{
    "question": "Does AI make developers faster?",
    "answer": "It depends on the study. In METR's randomized trial of 16 experienced open-source developers (February to June 2025), tasks took 19% longer when AI tools were allowed. A 2022 GitHub Copilot experiment with 95 freelancers finished one task 55.8% faster, and a 2024 Google trial of 96 engineers about 21% faster.",
    "period": "2022 to early 2026",
    "key_numbers": [
        {
            "value": "19%",
            "label": "Longer on tasks for experienced developers allowed AI tools",
            "note": "METR trial, Feb-Jun 2025; METR now calls it out of date",
            "stat": "web-development/experienced-developers-slower-with-ai-2025"
        },
        {
            "value": "55.8%",
            "label": "Faster on one JavaScript task with GitHub Copilot",
            "note": "Freelancer experiment, 2022, published 2023",
            "stat": "web-development/copilot-controlled-experiment-task-speed"
        },
        {
            "value": "21%",
            "label": "Less time on an enterprise task for Google engineers with AI (about)",
            "note": "Google trial, 96 engineers, published Oct 2024"
        },
        {
            "value": "-18%",
            "label": "METR's 2026 speedup estimate for returning developers, in METR's terms",
            "note": "Confidence interval -38% to +9%; METR calls it weak evidence"
        },
        {
            "value": "56%",
            "label": "Average security pass rate of AI-written code",
            "note": "Veracode tests of 100+ models since 2023 (vendor)",
            "stat": "web-development/ai-generated-code-security-pass-rate"
        }
    ],
    "tables": [
        {
            "id": "studies",
            "title": "Controlled studies of AI and developer speed",
            "columns": [
                "Study",
                "When",
                "Who took part",
                "Result with AI"
            ],
            "rows": [
                [
                    "Peng and others, GitHub Copilot experiment (arXiv 2302.06590)",
                    "May to Jun 2022",
                    "95 freelance developers recruited on Upwork, one JavaScript server task",
                    "55.8% faster (71 vs 161 minutes); 95% CI 21% to 89%"
                ],
                [
                    "Paradis and others, Google randomized trial (arXiv 2410.12944)",
                    "Published Oct 2024",
                    "96 full-time Google software engineers, one complex enterprise task",
                    "About 21% less time on task; wide confidence interval"
                ],
                [
                    "METR randomized trial",
                    "Feb to Jun 2025",
                    "16 experienced open-source developers, 246 real issues in their own repositories",
                    "19% longer; CI +2% to +39%"
                ],
                [
                    "METR follow-up, returning developers",
                    "From Aug 2025",
                    "10 developers from the first study, late-2025 tools",
                    "Estimated speedup of -18% in METR's terms (CI -38% to +9%); METR reads its raw results as some evidence for speedup"
                ],
                [
                    "METR follow-up, new developers",
                    "From Aug 2025",
                    "47 newly recruited developers, late-2025 tools",
                    "Estimated speedup of -4% in METR's terms (CI -15% to +9%)"
                ]
            ],
            "row_ids": [
                "peng-2022",
                "google-2024",
                "metr-2025",
                "metr-2026-returning",
                "metr-2026-new"
            ],
            "note": "Each study randomly assigned tasks or people to AI or no AI and measured time, not self-reports. A confidence interval that crosses zero means the study cannot rule out no effect. METR marks its 2025 result as out of date and says the 2026 data is only very weak evidence because some developers withheld tasks they did not want to do without AI."
        },
        {
            "id": "perception",
            "title": "METR 2025 trial: what developers expected and what was measured",
            "columns": [
                "Measure",
                "Change in speed with AI"
            ],
            "rows": [
                [
                    "Developers' forecast before the study",
                    "24% faster"
                ],
                [
                    "Developers' own estimate after the study",
                    "20% faster"
                ],
                [
                    "Measured in the trial",
                    "19% slower (tasks took 19% longer)"
                ]
            ],
            "row_ids": [
                "forecast",
                "self-estimate",
                "measured"
            ],
            "note": "16 experienced open-source developers, February to June 2025.",
            "source": "metr"
        },
        {
            "id": "code-quality",
            "title": "Security of AI-written code",
            "columns": [
                "Measure",
                "Value",
                "Period"
            ],
            "rows": [
                [
                    "Average security pass rate of code written by AI models",
                    "56%",
                    "2023 to 2026"
                ]
            ],
            "row_ids": [
                "security-pass-rate"
            ],
            "note": "Veracode, 2026 GenAI Code Security Report: more than 100 models tested on Veracode's own task set. A vendor benchmark of models, not a trial with developers; Veracode sells code security tools.",
            "source": "veracode"
        }
    ],
    "sources": [
        {
            "name": "METR research notes",
            "publisher": "METR (Model Evaluation and Threat Research)",
            "type": "study",
            "url": "https://metr.org/"
        },
        {
            "name": "arXiv preprint server",
            "publisher": "Cornell University (arXiv)",
            "type": "study",
            "url": "https://arxiv.org/"
        },
        {
            "name": "GenAI Code Security Report",
            "publisher": "Veracode",
            "type": "vendor",
            "url": "https://www.veracode.com/"
        },
        {
            "name": "Stack Overflow Developer Survey",
            "publisher": "Stack Overflow",
            "type": "survey",
            "url": "https://stackoverflow.co/"
        }
    ],
    "content_changed_on": "2026-10-30",
    "page_url": "https://digitaltreed.com/statistics/web-development/ai-coding-productivity",
    "csv_url": "https://digitaltreed.com/statistics/web-development/ai-coding-productivity.csv"
}