# AI in Science: Early Insights

arXiv: https://arxiv.org/abs/2609.28504 · HTML: https://mlsanity.com/paper/2609.28504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 95 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [The Widespread Adoption of Large Language Model-Assisted Writing Across Society](https://mlsanity.com/paper/2502.09747/) (2025)
- [Scientific production in the era of Large Language Models](https://mlsanity.com/paper/2601.13187/) (2026)
- [The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems](https://mlsanity.com/paper/2509.08713/) (2025)
- [Anthropic Economic Index report: Uneven geographic and enterprise AI adoption](https://mlsanity.com/paper/2511.15080/) (2025)
- [Is Your Paper Being Reviewed by an LLM? Benchmarking AI Text Detection in Peer Review](https://mlsanity.com/paper/2502.19614/) (2025)
- [The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?](https://mlsanity.com/paper/2401.02452/) (2024)
- [Reference Coverage Analysis of OpenAlex compared to Web of Science and Scopus](https://mlsanity.com/paper/2401.16359/) (2024)
- [Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers](https://mlsanity.com/paper/2511.21739/) (2025)
- [An analysis of the suitability of OpenAlex for bibliometric analyses](https://mlsanity.com/paper/2404.17663/) (2024)
- [Chaining Tasks, Redefining Work: A Theory of AI Automation](https://mlsanity.com/paper/2606.15960/) (2026)
- [Drivers and Barriers of AI Adoption and Use in Scientific Research](https://mlsanity.com/paper/2312.09843/) (2023)
- [Generalization and the Rise of System-level Creativity in Science](https://mlsanity.com/paper/2510.03240/) (2025)
- [Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud](https://mlsanity.com/paper/2607.10712/) (2026)
- [Google's AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy](https://mlsanity.com/paper/2608.00038/) (2026)
- [AI and the Decentering of Disciplinary Creativity](https://mlsanity.com/paper/2510.23734/) (2025)

## Abstract (arXiv)

Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements-- LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.