Compute Isn't the Constraint argued that the UK's sovereign AI fund is buying the wrong input. That post was prose. This one is the numbers behind it, drawn so you can check them, with the source data under every chart as CSV. The sources are at the end.
Who publishes
Share of global AI publications by fractional count, OpenAlex via OECD.AI. China's dip between 2010 and 2015 is in the source data.
The United States and the EU together produced over half of the world's AI papers in 2000. They produce under a quarter now. Volume moved east and it is not coming back.
Who gets cited
Papers in the year's 100 most-cited AI papers, by country of the authors' institutions, Zeta Alpha. The UK figure is only available for 2022.
Volume and influence are different things. The US has never had fewer than 46 of the hundred most-cited papers in a year, and in 2022 had 68. The UK's 16 in 2022 is a respectable third place, and 11 of those 16 came from one laboratory, Google DeepMind.
Where the people are trained, and where they work
Elite AI researchers, defined as authors at NeurIPS, ICML and ICLR, by country of undergraduate degree and country of current employer. MacroPolo Global AI Talent Tracker 3.0.
The American lead is imported. US universities produce a quarter of the world's top AI researchers and US institutions employ six in ten of them. China produces the largest share of anyone and keeps about a tenth.
Which way they move
Net flow of elite AI researchers, employer country minus undergraduate country. Same source as above.
The UK is on the right side of this chart by 92 people. It is the smallest positive number that still counts as a win, and the 65% retention rate behind it is the figure a fund should be trying to move.
What the money looks like
Private AI investment in 2025, Stanford AI Index 2026. The Europe figure covers generative AI only and is the comparable number the Index reports.
This is the gravity. A lab funded from a £500m pot competes for people against an industry that raised two hundred and eighty-six billion dollars last year. The researchers it trains are worth more to the companies on the left of the chart than to the state that paid for them.
How far behind the published frontier sits
Lag between the best open-weight models and the closed frontier, by how you measure it. Epoch AI, 2025 and 2026.
Pick the measure that flatters you and open weights are four months behind. Pick the one you cannot game and it is closer to ten. Measure by the compute actually deployed and it is sixteen. A team that trains on the public literature is a generation behind by construction, which is the whole argument of the earlier post in one bar.
The data
Every chart above has a CSV link. The charts and the CSV files come out of the same script, so the numbers in the pictures and the numbers in the files cannot drift apart.
Sources
- Trajectories and Comparative Analysis of Global Countries Dominating AI Publications, arXiv 2509.25298, using OpenAlex data from OECD.AI with fractional counting.
- The 100 most cited AI papers in 2022, Zeta Alpha's annual lists, which also give the 2021 and 2024 counts.
- The Global AI Talent Tracker 3.0, MacroPolo, Paulson Institute.
- The 2026 AI Index Report, Stanford HAI.
- Open models lag state-of-the-art closed models by 4 months and Open vs. closed AI: how behind are open models?, Epoch AI.
- UK's over-dependence on Google's DeepMind harms its global AI competitiveness, Verdict, for the DeepMind share of UK top-cited papers.
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