https://www.texaspolicy.com/ais-insatiable-appetite-for-energy-cant-be-
satisfied-by-renewables/

AI is bringing an unprecedented surge in energy consumption, whether
policymakers understand the energy implications or not.

In the realm of artificial intelligence (AI), where data crunching and
machine-learning algorithms reign supreme, the demand for energy has
emerged as a critical concern. Mark P. Mills, the executive director of
the National Center for Energy Analytics (an initiative I oversee at the
Texas Public Policy Foundation), argues that the energy requirements for
AI systems are far more substantial than most of us know. His insights
paint a sobering picture of the energy landscape that awaits us as AI
continues its relentless advance into every facet of modern life.

Mills contends that the computational intensity of AI applications, such
as deep learning and real-time data processing, is driving an
unprecedented surge in energy consumption. According to the International
Energy Agency, the global electricity consumption by AI alone could reach
1,000 terawatt-hours (TWh) annually by 2026, slightly more than the total
electricity consumption of Japan. The appetite will be formidable as it
becomes integral to industries ranging from health care to finance, and
transportation to agriculture.

At the heart of the debate lies a fundamental question: Can renewable
energy sources adequately power the AI revolution? Silicon Valley, home to
tech giants like Google, Facebook, and Tesla, has been a vocal advocate
for renewable energy solutions. Many of these companies have committed to
ambitious sustainability goals, including achieving carbon neutrality or
even operating entirely on renewable energy. Most of these promises are
hollow, at best, in that they rely on periodic renewable energy contracts
to make the claim that they�re 100 percent renewable while connected to a
grid stabilized and made reliable largely by traditional dispatchable
thermal power � nuclear, natural gas, and even coal.

California is the nation�s test case for renewables. It�s the state with
the most aggressive greenhouse gas reduction agenda. I voted against AB
32, the �California Global Warming Solutions Act of 2006,� which kicked
off this effort. Back then, California�s electricity prices were the
eighth most expensive in the nation and 44 percent above the national
average. Today, after installing all that �cheap� solar and wind energy,
California�s electricity prices are the second most expensive in the
United States, trailing only Hawaii, with consumers paying almost double
the national average.

Yet, while a grid dominated by renewables isn�t affordable, it�s also not
reliable. Mills argues that though renewable sources like solar and wind
have made significant strides, they face inherent limitations in meeting
the continuous and predictable energy demands of AI systems.

The reality is stark: AI operations require uninterrupted power to
function optimally. Unlike conventional electricity generation, where
output can be adjusted to meet fluctuating demand, renewable sources
depend on weather conditions and geographic location. This intermittency
poses challenges for maintaining the stability and reliability of the
power supply necessary for AI�s computational tasks, which often operate
around the clock. The same can be said of chip fabrication as well as
other industrial processes.

Moreover, the infrastructure needed to support AI�s energy demands goes
beyond generating capacity. Mills points out that the transmission and
storage of electricity � key components in ensuring a reliable energy
supply � are critical bottlenecks that must be addressed to accommodate
AI�s voracious appetite for power. Without substantial advancements in
grid technology and energy storage solutions, the scalability of renewable
energy in meeting AI�s needs remains a mirage � an expensive mirage.

A promising solution lies in the adoption of modular nuclear reactors and
nuclear power in general. These technologies offer the continuous and
reliable energy necessary for AI operations, providing a stable base load
that complements intermittent renewable sources. Nuclear power, with its
low carbon emissions and high energy density, is uniquely positioned to
support the energy-intensive demands 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. Only two new nuclear reactors have come online in the United
States in the past three decades � Vogtle Units 3 and 4, which connected
to the grid in July 2023 and April 2024 and �produce enough electricity to
power 1 million homes.� China, on the other hand, has 55 nuclear reactors
with 23 under construction, while India has more than 20 with seven more
under construction. Rather than reduce the red tape, Congress has sought
to pour subsidies on the problem � meaning 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 power. But green tech advocates remain steadfast in their belief
that renewables can and should power the AI future. However, the gap
between aspiration and practicality is widening, sparking interesting
political frictions in what used to be a close alliance.

The political and policy implications of this debate are profound. Germany
is a cautionary example of a nation that grappled with decarbonization
goals and commitments under the Paris Agreement, voluntarily starting the
process of deindustrialization in service of green goals � something
envisioned by the Morgenthau Plan in the aftermath of World War II as a
punishment and a means of preventing the Germans from having the capacity
to start another world war. Now Germany is faced with making a costly
volte-face on energy if it is to avoid being entirely dependent on China,
much less even try to participate in the AI space.

Furthermore, the economic dimensions of AI�s energy demands cannot be
overlooked. Mills warns that overlooking the scale of energy consumption
by AI could lead to supply constraints and price volatility in global
energy markets. For industries reliant on AI technologies � from
autonomous vehicles to smart grids � ensuring stable and affordable energy
sources is essential for long-term viability and growth.

AI is coming, whether policymakers understand the energy implications or
not. Since politicians aren�t likely to move fast enough, the fascinating
thing to watch will be the necessity-driven transformation of Silicon
Valley into a major energy-producing powerhouse.

--
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