Chatbots and AI copilots that can write fluent emails and computer code exploit statistical probabilities in natural or artificial language to predict the next token in any given sequence. But “the moment you try to breach that boundary of language, you start to run into problems, and that’s what people are running into now,” says Rohan Kodialam, a former data scientist at Citadel. “If you try to use [generative AI] on a real business application where it’s not just about saying stuff but about doing stuff, all of a sudden you are now in a much more difficult spot.”

The problem helps explain growing skepticism about the global AI boom. Last month an MIT project on “the agentic web” reported that despite up to $40 billion in enterprise investment, 95% of organizations are seeing no return.

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