Big AI reset ahead? Chris Wood doubts hyperscalers’ $990 bn capex estimates, highlights India’s reverse AI trade advantage

A note of caution is emerging amid the market frenzy for artificial intelligence, as Jefferies' global head of equity strategy, Chris Wood, questions the economic viability of the nearly $1 trillion in capital expenditures planned by US technology giants. The projection covers the anticipated spending by Microsoft, Amazon, Alphabet, and Meta over the next five years to build out their AI infrastructure.

The Trillion-Dollar Investment Question

The scale of the investment is staggering, with an estimated $200 billion earmarked for 2024 alone. This spending has fueled significant stock market gains for these hyperscalers, which now constitute 25% of the S&P 500's total market capitalization. Year-to-date, their stocks have surged between 14% and 37%, largely on the promise of AI-driven growth.

However, Wood raises a critical question about the return on this massive investment. The concern centers on whether the eventual revenue generated from AI applications will justify the immense upfront and ongoing costs, drawing parallels to previous technology investment cycles that ended in market corrections.

India's 'Reverse AI Trade' Opportunity

Wood's analysis also highlights a potential structural shift that could benefit India’s technology sector. He posits a “reverse AI trade,” where the high operational costs of running large language models (LLMs) in developed markets drive Western companies to outsource related application development and services to more cost-effective regions.

This trend could see major Indian IT firms, such as Tata Consultancy Services and Infosys, become key players in the global AI ecosystem. Instead of simply exporting services, India would import foundational AI models and export high-value application development built upon them.

Market Concentration and Risk

While maintaining an overweight position on the hyperscalers for now, Wood’s commentary underscores the risk concentrated in a handful of stocks driving the broader market. A potential “reset” or failure to monetize AI at the expected scale could have significant repercussions, challenging the current market narrative and potentially deflating what he terms a potential “tech bubble 2.0.”