The US Is Fighting for Control of AI. It Would Be Better Off Building Standards.

2026-04-29 · Source: Tech Policy Press · Field: Government & Public Sector — Public Policy & Governance, Regulatory & Compliance, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

The US government is struggling to assert control over advanced AI development, exemplified by the Trump administration's dispute with Anthropic over access to its models. Unlike the internet, which emerged from DARPA-funded protocols and US-managed domain name systems, commercial AI systems have developed largely independently of government involvement. The article argues that the US should prioritize establishing AI standards and governance architecture, similar to its historical role in internet interoperability, rather than attempting to strong-arm private AI firms. The economic stakes are significant, as leadership in AI standards could capture immense value over the next 30 years. While compute governance is often proposed, its effectiveness is limited as open-weight models proliferate. The US has an opportunity through initiatives like the Center for AI Standards and Innovation (CAISI) and its AI Agent Standards Initiative to lead in agent interoperability protocols, but aggressive tactics risk undermining international buy-in.

Key takeaway

For CTOs and VPs of Engineering assessing long-term AI strategy, recognize that the global AI landscape will be defined by interoperability standards, not national control over specific models. Your organization's ability to integrate and scale AI solutions will depend on these emerging protocols. Advocate for open, US-led standards development through bodies like CAISI to ensure a stable and predictable future for enterprise AI, rather than relying on government strong-arming that risks alienating international partners and stifling innovation.

Key insights

US leadership in AI requires establishing global standards for interoperability, not strong-arming private firms.

Principles

• Standards drive long-term value capture.
• Early governance architecture shapes future control.
• International buy-in is crucial for standards adoption.

Method

The US should increase government investment in AI research, bolster CAISI, and collaborate with frontier labs to build AI interoperability protocols.

In practice

• Focus on agent interoperability as a coordination chokepoint.
• Support NIST's CAISI and its AI Agent Standards Initiative.

Topics

• US AI Policy
• AI Governance
• AI Standards
• Agent Interoperability
• Anthropic Dispute

Best for: CTO, VP of Engineering/Data, Executive, Policy Maker, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Policy Press.