Meta and Microsoft are cutting employee use of Anthropic’s Claude AI and directing staff toward their own coding tools, The Information reported. The changes reflect tighter internal AI budgets while customer access to Claude through Microsoft platforms continues to expand.

Wrong: stated The Information's report on Meta and Microsoft cutting internal Claude use as confirmed fact, said Microsoft steered staff to "OpenAI frameworks," and listed the story under Microsoft's October 7 event. Right: the figures come from The Information's October 5 report, which neither company has publicly confirmed; staff were steered to tools such as GitHub Copilot and OpenAI's models; the story is not part of the event coverage.
Neither company is ending customer access to Claude through Microsoft platforms.
The move to new coding assistants does not remove risks tied to automated access to sensitive files and credentials.
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Microsoft has introduced a local edition of MAI-Code-1.1-Flash that runs on Windows PCs without server access. The model requires substantial memory and is coming to GitHub Copilot in experimental preview by the end of October, starting with Nvidia RTX Spark PCs.
At its Windows and Surface event, Microsoft opened preorders for the $2,599 Surface Laptop Ultra and the $5,999 Surface RTX Spark Dev Box, and said Copilot will be able to use files and act across Windows in the coming months.
NVIDIA and Microsoft opened RTX Spark preorders and made Microsoft Execution Containers generally available at the Windows AI and Surface event, aimed at running AI agents on Windows PCs. RTX Spark laptops, first introduced in June, are on preorder today with up to 128 GB unified memory and one petaflop of AI compute, shipping October 16.
Microsoft says Copilot on Copilot+ PCs will be able to use files and recent activity on your PC, take actions across Windows, and tap local AI models, with your permission. The features are expected to start rolling out in the coming months.
Microsoft demonstrated hybrid intelligence on Windows that runs AI models from 70 billion to 284 billion parameters locally. The approach keeps data on-device, supports offline use, and switches to cloud models for heavier tasks.