Math, not Frankenstein architecture - why Alteryx Inspire 2026 drew a hard line on what Large Language Models cannot do

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Enterprise AI demos have settled into a particular shape. Someone in a headset stands on a stage. A slide appears. Seven boxes are connected by arrows. An LLM sits in the middle, a customer data icon on the left, governance somewhere on the right, and a dashboard or Slack message on the far right. Someone says "end-to-end." Someone else says "agentic." You write it all down. You also know perfectly well that the integration work between those seven boxes will cost more than the entire product license.

This is the Frankenstein architecture. Stitched together from whatever the vendor needs the audience to believe. It is the default at most analyst events because, in those rooms, almost nobody is going to ask to drive.

During the Media, Influencer and Analyst Summit at Alteryx Inspire in Orlando, the company set us up in groups, each with a member of the product team...

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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