US AI policy shaped by corporate lobbying: Trump convenes tech elite to funnel public funds into private AI monopolies
Original framing: “Trump to meet AI leaders to discuss US investment in their companies” — BBC News - Science
The militarization of AI traces back to Cold War-era DARPA projects, where defense funding laid the groundwork for today’s surveillance economy. The 1990s 'tech boom' normalized the idea that public funds should subsidize private innovation, a pattern now accelerating with AI. Historical parallels include the railroad monopolies of the 19th century or the pharmaceutical industry’s patent regimes, where public investment led to private enrichment while externalizing costs onto society.
The meeting between Trump and AI leaders is not a neutral policy discussion but a symptom of a deeper structural crisis: the fusion of state power and monopolistic capital in shaping AI’s trajectory.
This nexus traces back to Cold War militarization and the 1990s neoliberalization of innovation, where public funds are funneled into private hands under the guise of 'investment,' while the risks—surveillance, inequality, and ecological harm—are socialized. Indigenous epistemologies, which reject the commodification of knowledge, and alternative governance models like cooperative AI or European data rights frameworks offer a counter-narrative to this extractive logic. Yet the marginalized voices most affected by AI—gig workers, Indigenous communities, and global South populations—remain excluded from these decisions, their labor and lands treated as raw materials for corporate enrichment. The trickster’s lens reveals the absurdity of this arrangement: a president who rails against 'big government' while orchestrating a corporate welfare bonanza, where the public purse is plundered in the name of 'innovation.' The path forward requires dismantling this nexus through antitrust action, public ownership of AI infrastructure, and the recognition of data sovereignty as a human right, ensuring that AI serves the many, not the few.