Skip to content

Can AI Make Art? And If So, Who Owns It? Novel Theoretical Frameworks for Understanding Artificial Intelligence, Creativity, and Ownership in the Digital Age

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Law, AI, and Intellectual Property

Abstract

The emergence of artificial intelligence as a creative force has fundamentally challenged traditional conceptions of authorship, creativity, and intellectual property ownership. This analysis presents six novel theoretical frameworks that transcend current academic discourse on AI art ownership, drawing from extensive empirical research, comparative legal analysis, and economic impact studies. Through examination of recent legal developments including Thaler v. Perlmutter [1], analysis of platform ownership models, and synthesis of market data showing a projected growth from $3.2 billion to $40.4 billion by 2033 [2], this work argues that AI art ownership exists within a complex adaptive system requiring dynamic, multi-dimensional approaches rather than binary legal categorizations. The analysis introduces the concepts of Temporal Ownership Dynamics, Algorithmic Authorship Gradient, Collective Intelligence Ownership, Platform Constitutionalism, Evolutionary Ownership Adaptation, and Economic Stratification of Creative Rights as foundational frameworks for understanding this emerging domain. These theoretical contributions provide both descriptive power for current phenomena and prescriptive guidance for future policy development in an era where artificial intelligence increasingly mediates human creative expression.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Trajectory Balance: Improved Credit Assignment in GFlowNets

It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...

Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al. · 302 citations · ⚡60

Related blog posts

MIT News · Artificial Intelligence Sep 30, 2026

This game-playing AI is the new champ at Stratego

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

GPT-Lab Sep 3, 2026

Adaptive AI Agents in Construction Workflows

Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.

GPT-Lab Aug 28, 2026

We built an AI factory for HVAC control

What does it take to trust AI-driven HVAC optimization? Our AI Model Factory combines agents, machine learning, reinforcement learning and deterministic checks in a governed workflow designed for messy, real-world building data. The post We built an AI factory for HVAC control appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.