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US-China AI Chip Race and Export Controls

The thesis

US export controls on AI chips are failing to contain China's cost-advantaged AI buildout, threatening US AI dominance and pressuring US AI hardware equities while benefiting Chinese and select non-US AI-exposed names.

How the score is derived

25%

2 of 8 tested indicators currently match this thesis.

The figure is the share of tested indicators whose latest reading matches the thesis. It describes market data already published, and is not a projection of what happens next.

Indicators last read 2026-09-10

Indicators tested against this thesis

Each indicator was selected to test one part of the thesis. The reading is compared with what the thesis implies, and the outcome is recorded either way — indicators that do not match are kept on the page.

  • NVDA quote

    Reading differs from the thesis

    Thesis implies:
    falling
    Latest reading:
    223.67
    Why this tests the thesis:
    Thesis says US AI chip dominance erodes; NVDA is the canonical US AI hardware proxy.

    NVDA quote above its long SMA (223.67 vs 197.09)

  • AMD quote

    Reading differs from the thesis

    Thesis implies:
    falling
    Latest reading:
    521.10
    Why this tests the thesis:
    Second US AI chip name should also weaken if China cost advantage bites into demand/pricing.

    AMD quote above its long SMA (521.10 vs 343.86)

  • ASML quote

    Reading differs from the thesis

    Thesis implies:
    falling
    Latest reading:
    1729.52
    Why this tests the thesis:
    ASML lithography exposure to China restrictions and AI demand shifts tests the chip-supply leg.

    ASML quote above its long SMA (1729.52 vs 1487.79)

  • China large caps (FXI ETF)

    Reading differs from the thesis

    Thesis implies:
    rising
    Latest reading:
    34.55
    Why this tests the thesis:
    If China AI cost advantage is real, Chinese large caps should re-rate higher despite export curbs.

    China large caps (FXI ETF) below its long SMA (34.55 vs 36.66)

  • Nasdaq-100 (QQQ ETF)

    Reading differs from the thesis

    Thesis implies:
    falling
    Latest reading:
    716.31
    Why this tests the thesis:
    Nasdaq-100 is heavily AI-weighted; thesis implies multiple compression on US AI complex.

    Nasdaq-100 (QQQ ETF) above its long SMA (716.31 vs 658.90)

  • Developed ex-US equities (EFA ETF)

    Reading matches the thesis

    Thesis implies:
    rising
    Latest reading:
    106.56
    Why this tests the thesis:
    Capital rotating out of US AI into developed-ex-US (e.g., Europe, Korea) tests the 'where it goes' leg.

    Developed ex-US equities (EFA ETF) above its long SMA (106.56 vs 101.73)

  • 005930.KS quote

    Reading matches the thesis

    Thesis implies:
    rising
    Latest reading:
    269000.00
    Why this tests the thesis:
    Samsung is a non-China AI hardware beneficiary if China is contained and Korea fills the gap.

    005930.KS quote above its long SMA (269000.00 vs 215703.75)

  • VIX close

    Reading differs from the thesis

    Thesis implies:
    rising
    Latest reading:
    15.72
    Why this tests the thesis:
    Geopolitical/export-control uncertainty around AI chips should lift equity volatility.

    VIX close fell over ~180d (27.29 → 15.72)

Narratives in this cluster

Each narrative was grouped here because it makes the same underlying claim. Narratives recorded as counter-evidence are kept in the cluster and weighed against it.

  • US Export Control Enforcement on AI ChipsSame claim
  • China AI Cost Advantage Threatens US AI DominanceSame claim

These clusters were selected because their indicator plans overlap with this one: the same published market series are used to test both theses. The overlap is computed from the plans themselves, not from what the narratives say.

How a cluster is built

Narratives are collected daily from tracked public sources, compared by meaning, and grouped when they make the same underlying claim. A single thesis is distilled from each group, and a plan of published market indicators is selected to test it. Deterministic code then fetches each indicator and records whether the reading matches what the thesis implies.

This page describes what the model grouped and measured. It is information about market data, not a recommendation, and not personal advice.

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