The platform looks like a tool. It isn’t. It is an architecture of governance, dressed as a utility.

To see this clearly requires multiple lenses. The Frankfurt School to expose the factory. Marcuse to map the psychology. Sara Ahmed to follow the money of feeling. Giorgio Agamben to name the political condition. Used together, they describe a single, interlocking system — what I’ll call the affective biopolitical culture nexus. Used separately, each describes only a fragment.

This article walks through all four. It compresses an academic paper. The full version, with citations and apparatus intact, is on Academia.edu (link at the bottom).

1. The Feed Is a Factory

Adorno and Horkheimer wrote Dialectic of Enlightenment in response to Hollywood and radio. They argued that under late capitalism, culture itself becomes industrial: standardized, formulaic, designed to manufacture passive consumers while wearing the mask of entertainment.

The social feed is the perfected version of what they described.

Hollywood guessed at audiences. Algorithms measure them in real time. A viral aesthetic — a dance, a meme, a particular grade of light — is detected, replicated, and distributed globally within hours. The cultural product is the same product, infinitely re-skinned. Netflix, Disney+, Prime, Crave: the menu simulates abundance. The output is the same easy-to-digest material engineered to keep you subscribed.

The illusion of choice is structural. So is the illusion of participation. Every single like, comment, and share is read by Adorno and Horkheimer’s critics as user agency. It isn’t. It’s free labour that feeds the system the data it needs to refine the next round of standardization. The user produces the culture and consumes it. The platform owns distribution, visibility, and monetization.

This is the culture industry.

2. The Happy Consciousness

Herbert Marcuse pushed the analysis inward. In One-Dimensional Man, he argued that advanced industrial societies no longer need terror to control their populations. They use seduction. The system installs false needs — needs that are not vital but superimposed by particular interests in repression. The need to relax and consume. To stay current. To perform a curated version of being alive.

The gratification of these needs produces what Marcuse called euphoria in unhappiness: a population fully satisfied by consumer goods, no longer able to recognize its own alienation. He called the resulting mindset the happy consciousness — a state in which only the existing reality is treated as rational, and any alternative is dismissed as unserious.

The personalized feed is the happy consciousness in interface form. It is a stream of one-dimensional positivity, calibrated to your dopamine, where any disruptive critique reads as jarring or pessimistic.

Marcuse also named the mechanism by which rebellion is absorbed: repressive desublimation. The system takes whatever is critical, packages it as a lifestyle, and sells it back. Anti-capitalist slogans on T-shirts. Burnout discourse as content. Political outrage that resolves into a share count and dies there. The energy that would have funded structural change becomes engagement.

Influencer culture is the fullest expression of this logic.

3. Outrage Is the Currency

Sara Ahmed’s contribution is the recognition that emotion does not merely exist between people — it circulates, accumulates value, and shapes collectives. She called this an affective economy.

In an affective economy, feelings stick. Happiness sticks to consumer goods. Stickiness is not natural; it is the product of repeated political and commercial association. Once an emotion has stuck, it polarizes — there is the us who shares the feeling and the them who refuses it. Those who refuse become what Ahmed calls affect aliens: estranged from the collective for failing to feel the right thing in the right direction.

Platforms run on this economy. Their business model requires engagement, and the most reliable producer of engagement is high-arousal emotion — particularly moral outrage and tribal solidarity. Algorithms detect emotionally charged content and amplify it, because it converts. Polarized communities are more active, more loyal, more profitable.

This is why every feed feels like an argument. The argument is the product.

Influencers operate inside the same economy at smaller scale. Their work is emotional labor: cultivating intimacy, trust, parasociality, and then transferring those affective bonds onto endorsed products. The follower mistakes the relationship for friendship. The platform mistakes nothing. It is metering the whole exchange.

4. Bare Life on the Platform

Giorgio Agamben supplies the political diagnosis. Drawing on Foucault, he argues that modern power is biopolitical — concerned with the administration and regulation of biological life. He distinguishes between bios, life that is politically qualified, and zoe, bare life, the simple fact of existing as a manageable body.

The figure that haunts his work is homo sacer — the Roman legal category of a person who could be killed without punishment but not sacrificed in ritual. Included in the system only through exclusion. Visible to the law only as an object of its power.

Agamben’s second key concept is the state of exception: the suspension of normal legal order in the name of crisis, which he argues has become a permanent paradigm of governance rather than a temporary measure.

Both concepts map onto platforms with disturbing precision.

Surveillance capitalism reduces human experience to behavioral data. The data-self is digital zoe: a depoliticized resource to be extracted, modeled, sold. Platforms operate as digital sovereigns of their territory. They write the law (Terms of Service) and reserve the right to suspend it (content moderation, shadow-banning, deplatforming). These decisions are opaque, inconsistent, and largely unaccountable. There is no due process inside the platform. There is only the sovereign’s discretion, dressed in the technical language of community guidelines.

The shadow-banned user is the digital homo sacer. Their voice has been killed, but no formal procedure was triggered. They are abandoned by the platform’s law and exposed to the arbitrary will of the platform’s power.

Meta’s 2025 shift to “Community Notes” sharpens the point. Outsourcing moderation to the user base reframes governance as participation while consolidating sovereign power. Users perform unpaid labor that looks like agency, generates affective investment, and reinforces the platform’s authority over discourse. The form is democratic, but the function is accumulation.

5. The Nexus

The four critiques are usually read separately. But I think they shouldn’t be.

The Culture Industry produces standardized cultural forms. The One-Dimensional Subject is the consciousness that consumes them happily. The Affective Economy is the engine that drives the whole circuit, because feeling is the most reliable producer of engagement. The Biopolitical Nexus is the foundation: every user reduced to a stream of data, governed by a sovereign that decides on the exception.

The cycle is self-perpetuating. Biopolitical control enables more precise affective manipulation. Affective manipulation deepens one-dimensional consciousness. One-dimensional consciousness fuels consumption of culture-industry product. Consumption generates more data for biopolitical management.

A factory, a screen, a market for emotion, and a sovereign. The same machine, viewed from four positions.

6. The Catastrophe of Liberation

Marcuse used a phrase that should be more famous than it is: democratic unfreedom. The condition of being comfortable, entertained, networked, and unfree.

This is the paradox the four thinkers leave us with. The system of control is not imposed from outside. It is something we participate in, perform, and prefer. Refusal feels like loss. Critique feels like negativity. Disconnection feels like death.

Each thinker leaves a small opening.

Adorno reserved hope for avant-garde art that refuses the formula. Marcuse named the Great Refusal — the rejection of false needs as a political act. Ahmed pointed to the affect alien, the figure who fails to feel what the collective demands and, in failing, sees clearly. Agamben sought a form-of-life that resists reduction to bare life.

All these are stances available to anyone who has noticed the apparatus and decided not to perform it on autopilot.

The first move is to name what the feed actually is: culture industry, one-dimensional engine, affective economy, biopolitical sovereign. It is the precondition for any response that doesn’t collapse back into the system it’s trying to leave.

Genuine autonomy, critical thinking, and political life are still possible. They are simply no longer the default. The default is the feed.

This essay compresses a longer academic paper. The full version, with full apparatus, citations, and the case studies of the influencer, the virality of conformity, and surveillance capitalism, is available on Academia.edu:

https://www.academia.edu/143406312/The_Affective_Biopolitical_Culture_Nexus_A_Frankfurt_Synthesis_of_Emotion_Repression_and_Digital_Sovereignty?source=swp_share

References (selected)

Adorno, T. & Horkheimer, M. (2002). Dialectic of Enlightenment. Stanford University Press. Agamben, G. (1998). Homo Sacer: Sovereign Power and Bare Life. Stanford University Press. Agamben, G. (2005). State of Exception. University of Chicago Press. Ahmed, S. (2004). Affective economies. Social Text, 22(2), 117–139. Ahmed, S. (2014). The Cultural Politics of Emotion (2nd ed.). Routledge. Marcuse, H. (1964). One-Dimensional Man. Beacon Press. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs. Gillespie, T. (2018). Custodians of the Internet. Yale University Press. Brady, W. J., et al. (2017). Emotion shapes the diffusion of moralized content in social networks. PNAS, 114(28), 7313–7318.

There are already too many options. The problem isn’t how to increase production anymore; it’s deciding what to keep. Generative AI became more costly than anticipated. It created a new problem that it can’t solve. Curation of what has meaning.

Human selection is the irreplaceable core of creative value in the age of infinite generation. The more AI floods the world with output, the more curation becomes a premium, scarce, and deeply human act because choosing what stays, what’s shown, and what means something requires judgment that machines fundamentally cannot replicate.

1. More Output is Less Novelty

Eric Zhou and Dokyun Lee analyzed over 4 million artworks on a major digital art platform and published their findings in PNAS Nexus in 2024. They wanted to know what happened when artists adopted text-to-image AI tools. The results were dramatic: adopters saw a 25% increase in creative output and a 50% boost in engagement (favourites per view). But that is not a win.

While peak content novelty increased—meaning the best, most unusual work got even better—average visual novelty declined over time. The typical AI-assisted artwork became more predictable, more stylistically flat. The tools expanded the ideas, but they pulled creators toward a kind of aesthetic convergence.

Zhou and Lee’s interpretation is key: AI is excellent at ideation. It can generate effective starting points, variations, and raw material. But it cannot perform filtering. It doesn’t know which of the thousand generated images is the one that resonates, the one that carries meaning, and the one worth showing. That remains a human skill. And in their data, the artists who thrived were the ones who learned to navigate, select, and refine AI outputs and not the ones who simply published everything the model gave them.

This created a productivity paradox. AI makes it easier to make more, but harder to make better. The bottleneck has shifted from production to curation.

2. The Authorship Effect: Why Labels Change Everything

Lucas Bellaiche and his team at the University of British Columbia conducted an experiment, published in Cognitive Research: Principles and Implications. They showed people identical images but told half the participants the image was made by a human and the other half that it was made by AI.

When labelled “human-made,” the images scored significantly higher on liking, beauty, profundity, and worth. Same image. Different story.

Two factors explained most of the gap: narrativity (the sense that there’s a story, a human experience, behind the work) and perceived effort (the belief that someone struggled, iterated, and cared). When people thought a human made it, they inferred intention, emotion, and a journey. When they thought an algorithm had made it, those inferences disappeared.

This finding was replicated by Federico Magni and colleagues in the Journal of Business and Psychology, who ran four experiments with over 2,000 participants. They found that evaluators consistently rated AI-attributed work as less creative—not because the output was objectively worse, but because they perceived less effort behind it. Effort, it turns out, is a proxy for meaning. And meaning is what we’re really judging.

Here’s why this matters for curation: A curator’s job is not just to pick good work. It’s to tell the story of why it’s good. To surface the effort, the context, the human stakes. Algorithms can rank by engagement or similarity, but they can’t explain why something matters. They can’t build the narrative about why the piece resonates.

3. The Cultural Limits of Algorithmic Taste

But what happens when curation itself is automated?

Jianyu Yan and colleagues surveyed 30 cultural institutions—museums, galleries, digital platforms—and published their findings in the Journal of Education, Humanities and Social Sciences in 2025. The concerns were consistent: algorithmic curation introduces bias, cultural homogenization, and a loss of interpretive agency.

And here’s the problem: Algorithms are trained on datasets, and datasets reflect the past. They encode what has already been valued, already been seen, already been clicked. They’re backward-looking by design. A recommendation engine can tell you what’s like what people have liked before. It cannot tell you what’s new in a way that challenges or expands taste. It cannot recognize work that’s culturally specific, locally meaningful, or ahead of its time.

Emotional resonance depends on human-centered cues like the viewer’s own lived experience. An algorithm can optimize for visual similarity or predicted engagement, but it can’t account for the embodied knowledge that a human curator brings to the table.

Adam Reynolds and Emiliano Ricciardi, writing in Psychology of Aesthetics, Creativity, and the Arts (2024) found that subjective emotional responses explain aesthetic appeal more than formal perceptual features. In simpler words, what makes a work land is not its pixel-level properties. It is only the human experience that it triggers.

Lauri Nummenmaa's 2023 research in Cognition & Emotion, mapping art-evoked feelings across thousands of artworks, found that emotional responses carry distinct bodily "fingerprints" — and that these responses are especially activated when human figures and human stories are salient.

Generative models struggle with culturally anchored complex narratives—the kind of work that draws on specific histories, subcultures, or marginalized perspectives. Without intentional human oversight, AI curation risks flattening the cultural landscape, amplifying dominant aesthetics and erasing the edges.

This is not a hypothetical risk. It’s already happening. And it’s why human curators, the people who understand context, who can read between the lines, who know what’s at stake, are more essential than ever.

4. What Curation Actually Is

Curation is not just “picking favorites.”

Curation is:

• Filtering — deciding what’s worth attention in a sea of noise.

• Contextualizing — explaining why something matters, where it fits, and what it’s in conversation with.

• Sequencing — arranging work so it builds meaning, tells a story, creates an experience.

• Stewardship — protecting cultural memory, elevating underrepresented voices, making long-term bets on what will endure.

None of these is an algorithmic task. They require cultural literacy and values. A curator makes choices that reflect what they believe is important, not just what’s popular or predicted to perform well.

The research backs this up. Zhou and Lee’s data show that the artists who succeeded with AI were the ones who developed strong selection skills—the ability to sift through hundreds of generated images and identify the handful worth refining. The ones who failed were the ones who treated AI as a vending machine: input prompt, output art, publish. That’s not curation. That’s automation. And the market can tell the difference.

Curation is now creative authorship. In a world where anyone can generate a thousand images in an afternoon, the creative act is no longer making the image; it’s deciding what it means and who needs to see it.

5. The Manifesto: Choose Like It Matters

We are entering an era of infinite mediocrity. Not because AI is bad, but because it’s too good at producing the average, the expected, the statistically likely. It flooded already every social platform, every feed, every inbox with competent, polished, utterly forgettable work.

The people whose work will be seen, remembered, and paid for are the ones who can curate. Who can look at a hundred options and choose the one that’s true. Who can say no to the fast and easy yes. Who can build a body of work that has a congruent point of view, a through-line, a reason to exist beyond “the algorithm made it.”

Algorithms optimize for engagement. Humans optimize for meaning. And meaning is what people will pay for.

Human curation is now the irreplaceable core of creative value. The more AI generates, the more your ability to choose—thoughtfully, intentionally, with conviction—becomes your competitive advantage.

Why This Matters For Your Creative Practice

1. Treat AI as a collaborator in ideation, not a replacement for judgment. Use it to generate options, explore variations, and break through your creative blocks. But never outsource the decision of what’s worth keeping. That’s your job, and it’s where your value lives.

2. Develop your selection skills as deliberately as you develop your production skills. Practice saying no. Practice challenging. Build a personal canon of work you admire and articulate why it’s good. Constantly train your eye, your ear, your taste. Curation is a muscle.

3. Tell the story of your choices. When you share work, explain why it matters. What you were trying to do. What you learned. What you rejected along the way. Context is what separates a portfolio from a feed. Narrative is always what makes people care. And in many cases, it has more value than reality.

4. Resist the pressure to publish everything. Volume is not a strategy. The artists in Zhou and Lee’s study who succeeded weren’t the ones who posted the most—they were the ones who posted the best. Quality is a curatorial act.

5. Invest in cultural literacy and contextual knowledge. The more you know the more knowledge you accumulate about history, about other disciplines, about the world in general, the better your curatorial judgment will be. Algorithms can’t read the room. You can.

6. Build systems that preserve your agency. If you’re using AI tools, choose ones that let you steer, iterate, and override. Avoid black-box systems that make decisions for you. Your creative process should amplify your judgment, not replace it.

References

Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., & Seli, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8(1), Article 42. https://doi.org/10.1186/s41235-023-00499-6

Nummenmaa, L., & Hari, R. (2023). Bodily feelings and aesthetic experience of art. Cognition & Emotion, 37(3), 515–528. https://doi.org/10.1080/02699931.2023.2183180

Reynolds, A. P. F., & Ricciardi, E. (2024). Subjective emotional instances surpass formal perceptual features in shaping the aesthetic appeal of artworks. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. https://doi.org/10.1037/aca0000720

Magni, F., Park, J., & Chao, M. M. (2023). Humans as creativity gatekeepers: Are we biased against AI creativity? Journal of Business and Psychology, 39(3), 643–656. https://doi.org/10.1007/s10869-023-09910-x

Yan, J., Wang, Y., & Liu, W. (2025). The application and challenges of artificial intelligence in contemporary art curation. Journal of Education, Humanities and Social Sciences, 60, 205–211. https://doi.org/10.54097/xnpkv747

Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3), Article pgae052. https://doi.org/10.1093/pnasnexus/pgae052