# Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams

arXiv: https://arxiv.org/abs/2605.27404 · HTML: https://mlsanity.com/paper/2605.27404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 80 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 17 of 17)

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- [Scientific production in the era of Large Language Models](https://mlsanity.com/paper/2601.13187/) (2026)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [Academic journals' AI policies fail to curb the surge in AI-assisted academic writing](https://mlsanity.com/paper/2512.06705/) (2025)
- [Reference Coverage Analysis of OpenAlex compared to Web of Science and Scopus](https://mlsanity.com/paper/2401.16359/) (2024)
- [Team size matters: Collaboration and scientific impact since 1900](https://mlsanity.com/paper/1410.8544/) (2014)
- [How should the advent of large language models affect the practice of science?](https://mlsanity.com/paper/2312.03759/) (2023)
- [Pandemics are catalysts of scientific novelty: Evidence from COVID-19](https://mlsanity.com/paper/2009.12500/) (2020)
- [Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers](https://mlsanity.com/paper/2511.21739/) (2025)
- [The Impact of Heterogeneous Shared Leadership in Scientific Teams](https://mlsanity.com/paper/2306.15804/) (2023)
- [Hidden Division of Labor in Scientific Teams Revealed Through 1.6 Million LaTeX Files](https://mlsanity.com/paper/2502.07263/) (2025)

## Abstract (arXiv)

The era of Big Science has long been defined by increasingly large and specialized research teams pushing the frontiers of knowledge. However, recent advances in artificial intelligence (AI), particularly large language models (LLMs), are beginning to reshape academic writing and scientific research, potentially disrupting the longstanding trend toward ever-larger teams and transforming other dimensions of research team structure. Drawing on 147,074 full-text publications from the PLoS family and the Nature portfolio since 2020, we examined whether and how AI-assisted writing influences team structure and team outcomes in science. Using multiple methods, including ordinary least square, quantile regression, Poisson regression, logistic regression and propensity score matching, we found that research teams using AI-assisted writing tend to be younger and smaller. Importantly, this shift toward more compact, junior-leaning teams does not come at the expense of scientific impact. On the contrary, we observed a higher probability of research teams that employed AI-assisted writing producing highly impactful publications. These results highlight the significant role of AI-assisted writing in reshaping not only how research is produced, but also how research teams are formed and assembled. Our findings call for policy improvements in research evaluation, funding, and training to address this emerging trend.