Whether used for construction, software development, small business ventures or otherwise, AI, with its seemingly unlimited potential, isn’t all-powerful. This burgeoning revolution is dependent upon a proper power supply to the massive data centers housing the vast array of computer processing chips, hardware, software and storage capability—the computing engines that comprise AI. Like all engines, the data center computing engines run on power; specifically, electric power.
Fundamental Questions:
- What are the projected power needs for AI and what sources of energy will fuel the AI revolution?
- Where will this power come from?
- How will the U.S. compete with other entities, principally China, India and Europe if it can’t power the massive and complex data centers that are required to provide AI?
The answer to the last question is simply that we must find, generate and/or produce whatever power is required. Failure is not an option.
To fully comprehend the magnitude of the anticipated power requirements for AI, it is important to understand what exactly electric power is and how much power is required in order to begin to appreciate the challenges and potential solutions to this critical problem.
Basic Numbers:
Electric power is described by terms such as watts, kilowatts, megawatts, gigawatts and terawatts. A watt is the basic unit of electric power output or transmission.
A lightbulb: A 100-watt electric lightbulb uses 100 watts of power per second to stay on at full power. That means 100 watts of electric energy is being transmitted to and used by that lightbulb every second.
Solar Panel: A typical single solar panel that is found on the roof of a house, when it is operating at maximum efficiency in direct sunlight, generates about 440 watts of electric power per second. That would only be enough power to light four and a half 100 watt electric lightbulbs. Most homes and appliances have requirements based on a kilowatt of electric power. A kilowatt is one thousand watts; a kilowatt hour (KWH) is a thousand watts of power being delivered for an hour.
A refrigerator: Another simple example of energy usage that we can all relate to is a refrigerator which typically requires 1.4 KWH of power per day. However, when we start talking about houses, then the overall energy needs to grow dramatically.
A home: A typical home’s power usage is approximately 30 KWH per day.
Now when we start talking about multiple houses, the energy demand is measured not in thousands of watts (kilowatts), but in millions of watts (megawatts). Megawatt usage per hour is shortened to MWH. Now a typical home consumes about 1.0 megawatts of power every month.
A small municipality: A small municipality with 1,000 homes would require 1,000 megawatts of power per month. When considering the electric power consumption of data centers which are necessary to provide the computing capabilities that actually make up AI—or any other complex computer-based information processing centers—units are measured in gigawatts, or one thousand megawatts, which is a billion watts. The example of 1,000 homes per month using 1,000 watts of power (1 megawatt) becomes 1,000 megawatts per month or one gigawatt per month.
The Hoover Dam: The Hoover Dam produces roughly two GWs of power per second, assuming the water level of Lake Mead is at the appropriate level. Now, a typical AI data center is projected to consume between 100 to over 500 MW of power per second. So, the newer larger-projected AI data centers—assuming they consume 500 megawatts of power—will each need 25% of the output of the Hoover Dam to operate continuously on a daily basis. Thus, ten new AI data centers would need two and a half new Hoover Dams. Currently, projections for new AI data centers run in the thousands; that means a dramatic need for more power. Thousands of new data centers would require thousands of new Hoover Dams.
Having projected the near-term energy requirements for data centers, let’s look back at the fact that existing data centers currently consume 1-2% of all global electricity and 4% of the total energy consumed in the United States. By 2030, that percentage is projected to grow to 3-5% of all electricity produced globally and 12-16% of all power consumed in the United States. Where will the energy to meet these new enormous energy requirements come from?
Currently, and for the foreseeable future, there are only a few discrete sources of power generation available. They are: (1) gas and oil power plants, with natural gas fired plants the predominate source; (2) nuclear power plants; (3) coal-fired power plants; and (4) renewable sources of power such as solar wind, hydro and geothermal, but predominately solar due to
the low efficiency and reliability of wind power. In 2024, natural gas supplied roughly, 40-45% of U.S. energy needs; coal provided roughly 15% of U.S. energy needs; nuclear energy provided roughly 20% of U.S. energy needs; while other renewables provided approximately another 20% of U.S. energy needs, with hydro power accounting for roughly 7%. However, the hydro energy component is essentially exhausted.
In terms of cost, while renewable energy costs are falling, there are critical issues with their reliability since they depend on wind and sunlight and limited hydro capacity. Over the next decade, while a small portion of the new power demands may be met by renewables, they will not address the dramatic increase in energy demand. The obvious and likely solution within the next decade to the need for increased energy demand will therefore be met by a combination of fossil fuel—principally natural gas and coal fueled powerplants and new nuclear energy power plants with some smaller contribution from renewables, primarily solar.
Since the projected number of AI-related data centers over the next 10 years is projected to be in the thousands, the corresponding number of power plants necessary to support the AI is likewise projected to be in the thousands. To quantify thousands of gigawatts of energy requirements, the conversation now shifts to terawatts.
Since the Hoover Dam produces two gigawatts of power, you need five hundred Hoover Dams to produce one terawatt of power. And if the typical nuclear power plant produces 500 megawatts of power, you would need 2,000 of those to produce a terawatt of power. As for coal plants, you would need 1,000 large coal plants to produce a terawatt of power. This is where conventional technology, specifically the design and construction of both massive data centers and the design and construction of massive sources of power generation enter the picture and become the controlling element to the growth of AI capability in the United States.
One caveat to this analysis is the always present hope that a new technology, or a breakthrough in existing technologies, will create a new, cheaper, more dependable, efficient and less risky energy alternative in the future. However, barring such a breakthrough, there will be a dramatic increase in natural gas, coal and nuclear power plants over the next decade. The topography of large areas of the country is now likely to change because of the energy demands of AI. The construction of thousands of new power plants will dot the landscape across America. Also, the environmental impacts as well as the need for massive construction technology and labor, as well as engineering capability will be pushed beyond all current capabilities. Pradoxically, conventional American engineering and construction sectors are now, and for the foreseeable future will be the key drivers in spurring the AI revolution.
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