Techno-federalism is a framework proposed by Jason Jia-Xi Wu (Harvard National Security Journal, 2025) that describes AI governance in both the United States and China as a tripartite interplay among central government, local government, and market power. In Wu's account, the tech industry operates as a third regulatory force, a co-regulator that reshapes center-local relations in both countries.
Background
Wu situates the framework against accounts of the US-China AI race that frame it as a "battle of values" between liberal democracy and techno-autocracy. Techno-federalism argues instead that both countries are converging toward a fragmentary governance model with three components: central or federal governments that set high-level objectives but lack the capacity to regulate AI comprehensively; local or state governments that fill the gaps and create a patchwork of regulations; and a tech industry that exploits jurisdictional variation, acts as a co-regulator through self-governance, and increasingly implements national security objectives.
Three governance domains
Wu maps the framework across three domains in each country:
| Domain | US | China |
|---|---|---|
| Software | California leads frontier model regulation | Cyberspace Administration sets rules; local enforcement varies |
| Hardware | Arizona leads semiconductor subsidies | Central direction plus local incentive competition |
| Infrastructure | NY/Delaware dominate financing law | Local datacenter/cloud policies with national coordination |
Relation to other framings
Wu's framework departs from binary characterizations of the US-China contest. Where the Eight Worlds Framework asks "is China racing?", techno-federalism describes China's strategy as itself fragmented between central direction and local experimentation. Where the AI and Authoritarianism framing portrays the Chinese Communist Party as monolithic, Wu argues that local governments and firms retain significant discretion.
The framework also accounts for regulatory incoherence within the United States. There is no comprehensive federal AI law, and states lead with inconsistent rules. Different legal frameworks apply different logic and can conflict: product liability via the AI LEAD Act, transparency via SB 53 and the RAISE Act, antitrust via algorithmic pricing cases, and export controls. In Wu's account, the tech industry fills these regulatory vacuums through self-governance and lobbying.
Wu argues that industry self-governance will grow as AI matures, that neither the United States nor China intends to let AI grow in unregulated markets, and that the modern "triple helix" of government, academia, and industry differs from its Cold War predecessor in that industry now leads rather than government.
Quantitative backing: FPF State of State AI (2025)
FPF's October 2025 survey of state AI legislation offers quantitative support for the framework's claims about fragmentation. The survey found 210 AI-related bills introduced across 42 states, with only 8 states introducing no qualifying bills, indicating that state-level AI regulation is a national rather than a California-specific phenomenon. Roughly 9% of those bills were enacted, a ratio consistent with Wu's description of a patchwork in which broad introduction yields thin legislative output. The survey identified 18 distinct bill types — covering automated decision-making technology, frontier models, chatbots, liability shields, sandboxes, and generative-AI transparency, among others — which Wu's framework reads as evidence of parallel and incoherent regulatory approaches. The FPF data also documented a shift away from sweeping frameworks on the Colorado AI Act model toward narrow, transparency-driven measures, a pattern Wu associates with broad regulation stalling while targeted disclosure advances and industry self-governance fills the remaining gap.
Illustrative cases
Illinois liability frameworks (2025–2026)
Two Illinois Democratic legislators have simultaneously advanced opposing AI liability frameworks at different levels of government. Sen. Richard Durbin (D-IL) is lead Senate sponsor of the federal AI LEAD Act (S. 2937), which creates strict liability for defective AI systems, including for "unexpected skills or behaviors," and uses floor preemption so that states may impose stronger protections. Sen. Bill Cunningham (D-IL) is sponsor of Illinois SB 3444, which grants frontier AI developers conditional immunity from liability for "critical harms" provided they publish a safety protocol and transparency report.
The result is that an AI firm operating in Illinois faces a Durbin-supported federal strict-liability regime nested inside a Cunningham-supported state liability shield. Wu's framework treats this kind of within-party, within-state framework collision as an equilibrium rather than a pathology.
California utility-AI bill (2026)
On June 2, 2026, California state Sen. Jerry McNerney (D) withdrew his bill to regulate AI use by electric and gas utilities, which would have limited automated decision-making in certain utility operations, after a Senate fiscal panel weakened it through forced amendments and industry opposition held firm. The episode bears on the fragility of state-level sectoral AI regulation: even a narrow, deployer-specific measure of the targeted, transparency- or process-oriented kind that the FPF survey identifies as the surviving form of state AI legislation can collapse before passage through fiscal-committee dilution and sustained industry pressure. Wu's framework anticipates that broad and narrow state frameworks alike frequently stall, leaving the regulatory vacuum that industry self-governance fills. (Source: insideaipolicy.com)
Relationship to other concepts
Techno-federalism extends AI Diffusion to include domestic federal-versus-state fragmentation, not only international competition. Export Controls (AI) function as a tool in the hardware governance domain, with industry shaping implementation, and Compute Governance maps to Wu's hardware and infrastructure governance domains. The AI LEAD Act's floor preemption, which allows stronger state protections, is an example of the federal-state dynamic the framework describes.
See also
- Techno-Federalism: How Regulatory Fragmentation Shapes the U.S.-China AI Race (Wu, Harvard National Security Journal, 2025)
- The State of State AI: Legislative Approaches to AI in 2025 — FPF survey of 210 state AI bills; quantitative backing for the fragmentation thesis
- AI LEAD Act (S. 2937) — Durbin-Hawley federal strict-liability model
- Illinois SB 3444 — Artificial Intelligence Safety Act — Cunningham state liability-shield model
- Digital Empires — Anu Bradford (2023) — Bradford's three-empires book provides the theoretical foundation techno-federalism specializes for AI
- Brussels Effect — Bradford's earlier framework for EU regulatory export; techno-federalism's US-state analog
- The Digitalist Papers (Stanford, Volumes 1–2) — Lessig, Pahlka, Persily essays engage fragmentation dynamics