In 1487, a twenty-three-year-old scholar proposed to defend nine hundred theses in Rome, in public, against any doctor of theology willing to argue them. Giovanni Pico della Mirandola’s Oration on the Dignity of Man — written as the opening speech for a disputation that was suspended before it could occur — argued that human beings possess no fixed nature, only the capacity to shape themselves through inquiry. The claim was radical because the alternative, the one the Church offered, was a fixed and given order: what to believe, in what sequence, on whose authority, with dissent named, tried, and punished.
Historians of the period have long located the deeper mechanism of that century’s transformation not in any single confrontation but in a shift in method. Lorenzo Valla’s 1440 philological demonstration that the Donation of Constantine — the document underwriting centuries of papal temporal authority — was a forgery is the example most often reached for here, and it deserves a more careful telling than the usual one. Valla did not defeat the Church by force, and he did not defeat it quickly: the demonstration was itself entangled with his employment under Alfonso V of Naples, then in dispute with the Papacy over territorial claims the Donation helped underwrite, and the document’s authenticity continued to be asserted and defended for generations after Valla’s philology had, in fact, settled the question. What Valla actually accomplished was narrower than “method defeated authority”: he showed that a specific claim could be tested against evidence, and shown false, by anyone equipped to read the Latin against its purported era — a capability, not an outcome. Whether that capability changes what an institution does, and how long that takes, is a separate question, and the gap between the two is part of what this essay’s own case has to reckon with. Vernacular translation of scripture, from Wycliffe through Tyndale, extended the same logic from philology to access — if the text is available in a language people can read, doctrine becomes something to evaluate rather than something to receive. The Renaissance did not abolish authority. It insisted that authority answer to method, evidence, and an individual capacity for judgment that could, in principle, be exercised by anyone who developed it — while leaving open, then as now, how long it takes for that capacity to change what authority actually does.
We think a structurally similar problem is forming now, and that the parallel is closer than metaphor. But the differences matter more than the similarity, and stating them precisely is the only way this argument earns the comparison rather than merely borrowing its drama.
What is actually converging
The claim that AI systems produce convergent output is not speculative. Kleinberg and Raghavan (2021), writing in PNAS, formalized what they termed algorithmic monoculture: when many decision-makers rely on the same or similar algorithms, their outputs and errors become correlated in ways that can reduce social welfare even as each individual system’s accuracy improves — a risk they compare to the vulnerability of biological monocultures to a single pathogen. Bommasani and colleagues’ widely cited 2021 report on foundation models, from Stanford’s Center for Research on Foundation Models, extended the concern directly to the current generation of AI: because so many downstream applications are built on a small number of shared base models, whatever patterns, gaps, or biases exist in those base models propagate outward to everything built on top of them.
The effect is measurable, not merely theoretical. A 2025 study evaluating twenty-one state-of-the-art language models against a dataset of genuinely diverse human preferences found that the models’ collective outputs aligned with only 41 percent of the variation present in actual human preferences — the models agreed with each other far more than humans agree with each other, regardless of which model or combination of models a person consulted.
This is a different phenomenon from the one that concerned an earlier generation of technology critics, and the difference is precise, not rhetorical. Pariser’s 2011 account of the “filter bubble” and Sunstein’s work on echo chambers described a curation problem: algorithms selecting which existing, human-authored content a person encounters, narrowing exposure without narrowing the underlying diversity of what had been written. The dependent variable in that literature is exposure — what you are shown from among things other people wrote. The dependent variable in algorithmic monoculture, and in what generative AI now does at far greater scale, is different in kind: the system does not select among existing positions, it generates the position itself, in the voice of a reasoning interlocutor, with no visible author and no indication that the same underlying convergence produced a substantial share of everyone else’s answer as well. Curation narrowed what people saw. Generation now supplies what people think they arrived at independently.
What this is not
This is not a claim that a Church exists, or that anything resembling one is coordinating the outcome. That distinction is the whole argument, not a footnote to it.
The Church Pico confronted was a single, doctrinally unified institution with an explicit canon, a named authority, and the coercive power to try, excommunicate, and execute dissent. The convergence described above has none of these properties. It arises from competing firms with different owners and different commercial incentives, converging anyway — because the available training data is drawn from a largely overlapping internet, because alignment processes optimize toward similarly cautious and inoffensive output, and because competitive markets reward resembling a safe consensus more than they reward visible divergence from it. No memo produced this outcome. No one needs to have intended it for it to occur, which is a harder problem to name and resist than one with an address in Rome, not a lesser one. And no AI system today can compel a person’s belief the way ecclesiastical courts once could compel conduct: there is no equivalent of trial, exile, or execution for the person who consults a different source or reasons differently. The influence here is structural, not coercive — closer to the lineage of McLuhan and Postman’s arguments about how a medium shapes thought by its form, or to Zuboff’s account of instrumentarian power operating through default and design rather than command.
Nor is this a claim that the convergence is evenly distributed in its effects, or that no pattern can be traced in what it converges toward. Noble (2018) documented that search systems trained on unrepresentative data reproduce and amplify racist associations already present in that data; Buolamwini and Gebru (2018) found that commercial facial-analysis systems misclassified darker-skinned women at rates dramatically higher than light-skinned men, because the benchmark datasets those systems were evaluated against were overwhelmingly composed of lighter-skinned subjects. Neither finding required a coordinating authority to produce; both are convergence toward a pattern, in the sense this essay uses the term, and the pattern is not neutral. The absence of a deliberate coordinating actor does not mean the convergence this essay describes is authorless in every sense. It means its authorship is diffuse and structural — traceable to whose language, whose faces, and whose labeled judgments the training and evaluation data privilege — rather than institutional and nameable in the way a Church’s was. That makes the convergence harder to hold any single party responsible for. It does not make the convergence pattern-free.
Nor is the core difference from the Renaissance case a matter of the public being more credulous now than then. Most people under Church doctrine also experienced it as simply how the world was, not as an imposed position to be weighed — awareness of doctrine as doctrine was always the province of disputants like Pico, not the general condition of the age. The sharper difference is speed and scale. Doctrinal convergence took centuries to establish and was repeatedly, visibly contested along the way, by philology, by translation, by print. Algorithmic convergence is being established across a global population within a handful of years of consumer deployment, largely without an equivalent visible act of contestation, because there is no canon anyone perceives themselves as being asked to accept. There is only an answer, arrived at, apparently, on one’s own.
Five theses
Thesis 1. Convergent AI output across competing systems is a documented phenomenon, not a speculative one, evidenced in formal models of algorithmic monoculture, in analysis of shared foundation-model components, and in direct empirical measurement of output homogeneity across current systems.
Thesis 2. This convergence differs in kind from earlier concerns about algorithmic curation and media concentration, because generative systems produce the position itself, in the form of independent reasoning, rather than selecting among visibly authored human content.
Thesis 3. Unlike centralized doctrinal authority, this convergence has no single coordinating actor and no coercive power over the individual; it is an emergent property of shared data, shared alignment methods, and converging commercial incentive, not a deliberate policy imposed by a nameable institution.
Thesis 4. The absence of a nameable authority makes this form of convergence harder to identify and resist than doctrinal control was, not because it is more coercive, but because it presents as a neutral, authorless answer rather than as an assertable position that invites a response.
Thesis 5. Because the deficit here is in the individual and institutional capacity to recognize convergence and exercise independent judgment against it, the appropriate response is closer to the humanist project of cultivating that capacity than to either a purely technical fix — mandating model diversity — or a regulatory one that risks recreating a single centralized authority over what counts as acceptable output.
Thesis 5 is the most contestable of the five, deliberately. It is a claim about what follows from the first four, not a claim that follows from them by necessity, and it is the one most directly at stake in whether AI Humanism, as an institutional response, is the right response at all.
Why the parallel still holds, precisely because the disanalogies are real
Pico’s actual wager was not that the Church was wrong about any particular doctrine. It was that human beings retained, and should exercise, the capacity to examine any claim on its merits rather than receive it on authority — and that this capacity had to be cultivated, through the studia humanitatis, not simply asserted as a natural right. The absence of a Church to argue with today does not remove the need for that capacity. It may increase it. A doctrine with a name can be identified, translated, and disputed — and, eventually, however slowly, revised when it fails the argument, as happened to the Donation of Constantine, even if the document’s political usefulness outlasted its philological credibility for generations. A convergence with no name and no author cannot be challenged in the same way, because there is no single claim to put to the test — only a pattern, distributed across systems that no one built to agree with each other, that agree anyway.
That is the argument for an institution organized, explicitly, around the cultivation of independent human judgment in an age of increasingly capable machines. Not because the machines are doctrinal authorities. Because they may be producing the effects of one without anyone having built one, and the only remedy on record for that condition is the same one the Renaissance proposed for the version it faced: people capable of examining a claim on its own terms, whether or not anyone can be named as having made it.
This essay’s claims are subject to challenge under the terms of the Institute’s Disputation Protocol. Editorial disclosure, including this publication’s AI review process, appears in the closing section of this edition.
References
Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. Stanford Center for Research on Foundation Models.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91.
Kleinberg, J., & Raghavan, M. (2021). Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences, 118(22), e2018340118.
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.
Sunstein, C. R. (2017). #Republic: Divided Democracy in the Age of Social Media. Princeton University Press.
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
Cultivating pluralism in algorithmic monoculture: The Community Alignment Dataset. (2025). arXiv preprint arXiv:2507.09650.
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