The Economy of Creative Abundance

AI is disrupting creation: it displaces scarcity and multiplies possibilities. Faced with an obsession for performance, the cultural sector can invent an economy of creative robustness.

Performance or Robustness: Which Model for Creation in the Age of AI?

Generative artificial intelligences are disrupting the economy of cultural creation just as they are doing in other sectors, even if we may not notice it yet: they profoundly modify the relationship between supply and demand, multiply production capacities, and redefine what constitutes value. Yet, unlike the corporate world which is rushing to optimize margins, the cultural sector faces a deeper opportunity: that of rethinking its economic models through the lens of logic based on abundance rather than scarcity, and robustness rather than performance.

Silicon Valley entrepreneurs work within a logic that biologist Olivier Hamant would call the “cult of performance”: creating massive value requires meeting immense demand with a rare supply, while possessing a lever to multiply one’s actions. AI, in this vision, constitutes the ultimate lever: it allows a single person to produce for thousands, to do “something a million times.” If applied mechanically to culture, this logic would lead to an even more radical industrialization of creation, where a few AI-equipped creators would saturate every market.

This dystopian scenario legitimately haunts many artists and cultural mediators. However, the specificity of the cultural field resists this economistic reduction. Creation is not measured solely in units produced, and cultural value far exceeds market value. As Hamant shows in his work on living systems, performance—the sum of efficiency and effectiveness—leads to evolutionary dead ends through over-specialization. Life, in reality, functions through robustness: the capacity to maintain a stable system despite fluctuations, at the cost of apparent “under-performances” (redundancies, diversity, slowness) which constitute its very adaptive strength. How can we re-elaborate and affirm the differences in nature between artistic or cultural projects and capitalistic logic? How can we conceive a robust cultural economy in the face of AI turbulence?

Scarcity Overturned: When Technical Abundance Meets Artistic Singularity

The cultural economy has long operated on the model of material scarcity: scarcity of production means (studios, cameras, printing presses), scarcity of distribution channels (galleries, theaters, publishers), and scarcity of technical skills. This scarcity structured professional hierarchies and economic models. A director with access to a professional camera and the knowledge of how to use it held a rare capital. A writer published by a major house benefited from a rare distribution channel. Generative AI brutally tips this equation by making production capabilities—once reserved for highly qualified or legitimized specialists—accessible at a lower cost.

Yet, this technical democratization does not dissolve all forms of scarcity; it displaces them. As Janet Murray observes in “Hamlet on the Holodeck” (1997), her foundational work on interactive digital storytelling, new narrative technologies do not destroy the value of storytelling itself; they create new spaces where this value can be expressed differently. What becomes rare is no longer the technical ability to produce an image or a text, but the ability to conceive coherent universes, to weave meaningful relationships, and to embody a singular artistic vision. Photographer Karin Crona, whose research spans different mediums, illustrates how the contemporary artist develops an intimate relationship with their tools—whether traditional techniques or AI—to build a signature aesthetic of their own.

This mutation recalls the shift from figurative painting to photography in the 19^th century. When photography appeared, many predicted the death of painting: why paint a portrait when it could be photographed in minutes? Yet, painting did not disappear. It freed itself from the obligation of mimetic representation to explore abstraction, expressionism, and texture, while photography also drew inspiration from painting to go beyond the snapshot. Generative AI provokes a similar displacement: it liberates creators from many technical execution tasks, allowing them to focus on the essential: vision, meaning, and relationship. Slavoj Žižek, who analyzed the Matrix trilogy as a philosophical machine (Badiou and During, 2003), shows how science fiction narratives constitute laboratories where our technological anxieties are experimented with. AI confronts us with a real-life version of these questions: what constitutes the irreducible value of the human in creation?

The Creative Leverage Effect: From Artisan to Orchestrator of Possibilities

In entrepreneurial logic, leverage refers to the ability to multiply one’s action through the use of external resources: capital, technology, networks. For the cultural sector, AI indeed constitutes an unprecedented lever, but according to modalities that escape simple productivity logic. A creator can now, alone in front of a screen, generate hundreds of images, musical variations, or narrative propositions. This multiplication does not necessarily mean a proportional increase in created value, but it radically transforms the creative process itself.

The artist becomes an orchestrator of possibilities rather than a solitary executant. This transformation is concretely observed in the AI creation workshops I facilitated in 2024 for some 400 participants. The relationship to the tool proves fundamental: just as Rembrandt ground his pigments to create unique colors, or filmmakers choose cameras and film stocks according to their vision, creators using AI develop an intimacy with their generative models. The choice of tools, the phrasing of prompts, the selection among propositions, and the sequence of iterations all constitute artistic gestures. The tool is no longer just an extension of the body but becomes a creative interlocutor, as Tim Ingold previously analyzed in his anthropological work on the relationships between humans and materials.

This collaboration opens the path to new cultural professions that we are only beginning to glimpse: curators of generative universes, sculptors of AI models, choreographers of narrative algorithms, and mediators between human intentions and machine productions. There are even emerging challenges regarding the conservation and mediation of the heritage of generative AI creations, as they are now part of our cultural history. These projects and roles are not limited to technical mastery; they require a profound understanding of the aesthetic, ethical, and cultural stakes of automated generation. Stéphane Mallard, in his essay on technological disruption (Disruption, 2018), reminds us that major transformations always eliminate certain jobs while creating others, often richer in relational and empathic dimensions. The challenge for the cultural sector is to anticipate these mutations rather than undergo them, training cultural actors in these new skills without renouncing traditional savoir-faire, which remains entirely relevant.

The multiplication of productive capacities does not mechanically lead to a devaluation of cultural production, contrary to what a purely quantitative reading might suggest. On the contrary, it allows for broader exploration, freer experimentation, and the multiplication of prototypes before selecting what deserves to be developed. This abundance of possibilities changes the very nature of the creative process: less linear, more intuitive and iterative, in dialogue with the ubiquitous machine, and more grounded in curation than in production *ex nihilo*. The post-AI artist looks less like a demiurge creating from nothing and more like a gardener cultivating an ecosystem of possible forms. This gardener metaphor resonates with Olivier Hamant’s thought: life does not control; it composes with fluctuations, cultivates diversity, and accepts apparent inefficiency as a source of resilience.

Creative Robustness: Cultivating Adaptability Rather Than Optimization

Olivier Hamant’s work on the robustness of living systems offers an enlightening conceptual framework for rethinking the cultural economy in the age of AI. Hamant makes a radical distinction between two logics:

• that of adaptation (anticipating the future and optimizing assets to prepare for it)
• and that of adaptability (building upon one’s weak points to create a diversity of solutions in the face of an unpredictable world).

Silicon Valley embodies the first logic: identify future demand, develop the most high-performance solution, and impose it massively. The cultural sector, however, would benefit from cultivating the second: maintaining a plurality of approaches, valuing margins and failures, and preserving spaces of non-optimization.

Concretely, a robust approach to cultural AI would involve resisting the temptation of standardization. Rather than seeking “the best” AI model for musical or visual creation, it would mean cultivating a diversity of tools, practices, and relationships with generative technologies. Rather than optimizing creative processes to maximize productivity, it would be appropriate to preserve apparently “unproductive” time for experimentation, fruitful error, and creative drift. As Hamant shows with the example of photosynthesis (which “wastes” 99% of solar energy but has ensured the survival of plants for billions of years), apparent inefficiency can constitute a long-term strategy for robustness.

This perspective directly challenges indicators of cultural “success”: number of views, listens, downloads, or spectators in theaters. These quantitative metrics, inherited from market logic and amplified by digital platforms, favor optimization at the expense of robustness. They encourage producing what already works, seeking to repeat proven formulas, and aiming for the maximum audience—which effectively kills innovation and diversity. A robust cultural economy would instead value singularity, unpredictability, and the capacity to surprise and unsettle. It would invest in projects whose “return on investment” cannot be measured immediately but which enrich the creative ecosystem in the long term.

AI can serve this robustness if we use it not to standardize but to diversify, not to replace the human but to augment their capacity for exploration. The workshops I have led show that participants develop extremely varied uses for the same tools: some use them to quickly generate sketches that they then rework manually, others to explore visual universes they would never have imagined alone, and still others to collaborate with the machine in a continuous creative dialogue. This plurality of approaches constitutes the very robustness of the system: if one path proves unproductive or problematic, a thousand others remain open.

An Economy of Relationship Rather Than Production

Entrepreneurial enrichment logics applied mechanically to culture via AI would lead to a dead end: an overabundance of standardized content, concentration of power, and devaluation of creative labor. However, if we choose other paths, AI can contribute to a true cultural abundance where value is measured not in quantities produced but in the quality of relationships woven, singularities expressed, and spaces of freedom preserved.

John Dewey, the pragmatist philosopher and major 20^th-century educator, stated in Art as Experience (1934) that art resides not in the objects produced but in the aesthetic experience lived—in the living relationship between the work and those who encounter it. This perspective invites us to think about the cultural economy differently: not as an industry for producing artifacts, but as an ecosystem of meaningful relationships. AI can serve this relational economy by facilitating shared creation, allowing more people to express themselves, and multiplying occasions for encounters between different sensibilities. It can also destroy it if it serves only to saturate attention with content algorithmically optimized to capture “available brain time.”

The choice belongs to us, collectively. Cultural actors, as carriers of humanist values, have a particular responsibility: to experiment with AI not to maximize their market share, but to cultivate creative diversity, critical thinking, and cooperation. This requires accepting uncertainty, renouncing total control over processes, and trusting the collective intelligences that emerge from the encounter between humans and machines. Anthropologist Tim Ingold reminds us that technology is never neutral but always relational: it configures our ways of being in the world and our ways of relating to others and to materials. Generative AI profoundly reconfigures these relationships. It is up to us to care for—in the sense of Bernard Stiegler’s *panser*—these new links: not to refuse them on principle, nor to adopt them blindly, but to tend to them, cultivate them, and orient them toward emancipatory rather than alienating ends.

Olivier Hamant proposes a concrete methodology for shifting toward robustness:

• first, identify the vulnerabilities of the system,
• then imagine stress tests (what would happen if...),
• finally, develop plural solutions rather than a single optimized solution.

Applied to the cultural sector with AI, this approach would invite us to ask:

• what are our vulnerabilities in the face of creative automation? (concentration of power, loss of savoir-faire, aesthetic uniformization)
• What would happen if AI models became inaccessible or were banned? (hence the importance of open models and technological sovereignty)
• How can we maintain a diversity of creative approaches? (through training, support for marginal practices, and valuing experimentation)

The economy of creative abundance with AI begins with a change of perspective: ceasing to see culture as a stock of goods to be produced and accumulated, and instead conceiving it as a flow of relationships to be nurtured and enriched. In this perspective, the question is no longer “how many films, books, or images can I generate?” but “what fertile encounters can I facilitate? What unheard voices can I help amplify? What spaces of freedom can I help preserve?” These questions, essentially relational and political, outline the contours of a cultural economy that would use technological leverage not to enrich a few, but to cultivate the common good.

The path I am calling for requires constant vigilance, prudent experimentation, and sustained collective reflection. It requires “slack in the gears,” in Hamant’s words: spaces of freedom, room for maneuver, and redundancies that seem inefficient but guarantee resilience. Most of all, it requires placing the human—not at the center as a hollow phrase, but in relationship: with other humans, with machines, and with the living world of which we are a part. For robustness, the biologist reminds us, is never conceived in isolation but always through links to territory, community, and ecosystems. A robust cultural economy will necessarily be an economy of the commons, of cooperation, and of mutual attention—the exact opposite of what an unquestioned adoption of Silicon Valley’s performance logic would produce.