AI Buildout Drives Resources Capital Inflows

• AI infrastructure buildout requires massive capital allocation across multiple layers: data center construction, GPU/semiconductor procurement, fiber-optic networking (e.g., Google's America-India Connect Initiative for intercontinental connectivity), and power generation capacity • Hyperscaler capex is concentrated in Information Processing Equipment, Software, and R&D within GDP components, with technology-related fixed investment increasing markedly relative to 2023-2024 baselines[3] • Defense AI applications span space-based infrared tracking, autonomous systems, counter-UAS (unmanned aerial systems) detection with the global counter-UAS market projected to grow from $2.08 billion (2025) to $19.06 billion (2035), and AI-powered cargo inspection systems[2] • Distributed computing optimization platforms (such as QuantumSpeed) target mining and resource extraction infrastructure, improving throughput and reducing overhead through advanced scheduling and latency reduction across up to 1,000 nodes[2] • Worker access to AI in enterprises rose 50% in 2025, with companies deploying ≥40% of projects in production expected to double in 2026, indicating accelerating operational AI integration across sectors[6]

The convergence of hyperscaler capex ($400B+ in 2026), corporate technology budget increases (averaging 10% across industries), and defense AI spending creates sustained structural demand for commodities essential to computing infrastructure: semiconductors, rare earth elements, copper for electrical systems, and energy resources. This represents a multi-year cycle distinct from cyclical commodity booms, as AI capacity constraints require continuous infrastructure expansion before revenue materialization. The resources sector benefits from both direct demand (materials for data centers and computing hardware) and indirect demand (energy for power-intensive AI operations). However, risks include potential demand destruction if AI revenue fails to materialize at projected levels, which could trigger severe price declines and reverse the current wealth effect supporting economic growth[3][7]. Geographic disparities in AI investment (U.S. outperformance vs. limited Canadian spillovers) suggest uneven commodity demand patterns by region and trading partner relationships[5].