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我们使用 NeuGBI 在相同的 Revelio Lab 数据集（3 亿条美国就业记录）上复现了“生成式人工智能作为与资历相关的技术变革”（哈佛商学院，2025）的研究。该论文的发现是：人工智能对初级职位的负面影响（-29.4%）远大于对高级职位（-5.8%）。NeuGBI 自主得出了相同的结论。 NeuGBI 发现的一点是该论文未提及的：在软件开发领域，几乎减半的是初级（L2）职位，而不是入门级（L1）职位。 NeuGBI 使用 NeuG（一个支持多跳关系图数据库）作为其查询引擎，使用超图重构进行分析，并打包了 LLM 可以调用的探索性技能，以分解问题并逐步深入。 NeuGBI 的关键能力是端到端的无偏采样——在 3 亿条记录上，复杂的多跳查询能在几秒钟内返回结果，而不是几小时。博客文章：https://graphscope.io/blog/tech/2026/06/16/NEUGBI-BLOG.html 原始论文：https://arxiv.org/abs/2603.10625

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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: <a href="https://graphscope.io/blog/tech/2026/06/16/NEUGBI-BLOG.html" rel="nofollow">https://graphscope.io/blog/tech/2026/06/16/NEUGBI-BLOG.html</a> Original paper: <a href="https://arxiv.org/abs/2603.10625" rel="nofollow">https://arxiv.org/abs/2603.10625</a>

展示 HN：自主复现哈佛大学关于人工智能就业影响的研究