Three Forces, One Fragmented Rulebook: What's Actually Driving Global AI Regulation in 2026

Global AI regulation in 2026 is being pulled in three directions at once: safety and ethics, infrastructure and economic competitiveness, and geopolitics and national security. Analysts at BNP Paribas Global Markets frame these as the industry's three critical "bears" — regulatory constraints of safety, the massive physical requirements of infrastructure, and the fragmentation caused by shifting geopolitics — and argue that navigating the year ahead demands a "Goldilocks" equilibrium between rapid innovation and essential oversight. Understanding which force is driving any given rule, and what trade-off it embeds, is the difference between a coherent compliance strategy and a reactive one.

Introduction: The Three Pillars of Global AI Regulation

The three drivers are not abstract categories; they map onto observable national behavior. Bruce Mehlman, writing on his Age of Disruption newsletter, notes that three decades of internet policy have produced profoundly different regulatory goals: the U.S. has prioritized innovation, the EU consumer protection, and China governmental control. Those inherited postures are now being applied to AI, producing rules that look similar on the surface but serve different masters.

The stakes have grown because the underlying economics have changed. BNP Paribas describes the AI-driven data-centre boom as a multi-trillion dollar story, with compute demand colliding with an unprecedented surge in data-centre power demand — a collision that forces regulators to weigh speed against oversight. Meanwhile, the technology itself crosses borders more easily than any regulator's writ, which is why, as a Stanford Cyber Policy Center chapter on generative AI governance observes, most major international organizations have proposed or are drafting AI initiatives. The result is a landscape where the same model may be governed as a consumer risk in Brussels, an economic asset in Washington, and a security matter in Beijing.

Driver 1: Safety, Ethics, and Societal Control

The safety driver starts from a specific premise: that early-stage regulation can improve consumer welfare even if it temporarily slows adoption. Brookings research on how regulation information affects managers' choices concludes that instituting rules early in AI diffusion may improve welfare through increased safety and better handling of bias and discrimination — while cautioning that innovation effects differ depending on whether one looks at firms consuming AI or firms producing it.

The flagship instrument here is the EU's AI Act, Regulation (EU) 2024/1689, which the European Commission describes as the first-ever comprehensive legal framework on AI worldwide, setting risk-based rules for developers and deployers with the stated aim of fostering trustworthy AI in Europe. Its substantive bite is compute-anchored: as a paper hosted on arXiv explains, the Act presumes models trained above 10^25 floating-point operations have high-impact capabilities, triggering classification as GPAI models with systemic risk under Article 51.

The sharpest trade-off inside this driver concerns open-weight models. The case for open release, as a CASRAI comparison summarizes it, is that wide weight access accelerates independent safety research and auditing that outside organizations could not otherwise perform. The case against: release can never be undone — no recall, no universal patch, no revocation once weights are downloaded. The arXiv paper adds empirical grounding to the restriction side, citing Qi et al. (2024), who showed safety alignment can be compromised by fine-tuning with as few as ten adversarially designed examples at a cost under $0.20 via OpenAI's APIs.

Yet locally executable open models may fall below the thresholds that trigger the most substantive obligations, a dilemma existing regulation does not fully resolve — and, the paper warns, restriction policies designed for resource-rich jurisdictions can deepen other jurisdictions' dependencies on foreign infrastructure rather than increase safety.

A parallel safety architecture is emerging in U.S. states. METR, the model-evaluation organization, summarizes frontier-AI safety and security obligations under California's SB 53, New York's RAISE Act, Illinois' SB 315, and the frontier AI portion of the EU AI Act — laws establishing incident reporting requirements, model evaluation standards, safety and security mitigations, internal governance practices, and whistleblower protections.

Driver 2: Infrastructure and Economic Competitiveness

The second driver treats AI less as a risk to be contained than as an industrial capability to be built. The physical dimension is now unavoidable: BNP Paribas ties the regulatory conversation directly to the data-centre boom, arguing that navigating 2026 requires more than computational scaling and that governments must manage the massive physical requirements of infrastructure alongside oversight. Power availability, land, and capital have become regulatory subjects in their own right.

The economic dimension is subtler. A Network Law Review article by Thibault Schrepel on regulatory overlaps and economic tradeoffs argues that existing data privacy and security regulations — including India's Digital Personal Data Protection Act and South Korea's PIPA — shape AI governance and carry real economic trade-offs, and that evidence now shows smaller models can sometimes outperform larger ones, complicating the assumption that bigger datasets and more compute always win. That finding matters for policy design: if scale is not destiny, compute-threshold rules may miss capable small models while burdening the wrong targets.

The trade-off embedded in this driver is a resource question. Safety-driven rules disproportionately burden smaller firms and entrants, as the Network Law Review analysis notes, while infrastructure-driven policy favors whoever can build. Governments must weigh fostering innovation against economic competition, and the two goals pull regulation in opposite directions: one wants fewer, lighter rules to keep domestic industries competitive; the other wants standards that make national AI ecosystems trustworthy enough to export.

Driver 3: Geopolitics and National Security

The third driver is the most fragmenting. BNP Paribas points to the rise of "Sovereign AI" and intensifying U.S.-China tech rivalry as forces fragmenting the global AI landscape and creating new compliance hurdles and supply chain constraints. The national-security logic can be concrete: the firm cites the Department of War's demand for "US-only" frontier-model access and requirements for U.S. citizens to use domestically hosted models as constraints that could hamper global infrastructure build-out or frontier model monetization.

Internationally, the geopolitical race has produced a crowded field of initiatives rather than a single order. The Stanford chapter catalogs the G7's Hiroshima AI Process — non-binding but influential frameworks, including its Guiding Principles and Code of Conduct — alongside the G20's AI Principles, EU-US Trade and Technology Council collaborative projects, a BRICS AI Study Group aligned with China's Global AI Governance Initiative, the Council of Europe's May 2024 Framework Convention (the first international AI treaty, requiring ratification by at least five states to enter into force), and the UN's High-Level Advisory Body and first AI resolution.

The trade-off here is between technological autonomy and global interoperability. Every sovereignty requirement — data localization, domestic hosting, export-style controls on model access — improves a government's grip on its AI stack while raising the cost of cross-border deployment. National governments are adopting different approaches based on their strategic priorities, and Mehlman's observation about divergent internet-policy traditions suggests those differences will persist rather than converge.

Comparative Framework: Drivers of Global AI Regulation

The table below compares the three drivers across a consistent set of criteria — primary objective, example policy instruments, and the innovation-versus-control trade-off each embeds — so that a safety-driven rule can be distinguished at a glance from a geopolitically driven one.

Three drivers of global AI regulation compared by objective, instruments, and trade-off
Regulatory Driver	Primary Objective	Example Policy Instruments	Innovation vs. Control Trade-off
Safety & Ethics	Safe, ethical, trustworthy AI; consumer welfare through risk control, per Brookings' finding that early-stage rules may improve welfare despite slowing adoption	EU AI Act (Regulation (EU) 2024/1689), the first comprehensive AI legal framework; frontier-AI laws (California SB 53, New York RAISE, Illinois SB 315) with incident reporting, evaluation standards, governance and whistleblower protections	Open-weight access accelerates independent safety research, but release is irreversible and fine-tuning can strip guardrails cheaply; restriction policies can deepen foreign-infrastructure dependencies in less-resourced jurisdictions
Infrastructure & Economic Competitiveness	Propel the multi-trillion-dollar AI data-centre market and national economic advantage	Data-centre and power policy; data privacy/security regimes that shape AI governance; hybrid regulatory approaches intended to avoid burdening smaller entrants	Lighter rules favor domestic competitiveness; heavier standards favor trust and exportability — and compute-threshold rules may miss capable small models
Geopolitics & National Security	"Sovereign AI," technological autonomy, and management of U.S.-China rivalry	"US-only" frontier-model access demands; domestic-hosting requirements; supply chain constraints; a crowded field of non-binding international frameworks (G7 Hiroshima, G20, BRICS, Council of Europe, UN)	Autonomy requirements improve governmental control but fragment the global landscape, raising compliance costs and constraining cross-border deployment

Global Fragmentation and Compliance Challenges

Because the three drivers produce rules with different objectives, the global landscape is fragmenting rather than harmonizing. The Network Law Review analysis defines the central pathology: free-riding, where firms exploit gaps in global regulatory alignment by developing AI in jurisdictions with weaker oversight, is set to remain in place without international coordination on baseline standards. For a multinational deploying the same model across markets, that means the weakest oversight anywhere in the footprint becomes an arbitrage opportunity — and a reputational exposure.

The compliance burden compounds because the instruments overlap without aligning. A frontier model may face systemic-risk classification in the EU, incident-reporting duties in California, data-localization rules in Asia, and "US-only" access demands from Washington — each triggered by different thresholds and definitions. The Stanford chapter's point that AI transcends any single regulator's boundaries is precisely why the G7, G20, BRICS, Council of Europe, and UN initiatives coexist without reconciling: they are geopolitical artifacts as much as safety instruments.

Fragmentation also has a distributional edge. The arXiv paper's warning that restriction policies designed for resource-rich jurisdictions deepen dependencies elsewhere means the safety driver and the geopolitics driver can interact badly: rules meant to prevent proliferation may instead concentrate capability in the countries that can afford compliant infrastructure.

Navigating the Fragmented AI Regulatory Landscape

Policymakers and technology executives should develop strategies that account for the divergent objectives and instruments of global AI regulation, focusing on adaptability to fragmented compliance requirements and identifying opportunities for international collaboration on specific standards. Practically, that means mapping each obligation in the stack above to its driving force before assuming rules will converge — a compute threshold, a hosting requirement, and an incident-reporting duty are not interchangeable burdens, and they will not soften on the same timetable.

For policymakers, the Network Law Review analysis points to a workable middle path: greater regulatory cooperation among major economies can reduce fragmentation and free-riding, and focusing on high-impact, low-resistance areas of consensus — with hybrid approaches and explainability measures such as model documentation and auditing — can balance innovation against responsible deployment without disproportionately burdening smaller firms. For executives, the same logic applies in reverse: the cheapest durable strategy is to build to the strictest plausible standard in areas where consensus is forming, and to keep deployment architectures modular enough to absorb jurisdiction-specific requirements.

Neither group should expect resolution soon. Monitor the emergence of new national AI legislation and international harmonization efforts, particularly regarding cross-border data flows and interoperability standards for AI systems — including the ratification trajectory of the Council of Europe's Framework Convention, which needs five ratifying states to enter into force. Those are the observable signals of whether the three bears are being tamed or merely multiplying.

Three Forces, One Fragmented Rulebook: What's Actually Driving Global AI Regulation in 2026

Introduction: The Three Pillars of Global AI Regulation

Driver 1: Safety, Ethics, and Societal Control

Driver 2: Infrastructure and Economic Competitiveness

Driver 3: Geopolitics and National Security

Comparative Framework: Drivers of Global AI Regulation

Global Fragmentation and Compliance Challenges

Navigating the Fragmented AI Regulatory Landscape

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