Research
Our research explores the mathematical, structural, and economic dimensions of AI systems — ensuring that as intelligence scales, its connection to human creativity, attribution, and insight is preserved.
The Economics That Decide AI Growth or Crisis
If an AI crisis comes, what drives it and how much could investors lose? A simple economic model reveals why AI companies need redistribution technology and structures to sustain the growth—and what happens when they don't.
The Economic Engine of AI
This article traces that evolution — from the initial dismantling of legacy ownership to the move towards service-distribution platforms — and explains why attribution capacity is now becoming a central determinant of AI’s economic future.
How a Transformer Model Loses Attribution: A Step-by-Step Example
As transformers grow more capable of understanding, they grow less capable of remembering who taught them.
Why AI Isn't Built for Attribution: Understanding Transformers
Transformer AI models excel at language generation but can't track sources—resulting in fabricated citations and misattributed quotes. This research explores why attribution fails in transformers, examines real-world consequences, and reviews emerging solutions for the provenance problem.