Futrell and Mahowald argue that the success of large language models should move the field away from the formal structures of generative linguistic theory. The limited success of these models falls short of formal linguistic theory in explaining both the character of human languages and understanding the trajectory of child language acquisition.

The diversity of variation captured in data can strongly affect the generalization of a learning system – even when that variation occurs along axes orthogonal to the generalization in question. Thus, I argue that data richness both distinguishes current language models from prior linguistic models and may still underlie their remaining linguistic data inefficiency.

This article examines how artificial intelligence (AI) became framed as a critical node of U.S.–China strategic competition between 2015 and 2023, arguing that “hybrid epistemic experts” – figures who straddle technical expertise, corporate leadership and policy influence – played a decisive role in shaping elite understanding of AI. Through a critical review of policy documents, news media, public statements and institutional developments, this article examines how the “U.S.–China AI Race” narrative did not emerge along the usual pathways of state-driven, top-down bureaucratic processes or traditional lobbying but was actively constructed and amplified by figures like former Google executive Eric Schmidt. Schmidt’s role as a hybrid actor allowed him to translate AI from a narrow technological domain into an existential competition requiring massive policy investment. This overarching capability was driven by AI’s speculative, technically complex and general-purpose nature, which has concentrated knowledge production in private hands, enabling hybrid actors to achieve disproportionate influence over AI policy discourse. This phenomenon raises concerns about democratic governance, the collapse of independent expertise and the self-reinforcing dynamics between private power and public policymaking in emerging technologies.

Lawrence Goodwyn’s Democratic Promise: The Populist Moment in America (1976), a pathbreaking book on the history of Populism and agrarianism, turns fifty in 2026. In this retrospective roundtable, a distinguished panel of historians of Populism discusses the book and its legacy, as well as its author and the relevant historiographical debates. Democratic Promise, which focused largely (although not exclusively) on Texas, was one of several works that pushed back against earlier analyses of the Populists, such as the largely negative portrayal in Richard Hofstadter’s The Age of Reform (1955). Goodwyn, who taught for more than three decades at Duke University, controversially introduced the idea of a “shadow movement” that effectively derailed the promise of Populism. Nevertheless, Goodwyn’s book was tremendously influential, and it still demands the attention of historians of the Gilded Age and Progressive Era.

Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyze externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input and can readily distinguish between real language and impossible languages.

This Article argues that international tax law has developed the characteristics of an asymmetric regime complex; contests over its normative content are playing out simultaneously in the OECD and in the UN. The OECD provides technical rulemaking capacity, while the UN serves to address claims about legitimacy and equity. Powerful states—especially China and the United States—have been exploiting the overlap between the two institutional arenas to try to press their competing visions for domestic and international tax governance.

Large language models (LLMs) challenge Chomsky’s long-standing mysterian view of the creative aspect of language use (CALU). By exhibiting fluent, situation-appropriate linguistic behavior and offering concrete mechanistic hypotheses, they provide the first viable scientific models of CALU. We endorse Futrell and Mahowald’s call to integrate LLMs into linguistic inquiry and suggest a bolder aim: elucidating the mechanisms underlying linguistic creativity.