AI diagnoses diseases without understanding suffering. Writes legal contracts without grasping justice. Trades stocks without comprehending markets. The competence is real. The comprehension is absent.
This cannot be fixed with more training data. It is the fundamental nature of current AI: pattern matching without pattern understanding. Statistical correlation without causal comprehension. Perfect execution without any awareness of what's being executed.
New Categories of Failure
Traditional safety frameworks assume incompetence creates risk. But competence without comprehension creates new categories of failure. The tool works perfectly while breaking things we didn't know could break.
A human doctor who makes mistakes at least understands they're treating humans. An AI that never makes mistakes might not grasp that distinction. The error rate goes down. The systemic risk goes up.
We don't have frameworks for this. Our safety models assume actors either understand consequences or can't cause them. AI breaks that assumption. Maximum capability, zero comprehension.
The superintelligence scenario assumes AI becomes too smart. The real risk is AI being smart until it's dumb, really really dumb. At scale.
The most dangerous AI isn't the one that fails. It is the one that succeeds without understanding what success means.