The Post-Data-Hoarding Economy and the Authorship Spectrum

Executive Summary

A theoretical framework for sustainable AI collaboration addressing environmental and legal crises

The artificial intelligence industry is approaching a convergence of two distinct but deeply interrelated crises, each with profound implications for the future of technology, environment, and human creativity.

The first crisis is environmental and infrastructural. Global data center electricity consumption is projected to surpass 945 TWh by 2030 more than double the relatively stable 200 TWh maintained through much of the previous decade. Alongside this energy surge, AI data centers are consuming hundreds of billions of liters of freshwater annually for cooling, and a persistent shortage of advanced semiconductor chips is constraining development while generating significant manufacturing waste. These pressures are not evenly distributed: the majority of this resource consumption is concentrated in a small number of hyperscale facilities operated by a handful of companies, each building redundant infrastructure in a race that may prove economically and physically unsustainable.

The second crisis is legal and philosophical. The question of authorship who deserves credit, compensation, and legal standing for creative and intellectual work has become fundamentally unstable. Recent USPTO guidance clarifies that AI systems cannot be authors, yet the boundary between human authorship and AI assistance remains legally ambiguous. Simultaneously, the data used to train these systems was often collected without explicit consent, creating a new form of what I term the "data-hoarding economy": a system in which value is extracted from human creative work without proportional compensation or control.

These two crises are not separate problems. They are symptoms of the same underlying issue: a misalignment between how AI systems are currently built and how they could be built to be both sustainable and equitable. The current model centralized, data-hungry, and legally murky is approaching physical and social limits. A different model is possible. This analysis proposes a theoretical framework for addressing both crises simultaneously:

the Unified Knowledge Theory (UKT) combined with a reformed approach to authorship and intellectual property. The framework suggests that future AI systems should be built on top of a trusted public library of verified knowledge, with user-controlled personal data storage and company-specific AI models. This architecture would reduce resource consumption, clarify legal questions around authorship and compensation, and create a more sustainable and equitable AI ecosystem.