As AI technology advances, it's not just about the algorithms and models. The energy required to power these advancements is substantial and growing. AI processing demands a significant amount of electricity, and as AI adoption accelerates, so does the need for a more robust power infrastructure. I want to share some concrete examples of how this impacts the demand for energy and then share some things that I think we can do to solve these issues.

For context, OpenAI’s ChatGPT alone consumes around 1 GWh a day, enough electricity to power 33,000 homes – and that is just one AI company. This is just a fraction of the demand driven by data centers, which are at the core of AI operations.

The growing energy needs of data centers

Data centers are the backbone of the digital economy, supporting everything from streaming services to cloud computing and AI applications. There are about 5,400 data centers in the United States, consuming roughly four percent of the country’s overall electricity. This demand will increase dramatically with the rise of AI.

In states like Utah, the grid demand sits between 3.5 and 4GW of electricity. However, if every proposed data center were constructed, that number could rise to 12GW, effectively tripling the state’s electricity demand. Utah’s situation is not unique. Across the United States, data centers are becoming significant consumers of electricity, with projections indicating they could require nine percent of total US power demand by 2030.

In Northern Virginia, the country’s leading data center market, these facilities require enough electricity to power 800,000 houses, which is more than a quarter of all homes in the state. With AI driving further growth, these numbers are only expected to climb.

Can the grid keep up?

The question arises: can our existing energy infrastructure support this surge in demand? Leaders in the AI and data center industries have voiced concerns about potential energy constraints. Mark Zuckerberg, CEO of Meta, has stated: “I actually think before we hit [computing constraints], we’ll run into energy constraints.” Similarly, Sam Altman, CEO of OpenAI, emphasized the need for breakthroughs in energy sources like fusion or significantly cheaper solar plus storage to meet future demand.

Already, we are seeing examples of energy constraints impacting data center development. In 2022, Dominion Energy paused data center connections in Northern Virginia due to insufficient capacity, highlighting the region's struggle to keep up with demand. More recently, Silicon Valley Power began limiting new data center proposals in Northern California to a maximum electricity allocation of 2 MW.

Solutions to the energy crisis

Addressing this challenge requires a multi-faceted approach. Expanding grid capacity through traditional means like building new power plants and transmission lines is essential but often slow and fraught with regulatory hurdles. However, new models for expanding grid capacity are emerging.

Renewable energy and storage technologies like Flywheel Energy Storage Systems (FESS) and Battery Energy Storage Systems (BESS) are critical in meeting immediate and long-term energy needs. These systems offer more flexibility and faster deployment compared to traditional grid expansion projects, providing a crucial buffer against energy shortages and enhancing grid stability.

FESS operates like a high-speed spinning wheel that can store excess energy in milliseconds and release it back into the grid when demand fluctuates. This rapid response helps smooth out the flow of electricity, much like a water tank stores extra water during a heavy rainstorm to release it later when it is most needed.

BESS, on the other hand, acts as a giant rechargeable battery, storing energy during periods of low demand and releasing it when the grid is strained. This balances the flow of electricity and prevents congestion, similar to how a reservoir holds and releases water to control the flow of a river.

FESS and BESS can also work together, acting like big storage tanks for electricity, to address the challenges of grid congestion and stability, mitigating the energy challenges posed by data centers. FESS offers a fast response, instantly absorbing sudden surges in energy demand, while BESS provides longer-term storage by capturing excess energy during low demand and releasing it when the grid is under pressure, such as during peak data center operations.

The role of AI in energy management

Interestingly, AI is not just a consumer of energy; it also plays a vital role in optimizing grid management. By leveraging AI, we can improve demand forecasting, optimize energy distribution, and enhance overall grid performance. AI technologies allow for real-time monitoring and adjustment of energy usage, ensuring a more efficient and reliable energy infrastructure.

A collaborative approach to energy

As we navigate this new landscape, it is clear that the surge in AI-driven power demand is reshaping our approach to energy. By embracing innovative solutions like decentralized power generation and storage, and harnessing AI's potential to optimize grid operations, we can ensure a future where technology and energy coexist sustainably.

The path ahead is challenging, but we can turn these challenges into opportunities and build a grid ready for the AI revolution with collaboration and innovation.