The internet has struggled with a question raised by Ella Langley’s record-setting country hit: How did a song that many people say they had never heard, by a singer they did not recognize, spend more weeks at No. 1 than any other song in the 68-year history of the Billboard Hot 100?

Langley’s “Choosin’ Texas” remained atop the chart for 24 weeks, surpassing the 22-week record held by Mariah Carey’s “All I Want for Christmas Is You.” Carey’s holiday song is a seasonal fixture that returns to the public conversation every winter. Langley’s run ended on the chart dated Oct. 10, when Taylor Swift’s “Patient Zero” debuted at No. 1. Carey’s song, which typically returns to No. 1 each December, would need three more weeks at the top to reclaim the record, according to Billboard and Billboard’s report on Swift’s debut.

Yet only 24% of U.S. music listeners knew who Ella Langley was in the second quarter of 2026, according to entertainment data and research company Luminate, which shared the figures with Fortune. Recognition had risen from 13% in the fourth quarter of 2025 and 17% in the first quarter of 2026, but still reached fewer than one in four U.S. music listeners.

A No. 1 hit, in other words, can still leave much of the public asking, “Who?” The disconnect raises a broader question: What happened to monoculture?

Monoculture refers to a shared set of cultural references—the songs, television shows and movies that people could assume others knew, whether or not they personally liked them. More recently, critics have also used the term in an almost opposite sense, to describe how algorithms and artificial intelligence can flatten culture into sameness.

Sharing references was easier when much of what people watched and heard came through a small number of television networks, radio stations and MTV. Industry gatekeepers determined what reached a mass audience. People did not need to be fans to know what everyone else was discussing.

That shared audience never included everyone, and it began to fragment before TikTok. But personalized feeds have taken the division further: What reaches a user increasingly depends on what a platform’s system believes that specific person wants to see. Fortune previously examined the relationship between algorithms, AI and monoculture in a separate report.

The feedback loop in action

TikTok emphasizes the personalization of its For You feed. As the company explains in its description of how recommendations work, “there’s no one For You feed.”

For example, a user might watch one Olivia Rodrigo concert clip and then feel as though every fifth video features another clip of the singer. TikTok says its system considers what users like and share, whom they follow and whether they watch a video to the end. It also evaluates the videos themselves, including their captions, sounds and hashtags. Language preference, country setting and device type are considered as well, although TikTok says those factors carry less weight.

Together, those signals help the system decide which content should receive more of a user’s attention. TikTok also says it tries to maintain variety, including by generally avoiding two consecutive videos that use the same sound or come from the same creator.

In the United States, the recommendation system is now overseen by the Oracle-backed joint venture that took over TikTok’s American operations in January. The venture has said it will retrain the recommendation algorithm using U.S. user data. Fortune reported on the ownership arrangement in December 2025.

Keep watching a particular type of content, and the algorithm may continue showing more of it. Researchers observed that feedback loop in a study published in EPJ Data Science in February. The researchers tested TikTok’s For You feed with automated accounts programmed around different interests.

The study found that TikTok increasingly recommended videos matching those interests, with the reinforcement typically beginning within the first 200 videos viewed. The feeds retained some variety, but as an account’s interests were amplified more strongly, it encountered fewer distinct hashtags. The experiments were conducted in 2024, before TikTok’s change in U.S. ownership. The study is available through Springer.

The findings help explain why one person may believe a subject is taking over the internet while a neighbor receives an entirely different feed and has no idea what they are talking about.

Users can also narrow their own selections. TikTok and other platforms offer controls such as TikTok’s “Manage Topics” setting, which allows users to indicate which subjects they want to see more or less of. Details on the feature were reported by TechCrunch.

Watching from all sides

The information used to personalize a feed can come from outside the app. Meta announced in June that it would use information shared with it by other businesses, including purchases made on other websites, to personalize users’ feeds and AI responses—not only advertisements.

Meta’s example was straightforward: Someone who buys a tent online might begin seeing more camping-related Reels. Users can manage the activity through a setting called “Activity from other businesses,” according to the company’s announcement.

What users watch and what they buy can both contribute to the calculation, shaping an individual corner of the internet tailored to that person.

Not everyone welcomes that convenience. New Yorker writer Kyle Chayka devoted a book to his concerns, Filterworld: How Algorithms Flattened Culture.

“These digital platforms and feeds, they kind of promise a great communal experience … but I think they’re actually kind of atomizing our experiences, because we can never tell what other people are seeing in their own feeds,” Chayka told NPR’s Fresh Air in 2024. The interview is available through NPR.

Chayka argued that this fragmentation takes some of the joy out of art and makes it harder for people to become excited about the same things. More advanced recommendation systems may leave users in separate bubbles. But the tension remains: If something does not interest a person, should it appear in that person’s feed merely to create a shared cultural experience? And if the content never reaches someone, how would that person know whether they might have liked it?

This story was originally featured on Fortune.com.