Regulating the Algorithm: Why Governments Are Losing the AI Policy Race

By Kenji Mori, Japan Sep. 20, 2025

Artificial intelligence has become the defining technology of the 21st century—transforming economies, rewriting social contracts, and redrawing the boundaries of human decision-making. Yet while the technology races ahead, governance lags dangerously behind. From Washington to Brussels to Beijing, policymakers are struggling to control an innovation that evolves faster than the laws meant to restrain it. The result is a widening governance vacuum in which private corporations, not public institutions, increasingly determine how intelligence itself is used.

The Policy-Innovation Gap

The speed of AI development has overwhelmed traditional rule-making cycles. According to the Stanford AI Index 2025, the average time from AI model release to global deployment has fallen from 18 months in 2018 to less than six months today. Meanwhile, drafting, negotiating, and enforcing comprehensive AI regulation can take years. This mismatch leaves governments perpetually reactive—legislating after the harm has already been done.

The European Union’s AI Act, passed in early 2025, is the world’s first major attempt at comprehensive regulation. It classifies systems by risk—prohibiting “unacceptable-risk” applications such as social-credit scoring and mandating transparency for “high-risk” uses like hiring or healthcare. But even the EU, hailed as the global regulatory leader, faces criticism for being too slow. Generative-AI tools such as ChatGPT and Midjourney reshaped industries years before the law’s enforcement mechanisms were operational.

The United States has fared worse. Despite its leadership in AI research, Congress remains divided over federal oversight. Regulation has been fragmented into state-level initiatives—like California’s SB 1047 requiring AI safety audits—and voluntary federal guidelines, such as the NIST AI Risk Management Framework. This patchwork creates loopholes that allow corporations to “jurisdiction shop” for the most lenient environments.

Corporate Capture of the AI Agenda

In theory, governments regulate technology; in practice, technology companies increasingly regulate governments. Tech giants now wield geopolitical power rivaling that of nation-states. The combined market capitalization of the top five AI firms—Microsoft, Alphabet, Amazon, Meta, and NVIDIA—exceeds US $10 trillion, more than the GDP of Japan and Germany combined.

These companies are simultaneously innovators, data custodians, and de facto policymakers. By defining safety standards, releasing open-source frameworks, and lobbying for self-regulation, they shape the regulatory conversation in their own interest. According to OpenSecrets, lobbying expenditures by the AI sector in the U.S. jumped 235 percent between 2020 and 2024. Microsoft alone spent over US $12 million in 2024 to influence AI policy.

This corporate capture extends globally. In the United Kingdom, the AI Safety Summit of 2023 was co-sponsored by leading firms, prompting critics to call it “a conference of the regulated writing their own rules.” In the absence of robust state capacity, private governance has become the default—an arrangement as convenient for industry as it is dangerous for democracy.

Geopolitical Fragmentation and the Race to the Bottom

AI governance is fracturing along geopolitical lines. The United States favors a market-driven model emphasizing innovation; the European Union prioritizes human rights and precaution; China integrates AI regulation directly into its national-security apparatus. Each model reflects distinct political values—and each complicates the possibility of a unified global standard.

The risk is regulatory arbitrage: companies exploiting the weakest regimes to deploy high-risk technologies first. The Carnegie Endowment for International Peace warns that “without cross-border coordination, the global AI landscape could mirror the climate-policy dilemma—fragmented, competitive, and ultimately ineffective.”

Developing countries face an even greater challenge. Lacking domestic expertise or infrastructure, many must import not only AI technology but also its regulatory frameworks. This “AI dependency” risks replicating digital colonialism, where local laws are subordinated to foreign corporate or geopolitical interests.

The Accountability Vacuum

The most alarming gap in AI governance is accountability. When an algorithm discriminates, causes financial loss, or contributes to a fatal accident, who is responsible—the developer, the deployer, or the algorithm itself? Current laws struggle to answer.

The European Commission’s Liability Directive (2025) proposes reversing the burden of proof, requiring companies to demonstrate that harm was not caused by their systems. But enforcement depends on access to proprietary data that firms often refuse to disclose, citing trade secrets. In the United States, Section 230 of the Communications Decency Act still shields platforms from liability for third-party content—an outdated provision ill-suited for AI-generated material.

The opacity of AI models compounds the problem. Deep-learning systems operate as “black boxes,” producing results even their creators can’t fully explain. As the OECD AI Policy Observatory notes, “without interpretability, accountability is functionally impossible.” Governments cannot regulate what they cannot understand.

The Global Push for AI Governance

Despite the chaos, signs of coordination are emerging. In 2024, the G7 Hiroshima Process produced guiding principles for “trustworthy AI,” emphasizing transparency, risk assessment, and human oversight. The UN Secretary-General’s High-Level Advisory Body on AI has proposed a global agency modeled on the International Atomic Energy Agency to monitor AI safety and compliance.

Meanwhile, countries like Singapore and Canada have launched innovative policy sandboxes allowing regulators and companies to co-test new AI applications under supervision. These experiments hint at a new regulatory paradigm—one that evolves dynamically alongside technology rather than chasing it.

Still, global governance remains largely aspirational. The proposed UN AI Accord, aimed at setting shared safety and ethical standards, has stalled over disputes between China and Western powers regarding surveillance and data-sovereignty clauses. The longer coordination drags, the greater the risk of catastrophic misuse—whether through autonomous weapons, disinformation, or economic destabilization.

Rethinking Regulation: From Reactive to Reflexive

The fundamental problem with AI policy is not ignorance—it’s inertia. Lawmakers continue to treat AI as an extension of the digital economy rather than as an existential governance challenge. To close the gap, governments must transition from reactive to reflexive regulation—anticipating risks before they scale.

Three shifts are essential:

1. Dynamic Regulation. Policy should update automatically in response to technological milestones. The EU AI Office, launching in 2025, is experimenting with adaptive standards tied to model-size thresholds and real-world performance audits—a model other regions could emulate.

2. Algorithmic Transparency Mandates. Governments must require full disclosure of training data, model architecture, and bias-testing procedures for any AI deployed in high-impact sectors such as healthcare, finance, or justice. Transparency is not antithetical to innovation; it is the foundation of trust.

3. Global Coordination Mechanisms. A binding international treaty on AI—akin to the Paris Climate Agreement—is needed to harmonize safety standards, liability norms, and export controls. Without it, competitive deregulation will continue to undermine global safety.

Conclusion

The world stands at a crossroads. Either governments reclaim authority over the technologies reshaping society, or they cede it permanently to private interests. The cost of inaction is not merely economic; it is democratic. As algorithms increasingly govern access to information, jobs, and justice, who governs the algorithms becomes the most important question of our time.

Effective AI regulation will not emerge from fear of innovation but from recognition of its power. States must move beyond reactive lawmaking and build institutions capable of evolving as fast as the code they oversee. In the contest between governance and technology, the winner will define the next century.

Works Cited

“AI Act: Europe Adopts the World’s First Comprehensive AI Law.” European Commission, 2025, https://commission.europa.eu/ai-act-2025.

“The AI Index Report 2025.” Stanford University Institute for Human-Centered Artificial Intelligence, 2025, https://aiindex.stanford.edu/report/2025.

“Artificial Intelligence and Democratic Governance.” Carnegie Endowment for International Peace, 2024, https://carnegieendowment.org/2024/09/15/ai-governance.

“NIST AI Risk Management Framework.” U.S. National Institute of Standards and Technology, 2023, https://www.nist.gov/ai/rmf.

“AI Safety and Global Regulation.” Organisation for Economic Co-operation and Development (OECD) AI Policy Observatory, 2025, https://oecd.ai/en.

“High-Level Advisory Body on Artificial Intelligence: Interim Report.” United Nations, 2024,

 https://www.un.org/en/ai-advisory-body.