Surreal illustration of an AI copyright trial.

AI's Copyright Conundrum: Will Generative AI Usher in a New Era of Creativity or Legal Chaos?

Soraya Malik in Tech & Innovation August 2025 • 4 min read.

"Navigating the complex interplay of copyright, artificial intelligence, and the future of content creation."

The rise of generative artificial intelligence (AI) is being compared to the dawn of the printing press or the internet. It promises to democratize creativity, automate tasks, and unlock new forms of expression. Goldman Sachs estimates that generative AI could drive a 7% increase in global GDP, injecting nearly $7 trillion into the world economy. However, this technological leap forward is intertwined with a web of legal and ethical dilemmas, particularly concerning copyright law.

At the heart of the debate lie two fundamental questions: First, should creators be compensated when their work is used to train AI models? This is the “fair use” standard issue. Second, can AI-generated content be copyrighted, and if so, who owns it? This is the “AI-copyrightability” question. These questions have ignited passionate debate among legal scholars, tech companies, artists, and policymakers.

This article delves into the economic implications of these two copyright issues. By exploring the potential impacts on AI development, creator incomes, and consumer welfare, it aims to offer insights for policymakers and business leaders navigating the evolving landscape of AI and copyright.

Fair Use vs. Strict Compensation: Balancing Innovation and Creator Rights

Surreal illustration of an AI copyright trial.

The “fair use” doctrine, a cornerstone of copyright law, allows for the use of copyrighted material under certain circumstances without requiring permission from the copyright holder. This typically includes commentary, criticism, education, and news reporting. However, the application of fair use to AI model training is hotly contested. AI companies argue that using copyrighted material to train their models falls under fair use, as it transforms the data into something new. Creators, on the other hand, argue that their work is being exploited for commercial gain without compensation.

The debate has already spilled over into the courtroom. Getty Images sued Stability AI for using its images to train AI models without authorization. The New York Times has also sued OpenAI and Microsoft, alleging copyright infringement and seeking billions of dollars in damages. The European Union is taking a tougher stance, with the latest draft of the AI Act mandating developers to disclose the copyrighted materials used for model training.

The Data Abundant Regime: When training data is plentiful, a generous fair use standard (allowing AI companies to use data without compensation) benefits AI development, creator incomes, and consumer surplus.
The Data Scarce Regime: When training data is limited, a strict fair use standard (requiring AI companies to compensate creators) may be preferable, as it incentivizes content creation and improves AI model quality.

The amount of available data is a critical factor in determining the optimal approach. A generous fair use approach could stifle creativity if creators are not incentivized to produce new content. Conversely, a strict fair use approach could hinder AI development by increasing data acquisition costs. The key is to find a balance that promotes both innovation and creator rights.

Charting a Course for the Future

Generative AI presents unprecedented opportunities and challenges for the creative industry. Navigating the complex intersection of copyright, AI development, and creator rights will require careful consideration and a willingness to adapt. By embracing a dynamic, context-specific approach, policymakers and business leaders can foster an environment that promotes innovation while ensuring that creators are fairly compensated for their work.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.17801,

Title: Generative Ai And Copyright: A Dynamic Perspective

Subject: econ.th cs.ai

Authors: S. Alex Yang, Angela Huyue Zhang

Published: 27-02-2024

Everything You Need To Know

What is the central debate surrounding AI-generated content and copyright?

The core debate revolves around two main questions: First, should creators be compensated when their work is used to train AI models, which is the 'fair use' standard issue. Second, can AI-generated content be copyrighted, and if so, who owns it, which is the 'AI-copyrightability' question. These issues spark passionate debate among legal scholars, tech companies, artists, and policymakers, making it crucial to understand the implications of each aspect.

How does the 'fair use' doctrine affect the use of copyrighted material in AI model training?

The 'fair use' doctrine, typically allowing the use of copyrighted material for commentary, criticism, education, and news reporting without permission, is being hotly contested in the context of AI. AI companies argue that using copyrighted material to train their models falls under fair use, transforming the data into something new. However, creators argue that their work is exploited for commercial gain without compensation, leading to legal battles like those involving Getty Images, Stability AI, OpenAI, and Microsoft.

What is the difference between 'The Data Abundant Regime' and 'The Data Scarce Regime' in relation to AI and copyright?

'The Data Abundant Regime' describes a scenario where training data is plentiful. In this case, a generous fair use standard, allowing AI companies to use data without compensation, benefits AI development, creator incomes, and consumer surplus. Conversely, 'The Data Scarce Regime' occurs when training data is limited. A strict fair use standard, requiring AI companies to compensate creators, may be preferable as it incentivizes content creation and improves AI model quality. The amount of available data is critical in determining the optimal approach to balance innovation and creator rights.

Why is the amount of available data so crucial in shaping copyright policy for AI?

The amount of available data determines the optimal approach to copyright policy. If data is abundant, a generous 'fair use' approach can stimulate AI development, increase creator incomes, and enhance consumer surplus. However, in a data-scarce environment, a strict 'fair use' approach, which requires compensation for creators, may be more beneficial by incentivizing new content creation and improving the quality of AI models. Finding a balance between these two regimes is key to fostering both innovation and protecting creator rights.

What economic impacts are highlighted regarding the intersection of AI and copyright?

The article focuses on the economic implications of two primary copyright issues: the 'fair use' standard and 'AI-copyrightability.' The potential impacts on AI development, creator incomes, and consumer welfare are central. A generous fair use standard may accelerate AI innovation but could potentially reduce creator incomes if they are not compensated for the use of their work. A strict fair use standard could increase costs for AI companies. Policy must be tailored to the data landscape to balance these effects and foster both AI progress and creator rights.