Creating generative percussions and its repercussions.
From CDs to MIDIs, MP3s to Napster, and iPods to Spotify, it's quite an understatement to say that during these transitional times, things were up in the air! Yet, notice how each disruption eventually became the norm within the industry. The music industry has always managed to find growth opportunities amid these changes. Each technological advancement reshaped how music was created, distributed, consumed, and monetized, bringing its own set of challenges. Now, with AI music on the horizon, the question arises: is it a threat, an opportunity, or perhaps both?
Today, the music business has been democratized, making it easier for talent to find their audience through self-release platforms. The long-standing challenge of distribution has been addressed, and the common sense of "own your masters" has transformed artists into entrepreneurs. However, with distribution being so easy, what's stopping hackers from uploading generative AI music onto streaming services, leading to increased competition in reach and discovery? The answer should be a resounding "No" from suppliers and delivery partners. There needs to be a stricter mechanism for identifying AI-generated content and rejecting its distribution on monetizable services.
Let’s talk about creation. Synthesizers, drum kits, and DAWs revolutionized how music is made, but these are just tools in the hands of creators—they aren't trained on anything. In contrast, generative AI can produce an entire song autonomously, whether you like it or not, potentially costing someone their job. A clear example of this is AI-generated music being used over production music or library music for micro-syncs. I'd like to believe that mature content production companies will continue to prefer music supplied by human artists.
Is AI in music generative or derivative? Let’s try to answer by understanding sampling. Sampling in music involves reusing a portion of a sound recording in a new work, and it's done by seeking permission from the original rights owners. An exaggerated, autonomous way of doing the same is AI music, as it’s all trained on copyrighted material. "Fair use" is a good argument that permits the usage of copyright, but it does not approve monetization or commercial purposes.
AI music companies may claim they're helping people rediscover the joy of play and exploration, but GarageBand executed it greatly almost two decades ago! However, these AI platforms can be useful when testing ideas or concepts before going into the studio. Their usage must be limited and should not in any way be monetized. AI should equip and enable artists to overcome creative challenges, not be a substitute. This distinct understanding will help in leveraging the power AI has, responsibly.
Conclusion
Let’s use AI to handle mundane and redundant tasks, freeing up our time and energy for creating art. Code is art too if you’d like to argue that the creators of AI music are being creative; indeed they are. But they’re probably fooling investors if they claim to be disrupting the music industry. Disruption means altering the way consumers, industries, or businesses operate. A capability that creates no real value and does not contribute to tangible industry growth is, in my view, merely a venture of amusement.
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