Nowadays, everyone believes they are capable of delivering production software. This is the actual disruption - it's not about the code, it's about the confidence people have.
Pieter Levels made a post on X that went viral. In it, he mentioned how AI was turning software into a commodity, and shared that he’d been canceling a bunch of SaaS subscriptions and instead building small custom solutions for himself using AI. The reactions to that post were like lightning. Indie hackers the world over suddenly started muttering: wait, why exactly am I paying for that? I could just build it myself.
It's a fair question. It's also a dangerous one.
AI has made it stupidly easy to get something working. You can go from idea to functional prototype in an afternoon. That's genuinely incredible.
The thing is, a "functional prototype" and something you can actually put into production is a different story altogether. And AI just handed everyone a running start toward the edge of that canyon.
Consider Figma for a moment. It is more than just "a design tool." It encompasses many years of addressing edge cases for live collaboration over unstable networks. It's conflict resolution when two people edit the same frame. It's accessibility compliance, enterprise SSO, version history that actually works, and a plugin ecosystem with thousands of integrations.
You can build a design tool demo in a weekend. You cannot build Figma. The gap between those two things is measured in years and hundreds of engineers.
Salesforce is in the same boat. Everyone loathes it. Everyone believes they can build something to replace it. Nobody who's tried has come away thinking it was simple. The product isn't the UI — it's the decade of workflow edge cases baked into every dropdown menu.
Here's the pattern I keep seeing. AI-generated code works great in isolation. One API, one database, one happy path. 🎉
Afterward, you establish a connection with a second system. Subsequently, a third one. Soon after, you have to manage retrying, partial crashes, expiry of auth tokens, rate limitations, and ensuring data consistency among services, as well as dealing with the vendor's API that, for some unknown cause, returns XML solely on Tuesdays.
AI-generated code may still not work for such complex multi-system integrations. Not because the models are bad -- they are scarily good at isolated problems. But real-world software is not an isolated problem. It is a thousand isolated problems duct taped together with error handling and prayers.
Here's my interpretation of what really transpired. AI did not actually make it easier to build software. It just gave the impression that software development was becoming easier.
It's essential. Very important.
When a compelling demo can be created at no cost, it becomes challenging for people to appreciate the efforts put in after the demo. The unglamorous work. The monitoring. The migrations. The "a customer in Japan found a bug that only happens with double-byte characters in the billing address" type work.
The gap between a demo and an actual product has always existed. AI just made it invisible to anyone who hasn't crossed it before. 😅
I'm not trying to imply that AI tools are negative. As a matter of fact, I rely on them all the time. They've changed how I work in ways I genuinely love.
However, I've observed a change in the narrative. People are associating "I built a working thing" with "I built a product." They are not identical statements.
The skill that matters most right now isn't prompting or vibe-coding. It's knowing what you don't know — recognizing when your working demo is 5% of the actual problem.
→ The demo proves the concept.
→ The product proves you understand the edge cases.
→ The business proves you can maintain it at 3 AM when something breaks.
AI made the first step a commodity but the other two are still as costly as ever.
What's the widest gap you've seen between a demo and the actual production version? I'd love to hear war stories.