But there are lots of areas where attempts to equate human neural networks with artificial ones quickly fall apart under scrutiny. Brandt carves out one domain where he sees human creativity clearly soar above its machine-made counterparts: what he calls “amplifying the anomaly.” AI models operate in the realm of statistical sampling. They do not work by emphasizing the exceptional but, rather, by reducing errors and finding probable patterns. Humans, on the other hand, are intrigued by quirks. “Rather than being treated as oddball events or ‘one-offs,’” Brandt writes, the quirk “permeates the creative product.”
STUART BRADFORD
He cites Beethoven’s decision to add a jarring off-key note in the last movement of his Symphony no. 8. “Beethoven could have left it at that,” Brandt says. “But rather than treating it as a one-off, Beethoven continues to reference this incongruous event in various ways. In doing so, the composer takes a momentary aberration and magnifies its impact.” One could look to similar anomalies in the backward loop sampling of late Beatles recordings, pitched-up vocals from Frank Ocean, or the incorporation of “found sounds,” like recordings of a crosswalk signal or a door closing, favored by artists like Charlie Puth and by Billie Eilish’s producer Finneas O’Connell.
If a creative output is indeed defined as one that’s both novel and useful, Brandt’s interpretation suggests that the machines may have us matched on the second criterion while humans reign supreme on the first.
To explore whether that is true, I spent a few days playing around with Udio’s model. It takes a minute or two to generate a 30-second sample, but if you have paid versions of the model you can generate whole songs. I decided to pick 12 genres, generate a song sample for each, and then find similar songs made by people. I built a quiz to see if people in our newsroom could spot which songs were made by AI.
The average score was 46%. And for a few genres, especially instrumental ones, listeners were wrong more often than not. When I watched people do the test in front of me, I noticed that the qualities they confidently flagged as a sign of composition by AI—a fake-sounding instrument, a weird lyric—rarely proved them right. Predictably, people did worse in genres they were less familiar with; some did okay on country or soul, but many stood no chance against jazz, classical piano, or pop. Beaty, the creativity researcher, scored 66%, while Brandt, the composer, finished at 50% (though he answered correctly on the orchestral and piano sonata tests).
Remember that the model doesn’t deserve all the credit here; these outputs could not have been created without the work of human artists whose work was in the training data. But with just a few prompts, the model generated songs that few people would pick out as machine-made. A few could easily have been played at a party without raising objections, and I found two I genuinely loved, even as a lifelong musician and generally picky music person. But sounding real is not the same thing as sounding original. The songs did not feel driven by oddities or anomalies—certainly not on the level of Beethoven’s “jump scare.” Nor did they seem to bend genres or cover great leaps between themes. In my test, people sometimes struggled to decide whether a song was AI-generated or simply bad.
How much will this matter in the end? The courts will play a role in deciding whether AI music models serve up replications or new creations—and how artists are compensated in the process—but we, as listeners, will decide their cultural value. To appreciate a song, do we need to picture a human artist behind it—someone with experience, ambitions, opinions? Is a great song no longer great if we find out it’s the product of AI?
Sanchez says people may wonder who is behind the music. But “at the end of the day, however much AI component, however much human component, it’s going to be art,” he says. “And people are going to react to it on the quality of its aesthetic merits.”
In my experiment, though, I saw that the question really mattered to people—and some vehemently resisted the idea of enjoying music made by a computer model. When one of my test subjects instinctively started bobbing her head to an electro-pop song on the quiz, her face expressed doubt. It was almost as if she was trying her best to picture a human rather than a machine as the song’s composer. “Man,” she said, “I really hope this isn’t AI.”
It was.
