Concentration of market power (Negative effects of increased market concentration)
The concentration of AI assets—encompassing data, hardware, and expertise—within a small group of global tech firms raises many concerns.564 Such a situation may stifle healthy competition, impede innovation, and potentially result in elevated costs for accessing AI technologies. Firms with control over essential resources for developing AI models may restrict access to these resources to prevent competition. For instance, if, in the future, training AI models increasingly relies on proprietary data, smaller organizations lacking access to such data might encounter significant barriers to entry and growth.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
3 - Other
Risk ID
mit750
Domain lineage
6. Socioeconomic and Environmental
6.1 > Power centralization and unfair distribution of benefits
Mitigation strategy
1. **Implement Proactive and Targeted Competition Policy Enforcement** Rigorously apply and modernize anti-trust and competition laws to scrutinize the vertical integration, exclusive agreements, and mergers of dominant technology firms (hyperscalers) that control essential AI assets, such as high-performance compute and proprietary training data. The primary objective is to prevent anti-competitive practices that create artificial barriers to entry, stifle challenger innovation, and lead to market lock-in, ensuring fair access to foundational AI resources for new entrants and smaller organizations. 2. **Mandate and Incentivize Open Standards and Interoperability** Develop and enforce regulatory standards that require open interfaces, data portability, and interoperability across the AI value chain, from foundation models to application layers, to mitigate vendor lock-in. Furthermore, provide significant public funding and policy support for the development of high-quality open-source models, data sets, and compute-sharing initiatives to create a viable, non-proprietary alternative to the concentrated assets of a few large firms. 3. **Establish a Publicly Governed AI Compute and Data Reserve** Create and strategically manage national or regional public computing infrastructure and non-proprietary, high-quality data pools. Access to this reserve should be preferentially allocated to research institutions, academic groups, and Small and Medium-sized Enterprises (SMEs) at subsidized rates. This intervention serves to directly lower the prohibitively high capital expenditure required for AI development, thereby fostering a more diversified and competitive innovation landscape outside of established market incumbents.