The article highlights the issues with public-private partnerships in the field of Artificial Intelligence (AI). It discusses how the consolidation of the AI industry and the capture of resources for AI development by large tech companies have hindered public-minded innovation. The lack of diversity in AI development and its impact on the types of AI being built is also highlighted. The article presents the launch of the National Artificial Intelligence Research Resource (NAIRR) as a proposed solution but raises concerns about its potential entrenchment of existing interests. It argues that public investment in AI must be critically examined to ensure that it truly benefits society and promotes innovation for the many rather than the few.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Public-private partnerships in AI | From consolidation of power to diverse innovation | Increased diversity and public-mindedness in AI innovation | Recognition of the need for public investment in AI |
| Market concentration in AI | From concentrated market to broader innovation | Increased competition and diversity in AI development | Desire to maintain U.S. technological competitiveness |
| Lack of diversity in AI development | From limited perspectives to inclusive development | More diverse voices shaping AI development | Desire for AI that serves the needs of the public |
| NAIRR pilot implementation | From pilot to full-scale implementation | Broader investment and resources in AI research and development | Recognition of the limitations of profit-driven innovation |
| Incentives for commercial AI actors | From narrow incentives to societal needs | Alignment of commercial interests with broader societal goals | Need to address conflicts between profit and public good in AI |
| Discrepancy between public and private investment | From imbalance to more equitable investment | Increased public investment in AI | Recognition of the importance of public investment in AI development |
| Need for clear articulation of AI benefits | From hype-driven models to meaningful societal benefits | Transparent evaluation of AI’s contributions to society | Demand for evidence of AI’s public benefits |
| Predatory business models and AI development | From profit-driven models to ethical and sustainable development | Responsible business models in AI development | Desire to avoid replicating past harmful practices |
| Litmus test for public investment in AI | From diversity and democratization to broader public good | Rigorous evaluation of AI investments and their societal impact | Recognition of the need to prioritize society’s benefit in AI investments |