The prototyping speed is real — I've had the same experience of getting an MVP skeleton in hours instead of days.
But here's what I've started paying attention to: the METR study found that developers predicted AI made them 24% faster but were actually 19% slower on measured tasks. The gap between how fast it feels and how fast it actually is might be the most important thing to understand about AI-assisted development.
For prototyping specifically, I think speed is the right metric. You're exploring, not building for production. But the moment you shift to "this is real code now," I'd watch out for that perception gap. The transition from "AI-fast prototype" to "production code I actually understand" is where I've seen the most time get lost.
Hi, I’m Jaideep Parashar, Founder of ReThynk AI, AI Strategist, and Author of 40+ books on Artificial Intelligence, Prompt Engineering, and AI Solutions for Global Problems. Pioneering AI Future!
That’s a very thoughtful and well-grounded observation. You’re right, the perception gap between feeling faster and actually being faster is one of the most important things to understand about AI-assisted development.
I really like your distinction between phases. For prototyping, speed is absolutely the right metric: you’re exploring the problem space, testing ideas, and reducing uncertainty. In that phase, AI’s ability to generate scaffolding quickly is a genuine advantage.
But as you said, the moment code becomes “real,” the metric shifts from speed to understanding, ownership, and maintainability. That transition, from AI-fast prototype to production-quality system, is where hidden time costs often appear. Refactoring, validating assumptions, aligning with architecture, and building confidence in the behavior can easily erase the initial gains if it’s not handled deliberately.
Your point about the METR study is a good reminder that felt velocity and actual throughput aren’t the same thing. AI changes where the time is spent, not whether thinking is required. If that thinking is deferred too long, it usually comes back with interest.
This is a very mature way to frame it: use AI for fast exploration, but be intentional about the handoff to “code we truly understand.” That handoff is where real engineering discipline still matters most.
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The prototyping speed is real — I've had the same experience of getting an MVP skeleton in hours instead of days.
But here's what I've started paying attention to: the METR study found that developers predicted AI made them 24% faster but were actually 19% slower on measured tasks. The gap between how fast it feels and how fast it actually is might be the most important thing to understand about AI-assisted development.
For prototyping specifically, I think speed is the right metric. You're exploring, not building for production. But the moment you shift to "this is real code now," I'd watch out for that perception gap. The transition from "AI-fast prototype" to "production code I actually understand" is where I've seen the most time get lost.
That’s a very thoughtful and well-grounded observation. You’re right, the perception gap between feeling faster and actually being faster is one of the most important things to understand about AI-assisted development.
I really like your distinction between phases. For prototyping, speed is absolutely the right metric: you’re exploring the problem space, testing ideas, and reducing uncertainty. In that phase, AI’s ability to generate scaffolding quickly is a genuine advantage.
But as you said, the moment code becomes “real,” the metric shifts from speed to understanding, ownership, and maintainability. That transition, from AI-fast prototype to production-quality system, is where hidden time costs often appear. Refactoring, validating assumptions, aligning with architecture, and building confidence in the behavior can easily erase the initial gains if it’s not handled deliberately.
Your point about the METR study is a good reminder that felt velocity and actual throughput aren’t the same thing. AI changes where the time is spent, not whether thinking is required. If that thinking is deferred too long, it usually comes back with interest.
This is a very mature way to frame it: use AI for fast exploration, but be intentional about the handoff to “code we truly understand.” That handoff is where real engineering discipline still matters most.