Here's an intriguing angle worth exploring: models don't actually see the world—they see the interface we construct for them. This distinction cuts to the heart of how artificial intelligence actually works.

Think about it differently. When we feed data into a model, we're not giving it raw reality. We're giving it our encoded version of reality—filtered, structured, and shaped by human decisions. Every preprocessing step, every feature selection, every data representation choice becomes the lens through which the model understands anything.

Scott Adams touched on something crucial here: how humans encode inputs is itself a powerful cognitive framework. It's not just philosophy—it's foundational to understanding why models behave the way they do. The interface isn't transparent. It's an active force shaping perception. That gap between the world and what models 'see' is where all the interesting problems live.