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Wednesday, November 11, 2026

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ON THE TAPE with Guy Adami

Guy Adami discusses Nvidia, Anthropic's IPO prospects, energy, gold and the AI trade.

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Circular AI financing keeps getting bigger. A record $63 billion vehicle, partly supported by Broadcom ($AVGO), will buy Broadcom chips for Anthropic, and it only pays off if enterprises adopt AI beyond coding. The vehicle borrows, buys Broadcom XPU accelerators (custom AI chips) and networking gear, and leases them to Anthropic. Deliveries are expected from 2027. Broadcom guarantees a residual value of $40B+. This is the latest example of the banks, hyperscalers, model labs and chipmakers are frontrunning AI demand they expect to be insatiable. Without faster enterprise adoption, that demand may not arrive, and the financing stays circular. AI has to spread from coding into the rest of the enterprise. Coding can be checked, because code has to compile and pass tests. Other domains need the same verifiability, which means codifying context that lives in employees' heads, or at best across email, Slack, WhatsApp and work-management platforms. Vendors that can codify that context should extract immense value. Palantir ($PLTR) is the closest so far. Palantir's ontology, a structured account of an enterprise's objects, relationships and rules that software can use to check a result, is critical because it is instrumental to verifiability. We think it can make an average Nemotron model state of the art for that enterprise's own work. If the result is good enough, enterprises will use it and achieve sovereign AI. Once that context is codified, enterprises will not want to blindly run closed frontier models on it and give their know-how away. Broader adoption depends on data sovereignty. The shift has been visible since Palantir's Alex Karp warned earlier this year that handing closed frontier labs their data, models and institutional know-how means losing control of the value. The path is intelligence sovereignty plus more verifiability, then greater enterprise adoption, which would justify this frontrunning. It would also benefit Nvidia ($NVDA), which needs enterprise adoption because hyperscalers and model labs are leaning toward custom chips.

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