Why AI Teams Should Never Treat Training Data As Evidence

Patrick Dajos - Founding Team at Hyperbound.

In Prometheus Rising, Robert Anton Wilson described people as living inside their own “reality tunnels.” In other words, we filter reality through attention, language, memory and prior beliefs. He also wrote that “the human mind is a verbalizing circuit.” Read today, that sounds unexpectedly close to one feature of a large language model (LLM): learned relationships between symbols can produce useful language outputs.

An LLM is not a digital brain; human cognition is embodied and extends far beyond language. But the brain remains our only demonstrated basis for human-level general intelligence, and it does not use everything it knows on every question. It selects a limited working set. Context selection may not only be a workaround for today’s models. It may be part of intelligent behavior itself.

Human intelligence depends on selection.

Psychologist Nelson Cowan estimated that working memory holds roughly four chunks under controlled conditions. Attention research similarly describes many signals competing while the brain prioritizes a few for deeper processing.

These limits force the brain to select. AI systems face an analogous practical constraint: A capable model can still fail when the decisive instruction, document or observation is missing from its active context.

I encountered this while building an AI system for analyzing datasets larger than a model could use reliably in one context window. Most of my engineering work moved into retrieval. Segmenting the material for semantic retrieval produced one of the largest improvements because the boundaries directly affected what the system could find. Straightforward questions often needed one vector-search pass. Complex analysis required a loop: inspect the initial results, identify gaps, reformulate the search, retrieve again, compare sources and verify material claims against the underlying records.