AI made POCs the default, not the exception

The question used to be whether a project was risky enough to justify a POC. Now the question is whether it’s certain enough to skip one.

Every software team eventually faces the same problem: you have an idea, you’re not sure it will work, and you need to find out before you commit weeks of engineering to it. The standard answer has always been a proof-of-concept. Build something rough, test the assumption, learn what breaks, throw most of it away, then build the real thing. The logic is obvious. What’s less obvious is why so many teams skip the step anyway.

For a long time, POCs occupied an awkward place in the development lifecycle. You reserved them for the scary bets: a new database, a rewrite you weren’t sure about, something that could waste a quarter if you guessed wrong. But the cost of a POC was high enough that you needed a strong hypothesis before starting.

AI changed that.

You can whip out complete functionality in a day today. It won’t be polished or maintainable, but it’s real enough to click through, run against a test API, show in a demo, and argue about in a review. The gap between an idea in someone’s head and something the team can actually react to has collapsed, and that changes the math on when a POC makes sense.

The question used to be whether a project was risky enough to justify a POC. The question now is whether it’s certain enough to skip one.

I’m not saying every POC is good, or that every single throwaway prototype is worth the disk space. Some teams will use the new cheapness to produce junk demos. That’s a discipline problem, not a tooling problem. The shit was always possible, it just took longer.

At SuperPlane, we’ve reorganized around this. We build something complete enough to test, not a mockup but an actual implementation that does the thing.

We test it ourselves first, clicking every path and noticing what feels wrong before showing anyone. Then we test it with the team, let people break it, and collect the “oh, we didn’t think about that” moments. Then we throw most of it away, keep the insights, and start the real implementation with a clean picture of the problem. We skip the design doc phase, the week of estimates before anyone touches code, and the standing argument about whether a project is big enough to justify exploring first.

This fits the broader pattern of what AI is doing to software work. It’s about lowering the cost of trying things: drafts, experiments, spike solutions, second versions, all the work that used to sit in the “too expensive to bother” bucket. POCs lived in that bucket for a long time. They don’t anymore.

If you’re still treating proof-of-concepts as a special ceremony for megaprojects, you’re paying for certainty you probably don’t have. Build the rough thing, learn from it, then build the real thing.