Disruption of the research software landscape through AI software generation

Specialized research software has historically been costly and time-consuming to create, but large language models (LLMs) have become capable enough at code generation to fundamentally change this. We describe how LLM-assisted programming disrupts the landscape by allowing researchers to build tools without support from software engineers, illustrate this with an example built rapidly by a single LLM-assisted developer, and discuss opportunities and risks. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. USD 39.95 Prices may be subject to local taxes which are calculated during checkout Subjects Code availability MOSS is openly available under an MIT license at https://github.com/StructuralNeurobiologyLab/MOSS. References Hocquet, A. et al. Nat. Comput. Sci. 4, 465–468 (2024). Schindelin, J. et al. Nat. Methods 9, 676–682 (2012). Berg, S. et al. Nat. Methods 16, 1226–1232 (2019). Sofroniew, N. et al. napari. Zenodo https://doi.org/10.5281/ZENODO.3555620 (2019). Makovetsky, R., Piché, N. & Marsh, M. Microsc. Microanal. 24, 532–533 (2018). (Suppl. 1). Last, M. G. F., Abendstein, L., Voortman, L. M. & Sharp, T. H. eLife 13, RP98552 (2024). Ouyang, W. et al. Preprint at bioRxiv https://doi.org/10.1101/2022.06.07.495102 (2022). Archit, A. et al. Nat. Methods 22, 579–591 (2025). OpenAI et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2303.08774 (2023). Jimenez, C. E. et al. in International Conference on Learning Representations 54107–54157 (2024). Peng, S. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.06590 (2023). Karpathy, A. X https://x.com/karpathy/status/1886192184808149383 (2025). Ronneberger, O., Fischer, P. & Brox, T. in Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, 2015). Jumper, J. et al. Nature 596, 583–589 (2021). Kirillov, A. et al. in Proc. IEEE/CVF International Conference on Computer Vision 3992–4003 (IEEE, 2023). Acknowledgements MOSS was developed using LLM-assisted programming tools including Anthropic’s Claude. We thank M. So-Last for helpful discussions on U-Nets. Author information Authors and Affiliations Contributions MOSS was developed by N.D.M. The manuscript was written jointly by N.D.M and J.M.R.K. Corresponding author Ethics declarations Competing interests J.M.R.K. owns shares of ariadne.ai ag. Peer review Peer review information Nature Methods thanks Robert Haase, Wei Ouyang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rights and permissions About this article Cite this article Medina, N.D., Kornfeld, J.M.R. Disruption of the research software landscape through AI software generation. Nat Methods (2026). https://doi.org/10.1038/s41592-026-03210-x Published: Version of record: DOI: https://doi.org/10.1038/s41592-026-03210-x