Renewable energy sources will not meet AI’s insatiable demand for energy

In the field of artificial intelligence (AI), where data processing and machine learning algorithms reign supreme, energy demand has become of utmost importance. Mark P. Mills, executive director of the National Center for Energy Analytics (an initiative I oversee at the Texas Public Policy Foundation), says the energy requirements of artificial intelligence systems are much greater than most of us realize. His insights paint a sobering picture of the energy landscape that awaits us as artificial intelligence continues its relentless advances in every aspect of modern life.

Mills says the computational intensity of AI applications, such as deep learning and real-time data processing, is driving an unprecedented increase in energy consumption. By 2026, global electricity consumption from AI alone could reach 1,000 terawatt-hours (TWh) per year, according to the International Energy Agency, slightly more than Japan’s total electricity consumption. The appetite will be huge, as it becomes an integral part of industries from healthcare to finance, transportation to agriculture.

At the heart of the debate is a fundamental question: Can renewable energy sources adequately power the AI ​​revolution? Silicon Valley, home to tech giants like Google, Facebook and Tesla, is a strong advocate of renewable energy solutions. Many of these companies have committed to ambitious sustainability goals, including becoming carbon neutral and even operating solely on renewable energy. Most of these promises are empty at best, as they rely on periodic renewable energy contracts to claim to be 100% renewable when connected to a grid that is stabilized and reliable mainly through traditional dispatchable thermal energy – nuclear, natural gas and even coal .

California is the nation’s test case for renewable energy. It’s the state with the most aggressive greenhouse gas reduction program. I voted against AB 32, the “California Global Warming Solutions Act of 2006,” which launched that effort. At the time, California’s electricity prices were eighth in the nation and 44 percent higher than the national average. Today, with all that “cheap” solar and wind installed, California’s electricity prices are the second most expensive in the United States, second only to Hawaii, and consumers pay almost twice the national average.

While a grid dominated by renewable energy sources is not affordable, it is also not reliable. Mills argues that while renewable sources such as solar and wind have made significant progress, they face inherent limitations in meeting the continuous and predictable energy demands of artificial intelligence systems.

The harsh reality is that AI operations require uninterrupted power to function optimally. Unlike conventional electricity generation, where power can be adjusted to changing demand, renewable sources depend on weather conditions and geographical location. This intermittency creates challenges for maintaining the stability and reliability of power needed for AI computing tasks, which often operate around the clock. The same can be said for chip manufacturing as well as other industrial processes.

Moreover, the infrastructure needed to meet AI’s energy needs exceeds the capacity of power generation. Mills points out that electricity transmission and storage—key elements of ensuring reliable power supply—are critical bottlenecks that must be addressed to satisfy AI’s voracious appetite for energy. Without significant advances in grid technology and energy storage solutions, the scalability of renewable energy to meet AI’s needs remains a mirage—a costly mirage.

A promising solution is the adoption of modular nuclear reactors and nuclear power in general. These technologies offer the continuous and reliable energy needed to run AI, providing a stable base load that complements intermittent renewable sources. Nuclear power, with its low carbon footprint and high energy density, is uniquely suited to handle the energy-hungry requirements of AI.

Unfortunately, the regulatory process for permitting new nuclear power plants resembles a plate of spaghetti with environmental lawsuits as the sauce on top. Over the past three decades, only two new nuclear reactors have been launched in the United States – Vogtle Units 3 and 4, which were connected to the grid in July 2023 and April 2024 and “produce enough electricity to power 1 million homes.” Meanwhile, China has 55 nuclear reactors, of which 23 are under construction, while India has over 20 with seven more under construction. Instead of cutting red tape, Congress has tried to fix the problem with subsidies, which means that if nuclear power does get built here, it will take too long and cost too much.

Silicon Valley’s techno-optimism and business plans must be fueled by reliable energy. However, green technology advocates remain steadfast in their belief that renewable energy sources can and should power the future of artificial intelligence. But the gap between aspiration and practicality is widening, causing interesting political friction in the once close alliance.

The political and policy implications of this debate are profound. Germany is a cautionary example of a nation that has struggled with decarbonization goals and commitments under the Paris Agreement, voluntarily embarking on a process of deindustrialization in the service of ecological goals – something that was envisaged in the Morgenthau Plan after World War II as a punishment and a way to prevent Germany from starting another world war. Now Germany faces a costly U-turn on energy if it wants to avoid total dependence on China, much less can it even try to participate in the artificial intelligence space.

Moreover, the economic dimension of artificial intelligence’s energy needs cannot be ignored. Mills warns that overlooking the scale of AI’s energy use could lead to supply constraints and price volatility in global energy markets. For industries that rely on AI technologies – from autonomous vehicles to smart grids – ensuring stable and affordable energy sources is crucial for long-term profitability and growth.

Artificial intelligence is coming, whether decision makers understand the energy implications or not. Since politicians probably won’t act fast enough, the necessary transformation of Silicon Valley into a major energy-producing powerhouse will be a fascinating thing to watch.