Data makes societies less intelligent

Information Paradox: Despite unprecedented information generation and analytical capacity, modern societies are less intelligent due to a breakdown in the ability to distinguish signal from noise.
Degradation of Signal: As information volume increases, the proportion of usable signal diminishes, leading to cognitive overload and difficulty in making informed decisions.
Fractured Interpretation: Shared frameworks for interpreting information have fragmented, resulting in incompatible conclusions drawn from the same datasets.
Feedback Loop Distortion: In degraded information environments, the mechanism by which systems learn from their outcomes is compromised, leading to ineffective and unlearnable decision-making cycles.
Learning Inequality: The crux of the problem is the failure in the collective learning cycle, where organizations that excel in preserving signal, sharing interpretation, and maintaining honest feedback will thrive amid AI-driven noise.
Need for Strategic Shift: The ability to learn collectively becomes more critical than raw intelligence, necessitating a shift away from the traditional assumption that more information equates to greater intelligence.