The Efficiency Trap: Creativity in the Age of Optimization

When OpenAI  announced this week that it had hired longtime Instagram and Meta partnerships executive Charles Porch as its first Vice President of Global Creative Partnerships, the move was widely interpreted as a natural next step in the evolution of artificial intelligence. Porch’s mandate is to build relationships across film, music, fashion, art, and sports; to serve as a bridge between Silicon Valley and the creative industries; and to help shape how emerging AI tools are adopted across mainstream cultural production. His first initiative, by his own description, will be a global listening tour with artists, studios, and cultural figures to better understand both their hopes and their concerns about AI.

The hire is not insignificant. It signals that the largest AI companies are no longer focused solely on building models and technical infrastructure but are now actively working to integrate those systems into the production of culture itself. Companies that once positioned themselves primarily as research-driven or tool-oriented are increasingly behaving like media platforms, forming licensing relationships with studios, exploring the use of celebrity likenesses in generative media, and attempting to build deeper ties with the industries whose work has helped train these systems.

This represents a meaningful departure from the way artificial intelligence was framed only a few years ago. Early narratives around generative AI emphasized its role as an assistive technology — a tool that could help individuals write, code, analyze, and prototype more efficiently. The stated ambition of many AI labs was to build general-purpose systems that would augment human capability and expand access to knowledge and creativity. As large language models and generative media systems have matured, however, the strategic horizon has widened. The question is no longer simply how AI can support creative work; it is increasingly how it might participate in the production and distribution of culture itself.

For the companies building these systems, this shift reflects the familiar arc of platform expansion. Foundational technologies are rarely content to remain tools; they tend to evolve toward control of the layers above them. I don't want to be too cynical, but I fear that cultural integration, in this context, is less about collaboration and more about entrenchment. Hiring senior figures from the entertainment and creator economies suggests an ambition not merely to assist creative work, but to position AI platforms as gatekeepers within the cultural economy itself.

And yet, beneath the strategic logic of this expansion sits a more complicated cultural question.It is not whether AI can assist creative work. It can. The more difficult question is whether the logic of efficiency and scale that drives technology companies should be allowed to reorganize the creative sphere without resistance, or at the very least without clearly articulated boundaries.

Because the risk is not that creativity disappears. It is that its underlying values begin to shift.

The Reasonable Case for Integration

There are thoughtful arguments in favor of bringing artists directly into the development of generative systems. Involving cultural practitioners early may lead to more nuanced tools. Creators who engage critically with emerging technologies can expose their limitations and push them in more interesting directions. Artists have consistently absorbed new technologies into their practice — from photography to synthesizers to digital editing — often expanding the vocabulary of their disciplines in the process.

But the historical comparison only holds up to a point. Most earlier creative technologies altered medium or technique; they did not operate through the large-scale statistical absorption of existing cultural output, nor were they built within corporate structures optimized primarily for speed, growth, and market dominance. The deeper tension, then, is not about whether artists use tools. It is about what happens when tools designed to increase output and efficiency begin to influence the norms and incentives of creative culture itself.

Efficiency is rarely neutral. It privileges certain outcomes over others and reshapes what gets rewarded. This is especially true when the underlying systems are probabilistic by design — trained on existing patterns and optimized to generate outputs that feel coherent, plausible, and familiar. When pattern recognition becomes the engine of production, the familiar is reinforced. What has already circulated widely becomes statistically safer to reproduce.

Over time, that dynamic does not eliminate creativity, but it does tilt the field. It nudges culture toward what is already legible, already validated, already market-proven. And when businesses built on these systems scale aggressively, that tilt becomes structural rather than incidental.

Creativity Was Never an Efficiency Problem

In most domains, technological optimization is unequivocally beneficial. We want faster logistics, more accurate diagnostics, more responsive infrastructure. When systems reduce waste or increase safety, the gains are tangible and immediate.

Creative practice, however, does not exist because it is efficient. It exists because it is human.

Art, design, writing, film, and music derive their value not simply from their final form but from the consciousness embedded within them. They carry biography, context, memory, and taste. They are shaped by constraint and by time. The friction of process — revision, experimentation, failure, refinement — is often inseparable from the meaning of the work itself.

When technologies designed to accelerate output enter this terrain, the question is not whether they are useful. It is whether they subtly alter what we understand creativity to be. If cultural production becomes primarily a matter of recombination and speed, authorship begins to blur. When process is compressed, the narrative of how something came into being becomes less central. And when that narrative fades, so too can the depth of attachment we feel to the work.

The Smoothing of Culture

Large generative systems are trained on vast datasets of existing cultural material and optimized to produce results that feel coherent and appealing. By design, they gravitate toward patterns that have proven legible and successful in the past.

At scale, this produces predictable dynamics. Aesthetic variation narrows as creators draw from similar tools trained on similar corpora. Familiar tropes proliferate because they are statistically reinforced. Markets already inclined toward what performs reliably find in these systems a mechanism that amplifies the familiar even further. Risk-taking becomes economically harder to justify, and cultural output becomes increasingly optimized for engagement rather than depth.

The result is not a dramatic collapse of originality but something subtler: a gradual smoothing. Work becomes more technically competent yet more interchangeable. Culture grows more abundant while feeling, paradoxically, less distinctive.

This is not a failure of individual artists. It is a structural outcome of scale.

Two Distinct Relationships to AI

It is important to distinguish between two fundamentally different ways these systems intersect with creative life.

The first is AI as medium. In this model, artists engage generative systems deliberately and critically. The technology becomes one material among many — something to interrogate, distort, and experiment with. The artist remains central, and the work reflects a conscious relationship to the tool.

The second is AI as production infrastructure. Here, generative systems are deployed primarily to accelerate output across entertainment, marketing, design, and media. The goal is efficiency and volume. Creative work becomes more easily scalable, more easily replicated, and more tightly linked to performance metrics.

Public discourse tends to collapse these into a single narrative of innovation, yet culturally they are distinct. One expands expressive range; the other restructures the economics of production. When technology companies formalize creative partnerships, the language often centers on artistic exploration, but structurally the incentives tend to align with integration at scale.

Scale, more than intention, is what ultimately reshapes culture.

A Shared Responsibility

Earlier this week, while speaking on a panel at KBIS on artificial intelligence and design, I was asked a question that lingers: Who bears responsibility for ensuring that we do not slide into this kind of cultural flattening? Is it the responsibility of designers and creative professionals? Editorial gatekeepers? Governments? Technology companies themselves?

My answer was simple: it is everyone’s responsibility.

None of us are passive participants in this shift. Designers choose which tools to adopt and how to use them. Technology companies choose where to direct investment and what kinds of systems to build. Media organizations and cultural institutions choose what to promote and what to question. Governments will inevitably decide how and whether to regulate. Each of these decisions shapes the cultural environment we collectively inhabit.

Most importantly, we must resist the quiet temptation to abdicate authorship over culture to large technology platforms. Culture has always been a shared construction, shaped by countless individual choices about what to make, what to use, and what to value. That remains true now. The future of creative work will be determined not by a single company or technology but by the cumulative decisions of the people who engage with them.

Design, Infrastructure, and Deliberate Boundaries

This distinction becomes especially visible in architecture and design.

In my own work, I have become increasingly intentional about where artificial intelligence belongs and where it does not. With Buildable Engine, the AI platform I founded, we focus almost exclusively on the operational layers of building: compliance, spatial logic, zoning constraints, and the technical rules that govern whether something can be constructed at all. These are necessary but time-consuming aspects of the design process that rarely generate meaning in themselves.

We made a deliberate decision not to use AI to generate aesthetic ideas.

That choice emerged from conversations with architects and designers who consistently pointed out that creativity was never the bottleneck. Compliance was. Documentation was. The invisible technical labor that consumes hours and leaves less space for reflection. Automating those constraints does not diminish authorship; it protects it. It returns time to the designer rather than replacing the designer.

There is a meaningful difference between using computational systems to strengthen infrastructure and using them to shape cultural expression. When AI helps carry the weight of rules and regulation, it clarifies the path from idea to reality. When it begins to dictate aesthetic output at scale, it risks standardizing the very thing that gives design its resonance.

The question is not whether AI belongs in creative industries. It is whether we are thoughtful about which parts of the process we choose to automate.

The Coming Cultural Divide

We may be moving toward a bifurcated landscape.

On one side will be abundant, AI-assisted cultural output — visually compelling, narratively coherent, and optimized for engagement. On the other will be work whose value is increasingly tied to authorship, provenance, and intentional process. As generative tools make image and content production ubiquitous, the question shifts from what something looks like to who conceived it, who crafted it, and what narrative it carries.

Process becomes part of value.
Human authorship becomes a marker of distinction.

When everything can be generated, origin begins to matter more, not less.

A Case for Cultural Deliberation

There are, of course, extraordinary opportunities for artificial intelligence that extend far beyond the pursuit of cultural relevance. Businesses across every sector still struggle with inefficiency, fragmentation, and operational complexity. Infrastructure remains outdated. Supply chains remain fragile. Housing, healthcare, transportation, and construction all present enormous technical challenges that would benefit from sustained investment and attention.

Artificial intelligence has the potential to address many of these structural problems, improving productivity and enabling organizations to function more effectively. It is worth asking whether this is where a significant portion of our collective focus and resources should be directed. Not every technological breakthrough needs to be translated immediately into entertainment or aesthetic production in order to justify its value.

It may be that the future of large language model companies lies increasingly in cultural integration and media adjacency. But for artificial intelligence more broadly, there remains enormous value in helping the world’s businesses and institutions operate more intelligently and efficiently. If AI can carry the technical burdens that slow progress in these domains, it could create more space for human creativity to flourish where it matters most: in the real world, among real people, shaped by real experience.

None of this suggests that AI should be rejected from creative fields, nor that artists should avoid experimentation. Cultural evolution is inevitable, and new tools will continue to reshape practice. What is less inevitable is the absence of reflection.

Before efficiency becomes the default organizing principle of creative life, it is worth asking what we are optimizing for and why. It is worth considering whether speed and scale are appropriate primary metrics in domains historically shaped by patience, singularity, and depth.

Technology companies will understandably pursue integration. Growth and adoption are built into their incentives. The broader cultural sphere, however, has a different responsibility. It must decide which values are worth preserving and where boundaries should remain intact.

Once infrastructure solidifies around a particular logic, it becomes difficult to unwind.

And creativity, in its most enduring forms, has never been governed by efficiency alone.

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