Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics
Jiasun Li
TL;DR
The paper asks whether generative AI could fully supplant human content creators. It develops a reduced-form information-economics model inspired by the Grossman–Stiglitz impossibility result to show an interior equilibrium where humans and AI coexist, rather than a complete takeover, with the equilibrium characterized by $R-C = r(\lambda^*) - c$ and endpoint conditions $r(0)=0$, $r(1)\approx R$. Key findings indicate that even at maximal AI performance, a positive share of human content creation persists, with $\lambda^*$ rising in $R$ and falling in $C$, $c$, and AI effectiveness. The work demonstrates how classic financial economics insights apply to AI-driven creative markets and offers a theoretical foundation for policy and strategy in the AI era, while outlining avenues for richer dynamic and heterogeneous extensions.
Abstract
With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives' futures. Specifically, as generative models' power further increases, will they eventually replace all human creatives' jobs? We argue that the answer is "no," even if existing generative AI models' capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox.