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The Department of Government Efficiency, or DOGE, has secured unprecedented access<\/a>\u00a0to at least seven sensitive federal databases, including those of the Internal Revenue Service and Social Security Administration. This access has sparked fears about\u00a0cybersecurity vulnerabilities<\/a>\u00a0and\u00a0privacy violations<\/a>. Another concern has received far less attention: the potential use of the data to train a private company\u2019s artificial intelligence systems.<\/p>\n The White House press secretary said government data that DOGE has collected\u00a0isn\u2019t being used<\/a>\u00a0to train Musk\u2019s AI models, despite Elon Musk\u2019s control over DOGE. However, evidence has emerged that DOGE personnel\u00a0simultaneously hold positions<\/a>\u00a0with at least one of Musk\u2019s companies.<\/p>\n At the Federal Aviation Administration, SpaceX employees\u00a0have government email addresses<\/a>. This dual employment creates a conduit for federal data to potentially be siphoned to Musk-owned enterprises, including xAI. The company\u2019s latest Grok AI chatbot model\u00a0conspicuously refuses to give a clear denial<\/a>\u00a0about using such data.<\/p>\n As a\u00a0political scientist and technologist<\/a>\u00a0who is intimately acquainted with\u00a0public sources of government data<\/a>, I believe this potential transmission of government data to private companies presents far greater privacy and power implications than most reporting identifies. A private entity with the capacity to develop artificial intelligence technologies could use government data to leapfrog its competitors and wield massive influence over society.<\/p>\n For AI developers, government databases represent something akin to\u00a0finding the Holy Grail<\/a>. While companies such as OpenAI, Google and xAI currently rely on information scraped from the public internet, nonpublic government repositories offer something much more valuable: verified records of actual human behavior across entire populations.<\/p>\n This isn\u2019t merely more data \u2013 it\u2019s\u00a0fundamentally different data<\/a>. Social media posts and web browsing histories show curated or intended behaviors, but government databases capture real decisions and their consequences. For example,\u00a0Medicare records<\/a>\u00a0reveal health care choices and outcomes. IRS and Treasury data reveal financial decisions and long-term impacts. And federal employment and education statistics reveal education paths and career trajectories.<\/p>\n What makes this data particularly valuable for AI training is its\u00a0longitudinal nature and reliability<\/a>. Unlike the disordered information available online, government records follow\u00a0standardized protocols<\/a>, undergo regular audits and must meet legal requirements for accuracy. Every Social Security payment, Medicare claim and federal grant creates a verified data point about real-world behavior. This data exists nowhere else with such breadth and authenticity in the U.S.<\/p>\n Most critically, government databases\u00a0track entire populations over time<\/a>, not just digitally active users. They include people who never use social media, don\u2019t shop online, or actively avoid digital services. For an AI company, this would mean training systems on the actual diversity of human experience rather than just the digital reflections people cast online.<\/p>\n Current AI systems face fundamental limitations that no amount of data scraped from the internet can overcome. When ChatGPT or Google\u2019s Gemini make mistakes, it\u2019s often because they\u2019ve been trained on information that might be\u00a0popular but isn\u2019t necessarily true<\/a>. They can tell you what people say about a policy\u2019s effects, but they can\u2019t track those effects across populations and years.<\/p>\n Government data could change this equation. Imagine training an AI system not just on opinions about health care but on actual treatment outcomes across millions of patients. Consider the difference between learning from social media discussions about economic policies and analyzing their real impacts across different communities and demographics over decades.<\/p>\n A large, state-of-the-art, or frontier, model\u00a0trained on comprehensive government data<\/a>\u00a0could understand the actual relationships between policies and outcomes. It could track unintended consequences across different population segments, model complex societal systems with real-world validation and predict the impacts of proposed changes based on historical evidence. For companies seeking to build next-generation AI systems, access to this data would create an almost insurmountable advantage.<\/p>\n A company like xAI could do far more with models trained on government data than building better chatbots or content generators. Such systems could fundamentally transform \u2013 and potentially control \u2013 how people understand and manage complex societal systems. While some of these capabilities could be beneficial under the control of accountable public agencies, I believe they pose a threat in the hands of a single private company.<\/p>\n Medicare and Medicaid databases<\/a>\u00a0contain records of treatments,\u00a0outcomes<\/a>\u00a0and costs across diverse populations over decades. A frontier model trained on new government data could identify treatment patterns that succeed where others fail, and so dominate the health care industry. Such a model could understand how different interventions affect various populations over time, accounting for factors such as geographic location, socioeconomic status and concurrent conditions.<\/p>\n A company wielding the model could influence health care policy by demonstrating superior predictive capabilities and market population-level insights to pharmaceutical companies and insurers.<\/p>\n Treasury data represents\u00a0perhaps the most valuable prize<\/a>. Government financial databases contain granular details about how money flows through the economy. This includes real-time transaction data across federal payment systems, complete records of tax payments and refunds, detailed patterns of benefit distributions, and government contractor payments with performance metrics.<\/p>\n An AI company with access to this data could\u00a0develop extraordinary capabilities<\/a>\u00a0for economic forecasting and market prediction. It could model the cascading effects of regulatory changes, predict economic vulnerabilities before they become crises, and optimize investment strategies with precision impossible through traditional methods.<\/p>\nValue of government data for AI<\/strong><\/h4>\n
The technical advantage<\/strong><\/h4>\n
Control of critical systems<\/strong><\/h4>\n