Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories

arXiv:2610.23212v1 Announce Type: new Abstract: Technological principles of openness, interoperability, and decentralization are integral to building an AI economy that is accountable to the public and truly responsive to local needs and constraints. However, Big Tech's firm control over essential elements of the stack (such as chips, cloud, models, data, and talent) limits valuable alternatives from reaching the scale necessary for sustained growth. Worse still, hyperscalers continue to enclose and extract value from publicly funded and volunteer-driven technologies without adequately distributing these returns. We see consolidated power within a few entities and "enshittification" of once-transformative innovations. Embedding ethical micro-level tools (like open data repositories or small-language models) into workflows is promising, but without a consistent innovation strategy, they also often end up perpetuating and being appropriated by the same exploitative trajectories. Instead, this whitepaper outlines a three-pillared holistic and intentional policy approach that i) actively pushes for the adoption of cooperative technologies in existing and emerging systems, ii) sets up collective institutional structures to sustain and maintain them in the long-term, and iii) introduces regulatory proposals that limit private entities from disempowering them through uncontrolled enclosure and extraction. Through desk research and purpose-specific case studies, we argue for the importance of such an approach in the Global South, where structural constraints around infrastructure,

Big Tech's control limits alternatives, and hyperscalers extract value without returns.

The paper identifies the problem of concentrated power in AI infrastructure limiting inclusive innovation, especially in the Global South, and proposes policy solutions for cooperative AI technologies. The approach could enhance equitable adoption and long-term sustainability but faces significant structural challenges and risks of exploitation. Evidence is analytical and policy-focused, not empirical outcome-based.

This is a policy whitepaper proposing frameworks rather than reporting empirical outcomes. It diagnoses real concentration risks and proposes remedies, but offers no evidence of adoption or impact yet, so the implication for flourishing is speculative and near neutral, tilted slightly positive for intent.

This academic paper outlines a theoretical policy approach to counteract perceived harms from concentrated AI power. It argues that Big Tech's control leads to exploitation and limits beneficial innovation. The proposed solutions aim to foster a more equitable and publicly accountable AI ecosystem. The implications are optimistic about the potential for policy intervention to steer AI towards public good.

The paper advocates for a policy approach that promotes cooperative AI technologies and limits the power of private entities. This implies a significant shift towards a more equitable and people-first AI economy, particularly in the Global South.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   26e24ed1-aee0-428d-8e15-8daac4883a69
           https://arxiv.org/abs/2610.23212
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +13  (Mixed or uncertain)
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CONFIDENCE 73%