Artificial Aesthetics: The Implicit Economics of Valuing AI-Generated Text

TL;DR

This study investigates whether users are willing to pay for aesthetic qualities in AI-generated text and finds that aesthetic improvements do not significantly influence monetary valuation in current LLM markets.

Contribution

It provides empirical evidence that aesthetic qualities in AI text do not significantly affect willingness to pay, suggesting aesthetic improvements are baseline expectations.

Findings

No significant link between aesthetic quality and willingness to pay.

Participants distinguish stylistic features but do not value them monetarily.

Aesthetic and functional attributes load onto a single perceived quality factor.

Abstract

Aesthetic qualities command measurable premiums in traditional goods markets. However, it remains unclear whether users are willing to pay for such qualities in AI-generated text. This paper estimates the willingness to pay for aesthetic attributes in large language model outputs using an online experiment with N = 117 participants. Participants evaluated responses from four anonymized models across academic, professional, and personal contexts, rated outputs along multiple dimensions, and submitted bids for access using a Becker-DeGroot-Marschak (BDM) mechanism. We find no statistically significant relationship between perceived aesthetic quality and willingness to pay. While participants systematically distinguish between outputs and exhibit consistent preferences over stylistic features, these differences do not translate into higher monetary valuation. Further analysis shows that aesthetic and functional attributes load onto a single latent factor, suggesting that users perceive quality as a unified construct rather than a separable aesthetic dimension. These results imply that, in current large language model (LLM) markets, aesthetic improvements function as baseline expectations rather than sources of price differentiation.