A Washington Post investigation published February 7, 2026, reveals that the unprecedented AI infrastructure spending spree by technology companies is creating significant resource shortages across multiple sectors of the economy, extending far beyond the widely reported constraints in computing chips and data center capacity.
The investigation documents how the hundreds of billions of dollars being channeled into AI data centers, networking equipment, and specialized hardware is fundamentally reshaping resource allocation throughout the U.S. economy. Electricians have become increasingly difficult to find for non-AI projects, construction timelines for conventional buildings are extending due to material diversions, and smartphones are expected to become more expensive for potentially years as memory chip production prioritizes AI workloads over consumer electronics.
The electrician shortage has emerged as a particularly acute bottleneck. Data centers require specialized electrical infrastructure capable of handling massive power loads, often measured in hundreds of megawatts for a single facility. The surge in data center construction has created fierce competition for qualified electricians, with AI infrastructure projects offering premium wages that pull talent away from residential construction, commercial real estate development, and industrial facilities.
Construction projects unrelated to AI infrastructure are facing delays and cost overruns as materials get redirected toward higher-margin data center contracts. Steel, concrete, cooling systems, and specialized building components that would normally flow to diverse construction projects are increasingly allocated to the AI buildout, where technology companies demonstrate willingness to pay premium prices to maintain aggressive deployment schedules.
The memory chip shortage represents perhaps the most concerning constraint for consumer technology markets. AI data centers consume disproportionate quantities of high-bandwidth memory (HBM) and standard RAM, creating supply imbalances as cloud providers outbid consumer electronics manufacturers for limited production capacity. Semiconductor fabs require 18-24 month lead times to bring new production capacity online, meaning shortages will persist well into 2027 even if new facilities begin construction immediately.
Smartphone manufacturers are warning that device prices will increase as memory costs rise due to AI demand. The same memory chips that power smartphones are essential for AI inference workloads, creating direct competition between consumer devices and data center deployments. With AI infrastructure projects willing to pay premium prices for immediate delivery, consumer electronics companies face difficult choices between accepting higher component costs or delaying product launches.
The resource diversion extends to promising innovations starved of investment funding. With venture capital flowing overwhelmingly toward AI infrastructure and foundation model companies—approximately 40% of global venture capital in 2025 went to AI startups—other technology sectors struggle to attract capital. Cleantech, biotech, and hardware innovation face funding droughts as investors redirect resources toward the AI boom.
The investigation highlights a fundamental tension in technology capital allocation. While AI infrastructure spending reached approximately $650-700 billion across major technology companies in 2026, that capital comes with opportunity costs throughout the economy. Electricians working on data centers cannot simultaneously build out electric vehicle charging infrastructure. Memory chips allocated to AI training cannot power consumer devices. Investment dollars committed to foundation models cannot fund renewable energy startups.
The broader economic impact remains difficult to quantify precisely, but economists interviewed by the Washington Post suggest the resource constraints could slow overall economic growth in sectors unrelated to AI. Construction delays affect commercial real estate development, residential housing, and industrial expansion. Consumer electronics price increases may dampen demand and slow technology adoption cycles. Reduced venture funding for non-AI sectors could limit innovation and job creation.
The findings raise questions about whether the current AI investment pace is sustainable or represents a misallocation of economic resources. While technology executives argue AI will generate enormous long-term productivity gains justifying current investments, the near-term economic costs include tangible constraints affecting consumers, businesses, and non-AI industries.
The Washington Post investigation provides crucial context for understanding AI economics beyond financial metrics reported by technology companies, documenting real-world consequences as the industry pursues infrastructure dominance.
