John Z Black
Brunswick, ME • (207) 245-1010 • contact@johnzblack.com
Michael Smith, 54, generated hundreds of thousands of songs using AI tools, created thousands of fake streaming accounts, and had those accounts listen to his songs around the clock across Spotify, Apple Music, Amazon Music, and YouTube Music. For years. He collected roughly $8 million in streaming royalties before the Southern District of New York had opinions about it.
He pleaded guilty last week.
A bot army, listening to AI music, in fake appreciation, generating fake revenue. It’s kind of funny if you think about it for a second. It’s also direct theft from every real artist on those platforms. That part is less funny.
Here’s why: streaming platforms don’t pay a fixed per-stream rate. They calculate a share of a total monthly royalty pool. If fake streams inflate the total stream count, every real artist’s percentage goes down. Smith’s bot network was siphoning from that pool constantly. At $8 million, this wasn’t a rounding error.
Streaming fraud isn’t new – playlist manipulation and click farms have existed for years. What changed is scale. When generating hundreds of thousands of songs becomes cheap and easy, even automated fraud detection has to process an enormous number of signals before patterns become actionable.
The DOJ case is a landmark whether anyone calls it that: the first major criminal prosecution for AI content fraud at scale. Wire fraud, federal court, guilty plea. Precedent established.
But precedent doesn’t fix the underlying incentive structure. AI music generation is cheap and getting cheaper. Fake streaming accounts require some overhead but nothing prohibitive. The royalty pool is large. Smith ran this for years before anyone stopped him, and someone else is already doing the math.
The real question coming for the music industry: should AI-generated content be eligible for the same royalty pool as human-created music? Or should it compete in a separated pool? That fight is coming. Smith is going to sentencing. Somewhere, someone is learning from his mistakes.
His main mistake was getting caught.