Jim Cramer’s argument is that rising borrowing costs are creating a divide: businesses and customers that rely on credit face more pressure, while lenders remain comparatively willing to finance AI and data-center projects. That is Cramer’s interpretation of the market—not proof that AI stocks are insulated from interest rates or that every AI-linked company has an easier path to financing.
What Cramer says is dividing the market
In commentary attributed to CNBC’s “Mad Money” host Jim Cramer and dated October 7, 2026, he argued that higher rates make stocks harder to own when investors must account for new uncertainty around government borrowing costs. A KhanList reproduction of the CNBC article reports that a $39 billion 10-year Treasury note auction took place that day and that the 10-year yield briefly reached 5.365%, described as its highest level since April 2002. Those figures are reported claims in the reproduction; they have not been independently verified here. KhanList’s reproduction of the CNBC article identifies CNBC as the original publisher.
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Cramer’s broader point is about financing exposure: higher rates can matter more to companies that need to borrow or whose customers depend on credit. He contrasted those businesses with AI-related projects that, in his view, still attract lenders. The source does not provide sector-wide measurements or comparable borrowing terms to establish how large that difference is.
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Which businesses Cramer sees as more exposed to credit
The reproduced article says Cramer named finance, housing, utilities, entertainment, retail, autos, and industrials as areas vulnerable to borrowing costs because the businesses themselves, their customers, or both rely on credit. The exposure can take different forms: a company may need to refinance debt, finance expansion, or sell goods and services to customers whose purchases depend on loans.
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That list is Cramer’s market framing, not a measured ranking of rate sensitivity. Individual firms within any of these sectors can have different debt loads, financing arrangements, customer bases, and ability to pass on costs.
Why he believes AI and data-center companies have an advantage
Cramer’s favored side includes data-center builders, semiconductor companies, power providers, and cybersecurity firms. His argument, as reproduced, is that the scale of expected AI demand makes lenders willing to fund some projects even when borrowing costs are rising. He said these businesses “seem to be able to borrow at their leisure,” and argued that data-center stocks—“aside from maybe Oracle”—were less tied to the rate at which the federal government borrows.
That is an account of perceived lender appetite and investor expectations, not evidence that AI companies can borrow without regard to rates. Higher financing costs can still affect project economics, debt service, and valuations; the source does not establish that AI-linked companies are immune or provide loan terms that would demonstrate a financing advantage across the sector.
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The SpaceX and Skydance examples—and what they do not prove
The reproduction says SpaceX was reportedly considering borrowing $40 billion to buy Nvidia chips for data centers, attributing the report to the Financial Times. It contrasts that financing story with Skydance-related debt whose bonds it says fell quickly. Neither the SpaceX plan nor the bond-price claim was independently checked against primary reporting or market records here.
Even if accurate, these examples would illustrate particular borrowers and transactions, not establish a general rule that AI-linked firms receive better terms than companies in other industries. The reproduced article does not provide comparable interest rates, bond yields, credit ratings, or deal terms for the examples.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read the “two markets” claim
Cramer’s contrast is most useful as a lens for asking which companies may feel tighter financial conditions—not as a clean division between winners and losers. A company’s exposure depends on both its own financing needs and whether its customers can keep spending when credit gets more expensive. The article offers no systematic sector comparison, so its examples should be treated as commentary rather than a measured forecast.
Quick Recap
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- Borrowing dependence: Does the business need to raise or refinance debt to operate or expand?
- Customer credit exposure: Do customers rely on loans or financing to buy its products or services?
- Lender appetite: Is there evidence about actual financing availability and terms, rather than an assumption based on an industry label?
- Project economics: Can expected demand support investment costs if financing becomes more expensive?
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