# Generative AI for Analysts

arXiv: https://arxiv.org/abs/2512.19705 · HTML: https://mlsanity.com/paper/2512.19705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [AI as "Co-founder": GenAI for Entrepreneurship](https://mlsanity.com/paper/2512.06506/) (2025)

## Built on by (most cited first, 1 of 1)

- [Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows](https://mlsanity.com/paper/2608.24842/) (2026)

## Abstract (arXiv)

We study how generative artificial intelligence (GenAI) reshapes financial analysts' information production. Using the 2023 integration of GenAI into FACTSET as a plausibly exogenous change in AI access, we find that FACTSET-associated reports become markedly richer--featuring 26% more distinct information sources, 24% broader topical coverage, and 21% more analytical methods--while also improving timeliness. However, these gains do not uniformly improve decision quality: relative forecast accuracy declines when analysts face greater information-processing demands. Yet, a machine-learning benchmark processing the same observable inputs shows no analogous deterioration, pointing to a human processing constraint rather than poorer underlying information. Placebo tests using other data vendors make a common platform-wide technology trend unlikely. Overall, GenAI relaxes information-acquisition constraints while making human attention a more important bottleneck.