As hiring signals, AI certifications are mostly worthless: they measure course completion, not engineering ability, and the credentials inflation of the past three years has made them noise rather than signal. We screen hundreds of AI engineering candidates, and the correlation between certificates and on-the-job performance is close to zero. What correlates: shipped systems, eval sets the candidate built, and repos where you can read their judgment.
Why certificates fail as signal
- They test recall of stable content, but AI engineering practice changes quarterly; most curricula are outdated on arrival.
- They can't test the core skill: deciding what to build, what 'good output' means, and when to distrust a model.
- Credential inflation: when everyone has the certificate, it distinguishes nobody, and the strongest candidates stopped bothering.
- They select for compliance temperament, which is exactly wrong for a field that rewards skeptical experimentation.
What actually signals skill
- 1A shipped AI feature with real users, and the candidate can explain what broke and how they found out.
- 2An eval set they built: nothing separates practitioners from course-takers faster.
- 3A readable repo: not polish, judgment. Error handling, testing choices, what they didn't build.
- 4Writing that explains a trade-off they made, engineers who reason clearly in prose reason clearly in systems.
- 5Depth in one production war story that survives thirty minutes of follow-up questions.
If you're the one deciding whether to get certified
- Skip the certificate; build one small, real thing and write two pages about what you learned. That's a portfolio.
- If your target employers are regulated enterprises with procurement checklists, get the checkbox cert, but know it's a formality, not a skill claim.
- Foundational courses are fine for learning, just don't confuse the learning with the proof. The proof is the shipped thing.
