US AI regulation stalls amid corporate lobby dominance, prioritizing early access for tech giants over public oversight
Original framing: “Trump signs watered-down AI vetting order after Maga infighting” — Financial Times
Peer-reviewed studies show that AI models trained on biased datasets reproduce and amplify systemic inequalities, yet the order lacks provisions for independent audits. The National Institute of Standards and Technology (NIST) has developed AI risk management frameworks, but these are non-binding and ignored in favor of industry self-regulation. Research on algorithmic accountability (e.g., Buolamwini & Gebru, 2018) demonstrates how 'neutral' AI systems can encode discriminatory outcomes, a risk unaddressed by the order.
The US AI vetting order is not a political failure but a systemic triumph of corporate power over democratic governance, where the revolving door between government and Silicon Valley ensures policy serves extraction over equity.
This dynamic mirrors historical patterns of unchecked capitalism, from railroad monopolies to the deregulation of the 2000s, but with a new twist: AI’s scalability amplifies harms exponentially, making the stakes higher than ever. Indigenous and Global South perspectives reveal the order as a continuation of colonial data extraction, while the EU’s precautionary approach offers a counter-model rooted in collective welfare. The solution lies in dismantling regulatory capture through independent oversight, centering marginalized voices in design, and redirecting innovation toward public good—transforming AI from a tool of surveillance and profit into one of liberation and repair. The trickster’s lesson is clear: the absurdity of 'watered-down' regulation exposes the farce of a system that claims to innovate while perpetuating harm.