The Future Isn’t Static: How AI and Archival Politics Undermine the “Digital Humanities Bust” Narrative

When The Chronicle of Higher Education recently resurfaced Timothy Brennan's seven-year-old critique—arguing that the Digital Humanities (DH) is a busted fad—it was a curious editorial decision. In “Revisiting DH Progress Since 2017,” I explore how the field has evolved substantially, propelled by new theoretical insights and, more crucially, advances in AI. By reprinting this article without considering how AI and DH have become intertwined, The Chronicle risks painting a stagnant portrait of a discipline that has, in fact, continued to grow in richness, scope, and social impact.

The Slow March of the “Bust” Narrative

It’s notable that The Chronicle’s reprint frames DH through a lens from nearly a decade ago, as though digital scholarship has remained stuck. This perspective neglects how quickly AI has transformed conversations in the humanities. Tools like large language models, machine learning algorithms for text analysis, and digital archives that leverage AI for cataloging and accessibility have shifted how many of us do research and pedagogy. It’s one thing to argue that some early DH projects oversold the glitz of technology or failed to deliver on overly ambitious promises; it’s quite another to overlook how these same tools, refined and reoriented by scholars, continue to spawn innovative engagements with text, authorship, and social critique.

AI, Memory, and the Politics of Archiving

The politics of archiving have long shaped what knowledge is preserved, who is remembered, and how history is constructed. Traditional university libraries, with their finite shelf space and institutional priorities, have historically favored the cultural “winners”—canonized authors, dominant intellectual traditions, and research areas backed by well-endowed grants. This material and institutional bias has often relegated marginalized voices, experimental scholarship, and emergent subfields to the periphery, if not outright oblivion.

AI-driven archival initiatives, however, hold the potential to disrupt these entrenched hierarchies. By digitizing vast arrays of texts, artifacts, and multimedia materials, they radically expand what can be preserved and accessed, offering a new paradigm for knowledge production. Unlike traditional archives bound by physical constraints and bureaucratic gatekeeping, digital archives can operate with far fewer limits on space, relevance, or institutional approval. The AI tools that facilitate their creation—through automated indexing, pattern recognition, and cross-referential analysis—can help unearth overlooked histories, challenge dominant narratives, and amplify perspectives that might otherwise be lost to obscurity.

Yet this shift is not without its complications. As I explored in "Archive Fever’s Freudian Impression: The Digital Humanities and the Ethics of Cyborgian Care," the role of AI in archiving is not merely technical but profoundly ethical and political. What is preserved, and how it is indexed, still reflects choices—choices shaped by algorithms, metadata structures, and the institutions funding these projects. AI itself does not escape ideological bias; it inherits the assumptions of its designers and the data sets it is trained on. If digital archives aspire to democratize knowledge, they must also reckon with how AI encodes historical exclusions, replicates epistemic biases, and potentially reinforces the very power structures it claims to dismantle.

This concern parallels critiques of the broader digital humanities (DH) movement, such as those raised in The Chronicle of Higher Education article, The Digital Humanities Bust. The initial promise of DH was to revolutionize the humanities through large-scale computational analysis, open-access knowledge, and institutional support for digital-first projects. Yet, as the article outlines, DH has often fallen short of its democratizing ambitions. Instead of dismantling academic hierarchies, DH initiatives have frequently served existing institutional interests—favoring projects with major grants, elite university backing, and a preference for quantifiable outputs over interpretive depth. Similarly, AI-driven archival initiatives, while expansive in scope, risk privileging what is most readily digitized, most easily categorized, or most aligned with dominant research agendas.

The question, then, is not simply whether AI can expand access to knowledge, but how it will shape the very terms of that access. Will AI-generated archives serve as tools for decentralizing authority and recovering lost intellectual traditions? Or will they, as Derrida warns in Archive Fever, subtly reproduce the logics of institutional power, consolidating control in new, less visible ways? The challenge for scholars, archivists, and digital humanists is to approach AI not as a neutral technology but as a site of contestation—one where the ethics of preservation, representation, and access must be actively negotiated.

In this light, digital archiving is not merely about expanding memory but about rethinking the politics of forgetting. AI may allow us to preserve more, but what it prioritizes, how it structures meaning, and whose voices it amplifies remain open questions—questions that demand ongoing critical engagement rather than blind optimism. Much like the digital humanities movement, AI in archival work risks becoming an infrastructural tool that, without sustained critique, simply reinforces preexisting academic and cultural hierarchies. If DH has faced a reckoning over its unfulfilled promises, AI-driven archives must actively avoid a similar fate by ensuring that their methods, priorities, and claims to democratization are continually interrogated.

Equal Shelf Space and Silenced Voices

In this light, the Chronicle article’s critiques of DH for failing to deliver grand scholarly revolutions seem to miss the core ethical dimension that has evolved over the past decade. The digital realm, in principle, allows for equal shelf space—at least compared to the heavily policed world of physical archives. Relegated voices—women writers, minority scholars, activists, ephemeral art forms, queer media—can find a lasting digital home without the high overhead costs or reliance on traditional gatekeeping structures.

This new archival model challenges the old, university-driven discourse of “prestige.” Suddenly, entire corpora deemed “marginal” by conventional academic measures can be cataloged, studied, and made widely accessible. This is a decisive break from the historical practice of “kissing the ring” of academic or institutional power brokers who decide which works are worthy of preservation. In this sense, DH—and especially the infusion of AI-based digital archiving—might finally disrupt the longstanding courtesies to the “discourse of the master” that overshadow so much institutional scholarship.

Getting Capitalism Out of the Humanities (Or at Least Loosening Its Grip)

Another key oversight in the “DH is a bust” viewpoint lies in ignoring how a digitally expanded archive can help disentangle scholarship from purely capitalist motives. Physical archives and print publications are expensive; space, materials, and labor costs constrain what gets produced or preserved. As a result, institutions often rely on funders, which means that the works likely to see the light of day are those that cater to external money. This can perpetuate a cycle where scholarship is guided by profit motives—directly or indirectly.

By contrast, robust digital archives (especially those powered by open-source or low-cost AI tools) can help scholars circumvent these structural traps. We can share knowledge widely without necessarily needing the blessing of large publishing houses or expensive institutional support. Certainly, digital infrastructures require funding as well, but the barrier to entry for maintaining an online repository or digitizing a collection is far lower than maintaining physical stacks or publishing expensive monographs. This democratization of the means of scholarly production could eventually help “get money out of the humanities,” at least to a greater extent than before, by divorcing the value of scholarly work from the market-driven constraints that typically dictate whose stories get told.

The Chronicle’s Missed Moment

So why would The Chronicle resurrect a seven-year-old argument that ignores these developments? Possibly, it’s an easy headline-grabber—pronouncing a “bust” or “death” of a field piques attention. Or maybe the editorial choice signals a reluctance to acknowledge how DH has quietly shifted from hype machine to serious reconfiguration of scholarly workflows, archives, and ethical commitments. Either way, the article risks doing a disservice to readers by omitting the ways that AI, and DH more broadly, have matured since the initial wave of enthusiasm.

At the heart of the matter is this: Digital Humanities was never just about flashy visualizations or trendy new labs. It’s an evolving framework that takes seriously how technology, archives, and scholarship intersect in ways that transform our understanding of knowledge production. In 2017 and beyond, that transformation has only grown more significant with the rise of AI and the parallel focus on archiving marginalized voices and decolonizing the institutional library. If The Chronicle wants to critique DH, it would do well to engage with these ongoing projects rather than rehashing critiques aimed at outdated illusions of what DH “promised.”

Conclusion: A Discipline Still in Motion

The Digital Humanities isn’t static, nor is it the monolithic, hype-fueled sphere some critics imagine. As AI technologies advance and as more scholars recognize the importance of equitable, open archives, DH continues to mature, offering increasingly powerful ways to disrupt traditional hierarchies of knowledge. In short, it’s not a bust; it’s part of a broader intellectual and ethical movement to remake the university’s relationship to scholarship, memory, and justice. Perhaps the most important lesson is that if we want to understand where DH is going—and what it can become—we should look to the latest work on AI-based archiving, psychoanalytic critiques of memory, and the dismantling of capitalist constraints in the humanities. The story is far from over—and, indeed, only getting started.