Where there’s a will there’s a way: ChatGPT is used more for science in countries where it is prohibited
Open accessAbstract
Abstract Regulating AI is a key societal challenge, but effective methods remain unclear. This study evaluates geographic restrictions on AI services, focusing on ChatGPT, which OpenAI blocks in several countries, including China and Russia. If restrictions were effective, ChatGPT usage in these countries should be minimal. We measured usage with a classifier trained to detect distinctive word choices (e.g., “delve”) typical of early ChatGPT outputs. The classifier, trained on pre- and post-ChatGPT “polished” abstracts, outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying our classifier to preprints from arXiv, bioRxiv, and medRxiv revealed ChatGPT use in approximately 12.6% of preprints by August 2023, with usage 7.7% higher in restricted countries. This gap emerged before China’s first major domestic LLM became widely available. To address whether high demand could have driven even greater use without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found higher usage in countries with restrictions. ChatGPT use correlated with increased views and downloads but not with citations or journal placement. Overall, geographic restrictions on ChatGPT appear ineffective in science and potentially other domains, likely due to widespread workarounds.
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LLMs represent a potentially transformative development for scientific work.
“LLMs represent a potentially transformative development for scientific work (15–21).”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [21]
The Conclusion (page not specified) reports ChatGPT use in scientific writing and suggests it may help scientists produce more outputs. It does not establish a transformative effect on scientific work more broadly; it reports only modest attention benefits and no statistically significant citation or journal-placement benefits. (LLM, conf=0.78)
Experimental — accuracy not yet measuredLittle is known about how the rise of LLMs is reshaping the public funding landscape.
“Despite growing attention to the use of AI in science (15–27), little is known about how the rise of large language models (LLMs) is reshaping the public funding landscape.”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [21]
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- Authors
- Bao, H., Sun, M., & Teplitskiy, M.
- Affiliations
- University of Chicago (US)
- Cold Spring Harbor Laboratory (US)
- University of Michigan (US)
- Corresponding author
- Teplitskiy, Misha
- Venue
- Quantitative Science Studies
- Publisher
- MIT Press
- Published
- 2025
- Volume
- 6
- Pages
- 716-731
- ISSN
- 2641-3337 (ISSN-L)
- OpenAlex
- W4409540401
- Cited by
- 7
- References
- 22
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 7.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantitative Science Studies (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- ChatGPT, AI regulation, AI-generated text detection, GPTZero, arXiv, China, Russia, scientific writing, language barriers, artificial intelligence in science
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