ffernandesclaro

I think this is a real friction point in the transition phase. Right now, many teams are still learning the difference between a fast AI-generated prototype and a production-ready system.

My guess is that, over time, the ecosystem will adapt with better expectations and frameworks, clearer handoff models, and more mature workflows around AI-generated work.

I'm the author. I've spent the last couple of years building functional products with AI tools instead of writing specs, and the results changed how I think about product development. The short version: I built a satellite monitoring module in 9 days that would have taken an engineering team 8-10 weeks. Not production quality, but functional enough for real users to test and give honest feedback. That feedback was better than months of pre-build research would have produced. The experience led me to develop two operational frameworks, SIGNAL and ATLAS, for rapid product discovery and construction. Both are running in production with real teams and real revenue. The article describes the economic argument underneath: the cost of specification (meetings, documents, reviews, estimation) is now often higher than the cost of just building a testable version. That inverts the logic of how we've organized product development for 30 years. Happy to answer questions about the practical side, what the tools actually produce, where the model breaks down, and what engineers think about non-engineers building software.