Policy & Governance
AI regulation, standards, and institutional choices.
AI and Antitrust: Why Competition Law Struggles with Foundation Models
A handful of companies control frontier AI development. OpenAI, Google DeepMind, Anthropic, Meta — and increasingly few others — determine what foundation models exist, what they can do, and on what terms the rest of the world gets to use them. This concentration is not incidental. It follows from the economics of the technology: training frontier models requires billions in compute, proprietary datasets at scale, and engineering talent that a few employers have locked up.
AI and Democracy: Deepfakes, Bots, and Informed Consent
Democracy requires informed consent. Voters must be able to evaluate claims, identify candidates' actual positions, and distinguish real information from fabrication. AI is systematically undermining each of these conditions — not as a side effect, but as a direct consequence of how the technology is built and deployed.
AI Cooperatives: Alternatives to Corporate Models
The dominant model of AI development follows a familiar script. Venture capital funds a startup. Engineers build a foundation model. The company scales. Returns flow to shareholders, executives, and early investors. The workers who label training data, the users who provide feedback, the communities whose culture and knowledge fill the datasets — they are inputs, not beneficiaries.
AI Executive Orders: What Changed and What Didn't
In October 2023, President Biden signed the most sweeping federal action on AI the United States had ever produced. Fifteen months later, President Trump rescinded it. This sequence tells you almost everything you need to know about the fragility of American AI governance — and about who benefits when regulation arrives by executive order rather than legislation.
The Influence Machine
OpenAI spent $260,000 on federal lobbying in 2023. By 2025, that figure had climbed to nearly $3 million. The company increased its lobbyist count from 3 to 18 in a single year. This trajectory tells you everything you need to know about where the AI industry believes the real decisions are being made.
The AI Safety Summit Circuit: Diplomacy or Theater?
Between November 2023 and February 2025, three international summits attempted to establish the foundations of global AI governance. Bletchley Park, Seoul, Paris — the circuit traced a familiar arc of diplomatic ambition meeting institutional reality.
The Venture Capital Trap: How Funding Structures Shape What AI Gets Built
There is a question that precedes all the familiar AI debates — about safety, about jobs, about regulation. It is this: who decides what gets built?
Artificial Dissuasion: Can Nuclear Deterrence Logic Apply to AI?
The Cold War produced the most consequential piece of game theory in human history: the doctrine of mutually assured destruction. Two superpowers, each capable of annihilating the other, reached a stable equilibrium in which neither used its weapons — precisely because the weapons existed. Deterrence worked not through goodwill but through mathematics. The logic was brutal and elegant: any first strike would trigger a retaliatory second strike, so no rational actor would strike first.
China's AI Strategy: State-Directed Development as an Alternative Model
China pursues AI development through state direction rather than market forces. Understanding this alternative reveals something important: the US model of corporate-led AI development is a choice, not an inevitability. Different governance structures produce different distributions of value, risk, and power.
Democratic AI Governance
AI governance is currently shaped by three actors: companies, governments, and technical experts. Missing from this list: the public.
The EU AI Act Explained
The European Union's AI Act is the world's first comprehensive AI regulation. It matters beyond Europe because companies operating globally often adopt EU standards everywhere. Here's what it actually does.
Europe's AI Paradox: Regulating What You Don't Build
Europe has the world's most comprehensive AI regulation. It does not have a single frontier AI lab.
Institutional Decoherence: When Technology Outruns Governance
The complaint that "regulation can't keep up with technology" has become a cliché. Like most clichés, it gestures toward something real while obscuring the mechanism. The problem is not that governance is slow and technology is fast. The problem is that different parts of society — political systems, legal frameworks, cultural norms, economic structures, international institutions — adapt to technological change at different rates, creating gaps between them. Those gaps become exploitable. And the actors best positioned to exploit them are the ones who least need the protection that governance provides.
Public AI
AI is built on public investment. DARPA funded the research that became deep learning. NSF grants supported the scientists. Public universities trained the engineers. The internet itself — the source of training data — was a government project.
Regulatory Capture in AI
When an industry shapes the rules meant to govern it, the result is regulation that protects incumbents rather than the public. This is regulatory capture — and AI policy is increasingly exhibiting its symptoms.
The Sovereign AI Movement
In early 2024, Nvidia CEO Jensen Huang told a room of world leaders that every nation needs its own AI. "It codifies your culture, your society's intelligence, your common sense, your history — you own your own data," he said. The term he used — "sovereign AI" — stuck. Within months, governments on every continent were citing it in policy documents and budget requests.
State-Level AI Regulation: The Patchwork Problem
Congress has not passed comprehensive AI legislation. As of early 2026, there is no federal framework governing how artificial intelligence systems are built, tested, or deployed across the United States. Into that vacuum, state legislatures have stepped — each writing their own rules, with their own priorities, at their own pace.
Stock Buybacks and AI
When AI cuts costs, where does the money go?
The Academic Brain Drain: Who Pays to Train AI's Best Minds?
The pipeline works like this. A student enters a public university. Taxpayers subsidize the tuition. Federal grants fund the lab. A professor — herself trained at public expense — supervises the dissertation. Five to seven years later, a newly minted AI PhD walks out the door and into a corporate lab offering four times the salary, a hundred times the compute, and a signing bonus that exceeds the annual budget of the research group that trained them.
The Digital Divide in AI
AI could narrow global inequalities. It could bring medical diagnostics to clinics without doctors, agricultural advice to farmers without agronomists, educational resources to students without schools. The technology is scalable. The marginal cost of deploying a trained model approaches zero.
Universal Basic Income and AI: The Debate Gets Real
For decades, universal basic income was a parlor game — a thought experiment debated by philosophers, a footnote in economics textbooks, an idea that united libertarians and socialists in mutual suspicion. Then AI started automating white-collar work, and suddenly everyone from Silicon Valley CEOs to presidential candidates was talking about writing checks.
Who Regulates Foundation Models
The most powerful AI systems in the world are currently regulated by... mostly their own developers. This is changing, slowly, unevenly, and with significant gaps.