Last week, Houston, Texas, hosted one of the world’s largest energy summits, the CERAWeek hosted by S&P Global, where energy executives from around the globe engaged in discussions about the potential of artificial intelligence (AI). The conversations revealed a fascinating narrative that AI is not only set to revolutionize exploration, drilling, and extraction techniques; it is also anticipated to create a massive new demand within the energy sector.

A consensus emerged during the conference: the endgame of AI is ultimately tied to energyThis understanding is quickly gaining traction in societies worldwide, as the intersection of AI technology and energy resources becomes increasingly evident.

So, why does AI have a strong connection with energy? To comprehend this relationship, it's essential to focus on three critical aspects: computing power, algorithms, and the volume of data

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Each of these elements plays a vital role in the AI landscape and its application in energy.

First, let’s discuss computing powerThis essential component stems from advanced chip technologyThe challenges in chip design and manufacturing are indeed formidable, yet the advancements in computing capabilities can be achieved over timeDespite the complexities, progress in chip-making is ongoing.

Next on the list is algorithmsOut of the entire landscape of algorithm development, roughly 20% relies on foundational breakthroughs in scientific theory that cannot merely be propelled by capital or engineer ingenuity; instead, these require real scientific advancementsThe remaining 80% of algorithmic improvement can be driven by machine learning techniques and data deployment, which are amenable to evolution over time.

The third factor, data volume, is crucialRegardless of the enhancement of computing power or the growth of algorithms, a plentiful supply of data is required—a bit like a car that won't run without fuel

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The volume of data ties directly to several factors, such as the depth of an economy and population densityCountries with large populations naturally generate more data, which enables AI to evolve more rapidlyAdditionally, having a variety of application scenarios also significantly influences data accumulationFor instance, while some economies may be robust, they might lack in online economic activitiesJapan is a prime example of this, where their offline retail sector is highly developed but online potential suffers.

When examining these three catalysts—computing power, algorithms, and data—one might conclude that the true competitive edge lies primarily in which entities can harness larger quantities of dataThis essence captures the core competition in the field of AI.

But where does data fundamentally originate? At its core, the competition for data is intertwined with the competition for electricity and energy resources

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As AI technology amplifies its demand for data, it simultaneously drives an insatiable need for energy.

John Ketchum, CEO of NextEra Energy, a leading utility company in the U.S., has pointed out that even though electricity demands plateaued for the past decade, growth rates for the next five years are forecasted to rise by an astonishing 81%. After a prolonged period of stagnation, American utility companies are now upping their projections for electricity usage—doubling their expectations over the past year.

The consumption of power required for generative AI technologies is also monumentalFor instance, OpenAI's ChatGPT consumes over 500,000 kilowatt-hours of electricity daily to process approximately 200 million user requests, which is equivalent to more than 17,000 times the daily electricity consumption of an average American household.

Toby Rice, the CEO of the largest natural gas producer in America, has cited a prediction that by 2030, energy consumed by AI could surpass residential consumption

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Looking at our own energy usage data, last year, we consumed over 9 trillion kilowatt-hours, a 7% increase year-over-year while GDP growth hovered around 5%. In the first two months of this year, electricity generation rose again, with an 8.3% increase compared to the previous year, vastly outpacing GDP growth.

Thus, the foundation for stronger AI systems is predicated upon an expansive volume of data coupled with abundant sources of electricity and energy, setting the stage for future competitions along these lines.

The increasing recognition that AI necessitates vast amounts of power has become consensus knowledge; however, the question of how to source that power remains hotly debatedErnest Moniz, the former U.SSecretary of Energy, emphasized that utility companies might have to rely more heavily on fossil fuels such as natural gas, coal, and nuclear power, perhaps necessitating the construction of new natural gas plants to accommodate this rising demand.

Many technology companies have proclaimed ambitions of carbon reduction, with low-carbon energy sources such as solar and wind energy being the preferred choices

While these clean energy sources hold great promise, they are primarily characterized by their inherent instability.

Data centers are not so much concerned with the cleanliness of their energy sources as they are about the stability of power supply; sudden outages pose a significant threat to their operationsThis has led many to consider nuclear power as a viable solution.

Yet, constructing large nuclear power facilities is often expensive and time-consuming, usually requiring anywhere from seven to ten years of development—an eternity for the fast-paced tech industryNatural gas remains an attractive alternative, yet it's still costly, while coal, albeit affordable, is criticized due to its carbon emissions.

Moreover, energy consumption cannot rely solely on non-renewable resources like coal, oil, or natural gasGiven the uninterruptible needs tied to AI computing power, there is a heightened requirement for consistent energy supply

Recent instances, such as blackouts in Ireland and other countries with high data center energy demands, illustrate the critical need for efficient electrical networks in the AI eraAs the optimization of solar energy utilization and impact control on power grids intensifies, the integration of energy storage solutions becomes increasingly paramount, representing a boon for China's renewable energy and storage enterprises.

Presently, China ranks as the world’s leading energy producer, boasting a diverse and clean energy supply network, with electricity generation reaching nearly 9 trillion kilowatt-hours in 2022, almost double that of the U.SIn the realm of photovoltaic energy, China has firmly established itself as a frontrunner globallyOnce reliant on foreign technology, the country has now emerged as the leader across the spectrum—from market size and technological advancements to manufacturing capabilities and comprehensive supply chains.

Indeed, solar power has overtaken hydropower to become China's second-largest energy source, following coal.

Energy storage, fundamentally, is not an energy generation sector, but rather an integral component that addresses the intermittency and volatility of renewable energy sources like solar and wind

A critical intermediary is necessary to store energy and balance renewable energy supply with demand in real-time.

Thus, energy storage emerges as an indispensable facet of the renewable energy sectorWithout effective energy storage solutions, the entire renewable generation capacity could only partially fulfill its potential.

Unquestionably, energy storage has solidified its role as a cornerstone of the green energy supply chain.

In 2020, China set ambitious goals to peak carbon emissions before 2030 and achieve carbon neutrality by 2060, a strategic vision commonly referred to as the "3060" agenda within the renewable energy sectorThis comprehensive plan underscores a strong commitment to developing a diverse portfolio of renewable energy sources, clearly indicating the future trajectory of the sector.

While the notion of "AI acting as a catalyst for the renewable energy industry to become a money-generating machine" may seem exaggerated, its potential impact in the not-so-distant future is undeniable