Validation Requirement

AI-generated code is trustworthy because you can test it. You write a function. You run the tests. They pass or they fail. The answer is right or it isn’t. That feedback loop exists independently of the AI. The model doesn’t have to be trustworthy. The test suite is.

Software has a full pyramid of validation. Each layer catches a different class of failure. Together they create a system where wrong answers surface before they cause damage.

That requirement doesn’t disappear in other domains. It has to be built there too. Without it, there’s no test suite for a legal interpretation. No assertion to write against a research summary. No failing build when an analysis draws the wrong conclusion. The output looks the same whether it’s right or wrong.

That’s an alarming problem. Not because AI models are especially unreliable, but because the mechanism that makes them trustworthy in software has to be deliberately built in any domain. Without it, the burden of validation falls entirely on the end user.

AI may simply not be that useful in any domain that depends on true facts without a validation strategy in place. The domain doesn’t matter. The requirement does.

Software engineers spent decades building that infrastructure. It didn’t happen automatically. Someone had to decide that untested code wasn’t acceptable, and then build the culture and tooling to enforce it. Any field that wants to use AI responsibly faces the same requirement.

The testing apparatus is what makes AI trustworthy. Without it, you’re not using a tool. You’re making a bet.