A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.
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The announcements, made at a live event in San Francisco titled "What's Next with AWS," landed just 24 hours after OpenAI and Microsoft publicly restructured their exclusive cloud partnership — a move that, for the first time, freed OpenAI to distribute all of its products across rival cloud providers. AWS CEO Matt Garman called it "a huge partnership" and said customers have been asking for OpenAI models inside AWS "from the very early days."
The amended agreement, disclosed simultaneously in blog posts from both companies, marks the most significant restructuring since Microsoft first invested $1 billion in OpenAI in 2019 — and it transforms what was once the most consequential exclusive technology alliance in a generation into something that more closely resembles a strategic but arm's-length commercial relationship.
Traditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI is stochastic and unpredictable. The exact same prompt often yields different results on Monday versus Tuesday, breaking the traditional unit testing that engineers know and love.
The most expensive AI failure I have seen in enterprise deployments did not produce an error. No alert fired. No dashboard turned red. The system was fully operational, it was just consistently, confidently wrong. That is the reliability gap. And it is the problem most enterprise AI programs are not built to catch.
The offering packages Gemini into a Dell-manufactured, Google-certified hardware appliance equipped with eight Nvidia GPUs and wrapped in confidential computing protections. Enterprises and government agencies can deploy the system inside Cirrascale's data centers or their own facilities, fully disconnected from the internet and from Google's cloud infrastructure. The product enters preview immediately, with general availability expected in June or July.
Enterprise AI is entering a new phase — one where the central question is no longer what can be built, but how to make the most of our AI investment.
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