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.

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 .

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.