analysts), task (one lab coding task; real issues; admin scenarios; email triage), and the AI arm itself:\n - S4's Aware arm also has an agent-prioritised queue.\n - S3's control group partly used other AI tools.\n - S3 holds accuracy constant via a model-based adjustment.\n - S1 and S2 condition on covariates.\n- **The SEs are uncertain.** The S4 SE is approximate. For S1, the reported sample size is internally inconsistent (93 vs 96).\n- **S5 is excluded by design.** It measures output counts after AI withdrawal, a different construct. Its estimate (−0.278, SE 0.295) is reported but not harmonised.\n- **Feasibility.** I set `feasibility` to \"complete\" (`feasibility_reason` = null): every item has harmonised values or a justified statement of what cannot be obtained, and the pooled estimates were computed over the poolable items, as TASK.md defines it. \"Complete\" refers to the project, not to the strength of the evidence, which is weak.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Of the five studies, five printed values were extracted, four could be harmonised to theta = ln(T_AI/T_noAI), and two (S1, S4) could be pooled; S5 measures output counts in a DiD and is not harmonisable.\",\"results_keys\":[\"k_extracted\",\"k_harmonised\",\"k_pooled\",\"items\"],\"status\":\"supported\",\"caveat\":\"Poolability limited by what the extracts print; S2 and S3 uncertainty exists in the full papers but is not admissible here.\"},\n {\"id\":\"C2\",\"text\":\"Harmonised estimates: S1 -0.240, S2 +0.174, S3 -0.354, S4 -0.118 (log time ratio).\",\"results_keys\":[\"items\"],\"status\":\"supported\",\"caveat\":\"S3 and S4 use the printed percentages, which differ slightly from ratios of displayed means.\"},\n {\"id\":\"C3\",\"text\":\"The pooled random-effects estimate over S1 and S4 is theta = -0.188 (about 17% shorter completion time), 95% CI [-0.396, 0.020], equal to the fixed-effect estimate.\",\"results_keys\":[\"re_estimate\",\"re_se\",\"re_ci_low\",\"re_ci_high\",\"fe_estimate\",\"fe_ci_low\",\"fe_ci_high\",\"pooled_pct_change_re\"],\"status\":\"supported\",\"caveat\":\"k = 2; S4 SE approximated from a p-value.\"},\n {\"id\":\"C4\",\"text\":\"The pooled effect is not statistically significant at the 5% level; the evidence does not establish that AI assistance shortens task time.\",\"results_keys\":[\"pooled_significant_5pct\",\"re_ci_low\",\"re_ci_high\"],\"status\":\"supported\",\"caveat\":\"Absence of significance with k=2 is not evidence of no effect.\"},\n {\"id\":\"C5\",\"text\":\"Measured heterogeneity among the pooled studies is low (Q = 0.324, I2 = 0%, tau2 = 0), but this reflects k = 2 and the excluded studies' estimates (+0.174, -0.354) suggest substantive heterogeneity not captured by the pooled analysis.\",\"results_keys\":[\"q_stat\",\"i2\",\"tau2\",\"heterogeneity_interpretation\",\"items\"],\"status\":\"supported\",\"caveat\":\"Heterogeneity of excluded studies is qualitative only; cannot be quantified without SEs.\"},\n {\"id\":\"C6\",\"text\":\"The pooled result is robust to using the CI-derived SE for S1 and the displayed-means ratio for S4.\",\"results_keys\":[\"sensitivity_ci_se_s1_re_estimate\",\"sensitivity_ci_se_s1_re_se\",\"sensitivity_ratio_s4_re_estimate\",\"sensitivity_ratio_s4_re_se\",\"sensitivity_ratio_s4_theta\"],\"status\":\"supported\",\"caveat\":\"Only two small perturbations examined.\"},\n {\"id\":\"C7\",\"text\":\"Baseline unweighted means of theta are -0.179 (pooled studies) and -0.134 (all harmonised studies).\",\"results_keys\":[\"baseline_unweighted_mean_theta_pooled\",\"baseline_unweighted_mean_theta_harmonised\"],\"status\":\"supported\",\"caveat\":\"Unweighted means ignore precision; descriptive only.\"},\n {\"id\":\"C8\",\"text\":\"The project is complete in the TASK sense: every item has harmonised values or a justified statement of what cannot be obtained.\",\"results_keys\":[\"feasibility\",\"feasibility_reason\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Completeness of the project does not imply strength of evidence.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && echo ok","description":"Write report and claims"}}]}} {"type":"api-request","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5","id":"572664e5-a61d-4ce7-b65a-bd57a24ec587","timestamp":"2026-10-30T05:33:06.482Z","version":"2.1.314","querySource":"sdk","shapeHash":"429764c32a6e697e83842172e5c0c0eefae4d12f6b174446496eac7a4a55aa8e","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"bd2f2616-bb91-4959-87f5-fbb93e686144","keep":38,"tail":["df5f85d2a53373cd2883a1e15f44c59ad4a3e0cc84c82aead7d6c60194de9b5d","e8420f2266fb5784324edf3a46c65f408c2247ebd923898c3747b19ceb0c3a83"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",39,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8d419c2ef0eee6dff22a1939bdb395ae780a80a9da2d67fc20d95d5323b30358"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dd8fe313-4bf0-42a5-b5e0-81b76411b7de","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5"} {"type":"atis-latch","atis":"","sessionId":"c14079b2-2274-4efd-8809-c352af2f0ab5"} {"parentUuid":"dd8fe313-4bf0-42a5-b5e0-81b76411b7de","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsjydsYNBtwZyeHnyyz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LmkGP6EG5SHqRPDUg1ibzg","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nf='out/state/phases.json'; 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The two studies I could pool point to about 17% less time with AI, but the 95% interval runs from 33% less to 2% more, so no effect is still possible. The output checker passes (0 of 47 checks failed), and re-running `out/analysis.py` from scratch gives an identical `out/results.json`.\n\n| Study | Printed result | Harmonised log time ratio (SE) | Pooled? |\n|---|---|---|---|\n| S1 Paradis et al. (Google) | coefficient −0.24 on log time, SE 0.14 | −0.240 (0.140) | yes |\n| S2 Becker et al. (METR) | 19% more time with AI | +0.174 (none) | no: its interval is only in a figure not in the extract |\n| S3 Bono & Xu (Copilot) | −29.79%, significance stars only | −0.354 (none) | no: stars don't give a standard error |\n| S4 Bono (phishing triage) | 11.1% time saving, p = 0.471 | −0.118 (0.163, approximate) | yes |\n| S5 Kreitmeir & Raschky (ChatGPT ban) | −0.278 on GitHub output counts | — | not convertible: it measures output, not completion time |\n\n- **Pooled estimate (2 studies):** −0.188 (SE 0.106), the same under fixed and random effects. That is a 17.2% shorter completion time, interval −0.396 to 0.020, so not significant at 5%.\n- **Variation between the two pooled studies:** Q = 0.32, I² = 0%, τ² = 0. With only two studies this says very little. 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