1. Introduction

Doctoral research has long been associated with rigor, discipline, and seriousness. The weight of the PhD—its years of study, the meticulous shaping of a dissertation, and its symbolic role in certifying scholarly maturity—often overshadows other dimensions of intellectual life. When I was invited to reflect on the meaning of the PhD in a talk in front of the PhD students of the Doctoral Programme in Strategic Communication, Advertising and Public Relations at the Universitat Autonoma de Barcelona in December 2024, I chose a deliberately provocative title: Pleasure, happiness, and Diversion. The title was planned to sound and feel almost transgressive for such a solemn event. Yet, in the tension between weight and play, rigor and joy, lies a fertile terrain for rethinking what doctoral research should mean in our time, and I wanted to pinch exactly that string, there.

This is an essay; it develops from that initial argument and draws from that earlier public talk to expand for a fuller academic reflection.¹ Still, as an essay it must be seen as a methodological revendication. The text moves between the personal reflection, the philosophical engagement, and the cultural diagnosis. In doing so, it aligns with Theodor Adorno's memorable 1958 defense of "The essay as form", identified as the only honest response to a world that resists systematic closure: "The essay, however, does not permit its domain to be prescribed. Instead of achieving something scientifically, or creating something artistically, the effort of the essay reflects a childlike freedom" and insists, "starts not with Adam and Eve but with what it wants to talk about; it says what occurs to it in that context and stops when it feels finished rather than when there is nothing to say" (Adorno et al. 1984, 151–2). This is, therefore, not a confession of methodological weakness but a claim for a different kind of rigour—one that takes the thinker’s situated experience as a legitimate point of departure, that works with concepts rather than merely applying them, and that accepts the risk of failure as the price of saying anything new. In what follows, I will flag a quarter of a century of experience in international higher education environments across Europe that have witnessed a progressive transformation—if not decay—of academia into a territory of bureaucratic measurement of research quality through impact, absurd indices of satisfaction and of citation, and publish-or-perish dramatic races; within that context, standardizing systems designed to measure success or impact of research have also led to a dramatic standardization of disciplinary and rhetorical dynamics of writing, publishing, and ultimately of thinking. Those same standardized patterns are promoted by AI such as algorithmic logics (H-index, journal ranking citation models, or even using Generative Pre-Trained Transformers to measure research quality (see the work by Thelwall 2024) and are the main feed for AI such as the Large Language Models to imitate and reproduce them at exponential super-human speed and flood the academic production with them. Both the efforts of measuring and the machinery enhancing them are damaging the epistemological gaps and encapsulate the academic industry while reducing our own human chances of critically thinking alternatives for the future, for society, and for ourselves. The use of the essay, as a style and as a method, is here an attempt to revitalize such a practice: autoethnographic in its attention to formative encounters (following Mulligan et al. 2025), with cheeky traces of orality such as the resource to digression and anacoluthon, and equipping the core conceptual argument with philosophical engagement with Arendt, Derrida, and others. Ultimately to vindicate essayistic style as refusal to pretend that thinking can be separated from the life that thinks it.

The provocation is a fitting and necessary start, because a PhD today is not what it was in the past. Historically, the modern doctorate emerged from the Humboldtian model of the university in the early nineteenth century, with its emphasis on Bildung—the cultivation of the self through research and scholarship (Ash 2006). The PhD was conceived not as a professional certification but as a formative intellectual journey. Over time, doctoral education became increasingly institutionalized, standardized, and bureaucratized. Bourdieu (1988) described the academic field as one structured by power, capital, and hierarchy, in which the doctorate functioned as a gatekeeping mechanism, reproducing social and cultural capital. More recently Barnett (2000) has argued that universities now operate in conditions of "supercomplexity," where knowledge is fragmented, contested, and constantly destabilized. Within this landscape, the doctorate risks being reduced to a technocratic exercise: a long, arduous project aimed at producing an original contribution to knowledge, but somehow dramatically stripped of the joy and public significance it once carried.

This tension—between formation and formalization, between intellectual joy and institutional weight, between learning and career shaping—is precisely why it is urgent to reclaim Pleasure, happiness, and Diversion as constitutive, if not definitory, dimensions of doctoral life. To do so is not to trivialize the seriousness of research, rather the contrary, it is to suggest that without these deeply human elements, the PhD loses its transformative potential, its purpose, and its capacity.

I began as a university student in the early 1990s, Internet was only a dream we had when discussing communication. Allow me some reflexive and autoethnographic bend, here. My first academic emails were with a PhD student finishing her dissertation. I still remember the sense of wonder in exchanging those early messages and sneaking into the computer room to receive and send the new messages. Around then, by the mid-nineties, my late Professor Badia, insisted I read Habermas and his Universal Pragmatics well before I could fully digest his arguments or the context of the work. Those encounters mattered—not because of the overwhelming content they supplied, but because they marked research as an adventure, as a space of intellectual stimulation. Doctoral research is never a solitary process: it is always shaped by people, friends, relationships, influences, evenings, networks, and obviously by drunkenness: "of wine, of poetry, or of virtue, as you wish" (Baudelaire n.d).

Yet, the conditions of doctoral research have changed radically since the 1990s. The expansion of digital infrastructures, the pressures of globalized academia, and the accelerating role of artificial intelligence compel us to rethink what doctoral research signifies. In an age of datafication of higher education (Gourlay 2024) and platformization (van Dijck, Poell, and de Waal 2018), where knowledge circulates through algorithmic logics and where generative AI can produce polished texts within minutes, the very rationale of doctoral study comes under scrutiny.

2. Writing a PhD in the Age of Artificial Intelligence

The twenty-first century has brought an extraordinary transformation in the conditions of research. Among the most consequential developments is the rapid rise of artificial intelligence. Once the domain of speculative fiction, AI now pervades everyday life, from predictive text on smartphones to large-scale systems capable of generating text, images, and code. For doctoral education, this technological shift raises pressing questions about what remains distinctive of the PhD, if machines can emulate scholarly practices.

Statistics alone are striking. The AI market has expanded at rates surpassing 100% year on year, with projections that it could add as much as US $15 trillion to the global economy by 2030 (PwC 2017). By 2025, Europol’s Innovation Lab estimated, 90% of online content could be synthetically generated (Europol Innovation Lab 2021). UNESCO (2023) has responded by issuing guidelines for higher education institutions on how to adapt to generative AI, recognizing both its potential and its risks. In research-intensive contexts, AI tools already assist in literature reviews (e.g., Elicit), qualitative data coding, quantitative modeling, and even text production. The Financial Times reported in 2023 that investment in generative AI had already exceeded €2 billion, signaling the growing centrality of AI for academic and commercial knowledge production (Financial Times 2023). As impressive as they are, these numbers feel old by the minute; because as equivocal as the notion of AI is, which is not artificial nor intelligent (Crawford 2021), and which hides an extension of computational capabilities under an enthralling aura that enters the contemporary culture and economy by capturing the sense of future of a whole generation. Indeed, "future" (Dunagan 2012) has become a political space of enquiry, debate, and exploration—and dystopia a source for public policy!

It is no exaggeration, then, to claim that doctoral research takes place under the shadow of machine intelligence; but this is not the first time technology has unsettled academic practice. The expansion of the Internet in the 1990s raised alarms about plagiarism and information overload, while the rise of big data in the 2010s reshaped methodologies across the sciences and humanities (Williamson 2017). Yet generative AI introduces a new scale of challenge, where previous technologies assisted research, AI now threatens to replicate it, raising existential questions about originality, authorship, and the very ultimate purpose of embarking on a PhD adventure.

Traditionally, the PhD is defined by its requirement for an "original contribution to knowledge." But what counts as originality when large language models can produce well-structured literature reviews, draft arguments, and simulate scholarly styles? As Floridi and Chiriatti (2020) argue, systems like GPT-3 (and its successors) are not genuinely intelligent in the human sense; they operate through probabilistic pattern recognition, recombining existing knowledge rather than generating new insights. Nevertheless, their outputs can be persuasive enough to mimic scholarly writing.

This creates a dilemma. If doctoral research is assessed primarily based on the written dissertation, and if writing itself can be automated, then the PhD risks being reduced to a technical formality. Dempere et al. (2023, 7) capture this tension in her discussion of ChatGPT in higher education: while AI can democratize access to writing support, it simultaneously destabilizes traditional models of assessment that equate authorship with intellectual labor: "noting its potential to improve the quality of writing and make research more accessible to non-experts while also posing challenges such as the authenticity and reliability of generated text and accountability and authorship issues." The danger is not simply plagiarism but an even deeper erosion of the meaning of authorship itself (Sarikakis, Krug, and Rodriguez-Amat 2017).

Here, it is important to note that originality in the human sense involves more than novelty of text. It entails situating research in relation to a field, articulating a problem, generating insights that contribute to ongoing conversations, stretching, recombining, remixing, expanding and opening the meanings and extensions of the field. Machines cannot experience curiosity, uncertainty, or the satisfaction of discovery. They cannot participate in the dialogical and social dimensions of research communities. What AI produces is a form of superficially fitting recognition; whereas what a PhD requires is a form of genuinely human drive, human care capacity, and the uncertainty that emerges from serendipitous findings.

The rise of AI intensifies those processes of datafication (Gourlay 2024) and of platformization in academia (van Dijck, Poell, and de Waal 2018, among others). Universities, and knowledge evaluation, increasingly rely on learning management systems, bibliometric tools, and analytics platforms that quantify research activity. Doctoral students are encouraged—sometimes required—to publish in indexed journals, to track citations, and to demonstrate "impact" through metrics. AI-driven tools now integrate seamlessly into these evaluative ecosystems (Thelwall 2024, among others), offering predictive analytics on research visibility, automated peer review suggestions, and algorithmically generated impact forecasts.

This technological transformation is not speculative; it is already visible in the empirical landscape of doctoral education. A systematic review of ninety-nine studies synthesizes responses from across higher education, revealing patterns of adoption and ambivalence that vary significantly by context (Liu et al. 2025). Within doctoral education specifically, surveys indicate that most students now use AI tools in their research, with common applications including proofreading and literature searching (Harris, Soriano, and Ralston 2025). Yet nearly half of doctoral students do not find AI tools helpful for communicating research findings—a striking result that suggests scholarly expression remains resistant to automation. Other research shows the efficiencies and ethical complications that arise when machines participate in interpretive processes: a collaborative self-study by Wilder and Calderone (2025) examined how a doctoral candidate and their supervisor used Google’s Gemini 1.5 to analyze interview data, finding that AI could enhance analytical depth and reveal hidden patterns, but only with careful human oversight to guard against data integrity risks and potential biases. Longitudinal research by Leite (2025) tracking AI adoption in higher education reveals that students who develop what researchers call "AI literacy" are better able to use these tools as supplements rather than substitutes for their own thinking, though the study also documents significant variation in adoption patterns across disciplines and institutional contexts. This literacy is not merely technical. Lu, Li and Qian (2025), studied 354 doctoral students, to find that AI literacy positively predicts innovative behavior, but this relationship is mediated by emotional engagement. Students who relate to AI with curiosity and critical awareness—who experience something like the pleasure of thinking with rather than being thought for—are more likely to use AI in ways that genuinely enhance their research. These empirical findings confirm that the question is not whether AI will be used, but how: whether it will serve the mechanization of thought or become a tool in the service of genuinely human inquiry.

While such systems seem to promise efficiency, they narrow the meaning of success and scholarship to what is measurable. AI in education futures often reinforces technocratic logics, privileging scale and optimization over humanistic inquiry. For doctoral students, this creates a paradoxical situation: on the one hand, academia accelerates the mechanics of research production; on the other, it reduces the value of intellectual exploration, surprise, chance, distraction, boredom, insight, and slow thinking; and AI obeys and enhances this dynamic nicely accelerating and hollowing the richness of the over-production.

The presence of AI in doctoral education also raises profound pedagogical questions. Should PhD programs ban AI tools, regulate their use, or integrate them openly into training? UNESCO’s (2023) guidance suggests a middle path: "Together with other forms of AI, ChatGPT could improve the process and experience of learning for students" (8). For UNESCO, educators must teach students both how to use AI responsibly and how to critically interrogate its biases, limitations, and ethical implications. Doctoral students thus need to become not just users of AI outputs but critical practitioners, capable of situating these technologies within broader epistemic and social contexts.

Some institutions have already begun to adapt. At the University of Sheffield, for example, AI literacy has been incorporated into doctoral training, with emphasis on understanding both the capabilities and the limits of generative systems. Similarly, academic publishers are revising authorship guidelines, clarifying that AI cannot be listed as an author, "Large Language Models do not currently satisfy our authorship criteria" though it may be acknowledged as a tool (Nature, online). These moves indicate a growing consensus: doctoral research cannot be divorced from AI, but it must articulate a human-centered approach to its integration.

3. Reclaiming the Human(-istic) Dimension

The challenge, then, is not to outpace AI but to reassert what is uniquely human in the doctoral journey and maybe picking the analogy about authorship and automated journalism (Montal and Reich 2017) to apply it to academic scholarship and to distinguish between what could be called "automated scholarship" and "augmented scholarship". The former entails outsourcing core intellectual labor to machines whereas the latter uses AI as a tool to expand, but not replace, human inquiry. The danger lies in conflating the two: if doctoral research becomes automated, its purpose as a formative intellectual experience is undermined. If, however, it becomes augmented, AI can free students from routine tasks, allowing them to focus on the deeper dimensions of curiosity, critical thinking, and social relevance. It is a thin hypothetic line, admittedly.

But this distinction provides the bridge to my central argument. The value of the PhD does not lie in its ability to compete with machines in efficiency or volume. It must lie in cultivating dimensions of research that are irreducibly human: the pleasure of discovering, the contribution to happiness as the public collective good, and the diversion as creative deviation. In times when AI threatens to mechanize knowledge, these values: pleasure, happiness, and diversion must be foregrounded as the raison d’être of doctoral education, and preserved as the human(-istic) fundaments of any research training.

4. Pleasure

The first dimension I want to defend for the doctoral journey is Pleasure (in capital P). At first glance, to link pleasure with the PhD might seem incongruous. Doctoral research is framed as a serious, even arduous undertaking: years of sustained labor, often marked by anxiety, and struggles impacting mental health: exhaustion, stress, frustration; uncertainty, and institutional demands. Yet, Pleasure, I argue, is central. Pleasure I mean, not only in the sense of distraction and entertainment, but also and particularly in the sense of pleasure as a response to the insatiable thirst for knowledge, the joy that arises when we explain, when we, driven by the desire of curiosity, chase questions that unsettle us, when that restlessness compels us to explore, and when in that process, the very act of thinking transforms who we are.

Historically, the association between knowledge and pleasure is deeply rooted. Augustine (4^th Century), somewhat naively from today’s view, already warned against the dangers of curiositas, that were also the force of his learning:

This I learned without any pressure of punishment to urge me on, for my heart urged me to give birth to its conceptions, which I could only do by learning words not of those who taught, but of those who talked with me; in whose ears also I gave birth to the thoughts, whatever I conceived. No doubt, then, that a free curiosity has more force in our learning these things, than a frightful enforcement (Augustine 2003, book I, para. 23).

Yet that same desire was also acknowledged as a fundamental human drive, under what scholastics would call libido sciendi. To seek knowledge is not a purely rational act; it is driven by affect, by restlessness, by an unease that pushes us forward. As Latour (2005) started his amazing book "like all sciences, sociology begins in wonder" (21). The English word wonder packs a powerful double sense, here: it denotes both curiosity ("I wonder what…") and fascination ("It is a wonder"). In my view, the true starting point of the doctoral adventure has to do with this doubleness: because it captures the epistemic condition of research. Doctoral study begins in that space where not knowing provokes desire, and where the object of inquiry exerts an irresistible fascination, and finding, is pleasure.

Galileo captured this sense in Discorsi (1638/1954), describing how the bustling activity of the Venetian arsenal—workers carrying materials, ships under construction, movements coordinated on a grand scale—opened "a large field to speculative minds" (37). What fascinated him was not only the mechanics of shipbuilding but the sheer complexity of coordinated human labor, a spectacle that invited endless questions. To watch with wonder is to be moved to think. Doctoral research, likewise, begins not in certainty but in astonishment.

This resonates with broader traditions in philosophy and science studies. Isabelle Stengers (2018) reminds us of slow science sharing that it is always entangled with wonder and with the affective investments of those who practice it. Knowledge is not simply a cold accumulation of facts, but a practice animated by curiosity, passion, and care. Brian Massumi (2002) likewise emphasizes affect as the motor of thought: thinking arises not in abstraction but in the intensity of encounters that compel us to respond. Sara Ahmed (2010), writing on the promise of happiness, notes how desires orient us toward certain objects and practices, shaping not only what we know but who we become. The doctoral journey is no exception: it is animated by desires that move us, sometimes restlessly, sometimes joyfully, toward unknown futures.

To acknowledge the role of pleasure in doctoral research is also to resist the colonization of research by metrics and productivity demands. Within neoliberal academia, doctoral work is often evaluated in terms of outputs: number of publications, citation counts, "impact." These measures, as Gill (2010) argues, contribute to a culture of anxiety, overwork, and precarity. Yet they rarely capture the lived experience of intellectual joy, the small moments of breakthrough, or the satisfaction of making sense of a complex problem. Instead, I sustain, pleasure resists quantification, but it is precisely what sustains research over the long haul.

It is worth contrasting this with the forms of "pleasure" that artificial intelligence seems to promise. Large language models, recommender systems, and digital platforms are designed to maximize engagement, to predict and deliver content that will keep users attentive. In this sense, AI commodifies curiosity: it transforms desire for knowledge into patterns of consumption, feeding us what is most statistically likely to satisfy. This is a dangerous reduction. As Zuboff (2019) has shown in her analysis of surveillance capitalism, the extraction of behavioral data reduces human curiosity to predictable inputs, flattening the richness of our intellectual drives. Doctoral pleasure cannot be reduced to such predictable loops. It involves risk, uncertainty, and the willingness to be unsettled.

This distinction between genuine intellectual pleasure and its algorithmic counterfeit finds empirical support in recent studies of how doctoral students engage with AI tools. Research on "perceived enjoyment" (PEN) as a factor in technology acceptance reveals that students are significantly influenced by the extent to which AI tools are enjoyable to use (Uludağ, Kılıç, and Çelik 2025; Cano and Nunez 2024). Indeed, some studies suggest that perceived enjoyment matters more than perceived usefulness in driving AI adoption among higher education students—a finding that should give us pause. At first glance, this might seem to vindicate pleasure as a motive for research. But the enjoyment these studies measure is precisely the kind AI is designed to deliver: frictionless, immediate, statistically optimized to match user expectations. It is the pleasure of consumption, not the pleasure of transformation; the pleasure of having questions answered, not the pleasure of being unsettled by new ones.

When a doctoral student takes pleasure in ChatGPT’s fluent summaries, they are experiencing something real—but it is the pleasure of the algorithm, not the pleasure of thought. This is where Paola Cantarini’s recent work on "Algorithmic Erotic Cognition" (2025) provides the theoretical language we need. Cantarini (2025) argues that AI systems do not merely assist cognition; they reconfigure the very structure of epistemic desire, displacing what she calls "the deferred pleasure of discovery" toward "instantaneous algorithmic gratification" (470). Drawing on Stiegler’s philosophy of technics and Bateson’s ecology of mind, she shows how the interval where epistemic desire traditionally resides—the space of wondering, of not knowing, of being drawn toward an unknown object—is compressed by interfaces that gratify before we have truly asked. Cognition shifts from abductive exploration to predictive consumption. And this is not merely a psychological shift: Cantarini (2025) anchors her argument in neuroscientific evidence from a 2025 study demonstrating that recurrent delegation of reasoning tasks to large language models leads to detectable reductions in neural connectivity in networks associated with metacognition, originality, and imagination.

To illustrate, consider the experience of returning to one’s dissertation drafts after several years. What at first seemed coherent and definitive now appears incomplete, provisional, perhaps even naive. Yet this is not failure; it is a sign of growth. The doctoral journey transforms us such that our earlier thoughts no longer suffice. Pleasure lies in this recognition—that research is not a straight line toward certainty but an evolving path of becoming.

My own encounter with this idea of research as a transformative Pleasure was shaped by the social psychologist Tomás Ibáñez. He offered a perspective that has deeply marked my own thinking. In February 2000 he also delivered an inaugural speech to the new cohortof doctorate students of Social Psychology at the Universitat Autonoma of Barcelona for the first International Congress of PhD Students. His memorable talk was playfully called Anchor in Objectivity or Sailing in Pleasure. Among many other things, Ibáñez argued that in social sciences objectivity is not a driver for knowledge or research. It is rather a stopping place where certainty works as an anchor; instead, what drives (or sticking to the metaphor, what sails) research is the pleasure of thinking new things. In his words:

Pleasure is what we have left to support our commitment to research. The pleasure of thinking, the pleasure of entering the confrontation game and the knowledge exchange. But above all, research is one, there are others, of course, but it is one of the various practices through which we can experience with some intensity the pleasure of living, the pleasure of feeling alive. And the reason is very simple. On the one hand, life is intrinsically change, it is constant modification, it is incessant transformation. Life, as we well know, ends when change ceases. On the other hand, thinking, thinking truly, seriously, and deeply is, necessarily, changing one’s thoughts. After having focused our thoughts intensely and productively on a given issue, we cannot continue thinking about it in the same way as we did before we had it under scrutiny. To think, Foucault said, is always the changing of thoughts. And since what we are is not independent of what we think, thinking is putting ourselves in the trance of changing ourselves, too. Thinking is entering the adventure of constantly becoming "other" of what we were. Life is change; thinking is change. When we live, we change, when we think we change. And that’s why thinking is one of the ways to savor the unmistakable pleasure of feeling alive. And this might be a sufficient reason to underpin our commitment to research (Ibáñez 2001, 36).²

The doctoral journey, then, is not only about producing new knowledge but about being transformed by the process. Pleasure lies in this transformation: the recognition that we emerge from the PhD not the same as we were when we entered. This pleasure of thinking, which Ibáñez identified with the experience of becoming other, is replaced, as Cantarini (2025) showed, by the pleasure of having one’s expectations met. The machine gratifies, but it does not transform. And in that gratification lies the danger: not that we will think with machines, but that we will cease to think at all, mistaking the fluency of response for the labor of discovery and the comfort of certainty for the risk of becoming other.

Too often, myself included, doctoral supervisors insist that students define a clear project at the beginning, a roadmap that ensures the feasibility of the project. And yet, the irony is that the true meaning of the research project only emerges retrospectively, when the journey is complete. What seemed like detours or digressions often reveal themselves as the heart of the argument. The dissertation process somehow makes sense backwards; and yet the pleasure of the doctoral journey lies in allowing this process to unfold—embracing uncertainty, deviation, and serendipity, rather than forcing knowledge into predetermined molds, or probabilistic models.

Pleasure, then, is not trivial. It is the motor of intellectual life. It is what sustains curiosity when deadlines loom, when archives are impenetrable, when data refuses to fit. It is also what distinguishes human research from machinic reproduction. Machines can recombine patterns of knowledge, but they cannot desire. They cannot feel the unease that compels us to ask new questions. They cannot take pleasure in discovery, in transformation, in wonder. Pleasure is the drive and the pathos, the passion that will ensure that your research is informed by originality, creativity, critical thoughts, and innovation. Only humans can aim for pleasure and feel it. And that is why pleasure must remain the first P of the PhD in times of artificial intelligence.

5. Happiness in the Neoliberal University

If pleasure sustains the doctoral journey through curiosity and transformation, happiness expands its scope. In the neoliberal university, happiness is increasingly instrumentalized. Over the past two decades, higher education has been swept into the orbit of "happiness industries" (Davies 2015), where student satisfaction scores, staff well-being surveys, and university "happiness rankings" are presented as key indicators of institutional success. In this framing, happiness is privatized and commodified: it becomes an individual achievement, something each student or academic must cultivate and display.

Doctoral students are particularly exposed to this discourse. They are told to manage their "work–life balance," to practice resilience, to safeguard their mental health. Support services and well-being initiatives, while valuable, often individualize responsibility, obscuring the systemic conditions that produce anxiety, precarity, and overwork. Rosalind Gill (2010) has shown how neoliberal academia produces a culture of silence around these "hidden injuries," shifting the burden of well-being onto the very individuals whose structural conditions undermine it.

Lauren Berlant (2011) takes this critique further with her notion of "cruel optimism." Happiness, she argues, can become an attachment to objects or promises that actually hinder our flourishing. In academia, the promise that the PhD will deliver personal fulfillment, career security, or intellectual recognition often proves to be an empty promise of cruel optimism. Many doctoral graduates face precarious futures, with diminishing opportunities for permanent positions. The injunction to "be happy" can thus become one more burden, one more metric of self-discipline in a system structured by insecurity.

When happiness is reduced to private satisfaction, it aligns neatly with the neoliberal emphasis on competition, individual achievement, and performance. A PhD becomes a credential to secure personal advancement, its value measured by individual career trajectories rather than collective contributions. Happiness, in this sense, is narrow, fragile, and easily co-opted by the very systems that undermine genuine well-being.

When Hannah Arendt wrote about happiness, she appealed to a very different perspective. For her, happiness cannot be confined to the private sphere. In On Revolution (1963), she revisits the United States Declaration of Independence when on the 4th of July of 1776 the United States Congress made, signed, and posted this unanimous declaration and its invocation of the "pursuit of happiness". This is what the text says:

When in the course of human events it becomes necessary for one people to dissolve the political bands which have connected them with another, and to assume among the powers of the earth the separate and equal station to which the laws of nature and nature’s God entitle them, this in respect to the opinions of mankind requires that they should declare the causes which impel them to the separation. We hold these truths to be self-evident, that all men are created equal, that they are endowed with their creator with certain unalienable rights, that among these are life, liberty, the pursuit of happiness (Jefferson 1776, para. 1–2).

True, this statement only talks about "men" and only refers to "the United States" and was written in a particular moment in history—in the 18th century—when slavery was common and unmentioned. Still, this is a very important epochal statement, that also appealed the attention to the philosopher Jacques Derrida. In his beautiful text "Signature, Event, Context" from 1971, he paid attention to this same document, the US Declaration of Independence and dropped the question: who is signing this paper? The Declaration starts with the famous formula "We, the people", and he identified here the paradox: if the document is a declaration of independence, this "the people" can only become a signatory entity with the actual independence to sign. This is, the document could only give validity to the signatories after the document was signed. This led Derrida to a rich, stimulating, and pleasant exploration of the idea of a signature. From there, he explored the idea of signature as a form of certification that establishes that one (who signed) has been there (to sign); but the one (who signed) is not there anymore. The signature, Derrida wrote, stands as something that remains for the future, from the past, as a witness of the present. In his words: "By definition, a written signature implies the actual non-presence of the signer, but it also marks and retains his having been present in a past, and he will remain in the future" (Derrida 1988, 8).

I have worked with Derrida’s text many times. I like the bending of the thinking, the paradox in between; the absence and the presence, and the signature as something that remains. If you think of digital data and research for a second (Rodriguez-Amat 2021), for instance, the data that we use to track ourselves when we go for a run, or the data owned by Google asking where you have been, and if you have been at a certain place and if you liked it or not. The phone and the data bits that are collected by your phone company, and extensively by the data platforms that own the apps, does the same thing. Your phone signs: it stores a presence in a spot, a signature, a geo-coordinate matched to a timestamp. The phone and the information byte certify that you were there, in the past, as a witness that will stay for the future, even if in the present you are not there anymore.

Imagine then algorithms in droves reaching for the right formula that anticipates where you will be, or how you will go, and how many people will be there, or if many liked it or not. Even if you have never been there, the signature of others works as training material for your experience, and vice versa. I know. Geo data is fascinating, and the text by Derrida about the notion of signature very interesting… But this is a digression. I want to get back to the "h" in PhD that stands for happiness. So let me go back to that Declaration of Independence, Hannah Arendt, and the inalienable right, to the collective "pursuit of happiness".

Contrary to readings that would easily equate happiness with individual well-being, Arendt revisited the Declaration of Independence and with it, she brought forward what that revolutionary generation understood as happiness. Happiness was taken in public, collective, common terms: happiness meant the right to participate in political life, to act with others in shaping the common world.

This distinction of the meaning of happiness is sharpened in The Human Condition (1998), where Arendt identifies the public sphere as the space where we appear to one another, act together, and sustain a shared world. Happiness, there, is not a private feeling but a collective condition made possible by freedom and action. It arises when we recognize ourselves as part of a common project, when we act in ways that sustain rather than retreat from plurality.

This idea of happiness explored by Hannah Arendt is very political, because it connects directly to the notion of citizenship. There are three features of the public sphere and of the sphere of politics in general that are central to Arendt’s conception of citizenship. First, citizenship is a constructed quality, not something we have by nature. It is therefore artificial. This matters because it makes citizenship human, ethical, and product of a decision, not something natural, or objective.

Second, citizenship is also a spatial quality. It is not something that happens in the void, but something that is connected to the space we share, the city, the polis, the country. Note how this hits frontally many discourses on migration today, particularly those that talk about illegal immigrants. For Arendt, this idea is a very unhappy aberration.

And third, citizenship must be built on the distinction between public and private interests. This separation implies that private interests such as the economic interests or political career, or personal interests cannot enter or alter the conditions for the public discussion. Happiness for Arendt is a public and social good, and it is an active interaction in society that ensures living together in a community to be able to understand each other and to happily share the world.

In contrast, the notion of the idiot for the Greek, referred to someone who did not engage or participate in public life, the one who did not join the public discussion and remained selfishly thinking about their own personal interests. Whereas initially the notion was not particularly negative, allow me to use it to claim that the PhD work cannot be idiotic. Instead, it must contribute to the collective public and common good: a PhD must engage with the affairs of our society and must offer an engaging reflection destined to improve our social world, ideally by participating and offering the chance of a clean, fair conversation.

For doctoral research, this shift is crucial. If the PhD is pursued only for private happiness—career advancement, titles, or self-fulfillment—it risks becoming self-referential, detached from the public good, mechanical. But if it is oriented toward public happiness, then the value of the research project can be checked by its capacity to intervene in shared problems, to generate knowledge that supports collective flourishing, the improvement of society and the world. The dissertation, then, is not just a document for private advancement but an act of world-making, a contribution to collective happiness.

Taking happiness seriously as a criterion for doctoral work would mean asking not only whether a dissertation demonstrates originality but also whether it contributes to the collective good. A project on climate change, for example, cannot be judged solely on its novelty; it must also be evaluated by how it speaks to urgent planetary challenges. A dissertation on communication or cultural policy must be assessed not only on its theoretical sophistication but on its potential to inform practices that strengthen democratic life.

The climate crisis offers a telling example. For years, individuals were urged to change their diets, reduce their carbon footprints, or consume differently—small acts of private virtue. While important, these obscured the structural drivers of the crisis: fossil fuel industries, global inequalities, extractive economies. Similarly, doctoral research cannot be limited to private virtue (personal advancement, academic prestige). It must engage structural questions. Happiness, understood in Arendt’s sense, reminds us that the ultimate criterion is whether research contributes to a better livable shared world.

The philosopher Marina Garcés (2018) captures this relational, collective, and shared understanding in her Carta a Jordi Cuixart, written to the Catalan activist while he was imprisoned. "Tenim ganes de dir-nos"—"we feel like telling each other things." For Garcés, the act of speaking to each other, of naming each other, is itself a form of resistance and a form of vitality; a form of transformation and extensively a revolutionary form of happiness. Happiness is not the solitary joy of achievement but the collective joy of dialogue, of recognizing each other, ourselves in relation to others.

In terms of doctoral work, this is similar: a dissertation is never purely solitary. The work ought to emerge from conversations with supervisors, peers, texts, and publics. Its meaning lies not in isolation but in its circulation. Happiness in doctoral research, then, is inseparable from the joy of communication—of telling each other things, of making knowledge that others can use, respond to, and build upon.

Artificial intelligence can simulate that dialogue, but in pretending it often invents, hallucinates, and in any case, it does not become happier; it does not make the world a better place. AI can repeat or produce words in response to prompts, but it cannot care about being heard or about sustaining a common world. This is where the doctoral journey reveals its irreducibly human dimension. The happiness that matters is not the optimization of private well-being, but how research is called to contribute to the cultivation of a better shared life.

To reclaim happiness for doctoral research, we must resist the neoliberal pull of privatization and individualism and embrace instead its Arendtian public dimension. This means recognizing dissertations not merely as instruments of individual advancement but as interventions in collective conditions. It means evaluating doctoral work not only by originality but by its contribution to public happiness. It means teaching doctoral students not only to publish but to engage—to see their research as a form of political action in Arendt’s sense, a way of sustaining the shared world.

Such a politics of doctoral happiness aligns with the triad of values that guide this essay. If Pleasure is the motor of curiosity, and diversion the safeguard of creativity, then happiness is the horizon of responsibility. Together, they secure the Ph of the PhD as a fundamental human practice that matters in times of artificial intelligence.

But Pleasure and happiness still need the third aspect, as a mark of humanness in the ecosystem of AI and doing a PhD. The third aspect is Diversion.

6. Diversion

As it happened before, pleasure is not strictly about pleasure but intellectual change. Happiness is not strictly about getting presents but about form of social commitment. In the case of Diversion, it happens similarly. As much as the PhD and academia in general works around multiple cooking speeds and schedules, calendars, plans and agendas and deadlines, deadlines, and deadlines; a proper PhD can distinguish itself from an artificial intelligence generated product because it must embrace diversion, or several, to succeed.

The funny thing about the word diversion is that in Spanish it reminds of the word diversión (which refers to leisure, or fun); instead, in English, the meaning for deviation, and dispersion. This equivocal translation gap opens a powerful range that helps to explain the meaning I wanted it to have, here. Doing a PhD is not about following a straight line of work; it must be full of intellectual detours, playful explorations, and deviations from normative academic trajectories that make scholarly work both generative and humane.

Diversion is a call inviting the PhD student to wander, and with wandering, also comes the getting lost. This idea might be a little bit daunting and scary and provocative, but not only. It also comes with a proper voyage; there is getting lost in every proper voyage, and in every proper adventure. But getting lost is often a condition to break new knowledge, to serendipitously find new solutions to old problems. Internet, this space of uncertainty insists that Einstein said—nobody knows if it’s true—that "problems can never be solved by the way of thinking that first created them." And maybe this whole idea of lateral thinking, of daring to change patterns and paths, is sufficient to make my point of deviation and diversion. Every PhD needs to embark on an exploration of something yet unknown, and what I am trying to state here is that it should happen without fear.

Getting lost is something that, for me, must also carry a principle of challenge and of courage, and even of transgression. And this has two sides: first, the side that activates the fundamental figure that I have barely mentioned in this text, but that is very important: the supervisor. Getting lost is the most beautiful and, I believe, enriching challenge in the relationship with the supervisor. In German, the doctor parent. In Spanish, the director; In Japanese, the Sensei. With them, with the dissertation supervisor, disagreement, transgression, and wandering away from the paths set are healthy ways for the PhD student to establish their own ways. A good supervisor knows that. They support and play along with it. Supervisors accompany the student in a process, and throw a cable if and when it is needed.

The second side that this notion of diversion activates is probably richer and more difficult. It is the transgression of the discipline borders. This sounds so violent already. Discipline has these two meanings: the meaning that refers to a field of work, the territory of knowledge; and the other meaning for the word "discipline" is the rigor, the norms, and the authority. But if a PhD student wants to acquire their own voice as an independent scholar, challenging the authority is a condition to acquire some of that authority, too. The authority of the author of a research project that holds itself and that discusses with the tradition of knowledge and challenges its authority by following the rule too.

Katharina Niemeyer (2020), a scholar from the University of Quebec in Montreal, has picked a way of stating it when she writes about the "undisciplined."

As a media theorist with background in media philosophy and semiotics, it is precisely this "in-between more than two" that makes me passionate about what we do. Similar to research in particle physics, we observe it, we get closer to it and sometimes create it—but luckily, we can never completely grasp it. For the future of media theory and (media) art, we have to continue to be undisciplined and we need to keep on decolonizing our perspectives, cultural bias, minds and research, we need to keep on translating (Steinberg and Zahlten), rebelling against uniformization of research and thinking and we need to keep on exploring alternative ways of approaching and doing (media) theory (Niemeyer 2020, 57).

This understanding allows for embracing failure, detour, and divergence. Doctoral scholars cultivate intellectual resilience, ethical reflexivity, and epistemic imagination that make of diversion a political form of a counter-hegemonic strategy, enabling researchers to explore lines of liberation and to fail as in Halberstam’s (2011) "queer art of failure": undiscipline, and diversion are also essential markers of humanity in a territory of knowledge parceled, categorized, and ruled by Artificial Intelligence.

7. AI and the Future of Doctoral Research

The rise of generative artificial intelligence presents both opportunities and dilemmas for doctoral research. AI tools—from literature-sifting platforms like Elicit to large language models such as ChatGPT—promise new efficiencies, reducing the time needed for data handling, literature reviews, or even preliminary drafting (Floridi and Chiriatti 2020). They exemplify the augmented scholarship: computational partners that expand the researcher’s reach, while leaving critical interpretation and judgment to the human scholar.

But this augmentation comes with profound risks. AI-generated text is always derivative, secondary, bound to and as good as the data that feeds it. It may reinforce biases, flatten complexity, or simulate critical reasoning without ever truly engaging in it. The danger for doctoral education is that the PhD—long defined by the "original contribution to knowledge"—risks being hollowed out if originality is outsourced to algorithms; which raises further debates over plagiarism, authorship, and AI-assisted work that already complicate the line between legitimate support and intellectual abdication.

Universities and agencies are responding with guidelines. UNESCO (2023) calls for responsible and transparent use of AI in higher education, while Europol (2021) and others warn of risks to epistemic integrity. Yet policy alone cannot resolve the deeper question about what it means to do a PhD in a world where machines can mimic scholarship.

The answer, I argue, is not to banish AI but to reposition it. AI can process, summarize, and recombine, but it cannot desire, wonder, or care. It cannot experience the joy of an unexpected finding, the ethical weight of representing others, or the creative exhilaration of getting lost in a question. In doctoral research, these human experiences matter more than efficiency. They are the very conditions that make the PhD transformative rather than transactional.

Thus, the doctoral task today is double: to learn how to work critically with algorithmic systems, and at the same time to safeguard the human(-istic) essence of scholarship. AI may be a powerful co-actor in Latour’s (2005) sense, but it cannot replace the irreducible human dimensions of inquiry. These are, as I have argued, the capacity to seek with Pleasure, to contribute toward public Happiness, and to wander through Diversion.

8. Conclusion

In times when artificial intelligence unsettles the foundations of scholarship, the PhD is not obsolete; it is urgent. Its meaning does not reside in efficiency or in the production of yet another PDF uploaded to an institutional repository. Its value lies in cultivating what no machine can simulate: the thrill of curiosity, the responsibility of contributing to the common world, and the courage to take detours that no algorithm would ever dare to compute.

A dissertation, at its best, is not just a piece of writing. It is an adventure, a declaration, a signature that says: I was here, I thought this, and I invite you to think further with me. It is a human gesture that refuses to be reduced to metrics, patterns, or predictive models. To do a PhD today is to affirm that research is not merely a technical exercise but a way of inhabiting the world with imagination, risk, and joy.

If AI can produce fluent text, only human beings can turn thinking into living. Only humans can make of research a collective act of care, an occasion for transformation, and a practice of freedom. And if doctoral education is to have a future, it will not be because it outpaces the machine, but because it insists on what machines cannot touch: the joy of discovery, the responsibility of the common good, and the generative play of intellectual wandering.

Or, to put it simply: the PhD will survive—and matter—for as long as we defend it against algorithmic gratification and replace the consumptive enjoyment with transformative Desire and Pleasure; if we succeed at challenging neoliberal privatization with the common good of happiness; and if we overtake the algorithmic optimization of the calculated recommendation models into a wander, undisciplined, diversion.

Competing Interests

The author has no competing interests to declare.

Acknowledgements

The author wants to thank Dr. Jose Maria Blanco and team for the invitation to deliver the lecture at the Universitat Autonoma Barcelona that inspired this text.

Use of Artificial Intelligence

I acknowledge the use of a large language model (DeepSeek, February 2026 version, https://www.deepseek.com) on multiple occasions between January and February 2026 for the purpose of reference verification, formatting assistance, drafting correspondence with reviewers based on bullet point inputs. The conversation history with the AI assistant includes: verifying source validity against search results; correcting citations for example for Harris, Soriano, and Ralston (2025), Leite (2025), Wilder and Calderone (2025), and Mulligan et al. (2025); using the bullet points and the table to draft structured responses to reviewer comments. The outputs from these interactions were used to identify missing citations in the original text, correct reference formatting, structure responses to peer reviewers, and strengthen the empirical grounding of the manuscript. All AI-generated suggestions were verified against original sources or substantially rewritten by the author. I take full responsibility for the content of all AI-generated outputs used in my research and for the final manuscript.