Human Perception and LLMs Share the Same Two-Step Signal-Rendering Process

A software developer argues that both human brains and large language models function as 'rendering engines,' converting raw input signals into usable outputs rather than passively receiving objective reality. The author draws on a real-world example of two analysts interpreting identical backtest data in opposite ways, attributing the difference to the interpreter rather than the data itself. The piece challenges a common misconception in both quantum physics discussions and AI discourse — that the observer 'collapses' or changes the world — arguing instead that only the observer's output changes. Using the analogy of color perception and sunset responses, the author illustrates how different training histories in both humans and AI models produce different outputs from identical inputs. The article is the first in a three-part series and includes a C++ code sketch to model the concept as a sampling problem.