AI Summary & Key Takeaways
AI InsightThe findings indicate that, in these two examples, the minimal prompt is accompanied by a more detailed style description displayed by the platform, whereas the description associated with the elaborated prompt selectively retains, renames, and restructures elements of the original prompt.
📌 3 Key Takeaways
Abstract
This paper examines the role of language in prompt-based music production through the case of the AI music generator Suno and questions the widespread claim that such systems democratize music production. Building on Agamben’s (2022) concept of command, the paper conceptualizes the prompt as a command that initiates production. Drawing on Bourdieu’s (1986) concept of cultural capital, the paper considers how linguistic competence may shape prompt construction. The paper adopts an interpretive case study approach. For Suno, the paper tested one minimal and one elaborated text-based prompt in the platform’s freely accessible v4.5 Simple mode and compared the first musical output presented for each prompt. The minimal prompt consisted of a brief, general description, whereas the elaborated prompt included linguistic detail and musical terminology specific to the selected genre. The analysis focused on rock music because its well-established terminology – such as riff, distortion, power chord, pre-chorus, and guitar solo – enabled a clearer comparison of how linguistic and musical specifications were reflected in the musical outputs. The findings indicate that, in these two examples, the minimal prompt is accompanied by a more detailed style description displayed by the platform, whereas the description associated with the elaborated prompt selectively retains, renames, and restructures elements of the original prompt. Thus, algorithmic interpretation is defined as a theoretical account of how the platform maps the user’s command to the resulting musical decisions. The paper concludes that the musical output can be understood as the asymmetrical co-production of the user’s command, shaped by cultural capital, and the platform’s algorithmic interpretation, with the latter occupying a structurally dominant position in this relationship. These findings suggest that the widespread discourse presenting AI music generators as democratizing music production may overlook the distinction between access to production and control over the production process.