AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers

Paper: arXiv 2609.08166

Authors: Alexander Crosier, Kyle Onghai, Ronnie Sircar

Abstract

The twenty-first century’s transformative technology, artificial intelligence, is increasingly constrained by the twentieth century’s transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a strong mathematical framework, including deterministic and stochastic models, and formulates a stochastic control problem. While it outlines a simulation on ERCOT data, the empirical rigor could be enhanced with more detailed backtesting and sensitivity analysis. The novelty lies in applying advanced quantitative methods to the critical and emerging problem of AI data center energy demand.

Research Flowchart

  flowchart TD
    A[Research Goal: Optimize Infrastructure for AI Data Centers amid Energy Constraints] --> B{Key Methodology Steps};
    B --> C{1. Deterministic Model Analysis of Supply-Demand Growth};
    B --> D{2. Stochastic Modeling of Data Center & Electricity Supply Expansion};
    B --> E{3. Stochastic Control Problem: Revenue-Maximizing Generation Expansion};
    C & D & E --> F[Data/Inputs: Data Center Load Growth, Generation Capacity, Market Prices, Supply/Demand Growth Rates];
    F --> G[Computational Processes: Numerical Simulations, Optimization Algorithms];
    G --> H[Key Findings: Rapid demand growth needs new generation, but uncertainties (load forecasts, execution risks, value cannibalization) weaken investment incentives, leading to unstable electricity prices.];

Cited References (33)

1. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python (2020) — link
2. Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing (2026) — link
3. Fracking, Renewables & Mean Field Games (2017) — link
4. Capacity of electric power plants (2025) — link
5. ERCOT Monthly Operational Overview: December 2024 (2025) — link
6. A Mean Field Game for Capacity Expansion Modeling (2025) — link
7. A Mean Field Games Model for Cryptocurrency Mining (2024) — link
8. Exploration and Exhaustibility in Dynamic Cournot Games (2012) — link

0 of 33 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.