AI Is Flooding Codebases With Code No One Has Time to Read

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The rise of AI-assisted development has dramatically lowered the cost of software engineering, enabling individual developers to generate 8–10 substantial merge requests per day by running multiple AI agents in parallel. This surge in code output is fundamentally changing daily workflows, with engineers spending less time writing code in an IDE and more time directing AI across several concurrent problem-solving threads. The sheer volume of code being produced means developers can no longer realistically read all of it, a shift the author describes as an unavoidable consequence of mass parallelisation. Traditional software development lifecycle stages — such as code review, deployment, and debugging — were designed around human-scale output and are struggling to keep pace. Some in the industry are already coining the term 'Agentic Development Lifecycle' to describe this new reality, though the deeper challenge remains adapting processes and oversight to match AI-driven productivity.

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