AI Boom Faces Headwinds from Global Trade Tensions and Tariffs

Despite the ongoing surge in corporate investments in artificial intelligence, warning signs are emerging that geopolitical tensions and economic instability may soon slow the momentum. The trade conflict between the United States and China, reignited by renewed tariffs, is casting uncertainty over industries ranging from tech to energy.

Investors are now eyeing the upcoming earnings reports from major players like Alphabet and Microsoft, along with power providers such as Vistra and Constellation Energy, to assess the impact of the escalating tariff battle on AI infrastructure development. These companies are integral to supporting the energy needs of sprawling data centers that fuel AI growth.

Across sectors, clients from retail to automotive are reportedly reining in spending amid rising uncertainty. This conservative shift is already being reflected in a more cautious approach to data center expansion, a trend analysts say could put downward pressure on AI investment.

While both Google and Microsoft have reaffirmed their ambitious capital expenditure plans for 2024, together totaling a projected $155 billion, nearly half of what analysts expect “Big Tech” to allocate to AI, the pressure is mounting. With China excluded from a recent 90 days tariff relief window, supply chains remain vulnerable. As a key supplier of AI-related hardware, China’s diminished accessibility could pose significant cost challenges.

The re-imposition of U.S. tariffs on Chinese imports, now as high as 145%, threatens to sharply inflate the cost of critical electronics and infrastructure. According to Pat Lynch, a senior executive at CBRE, the added costs and supply disruptions may force global suppliers to prioritize other markets, straining U.S. projects further.

A slowdown in AI capital outlays could ripple through the broader U.S. economy. J.P. Morgan analysts projected that data center investment alone could add up to 0.2% to GDP growth between 2025 and 2026.

The market has already priced in some of the risks. High-performing tech stocks, often referred to as the “Magnificent Seven”, have collectively lost nearly $5 trillion in market value since peaking late last year. AI chip leader Nvidia, once the world’s most valuable firm, has seen its stock drop by about 26% this year, while Alphabet has declined around 20%.

Signs of Strategic Pullback

There are indications that firms are dialing back their expansion efforts. Analysts at TD Cowen recently noted that Microsoft had shelved several projects involving approximately 2 gigawatts of planned power usage in the U.S. and Europe over the past six months due to oversupply.

In a recent LinkedIn post, a senior Microsoft executive acknowledged that while the company remains committed to growth, it is reassessing the pace of its projects in light of evolving customer needs. Similarly, Amazon has delayed some new data center lease agreements, though the company described this as routine capacity management.

Analysts from Wells Fargo observed that cloud giants, also known as hyperscalers are becoming more selective in committing to large-scale infrastructure projects, particularly those requiring high energy consumption.

According to estimates from LSEG and Visible Alpha, Microsoft is projected to post its slowest revenue growth in nearly two years, with its cloud platform Azure expected to see its weakest performance in over 12 months. Similar slowdowns are anticipated for Alphabet, Amazon, and Apple, despite the marginal boost from a weaker U.S. dollar.

Still Betting on the Long Game

Nevertheless, many investors remain optimistic. The long-term potential of AI, they argue, continues to justify the heavy upfront costs. Earlier this month, Amazon CEO Andy Jassy reaffirmed the importance of staying competitive in the AI race, defending his company’s current level of spending.

Eric Schiffer, CEO of the Patriarch Organization, echoed this sentiment, saying the market is overly focused on short-term returns and underestimating future gains. He believes significant payoffs from hyperscalers could begin materializing within 12 to 18 months.

“The big tech firms simply can’t afford to fall behind in the AI race” Schiffer said.