AI and Authenticity

In recent months we’ve seen a wider debate over the use of AI in both academic research and among students pursuing higher education. But much anti-AI positioning seems to rely on its weakest argument. Yes, there are legitimate questions about veracity — about truthfulness, accuracy, and hallucinations — and about deskilling, particularly with regard to students. But the least convincing claim on the anti-AI side is that of authenticity, because authenticity is such a deeply problematic category. We’re never simply authentic; Adorno taught us that in the 1960s in The Jargon of Authenticity. We’re always-already embedded in a social order, which means we benefit from or are disadvantaged by the social relations that we find ourselves caught up in. We’re enmeshed in a social fabric that punishes or rewards us, conferring unfair advantages or disadvantages, as the sociologist Pierre Bourdieu pointed out over decades of research. There is, therefore, no natural, spontaneous self with which we can authentically be in communication. We’re augmented or disadvantaged by the mere fact of being social beings or products of societies.

Second, Donna Haraway’s work on the cyborg shows us that the state of humanity is, in many ways, that of being caught in and augmented by technology. There’s no state of nature for human beings — the typewriter was already a kind of cyborg beginning, an intermeshing of human and machine, intensified to be sure with the rise of the personal computer and further accelerated with the advent of AI. But these are differences in degree, not kind. The philosopher Bernard Stiegler’s Technics and Time project argues that thought is constituted by the technical. Our thought is always-already algorithmic, structured by cultural patterns, and we should include technical tools in that patterning. So there is no pre-cultural, pre-technological way of thinking or writing that would be fully “natural.”

Now, some of the opponents of AI in higher education are not making a metaphysical argument so much as giving voice to a narrower worry about the problem of assessments: Are we able to assess students’ capabilities if AI usage becomes (too) widespread? But this is essentially a question of monitoring, easily resolved if one so desires by rolling out supervised on-campus exams rather than take-home assessments or the like. Student theses and dissertations are a different matter, but again we might ask: Is the future of writing-with-AI perhaps something bolder, more interesting than indexing the authentic self, aimed instead at constructing new hypotheses, concepts, theoretical frameworks, and research agendas, as well as practical interventions? Machine tools could help embolden students to think the world anew, alongside their instructors, in collaborative ventures. Deskilling can be avoided if instructors are able to show that using AI to pursue this sort of work is harder, not easier — and potentially more exciting. If higher education is to remain viable in the age of AI, then these are the sorts of questions we must begin to ask ourselves.

Cory Doctorow’s recent book, The Reverse Centaur’s Guide to Life After AI, touches on many of these issues. In the book, Doctorow distinguishes the “centaur” (a human who is in command of a machine) from the “reverse centaur” (a human who is reduced to the machine’s appendage). The task for academia is to ensure a centaur-like relationship with AI, for researchers and students alike; that is, ensuring that humans are still making decisions along with AI rather than becoming subordinated to AI.

Finally, we should ask why authenticity should matter so much anyway. Following Deleuze and Guattari’s What Is Philosophy?, we might say that the whole purpose of thinking and of concepts is to create something new, to build something different in reality. The point isn’t simply to index or reflect reality. For that, we don’t really need thinkers; we can just have stenographers. It’s difficult, of course, to accurately portray reality, but we’re also — in addition, and perhaps more so — in the business of trying to construct something that is alive, something vital. Marx said as much in his 11th thesis on Feuerbach: “The philosophers have only interpreted the world, in various ways; the point is to change it.” We should ask ourselves what it is we are trying to accomplish with writing. And to that end, we might ask what is now possible to achieve with AI that wasn’t possible before, and whether it might augment — there’s that word again — our forces, our potentia, our powers.

*Methods note: This research note, which began as a voice memo in Slack transcribed by Groq’s Whisper API via Hermes, was lightly polished for clarity by Claude Fable 5 and extensively human-edited; multiple instances of Fable 5 provided editorial feedback on the draft.