The Race for Power in the AI Era: A Systematic Review of Time-to-Power Constraints, Data-Centre Location, and Sustainable AI Infrastructure
Keywords:
AIRSF, Artificial intelligence, Carbon-aware computing, Data centers, Grid interconnection, Siting, Sustainable AI infrastructure, Time-to-PowerAbstract
Artificial intelligence (AI), generative AI, large language models, and accelerated computing are redefining the physical demands of digital architecture. The feasibility of deploying large-scale AI infrastructure is not just determined by computational efficiency, but even more so by the availability and local timing of electrical capacity to energize the systems that power them: whether nearby land has access to abundant deep-well water, which is increasingly attracting interest; what sources of low-carbon electricity are available, when they are available, and whether they can be delivered in time; what cooling resources are available across regions; and where high-bandwidth, low-latency network connectivity exists are also being examined. This review brings together literature on AI compute growth, electricity demand from data centers, data-centre siting and span of control in grids, renewable-energy procurement and scheduling, carbon-aware computing and cooling, and water use. Evidence published primarily from 2020 to 2026 is synthesized using a structured systematic-review approach, consistent with PRISMA 2020 principles, while earlier work laying the foundations of this evidence base remains referenced. The evidence base consists of empirical studies, engineering analyses and modelling work, industry reports and systems research that cannot be compared statistically through meta-analysis; hence, thematic synthesis is used. The central concept developed in this review is Time-to-Power (TTP), defined as the elapsed period between a sufficiently advanced infrastructure commitment (site control, interconnection request or equivalent planning milestone) and the availability of the required continuous electrical capacity at the point of connection. TTP is treated as a conceptual deployment constraint rather than an established industry-standard metric. The review further demonstrates that AI data-centre location is becoming a multi-resource decision involving grid capacity, interconnection timing, land, fiber, renewable resources, tariffs, climate, cooling options, and water availability. Sustainability requires temporal and spatial consideration of carbon intensity, renewable matching, workload flexibility, and both direct and indirect water use. These findings are organized into the proposed AI Infrastructure Readiness and Sustainability Framework (AIRSF), comprising five layers: compute demand, power readiness, location, sustainability, and deployment decision criteria. AIRSF is not presented as an empirically validated optimizer; it provides a research and decision architecture that can be operationalized with real project, grid, workload, cost, and environmental datasets. Future priorities include standardized TTP definitions, spatial-temporal workload orchestration, grid-interactive AI data centers, integrated carbon–water–power optimization and empirical AIRSF validation.
References
International Energy Agency, Energy and AI. Paris, France: IEA, 2025.
A. Shehabi et al., 2024 United States Data Center Energy Usage Report, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, LBNL-2001637, 2024.
S. J. Smith et al., United States Data Center Energy Usage Report: 2025 Update, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, LBNL-2001758, 2026.
S. Snider, “Why AI data center projects face years of delays after approval,” Data Center Knowledge, May 12, 2026.
S. M. Kerner, “Power-constrained data architecture curbing AI ambitions,” TechTarget, May 13, 2026.
N. Coelho, “How long it actually takes to power a data center in 2026: A U.S. market-by-market reality check,” Construction Owners, Jul. 1, 2026.
J. Haubert, “Time-to-power gap: Big generation’s Achilles’ heel in the AI data center race,” Enverus Intelligence Research, May 27, 2026.
E. Masanet, A. Shehabi, N. Lei, S. Smith, and J. Koomey, “Recalibrating global data center energy-use estimates,” Science, vol. 367, no. 6481, pp. 984–986, Feb. 2020.
A. Shehabi et al., United States Data Center Energy Usage Report, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, LBNL-1005775, 2016.
E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for deep learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Jul. 2019, pp. 3645–3650.
International Energy Agency, Electricity 2024: Analysis and Forecast to 2026. Paris, France: IEA, 2024.
International Energy Agency, Electricity 2026. Paris, France: IEA, 2026.
I. E. Arciniegas Rueda, A. Smidt, R. Wang, H. M. Park, D. Gill, and H. van Soest, Evaluating Potential Artificial Intelligence Energy Capacity at Different Data Center Sites, RAND Corporation, Santa Monica, CA, USA, RR-A3845-3, 2026.
N. Lei, J. Lu, A. Shehabi, and E. R. Masanet, “The water use of data center workloads: A review and assessment of key determinants,” Resources, Conservation and Recycling, vol. 219, Jun. 2025.
A. S. Luccioni, S. Viguier, and A.-L. Ligozat, “Estimating the carbon footprint of BLOOM, a 176B parameter language model,” Journal of Machine Learning Research, vol. 24, pp. 1–15, 2023.
P. Li, J. Yang, M. A. Islam, and S. Ren, “Making AI less ‘thirsty’: Uncovering and addressing the secret water footprint of AI models,” Communications of the ACM, vol. 68, no. 7, pp. 54–61, Feb. 2023.
M. Grahovac, P. Ehrlich, J. Hu, and M. Wetter, “Model-based data center cooling controls comparative co-design,” Science and Technology for the Built Environment, vol. 30, no. 4, pp. 394–414, Dec. 2023.
A. Radovanović et al., "Carbon-Aware Computing for Datacenters," in IEEE Transactions on Power Systems, vol. 38, no. 2, pp. 1270-1280, March 2023.
M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, 2021.
D. A. Sartor, B. Shen, and A. Shehabi, Repurposing Coal Assets for a Decarbonized Digital Economy. Berkeley, CA, USA: Lawrence Berkeley National Laboratory, 2023.
N. M. Frick and V. Srinivasan, 2025 Large Load Literature Review. Berkeley, CA, USA: Lawrence Berkeley National Laboratory, 2025.
T. W. Kirchstetter, S. J. Smith, A. Shehabi, and D. A. Sartor, DOE Data Center Load Flexibility Workshop Summary. Berkeley, CA, USA: Lawrence Berkeley National Laboratory, 2025.
