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The article analyzes US grid capacity constraints and models the shortfall that must be filled by behind-the-meter power solutions for datacenters, projecting over 40GW by 2028.
Amazon has a leading position in data center capacity and power for AI, but Google is expected to close the gap by 2030.
Discusses the cracking of nuclear regulatory barriers, driven by hyperscalers' demand for energy, and highlights key thinkers on the shift.
This report from Multiples.vc provides public AI valuation multiples as of June 2026, covering hyperscalers, semiconductor supply chain, neoclouds, and other segments with median forward revenue multiples and growth rates.
An analysis of declining token prices for AI models despite new releases like GLM 5.2 and Kimi 2.7, suggesting possible diminishing returns from expensive models.
An academic study presented at the Americas Conference on Information Systems maps five systemic tensions from AI's data-centre boom, including energy paradox, water strain, hyperscaler dominance, sovereignty erosion and urban displacement, highlighting the growing environmental and social costs.
Cisco is repositioning itself as a core AI infrastructure supplier, reporting strong hyperscaler orders and a broadening market beyond GPUs to networking, security, and observability. The company raised its FY26 hyperscaler AI infrastructure order expectation to ~$9B and highlighted Silicon One as a critical differentiator.
Michael Burry warns that the AI boom may be built on temporary demand from hyperscalers training models, creating a 'bullwhip effect' that could lead to severe oversupply and a sharp correction for Nvidia.
Gulf states' AI ambitions are threatened by their heavy reliance on a few vulnerable undersea cables, risking disruptions to data flows and economic transformation.
The article argues that AI is too expensive to be economically viable for most companies, with hyperscalers spending trillions on data centers but failing to generate proportionate AI revenue. It suggests only hardware suppliers like NVIDIA benefit from the current AI bubble.
The article argues that the high capital expenditure, power infrastructure, and GPU costs make AI development economically unsustainable for all but the largest hyperscalers like Google, Microsoft, Amazon, and Meta.
a16z's Charts of the Week highlights a surge in Codex installs and analyzes how hyperscalers like Amazon and Google are generating massive 'other income' through tech investments, notably in Anthropic.
Andy Masley pushes back against the argument that data center construction is causing farmland loss, citing data that farmers have sold large amounts of land historically without affecting food access.