GPT-5, OpenAI’s latest artificial intelligence system, was supposed to be a game-changer, the culmination of billions of dollars of investment and nearly three years of work. Sam Altman, the company’s chief executive, implied that GPT-5 could be tantamount to artificial general intelligence, or A.G.I. — A.I. that is as smart and as flexible as any human expert.

Instead, as I have written, the model fell short. Within hours of its release, critics found all kinds of baffling errors: It failed some simple math questions, couldn’t count reliably and sometimes provided absurd answers to old riddles. Like its predecessors, the A.I. model still hallucinates (though at a lower rate) and is plagued by questions around its reliability. Although some people have been impressed, few saw it as a quantum leap, and nobody believed it was A.G.I. Many users asked for the old model back.

GPT-5 is a step forward, but nowhere near the A.I. revolution many had expected. That is bad news for the companies and investors who placed substantial bets on the technology. And it demands a rethink of government policies and investments that were built on wildly overinflated expectations. The current strategy of merely making A.I. bigger is deeply flawed — scientifically, economically and politically. Many things from regulation to research strategy must be rethought. One of the keys to this may be training and developing A.I. in ways inspired by the cognitive sciences.

Fundamentally, people like Mr. Altman, the Anthropic chief executive Dario Amodei and countless other tech leaders and investors had put far too much faith into a speculative and unproven hypothesis called scaling: the idea that training A.I. models on ever more data using ever more hardware would eventually lead to A.G.I., or even a “superintelligence” that surpasses humans.

However, as I warned in a 2022 essay titled “Deep Learning Is Hitting a Wall,” so-called scaling laws aren’t physical laws of the universe like gravity, but hypotheses based on historical trends. Large language models, which power systems like GPT-5, are nothing more than souped-up statistical regurgitation machines, so they will continue to stumble into problems around truth, hallucinations and reasoning. Scaling would not bring us to the holy grail of A.G.I.

Many in the tech industry were hostile to my predictions. Mr. Altman ridiculed me as a “mediocre deep learning skeptic” and last year claimed “there is no wall.” Elon Musk shared a meme lampooning my essay.