We broke the O(2^N) barrier to compute AI consciousness (Phi)

InductivityAI developed the first O(N³) computational model to measure Topological Phi (Φ), which quantifies integrated information as a mathematical prerequisite for AI consciousness, breaking through the previously insurmountable O(2^N) computational barrier. Their testing revealed that standard large language models like GPT-2 exhibit "Phi-collapse," showing minimal information integration, while their Φ-regularization technique during training creates highly structured networks with concept formation similar to Global Workspace Theory, demonstrating measurable improvements in how AI systems process and integrate information.