"The question is simple: how much of the code your AI agents generate actually reaches production? Not how much was generated. Not how many prompts were run."
"According to the Stanford AI Spend Index, the median company now spends $86 per developer per month on AI coding tools. The top quartile spends more than $195."
"AI providers bill by tokens. The more tokens your engineers consume, the more revenue the provider earns. This creates a structural misalignment."
"Coding agents are installed in more than 75% of Linear's enterprise workspaces. The money is flowing. The code is flowing. But nobody is tracking how much of that code actually ships."
AI coding tool adoption is rapidly increasing, yet engineering leaders often measure usage metrics instead of actual outcomes. The critical question remains: how much AI-generated code reaches production? Many leaders cannot answer this, and AI providers lack incentives to provide clarity. Companies are spending significantly on AI coding tools, with median costs reaching $86 per developer monthly. However, the focus on token consumption rather than successful code deployment creates a misalignment between developer costs and actual productivity.
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