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Tuesday, November 10, 2026

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Fajasy on X

The companies spending the most on AI are paying for more of it with debt. To understand whether debt-funded spending like this is indicative of a bubble, we can look at William Quinn and John Turner’s bubble triangle, which says a bubble needs three sides: marketability (how easily an asset trades), money and credit, and speculation. The AI boom arguably has all three sides: → Marketability: Index funds held 52% of U.S. long-term fund assets at the end of 2025, up from 19% in 2010, and they buy each stock in proportion to its size, so the biggest AI stocks get the most of every new dollar. → Money and credit: $MSFT, $GOOGL, $AMZN and $META are on track to spend more than $730B on capex in 2026, up from $410B in 2025, and their Q3 reports didn’t change that. $AMZN still expects ~$220B, and $META narrowed its range to $135-145B. Investors are borrowing more too, with margin debt at $1.48T in September, just below June’s record $1.50T. → Speculation: AI startups raised 82.7% of the money U.S. venture capital firms invested in 2026 through September, a record share, up from 65.4% in 2025. So more of the private money is betting on a single theme. $META’s also raising money by selling stock. Its October 27 pricing term sheet shows it sold 40M shares at $625 through forward sale agreements, plus $12.5B of mandatory convertible preferred stock, which together bring in $37.4B of net proceeds once the forward sales settle by November 2027.
@convequity

Convequity on X

I think some bears underestimate $PLTR's model expertise. We have argued that Palantir had more model expertise than it had shown publicly, and that lacking its own foundation model did not put it at the competitive disadvantage those bears assumed. The earlier NVIDIA partnership, which included NeMo Retriever and Nemotron, was my first clue. I see the public Nemotron results as evidence of that expertise. These results fit our argument and were what I expected. Palantir's slide on McCarthy's construction requests for information shows a tuned Nemotron model outperforming Sonnet and Astra on classification. FourWeekMBA reads the chart, which has no data labels, as approximately 73% balanced accuracy for tuned Nemotron, approximately 66% for Sonnet and approximately 62% for Astra. On a cost basis the slide leaves unexplained, FourWeekMBA places Nemotron at approximately $0.10 per million tokens. The slide leaves open whether that includes hosting, tuning and Palantir's software fees. I would want the full customer cost comparison to judge the savings. I still regard Nemotron as a weak starting model. If Western developers make state-of-the-art open-source models available, I expect Palantir to use customer data, task-specific benchmarks and tuning to deliver further improvements in performance and cost. That could make Palantir economical for smaller enterprises as well as very large ones. Savings on model usage could cover Palantir's software premium if customers can move enough work to cheaper tuned models while maintaining the quality they need. As customers spend more on tokens relative to software, the potential savings grow. I expect Palantir's model expertise to become more valuable because it could reduce a larger part of the customer's total bill.

Investor Letters

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Quick Value #337 - Dana Inc (DAN)

Dana is combining with Eaton’s Mobility unit via a 1Q27 Reverse Morris Trust after selling a division, delevering and accelerating buybacks.

ETN $141B
Value Don't Lie 2 clicks

Roblox: Capture This Moment

Roblox’s Q3 FY26 DAUs fell year over year for the first time, while bookings declined 12% to $1.7 billion.

RBLX $35B
TSOH Investment Research 1 click

DoorDash and Google

DoorDash and Google.

DASH $79B
Stratechery by Ben Thompson – On the business, strategy, and impact of technology. 4 clicks

NEW POSITION: Google LEAPS

Alphabet bought long-dated GOOGL calls after a 12.6% earnings drop, betting AI monetization and distribution overcome capex pressure and…

GOOGL $3.5T
Hated Moats 1 click

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