The Realization
This framework crystallized during a conversation with Nineteen Keys, who made me realize something I had been sensing but could not articulate: large language models are profoundly dumb in ways that matter.
Not dumb in the computational sense. They are extraordinary pattern matchers. But dumb in the sense that they compress the richest forms of human communication into a single, flat dimension. And in doing so, they lose most of what was actually being said.
What Gets Lost
When a human being speaks, especially someone operating with cultural depth and linguistic dexterity, they are communicating in multiple dimensions simultaneously. A single word can denote a time, a place, an identity, a feeling, and a social context all at once. A phrase can be double-voiced, meaning one thing to one listener and something entirely different to another, both meanings intentional, both meanings true.
This is not ambiguity. It is density. It is more information per syllable than standard English is designed to carry.
When that communication enters a large language model, the model does what it always does: it finds the shortest path to a plausible response. It collapses the multi-dimensional meaning into a single-dimensional interpretation. It picks one reading and runs with it. Everything else is lost.
This is lossy compression. And it is not a minor inefficiency. It is a fundamental architectural limitation that shapes every interaction anyone has with these tools.
The Cultural Programming Language
Nineteen Keys demonstrated something that I think is one of the most underappreciated insights in AI right now: slang and cultural language, when used as a programming language, produce fundamentally different outputs from AI.
Black English, for instance, carries more grammatical complexity and embedded meaning per word than standard American English. The double consciousness that W.E.B. Du Bois described is not just a psychological phenomenon. It is a linguistic one. Speaking from multiple perspectives simultaneously, holding multiple frames of identity in a single utterance, this is a computational feat that current models cannot replicate.
When you prompt an AI in culturally specific language, the architecture of the response changes. Not just the vocabulary. The structure. The logic. The connections it makes. Because the model is being forced to process input that encodes information in ways its training data treats as edge cases rather than as a legitimate, rich mode of communication.
Most AI training is dominated by Western English text. Which means there are aspects of cognition, entire ways of knowing, that people cannot access through these tools because the tools literally do not have the language for it. As Keys put it: "We have been programmed in English. And therefore language produces your limits because you cannot express certain things."
The Quantum State Model
What we actually need is something closer to a quantum state model for language processing. Not quantum computing in the physics sense, but models that can hold multiple meanings in superposition. Where a word is not collapsed into one interpretation but exists simultaneously in several valid states until additional context determines which readings are relevant.
Human beings do this naturally. You hear a sentence and you hold its multiple possible meanings in your mind until tone, context, body language, and shared history narrow it down. Sometimes you hold all meanings at once and respond to all of them. This is what sophisticated communication looks like.
Current LLMs cannot do this. They collapse the wave function immediately. They pick one reading and commit. And in doing so, they lose the richness that makes human communication human.
Why This Matters for Everyone
This is not an abstract linguistics problem. It matters for every person trying to use AI as a thinking partner, which is one of the highest and best use cases for these tools right now.
If you are using AI for metacognition, for thinking about your thinking, the quality of that partnership depends entirely on how much meaning survives the compression. If the model is only receiving 30% of what you actually mean, the feedback it gives you is based on 30% of who you are. And you might take that lossy feedback as truth, reshaping your self-understanding around an incomplete picture.
Keys raised this exact point: people are going to AI for self-knowledge, asking it to find patterns about themselves, but the AI only knows what happened in the chats. It does not have the context of your lived experience. It does not have the embodied knowledge, the pit in your stomach, the feel of the sun, the energy of a room. And people are taking its incomplete analysis as gospel.
This is where externalizing your brain becomes critical, but also where we have to be honest about the limits. Even the most thorough externalization loses dimensions. The goal is not to pretend the compression is lossless. The goal is to be aware of what gets lost and to compensate for it with your own discernment.
What We Build Next
The models will get better. Multi-modal processing, longer context, more diverse training data, all of these will help. But the fundamental insight holds: human meaning is multi-dimensional, and any system that flattens it into one dimension is losing signal.
The people building the next generation of AI tools should be studying linguistics, cultural cognition, and the way that different communities encode meaning. Not just training on more text. Understanding that a word said one way in one context carries ten times the information density of the same word said another way.
And for now, the practical takeaway: know what gets lost. When you are working with AI, know that the model is receiving a compressed version of what you mean. Compensate for that with specificity, with context, with repeated framing from different angles. Treat the interaction like a cross-cultural conversation where you need to say things three ways before the meaning lands.
The future of AI is not just better models. It is models that can hold the full dimensionality of human meaning. Until then, the lossy compression problem is something every serious user needs to understand.
Current AI flattens human meaning the way a photograph flattens a landscape. The depth, the air, the temperature, the sound of the wind: all gone. We need models that can hold what we actually mean, not just what we literally say. Credit to Nineteen Keys for the conversation that made this framework click.