Technological Sovereignty

Regulatory approaches

Instruments engage technological sovereignty through different modalities, not a shared template — a distinction the coverage matrix's verdict labels do not capture. The United States worked chiefly through trade and industrial policy, not AI-specific law: Executive Order 14110 (2023) tied agencies to the CHIPS and Science Act of 2022, and the operative levers have been Bureau of Industry and Security export controls and a new ECCN (4E091) on frontier model weights trained above 10^26 operations (Sidley Austin 2025). China's Interim Measures for Generative AI Services (2023) Art. 4 require providers to uphold Core Socialist Values atop a self-reliance doctrine. The EU AI Act (Regulation (EU) 2024/1689) frames capacity via internal-market language (Art. 1(1)) and the AI Office — part of what scholars read as a 'geo-dirigiste' turn blending security and competitiveness logics ¹.

Key fault lines

The debate divides along contested axes the coverage map does not adjudicate. The sharpest is openness versus protectionism: backers of a self-contained stack such as EuroStack argue decoupling from non-European suppliers is necessary for autonomy. Critics note scholarship distinguishes legitimate technology sovereignty from costly near-autarky ², and warns sovereignty efforts can re-incorporate dominant US clouds, undermining the autonomy they invoke ^3,4. A second axis is terminological: research finds no singular EU meaning — Gaia-X carried six conceptions ⁵ — so critics argue the frame should be 'unthought' ⁶ and call EU AI-based security sovereignty a 'false promise' ⁷.

Trajectory / what's changing

The sovereignty agenda has accelerated since the catalogue's baseline. The second Trump administration rescinded Executive Order 14110 and the AI Diffusion Rule (BIS, 2025), replacing presumption-of-denial with case-by-case licensing for advanced chips to China and Macau (BIS, January 2026) and a 25% semiconductor tariff. The EU moved from framing to building: InvestAI committed €20bn toward AI 'gigafactories', and the Council extended EuroHPC's mandate to gigafactory deployment for 'sovereign access' to compute (Regulation (EU) 2021/1173 amendment, January 2026) — though critics note the objective stays under-specified and may serve incumbent industrial interests rather than European publics ⁸. India's IndiaAI Mission scaled GPU subsidies, even as test-time-compute scaling reframes inference capacity as a first-class capability lever ⁹.

The ledger: price tags and boomerang effects

The build and restrict strategies now carry price tags and early results, and the two ledgers diverge. On the build side, the EuroStack analysis published by the Bertelsmann Stiftung under innovation economist Francesca Bria priced a self-determined European stack, spanning semiconductors, networks, cloud, software, quantum, and data/AI, at roughly a decade and around 300 billion euros by 2035, proposing a 10 billion euro European technology fund as a first step and a 'Buy European Act' prioritising European-made digital products (Bertelsmann Stiftung, 13 Feb 2025). Brussels' actual commitment came two days earlier at the Paris AI Action Summit: InvestAI, launched by von der Leyen to mobilise 200 billion euros for AI investment, including a new 20 billion euro fund for AI gigafactories, pitched as 'akin to a CERN for AI' (European Commission, 11 Feb 2025). The legal machinery followed within a year: an amendment in force from 20 January 2026 rewrites Regulation (EU) 2021/1173 so the EuroHPC Joint Undertaking can deploy AI gigafactories supporting 'the full AI lifecycle, including the development, training and large-scale inference of very large AI models', and adds a dedicated quantum-technologies pillar, with strategic autonomy and technological sovereignty written into the mandate (Council Regulation (EU) 2026/150, 16 Jan 2026). On the restrict side, the measurable effects ran against the instrument. BIS rescinded the AI Diffusion Rule on 13 May 2025, two days before its compliance deadline, saying it would have 'stifled American innovation', while warning that using Huawei Ascend chips risks violating US export controls (BIS, 13 May 2025). The remaining controls bit, but ambiguously: Nvidia's share of China's AI-accelerator market fell from roughly 95% to zero, a market that had supplied 20-25% of its data-center revenue, and Jensen Huang says the policy 'has already largely backfired' (Tom's Hardware 2026). The demand did not disappear; it moved. Huawei expects Ascend revenue of roughly $12 billion in 2026, up from about $7.5 billion, on orders from Alibaba, ByteDance and Tencent (Tom's Hardware 2026). Together the ledgers sharpen the fault lines above: denial-based sovereignty accelerated the substitution it aimed to prevent, while Europe's construction-based variant mobilises 200 billion euros for AI alone against the 300 billion euros Bria's team estimates the full stack requires, a gap that measures the distance between announcement and autonomy.

Historical primacy & cross-jurisdiction tension

First addressed by UK Pro-Innovation Approach to AI Regulation (White Paper) on 2023-03-29 (implicit). Subsequent regimes have either codified, diverged from, or remained silent on this baseline.

• Forum-shoppingExecutive Order 14110 on Safe, Secure, Trustworthy AI↔Executive Order 14179 — Removing Barriers to American Leadership in AI
• Forum-shoppingInterim Measures for Generative AI Service Management↔OECD AI Principles (Recommendation)
• Forum-shoppingAfrican Union Continental AI Strategy↔Council of Europe Framework Convention on AI

Silent regimes — gap signal

Instruments that do not address Technological Sovereignty — candidates for future policy work.

• Executive Order 14179 — Removing Barriers to American Leadership in AIUS
• OECD AI Principles (Recommendation)OECD
• Council of Europe Framework Convention on AIcouncil_of_europe
• NIST AI Risk Management FrameworkUS
• Bletchley Declaration on AI Safetyglobal
• Seoul Declaration on Safe, Innovative and Inclusive AIglobal
• NIST AI RMF Generative AI ProfileUS
• California SB-1047: Safe and Secure Innovation for Frontier AI Models ActUS
• India Digital Personal Data Protection Act + AI Advisory (MEITY)IN
• Brazil AI Bill (PL 2338/2023)BR
• Anthropic Responsible Scaling Policy (RSP) v2US
• OpenAI Preparedness FrameworkUS
• Google DeepMind Frontier Safety FrameworkUS
• Meta Frontier AI FrameworkUS
• UK-US AI Safety Institute Memorandum of Understandingglobal
• White House Voluntary AI CommitmentsUS
• Japan METI AI Guidelines for BusinessJP
• General Data Protection Regulation (GDPR)EU
• EU General-Purpose AI Code of PracticeEU
• OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)US
• GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource GuideUS
• DoD Responsible AI Strategy and Implementation PathwayUS
• FedRAMP AI Cloud Procurement GuidanceUS
• DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)US
• California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)US
• California SB 243: Companion ChatbotsUS
• California SB 942: AI Transparency ActUS
• Revised Product Liability Directive (Directive (EU) 2024/2853)EU
• UNESCO Recommendation on the Ethics of Artificial IntelligenceUNESCO
• Directive (EU) 2024/2831 on improving working conditions in platform workEU
• Provisions on the Administration of Deep Synthesis of Internet Information ServicesCN
• New York RAISE Act: Responsible AI Safety and Education ActUS
• TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)US
• UN Global Digital CompactUN

Does governance work? — the social-science evidence

What the peer-reviewed social science shows: whether the harm this topic addresses is empirically real, and whether governance of it works. The badge is the epistemic status of the evidence(not the policy debate) — “thin” or “absent” efficacy evidence is itself a finding (the “second silence”). Each epistemic-status label is Policy Window's editorial assessment of the cited evidence base (a structured classification), not a verdict any single source issues.

• Is the harm real?evidence: contested

  The structural fact that compute capacity is geographically concentrated is well-measured: Lehdonvirta, Wú & Hawkins find only ~33 countries host facilities with AI-accelerator hardware and roughly 24 have the capacity to train full-scale foundation models, the Stanford AI Index 2026 reports low-income countries collectively hold ~0.1% of global data-centre compute (the US hosting >10x any other nation), and Cottier et al. document amortized frontier-training cost rising 2.4x/year (95% CI 2.0-3.1x) toward $1B+ models by 2027. But this is a political-economy FRAME, not a documented harm, and the core contested claim of the topic, that the cost curve locks mid-sized economies OUT of capability, is empirically cut both ways: a feasibility study of Brazil and Mexico (Malagon et al. 2025) estimates usable (non-frontier) 10-trillion-token sovereign models are fiscally viable at roughly $8-14M on H100 hardware, and DeepSeek-style efficiency gains (V3 trained for ~$5.5M, ~11x less compute than Llama 3 405B) show frontier-adjacent performance at a fraction of prior compute, so whether domestic frontier-tier capability is foreclosed for middle powers remains genuinely unsettled.

  Sources: Lehdonvirta, Wú & Hawkins 2024 (Compute North vs. Compute South, Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics & Society 7:828-838); Cottier, Rahman, Fattorini, Maslej & Owen 2024 (The Rising Costs of Training Frontier AI Models, arXiv:2405.21015); Stanford AI Index 2026 (Maslej et al., Stanford HAI); Malagon, Ulloa Ruiz, Sandoval Plaza, Rosario Bolívar, García Mesa & Alvarado Morales 2025 (The Feasibility of Training Sovereign Language Models in the Global South: A Study of Brazil and Mexico, arXiv:2510.19801)

• Does governance work?evidence: absent

  There is no rigorous impact evaluation showing that technological-sovereignty policies (on-shore compute mandates, national foundation-model champions, talent-retention schemes such as EuroHPC AI Factories or India's IndiaAI Mission) actually deliver sustained domestic capability or strategic autonomy; these programs are recent, utilization and cost-per-GPU-hour are largely unpublished, and no counterfactual study exists. The closest analogue evidence base, the industrial-policy literature synthesized by Juhász, Lane & Rodrik, finds that properly-identified studies are more favorable than older correlational work suggested but that outcomes depend heavily on instrument design and structural context, and the older national-champion record warns of subsidized 'zombie' firms and government capture, so the closest analogue is mixed and the direct evidence that the sovereignty rule works is simply missing.

  Sources: Juhász, Lane & Rodrik 2024 (The New Economics of Industrial Policy, Annual Review of Economics 16:213-242); Ahmed & Wahed 2020 (The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research, arXiv:2010.15581); IndiaAI Mission (Indian Cabinet, March 2024); EuroHPC Joint Undertaking AI Factories (2024 regulation amendment; no published impact evaluation)

Related topics

Topics with similar coverage patterns across the tracked instruments.

• Environmental Impact of AI Training