Is AI Slowing Down the Energy Transition?

While we could choose any number of metrics to gauge the current stresses on the power grids, in this report we’ll focus on three key measures:

• Consumption relative to theoretical full capacity
• Consumption relative to actual production
• Growth in consumption relative to growth in capacity

First, let’s examine the total electricity consumption in each country/region as a percentage of full theoretical capacity. In this context, we consider full theoretical capacity to be the total amount of electricity that the power grids could generate in a year if all power plants were operating at full capacity each day.

On the surface, these calculations may suggest that the power grids have ample excess capacity, with none of the countries/regions consuming more than 50% of full theoretical capacity in a given year. However, it’s important to keep in mind that power plants may not be able to continuously produce power. According to the Department of Energy, nuclear plants had an average capacity factor of 93% in 2021, meaning they could reliably produce 93% of full theoretical capacity three years ago. But solar could only produce 25%. These dynamics suggest that there may not be as much excess capacity as one might think.

We can also look at the ratio of electricity consumption to electricity generation. Here the U.S. stands out as an interesting datapoint: electricity consumption was actually slightly higher than electricity production, meaning that the U.S. had to rely in part on imported electricity. While it is true that there are proposals (such as the Champlain Hudson Power Express) for more grid interconnections between Canada and the U.S., lengthy and complex permitting processes mean that it may take time for more of these interconnections to become available.

Meanwhile, in 2023, Europe and Asia consumed all of the power that was produced in their respective continents. So overall, on this metric, the electrical grid may be somewhat strained across regions.

One final metric to consider: the growth in energy consumption vs. energy capacity over the past 10 years. If energy consumption is growing slower than energy capacity, then over time this might help ease pressures on the electricity grid, assuming that the new power plants have a similar average capacity factor as the old power plants.

In the U.S., Europe, and Asia, the cumulative average growth rate in electricity consumption was slightly slower than the cumulative average growth rate in electricity capacity, suggesting that perhaps there may have been a narrow reprieve with the passage of time. But nevertheless, the totality of the three metrics suggests that the overall electrical grid may still be somewhat fragile.

Part 2: AI’s incremental impact on energy consumption and carbon emissions

As AI becomes increasingly integrated in our daily lives and its capabilities continue to advance, energy consumption has seen a significant surge. A recent study found that generating a single image using generative AI requires as much energy as fully charging a smartphone.1 Moreover, data centers use up 10 times more power for these AI-related tasks than they do for a normal search. The computational power required to develop machine learning models has doubled every five to six months since 2010.2 This dynamic presents a significant challenge for policymakers and tech leaders alike: balancing the need for AI-driven growth with the imperative to reduce emissions and meet global climate targets.

In 2022, the International Energy Agency (IEA) estimated that data centers, cryptocurrencies and AI consumed almost 2% of the total global electricity demand.3 Exhibit 2 shows the IEA’s projections through 2026, which predict that global electricity consumption of these technologies will range between 620 to 1,050 terawatt hours (TWh), up from 460 TWh in 2022. To put this into perspective, this increase is roughly equivalent to adding at least one Sweden or, at most, one Germany’s entire electricity consumption to the grid, according to the IEA.

Exhibit 2: Global electricity demand from data centers, AI and cryptocurrencies, 2019-2026

This rising energy demand is leading to higher emissions. The power demand growth in data centers is projected to more than double data-center-related CO2 emissions by 2030 compared to 2022 levels, potentially representing 0.6% of global energy-related emissions.4 In fact, a recent study conducted by Cornell University5 estimates that training a large language model (LLM) like GPT-3 (with around 175 billion parameters) can emit around 552 metric tons of carbon dioxide. This is akin to one coal-fueled power plant working continuously for around 10 straight hours.6 The fact that training LLMs requires thousands of GPU hours and systematic retraining to remain relevant further increases energy consumption and emissions.

Such trends are raising concerns about whether companies will be able to meet their climate targets set before the AI boom. The Magnificent Seven (Mag 7) companies, for example, are finding it increasingly difficult to reduce their emissions. The energy-intensive nature of AI is forcing them to explore alternative energy sources, such as onsite power generation and even nuclear energy, to maintain operations without further escalating their carbon emissions. Google’s recent sustainability report revealed that they experienced a 48% increase in total greenhouse gas emissions from 2019 to 2023 and a 37% rise in Scope 27 emissions between 2022 to 2023 alone. This is a trend we are seeing in other technology companies, whereby companies are seeing sharp increases in their carbon footprints rather than the opposite.

As the environmental impact becomes more prominent, some tech companies are attempting to meet their net-zero commitments through offsets, such as investing in clean energy projects. This approach is often chosen because current sustainability reporting practices allow for carbon-emission reductions to be accounted for through global investments rather than solely through direct reductions in emissions. For example, one company is matching 100% of its electricity consumption with renewable energy purchases. This has brought media attention to the issue, with some individuals worried that these companies may be too reliant on creative accounting to deal with rising carbon emissions —rather than confronting the problem with technological and market improvements. Notably, these offsets do not eliminate the companies’ underlying emissions, which will continue to grow as long as data centers rely on fossil fuels for power generation. While offsets can help balance a company’s carbon ledger, using them ultimately just means that as a company’s emissions increase, their investments grow in tandem—without reducing the company’s overall carbon footprint.

Other AI companies are opting to set operations in regions with abundant energy supplies—such as Iceland—in an attempt to mitigate their environmental impact without making substantial changes to their operations.8 However, accessing renewable-heavy grids presents challenges. For instance, more than half of the new utility-scale solar capacity in the U.S. is planned for just three states: Texas (35%), California (10%), and Florida (6%)9, highlighting the potential for difficulties for companies not located in or near these regions seeking to access renewable energy. This limitation forces companies to either relocate their operations—which is a costly and logistically complex process—or continue relying on fossil fuels, which undermines their sustainability goals.

Part 3: Offsets and Efficiency

While AI’s increasing energy demands are a concern, the technology also offers potential to support the energy transition. Much of the recent discussion in the media concerning AI has been centered around generative AI and large language models, but AI is a broad term that encompasses a myriad of technologies, some of which can be incredibly powerful in reducing energy usage. In a recent interview in London, Bill Gates suggested that AI could drive more than a 6% reduction in overall energy consumption.10

A recent report by Boston Consulting Group (BCG) supports this view, predicting that AI could help mitigate 5% to 10% of global GHG (greenhouse gas) emissions by 2030. AI’s ability to optimize operations will be central for this to be the case, particularly in the energy, transportation and agriculture sectors, which are major sources of emissions. For instance, AI can improve energy use in buildings, reduce waste in supply chains, and enhance the efficiency of renewable energy sources.11

AI can also enhance grid management by better predicting energy supply and demand and enabling real-time monitoring, predictive analytics and dynamic control of energy storage assets. This helps minimize emissions while maximizing the value of energy, making it a more attractive option. For example, machine learning models can improve the accuracy of renewable energy forecasts, helping companies better align energy supply with demand. This also enables load shifting, where energy use is timed to match peak renewable energy output, cutting costs and encouraging more investment in renewables.12 AI can also be used to optimize the use of battery storage systems by predicting when to store excess renewable energy and when to release it, ensuring a consistent energy supply when the sun isn’t shining, or the wind isn’t blowing.

Additionally, AI and deep learning technologies are already being used to improve the energy efficiency of data centers. In the case of Google, in 2016 the company said it was using technology to reduce the amount of energy needed to cool down its data centers by 40%, meaning a 15% improvement in overall power usage effectiveness. This is an example of how, as technology continues to evolve, so too will the strategies for managing its impact on the environment.

Ultimately, while the rising energy demands associated with certain types of AI present significant challenges, other AI applications also hold considerable potential to drive energy efficiency and support the transition to a low-carbon economy. As AI continues to evolve, its role in optimizing energy use, improving renewable energy integration, and enhancing the efficiency of data centers will be crucial in balancing the environmental impact of its own growth. By leveraging AI's capabilities, there is potential to mitigate its energy footprint and contribute to a more sustainable future.

Part 4: Investment implications

In the earlier parts of this article, we showed that even before the recent excitement about AI, electrical grids in many parts of the world were already under significant pressure. And with the rapid development of electricity-intensive AI capabilities, the electrical grids could be under even more strain in the years to come. While it’s true that there may be efficiency savings, it can be challenging to quantify the exact amount of efficiencies generated from smarter electricity consumption.

These trends have several important implications, both direct and indirect. On the direct side, higher electricity consumption could significantly increase revenue for utility companies, giving a boost to a sector that is sometimes regarded as more slow and steady by investors.

Beyond the utility companies, the higher electrical demand could also lead to more traction for companies that build the infrastructure necessary to generate power. For instance, the companies that manufacture rooftop solar panels or produce the wind turbine shafts may see a boost in business.

Moreover, innovative companies that are able to create solutions for enhancing energy efficiency could also see healthy tailwinds amid rising electrical consumption. From software companies that leverage AI to generate smarter energy allocation algorithms, to companies that can create the physical infrastructure to better store energy with minimal loss, a wide range stand to potentially benefit.

And of course, data centers—a sector which is already seeing a significant boom—might continue to reap the rewards from the rollout of AI and potential changes to energy consumption and production patterns.

While some of these companies will be publicly listed, others may be backed by venture capital firms or private equity funds—an important reminder of the power of private markets for accredited investors.

Ultimately, AI creates opportunities and challenges alike for investors. Navigating both effectively can play a crucial role in generating strong investment outcomes.

¹ Carnegie Mellon University & Hugging Face 2024 - https://arxiv.org/pdf/2311.16863

² IEA, 2023 - https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple

³ IEA , 2024 - https://iea.blob.core.windows.net/assets/18f3ed24-4b26-4c83-a3d2-8a1be51c8cc8/Electricity2024-Analysisandforecastto2026.pdf

⁴ Goldman Sachs, 2024 - https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand

⁵ Patterson et al, 2021 - https://arxiv.org/pdf/2104.10350

⁶ A typical coal-fueled power plant uses 1.14 pounds per kilowatt hour (KWh), according to the US Energy Information Administration.

⁷ Scope 2 emissions are indirect GHG emissions associated with the purchase of electricity, steam, heat or cooling.

⁸ Nature, 2024 - https://www.nature.com/articles/d41586-024-01137-x

⁹ EIA, 2024 - https://www.eia.gov/todayinenergy/detail.php?id=61424

¹⁰ Techspot, 2024 - https://www.techspot.com/news/103617-bill-gates-dont-have-worry-about-ai-energy.html

¹¹ BCG, 2023 - https://www.bcg.com/publications/2023/how-ai-can-speedup-climate-action#:~:text=1.,related%20adaptation%20and%20resilience%20initiatives.

¹² IEA, 2023 - https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple

© Russell Investments Group, LLC. 1995-2024. All rights reserved. This material is proprietary and may not be reproduced, transferred, or distributed in any form without prior written permission from Russell Investments. It is delivered on an "as is" basis without warranty.

Products and services described on this website are intended for United States residents only. Nothing contained in this material is intended to constitute legal, tax, securities, or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. The general information contained on this website should not be acted upon without obtaining specific legal, tax, and investment advice from a licensed professional. Persons outside the United States may find more information about products and services available within their jurisdictions by going to Russell Investments' Worldwide site.

Russell Investments is committed to ensuring digital accessibility for people with disabilities. We are continually improving the user experience for everyone, and applying the relevant accessibility standards.

Russell Investments' ownership is composed of a majority stake held by funds managed by TA Associates Management, L.P., with a significant minority stake held by funds managed by Reverence Capital Partners, L.P. Certain of Russell Investments' employees and Hamilton Lane Advisors, LLC also hold minority, non-controlling, ownership stakes.

A message from Advisor Perspectives and VettaFi: To learn more about this and other topics, check out some of our webcasts.

© Russell Investments

Read more commentaries by Russell Investments