James Broughel

Major technological revolutions often prompt doubts about whether existing economic measures can keep up. Artificial intelligence is no exception.

AI will reduce transaction costs and improve many markets. But it will also make some problems cheaper to create, from fraud to pressure on infrastructure.

Industrial-scale cooling equipment at a large data center in Loudoun County, Virginia. AI will solve many problems, but it will create new ones as well, from pressure on local infrastructure to fraud.

Government data are usually described as a public good. A dataset collected once can be used by many researchers for many projects.

Senator Bernie Sanders introduced the American A.I. Sovereign Wealth Fund Act, proposing a one-time 50% stock tax on large AI companies. This would create a $7 trillion public fund, managed by an independent commission, distributing 5% of its value annually to Americans. The initiative asserts public ownership is justified because AI relies on a vast intellectual commons and taxpayer-funded research. While the concept of public equity in AI gains bipartisan traction, critics argue Sanders's 50% claim could deter capital formation, and raise concerns about governance and premature dividend payouts. Despite these issues, the proposal is seen as a serious effort to ensure the public shares in AI's extraordinary, publicly-supported gains.

AI-generated fake receipts are making expense fraud cheaper, easier, and harder to detect, forcing companies to rethink how they verify employee claims.

OpenAI CEO Sam Altman has proposed offering the US government a 5% equity stake in the company, valued at $42.6 billion, and suggested other leading AI developers contribute similarly to a public investment vehicle. Modeled on the Alaska Permanent Fund, the goal would be to allow the public to share in AI's significant upside, acknowledging its foundation in public inputs and federal research. The idea garners rare bipartisan interest, potentially providing AI companies legitimacy and addressing public anxiety about the technology's future impact.

Many of the world's most profitable companies, like Saudi Aramco ($106.2B net income, 97.6% state-owned) and TSMC ($36.5B net income, initially 48% state-funded), demonstrate that government ownership does not guarantee corporate failure. Other successful examples include Norway's Equinor, Singapore Airlines, and Malaysia's PETRONAS. These entities thrive due to varied ownership models, but share common success factors. Key among these are maintaining distance between government owners and company operations, embracing market discipline through public listings, and ensuring professional governance.

Washington is weighing a FINRA style regulator for AI. FINRA's own record suggests caution, however, and the standards bodies AI already has can do most of the job.

President Trump's move to establish a US sovereign wealth fund and take a stake in Intel has revived debate on government ownership. Historically, economists viewed state-owned enterprises negatively due to inefficiency and political interference, favoring privatization. However, research evolved as SOEs persisted globally. A new consensus, supported by the World Bank, IMF, and OECD, acknowledges that state ownership can succeed under strong governance. Key factors include professional management, competitive neutrality, clear mandates, and transparent operations, separating ownership from regulation. Trump's initiatives, like the passive Intel stake, will be judged by these modern standards, emphasizing institutional design over simple ownership type.