This article aims to characterize Generative linguistics (GL) contribution to artificial intelligence (AI), alluding to the debate among linguists and AI scientists on whether linguistics belongs to humanities or science. In this article, I will try not to be biased as a linguist, studying the phenomenon from an independent scientific perspective. The article walks the researcher/reader through the scientific theorems and rationales involved in AI which belong from GL, specifically the Chomsky School. It, thus, provides good evidence from syntax, semantics, language faculty, Universal Grammar, computational system of human language, language acquisition, human brain, programming languages (e.g. Python), Large Language Models, and unbiased AI scientists that this contribution is huge, and that this contribution cannot be denied. It concludes that however the huge GL contribution to AI, there are still points of divergence including the nature and type of language input.

As a point of departure for this essay, in their recent Op Ed in The New York Times Noam Chomsky and two of his academic colleagues--Ian Roberts, a linguistics professor at the University of Cambridge, and Jeffrey Watumull, a philosopher who is also the director of artificial intelligence at a tech company--tell us that "however useful these [AI] programs may be in some narrow domains (they can be helpful in computer programming, for example, or in suggesting rhymes for light verse), we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects…." They continue: "Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility." Readers might take these comments to mean current AI so differs from how humans communicate that predictions that AI will displace humans in any but a few minor domains is hype. The new Chatbots, painters, programmers, robots, and what all are impressive engineering projects but nothing to get overly agitated about. Current AI handles language in ways very far from what now allows humans to use language as well as we do. More, current AIs' neural networks and large language models are encoded with "ineradicable defects" that prevent the AIs from using language and thinking remotely as well as people.