Goldman Sachs is forecasting AI-related capital expenditure to reach $905 billion by 2027, a projection that underscores just how aggressively the technology sector — and the institutions financing it — are committing to the AI buildout. The figure encompasses infrastructure spending across data centres, custom silicon, power systems, and the broader supply chain that feeds large-scale model training and inference.
For macro and markets investors, the number is a directional signal as much as a forecast: Goldman's willingness to put a near-trillion-dollar figure on the cycle suggests the bank sees the current wave of AI investment as structural, not a short-term capex bubble. That framing has direct implications for semiconductor names, hyperscaler earnings, and energy infrastructure plays that sit in the AI spending chain.
Frequently asked questions
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What factors contribute to the projected $905 billion in AI capex by 2027?
The projection includes spending on infrastructure such as data centres, custom silicon, power systems, and the broader supply chain necessary for large-scale AI model training and inference.
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How does Goldman Sachs view the current wave of AI investment?
Goldman Sachs perceives the current wave of AI investment as a structural trend rather than a short-term capital expenditure bubble, indicating a long-term commitment from the technology sector.
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