by Robin Manley
Leif Weatherby is an Associate Professor of German at New York University, where he directs the Digital Theory Lab. Robin Manley spoke with Dr. Weatherby about his latest book, Language Machines: Cultural AI and the End of Remainder Humanism (University of Minnesota Press, 2025), which argues that Large Language Models (LLMs) have effected a separation of cognition from language and computation in a form that corresponds to earlier structuralist theories.
Robin Manley: In the introduction to Language Machines, you argue that today’s prevailing frameworks for AI research all share a common theoretical mistake, which you call “remainder humanism.” This goes for the AI-skeptical linguistic arguments made by scholars like Emily Bender and Noam Chomsky, but also for the more social-scientific literature on AI bias and ethics, and even for the otherwise quite-opposed tradition of AI risk and alignment research. What is remainder humanism, and how does it limit these different approaches to thinking about LLMs?
Leif Weatherby: Remainder humanism is the term I use for us painting ourselves into a corner theoretically. The operation is just that we say, “machines can do x, but we can do it better or more truly.” This sets up a kind of John-Henry-versus-machine competition that guides the analysis. With ChatGPT’s release, that kind of lazy thinking, which had prevailed since the early days of AI critique, especially as motivated by the influential phenomenological work of Hubert Dreyfus, hit a dead end. If machines can produce smooth, fluent, and chatty language, it causes everyone with a stake in linguistic humanism to freak out. Bender retreats into the position that the essence of language is “speaker’s intent”; Chomsky claims that language is actually cognition, not words (he’s been doing this since 1957; his NYT op-ed from early 2023 uses examples from Syntactic Structures without adjustment).
But the other side are also remainder humanists. These are the boosters, the doomers, as well as the real hype people—and these amount to various brands of “rationalism,” as the internet movement around Eliezer Yudkowsky is unfortunately known. They basically accept the premise that machines are better than humans at everything, but then they ask, “What shall we do with our tiny patch of remaining earth, our little corner where we dominate?” They try to figure out how we can survive an event that is not occurring: the emergence of superintelligence. Their thinking aims to solve a very weak science fiction scenario justified by utterly incorrect mathematics. This is what causes them to devalue current human life, as has been widely commented.
Basically, we have two brands of “humanism” that both utterly fail to pay attention to the actual technologies at hand. The bright line between machine and human that they take as a premise prevents them from looking at language as such, and so allows them to go on letting either reference or intent be the essence of language. That’s a backhanded way of accepting the premise that cognition is utterly separate from culture, which is a serious scientific debate.
The position I take here is that LLMs don’t really solve that debate for human minds but do extend our ability to see truly cultural systems in action and give us some reasons to revisit that debate. Literary scholars dropped out of serious discussions of this type of issue about a generation ago, maybe even two, as I narrate polemically in chapter one, “How the Humanities Lost Language.” LLMs are calling us back into this world, to which we need to catch up fast.
RM: In order to overcome these pitfalls, you turn to French structuralism—a tradition that articulated similar theoretical concerns long before LLMs. In the first chapter, you contrast the structuralist account of language with two others: the “syntactical” approach developed by Chomsky that was so pivotal in the development of linguistics and the “statistical” view that has been more influential in cognitive and data science. To be very schematic: in the syntactical view, the output of an LLM is nothing more than a party trick, as it has no relation to the internal cognitive laws of language; whereas in the statistical view, the output of an LLM is indistinguishable from human writing. How does LLM output appear in your structuralist view? What does this suggest for how we think about the familiar question of intelligence?
LW: I’m not sure if the modelers would claim indistinguishability, though they seem bullish. It’s a weird area, which the book dwells on. On the one hand, we’re pretty sure these systems don’t do anything like what humans do to produce or classify language or images. They use massive amounts of data, whereas we seem to use relatively little; that data is highly structured, ours is not; etc. On the other hand, LLM outputs are indistinguishable from human writing in one important sense, which is that we have no tools to consistently identify which is which. Teachers and professors believe they can tell, but they’re basically wrong. We can often see little traces that are obvious—“sure, let me help you with that,” the overuse of the word “delve,” etc.—but studies show that neither human eyes nor quantitative tools can make this separation well. My university turned off the plagiarism software we previously relied on, Turnitin, almost as soon as ChatGPT was released, and hasn’t turned it or anything else back on.
We’re in this uncanny area where writing has been wrested away from us. It feels like language has gone the way of vision, music-making, and so many other essentially “human” capacities that were extended into media applications—gramophone, film, typewriter, to invoke Friedrich Kittler’s triumvirate of analog media. Language is an odd one, though, because it has no analog counterpart. It is the example of artificiality, made up of symbols that cut through its analog medium, sound. Because these systems work from text, there’s not even the element of sound; we’re faced with pure textual symbols (tokens) interacting with mathematical techniques of a particular variety. The math is relatively simple but not very elegant, and somehow all this results in language—real language, I claim. The talk out there about “synthetic text,” a phrase the AI critics use, makes no sense to me. Since Socrates, we have understood text as the demarcator of artificiality, of “synthesis.” All text and all language is synthetic—a point that I have developed in conversation with the philosopher Beatrice Fazi, who argues that these systems contain a structuralist distribution of concepts as well.
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