On the Knowledge Problem, Tacit Knowledge, and Artificial Intelligence is an article we need to study. It begins:
In May 2023 the MIT economist Daron Acemoglu, who went on to share the 2024 Nobel prize in economics, asked a question on Twitter. Friedrich Hayek argued in 1945 that central planning fails because no planner can collect and compute all the information an economy runs on. “What if computational power of central planners improved tremendously?” Acemoglu wrote. “Would Hayek then be happy with central planning?”
About thirty economists sent him the same reply, according to Shruti Rajagopalan, who hosts the Mercatus Center’s Ideas of India podcast: Hayek never made a computational argument. He made an argument about the nature of knowledge itself. Acemoglu reached the same conclusion in print that June. In an essay titled “Hayek vs. AI Socialism”, he quoted Hayek’s warning that the relevant knowledge “by its nature cannot enter into statistics” and concluded that “not even an all-powerful large language model” could deal with it. AI socialism, he wrote, “offers only a superficial critique of Hayek.” That distinction is the whole story, and it explains why a model trained on most of what has ever been written still cannot do what a well-trained human mind does.