London-based Applied Computing, which is developing AI models for energy operations, raised $20M led by KBR, with participation from Databricks Ventures

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Subquadratic: the LLM built for 12M-token reasoning — SubQ can reason across entire codebases and document sets in one pass with no RAG workarounds. Read how SubQ 1.1 Small holds near-perfect retrieval out to 12M tokens.

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Microsoft's Maia 300 swaps raw compute for a 2.5x memory bandwidth gain as it moves to make in-house silicon its main AI engine while claiming a 40% efficiency bump — could going the Google route be the pin that pops the Nvidia AI bubble?

* Microsoft will make Maia its main inference engine at multi-gigawatt scale, following Google and Amazon into in-house silicon * Microsoft's upcoming Maia 300 trades a modest 36% compute gain for 2.5x Maia 200's memory bandwidth, betting on memory-bound decode, with production in 2027 * Hyperscalers supply just