Carla Rover once spent 30 minutes sobbing after having to restart a project she vibe coded.
Rover has been in the industry for 15 years, mainly working as a web developer. She’s now building a startup, alongside her son, that creates custom machine learning models for marketplaces.
She called vibe coding a beautiful, endless cocktail napkin on which one can perpetually sketch ideas. But dealing with AI-generated code that one hopes to use in production can be “worse than babysitting,” she said, as these AI models can mess up work in ways that are hard to predict.
She had turned to AI coding in a need for speed with her startup, as is the promise of AI tools.
“Because I needed to be quick and impressive, I took a shortcut and did not scan those files after the automated review,” she said. “When I did do it manually, I found so much wrong. When I used a third-party tool, I found more. And I learned my lesson.”
She and her son wound up restarting their whole project — hence the tears. “I handed it off like the copilot was an employee,” she said. “It isn’t.”
Rover is like many experienced programmers turning to AI for coding help. But such programmers are also finding themselves acting like AI babysitters — rewriting and fact-checking the code the AI spits out.
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A recent report by content delivery platform company Fastly found that at least 95% of the nearly 800 developers it surveyed said they spend extra time fixing AI-generated code, with the load of such verification falling most heavily on the shoulders of senior developers.
These experienced coders have discovered issues with AI-generated code ranging from hallucinating package names to deleting important information and security risks. Left unchecked, AI code can leave a product far more buggy than what humans would produce.
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