Independent research shows AI can accelerate bounded development tasks — and that the gains depend on operator skill, task type, integration, and human review. That's exactly why we sell the operating system and orchestration, not “AI magic.”
A controlled study found developers with GitHub Copilot completed a defined JavaScript task 55.8% faster than a control group. We treat this as evidence that AI can accelerate certain bounded development tasks — not proof that whole companies or every project move faster.Peng et al., “The Impact of AI on Developer Productivity” — controlled experiment (GitHub Copilot). arxiv.org/abs/2302.06590
McKinsey reported developers completed certain coding tasks up to twice as fast with generative AI, with productivity value in drafting, correction, refactoring, root-cause analysis, and system-design generation. We use this for task-level acceleration — not as proof an entire MVP is always 2× faster.McKinsey & Company, “Unleashing Developer Productivity with Generative AI”; McKinsey Global Institute, “The Economic Potential of Generative AI.”
Research on collaborative open-source work found project-level gains from Copilot and increased integration time, with core developers (deeper project knowledge) benefiting more than peripheral contributors. AI creates the most value when directed by someone who understands the project, controls the context, and can evaluate the output.Song, Agarwal & Wen, “The Impact of Generative AI on Collaborative Open-Source Software Development” (working research).
Results are mixed by task, developer, project maturity, and evaluation method. AI can produce incorrect outputs, integration work, security risk, review burden, false confidence, and extra correction time. That's why we use structured protocols, human review, testing, security passes, and deployment checks. We sell controlled acceleration — not unsupervised automation.
Every external numeric claim carries a citation; every project count cites the repository or the project brief.