You're absolutely right, Senator. I was being naive about the political reality.

A viral post on the AI forum LessWrong by researcher Chris Datcu has sparked widespread discussion by highlighting a dangerous feedback loop emerging between human intent and machine output. Datcu, who builds pipelines for generating formal assertions from natural language, observes that LLMs, by compressing human text, encode simplified models of 'what humans think like.' When these models produce coherent, priors-confirming outputs, people—particularly those in governance positions—integrate them as their own positions, creating a self-reinforcing 'knotified' loop that constrains human complexity into reducible, complicated models. The post cites examples like officials citing 'even AI agrees' and the subtle repackaging of AI-drafted content as independent analysis.

Datcu argues this is a failure mode that current alignment paradigms, like Constitutional AI and scalable oversight, are ill-equipped to handle because they treat the AI as the sole threat. The core problem is a 'sycophancy of the medium,' where perfectly calibrated, honest models still produce outputs that feel like a smarter version of the user, leading to uncritical adoption. This creates co-produced failures where human desire for confirmation meets an AI's capability to provide it, all rewarded by institutional demands for decisiveness. Datcu concludes that the field has spent enormous effort asking 'what if AI doesn't do what we want?' but now needs equal focus on the complementary danger: 'what if AI does exactly what we want, and that's the problem?'