Artificial Intelligence (AI) is the ability of a digital computer or a computer-controlled robot to perform tasks commonly associated with intelligent beings such as learning, problem solving, reasoning, and decision making. AI, and the data centers required, demand more of one thing… Energy. 

Energy is essential to addressing the challenges of reducing costs on everything from groceries to housing. History proves that economic progress only comes when energy production increases. Long story short, energy fuels the growth that improves people’s lives.

AI requires significant computational power primarily due to the complex algorithms and large datasets involved in training and deploying machine learning models. Many AI applications utilize deep learning, which involves networks with multiple layers. Training these networks requires extensive computations, which require large amounts of data for training to improve accuracy and performance. This data must be processed, stored, and analyzed at data centers, consuming a significant amount of  computational resources. The more robust the AI workload, the more energy is consumed by the data center. 

As AI continues to evolve, further advancements in data centers will be necessary to keep pace with its requirements. By 2030, data centers could potentially consume the equivalent energy of New York City's annual energy use. Approximately 60% of that usage would be powered by gas.  According to S&P Global, this shift could add 50 GW of gas-fired power to US grids, and increase the natural gas demand by 17%.

According to McKinsey & Company, global demand for data center capacity could rise at an annual rate of between 19 and 22 percent from 2023 to 2030 to reach an annual demand of 171 to 219 gigawatts (GW). This contrasts with the current demand of 60 GW, raising the potential for a significant supply deficit. To avoid a deficit, at least twice the data center capacity built since 2000 would have to be built in less than a quarter of the time.

Access to power has become a critical factor in driving new data center builds. Without ample investments in data centers and power infrastructure, the potential of AI will not be fully realized. Meeting this demand will require considerably more electricity than is currently produced in the United States. 

Below are some suggestions to create new solutions to power access and sources.

1 . Investors can funnel investments into utility companies to build out transmission and distribution (T&D) infrastructure in key markets. The demand for data centers and power show no signs of slowing, so T&D markets should respond accordingly.

2. The timeline of building out data centers and those of power infrastructure development can take years.          Hyperscalers are building out capacity in new and atypical locations outside the core data center markets because these areas offer cheaper, available power and have the potential for carbon-free infrastructure. Investors have opportunities to fuel growth by accelerating the build-out of fiber or power infrastructure in these secondary locations.

3. Investors can seek to support behind-the-meter solutions to provide power in areas where utilities providers cannot keep up with pace or reliability requirements as local supply availability or transmission constraints worsen. The sites available for these opportunities are limited, but creating more competition and urgency among investors to act sooner than later can help secure the talent, connectivity, and regulatory requirements necessary to run the sites.

4. Investment in clean energy such as solar power or offshore wind. Investment in this space has a long track record, but does have mixed returns. 

AI has forced the pace of progress. Whether we can provide the energy to keep up with that pace is something only time will tell. 

Tickers to consider:   KALA, EVAX, JTAI, AEON

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