Analysis on the infrastructure, engineering, and economics behind modern technology and AI.
Why AI data centers are switching to liquid cooling: how rack power rose from 8 kW to 120 kW, why water moves heat so much better than air, and how direct-to-chip and immersion cooling compare on density and cost.
HBM explained: how stacked high-bandwidth memory works, where figures like 819 GB/s and 1.2 TB/s come from, what the 18.75× HBM-vs-DDR5 comparison really measures, and why memory limits AI hardware.
Understand HBM memory, TSMC CoWoS advanced packaging, and why making compute dies is only one step in delivering Nvidia AI accelerators.
AI cloud specialists sell access to clusters of Nvidia GPUs. CoreWeave shows how long-term capacity contracts can support rapid growth while creating financing, customer, and hardware risks.
ASML's extreme ultraviolet lithography systems turn chip designs into silicon. How the $400 million High NA generation works, why chipmakers consider it, and where the bottleneck lies.
AI data centers depend on transformers, switchgear, liquid cooling, optical links, power electronics, and microgrids. These industrial suppliers may determine how quickly new computing capacity becomes usable.
How do data centers earn money? Operators sell power, space and connections on contracts — Equinix earns over 90% of revenue as recurring fees. See how colocation, hyperscale leases, utilization and financing decide profit.
Microsoft, Google, Amazon, and Meta are moving beyond buying electricity. Their nuclear, geothermal, grid, and power-development deals reveal how energy is becoming a strategic part of the AI business.
Why do AI data centers need so much power? Explore GPU electricity demand, cooling overhead, grid bottlenecks, and a worked electricity-cost example.
AI looks like software, but the machine behind it is physical: chips, servers, networks, cooling, power, land, and capital. A look at who actually profits from the AI infrastructure buildout.