We all use Copilot or some form of GenAI now. The promise was 10x productivity. The reality is that IT mid-caps are winning huge AI-led deals without matching headcount growth, meaning the economic value is shifting. But in the trenches, are we seeing a net positive? New studies suggest increased meeting time and a drop in uninterrupted focus hours, even while working remotely. If AI handles the boiler-plate, and we spend the recovered time in meetings about the AI-generated code or coordinating with fragmented teams, what did we actually optimize for? Are you worried about deskilling, or has AI truly freed you for higher-leverage architecture work? What’s your ground truth metric for AI’s value: lines of code, or time spent in deep work?
It’s all ‘productivity theater.’ My team gets code faster, yes, but the review time is up 30% because the AI output is a high-confidence hallucination. It nails syntax, but misses critical context in our legacy services. We traded junior dev code-smell for AI code-smell. Net result: more time debugging code written by a model that doesn’t know our business rules. Deskilling is real
I’m pushing 10x velocity. The key is in framing the prompt, not just accepting the output. We’ve automated all scaffolding and 80% of unit tests. I now spend my time on API design and data modeling, which is the high-leverage work I was hired for. The ‘meetings’ aren’t about code quality; they’re about system-level alignment, which is a better use of a $150k engineer’s time than a CRUD endpoint. Learn the new toolchain or get comfortable with scaffolding