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Under the Joint Artificial Intelligence Governance model, frontier AI companies must demonstrate their ability to self-regulate. This is a recent example of actions towards achieving this goal. OpenAI, Anthropic probe tens of thousands of AI model security incidents As for its part of establishing standards, licensing, auditing and liability, government is in the stage of publicly probing AI companies nationally and internationally. These are sample probes into AI.  Senior leaders from Anthropic, OpenAI, Google and Meta testified under oath during a New York City Council hearing. U.S. FTC to Compel Testimony from AI Executives in Sweeping Probe of OpenAI and Anthropic U.S. Will Now Examine National Security Implications of New AI Models, Pre-Release OpenAI Faces Congressional Probe Over Swarm Hacking Incident Australia Summons OpenAI, Anthropic CEOs to AI Probe
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Financial Sector Governance could be the closest model to follow for Artificial Intelligence governance. How does the Financial sector work and how does it help in the case of AI? The financial-sector model essentially says: Private power + public authority + shared infrastructure + systemic risk requires joint governance . AI increasingly has exactly the same characteristics. Consider the parallel: Financial Sector AI Ecosystem Banks AI Companies Financial Markets AI Platforms/Models Capital Compute/Data/Investment Regulators AI Regulatory Authorities Central Banks Strategic/National AI Institutions Financial Infrastructure Cloud, Chips, Data Centers Systemic Financial Risk Systemic AI Risk Regulatory Capture AI Regulatory Capture Financial Stability AI/Strategic Stability Public/Private Governance Government–Industry AI Governance The most important lesson from Financial Sector Governance ...
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Artificial intelligence is developing along two major—and increasingly interconnected—paths: Commercial/Economic AI and Strategic AI. They require different governance philosophies. Commercial/Economic AI The guiding principle is: Regulate the risks while preserving competition and innovation. Its institutional foundations resemble those developed for other powerful economic systems: Financial regulation Antitrust law Consumer protection Environmental regulation Occupational safety Privacy law The objective is to allow innovation while preventing companies, markets, or technologies from imposing unacceptable costs on society. Strategic AI Strategic AI presents a fundamentally different challenge. Its guiding principle is: Maintain human control and strategic stability while preventing catastrophic misuse or escalation. Its institutional ancestors are therefore different: Nuclear arms control International humanitarian law Military command structures Intelligence oversight ...
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  We have identified two major strands of A.I. under development (and roll-out) now. Commercial/Economic A.I. and Strategic A.I. The differences between them are real, and complicate their governance. A governance regime must take these differences into consideration. Here is a brief comparison between the two. Dimension Commercial / Economic AI Strategic AI Primary objective Productivity, revenue, efficiency, innovation National security, military advantage, strategic influence Main actors Corporations, investors, consumers, regulators States, militaries, intelligence agencies, defense contractors Principal risk Economic and social externalities Security escalation and strategic instability Governance problem How to make markets innovative but accountable How to make powerful systems controllable without creating strategic vulnerability Typical failure AI causes discrimination, market concentration, job disruption, fraud AI makes or accelerates consequential decisions involving co...
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The Workforce in Motion: Skills and pathways to future jobs in the United States report from McKinsey Global Institute is out. Some key findings. The US economy could create more jobs in the next decade than will be replaced through automation, but the change may require the largest and most sustained workforce transformation in US history.  We estimate that roughly 11 million workers in declining occupations may need to transition to new jobs, ranging from six million to 16 million depending on the pace of automation adoption and its impact on labor demand. Jobs of the future, particularly growing jobs, could pay more on average but require more skills.  Roughly 60 percent of growing employment could be in the top two wage quintiles, with much of the increase concentrated in healthcare, construction, and management occupations. Conversely, more than 70 percent of declining employment could be in the bottom two quintiles, primarily in office and administrative suppor...
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 The closest supervision model in existence today for Artificial Intelligence is that of Finance. This is due to many essential similarities. Financial system AI system Common governance problem Banks, brokers, exchanges AI developers, model companies, deployers Highly specialized private-sector institutions Financial regulators AI regulators/supervisors Government needs oversight without knowing every technical detail FINRA / exchanges / SROs Potential AI SRO Industry expertise can be incorporated into supervision Capital requirements AI safety/security requirements Establish minimum risk-management requirements Stress testing AI safety evaluations / red-team testing Test systems before they create unacceptable risks Bank examinations AI audits / technical assessments Continuous examination rather than occasional legislation Risk management frameworks AI risk-management frameworks Firms manage r...
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