While AI capabilities continue to advance, its infrastructure remains fragmented and ill-suited for cross-organizational and cross-sector collaboration. This structural gap forces institutions to choose between regulatory compliance and effective data sharing—particularly in privacy-sensitive domains. Centralized AI architectures exacerbate the issue by limiting input diversity and reinforcing siloed decision-making, leading to brittleness, bias, and inefficiencies.

The result is a growing trust bottleneck: increasing regulatory burden, security vulnerabilities, and reduced capacity to realize trustworthy, high-impact AI innovation.