Open any AI naming tool. Paste in a brief. Wait a few seconds.
Then watch the same dozen words show up, rearranged in slightly different order.
Flux. Pulse. Nexum. Atlas. Axera. Vectra. Tivra.
You've seen them before. Everyone has. That's not a coincidence. It's a structural problem.
AI is trained on what already exists.
It learns from patterns across millions of brand names, product launches, and startup announcements. The result? It gets very good at producing names that look and sound like other names. Not names that stand apart from them.
This is called linguistic averaging. The model finds the statistical center of what "a good name" looks like and heads straight for it. Short. Punchy. Vaguely technical. Ends in a vowel or an "x." Safe enough to feel modern. Boring enough to disappear.
The problem isn't that AI generates bad names. It's that it generates consensus names. Names that no one hates and no one remembers.
There's also a pattern convergence effect that makes this worse. Every naming brief that goes into an AI tool trends toward similar outputs because the underlying data pulls in the same direction. You can change the prompt. You can tweak the category, the personality, the tone. But the gravitational pull toward the linguistic center is strong. Axera works for a car, a fintech, a health startup. Which is exactly why it works for none of them.
Great names don't come from the middle. They come from a decision. A point of view. An instinct that something unexpected is actually more right than something expected. That's judgment. And AI doesn't have it.
It can remix. It can pattern-match at scale. It can surface ideas faster than any human team. But it can't tell you when something is boring. It can't recognize that a name lands because it breaks a rule rather than follows one.
That's the gap. Not effort. Not speed. That human place where discernment and courage work together to create something new..