Trump Eyes Pentagon AI Program to Shape Minerals Pricing for Trade Bloc Strategy
Former US President Donald Trump is exploring the use of a Pentagon-backed artificial intelligence program to influence pricing mechanisms for critical minerals within a proposed trade bloc framework, according to sources familiar with the discussions. The move signals a sharper focus on technology-driven policy tools to secure strategic resources amid intensifying global competition.
The AI program, developed under the US Department of Defense to analyse supply chains, demand patterns, and geopolitical risks, is being evaluated for its potential to guide pricing benchmarks for minerals considered vital to national security. These include rare earth elements, lithium, cobalt, nickel, and other inputs essential for defence systems, clean energy technologies, and advanced manufacturing.
The approach under consideration would align mineral pricing with strategic trade partnerships, potentially offering preferential terms to allied nations while limiting exposure to rival suppliers. Supporters argue that such a framework could reduce dependence on adversarial countries and stabilise long-term access to critical resources.
Pentagon officials have previously highlighted the role of data analytics and AI in anticipating supply disruptions and market manipulation. Integrating these tools into trade policy could allow the US to respond more quickly to pricing shocks and coordinate actions with partner countries in a structured trade bloc.
However, the proposal is likely to raise concerns among global commodity producers and trading partners, who may view AI-influenced pricing as market intervention. Analysts warn that excessive control over pricing mechanisms could distort markets and invite retaliatory measures.
The development underscores how critical minerals are increasingly at the centre of trade, defence, and technology policy. As geopolitical rivalries deepen, the use of advanced analytics to shape economic strategy reflects a broader shift toward data-driven decision-making in global resource management.