Artificial intelligence systems are becoming gigawatt-scale, always-on workloads that today’s power systems were not built to carry. Capacity expansion and short-term coordination alone cannot close this gap. Here, we argue that alignment requires a structural perspective: enabling energy and computation systems to interpret each other’s structural organization and evolving constraints.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 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
References
International Energy Agency. AI is Set to Drive Surging Electricity Demand from Data Centres while Offering the Potential to Transform How the Energy Sector Works https://go.nature.com/3Zz6yda (2025).
Mills, J. NVIDIA Launches Omniverse DSX Blueprint, Enabling Global AI Infrastructure Ecosystem to Build Gigawatt-Scale AI Factories https://blogs.nvidia.com/blog/omniverse-dsx-blueprint/ (2025).
Xie, L., Li, N. & Poor, H. V. Sustainable electrification in the era of AI. Nat. Rev. Electr. Eng. 1, 493–494 (2024).
US Department of Energy, Office of Nuclear Energy. Advantages and Challenges of Nuclear-Powered Data Centers https://go.nature.com/4bOaCO9 (2025).
International Energy Agency. Energy Supply for AI https://go.nature.com/4kz1hvJ (2024).
Lin, H. et al. Power system and communication network co-simulation for smart grid applications. IEEE ISGT (2011).
Ruan, J., Xu, Z. & Su, H. Towards interdisciplinary integration of electrical engineering and earth science. Nat. Rev. Electr. Eng. 1, 278–279 (2024).
Censi, A. Uncertainty in monotone codesign problems. IEEE Robot. Autom. Lett. 2, 1556–1563 (2017).
Helbing, D. Globally networked risks and how to respond. Nature 497, 51–59 (2013).
Liu, Y., Slotine, J. & Barabási, A. Controllability of complex networks. Nature 473, 167–173 (2011).
Acknowledgements
The authors acknowledge support from the National Natural Science Foundation of China (72331009), the Shenzhen Key Lab of Crowd Intelligence Empowered Low-Carbon Energy Network (ZDSYS20220606100601002), the Australian Research Council Research Hub (IH180100020), and the Australian Research Council Linkage Project (LP240200586).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Lu, X., Qiu, J., Wang, X. et al. Structural alignment for energy–computation co-design. Nat Rev Electr Eng (2026). https://doi.org/10.1038/s44287-026-00273-y
Published:
Version of record:
DOI: https://doi.org/10.1038/s44287-026-00273-y
