Great point. The lowered cost of producing software means our baseline expectations need to shift. Since it’s easier to build now, the 'barely working' MVP shouldn't cut it anymore. Founders are now obligated to deliver much more reliable, higher-quality prototypes from day one.
This resonates hard with me. In the accounting world, everyone said 'AI will replace bookkeepers' for years. What actually happened? Excel is still everywhere, QBO still trips up users, and the firms thriving aren't the ones who adopted the most tools — they're the ones who figured out how to combine AI with solid process design. Easy building creates a paradox: more options but same limited attention. The firms winning now are the ones who went narrow and deep, not broad.
This maps directly to what I see in retail AI deployments. The prototype-to-production gap isn't just technical infrastructure - it's knowledge transfer.
The instrumentation exists, but the team inheriting the system doesn't know why certain metrics were chosen or what domain logic shaped the engagement rules. The system runs, but the reasoning that made it work doesn't transfer. Different domain, same handoff problem.
Similar experience I documented here from AI PoC → production gap in retail/ecommerce angle.
App ships, demo lands, team celebrates, then real users arrive and the simplest questions become unanswerable. Which users are real? Where do they drop? What actually changed after the last release?
Not because the team isn’t smart. Because the system was never built to answer those questions.
Cannot agree more: the app is the entry point, not the product.
Most founders only discover that after they’ve already crossed the line.
Great point. The lowered cost of producing software means our baseline expectations need to shift. Since it’s easier to build now, the 'barely working' MVP shouldn't cut it anymore. Founders are now obligated to deliver much more reliable, higher-quality prototypes from day one.
This resonates hard with me. In the accounting world, everyone said 'AI will replace bookkeepers' for years. What actually happened? Excel is still everywhere, QBO still trips up users, and the firms thriving aren't the ones who adopted the most tools — they're the ones who figured out how to combine AI with solid process design. Easy building creates a paradox: more options but same limited attention. The firms winning now are the ones who went narrow and deep, not broad.
the hard part is distribution!
Yes, distribution is incredibly hard. And incredibly powerful. It is the primary reason why Microsoft dominates enterprise software.
This maps directly to what I see in retail AI deployments. The prototype-to-production gap isn't just technical infrastructure - it's knowledge transfer.
The instrumentation exists, but the team inheriting the system doesn't know why certain metrics were chosen or what domain logic shaped the engagement rules. The system runs, but the reasoning that made it work doesn't transfer. Different domain, same handoff problem.
Similar experience I documented here from AI PoC → production gap in retail/ecommerce angle.
https://opplearner.substack.com/p/i-vibe-coded-something-good-that
The prototype works. The system doesn’t.
Seen this pattern repeatedly.
App ships, demo lands, team celebrates, then real users arrive and the simplest questions become unanswerable. Which users are real? Where do they drop? What actually changed after the last release?
Not because the team isn’t smart. Because the system was never built to answer those questions.
Cannot agree more: the app is the entry point, not the product.
Most founders only discover that after they’ve already crossed the line.