AI companies are hiring the wrong engineers.

Not because good engineers don't exist. Because they're filtering for the wrong thing.
The job posts say: "optimize inference costs", "reduce latency", "fine-tune prompts at scale."
What they don't say: "talk to users", "figure out why people churn", "build something someone actually wants."
So they end up with teams that can make the model faster but can't make the product matter.
Token efficiency is an operations problem.
Product-market fit is a people problem.
Confusing the two is why most AI tools ship clean, fast, and completely ignored.
The companies that figure this out first aren't hiring prompt engineers.
They're hiring people who lose sleep over why users don't come back.