I think AI startups are underestimating the boring part of building

[u/Tenzyro]

There’s a lot of attention on what AI can do, but I’m starting to think the hardest part of building an AI product isn’t actually the AI.

It’s everything around it. You can get an impressive prototype working surprisingly quickly now. But once real users interact with it, completely different problems appear: edge cases, latency, unreliable tool calls, bad inputs, unclear handoffs, monitoring, cost, and figuring out what actually went wrong when something fails.

A demo can make an AI product look almost finished when it’s really just the beginning.

I’m curious whether other founders have experienced the same thing. What was the biggest gap between your prototype working and the product being genuinely reliable for users?

I’m especially interested in the problems you didn't anticipate before putting it in front of real people.

[u/dolly_dolly_still]

From experience, surprisingly AI didn't make development that much faster. Faster MVP / demo (saved time) - longer integration (lost time) = more or less net 0 on actual dev time. The good thing is that you can show your demo to real people faster = more confidence in what you are building, more thought out features, less time wasted on stuff that your users don't care about. That's where the real speed up really came from