What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music

Abstract: Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This raises a central question for platform design: how should creators be compensated when their work is used to train generative AI models that in turn produce commercial outputs? We develop a framework for fairly compensating creators in generative-music markets, where each creator's payment depends on a data-attribution score estimating their contribution to model outputs. Compared to past compensation frameworks, our framework has two unique considerations: (1) attribution is traced to entire creator catalogs, not individual songs, and (2) the informativeness (signal-to-noise ratio) of the attribution score is an input to the payment mechanism. The framework yields a closed-form payment rule per creator and measures the welfare cost of inaccurate attribution for both creators and the platform. Whether the welfare-optimal contract is royalty-based or takes the form of fixed-fee licensing depends on how informative attribution is for that creator's catalog. We show that better attribution translates directly into welfare gains for both creators and the platform, yet under multi-platform competition a platform only captures gains from attribution improvements when its signal becomes the most precise in the market. To ground our framework in empirical behavior, we train acoustic and symbolic music generation models and measure the informativeness of scalable attribution techniques against a leave-one-catalog-out ground truth. Our experiments reveal that noisy attribution signals push payment toward fixed-fee licensing and diminish welfare for both creators and the platform, providing an economic motivation for further research on improved attribution.

• The paper's main contribution is a formal market framework integrating attribution informativeness into optimal contract design for generative music.
• It demonstrates that high attribution precision shifts compensation towards royalty-based models while low precision favors fixed-fee structures.
• Empirical evaluations reveal that current attribution methods are too imprecise for royalty-based compensation, underscoring a need for improved calibration.

A Market Framework for Attribution-Aware Compensation in Generative Music

Motivation and Problem Statement

The proliferation of generative AI in music production creates unprecedented challenges for fair compensation of the underlying rightsholders whose catalogs power model training. Traditional licensing mechanisms—flat catalog fees or blanket licenses—are agnostic to the differential value contributed by individual catalogs and fail to align payments with platform revenue derived from generated outputs. This problem is accentuated as AI-generated music increasingly saturates commercial platforms, displacing traditional licensing revenue streams and raising legal, economic, and social stakes for stakeholders.

Framework Overview

The paper develops a formal market framework for compensation in generative music, coupling explicit data attribution methods with contract theory primitives. The central departure from prior art is the explicit modeling of attribution informativeness—quantified as signal-to-noise ratio (SNR) of attribution scores—and its integration as a control variable in optimal contract design. Attribution is computed at the catalog (not per-song) granularity, consistent with the grouping of training data per licensing counterparty, and for each creator, payment splits between fixed-fee and royalty components depend endogenously on attribution informativeness and risk aversion.

Figure 1: The attribution-aware compensation framework for generative music. Each creator's contract form follows from how precisely their contribution can be attributed to a generated output.

Formally, for creator $j$, the informativeness $\mathcal{I}_j$ is defined as the OLS slope of the true value $a^*_j$ on the measured attribution $\hat{a}_j$. Contracts take linear form $(f_j, \rho_j)$, mixing a fixed payment with a royalty on attributed value, incentivized under mean-variance utility reflecting creators’ liquidity constraints. The main theoretical result is a closed-form expression for the welfare-optimal royalty rate:

$$\rho_j^* = \text{clip}\left(\frac{\mathcal{I}_j}{\alpha_j}, [0,1]\right)$$

with $\alpha_j$ capturing risk aversion.

Theoretical Contributions

Informativeness and Contract Structure

The framework establishes a regime-dependent split between fixed-fee and royalty payments—precision (i.e., high SNR) yields royalty-dominant contracts, while noisy attribution (low SNR) favors fixed-fee compensation. Theoretical results yield explicit formulas for welfare loss $\mathcal{L}_j$ due to erroneous attribution, which is quadratic in $(1-\mathcal{I}_j)$, concentrating the welfare gap among creators whose contributions are poorly measured.

Multi-Platform Competition and the Attribution Moat

A multi-platform Bertrand-competition model is introduced, demonstrating that only the platform with the most precise attribution can internalize welfare gains from increasing attribution fidelity; trailing competitors have no incentive to improve attribution until they overtake the leader. This strategic bottleneck—a quantified attribution moat—predicts systematic underinvestment in attribution improvement except from market leaders.

Market and Welfare Impacts

The analysis identifies two robust failure modes in status-quo compensation: (1) uniform compensation disproportionately excludes distinctive creators with unique styles—whose contributions cannot be replaced by other catalogs—leading to superlinear collective welfare loss; (2) high royalty rates, when coupled with poor attribution, can perversely reduce effort or even drive exit among high-value creators due to excessive risk loading. These considerations position attribution informativeness as an operational and policy-critical primitive, with explicit formulas identifying where institutional intervention or policy oversight is necessary.

Empirical Evaluation

Measurement uses two model families: latent audio diffusion (Stable Audio Open) and symbolic LLMs (Anticipatory Music Transformer), each combined with scalable attribution methods (EKFAC, D-TRAK, LoGra, TRAK). For audio, 22 artist catalogs are evaluated; for symbolic, 100 commercial catalogs.

Figure 2: Attribution measurement on two settings, showing that nearly all current attribution estimates lie below the threshold needed for royalty-based compensation to beat fixed-fee licensing.

Empirical diagnostic reveals:

• All tested methods yield attribution signals too imprecise for royalty-based compensation to be welfare-optimal for the vast majority of creators. In audio, at most 1 out of 22 catalogs would receive a positive royalty weight under the benchmark, and the rest fall robustly in the fixed-fee regime.
• Methods agreeing on creator ranking differ by orders of magnitude in calibration scale, impacting contract values materially—highlighting the necessity of evaluating attribution scores with cardinal calibration, not just rank correlation.
• Symbolic and audio model settings, despite significant architectural and modality differences, both yield the same contract diagnosis, pointing to a deficit in attribution precision as a systemic limitation of current ML methods, not a quirk of a particular implementation.

  Figure 3: Framework prescription across the measured pool, illustrating that all but outlier catalogs fall into the fixed-fee optimal contract regime (at $\alpha = 2$).

Diagnostic and Differential Analysis

Detailed per-creator diagnostics confirm high variance in attribution signal calibration and significant cross-method disagreement in absolute scale, though not in ranking. Notably, larger catalogs display higher ground-truth signal variance, inducing higher informativeness, reinforcing the theorized link between catalog size, distinctiveness, and attribution-based welfare impact.

Figure 4: Per-creator Pearson correlation statistics with confidence intervals, confirming the high uncertainty in detected attribution signal at the individual level.

Further, the framework yields actionable water-filling rules for prioritizing attribution-R&D investment: the marginal welfare return is maximized for creators occupying the mid-informativeness regime, strongly supporting targeted rather than uniform improvement efforts.

Figure 5: Ground-truth signal variance scales with catalog size; the marginal welfare return from precision improvement peaks in today's dominant precision range.

Competitive Dynamics and Institutional Implications

Bertrand competition simulations using two attribution methods (EKFAC vs. D-TRAK) empirically instantiate the theory: for most creators, surplus differences between platforms are negligible (representing dead zones for underinvestment), whereas outlier catalogs drive the strategic advantage for a leading platform.

Figure 6: Bertrand competition shows winner-take-all surplus allocation and wide variation in dead-zone gap sizes, confirming the attribution moat.

The analysis demonstrates the limitations of contract-mediated solutions: (1) risk-averse creators can always smooth reported attribution scores to game the linear contract with impunity, unless attribution computation is centralized; (2) asymmetric information about attribution accuracy produces irreducible welfare losses that no contract can fix.

Conclusion

This paper delivers a comprehensive contract-theoretic and empirical framework for attribution-aware compensation in generative music. Its principal contribution is to operationalize attribution precision as a first-class economic and design variable, with metrics and benchmarks tailored to the realities of generative AI music platforms. The main practical impact is to anchor industry and policy discussion around measurable attribution calibration, clarify the conditions under which royalty-based compensation is feasible, and outline both contract-theoretic and institutional boundaries for sustainable compensation.

Extensions include dynamic contracts, cross-media modeling, and multi-stage data value chains involving synthetic training reuse, as the framework is modular and contract design separable from attribution technology.

Key explicit findings include the claim that currently no scalable attribution method achieves sufficient precision for royalty regimes to be welfare-optimal, reinforcing the empirical necessity of fixed-fee compensation for today’s music AI platforms and identifying explicit, measurable targets for attribution improvement.