Evals are part of your business moat 🧠

Over the years, I’ve learned that if you want Engineering to move with confidence and build real systems with LLM/AI/ML embedded, you need evals. There’s no way around it.

Evals are what turn “we think this is better” into “we know this is better.” They’re how you ship, iterate, and adopt new models without guessing or breaking production.

At companies that are doing this right, people are spending significant time building, maintaining, and evolving evals. That work isn’t overhead. It’s core infrastructure. It’s part of the moat.

I think this becomes a major function in most serious AI companies.

“Evals Engineer” probably becomes a real title.

The teams that can measure performance clearly will move faster, upgrade models faster, and build better products than the ones that can’t.

Curious if others are seeing the same shift.