Michael Smith, a North Carolina musician, has been indicted for orchestrating a massive $10 million streaming fraud scheme involving AI-generated music and automated bots. Between 2017 and 2024, Smith collaborated with an unnamed music promoter and the CEO of an AI music company to artificially inflate streaming numbers on major platforms like Spotify, Amazon Music, Apple Music, and YouTube Music. By leveraging AI-generated songs and utilizing bots, Smith manipulated streaming data to secure substantial royalty payments.
The scheme involved acquiring hundreds of thousands of AI-generated tracks and uploading them to digital music platforms. Smith then employed automated bots to stream these tracks billions of times, circumventing the platforms’ anti-fraud systems. To further evade detection, he used virtual private networks (VPNs) to obscure the origin of the streaming activity. This meticulous approach allowed him to evade the platforms’ fraud detection mechanisms and accumulate significant financial gains.
At the peak of his operation, Smith utilized over 1,000 bot accounts, which were managed through 52 cloud services accounts, each housing 20 bots. With an estimated streaming capacity of approximately 636 songs per day per account, the fraudulent streams totaled around 661,440 daily. Smith’s calculated earnings from this manipulation amounted to approximately $3,307.20 daily, $99,216 monthly, and over $1.2 million annually. By February 2024, Smith boasted of generating over 4 billion streams and $12 million in royalties through his fraudulent activities.
Smith now faces severe legal consequences, including charges of wire fraud, wire fraud conspiracy, and money laundering conspiracy, each carrying a maximum sentence of 20 years in prison. U.S. Attorney Damian Williams highlighted the severity of Smith’s actions, emphasizing that his fraudulent scheme deprived legitimate musicians, songwriters, and rights holders of rightful royalties. This case underscores the critical need for robust fraud detection mechanisms in the digital music industry.
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