The Real Power Behind AI: Energy, Infrastructure, and Innovation

Artificial intelligence is transforming industries, from accelerating vaccine research to improving government services. But behind the breakthroughs is a less celebrated reality: AI consumes energy at a staggering scale. 

In 2024, data centers worldwide consumed about 415 terawatt-hours (TWh) of electricity, roughly 1.5% of global demand. By 2030, this figure could more than double to ~945 TWh, with AI workloads driving much of that increase. 

In the United States, demand from data centers could nearly triple by 2028, consuming up to 12% of national electricity. Generative AI alone uses about four times more power per operation than conventional cloud workloads. 

For governments, nonprofits, and civic innovators, these numbers matter. AI’s potential to improve equity and access cannot be realized if the energy and infrastructure supporting it are unsustainable or inaccessible. 

At OpenEyes Technologies, we believe the real power behind AI lies in how we manage energy, infrastructure, and equity together. 

The Energy Challenge Behind AI Growth 

1. Rising Demand, Rising Costs 

AI models are computationally and therefore electrically intensive. Training a large-scale model can consume as much electricity as hundreds of U.S. households in a year and once deployed, inference requires continuous compute and cooling. 

Goldman Sachs projects that data center electricity demand will rise by 165% between 2023 and 2030, with AI driving much of that growth. 

2. Environmental Burden 

A recent study estimates that U.S. data centers consumed over 4% of national electricity in 2023, 56% of which came from fossil fuels, emitting more than 105 million metric tons of CO₂. That represents about 2.2% of total U.S. emissions. 

Efficiency gains have helped (average Power Usage Effectiveness [PUE] improved from 2.5 in 2007 to ~1.58 in 2023, but these improvements cannot keep pace with the exponential growth in AI workloads. 

3. Risks for Mission-Driven Organizations 

• Cost escalation: Rising energy costs make AI adoption prohibitive for nonprofits. 

• Infrastructure mismatch: Many regions lack the reliable power grids or cooling systems needed to support AI deployments. 

• Equity risks: Concentration of AI in energy-rich regions threatens to leave underserved communities behind. 
   

Infrastructure at a Crossroads 

1. Legacy Systems Under Pressure 

Global electricity grids were not built for the exponential growth of AI workloads. The Center for Strategic and International Studies has called electricity supply the “bottleneck on AI dominance”, noting that power availability now dictates where AI can be deployed. 

2. Unequal Access to AI Infrastructure 

Energy-rich regions can attract investment, expand data centers, and accelerate digital transformation. Underserved regions face rolling blackouts, insufficient grid capacity, or high costs, making equitable AI adoption impractical. 

For GovTech and nonprofit technology, this imbalance raises a central concern: if infrastructure is uneven, AI may deepen divides rather than bridge them. 

Equity-Driven Design for Sustainable AI 

1. Defining Equity-Driven Design 

Equity-driven design ensures AI systems are accessible, sustainable, and accountable. It emphasizes: 

• Accessibility: Tools that function in low-bandwidth, infrastructure-constrained environments. 

• Efficiency: Energy use treated as a core design parameter. 

• Transparency: Clear reporting on energy costs, environmental impact, and system performance. 

2. Why It Matters 

Without equity-driven design, AI risks becoming a privilege of the few. For nonprofits and governments, embedding equity and efficiency into system design is what enables digital transformation at scale, not just in wealthy regions, but everywhere. 

Solutions in Action: Pathways to Responsible AI Infrastructure 

1. Energy-Aware AI Strategies 

• Model optimization – Distilling large models into smaller, efficient versions. 

• Carbon-aware scheduling – Running workloads in greener grids or off-peak hours. 

• Hardware efficiency – Leveraging accelerators optimized for lower power use. 

• Measurement and transparency – Publishing energy metrics for accountability. 

2. Infrastructure Innovation 

• Green data centers powered by renewables, designed with advanced cooling. 

• Edge and hybrid computing to process closer to users, reducing central load. 

• Public-private partnerships between governments, utilities, and nonprofits to modernize infrastructure. 

OpenEyes in Practice 

Our tools are designed with these principles in mind: 

1. Survey Platform – A lightweight, bandwidth-conscious platform that enables feedback collection in low-infrastructure environments. 

2. Feedback AI – Real-time analysis of citizen input, optimized for energy-efficient deployment. 

Both solutions illustrate how AI infrastructure and equity-driven design intersect: enabling nonprofits and public agencies to deliver impact without unsustainable energy costs. 

Future Outlook: What Comes Next 

1. Policy and Standards 

Governments and multilateral bodies are beginning to require sustainability reporting for AI systems. Future GovTech procurement will likely favor vendors who can demonstrate energy efficiency and responsible infrastructure practices. 

2. Equity at Scale 

Digital equity is shifting from aspiration to necessity. Nonprofits and governments must ensure AI systems reach all communities, not just those with infrastructure advantages. 

3. Innovation Through Collaboration 

The path forward will depend on collaboration between utilities, governments, nonprofits, and technology providers. No single actor can solve the infrastructure challenge alone. 

Conclusion: Building Sustainable, Inclusive AI Together 

AI’s potential is vast, but so is its energy demand. The real power behind AI lies not only in algorithms, but in the energy that sustains them, the infrastructure that enables them, and the equity that ensures access. 

At OpenEyes Technologies, we believe responsible AI adoption must be: 

• Energy-aware 

• Infrastructure-resilient 

• Equity-driven 

For GovTech leaders, nonprofits, and civic technologists, this is both a responsibility and an opportunity: to shape AI into a force for inclusion rather than exclusion.