The promise of AI-powered software development is profoundly appealing: describe what you want, and watch the product materialize. However, as AI coding tools matured through 2025, research and real-life production experiences revealed a complex reality. Productivity gains are real, but they are highly conditional, unevenly distributed, and often absorbed by downstream bottlenecks.
As software engineers and industry experts, we often find ourselves in a position to advocate for open-sourcing parts of our companies’ codebases. While the business’s knee-jerk reaction might be hesitation (“Why would we give away our code?”), there are compelling strategic advantages to opening the sources. In my experience, technical
