Building got cheap this year. Judging whether the thing is actually good did not. In 2026 the eval is your spec, and it's the step nearly everyone leaves out.
There's a moment now that didn't exist three years ago. You describe a feature, an agent writes it, and forty minutes later it's running. It works, sort of. It answers, it responds, it does the thing. And you sit there with a genuinely hard question you have no tool for: is it any good?
That question used to answer itself. Old software was deterministic same input, same output, and you wrote a test that either passed or failed. AI features don't work like that. Ask the same thing twice and you get two different answers, both plausible, one subtly wrong. "It ran without erroring" tells you almost nothing about whether it's right, helpful, or safe. The build got easy. The judging got hard. And the gap between those two is where most maker products quietly fall apart around week three.
The discipline that closes the gap has a name this year: eval-driven development. The idea is boring and it's correct. Before you tune a prompt or swap a model, you write down what "good" means as a set of concrete examples, and you check every change against them. Not vibes. Not "seems better to me." A fixed set of cases with expected behavior that you can rerun. The whole shift in how serious teams build with AI in 2026 is the move away from vibes-based iteration toward something you can actually rerun and compare. Eval platforms are multiplying for a reason the demand is real.
