Where AI actually fails in production
Demos are misleading - an AI feature that looks flawless in a 5-minute demo often breaks on edge cases: unusual inputs, adversarial users, rare formats, or simply running at scale across thousands of real (messy) cases instead of a few clean examples. The gap between "impressive demo" and "reliable product" is the single biggest thing separating real AI products from hype.
Common failure modes: hallucination on rare/specific facts (A5), inconsistent output format breaking downstream code, silent failures when a tool call errors out, and cost blowing up at scale in ways that weren't visible in testing. Serious teams stress-test with adversarial and edge-case inputs, not just happy-path demos - this is what evals (C3) are actually for.
Advisory habit: whenever you see an impressive AI demo (a pitch, a competitor's feature), ask yourself "what messy real-world input would break this?" - practicing this instinct is most of what "advisory-level literacy" actually is.
Why can an AI feature look flawless in a demo but break in production?