Everyone can tell you let the algorithm choose

How consumption became identity performance.

Your Spotify Wrapped isn’t a reflection of your taste. It’s a blueprint for who you’re becoming.

When millions of users decided which slides to share last December hiding the “embarrassing” popular artists, skipping certain statistics, showcasing only the data that matched their aspirational identity, they revealed something about contemporary identity construction. A Harper’s Bazaar analysis of Spotify Wrapped psychology found that users explicitly curate which aspects of their Wrapped they highlight versus hide as “a means of creating the online identity we want to project.” Research from The Dartmouth on the platform’s evolution into a social network found students openly admitting they wouldn’t want certain mainstream artists appearing in their results. The curation itself is the point: consumption is no longer about what you own. It’s about what you choose from an infinite catalog and who’s watching you choose it.

How cultural capital moved from bookshelf to feed

Pierre Bourdieu, the French sociologist who spent decades studying how social classes reproduce themselves, built part of his theory of cultural capital around objectified assets. The bookshelf, the art collection, the wine cellar. Physical proof of taste, education, refinement that could be displayed and decoded by others who knew the same social codes.

But when streaming services cost $15 a month and anyone can access the entire history of recorded music, physical ownership means nothing.

What matters now is selection and timing. Not what you own, but what you consume. More importantly: how you discovered it and when you engaged with it.

Before, status came from collecting things or numbers such as followers, airline miles, designer handbags. Now, you signal through knowledge, taste and membership in micro-communities. Your reading stack carries more weight than your bookshelf.

But the platforms mediating consumption aren’t neutral. They’re actively constructing taste.

Why the algorithm makes you predictable

Netflix creates granular taste profiles based not on demographics but on content properties. Psychological thrillers with strong female leads and nonlinear narratives. A 45-year-old lawyer in Toronto can share a taste profile with a 22-year-old student in Berlin based on these micro-preferences.

Brands stopped targeting demographics and started creating taste communities.

Here’s what’s different: the algorithm doesn’t just reflect taste, it shapes it. Every recommendation is a nudge toward profile coherence. The system rewards consistency, punishes eclecticism, gradually herds users into increasingly specific niches.

You watch what the algorithm serves. It learns from that choice. It serves more like it. The pattern reinforces. “Person who watches [X]” hardens into identity. The taste profile becomes you.

The question shifts from “Am I choosing this?” to “Is this choosing me?”

The curation class: how status separates algorithmic consumers from active curators

The universal content access hides a sophisticated class hierarchy. The distinctions aren’t highbrow versus lowbrow anymore, they’re algorithmic versus analog, curated versus passive, early versus late.

Consider film consumption. Criterion Channel subscribers signal something specific. Not because the content is objectively better, but because choosing a boutique streaming service dedicated to film d’auteur indicates cultural priorities. It’s Bourdieu updated: knowing which directors matter, the subscription as status object, ability to discuss in accepted critical vocabulary.

But you can’t buy your way in anymore. Knowing about content isn’t enough. You need consumption history at the right time, through the right channels, in the right context.

Being into a band “before they were famous” still carries weight. Discovering an artist through algorithmic recommendation versus trusted curator signals different things about your cultural literacy. The same content consumed through different pathways produces different status outcomes.

Content consumption patterns map onto clear class quadrants. Longform Substack essays versus TED Talks versus reality TV each carries signifiers. But the real distinction is active curation versus algorithmic passivity.

Those who maintain curated RSS feeds, follow specific critics, actively seek content rather than accept recommendations, they occupy a different position in the status hierarchy than those who let the algorithm feed them.

This is the new elite: the curators. The ones who resist the feed. The ones who build their own discovery systems instead of outsourcing taste formation to recommendation engines.

They spend time in forums, follow independent critics, maintain reading lists curated by humans not machines. They know which podcasters to trust, which Substacks matter, which film critics see what others miss. They’ve built taste networks that operate outside algorithmic mediation.

It’s not about having better taste. It’s about how that taste gets formed. The algorithm optimizes for engagement. Human curators optimize for meaning. You can usually tell the difference.

When consumption becomes moral performance

Content consumption has become ethical performance. Every click is a vote, every view a values declaration.

Your streaming choices signal your politics. Your reading list indicates your ideological position. Your podcast subscriptions reveal tribal affiliation. In some circles, watching certain filmmakers’ work requires a disclaimer about separating art from artist. In others, consumption itself is considered endorsement.

This creates curation exhaustion, the constant performance of identity through consumption becomes labor. You can’t just watch something because you enjoy it. You have to consider what watching it says about you. Does it align with your aspirational identity? Will it produce the right data trail? What will it communicate to anyone who checks your Letterboxd?

The platforms understand this dynamic. Year-end recaps frame listening habits as cultural report cards. The design is built for social sharing, transforming private consumption into public performance. You’re not reflecting on your year, you’re broadcasting your taste profile as an identity referendum.

Users curate accordingly. Some share everything proudly. Others hide embarrassing data. The curation itself reveals the stakes: consumption data has become identity data.

We’ve entered an era where consumption is virtue signaling through media. Supporting the “right” artists, watching films by underrepresented directors, reading diverse voices—these are status markers, ways of performing values while building cultural capital. The algorithm tracks it all, feeding content that aligns with performed values, tightening the feedback loop.

The algorithmic identity trap

Your content diet is simultaneously a reflection of who you are and a blueprint for who you’re becoming. The algorithm learns from past behavior to shape future consumption, which shapes your future self.

Every recommendation nudges your taste. Every piece of content feeds the profile. Over time, your algorithmic identity (the person the platform thinks you are)converges with your actual identity. Or the reverse: your actual identity aligns with your algorithmic profile.

The system is designed to make you more coherent, more predictable, easier to model. It reduces the friction between who the algorithm thinks you are and who you actually are, not by getting smarter about you, but by shaping you into someone more consistent with your profile.

The question is whether there’s still space for discovery outside your profile. For randomness. For the identity formation that comes from stumbling into things that don’t match your taste community.

The algorithm replaced serendipity with what we might call “strategic serendipity” carefully calculated surprises designed to expand your profile in predictable ways.

How to build taste outside the algorithm

But there’s a growing counter-movement. People building discovery systems outside algorithmic feeds.

They’re finding creators on Patreon before platforms amplify them. Attending local readings and film screenings instead of waiting for streaming homepages. Buying directly from artists on Bandcamp instead of using streaming services. Following newsletter recommendations from trusted writers instead of letting the algorithm serve up articles.

This is what off-grid looks like in the content economy: maintaining human-curated pathways for discovery. Building taste networks through real relationships instead of recommendation engines. Choosing friction, the work of finding things yourself, as a feature, not a bug.

These off-grid curators treat discovery as an active practice. They maintain RSS feeds. They follow specific critics across platforms. They join Discord servers and Slack communities where humans recommend things to each other. They attend IRL events (book clubs, film societies, listening parties)where discovery happens through conversation, not data.

The offline matters more than ever precisely because it’s where algorithmic mediation breaks down. What you hear at a dinner party, what your friend insists you read, what someone passes you at a show, these don’t feed your profile. They don’t optimize for engagement. They’re just discovery for its own sake.

It’s recognizing that algorithmic recommendation, for all its efficiency, narrows taste over time. It makes you more predictable. The off-grid approach is inefficient by design because that inefficiency is where you find things that don’t fit your profile. Things that change you instead of confirming who you already are.

Every piece of content you consume feeds a profile.

Every piece shapes future recommendations. Every recommendation nudges identity formation. The loop tightens until the person you are and the person the algorithm thinks you are converge into the same thing.

The real product isn’t content, it’s the self that content helps construct.

This system doesn’t require conspiracy. It doesn’t require intention. It just requires optimization for engagement at scale. The rest follows structurally: more coherence, more predictability, more convergence toward algorithmic legibility.

The question isn’t whether this is good or bad. The question is whether you’re aware it’s happening while you’re choosing what to watch tonight.

I’m Kima Sargsyan, a strategist and futurist studying the patterns and tensions that move the world. If you love this newsletter and need more:

• My LinkedIn where I post early-stage ideas and observations before they evolve into analysis. Follow me on Instagram.

• Invite me to speak when you need someone to challenge your team’s assumptions about community, culture, and what consumers actually want versus what we tell ourselves they want. DM on Substack.