Abstract

Artificial intelligence (AI) is advancing toward increasingly general and autonomous systems, intensifying concerns about safety, governance, and societal impact. While technical alignment research and regulatory approaches have been widely examined, venture capital (VC) a key upstream institution shaping frontier AI trajectories remains underexplored. This study reveals that venture capital functions as a governance mechanism: by embedding incentives, control rights, and investment time horizons, it systematically shapes documented safety practices, transparency norms, and deployment pacing across frontier AI organizations. An interdisciplinary review is conducted integrating AI governance, innovation economics, and labor-market research, and a VC positioning typology, Accelerator, Guardian, Neutral Investor, and Bridge Builder is developed and linked to observable governance expectations and oversight mechanisms. Comparative case analyses of OpenAI, Anthropic, Google DeepMind, NVIDIA, Microsoft, and Scale AI synthesize publicly documented governance features, including evaluation pipelines, staged-release controls, auditing practices, and disclosure norms. These findings are triangulated with global survey evidence documenting productivity gains alongside risks related to labor disruption, compute concentration, and uneven governance readiness. Because the evidence is drawn from publicly documented cases and secondary surveys, the study advances a conceptual framework and testable governance propositions rather than causal estimates. The review identifies a structural tension between acceleration-optimized investment models and the long-horizon stewardship demands of frontier AI governance, motivating hybrid policy investment approaches that align capital allocation with AI safety and societal resilience.

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