# AI Infrastructure Costs Soar: Goldman Sachs Tracks $7.6 Trillion Through 2031

> Goldman Sachs has put hard numbers on the AI build-out, and they are staggering. Its baseline model implies roughly $765 billion in AI capital expenditure in 2026, scaling to $1.6 trillion by 2031, for a cumulative $7.6 trillion across compute, data centers, and power over the period. The breakdown is instructive. GPUs account for about 39 percent of data center spending, and cloud rental for chips like the H100 runs from $2.50 to well over $6.50 an hour. Perhaps the most sobering figure is that 80 to 85 percent of enterprises miss their AI infrastructure forecasts by more than 25 percent, a sign of how poorly understood these costs still are. The analysis is a reminder that beneath the model launches sits a physical, capital-hungry industry whose economics will ultimately decide how far the boom runs.

_Section: [Interesting AI Articles](https://www.wortins.com/articles) · Source: Goldman Sachs · Published Friday, August 28, 2026_

## Wortins' read

Goldman Sachs has put hard numbers on the AI build-out, and they are staggering. Its baseline model implies roughly $765 billion in AI capital expenditure in 2026, scaling to $1.6 trillion by 2031, for a cumulative $7.6 trillion across compute, data centers, and power over the period. The breakdown is instructive. GPUs account for about 39 percent of data center spending, and cloud rental for chips like the H100 runs from $2.50 to well over $6.50 an hour. Perhaps the most sobering figure is that 80 to 85 percent of enterprises miss their AI infrastructure forecasts by more than 25 percent, a sign of how poorly understood these costs still are. The analysis is a reminder that beneath the model launches sits a physical, capital-hungry industry whose economics will ultimately decide how far the boom runs.

## Source

[Read the full story at Goldman Sachs](https://www.goldmansachs.com/insights/articles/tracking-trillions-the-assumptions-shaping-scale-of-the-ai-build-out)

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