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There\u2019s an old saw in management: What you measure matters. And, typically, you get more of whatever you\u2019re measuring.<\/p>\n
Software engineers have debated productivity metrics for decades, starting with lines of code. But as the new generation of AI coding agents delivers more code than ever, what their managers ought to be measuring is less clear.<\/p>\n
Enormous token budgets \u2014 essentially, the amount of AI processing power a developer is authorized to consume \u2014 have become a badge of honor among Silicon Valley developers, but that\u2019s a very weird way to think about productivity. Measuring an input to the process makes little sense when you presumably care more about the output. It might make sense if you\u2019re trying to encourage more AI adoption (or selling tokens), but not if you\u2019re trying to become more efficient.<\/p>\n
Consider the evidence from a new class of companies operating in the \u201cdeveloper productivity insight\u201d space. They\u2019re finding that developers using tools like Claude Code, Cursor, and Codex generate a lot more accepted code than they did before. But they also find that engineers have to return to revise that accepted code far more often than before, undercutting claims of increased productivity.<\/p>\n
Alex Circei, the CEO and founder of Waydev<\/a>, is building an intelligence layer to track these dynamics; his firm works with 50 different customers that employ more than 10,000 software engineers. (Circei has contributed to TechCrunch in the past, but this reporter had never met him before.)<\/p>\n He says that engineering managers are seeing code acceptance rates of 80% to 90% \u2014 meaning the share of AI-generated code that developers approve and keep \u2014 but they\u2019re missing the churn that happens when engineers have to revise that code in the following weeks, which drives the real-world acceptance rate down between 10% and 30% of generated code.<\/p>\n The rise of AI coding tools led Waydev, founded in 2017 to provide developer analytics, to totally rework its platform in the last six months to address the proliferation of rapid coding tools. Now, the company is releasing new tools that track the metadata generated by AI agents, offering analytics on the quality and cost of their code to provide engineering managers with more insight into both AI adoption and efficacy.<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tSan Francisco, CA<\/span> While analytics companies have an incentive to highlight the problems they find, the evidence is mounting that large organizations are still figuring out how to use AI tools efficiently. Major companies are noticing \u2014 Atlassian acquired DX, another engineering intelligence startup, for $1 billion last year, to help its customers understand the return on investment on coding agents.<\/p>\n The data from across the industry tells a consistent story: More code is being written, but a disproportionate amount of it isn\u2019t sticking.<\/p>\n GitClear<\/a>, another company in this space, published a report<\/a> in January that found AI tools increased productivity, but also that its data showed \u201cregular AI users averaged 9.4x higher code churn than their non-AI counterparts\u201d \u2014 more than double the productivity gains the tools provided.<\/p>\n Faros AI, an engineering analytics platform, drew on two years of customer data for its March 2026 report<\/a>. The finding: code churn \u2014 lines of code deleted versus lines added \u2014 had increased 861% under high AI adoption.<\/p>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n