The China AI Disruption Thesis: Why the Sell-Side Is Six Months Late

Djellal Djouad

MPRA Paper from University Library of Munich, Germany

Abstract: This paper presents a structural derivatives framework for the repricing of the US AI infrastructure buildout. We formalize a nine-element analytical framework - five operational vectors and four geopolitical fronts - that converges on a 25-40% re-rating of pure-play AI infrastructure equities by Q1 2027. The five vectors are: (1) token commoditization, documented by the DeepSeek V4 Pro permanent pricing at 1/29th of frontier US output pricing; (2) Chinese hardware cost parity, with Huawei Ascend 910C achieving a 2.0-2.3x cost-per-performance advantage over NVIDIA H200 on inference workloads; (3) the US power grid bottleneck, with PJM 2026/2027 capacity auction clearing at $329.17/MW-day; (4) China's parallel energy buildout, with 12-24 month project timelines versus 4-7 years in the US; and (5) the hyperscaler bond wall, with $230-240B forecast 2026 issuance. We construct three trade families - equity dispersion, credit expression, and cross-asset hedges - and present an eleven-catalyst falsification calendar with probability-weighted thresholds. Working paper adapted from: CrossVol Research (2026). The China AI Disruption Thesis: Why the Sell-Side Is Six Months Late. Amazon Kindle, ASIN: B0H11WH3R9. https://www.amazon.com/dp/B0H11WH3R9

Keywords: AI infrastructure; compute commoditization; DeepSeek; hyperscaler bond wall; PJM capacity auction; Chinese hardware parity; semiconductor dispersion; CDS basis; grid constraint; geopolitical risk premium (search for similar items in EconPapers)
JEL-codes: F3 F61 F62 F65 G10 G13 G14 G15 G17 (search for similar items in EconPapers)
Date: 2026-06
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