AI capital expenditure is too overheated? Goldman Sachs: This is just the beginning.
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Recently, massive capital expenditures in the AI sector have sparked market concerns about their sustainability. A new research report from Goldman Sachs clearly reveals that the current scale of AI investment is far from overheating, and this level of investment is sustainable, meaning the macro story of AI infrastructure construction remains robust.
On October 19, according to Zhuifeng Trading Desk, Goldman Sachs’ latest research report indicates that the size of AI investment is not excessive. The current technological context continues to support AI capital expenditures, and the proportion of AI-related investment to U.S. GDP is much lower than in previous technological cycles.
At the same time, they expect that productivity improvements brought by AI will generate $8 trillion in capital income for U.S. companies, far exceeding the current and foreseeable total AI investment.
The AI Investment Boom Is Sustainable
Since mid-2023, investment in AI infrastructure has continued to accelerate. In 2025 alone, publicly listed U.S. companies are expected to increase revenue by approximately $300 billion in AI-related infrastructure investments. Data from U.S. national accounts shows that AI-related expenditures have grown at an annualized rate $277 billion higher than in 2022.
Since September, OpenAI has announced a series of major investment agreements: a $300 billion collaboration with Oracle, a $100 billion investment from Nvidia, a strategic partnership with AMD to deploy 6GW of GPU computing power, and a partnership with Broadcom to deploy 10GW of custom AI chips.
The report notes that the technological context continues to support AI capital expenditures, mainly for two reasons:
On one hand, the productivity gains are significant. Goldman Sachs estimates that full application of generative AI will boost U.S. labor productivity by 15%, a process that will gradually unfold over the next decade. Academic and corporate cases show that AI application can result in an average productivity boost of 25-30%, though currently only 2.5% of jobs face automation risk, mainly concentrated in programming, customer service, and consulting fields.
On the other hand, demand for computing power continues to rise. The growth rate of AI model scale (annual average 400%) far exceeds the rate at which computing costs are falling (annual average 40%). The annual growth rates in demand for training queries and cutting-edge models stand at 350% and 125%, respectively. Although energy efficiency has improved, it is difficult to offset the expansion in demand. As long as the growth of computing power demand outpaces cost decreases, AI infrastructure investment will continue to be driven forward.
Current AI Investment Scale Is Not High
The report notes that although nominal AI infrastructure investment has hit new highs, it is not excessive compared to historical technological cycles. Historically, the peaks of investment in railroads, electrification, IT and other technological cycles have accounted for 2-5% of GDP, while the current proportion of U.S. AI investment is still less than 1%.
Goldman Sachs estimates the productivity improvements brought by generative AI will create $20 trillion in present value for the U.S. economy, of which $8 trillion will flow to U.S. companies as capital income. Even under pessimistic or optimistic assumptions, this range is between $5 and $19 trillion, significantly exceeding current and future AI investment totals. More importantly, this estimate does not account for overseas profits, new profit pools, or potential gains brought by AGI (Artificial General Intelligence).
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