Snow Bunnies Vol. 6: Continue Watching?

Recommendation Algorithms Are 'For Us, By Us'

The Onion, which used to satirize power, ran a fake news item this week: “GigSlave Goes Public With $84 Billion Valuation.” Its attempt at humor reveals some core assumptions of the Elon-hating counter-elite: first, that convenience is a moral vice; second, that app usage is more like modern slavery than voluntary exchange; and third, that market incentives align with evil—and implicitly antiblack—objectives.

For ordinary tech-illiterates, these sentiments typically surface as conspiracy theories about ‘The Algorithm’. Today, most such rants are impotent and trite, but it used to be far easier to whip up a mob over your feed. A prime example was Elsagate in 2017, when anti-pedophile vigilantes accused YouTube of fueling demand for fetish videos that put children’s icons—like Elsa or Spider-Man—in sexually humiliating scenarios.

Antiracists are asking, ‘Why Do All These Homosexuals Keep Sucking My Cock?’

Recommendation algorithms—like YouTube’s ‘Up Next’ engine—were among the first broad-based consumer applications of AI, predating ChatGPT by decades. The disruption came when social media learned to satisfy what you personally craved, rather than relying on friend-curated feeds. This victory for individual freedom was a catastrophe for professional gatekeepers—and we’ve watched them flail ever since.

Nude Africa Undresses Our Desires

Early social networks like Facebook began by showing posts in reverse chronological order—freshest updates first. But as user-generated content ballooned, time-ordering alone meant compelling posts easily got buried. In 2009, Facebook introduced an algorithm to sort the News Feed by relevance (factoring in the poster’s relationship to viewer, among other signals). By today’s standards, EdgeRank was primitive.

Meanwhile, future North Carolina lieutenant governor Mark Robinson was posting on a forum called Nude Africa. His candid remarks contradicted the pro-life, anti-trans stances he publicly championed. On a celebrity’s abortion, he boasted, “I don’t care. I just wanna see the sex tape!” On the Trans Question, he raved, “I like watching tranny on girl porn! That’s f*cking hot! It takes the man out while leaving the man in!”

Which side of Robinson was real—the private horndog or the public moralist? Netflix, in a broader sense, answered that question in 2017 by ditching its 5-star rating system. The company found that relying on implicit feedback (how long you linger) was far more reliable than explicit ratings (how you scored a movie). User retention spiked, and in 2018, TikTok pushed that principle even further. Was this to be the End of History?

Devouring Mommy Meets Indulgent Al

As engineers tuned algorithms to capture our rawest preferences, a subtle distortion emerged. Starting in the early 2010s, social media empowered women, Africans, and sundry ‘marginalized groups’ to unleash shame culture at unprecedented scale. Netizens cowered while tech leaders found themselves offsides, dutifully obeying government requests to censor critics, suppress stories, and deplatform presidents.

During this era, Twitter became the favored social platform for political elites. Like TikTok, it used machine learning to show you posts from accounts you didn’t follow, yet it uniquely blended personalized ranking with platform-wide trend detection. If something went truly viral, everyone saw it. The phrase “Twitter is not real life” rang as hollow cope, because users knew firsthand how the app drove social contagions.

Back when Twitter ‘likes’ were public, some users acted bravely—or just recklessly.

Memes became nightmares for the richest father on Earth in June 2022 when Xavier Musk declared a name change to Vivian. Elon, radicalized by the ‘death of his son,’ bought Twitter and handed it to a cadre of mission-focused engineers. By 2023, he had open-sourced Twitter’s algorithm and started overhauling the site’s culture. Then, in 2024, X’s Director of Engineering, Wang Haofei, announced ‘likes’ would go private.

Wang explained that when likes are public, people perform for the crowd, confounding both machine learning models and literal-minded autists. A relic of this ‘private truths, public lies’ culture is the r/raceplay subreddit, where a writer noted a ‘BLACK LIVES MATTER’ banner coexists with black women calling themselves ‘apes’ and ‘beasts’ while flirting with white men who call them slurs in the comments.

Data ‘Consent’: The Pig’s New Lipstick

Opponents of progress love weaponizing high-minded notions like ‘informed consent.’ One egregious example involves disappearing Paleoindian skeletons from museum collections. The real motivation behind this enforced ignorance is religious—to preserve modern tribes’ origin myths. Yet labs are told to destroy humanity’s heritage under the pretense that ‘descendants didn’t give consent.’

But who measures the cost of our consent-by-default culture, where we slap ‘No Human Is Illegal’ across the storefront while indulging contradictory urges in the backroom? In his 2017 book Everybody Lies, data scientist Seth Stephens-Davidowitz analyzed Google searches that users assume are private. Many of his findings, if taken seriously, would alleviate needless suffering over life’s most intimate anxieties:

• There are twice as many complaints that a boyfriend won’t have sex as a girlfriend won’t have sex.

• People are seven times likelier to ask Google whether they’ll regret not having children than whether they’ll regret having them.

• Over 40 percent of penis-size complaints lament that it’s too big—not too small.

Millions pose these questions daily: the man worried he’s too small while his wife winces because he’s too big; the woman fretting about having another kid. They aren’t asking for facts so much as reassurance that they’re not alone. The algorithm acts like a mirror—unlike a human tastemaker with his own motives, scolding you, “Take the calipers off the skull!” It’s your private confessional, and that’s precisely why it works.