Just Security: “In May 2026, the Pentagon announced that it had reached deals with eight AI companies—SpaceX, OpenAI, Google, NVIDIA, Reflection, Microsoft, Amazon Web Services, and Oracle—to “deploy their advanced AI capabilities on the Department’s classified networks for lawful operational use.” These deals came amid a public dispute between another top AI company, Anthropic, and the Department of Defense. Anthropic’s Claude large language model (LLM) had been integrated into the military’s classified systems as part of a pilot program. That contract had incorporated usage restrictions that prohibited the use of Claude for mass domestic surveillance and fully autonomous weapons systems. The Pentagon wanted to eliminate these restrictions and allow the deployment of Claude for “any lawful use.” When Anthropic refused, the Pentagon moved to blacklist the company from defense contracting; Anthropic has sued. According to the Pentagon’s Chief Technology Officer, no further restriction was needed, because mass surveillance of Americans is already barred by law and Pentagon policies. Except for Anthropic, AI companies have mostly gone along with this construction, with some (e.g., OpenAI) stating that their deals with the Defense Department ban mass domestic surveillance.
These reassurances obscure the central question: what counts as mass domestic surveillance? They rest on the unstated assumption that communications metadata and other forms of commercially available information—the detailed records of Americans’ movements, communications, and associations that the government (including the military) purchases from commercial data brokers—are outside the envelope of what counts as surveillance. The government also does not count foreign intelligence programs as mass domestic surveillance even though they sweep up Americans’ communications without a warrant. Yet both types of collection result in the acquisition of vast quantities of Americans’ information, posing serious risks to their privacy and civil liberties. Large language models only increase these risks by making it faster and easier to analyze data across large populations and generate inferences about Americans’ beliefs, associations, and behavior.
