We used NeuGBI to replicate "Generative AI as Seniority-Biased Technological Change" (HBS, 2025) on the same Revelio Lab dataset — 300M U.S. employment records.

The paper's finding: AI disproportionately affects junior positions (−29.4%) vs. senior (−5.8%). NeuGBI arrived at the same conclusion autonomously.

One thing NeuGBI found that the paper didn't: within software development, it's specifically junior-level (L2) positions that nearly halved, not entry-level (L1).

NeuGBI uses NeuG (a graph database with multi-hop relationship support) as its query engine, Hypergraph reconstruction for analysis, and packaged exploratory Skills that an LLM can invoke to decompose questions and drill down step by step.

The key capability of NeuGBI is end-to-end unbiased sampling — on 300M records, complex multi-hop queries return in seconds rather than hours.

Blog post: https://graphscope.io/blog/tech/2026/06/16/NEUGBI-BLOG.html Original paper: https://arxiv.org/abs/2603.10625