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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.

NVDA $4.2T
WHAT ARE WE DOING? “Contrarians At The Gate” 5 clicks

From X

@convequity 3 clicks

Convequity on X

$NET's AI hosting is limited by the physics of its edge footprint and by the role of its products. Carrier-hotel sites built for fast network traffic are a different proposition from purpose-built AI datacenters. Cloudflare’s own engineering disclosures acknowledge power constraints, and management distinguishes its strategy from the hyperscalers’ GPU spending race. Our assessment starts with a two-H100-per-server edge configuration. H100 PCIe and NVL-class cards operate around 350–400 watts per GPU, versus up to 700 watts for H100 SXM. Hyperscalers commonly pack eight SXM GPUs into a machine and connect those machines into dense clusters. The distinction is power, cooling, memory bandwidth and GPU interconnects — not simply whether a model can run. The existing edge footprint cannot acquire hyperscaler-class cooling and power density through server upgrades. Dense, liquid-cooled Blackwell and Vera Rubin systems require a different infrastructure commitment. We see little chance that NET’s existing edge footprint becomes a competitive home for those dense clusters. For sustained, large-model inference, our assessment is that sparse edge GPUs cost more per token than dense AI clusters. Geographic proximity saves network transit time, but it does not guarantee a faster first token or a cheaper response. Greater compute capacity, faster interconnects and efficient batching can outweigh the edge’s network advantage. We expect heavy training, frontier-scale hosting and the most demanding next-generation inference to remain concentrated with hyperscalers. Workers AI’s progress with larger open models is real. Hosting those models does not establish an economic advantage over dense clusters. AI Gateway also lets customers route requests to external providers, matching the model and infrastructure to the task. Cloudflare can still win substantially. More agents mean more requests to secure, more Workers executions, more orchestration and more demand for fine-grained access controls. Those workloads build on infrastructure and capabilities Cloudflare already has. On the SaaS Survival Scorecard, we score $NET at 2.5 for Model-Layer Leverage. That reflects its substantial work building an agent harness across inference, orchestration and execution, while stopping short of model post-training and reinforcement learning.
@convequity 2 clicks

Convequity on X

Anthropic's $518bn of planned compute obligations is about the same size as $AMZN's entire stack of debt, leases and purchase commitments. Convequity's ABBX barometer adds up borrowings, leases, purchase obligations and leases not yet started for each hyperscaler. On that all-in measure, AMZN and Anthropic are roughly level. 1. Against valuation, the number looks normal. At the $2 trillion valuation being debated, Anthropic's obligations are about a quarter of its value. That is close to $GOOGL's ratio and inside the range of the four big hyperscalers. 2. Against revenue, it is large but not extreme. Anthropic told investors its annualized run rate was above $65bn at the end of July. The obligations are about eight times that, spread over roughly a decade. A run rate is the latest month multiplied by twelve, not full-year booked revenue, and the hyperscalers fund their stacks from large, established cash flows. 3. Reuters reports that about 80% of the obligations are non-cancelable or payable regardless of usage, while many of Anthropic's largest customers are not on long-term contracts. I think the size is about right. It fits a $2 trillion company, and at the July pace Anthropic can grow into it. The risk is in the terms: most of the cost is locked in for years, while much of the revenue can leave at short notice. The number to watch now is how much of Anthropic's revenue is under long-term contract.

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