Dev Nag has been in the trenches with AI coding tools for several years and is the CEO of QueryPal, a software company focused on AI-powered ticket generation. He describes the process of refactoring and maintaining AI-generated code as surprisingly challenging. “The code often lacks consistency in style and naming conventions, which can make a codebase feel disjointed,” he says. “I’ve spent many hours cleaning up and standardizing AI-generated code to fit a project’s conventions.”

Dhaval Gajjar, CEO of IT services and consulting company Pranshtech Solutions, CTO of SaaS development company Textdrip, and an experienced software developer, agrees. “AI-based code typically is syntactically correct but often lacks the clarity or polish that comes from a human developer’s understanding of best practices,” he says. “Developers often need to clean up variable names, simplify logic, or restructure code for better readability.”

To Travis Rehl, CTO at Innovative Solutions, which migrates, modernizes, and builds next-gen systems on the cloud, the oddness of working with AI-written code in order to refactor or maintain it can go deeper. “When the AI has employed unfamiliar patterns or libraries, it can be challenging to refactor without a deep understanding of these choices,” he says. “There’s also the risk of breaking intricate dependencies that the AI might have created. It’s definitely a different experience. You’re often working with code that feels both familiar and alien at the same time. The AI might use approaches that seem unconventional to human developers, leading to ‘Why did it do it this way?’ moments.”