When AI Collapses Fact and Assumption

Blended inference is the baseline response mode of LLMs. Smooth prose is the goal. In software, that smoothness can hide the boundary between grounded analysis and inferred assumptions. The generation process does not distinguish between a token the model can support and one it filled in. Everything comes out at the same confidence level. I ran a small experiment on a Python caching service by asking: We’re seeing latency spikes on our report generation API. What should we look at? The baseline response correctly identified concrete areas to improve in the file: no lock or request coalescing on cache miss, a cleanup job that scans all of Redis, a stale flag that never gets checked, and synchronized TTL expiry. In the same answer, at the same confidence level, it also said things like: “If this runs periodically on the same Redis used by the API, it is a strong candidate for periodic spikes.” “If many hot reports are created around the same time—after deploy, after nightly prefetch, aft