James Broughel is a Contributor at Forbes. He covers breaking news related to state and federal regulation, with expertise spanning a broad range of issues from artificial intelligence to nuclear power. Broughel's work has been featured in prominent outlets such as The Wall Street Journal, Yahoo Finance, and The Hill.
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James Broughel's coverage suggests a strong focus on government announcements, legal policy regulation, and data citations within the themes of politics, government & politics, finance & economy, and environment. His articles cover an array of complex topics such as AI economics, environmental regulation, and permitting reform.
When reaching out to James with pitches or expert insights related to his coverage areas, consider providing well-researched analysis on government policies or regulations in the fields of AI technology and its economic implications. Also consider offering expertise regarding cost-benefit analysis in regulatory decision-making processes or insights into the impact of government actions on for-profit and non-profit organizations.
It's important to note that while there is no specific geographic focus mentioned for James' work; however it would be beneficial to offer examples from different regions if relevant to provide a broader perspective.
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Wall Street has spent the past year pricing in an artificial-intelligence revolution. Now investors are starting to question whether the boom has gotten ahead of reality.
Major technological revolutions often prompt doubts about whether existing economic measures can keep up. Artificial intelligence is no exception.
GDP does not need to be redefined for the AI age. The harder measurement problem is how price indexes account for rapidly changing quality and falling prices.
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.
