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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” 4 clicks

From X

@StableBread

Fajasy on X

35% of TOYO Co., Ltd’s $TOYO H1 2026 revenue came from one customer it doesn’t name, and 22% came from companies under its own controlling shareholder. $TOYO manufactures solar cells, the processed silicon wafers that turn sunlight into electricity, and assembles some of them into panels (modules). Its cells use TOPCon (tunnel oxide passivated contact), a common high-efficiency silicon cell design. $TOYO runs three factories: 1) Ethiopia (4 GW of cells): Started production in April 2025, six months after $TOYO announced the plant, added a second 2 GW line in August, and reached its 4 GW nameplate in October 2025. 2) Vietnam (2 GW of cells): Running since H2 2023, but its cells now go to non-U.S. markets like India and Taiwan, since the U.S. Commerce Department set anti-dumping and countervailing duties (taxes on imports sold below cost or subsidized) on Vietnamese cells in April 2025. 3) Texas (2 GW of modules): A leased 567,140-square-foot plant near Houston that started its first 1 GW line in October 2025 and had completed its second by early October 2026. It assembles panels from $TOYO’s Ethiopian cells. VSUN (Vietnam Sunergy JSC) is the Vietnamese panel maker $TOYO was carved out of, and both companies sit under the same majority shareholder, Abalance, a Japanese solar group. VSUN is still $TOYO’s lender, owed $51.4M including interest, and a past wafer supplier through its Chinese subsidiary. $TOYO’s $261.0M of H1 2026 revenue came from four lines: → Third-party cells (56.5%): $147.6M of cells sold to outside module makers. → Related-party sales (22.0%): $57.5M of cells and modules sold to VSUN and its affiliates. → OEM services (14.2%): $37.2M of fees for cells made under one customer’s name, which $TOYO books net, counting only its fee as revenue. → Third-party modules (7.2%): $18.7M of Texas-made panels. Upstream, 70% of the polysilicon (the refined silicon that wafers are cut from) in $TOYO’s Ethiopian cells comes from a U.S. producer and the rest from OCI’s plant in Malaysia, Rhone Resch, $TOYO’s Chief Strategy Officer, said on the Q2 call. Resch also said its 2026 wafers come from non-China production, “including a designated facility in Indonesia.” $TOYO markets its supply chain as “non-FEOC,” free of any foreign entity of concern. U.S. buyers care, since the One Big Beautiful Bill Act (OBBBA) denies the main clean-power tax credit to projects with too much equipment from Prohibited Foreign Entities, a label aimed at Chinese suppliers.
@SouthernValue95 1 click

SouthernValue on X

Ramp data shows VC-backed companies are spending 10x more per employee/month on AI vs. PE-owned and other companies. A bearish interpretation could be: token spend is being driven by VC-backed companies, making demand vulnerable to swings in capital cycle. Valid! However, on a longer timeframe, I think this is quite interesting as a leading indicator for the direction of software companies (VC-backed companies are mostly software or service-as-software companies). New companies are being built with a larger ratio of tokens vs. humans. Look at Anthropic itself, which pays 1/6 as much on intelligence as its customers (since it sells tokens marked up at 85% GM): Anthropic is spending ~9x more on compute than human salaries despite having access to tokens at cost. Cash payroll is annualizing ~$2B for a company with ~$60B of run-rate revenue in 1H26. Anthropic employs just 5k people. Wild efficiency in human capital. I suspect that the great software companies of the next decade, both new and incumbent, will insert AI into every aspect of their products so that using them feels like magic, and work happens seamlessly. Building a presentation in Claude feels like magic: I tell it what I want to put on a slide, and it appears like magic. As a user of Ramp, their products feel the same way: I make a purchase, take a picture of a receipt, and the expense is logged into my accounting system appropriately without the need for a human to type into a blank box. I think we will see most software become AI-native or be replaced by software that is. This is a significant opportunity for incumbents and startups alike. And I think this is a huge source of future token spend that will go to $AMZN and $GOOG where most software companies are built (on AWS and GCP) and where AMZN and GOOG are building the platforms for software companies to build AI capabilities into their products with choice around compute and model, embedded safety and governance, and unlimited scale. Amazon CEO @ajassy said on a podcast this week that AI spend is currently a "barbell-shape" with insatiable compute appetite of the AI labs on one side, and corporates using AI for cost savings on the other. In the middle is tokens embedded into "core workloads" which he says is growing but small today, but he thinks in 5 years might be the biggest category of token spend.
@convequity 1 click

Convequity on X

We think AI power users are overestimating how far enterprise adoption has progressed. Anthropic’s S-1 says over half the Fortune 500 were $100k+ annual customers as of September. Among those publicly traded customers, Anthropic spending averaged under 0.1% of total expenses in Q3 2026. That measures spending on one provider, so it cannot establish whether their AI deployments are succeeding. But the expectation gap is familiar. When you spend all day using the best models, it is easy to assume the rest of the economy is close behind. Building and running an enterprise’s own AI applications is a much harder undertaking. In Part 2 of our recent $NVDA report, we argued that successful adoption remains concentrated in a few exceptionally capable organisations, with Palantir ($PLTR) and coding agents the clearest examples. For Nvidia, adoption through clouds and model providers still leaves compute demand concentrated in intermediaries with the scale and incentive to develop their own chips. It needs more enterprises buying its compute directly. This is why we continue to see Palantir as a strategic enabler. The ability to help enterprises put AI into production remains scarce, even as access to capable models spreads. From Convequity’s “Nvidia at 16x Earnings: Bargain or Value Trap?” (Pt.2), 20 Oct 2026.

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SE $48B
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