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Replicating a Harvard study on AI's employment impact – Autonomously

Replicating a Harvard study on AI's employment impact – Autonomously

by robeenly·Jun 16, 2026·3 points·0 comments
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AI Analysis

●●SolidBig BrainDark Horse

Found L2 jobs halved while L1 stayed stable — original Harvard paper missed this.

Strengths
  • •Graph database handles 300M records with multi-hop queries in seconds.
  • •LLM-driven Skills decompose questions autonomously without human intervention.
  • •Discovered novel finding the original research team didn't identify.
Weaknesses
  • •Graph-based BI already exists in Neo4j, TigerGraph, and similar platforms.
  • •Unclear if this generalizes beyond employment data or requires heavy customization.
Category
AI/ML
Target Audience

Data analysts, research teams, business intelligence engineers

Similar To

Neo4j · TigerGraph · ThoughtSpot

Post Description

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

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