Inflation may have slowed significantly in June, but one Wall Street strategist said it remains an issue for AI chips, one of the most popular products on the market.
Julia Herman, global market strategist at New York Life Investment Management, said “chipflation,” or rising prices for AI-related logic and memory chips, will be the next headwind to test the strength of AI trade.
“Hyperscalers face a balance with rising input costs (higher chip prices, higher energy and utility costs), but the prospect of a return on investment is still several years away,” Harman told Business Insider. “We believe this environment is poised to test conviction. Investors may be willing to tolerate volatility and the outlook for slower profitability if they continue to believe in the long-term potential of AI trading.”
Chip stocks have been under pressure lately as investors have begun to scrutinize the economics of the AI boom. Sector leaders have fallen in recent weeks as investors dumped hot stocks such as memory makers and other hardware stocks, even though early earnings reports beat expectations by a wide margin.
In Herman’s view, Asian markets are showing worrying growth, reaffirming her thesis on chipflation.
“One of the best indicators of chipflation in memory capacity is the Korean DRAM memory export price index,” he said. “In past cycles, the price growth rate of memory chips peaked at about 100% year-on-year growth, but currently Korean memory chip prices are growing at 370% year-on-year growth.”
Herman’s paper on the impact of more expensive chips focuses on the idea that chips are a double-edged sword and a market driver that impacts AI trading in several ways.
While rapidly rising chip prices are a sign of strong demand, they also have the power to stem the AI boom, as rising chip prices mean higher costs for companies that have already spent billions of dollars building AI infrastructure.
“Volatility related to semiconductor leadership has been primarily up this year, but that volatility works in both directions and could test investor confidence,” he said. “We are most focused on quality across the AI supply chain, including large semiconductor companies, and define quality as high profitability, low to moderate revenue volatility, and strong interest rate coverage.”