AMD

Nvidiaโ€™s CEO has warned that U.S. export controls have effectively halted the companyโ€™s China business, sharpening the stakes for AI leadership, supply chains, and enterprise buyers. He indicated the company is modeling China sales at effectively zero for the next two quarters under current rules, acknowledging that the revenue loss
Nvidiaโ€™s latest quarter signals that AI infrastructure spending is not cooling and is, in fact, broadening across clouds, sovereigns, and enterprises. Nvidia delivered $57 billion in revenue for the quarter, up more than 60% year over year, with GAAP net income reaching $32 billion; the data center segment accounted for
SoftBank has exited Nvidia and is redirecting billions into AI platforms and infrastructure, signaling where it believes the next phase of value will concentrate. SoftBank sold its remaining 32.1 million Nvidia shares in October for approximately $5.83 billion, and also disclosed a separate $9.17 billion sale of T-Mobile US shares
Google has unveiled nextโ€‘generation TPU accelerators with up to a 4x performance boost and secured a multiyear Anthropic commitment reportedly worth billions, signaling a new phase in AI infrastructure competition. Google introduced new Tensor Processing Units that deliver roughly four times the performance of prior generations for training and inference
Qualcomm is moving from mobile NPUs into rack-scale AI infrastructure, positioning its AI200 (2026) and AI250 (2027) to challenge Nvidia/AMD on the economics of large-scale inference. The company is translating its Hexagon neural processing unit heritageโ€”refined across phones and PCsโ€”into data center accelerators tuned for inferencing, not training. AI200 and
SoftBank has reportedly approved the final $22.5 billion tranche of a planned $30 billion commitment to OpenAI, tied to the AI firmโ€™s shift to a conventional forโ€‘profit structure and a path to IPO. The investment completes a massive $41 billion financing round for OpenAI that began in April, making it
Appleโ€™s new M5 chip is a material step in local AI compute that will ripple into enterprise IT, developer tooling, and edge networking strategies. M5 is built on a thirdโ€‘generation 3โ€‘nanometer process and reworks Appleโ€™s GPU as the center of gravity for AI. The 10โ€‘core GPU adds a dedicated Neural
HUMAIN, a Saudi PIF-backed AI company, introduced Horizon Pro, an โ€œagentic AIโ€ PC built on Qualcommโ€™s Snapdragon X Elite, positioning it as a new class of Windows laptop where on-device AI drives workflows, decisions, and user interaction. At Qualcommโ€™s Snapdragon Summit in Maui, HUMAIN CEO Tareq Amin unveiled the Horizon
New analysis from Bain & Company puts a stark number on AIโ€™s economics: by 2030 the industry may face an $800 billion annual revenue shortfall against what it needs to fund compute growth. Bain estimates AI providers will require roughly $2 trillion in yearly revenue by 2030 to sustain data
The CPU roadmap is strategically important because AI clusters depend on balanced CPU-GPU ratios and fast data pipelines that keep accelerators fed and utilized. Even as GPUs carry training and inference, CPUs govern input pipelines, feature engineering, storage I/O, service meshes, and containerized microservices that wrap models in production. More
OpenAI and NVIDIA unveiled a multiโ€‘year plan to deploy 10 gigawatts of NVIDIA systems, marking one of the largest single commitments to AI compute to date. The partners outlined an ambition to stand up AI โ€œfactoriesโ€ totaling roughly 10GW of power, equating to several million GPUs across multiple sites and
Gartnerโ€™s latest outlook points to global AI spend hitting roughly $1.5 trillion in 2025 and exceeding $2 trillion in 2026, signaling a multi-year investment cycle that will reshape infrastructure, devices, and networks. This is not a short-lived hype curve; it is a capital plan. Hyperscalers are pouring money into data

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