• What Taste Collapse actually is
• The numbers behind the narrowing
• The homogenization paradox
• How taste actually develops
• The convergence nobody notices
• What this costs
• There’s no going back to bargain bins

I haven’t actively searched for music in months.

That thought hit me while reading a Reddit post where someone said nearly the same thing. “Part of my personality used to come from weird, accidental discoveries,” they wrote. “A random album from a bargain bin. A forum thread I found at 2 am.” They wanted to know: “Do we become more ourselves because everything is personalized, or less ourselves because we’re no longer bumping into the unexpected?”

The post had 185 upvotes and 88 comments of people recognizing themselves in it. Not angry comments. Confused ones. People trying to put words to a feeling they couldn’t quite name.

I think the word is Taste Collapse.

§What Taste Collapse actually is

It is not a filter bubble. Eli Pariser coined that term in 2011 to describe how algorithms isolate you from opposing viewpoints. That’s an information problem. Taste Collapse is a development problem. It’s what happens when the process of forming preferences gets outsourced to machines before you ever get the chance to form them yourself.

Human taste doesn’t develop through optimization. It develops through friction. The weird album your older cousin left in the car. A late-night channel surf that landed on a documentary about competitive birdwatching and somehow kept you watching for two hours. Those encounters weren’t efficient. That was the point. The randomness was doing something. It was building a version of you that hadn’t existed before.

Algorithms remove that friction entirely. They don’t just predict what you like. They prevent you from developing new preferences by never exposing you to the uncomfortable or the unfamiliar. What’s left isn’t personalization. It’s a narrowing loop that feels personal because the walls are shaped to your data, but produces convergence because every user’s data follows the same engagement patterns.

§The numbers behind the narrowing

YouTube’s chief product officer said in 2018 that 70% of watch time on the platform comes from algorithmic recommendations. Not searches, not subscriptions. The algorithm deciding what you see next.

Netflix reported in 2017 that 80% of content watched on the platform is discovered through its recommendation engine. The search bar exists, but four out of five viewing decisions are made by the system.

Spotify’s own data shows that at least 30% of all streams come from algorithmic recommendations, and more than 40% of new artist discoveries happen through algorithmic playlists like Discover Weekly and Release Radar. The company frames this as a feature. For listeners, it means nearly half of what they think of as “discovering something new” was selected for them.

Those percentages describe a world where most cultural consumption is already curated by machines. And the trend line is clear: the algorithms keep getting better at prediction and worse at expansion. Increasingly good at giving you what you’ve already shown you like. Increasingly bad at giving you what you didn’t know you’d love.

§The homogenization paradox

On r/TheoryOfReddit, a post titled “Why does Reddit feel so different now” collected 156 upvotes and 86 comments. People described the platform as “repetitive” and “flattened.” Another user noticed the same conversations happening endlessly, the same content patterns in every search. They could feel something had changed but couldn’t name the mechanism.

A 2024 MIT paper examined the long-term effects of recommendations on consumption patterns and found that the common assumption about filter bubbles, that algorithms make everyone see different things, may be less accurate than the opposite: algorithms are making everyone consume more similar content. The personalization is superficial. The underlying consumption patterns converge.

Kyle Chayka documented this in his 2024 book “Filterworld: How Algorithms Flattened Culture.” His argument is that despite the promise of personalization, the net result of algorithmic recommendation is cultural homogenization. From cafe aesthetics to music playlists to Netflix queues, algorithmically optimized experiences converge on the same safe, engagement-maximizing patterns worldwide. A coffee shop in Tokyo looks like a coffee shop in Brooklyn, not because anyone planned it, but because both were optimized for the same Instagram algorithm.

Chayka calls it a “flattening.” I think there’s a more precise way to describe what’s happening. The algorithm isn’t just flattening what exists. It’s preventing what doesn’t yet exist from forming.

§How taste actually develops

Taste requires discomfort. This seems obvious when you say it out loud, but the entire recommendation industry is built on the opposite assumption.

You hear music you don’t understand, and either something clicks or it doesn’t. You read a book that frustrates you, and six months later find yourself thinking about it. You stumble into a genre you’d never have chosen and realize it speaks to something you didn’t know was there. The developmental function of surprise is not a bug in human cognition. It is the mechanism by which identity forms.

Recommendation algorithms are designed to minimize surprise. Every interface is engineered to reduce the distance between the user and the content they’re most likely to engage with right now. “Likely to engage” is measured by patterns from the past. The system looks backward to decide what you see next. Which means the space for the genuinely new, the thing that doesn’t match your history, the thing that might change your history, gets smaller with every interaction.

Think about how you used to discover things. Browsing record store bins and finding an album because the cover art was strange. Walking through a library and pulling something off the shelf because it was next to the thing you came for. Every one of those encounters had friction built in. The friction was the point. It forced you to process, to reject, to occasionally be converted. That process is how you become someone with taste rather than someone with a consumption history.

§The convergence nobody notices

The cruelest part is that Taste Collapse feels like personalization. Your feed is yours. Your Discover Weekly is yours. Your Netflix homepage is yours. No two people see the same thing. But the underlying logic that selects what you see is the same for everyone: maximize engagement, minimize churn, optimize for return visits.

When the same optimization function runs on billions of people, it produces a bell curve. Everyone drifts toward the middle. Not the same middle, exactly. Your middle and my middle look different on the surface. But the variance shrinks. The tails disappear. The challenging, the slow-burn, the thing that takes three listens before it clicks: these get systematically deprioritized because they don’t produce immediate engagement signals.

Spotify’s algorithm measures skip rates, save rates, and playlist adds. A song that hooks you in the first five seconds performs better on every metric than a song that reveals itself on the third listen. So the algorithm promotes the hook and buries the grower. Across millions of users, this means the pool of music that gets recommended narrows toward a specific kind of listenability. The long tail of the catalog is technically available. Functionally, it’s invisible.

Netflix has the same dynamic. The catalog grows but effective diversity shrinks. More content exists, and less of it reaches you, because the recommendation engine defaults to whatever is most likely to prevent you from pressing the back button.

§What this costs

I keep thinking about what this actually costs. Not money. Not time. Something harder to measure.

When a teenager in 2010 browsed YouTube by clicking related videos down increasingly weird rabbit holes, they were building something. Not on purpose, and not efficiently. But it was theirs. They were creating a relationship with culture that was uniquely theirs, shaped by accident and boredom and the particular sequence of wrong turns that only they took.

When a teenager in 2026 opens TikTok, the For You page starts feeding them content before they’ve asked for anything. The algorithm has already decided who they are based on the first few interactions. It reinforces that profile with every swipe. The identity doesn’t form through friction. It forms through confirmation. And confirmation, repeated at scale, produces not individuals but demographic clusters. People who think they have taste but actually have a consumption pattern.

I notice it in myself. My listening has narrowed. Not because I chose fewer genres, but because the choices are made before I arrive. I open the app and the recommendations are already there. Already tailored. Already safe. The surprise is gone. And surprise, it turns out, was not a luxury. It was the raw material.

§There’s no going back to bargain bins

I’m not arguing we should delete our accounts and return to the record store. The record store wasn’t fair either. It gatekept by geography, by economics, by the taste of whoever stocked the shelves. The algorithm democratized access to everything. That was real and good.

But access and discovery are not the same thing. Access means it exists somewhere in the catalog. Discovery means you actually encounter it. And the way algorithmic systems have evolved, access has expanded while discovery has narrowed. More music is available than at any point in history, and people are listening to a smaller and smaller slice of it.

The fix probably isn’t better algorithms. “Serendipity engines” have been proposed by researchers for years, and the fundamental problem is that serendipity, by definition, can’t be engineered without ceasing to be serendipity. You can add a randomness parameter. But if the user knows the random recommendation is random, it doesn’t feel like discovery. It feels like noise. The experience of finding something unexpected requires the expectation of finding nothing.

The fix might be friction. Deliberate, structured friction. The bookstore employee who says “I know you came in for the new thriller, but have you read this?” Human curation by people who have taste and are willing to impose it. Playlists made by someone who listened to the whole album, not just the tracks that tested well.

Or maybe it’s simpler than that. Maybe it’s just knowing what’s happening. Knowing that the feeling of “I don’t discover things anymore” isn’t laziness or age. It’s architecture. The system is working as designed, and what it’s designed to do is keep you where you are.

Taste Collapse doesn’t announce itself. It just slowly replaces the question “what might I like?” with the answer “here’s what you already like.” Eventually the question stops getting asked.

Sources:

• YouTube CPO on 70% algorithmic watch time (2018 panel, reported by Quartz)
• Netflix: 80% of content watched from recommendations (2017 company statement)
• Spotify: 30%+ of streams from algorithmic recs, 40%+ of new artist discovery from algorithmic playlists (Spotify Fan Study, April 2024)
• “Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns” (MIT, 2024)
• Kyle Chayka, “Filterworld: How Algorithms Flattened Culture” (Doubleday, 2024)
• “What happens to human taste when algorithms get too good at predicting it” (r/Futurology, 185 upvotes, 88 comments)
• “Why does Reddit feel so different now” (r/TheoryOfReddit, 156 upvotes, 86 comments)
• Eli Pariser, “The Filter Bubble: What the Internet Is Hiding from You” (2011)

Originally published at https://noahaust2.github.io/strategist-dashboard/blog/the-taste-collapse.html