The infrastructure behind AI shapes its cost, speed and availability. Follow developments in semiconductors, cloud platforms, data centers, networking and energy. Our briefings distinguish announced capacity and investment plans from deployed systems, and explain how those changes could affect the people building and using AI.
AWS, Google Cloud and Azure reported strong growth for the September quarter. Amazon's cash-flow figures show the cost of the buildout, while each company's cloud metric covers a different business scope.
Oracle and Google plan to make Google TPUs available through Oracle Cloud Infrastructure beginning in 2027, making OCI the first hyperscaler outside Google to offer the accelerators.
NVIDIA and Nokia say T-Mobile completed an AI-RAN call on a live U.S. network while European operators advanced trials that combine 5G radio and AI workloads.
External key management and confidential virtual machines reach general availability, while other local-cloud and AI controls remain previews or future releases.
Vertiv says it completed a high-density direct-to-chip liquid-cooling deployment at CapitaLand India Trust’s CLDC Mumbai01 facility, supporting 30 MW of AI-ready capacity.
Microsoft and NVIDIA say RTX Spark systems are now available globally. NVIDIA advertises up to 1 PFLOP FP4 and 128GB memory; those are vendor specifications.
Microsoft says Maia 200 is already serving production workloads and plans multi-gigawatt Azure Maia deployments beginning in 2027, with Maia 300 production deployment also planned for that year.
The new partner-built configuration keeps the GB10 Grace Blackwell platform, supports models up to 100 billion parameters, and can link two systems for 128GB of pooled memory.
AWS CEO Matt Garman says the five-year Built Together program will fund education, workforce and energy-efficiency projects while Amazon adopts new transparency and operating commitments.
CoreWeave made its Forge AI development platform generally available and said Cognition is the first production customer using NVIDIA Vera Rubin NVL72 systems on its cloud.
The companies announced a Cerebras-powered Gimlet Cloud deployment targeting up to 3,000 tokens per second; the first datacenter remains a forward-looking plan.
Mistral announced a Munich industrial AI hub and collaborations with German industry and research partners, alongside a company target to build one gigawatt of European compute by 2030.
Samsung and affiliates pledged a combined $1 billion to Helix Digital Infrastructure, backing a planned buildout of AI data centers and supporting power and network assets.
Google DeepMind described a planned persistent-memory layer for Private AI Compute, using encrypted storage and device-held keys to support cross-device context.
NVIDIA launched DSX Ready to qualify battery-storage and liquid-cooling products for AI factories, while stressing that product qualification does not replace site-level engineering.
CXMT says its fifth-generation DRAM platform and two 24Gb LPDDR5X products have entered mass production, while yield, output and customer adoption remain undisclosed.
INNIO announced a planned 450 MW Jenbacher order for U.S. data-center projects, highlighting behind-the-meter power as an infrastructure response while sites and operating results remain undisclosed.
Oracle says fiscal Q1 cloud-infrastructure revenue rose 121% to $7.4 billion as it added 850 megawatts of data-center capacity and delivered more than 300,000 GPUs.
Texas Governor Greg Abbott ordered a PUCT/ERCOT audit before data-center projects move through the grid interconnection process; the Financial Times reported on September 13 that the pause is now braking new approvals.
d-Matrix says its next-generation Raptor inference XPUs will plug into NVIDIA’s NVLink Fusion and MGX rack architecture, with initial availability targeted for Q4 2027.
NVIDIA says Australian cloud and infrastructure partners are expanding land, power, cooling, and shell capacity for up to 2 gigawatts of DSX AI-factory buildout by 2027.
OpenAI has detailed how Habitat, the online storage layer behind ChatGPT, the API, Codex, and internal services, grew from a small Python library into a distributed platform handling more than 70 million requests per second and more than 500 petabytes.