Whether AI Before How

CTTL Symposium on Teaching and Learning in the Context of AI | Friday, October 2, 2026

A note at the start on “AI” as a term. My talk is focused on “generative AI.” One of the things that makes AI difficult to discuss is that it has no agreed upon meaning; a fact that largely results from the concerted efforts of advertisers and salespeople—in this way “AI” is the new “organic.” I myself follow SLU’s own Fr. Walter Ong, S.J. and his assertion that the most “natural” thing about humans is their “artifice”—our capacity to make things through which we extend ourselves. I think of writing, for instance, as already artificial intelligence insofar as it is thoughtful action achieved in concert with and through a set of tools and techniques: a shared language, the alphabet, pen and paper, or a keyboard and word processor. I here take aim at a specific, identifiable technology developed and maintained by nameable entities pursuing demonstrable ends rather than the abstract concept of “artificial intelligence.” Tools that are widely available and promoted at SLU: such as Microsoft’s Co-Pilot, which is built from the work of Open AI, and Claude, a product of Anthropic, which broke off from Open AI. How these companies operate, how they acquire the data for their large language models, how those LLM’s are organized and moderated, the natural and human resources required to build, maintain, and further develop them, and the legal, financial, and political deal-making that protects them are all well-documented. There can be no denying with whom we have partnered in this enterprise.

***

My argument is a simple one: the question of ethics is nullified when it is reduced to the question of how we can use AI. But the ethical question we ought to be asking is whether. The question of whether is all the more pressing in light of SLU’s new strategic plan, which skips right over whether with its demand for “AI fluency,” its demand that we (by 2027), “embed [AI] in every undergraduate and graduate program” “so that every graduate can use emerging technologies capably, critically, ethically, and responsibly” (10, emphasis added). How insists that we think the ethical implications of generative AI as having nothing to do with its making, its makers, or their motives. How perversely suggests that the ethical onus is only on us as users (a nice sleight of hand arrangement for those who sold it to us).

And more often than not it is only how that we are asking. And the party line is that how is all we can ask because “generative AI is inevitable.” But to tweak a turn of phrase from Samuel Johnson, inevitability is the last refuge of a scoundrel. A quick journey through history will find little evidence that anything is inevitable. What we find are false starts, serendipity, and a litany of unintended consequences and uses. We also find choices, many imposed asymmetrically by those with more power and capital. But even presuming that some things are inevitable, there is no evidence that generative AI is. Two realities, in fact, point to the weaknesses of any claim to generative AI’s inevitability.

First, one of the biggest debates amongst the leading generative AI companies is how exactly to make it make money. OpenAI, Anthropic, Microsoft, Google, and Meta are all wagering on different profit-generating schemes at the same time they are borrowing money at an alarming rate. They must borrow because the dominant form of generative AI requires massive outlays of capital in order to secure the unfathomable amount of resources needed to make it. Their investment models also take the form of insular systems of resource consolidation wherein, for example, OpenAI is propping up Nvidia (the computer chip maker), which then “invests” back into Open AI. Nobody would argue that a snake eating it’s own tail is inevitable. Furthermore, this seemingly inevitable technology is raising capital in ways adversely affecting the U.S. bond market. This country’s economy is made all the more precarious by the rush to build and everywhere embed generative AI, which, polls show, citizens are increasingly skeptical of.

Second, then, are the politically suspect ways that Big Tech companies aggressively short circuit democratic attempts to regulate it. Such as the proposed (and for the moment shelved) ten-year moratorium, funded and promoted by AI companies, preventing states from regulating AI. This technology is touted by its own makers as world changing, yet any attempts by that world to have a say are fought tooth and nail. Furthermore, there are Big Tech’s attempts to secure regulatory capture, which in addition to subverting democratic accountability likewise squeezes out alternative approaches to AI. These concerted efforts to thwart democracy reveal generative AI as far from inevitable, especially in the face of an increasingly skeptical public, which includes college students booing AI boosters giving commencement addresses—students who know full well that training them to use AI is training them against their own interests. What employers want, it might come as a surprise, is not always what we might want for our students; neither is it what our students might want for themselves. AI skepticism is strongly felt and loudly voiced by the citizens of a democracy, which Big Tech in broad day light routinely attempts to subvert.

These economic and political realities evidence an industry that is anything but inevitable. Hence, the amount of pressure on educational institutions from grade schools to graduate schools to adapt and embed AI. They need us to secure their future. And our participation, however discerning, necessarily strengthens their position and so exacerbates the litany of harms that are falling, always asymmetrically, upon the world as a result. Generative AI implicates us all. If we go all in on AI it will not be because it is already inevitable but because we have decided that it should be.

***

Our mission and identity as a university matters, and it should be the grounds for any conversation about the ethics of generative AI. We need to be able to ask out loud and in public, should we as a university community embed in “every undergraduate and graduate program” something that is demonstrably made of practices none of us would find to be ethical? Is there an intentional, critical, or ethical use of AI that changes how and by whom it is made? It might very well be that we, as a community, find that some of these costs are sometimes worth it. But that is not the conversation we are having right now. The conversation we are having now, grounded in how, risks turning our institution into a non-IRB approved experiment upon the minds and bodies of our students and binding our fate as a credible institution of higher learning to snake oil salesmen with little regard for us or our collective values. 

And yet, most of the university-wide cases for generative AI are grounded in either inevitability or market demand. If the mission is mentioned, it is always in an ancillary position. The mission, in other words, is left to guide how. It simply is not  (cannot be) allowed to discern whether. I know this because I have yet to hear a mission-grounded case made for the embedding of AI in every program at SLU. I wish the authors of the strategic plan had asked the office of Mission and Identity to develop a case for AI (not a guide to it) using the four Universal Apostolic Preferences. Or a strategic plan that, borrowing from Magnifica Humanitas, Pope Leo XIV’s recent encyclical, had “the courage to insist on a further condition: the possibility of openly discussing the ethical frameworks involved and subjecting them to shared standards of justice. Otherwise, those who control AI will impose their own moral vision, which will become the invisible infrastructure of these systems” (85, emphases added). How does one use AI “in service of the greater good” when so much of generative AI is the threat (economic, environmental, political, cognitive, moral, and spiritual) to that greater good. I think it reasonable and necessary that this Catholic, Jesuit institution of higher learning, an institution far older and more durable, more socially and morally valuable than any over-leveraged tech start up, can and should stage a much better moral reckoning with AI. I think we can and should do better than “it is inevitable.” We are better than that fait accompli.