At a Glance
AI’s voracious appetite for computing power will spur growth in data centers, from today’s 50–200 megawatts to more than a gigawatt.
AI will also transform edge computing, as small, domain-specific language models will support tasks requiring lower latency.
These changes will strain already-stressed supply chains as leaders vie for resources, especially labor and electricity.
As data centers and edge computing evolve, enterprises may need to reassess market positions and revisit strategic ambitions.
AI’s need for computing power will radically expand the scale of large data centers over the next five to 10 years. Today, big data centers run by hyperscale cloud service providers range from 50 megawatts to more than 200 megawatts. The massive loads demanded by AI will lead these companies to explore data centers in the 1 gigawatt and higher range. That will have huge implications on the ecosystems that support these centers (including infrastructure engineering, power production, and cooling), and affect market valuations. The architectural requirements for achieving the necessary computing, electrical power, and cooling density for gigawatt data centers will influence the design of many smaller data centers (see Figure 1).
The ubiquity of AI will also change the nature of edge computing. Domain-specific language models—smaller, simpler, and optimized for specific purposes—will be necessary to handle computing loads that may require faster response, lower latency, or are able to use a simpler model due to a narrow focus. Innovation at the edge will extend to the form factor of user devices, which will also change to meet the needs of people engaging with AI.
The implications of these changes will be transformative across a number of critical dimensions, including speed of technology development, sector leadership, power generation and consumption, construction and industrial supply chains, environmental considerations, market economics, national security interests, and financing and investment. To remain in the top tier of the market, leaders will need to make unprecedented levels of investment in technology infrastructure. If large data centers currently cost between $1 billion and $4 billion, costs for data centers five years from now could be between $10 billion and $25 billion.
Strain on resources
The power demands and price tags of these large data centers will impose limits on how many can be built and how quickly. The scramble to acquire AI resources is already creating extreme competition for resources at the high end of the market, and growing data center requirements will further strain capabilities.
Power consumption is one critical example. Utilities are already fielding requests from hyperscaler customers to significantly expand electrical capacity over the next five years. Their needs will compete with rising demand from electric vehicles and re-shoring of manufacturing, stressing the electric grid. Growth in electricity demand has been essentially flat for the last 15 to 20 years, but investments to expand and strengthen the grid and add new power sources (including on-site generation and renewables) will need to increase significantly.
Infrastructure providers and technology supply chains, including networking, memory, and storage, are also investing to meet the demands for high-performance compute from hyperscalers, digital service companies, and enterprises. Large data centers will push the limits and unleash innovation in physical design, advanced liquid cooling, silicon architecture, and highly efficient hardware and software co-design to support the rise of AI.
Large data centers are major construction efforts, requiring five years or more. Demand for construction and specialized laborers—as many as 6,000 to 7,000 workers at peak levels—will strain the labor pool. Labor shortages in electrical and cooling may be particularly acute. Many projects occurring at once will stress the entire supply chain, from laying cables to installing backup generators.