About
AI Gap Clock is a public, sourced scoreboard of measurable US–China AI capacity — infrastructure and research volume a reader can check against named publications. It is not an oracle for “who is winning AI.”
How the lanes connect. Chips, power, and memory are infrastructure — who can build and feed accelerators. Papers, notable models, and Arena are research and published capability. Government packages are policy fuel, not a yearly budget. The lanes inform one another; they do not sum to a score.
The board is published as AI Gap Clock, a public scoreboard. There is no institute behind it, and no methodology paper to cite. The sources are the publications named on each tile.
Methods
The digits tick the way a debt clock ticks: they interpolate between published source prints. That motion is display only. It is not live, not real-time, and not a new number.
Primary is the checkable board: Measured counts and Official estimates a reader can open and verify. Estimates is the quieter row — modeled reconstructions, or government packages that are hard to compare. Do not subtract papers from models. There is no unitless hero gap.
Notable-model counts are Epoch AI’s family as printed by the Stanford AI Index — same lane, two hats. The Index reports Epoch’s bar; Epoch curates the family. That is not a second independent census.
The frontier performance tile is the 2026 Index Technical Performance chapter’s Arena Elo gap (March 2026: 2.7%), not the R&D chapter’s paper and notable-model counts. End-2023 gaps of 17.5–31.6 points on MMLU / MMMU / MATH / HumanEval are a different yardstick from the 2025 Index.
Every figure names a public primary and an as-of date. The board draws on Epoch AI, Counterpoint, the IEA, the Stanford AI Index, NIST / CHIPS, and the other publications listed on Sources. If a figure cannot name a primary and a date, it stays off the board or is marked EXAMPLE.
Social posts, forums, blog rankings, and Wikipedia-only cites are not used.