Has AI Gotten Good Enough to Predict My Taste? I Had to Know.

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I often wonder how predictable I am.

I like to think that my preferences in films and music, my interest in philosophy, or even the echoes of my childhood obsession with anime and manga define a pattern that's as unique as my fingerprint. Most of us walk around with some version of this assumption, I think. We may consciously cultivate our tastes, but at the end of the day we feel they're ineffable. No machine could learn to reproduce a pattern so nuanced, so tethered to our unique experience of the world.

But what if that's not true? What if my taste, and yours, is well within the reach of AI to understand—and to predict?

I've wrestled with some form of this question for years. Early in my career, I worked at a company building recommendation engines for e-commerce sites. I became fascinated by the idea of a universal discovery engine: an opposite of Google Search that would surface content you don't even know you want yet.

Building something like that would be transformative. When Netflix suggests a movie, instead of a just-okay recommendation, you'd end up binging everything the director has ever made. Every new song would hit you deep in your soul. It would be a profoundly more satisfying way to navigate the oceans of content at our fingertips, not to mention a huge business opportunity.

The explosion of generative AI made me wonder if machines had become sophisticated enough to power a discovery engine to encode and decode our tastes. To find out, I ran an experiment on myself.

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Can taste be automated?

Before we get to the experiment, we need to address the taste discourse. AI is improving fast in acquiring a whole range of skills, but many smart people—including Every CEO Dan Shipper—have gravitated toward taste as a way that humans will continue to differentiate themselves from the machines.

Replicating taste with AI seems impossible because our real preferences hide in the shadows—we read without liking, enjoy without sharing. The platforms that know us best guard their data like dragons. And taste itself shapeshifts through lived experience: A chef's palate emerges from years of burnt soufflés and perfect reductions, from connecting flavors to emotional memories—creating judgments no algorithm can fake.

Our taste continually evolves, shifting with exposure to new influences, life experiences, and cultural trends. A music enthusiast who once dismissed electronica might develop an appreciation for it after attending a transformative live performance. A reader's literary preferences might expand dramatically during a period of personal upheaval.

While these barriers make automating taste seem hard, they don't necessarily make it unachievable. The very qualities that make taste difficult to replicate—its personal nature, evolution over time, and basis in lived experience—might provide the foundation for a new approach.

Why LLMs see what Netflix can't

Traditional recommendation engines work like a lazy matchmaker—"you watched sci-fi, here's more sci-fi." Netflix might know I watched three sci-fi movies last week, but it can't grasp why I loved the philosophical undertones in one while finding another's action sequences tedious. Facebook's algorithm might surface posts similar to others I've liked, but it misses the subtle distinction between content I genuinely find meaningful versus what I casually toss a "like" to while scrolling.

True taste operates at a much finer resolution. LLMs are different. They contain billions of parameters that encode rich contextual understanding about cultural references and subtle associations between concepts. Where a traditional collaborative filtering model might know that users who liked Movie A also liked Movie B, an LLM "understands why"—it "knows" that both films share noir aesthetics, feature morally ambiguous protagonists, or were influenced by German Expressionism.

This deep contextual knowledge, built from training on vast amounts of text, enables more sophisticated and personalized assessments of what aligns with an individual's taste.

So I decided to find out if this was true. Could an LLM actually predict what would catch my eye in my daily scroll? I gathered six months of my own browsing data and ran four experiments. The results were unsettling.

Become a paid subscriber to Every to unlock this piece and learn about how:

1. AI struggles to predict taste without context
2. Personal preference descriptions dramatically improve prediction accuracy
3. Comparative judgments outperform absolute assessments
4. The 80 percent solution reveals both AI's potential and limitations
5. The gap between algorithmic prediction and human uniqueness is narrowing