The first opportunity is in copper: “Generative AI tools like ChatGPT run on large language models hosted on thousands of servers in massive data centers. These data centers require cooling systems to help the servers run more efficiently, as well as a power infrastructure consisting of transformers, generators and transmission lines. Most of these elements require copper. The construction of a $500 million Microsoft data center near Chicago required 2,177 tons of copper, for example.
“If the projections by the hyperscalers are right, data centers constructed over the next eight years will require one million tons of copper in the US alone,” Franz says. “And you’re going to have to think globally about this build-out.”
Demand for copper in electric vehicles, clean energy technology and the modernization of the US electric grid is already expected to create growing deficits.”
The second opportunity is in supplying power: “AI, like just about any advanced technology, needs power. A lot of power. Data centers could consume as much as 9% of total US electricity output by 2030, more than double current usage, according to the Electric Power Research Institute. “The demands on the grid from both data centers and electric vehicles are going to drive an increase in consumption we haven’t seen in about 20 years,” says Cheryl Frank, an equity portfolio manager.” [There is a chart in this article which shows that over the next 6 yrs, AI will be the single biggest driver of growth in power demand.]
The third opportunity will be in capital equipment: “Substantial capital equipment needs to build out data centers and increase power generation globally are driving demand for a range of industrial companies, in some cases leading to shortages. For example, energy equipment maker GE Vernova expects its $6.4 billion backlog of gas powered turbines needed for backup generators and other electrical equipment to triple by the end of 2024.
Because AI chips generate a great deal of heat, data centers require advanced liquid cooling systems to prevent equipment failure and improve energy efficiency.”
The fourth opportunity will be in – counterintuitively – people: “…the rollout of AI faces a potential human resources shortage. “We’re starting to hear companies say there is an actual shortage of AI engineers who can build foundational models, as well as a shortage of people able to implement AI systems at the enterprise level,” Franz says.
According to a recent Salesforce survey, 60% of public sector IT professionals identified a shortage of AI skills as their top challenge to implementing AI.
Without experienced people leading the rollout, adoption will likely be slower and take more time to generate the efficiencies the technology can provide. “I think professional services companies like Accenture and Oracle will play an important role in helping enterprises determine their AI strategies,” Frank adds. “There will be a lot of people in this chain.””
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