The obsession with powerful large language models overlooks the developing world.
Bhaskar Chakravorti
the dean of global business at Tufts University’s Fletcher School of Law and Diplomacy.An illustration shows a tech-textured shape being pulled down to include the Southern hemisphere.Sebastien Thibault illustration for Foreign Policy
The artificial intelligence industry seems poised for a crash. Spending on AI infrastructure is expected to hit $2.8 trillion by 2029, and it is hard to imagine how any potential financial returns can justify this as a rational investment decision. Analysts across Wall Street, the International Monetary Fund (IMF), and the Bank of England are already voicing their concerns.
One of the biggest challenges in getting people to pay for the industry’s products is that there are not enough use cases—the term most frequently offered, which means value-creating jobs done by AI—to justify the expense. The irony is that while use cases for big AI, built for power, are hard to find, there is no shortage of use cases for simpler forms of AI—what I call “small” AI—which could be deployed for narrower purposes across the developing world. Not only are the use cases plentiful, the need for solutions to long-standing problems is urgent, and the impact could be felt by 6.7 billion people who populate low- and middle-income countries.
Getting the AI industry away from its singular obsession with building ever more powerful large language models will not be easy. The goal of OpenAI’s CEO, Sam Altman, is artificial general intelligence (AGI), a form of superintelligence that will require adjusting hundreds of billions of parameters in AI models for months and maybe years. The repeated computations required involve immense processing power and high-performance computing that, in turn, consume massive amounts of electricity. Altman has reportedly signed an agreement with Microsoft that says OpenAI will only have achieved AGI when its AI delivers $100 billion or more in profits. Given that OpenAI’s revenue target for 2025 is $13 billion against $1 trillion of investment, achieving AGI will take time.
As fears of an AI bubble grow, and as the U.S. and global economies have become a giant bet on AI, it is a good time to ask: Can the technology be directed toward more immediate needs?
Across this big AI ecosystem, the staggering pace of investments—at the equivalent of $1,800 per American—now add up to 2 percent of the United States’ GDP and have artificially boosted GDP growth by 0.7 percent. OpenAI alone has locked in $1 trillion in deals this year, giving it the power to harness the equivalent of 20 nuclear reactors. This kind of spending is problematic beyond the missing use cases: Uncontrolled AI development comes with many unresolved risks; the AI boom has taken over the critical U.S. venture capital sector, crowding out a wider cross-section of innovations; and it is masking serious vulnerabilities in the overall economy.
To get a sense of why big AI appears to be an investment in too much power with too little purpose, consider OpenAI’s own data, which reveals that people are using the product mostly as a personal assistant for simple tasks. The uses of AI today are primarily in areas where we have substitutes and other tools at our disposal. Also, the users are mostly in rich countries where affordable alternatives are available. The customers that could justify the massive spending would be businesses, but these businesses are also hard-pressed to find use cases: A 2025 MIT report found that 95 percent of surveyed businesses failed to find any financial return on their AI initiatives.
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