Jul. 7 at 4:31 PM
Goldman baseline model implies
$765B in annual AI CapEx in 2026, growing to
$1.6T in annual CapEx in 2031
Debates about the scale of AI infrastructure investment are often framed as a referendum on demand: whether AI adoption, monetization, or productivity gains will ultimately justify trillions of dollars of capital investments. But the amount of capital required to support today’s AI ambitions is not a single, fixed number—it is highly sensitive to a small set of structural assumptions about how the infrastructure itself is built and renewed.
The implication is not that current estimates are obviously too high or too low, but that they are far more conditional than they appear & so may shift over time. As assumptions around technology progress and system design and market demand behavior shift, estimates of required capital will move with them. For investors and operators, critical questions remain: What fundamental assumptions do we have about the future, and how resilient to changes in those assumptions are our plans?
There is a certain circularity hinted at in this analysis. Much of it has focused on how difficult it will be to deploy trillions of dollars of capital against the physical, institutional, and economic constraints we have described. But if the ecosystem does manage to conquer those constraints—if the infrastructure is built, the bottlenecks are cleared, and the cost of compute continues to fall—then the history of technology suggests that the result may not be surplus capacity but rather a new wave of demand and use cases that could not have existed at higher price points. The success of the build-out for today’s AI ambitions may be what ensures that it is not enough for tomorrow’s technological opportunities.
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