The relentless arms race among AI hyperscalers to amass more and more computing power will eventually hit a wall, according to Gary Marcus.
That’s because the enormous amounts of capital expenditures have failed to clean up errors their large language models produce while also removing any technical moats that might give them a competitive edge.
“This has led to price wars coexisting with high operating expenses (needed to run bigger data centers to train and operate new models) and low or even negative margins, since all are building more or less the same product,” Marcus wrote in a Financial Times op-ed on Thursday.
The professor emeritus of psychology and neural science at New York University, who has long been skeptical of the AI boom, also pointed out that more U.S. companies are using cheaper, open-source Chinese AI models.
In fact, even hyperscaler Microsoft may make China’s DeepSeek available for its Copilot Cowork AI agent, according to Axios, and is looking at open-source models as lower-cost alternatives to Anthropic and OpenAI products.
