The worldwide build-out of data center infrastructure is accelerating rapidly. Consensus estimates indicate that technology firms are projected to commit approximately $800 billion in capital this year to expand AI infrastructure. That investment is anticipated to keep climbing. According to research from McKinsey & Co., global AI capital expenditure is expected to reach $7 trillion by 2030. The consulting firm characterizes this as “one of the largest infrastructure build-outs in modern history.”
Some market observers take a more measured view. Research from AllianceBernstein notes:
The practical applications of AI are gradually coming into focus, though it remains too early to chart a path forward with high confidence. Still, memories of the dot-com era frequently surface in discussions with clients, alongside parallels to the nineteenth-century railroad boom and other historical episodes in which the uptake and economic advantages of emerging technology took considerably longer than investors initially anticipated—and where the eventual winners were far from obvious.
Unlike earlier capital expenditure cycles, however, AI-focused enterprises are substantiating the excitement with genuine revenue expansion. The sector is also led by firms with substantial valuations and fundamentally profitable operations, giving them the capacity to raise capital and invest aggressively over the long term. This dynamic has led Goldman Sachs to suggest that elevated capex projections could actually understate the magnitude of investment in 2026.
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Rather than the consensus projection of $800 billion in AI capex this year, Goldman Sachs believes the actual figure will approach $1 trillion. Due to differences in reporting methodology, the bank’s analysts anticipate that U.S. spending may fall short of expectations. Conversely, global spending and investment by private AI companies should exceed projections by more than $200 billion. This is because, according to Goldman Sachs, most market estimates “do not include investment in AI by private companies or by companies outside the U.S.—including firms in Asia.”
How should investors position themselves for AI capex that exceeds expectations outside the United States? Two clear strategies stand out.
1. Back the global AI frontrunner
In the realm of AI investing, Nvidia (NASDAQ: NVDA) is virtually impossible to overlook, regardless of one’s investment approach. Nvidia stands as not only the foremost AI company globally but also the largest company in the world, full stop. Its graphics processing units (GPUs) are broadly considered the finest available. While hardware leadership tends to be cyclical, Nvidia’s substantial investments in software—particularly its commitment to the CUDA developer platform—render its products considerably more durable than those of typical hardware vendors. Additionally, Nvidia’s access to capital provides a meaningful edge at a time when customers are pushing for GPU production to scale as rapidly as possible.
U.S. companies represent Nvidia’s largest customer base, and approximately 10% of its revenue originates from Taiwan. Chinese firms were once significant revenue contributors, but that channel has largely disappeared—it is evident that Nvidia operates as a U.S.-centric enterprise.
So what makes the company appealing in light of Goldman Sachs’ projections? Because Nvidia retains the industry’s premier hardware, and it is investing aggressively in international expansion. Last year, it announced more than 40 international partnerships, up from just 15 the prior year. Nvidia is already on track to surpass those figures in 2026.
According to one industry analyst, Nvidia’s “growth story used to be American hyperscalers. Since 2025, their geographical mix has turned international.”
2. Invest in AI stocks with Asian exposure
Investors can alternatively target AI stocks that already have exposure to Asia. Qualcomm (NASDAQ: QCOM), for instance, generates nearly half of its 2025 revenues from China. More than a quarter of sales originated from South Korea and other international markets.
Qualcomm is primarily focused on producing high-end mobile chipsets for smartphones. As AI adoption shifts toward edge devices, the company has a direct opportunity to benefit. The company is also expanding more aggressively into data center components. Qualcomm projects at least $15 billion in data center revenue by 2029.
To be sure, Qualcomm is not nearly as tied to AI as Nvidia—at least not yet—but that picture is changing rapidly. Trading at just 19 times earnings, Qualcomm presents a much more affordable entry point into the AI economy, though geopolitical risks and a stagnating core business add complexity to the investment case.
