Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics

TL;DR

This paper argues that generative AI models are unlikely to fully replace human creatives because economic incentives and the need for up-to-date information limit their capabilities, creating a paradox similar to market efficiency in finance.

Contribution

The paper introduces an economic theory analogy from finance to explain the limitations of generative AI in replacing human creativity, highlighting a paradox involving incentives and information freshness.

Findings

Generative AI cannot fully replace human creatives due to economic and informational constraints.

A paradox exists where AI's inability to generate current content limits its usefulness.

Economic incentives prevent complete reliance on AI for creative content.

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.