In the last ten days or so, I've been devouring political podcasts at a rate I previously would have said was detrimental to one’s mental health. Trump this, Biden that—geriatrics all around. Yet, the Ezra Klein Show provided a series of bipartisan perspectives that’s made election-year politics feel important (always the case) and engaging. The series ended, but I continued my Ezra Klein kick, stumbling into an episode called "How to Discover Your Own Taste.”
In this episode, Ezra speaks with Kyle Chayka, author of Filterworld: How Algorithms Flattened Culture. This book describes the propensity of algorithms to homogenization culture, capturing a broader base and bringing them into a shared arena of sterilized pop culture. Marvel films, indie music, and Stanley Quenchers. In a world where it's easy enough to know what everyone else likes, it begs the question: what do I actually like and why?
“The generic human is the ideal consumer in capitalism. It's the ideal worker in industrialization.” — Kyle Chayka
Throughout the conversation they describe the process of discovering taste or style as a sort of personal superpower. Guided by an ineffable internal compass, we uncover niche corners of music, clothing, philosophical/spiritual belief—any area of life—and affix them to our identity.
The internet was ideal for this. It provided vast expanses of culture, and with just a bit of sleuthing on our end—a sort of “pulling” effort—we could come away with a sense of individualism or unique personhood. This was often expedited by connections and recommendations from real humans at the other end.
Algorithms have largely taken this way. We’re systematically pushed toward the broad interests of others, the popular becomes more popular, and the gold nuggets tucked away in the web’s nooks and crannies become increasingly difficult to find. And it stands to reason that forms of culture—art, entertainment, knowledge, etc.—which are difficult to access will inevitably experience a decline in demand, and therefore supply.
The episode’s bright spot came in the form of blogs, newsletters, and other forms of curated online spaces. The Marginalian (formerly known as BrainPickings) comes to mind as a prime example. Collections of unique, often weird morsels of another's taste, organized and shared for others’ enjoyment.
The background reading and writing of Kyle’s Newsletter is a recursive process. Over time it’s given me a broader base of knowledge to work from, but also made me less sure that I really “know” anything about the world in an absolute sense. Above all, writing is a pleasurable puzzle. I feel grateful to have a community of readers, for resisting the unconscious recommendations of a machine and placing your trust in the thoughts and words of a real human being.
At the end of each episode, Ezra asks the guests for three book recommendations. Most often, these are books I've never heard of. But in this episode, I'd heard of all three, and two were high up in my "To Be Read" list. There's an internal spark when we discover something on our own, without the input of algorithms, and it creates a bridge to something we've enjoyed or appreciated in the past. As if this bridge is telling us, you’re on the right path. Keep going. Keep trusting.
As always, thanks for reading :)
"There's an internal spark when we discover something on our own, without the input of algorithms, and it creates a bridge to something we've enjoyed or appreciated in the past."
This really struck a chord with me as something I've felt for a long time but never seen articulated. There is something magical about discovering something on your own. There also seems to be a natural yearning to share the magic of this discovery with others.
I had never considered the nature of the discovery of content to be that important but I think you're onto something. It makes me think about the difference between browsing the "old" internet and the "new". The conscious act of navigating a trail of hyperlinks left by users in reddit threads and forums vs the unconscious consumption via suggestion algorithms on TikTok and YouTube.