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AI Infrastructure
AI infrastructure is capital-intensive, geopolitically sensitive, and increasingly constrained. Credence Wire covers hyperscaler investments, chip ecosystems, energy demands, and the buildout race shaping who can train and deploy at scale.
Edited by Sara Chen, Tomasz Kowalski, James Okafor
Updated June 7, 2026
Latest AI Infrastructure Stories
Latest AI Infrastructure Coverage
Wall Street Demands ROI Proof as Hyperscaler Capex Tops $452 Billion
Alphabet, Amazon, Microsoft, and Meta are on track to spend nearly half a trillion dollars on AI infrastructure in 2026 while software multiples compress.
Meta Explores Leasing Excess AI Compute as Capex Scrutiny Intensifies
Executives discussed selling reserved GPU capacity to enterprise customers after internal forecasts showed utilization gaps in several U.S. regions.
Hyperscalers Back Geothermal AI Compute Campuses to Escape Grid Bottlenecks
Fervo Energy and Crusoe commissioned a 50-megawatt Nevada plant routing enhanced geothermal power directly to GPU clusters.
Fervo Energy and Crusoe Launch Geothermal-Powered AI Compute Pilot in Nevada
A 50-megawatt campus will route enhanced geothermal electricity directly to GPU clusters, testing whether firm clean power can undercut grid-constrained sites.
OpenAI and Broadcom Unveil Jalapeño Chip Built for Large-Scale LLM Inference
The custom ASIC, developed in nine months with OpenAI roadmap input, targets better performance per watt than general-purpose accelerators in data-center serving workloads.
IBM Unveils Sub-1 Nanometer Chip Technology With Nanostack 3D Architecture
A 0.7-nanometer node prototype packs nearly 100 billion transistors on a fingernail-sized die, with production possible within five years.
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Frequently Asked Questions
How much are hyperscalers spending on AI infrastructure?+
Microsoft, Google, Amazon, and Meta collectively announced over $300 billion in capital expenditure for 2025, with AI data centers and GPU clusters representing the largest share of incremental spend.
What is the energy challenge for AI data centers?+
Training large models and running inference at scale requires enormous electricity — a single large training run can consume gigawatt-hours. Data center operators are competing for power grid access and exploring nuclear, solar, and on-site generation.
Should enterprises use cloud or on-premise AI infrastructure?+
Cloud offers flexibility and access to the latest hardware without capital commitment; on-premise suits organizations with strict data residency requirements or predictable, high-volume workloads where reserved capacity is more cost-effective.
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