AI-assisted development is producing MVPs at a pace that would’ve seemed absurd two years ago. A developer paired with business expertise can go from concept to working prototype in about an hour. Y Combinator reported that 25% of startups in their Winter 2025 batch had codebases that were 95% AI-generated.

The production velocity is real. What’s also real is the gap between “working demo” and “production-ready.”

In our experience, that ratio is averaging about 2:1. One hour to build the working prototype, then two more hours to get it hardened, tested, secured, and deployable at scale. That’s with mature automated pipelines and established deployment processes. Without those, the ratio gets worse.

It’s like building a prototype car versus building the assembly line. The prototype proves the idea works. The assembly line proves you can deliver it repeatedly and at quality.

Fast Company reported on the “vibe coding hangover” in late 2025, with senior engineers describing “development hell” when AI-generated prototypes hit production environments that weren’t ready for them. We’ve seen the same thing. The MVP is easy. The pipes behind it are the constraint.

What’s interesting is that the organizations absorbing AI output fastest right now aren’t the ones with the biggest AI budgets. They’re the ones that invested in deployment infrastructure, testing automation, and change management maturity before AI showed up. That unglamorous work is paying off in ways nobody predicted when they were doing it.

If your teams are producing faster than your infrastructure can deploy, that’s your signal. Not more AI tools. Better pipes.

What are you seeing in your organizations? Is the MVP-to-production gap showing up for you too?