U.S. Data Center and IT Investment

A central cross-category consideration is whether current spending on the AI buildout is sustainable. High-investment respondents typically conclude that it is: “I now think most of this investment is backed by real, growing demand rather than hype: demand looks like it's outrunning supply, with power and grid connections the main bottleneck rather than any lack of appetite. So I expect the buildout to keep climbing.”; “Enterprises have barely begun moving substantial production workloads into persistent AI systems.” Low-investment respondents often express skepticism: “In all cases there is an overcapacity possibility that would need to be accounted for.”; “The historical trend line is unsustainable.”

Several respondents note a buildout that migrates offshore wouldn’t be captured by a measure of US private investment. As one writes, “China’s push toward domestic chip supply chains decoupled from the HBM[high bandwidth memory]/memory crunch, plus Gulf-state sovereign buildouts, means a growing share of new AI infrastructure investment increasingly lands outside the US NIPA [national income and product] accounts entirely—this doesn't shrink global AI capex, but it directly caps the US-specific structures and equipment lines [this] question asks about.” Another respondent notes that “nationalization moves spend out of private accounts,” as well.

Many low-investment respondents stress that this category (along with the information technology equipment category below) is economy-wide: “Data centers are under 5% of [electrical and communication structures]’s level—the key fact about [this] category, since it makes [it] insensitive to a data center boom.”; “Even a dramatic AI-driven surge arrives diluted.” High-investment respondents tend to acknowledge this factor, but emphasize that the scale of new load is large enough to show up regardless: “The more data centers that are actually completed, the more obvious it becomes that generation, transmission, distribution, substations, fiber routes, and communications infrastructure need to be rebuilt around a load-growth regime that the United States has not experienced for decades.” Other high-investment respondents point to factors unrelated to AI: “The electrical grid is in desperate need not only for data centers, but also electric cars and tools. As climate change gets worse, there may be more of a push…to move away from gas power to electricity.”

Low-investment respondents often note that the real level of investment has barely moved in recent years: “[It] has been flat for five years despite constant talk of grid investment.”; “Fifteen years essentially flat, through the shale revolution, the renewables boom, and the first years of the AI surge.” Several high-investment respondents read the same flatness as having led to a backlog that will lead to a spike in investment in the years to come: “My 2028 figure has the bottleneck forcing real acceleration, and by 2035 I expect a construction boom of a scale not seen since the 1970s.”

Some low-investment forecasters argue that utilities will struggle to expand quickly and overcome supply chain constraints given that “utilities are slow companies and permitting/construction is slow even if there is demand.” One adds that investment in this domain “is prohibitively expensive, politically tricky for governments to fund, and presents all the same permitting, design, construction labor, and other costs that create bottlenecks for the data centers.” High-investment respondents tend to acknowledge this element, but believe demand—along with “money, political influence, and geopolitical expediency”—will “force the issue toward the end of the decade.”

High-investment respondents often cite the short life of hardware as a key reason investment in this sector will remain robust: “Structures [like data centers] last twenty-five years and servers last three or four. So once the installed base is large, replacement demand drives the equipment number even when construction stops growing.”; “Even if the growth of data centers slows, there will still be a demand for replacement of these systems.” Low-investment respondents don’t dispute that hardware has an existing shelf life, but instead point to reasons why this factor might not be enough. As one writes, “Open and smaller models could shift more inference onto existing or edge hardware, foreign supply constraints could limit quantities, and the late-2020s buildout could leave the economy digesting a mountain of underutilized equipment.”

Respondents from both poles broadly agree hardware is getting cheaper per unit of performance; low-investment respondents typically argue that this will suppress total investment. One notes, “Real dollar spending can stagnate even as unit compute rises sharply, since the [cost of compute]...historically falls,” and another that “prices and efficiency improvements offset some of the increase in real investment.” High-investment respondents instead tend to focus on recent confirmed growth rather than projections: “We have already seen unprecedented growth in computers, communication equipment, and other apparatus in 2025, a nearly one third jump driven by the server and GPU surge”; “BEA printed +45.9% annualized real growth in Q1 2026.”

Several high-investment respondents point to the likelihood that AI will diffuse broadly across the whole economy, and that as “AI becomes embedded throughout enterprise computing” it will begin “producing persistently higher equipment investment than before the AI boom.” One notes that we will also “have more robots interacting with the physical world and they will need IT support.” Low-investment respondents tend to emphasize factors like “memory/transformer scarcity” as reasons IT investment may remain muted, regardless of demand.