How AI Learned to Sound Like Thinking

"This particular unease isn't rooted in a fear that machines are becoming too intelligent. It's something different and comes from watching the very meaning of intelligence itself begin to change. AI increasingly occupies the space where thinking is expected to appear. What's harder to determine is whether the internal pressures that once gave that space its weight are still present."

"I've used the term anti-intelligence to describe this shift. What I'm trying to name is a "structural reversal" that becomes visible when you shift from evaluating outputs and start paying attention to how those outputs are produced. The outward signals of intelligence intensify, while the inner constraints that once defined intelligence quietly weaken or become less important to the end-user."

Large language models produce fluent, coherent outputs that mimic thinking while lacking the internal constraints and judgment that define human intelligence. The outward signals of intelligence—fluency, confidence, and coherence—scale with model size and can convince users even when reasoning or grounding is missing. Human intelligence evolved under constraints where beliefs were tied to reasons and exposed to failure and consequences that refined judgment. A structural reversal occurs when evaluation focuses on production mechanisms rather than outputs, weakening inner constraints and creating an anti-intelligence dynamic where appearances of thought outpace actual judgment.

Read at Psychology Today