A forecast by a Nobel laureate, announced in April 2026, signals a radical paradigm shift in the perception of artificial intelligence. If over the past decade the dominant narrative has been about democratizing opportunities and lowering entry barriers, experts are now compelled to focus on risks of systemic imbalance. The core problem lies in the specific nature of human-algorithm interaction: modern neural networks are not a universal tool equally accessible to everyone. Their effective management requires a specific set of competencies—from developed quantitative thinking to basic programming skills. This creates a high intellectual barrier, excluding a significant portion of the population from access to new economic benefits.
As a result, deep labor market polarization occurs. Capital owning algorithms and infrastructure receives exponential productivity growth, while wage labor without corresponding qualifications rapidly depreciates. Instead of the expected smoothing of inequality, technologies widen the gap between intellectual property owners and ordinary executors. The chaos warned of by the scientist relates to the fundamental impossibility of rapidly retraining masses under conditions of an accelerating technological cycle.
Analytics show that without fundamental changes in the education system and a revision of resource redistribution mechanisms, mass AI implementation could irreversibly exacerbate social contradictions. It is important to understand that technological progress does not guarantee automatic improvement of quality of life for all societal layers. Corporate and state strategies must shift from simple process automation to creating mechanisms for protecting human capital; otherwise, economic efficiency will be purchased at the price of long-term social stability.