How much is a credit worth? Market framework for compensation with attribution

The emergence of generative artificial intelligence in the music industry has opened a fundamental debate: how to fairly compensate creators whose works serve as the basis for training models that then produce new songs with commercial value? This dilemma is not only technical, but deeply economic and legal. Platforms that commercialize AI-generated music face the challenge of designing compensation mechanisms that recognize each artist's contribution, but exact attribution is extraordinarily complex. Unlike traditional royalty systems, where each play is associated with a specific work, here the model learns from entire catalogs, not individual tracks. The attribution signal —that is, how much a particular creator has influenced the final result— is often noisy and unreliable. When that signal is weak, contracts tend to set flat fees instead of royalties, which reduces the well-being of both the artist and the platform. Improving attribution accuracy not only benefits creators, but also allows platforms to capture greater value, especially if they manage to be the most accurate in the market. In this context, companies need artificial intelligence solutions for businesses that integrate robust traceability and data analysis systems. Q2BSTUDIO, as a company specialized in software development, offers custom applications that allow implementing personalized attribution models, along with AWS and Azure cloud services to scale the processing of large volumes of information. Additionally, its cybersecurity capabilities ensure the protection of creators' sensitive data, while business intelligence tools like Power BI facilitate the visualization of contribution metrics. Building a fair market for generative music requires combining custom software with AI agent algorithms capable of continuously refining attribution. Only then can we move towards an ecosystem where credit —and compensation— faithfully reflects the real value of each work.