Post-mehification
How to monetize creative risk-taking
In her guest post here on the Sociology of Business, Beth Bentley wrote about how “everything looks the same, and everything is derivative of something else,” a build on her analysis of how algorithm is “making us less interesting.”
Back in 2020, I wrote about algorithm’s impact on culture. My approach was that sameness in our taste choices — in music, fashion, interior design, entertainment, or physical looks — has less to do with the algorithm and more with us:
Similarly as our ancestors had to, as a matter of survival, quickly decide if a fellow Neanderthal was a friend or a foe, modern humans use social signals to quickly orient themselves in the world.
On a daily basis, they actively classify one another by lifestyle, values, interests, and projected and perceived social standing. Based on taste displays, they make snap decisions whether a person is like them or far away in taste space, and thus foreign. Feeling cozy in our own taste space is largely responsible for Zara collabs, AI face, Spotify hits, Twister, Shogun and Gladiator reboots. Cultural “simplification” appeals to human tendency to revert to the recognizable and the familiar. Thanks to it, Spotify is now a music genre, one shorter and with memorable hooks in the first 30 seconds, in addition to being a streaming platform. TikTok is a shopping, entertainment, and a music label.
Faulty as they may be, snap judgements overpower decision-making. Processing complexity of any person, choice, or a situation is time consuming and resource-draining. Snap judgements simplify the world. A very few people have the time or the attention to sift through the entirety of culture.
Five years later, I am less interested in why everything is meh and more in how to stand out in culture where blandness is default — and how to turn blandness into strategy.
The obvious solution against blandness is to create something surprising, unexpected and different. The obvious problem is that something that’s different won’t make it into the mainstream.
To have something original means to stay niche. The unexpected happens offline, in the analog world. Subcultures, taste communities, small groups all create their own cultural output — lo-fi music, experimental film and art, secret retail destinations, zines, board games clubs. There’s joy of discovery, an experience, and an emotional connection there. A24 makes a lot of movies, some of which became mainstream hits, and others are for the true fans (Beau is afraid, by Ari Aster, is niche taste; Hereditary, by the same director, is not, which is why it’s getting a prequel).
Since the very beginning of cultural markets, producers (not creators) of cultural output strived to take the risk out of it. The entire industry structure of early Hollywood was designed to minimize risk through scale: studios owned both the cinemas where their movies were shown and the movie stars starring in them.
A hundred years later, in the infinitely more complex production-distribution-talent world, Hollywood remains organized around the same principle of risk-minimization, generating reboots, sequels and franchises — basically, owning IP and monetizing it to death.
Creative risk-taking is risky for business.
ON Running (there’s a case study on ON coming next week, so make sure to pay to access it) was a risk. It came into a crowded market, shaped by a few dominant, established, players, and introduced something that didn’t look like anything else. It was originally made for a specific community. It was niche, functionally and aesthetically.
ON reached scale and popularity through wild product innovation powered by celebrity (originally Roger Federer, now Zendaya, FKA Twigs, etc). Celebrities, like algorithms, are shortcuts. They amplify great product but cannot compensate for a mediocre one.
In the winners-take-all market, ON is an outlier. Cultural winners-take-all markets, like music, entertainment, fashion, art, design, literature, are defined by a few massive incumbents. To stay massive, they need to sell a lot of whatever they are selling — fashion items, movies, songs, books — and to do that, they need to appeal to the largest number of people.
The problem is not the algorithm, the problem is scale.
Scale is created by formulas. In entertainment, there’s a formula for talent, plot, title, release date, etc that is most likely to work. Netflix honed this data crunching to an
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