Software developers are writing twice as much code with AI. Is that a problem?

Software developers across all industries are embracing AI coding tools en masse, and finance is no exception. Investment banking quants are calling CoPilot a "revelation" while fintech firms are generating 50% of their code through agentic tools like Cursor. These tools have caused some metrics of developer productivity to skyrocket, but are there any unforeseen consequences?

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Greptile, an AI code review startup, released a report yesterday on "The State of AI Coding" in 2025, in which it analyzed how the adoption of AI tools affected various productivity metrics within its internal teams. It found that lines of code (LoC) per developer rose by 76% overall from March to November, and rose 89% for medium-sized teams with 6–15 members. The size of pull requests rose too, but to a much smaller degree of 33%, while the number of lines changed within each pull request rose by just 20%. Developers, therefore, are writing a lot more code, putting out many more pull requests but, proportionally, are editing much less of their code compared to their output. 

That's not necessarily a bad thing. The report doesn't assess the quality of the code, and it's unclear whether the number of bugs found or fixed was meaningfully changed as a result of the increase in velocity. It does, however, suggest a paradigm shift away from code evaluation towards content production. 

This can cause complications, as anyone who has had their productivity assessed based on LoC will attest. Receiving praise or recognition for the amount of code you produce will lead some developers to write more code just for the sake of it, even if it could be written more cleanly in fewer lines. One FAANG engineer on Hacker News responding to the report said they had seen "an uptick in the number of lines on pull requests, partially due to AI coding tools and partially due to incentives for performance reviews." More lines of code doesn't inherently mean lower quality code, but other responses said that lines of code should be treated as liabilities, rather than assets, and managed accordingly. 

If these metric increases are similar in other companies, it has widespread implications as AI tools increase in popularity. The report found that OpenAI's software development kit had been downloaded ~130m times since 2022, while Anthropic's had been downloaded over 30m times. If all the developers using these kits double the lines of code they produce, there's now hundreds of millions of extra lines of code being produced per year while the number of developers around to assess and review that code remains largely the same. Even if the code itself is no less buggy or defective than code produced before the age of AI, the odds of those defects slipping through the cracks greatly increases. That's not to mention the abundance of tech debt that could be created from superfluous code production that would eventually need to be rewritten.

Banks, given their highly regulated nature, will bottleneck this productivity for their developers somewhat; you often need to ask for permission to make even small changes to a bank's codebase. Nonetheless, they have been building AI tools with a particular emphasis on reducing the number of 'developer hours' spent on a given task like rewriting legacy code. If some of those freed up developer hours are put towards analyzing the abundance of new code, great! If those hours are used as an excuse to cut headcount, the same problem may apply.

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