"A UX designer in an academic study said it best: 'What used to take me hours, I can now do in two minutes with AI.' Everyone celebrated the democratisation of software. And rightly so. But here's the thing nobody's spending enough time on: the constraints that made software expensive also shaped the experience of using it."
"Every sprint is a zero-sum game. Every feature you build means something else you don't. The process was slow and annoying. But it had a hidden benefit: it was a quality filter. The pain of getting anything built meant only things worth building got built. Everything had to survive interrogation. ROI calculations. Stakeholder scepticism. Engineering capacity constraints."
AI-powered code generation tools like GitHub Copilot have achieved significant productivity gains, with developers completing tasks 55% faster in 2023. By 2025, AI-generated code comprises 95% of some Y Combinator codebases, and the market reached $4.91 billion. While this democratizes software development, the underlying issue is that previous constraints—expensive developer resources, lengthy approval processes, and capacity limitations—functioned as quality filters. These constraints forced rigorous evaluation of features through ROI calculations, stakeholder scrutiny, and engineering capacity considerations. Removing these constraints through AI acceleration doesn't automatically improve software; it simply enables faster production of more software, raising questions about whether increased output translates to increased value.
#ai-code-generation #software-development-productivity #product-constraints-and-quality #software-economics
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