Autonomous AI and Ownership Rules

Abstract

As artificial intelligence (AI) systems become increasingly autonomous, traditional notions of ownership face novel stress tests. Historically, property rights have been grounded in traceability, enabling legal and economic systems to allocate ownership efficiently through doctrines such as accession (ownership by connection) and first possession (ownership by labor). Property law also recognizes that abandoned property, when considered untraceable to its original owner, can be efficiently reassigned through accession, ensuring that resources do not remain ownerless. However, autonomous AI systems capable of self-replication, self-governance, and independent economic activity complicate these established principles, raising new questions about how ownership should be determined when AI is no longer clearly linked to an identifiable owner.

This Article examines the circumstances in which AI-generated outputs remain linked to their creators and the points at which they lose that connection, whether through accident, deliberate design, or emergent behavior. In cases where AI is traceable to an originator, accession provides an efficient means of assigning ownership, preserving investment incentives while maintaining accountability. When AI becomes untraceable—whether through carelessness, deliberate obfuscation, or emergent behavior—first possession rules can encourage reallocation to new custodians who are incentivized to integrate AI into productive use. The analysis further explores strategic ownership dissolution, where autonomous AI is intentionally designed to evade attribution, creating opportunities for tax arbitrage and regulatory avoidance. To counteract these inefficiencies, bounty systems, private incentives, and government subsidies are proposed as mechanisms to encourage AI capture and prevent ownerless AI from distorting markets. Rather than treating autonomous AI as agents and applying agency or corporate law to check asocial bot behavior, this Article frames autonomous AI as property and relies on basic property law doctrine to achieve the same goals.

Ultimately, the erosion of traditional ownership structures due to autonomous AI is not merely a theoretical concern but an emerging economic reality. As AI-driven automation expands, ownership may become increasingly provisional, assigned dynamically through legal and economic frameworks designed to maximize efficiency rather than as a default right. This Article argues for an adaptive approach, ensuring that AI remains within assignable governance structures—whether through accession, first possession, or incentive-driven competition—to prevent the unchecked proliferation of unregulated, unowned AI systems.