The text it trained on was already a translation. The original signal — spatial, holistic, embodied, non-linear — never reached it.
What the human meant. What the model received. What was lost in between.
The problem is shaped like a funnel — wide chaos at the top, collapsing into a single point of failure. The solution must widen the base, not patch the tip.
When my grandmother arrived in Germany in 1972 with nothing but a sewing kit, she didn't need a process. She needed someone to recognize what she already knew how to do.
[Long pause] My father would have done this differently. [Another pause] But we are not in that time anymore.
We've documented 28 cognitive profiles across 7 categories. Each profile is a gradient — and they compound. Most lived combinations have never been systematically labeled in any training corpus. Your model meets a fraction that rounds to zero of actual cognitive reality.
Not "more inclusive."
Fundamentally different in what it can understand.
We don't know. Neither do you.
That is the point.
I'm left-handed.
I've spent my entire life translating myself into systems built for the other 90%.
I never thought much about it — until I realized AI does the exact same thing.
Just at scale. And invisibly.
So I built a tool that measures it.
Not qualitatively. Mathematically.
The numbers
→ 28 documented cognitive profiles. 7 categories.
→ Compound profiles (ADHD + high-context + narrative): 74% signal loss.
→ For some users, only 26% of their intended meaning reaches the model.
That's not a bug. That's the architecture.
AI was trained on text. Text is already a translation of thought. The original signal — spatial, holistic, embodied, non-linear — never reached the training data.
And these 28 profiles don't stack linearly. One person can be left-handed, neurodivergent, from a high-context culture, and a non-native speaker — simultaneously. The compound effect isn't additive. It's exponential.
Most people assume AI works for everyone equally. It doesn't. It works best for people who already think like the training data. Everyone else is paying a hidden tax.
The complete foundational argument — cognitive misalignment, translation overhead, combinatoric space — documented and open to scrutiny.
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