AI coding tools have become essential in software engineering, with over half of engineering teams now using them consistently. According to a Jellyfish report, 64% of companies generate most of their code with AI, and this figure could rise to 90% within a year. The shift is driven by substantial productivity gains rather than improved code quality, as AI is now viewed as a primary engine for development. High-performing teams report double the pull request throughput over three months compared to lower adopters. Furthermore, autonomous agents are increasingly managing routine coding tasks, although they currently account for a small share of overall production. As AI tools become standard in engineering workflows, organizations focus on integrating AI strategically to sustain high throughput and maintain a competitive edge. This trend emphasizes the importance of monitoring operational output rather than assuming faster production leads to better code quality, reshaping how teams plan, execute, and scale their projects.
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