I’ve been digging into how teams actually use AI in production workflows beyond the hype, and I keep wondering where the real limits are. In my last project we introduced AI-assisted coding and test generation across a small backend service, and at first it felt like everything sped up massively. But after a few weeks we started noticing a different pattern: faster code output, but more time spent in review and debugging than before. It made me question whether we were improving the system or just shifting effort around. I also came across this breakdown in site and it basically confirmed that different teams are seeing very mixed results depending on how they integrate AI into the whole workflow.
13 Views