Who's Ahead in the Global AI Talent Race?

These rules set the parameters for generating results from both the 2022 and 2025 cohorts. Python scripts were applied to these rules, and Claude Code and OpenAI Codex helped write and test the scripts. In contrast, Python was used primarily to scrape the NeurIPS dataset in the 2023 edition and was not used to apply rules and set parameters for machine labor to generate the full results (see machine labor vs. human labor below).

Based on the 2025 dataset of unique authors of accepted papers at NeurIPS, a subset of unique authors that have complete career paths—undergraduate origin, graduate education (including qualifying “No graduate school” cases), and current workplace—was used to generate results.

Country labels are assigned based on the country of the recorded institution or employer. For multinationals, this may be the institution’s home country rather than the researcher’s physical office. Each comparative chart for 2022 vs. 2025 shows the same six categories in both years; all remaining countries or regions are grouped as “Other.” Net gains and losses compare undergraduate origin with current workplace; they do not track individual migration. Hong Kong is grouped with China; the United Kingdom is separate from Europe; and Singapore is treated as its own category.

We used Claude Code and OpenAI Codex to support the initial development, review the scripts, and run the results for each career stage locally on a Mac. Once the human researcher set the rules, Python scripts were implemented to download records, link identities, aggregate results, and produce data visualization figures. The human researcher chose the cohorts and definitions, decided how to handle missing information and assign institutions and countries, reviewed outputs and exceptions, and approved corrections. No LLM was used to generate career histories, which came entirely from the OpenReview data.

Claude Code was primarily used for the initial run of results and debugging. For instance, if a script failed or produced an unexpected result, the affected step was debugged and rerun, and the human researcher reviewed the process and final output. Select records and intermediate outputs were also checked against the original OpenReview histories. The human researcher also checked records and intermediate outputs flagged by the scripts, as well as institution matches that required a judgment call, against the original OpenReview histories.

The workflow was developed for the 2025 dataset, then applied to the 2022 cohort as a back test to ensure results integrity and that comparisons would be feasible. For that, we mainly used OpenAI Codex to review the code for the 2022 reconstruction and cross-year comparisons. The reconstruction exposed a profile-linking error in the automated workflow: Renamed or merged OpenReview profiles could be treated as missing. For instance, in the preliminary build of the 2025 cohort, 1,705 of 25,682 authors, or 6.6 percent, were incorrectly treated as having no profile. The rule was corrected, and both cohorts were rerun before the final comparison.

Quality control combined automated consistency checks with targeted researcher review. During each stage, the scripts generated predefined flags and error messages. The researcher reviewed these diagnostic outputs with Codex to identify their causes, corrected the relevant code or rule where necessary, and reran the affected stage before proceeding. The researcher also checked institution matches that required human judgment against the underlying OpenReview records. The aforementioned renamed profile problem is an example of this stop, diagnose, correct, and rerun process.

Two further checks were conducted after the workflow was completed. A from-scratch rebuild using a separate implementation reproduced the core cohort counts for both years. An additional independent AI review of the handover files reproduced the key row counts and headline career paths and identified and corrected three numerical errors in the supporting documentation.

Together with the eighteen automated consistency checks for the two editions, this quality control process provided strong evidence that the main aggregate results were produced consistently from the data inputs and stated rules. Manual human verification was selective and did not involve re-coding a random or stratified sample of profiles, so the study cannot report a record-level accuracy rate. However, the four reconstructed back tests of 2022 data differed from the previous edition’s hand-coded estimates by 1.7 to 3.2 percentage points, which is in line with the rough estimate of the previous edition’s uncertainty range of +/- 5 to 7 points.

Confidence is highest in the aggregate counts reproduced by the checks and in the finding that China overtook the United States as the leading work location in the 2025 results. It is somewhat lower for individual career profiles and their physical work locations. Results for Europe are less consistent: Its share of AI talent rises slightly in the full career path sample but falls slightly in the broader sample, making it a bit harder to conclude about Europe’s direction of change.

Four additional checks tested the main counts and outputs from different directions:

1 Tracks are not assigned separate weights. In the people-based figures, each unique author is counted once after identities are linked across papers and tracks. In the institution rankings, each accepted paper contributes one unit of paper credit, divided equally among all listed authors; each author’s share is assigned to the institution where that researcher worked in the relevant observation year. The Position Papers track was introduced in 2025 and therefore has no 2022 counterpart.