Downsides of AI aiding coders


“Generating more code is awesome,” said Martin Reynolds, field CTO at Harness, as developers are predominantly feeling more productive with AI coding assistants. “But that generates more of everything else that happens after the code —and there’s a lot down that line. From security testing to unit testing, the overall governance, the amount of deployments that happen, the number of dependencies that get pulled in.”

The majority of developers interviewed have problems with at least half of all deployments aided by AI coding assistants. 

The report found that 67% of developers spend more time debugging AI-generated code, while 68% spend more time resolving security vulnerabilities. 

While AI speeds up code writing toward the start of the software development lifecycle, more code means a greater demand for code reviews, security validation and quality assurance. This demand is so far nowhere near being met.

Add to this, when AI adoption increases, delivery stability and throughput decreases. Despite all these downsides, AI adoption will continue to rise in software delivery in 2025. 



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