"“But the reality is that a lot of developers are much earlier in the curve,” he said. “The expectations of businesses are getting ahead of where the developers are in terms of their mental model and in terms of the training that they're providing, the enablement they're providing to make their teams comfortable with the tools, and the rate at which these tools are evolving.”"
"“I absolutely have customers who've told their developers, 'You don't write code anymore. You review code. No one should write a line of code unless for some reason you failed after three attempts getting GenAI to do it,'” he said. “I have customers like that. I don't know if I should name them, but absolutely.”"
"“It's an exciting time to be adopting these tools and learning these tools, but it puts a lot of pressure on the developer,” he said. “It puts this expectation of being more productive.” Not everyone manages that, and Sambol said he has a lot of sympathy for developers who have been directed to use AI tools without training and organizational guidance."
"Generative AI models will produce a lot of code quickly, he said, and because the code seems correct initially, it often gets pushed forward. “If it's not creating bugs en masse today, it'"
Manager enthusiasm for AI tools has grown faster than developers’ ability to learn and use them effectively. Adoption levels vary widely across organizations, from directives to stop writing code and rely on GenAI after repeated attempts, to cautious rollouts driven by compliance and industry risk concerns. The shift creates pressure on developers to be more productive while tools evolve rapidly. When teams receive insufficient training and organizational guidance, generated code can be pushed forward because it appears correct at first. This can lead to accumulating technical debt as issues emerge later, even if bugs are not widespread immediately.
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