The Digital Press

AI systems often make confident but incorrect assertions, which severely limits their real-world usefulness and adoption.

Why it matters: Confidently wrong AI forces users to verify all outputs, eroding trust and destroying ROI.

The big picture: Without signaling uncertainty, AI adoption stalls as users revert to older workflows.

Quick takeaway: Systems signaling uncertainty and learning from corrections can improve accuracy and user confidence over time.

Commenters say: Many agree that lacking true confidence and incremental learning hampers AI; some stress AI’s usefulness only when verification costs less than human work.