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Powering Progress But at What Cost? The Staggering Energy Appetite of AI

Recent reports paint a concerning picture. Google AI alone uses enough electricity in 1 second to charge 7 electric cars.

Image credit: Israel Palacio

As artificial intelligence continues to transform various aspects of our lives, concerns are growing about its voracious appetite for energy. Recent studies and reports paint a concerning picture of AI's electricity consumption, with a particular focus on Google's AI-powered search feature.

Introduced in May 2024, generative AI in Google search has come under scrutiny not only for reducing organic traffic to websites, diminishing the importance of traditional rankings, and occupying more screen space, but also for its substantial energy consumption. According to the latest research:

Alex de Vries, a PhD candidate at the VU Amsterdam School of Business and Economics and the founder of Digiconomist, estimates that by 2027, the worldwide electricity consumption for newly manufactured AI servers could increase by 85.4–134.0 TWh annually. This projected increase in electricity demand is comparable to the total annual electricity consumption of countries like the Netherlands, Argentina, and Sweden.

This exponential growth in energy demand is driven by the increasing complexity and scale of AI systems, which require vast amounts of computational power to train and run. As AI becomes more ubiquitous in various applications, from natural language processing to computer vision, the energy demands of these technologies are escalating.

Image credit: Shane Rounce

Tech giants like Microsoft, Google, Amazon, and Meta have made significant sustainability commitments, such as aiming to power their operations with 100% renewable energy. Although, the rapid growth in data center capacity and AI computing is outpacing the ability of renewable energy installations to keep up. This is leading to increased electricity usage and carbon footprint from data centers.

Critics argue that while these companies are procuring renewable energy, they are still operating off the same power grid as everyone else and may be limiting the availability of green energy for other users. The industry's green energy claims have been criticized as misleading, as the companies are not necessarily reducing overall emissions but rather shifting the burden to other grid users.

This tension between the tech industry's reliance on AI and its environmental impact is a growing concern. As these companies continue to invest heavily in AI development and deployment, the strain on the power grid and the associated carbon emissions are becoming increasingly problematic.

Innovative Solutions and Controversies

In response to the growing energy demands of AI, major tech companies are investing in experimental clean-energy projects, though their long-term success remains uncertain.

Microsoft has partnered with fusion startup Helion, backed by OpenAI CEO Sam Altman, to purchase electricity from Helion's first fusion power plant, expected to be operational by 2028. Fusion power holds promise as a clean, abundant energy source to meet AI's needs, but the technology remains highly experimental and its timeline for commercialization is uncertain.

In addition to Helion, Sam Altman chairs Oklo, a company developing small modular nuclear reactors. These could potentially provide a carbon-free energy source for AI applications, but they face regulatory hurdles and public skepticism about nuclear power.

Google is exploring geothermal power, which taps into the Earth's natural heat to provide a reliable and renewable energy source for data centers. However, the feasibility and scalability of this approach are still being evaluated.

Critics argue that some companies' renewable energy contracts may be accompanied by fossil fuel expansions, raising concerns about the true environmental impact of these efforts and the industry's commitment to sustainability.

The Secrecy Surrounding AI Energy Use

Major tech companies like Meta, Microsoft, and OpenAI are reluctant to share detailed information about their AI systems' energy usage, making it difficult to accurately assess the scale of the problem. The Energy Information Administration currently doesn't calculate data centers' energy use due to low cooperation rates in their last survey. This lack of transparency hinders the public's understanding and regulators' ability to mitigate the issue.

Despite deliberations behind closed doors about AI's ecological footprint, tech companies' lobbying has kept these concerns from being addressed in state or federal legislation. In February 2024, Senator Edward Markey introduced legislation to create voluntary reporting guidelines on how AI is affecting the environment, but researchers argue that relying on companies' voluntary disclosures will never be sufficient. The lack of mandatory reporting has allowed the tech industry to operate with limited accountability when it comes to the environmental impact of their AI-powered technologies.

The Impact on Utilities and Ratepayers

The energy-intensive nature of AI applications is also straining local power grids and utilities, with the costs often passed on to ratepayers. In Kansas, where Meta is building a data center, the local utility Evergy announced that it would delay the retirement of its coal plant by five years to meet the increased demand.
Similarly, in Northern Virginia, home to "data center alley," Dominion Energy paused new data center connections in 2022 due to grid constraints. These examples illustrate the broader impact of AI's energy demands on local communities and the challenges faced by utilities in keeping up with the industry's growing power needs.

Balancing Progress and Sustainability

The rapid adoption of AI technologies has outpaced regulatory efforts to monitor and mitigate their environmental impact. As we embrace the transformative potential of AI, it's vital to address the significant energy costs associated with these technologies. Balancing progress with sustainability will require increased transparency from tech companies, more robust regulatory frameworks, and continued innovation in clean energy solutions.

The stakes are high, as the unchecked growth of AI's energy consumption could undermine efforts to combat climate change and jeopardize the long-term sustainability of our energy systems. By tackling these challenges head-on, we can make the most of AI while ensuring that its development and deployment align with our environmental goals and the well-being of our communities.