Algorithmic Taste: Data, Analytics, and the Marketization of Aesthetics

How value is being coded

In the age of endless feeds, art is no longer just looked at—it’s tracked, tagged, and quantified. We scroll through grids, filter by hashtags, check viewing rooms for click rates, and watch auction estimates tick upward like share prices. In this landscape, a new sensibility emerges: algorithmic taste—a logic of value that does not grow from dialogue, history, or critique, but from pattern recognition, engagement metrics, and market analytics.

Taste, once the domain of the connoisseur or the institution, is now a data stream—code rendered cultural. But what happens when the algorithms that optimize our playlists and product ads begin to shape our museums, studios, and imaginations?

The Seduction of the Dashboard

Platforms like Artnet, Artprice, and Artfacts offer users a crystal ball of market data: auction results, exhibition histories, visibility indexes. Some advisors and collectors subscribe to private databases that chart “artist performance” like a hedge fund manager watching stocks. We are invited to believe that artistic value can be modeled, predicted, even gamified.

It’s easy to see the appeal: in a notoriously opaque art world, data seems to offer clarity. But clarity can also flatten. Algorithms are trained on what has already succeeded, biasing the future toward the past. Like Spotify’s recommendation engine that keeps us in a sonic loop, art data favors the predictable: names with prior sales, institutions with reach, works that align with market taste.

The Feedback Loop of Visibility

Instagram, once a tool for access and experimentation, has become a market amplifier. An artist’s “value” is increasingly tied to social media engagement, inclusion in fair booths, and museum exposure—all of which are fed back into data platforms, further solidifying their position.

This system produces a feedback loop where art that performs well, performs better—regardless of content, context, or critique. The algorithm does not ask whether a work is radical, uncomfortable, or historically relevant. It asks whether it got enough saves. In this system, aesthetics risk being reduced to surface legibility and virality.

The New Gatekeepers

In theory, data democratizes. In practice, it consolidates. Large galleries and wealth-backed collectors have more access to analytics tools, better positioning to manipulate markets, and more capital to secure the visibility metrics that algorithms reward. Smaller galleries and artists working outside the Western institutional circuit risk becoming invisible to the algorithm—not for lack of quality, but for lack of legibility.

This redefines gatekeeping: no longer just critics and curators, but platform designers, engineers, data analysts, and market consultants. The algorithm is not neutral—it is built from and for the logic of capital efficiency, not cultural depth.

Art as Data, Artist as Brand

This shift also changes the role of the artist. Artists are increasingly expected to produce coherent visual brands, maintain regular output, engage digital audiences, and optimize their visibility. The work is not just the artwork—it’s the content, the metrics, the narrative. The artist becomes an entrepreneur of their own algorithmic desirability.

For younger artists, this pressure is especially intense. Success becomes about alignment with market rhythms, not long-term evolution. Burnout, brand rigidity, and speculative volatility follow.

Resistance and Rethinking

Still, resistance is possible. Artists like Hito Steyerl, Trevor Paglen, or collectives like DIS actively interrogate the aesthetics of data and visibility. Some galleries refuse to participate in certain metrics. Curators and institutions can rethink what counts as “impact”—shifting from audience size to depth of experience.

But resistance must be more than a gesture. It requires a rethinking of value itself—beyond pricing, beyond engagement, toward forms of knowledge, presence, and exchange that the algorithm can’t parse.

Aesthetic Consequences

What happens to art when it is shaped by what works in data? We risk entering an era of algorithmic formalism: art made not to provoke, but to optimize; art that mimics art; art that confirms what the algorithm already believes.

We have to ask: do we want an art world optimized for clarity, speed, and sales? Or do we want one that still values the unquantifiable: dissonance, complexity, time, and silence?

Because if we forget how to value what the algorithm cannot see, we may soon find ourselves in a market full of art—and empty of meaning.

Title: Algorithmic Taste: Data, Analytics, and the Marketization of Aesthetics

Type: Technocultural Essay / Art & Technology Critique

Description:

An exploration of how algorithmic systems, engagement metrics, and market analytics are reshaping what is seen, valued, and produced in the contemporary art world. This essay interrogates the rise of “data-driven taste,” the pressures on artists to optimize their output, and the risk of aesthetic flattening in a system calibrated for efficiency, not meaning.