Nvidia

Nvidia announced on Wednesday, October 14, a five-year donation of computing resources valued at 250 million dollars to strengthen research in the United Kingdom into the safety and security of agentic AI.

For years, Nvidia's grip on China's data center AI chip market looked unshakeable. New figures from research firm IDC, reported by Reuters on October 13, show how quickly that position is eroding. Nvidia's share of the market fell to 34% in the first half of 2026, while Huawei climbed to 28% — close enough that the race for China's AI hardware business now looks genuinely competitive.

Microsoft has made its biggest move yet to run its artificial intelligence services on hardware of its own design. On Monday the company unveiled Maia 300, the third generation of its custom AI chip, and said it plans to deploy potentially more than a million of them starting next year in multi-gigawatt installations. Chief Executive Satya Nadella described the rollout as the company's biggest bet yet on its own silicon, and Azure executive Scott Guthrie said Maia is intended to become the primary system for running AI inference across Microsoft's products and services.

Washington just put AI companies to work for science — on an unprecedented scale.

At the White House's "Science: A New Golden Age" summit on October 8, the Trump administration announced that eleven technology companies had committed $2.4 billion in AI tools and compute credits to the Genesis Mission, the federal initiative using AI to accelerate scientific discovery across energy, health, space, and national security. The pledges are part of a broader package the administration billed as a $6 billion-plus push for U.S.-led innovation.

Nvidia is circling the open-source coding world — and it already owns a seat at the table.

CoreWeave built its reputation selling raw AI compute. Now it wants to sell the software layer on top of it. At its Fully Connected conference, the company unveiled CoreWeave Forge, a development platform that folds model serving, observability, post-training, and evaluation into a single environment — and it comes with a striking performance claim: a preview feature called RL Rollouts that improved model reload latency by 15x in testing.