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Sunday, November 15, 2026

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Compute and infrastructure expenses are expected to reach ~$13.0bn in Q4 as the $SPCX contract ramps. Meanwhile, revenue is expected to reach $18.6–20.3bn in Q4, up ~10–20% QoQ, with the competitive environment and holiday seasonality as potential headwinds. That puts revenue per compute dollar at ~1.5x. Meanwhile, roughly $80/$120bn of payments are against disclosed commitments in 2027/28 that help underwrite the entire global AI trade. The harder question is how quickly and profitably can API usage grow next year? Coding has been a clear winner. But sustaining growth requires either deeper usage within the industry and/or broader deployment across other high knowledge-intensive industries (finance, healthcare, legal). For the former, a ceiling is emerging, especially when sophisticated clients choose to train their own models to avoid the closed-source tax (e.g., Palantir). For the latter, those opportunities are large but much harder to capture, as turning them into reliable production systems requires data integration, domain-specific knowledge, corporate and regulatory approvals, and sometimes an entire organizational workflow redesign. Anthropic’s own $100m program to train 10,000 deployment engineers illustrates the work still required. However, wherever you look, the most sophisticated industry players choose to train their own models, e.g., Harvey AI using Kimi. Given this dynamic, it's not hard to paint a scenario where the“revenue per dollar of compute” metric continues to decline further in 2027 as industry capacity ramps, and hence one can see why the market is becoming extremely skeptical of the entire AI trade, given the labs are essentially underwriting the entire semiconductor industry at this stage. Where am I underestimating the upside: usage intensity (AGI? Humanoid Robots?), deployment speed in legacy industries? Or the entire cost curve?

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