When AI Becomes a Co-Author, Who Owns the Work?

Key Takeaways

• AI agents are transitioning from task executors to creative collaborators.
• Infrastructure investments (chips, compute) create downstream authorship questions.
• Current productivity metrics ignore value distribution and responsibility gaps.
• Creative and policy sectors face unique governance challenges with AI co-creation.
• Organizations need frameworks defining ownership, attribution and accountability.
• Early governance design determines who benefits from AI-human collaboration.

As AI agents move from automation to co-creation, infrastructure investments reveal a governance gap. Who owns ideas generated collaboratively?

The productivity promise of AI agents is seductive: autonomous systems that draft policy briefs, generate creative content and execute complex workflows without human intervention. Yet as organizations invest billions in AI infrastructure — from specialized chips to large action models (LAMs) — a fundamental question remains unanswered: When AI moves from tool to collaborator, who owns the output?

Table of Contents

• Who Owns Content Created Using AI?
• 1. The Infrastructure Illusion: Why Chips Matter for Authorship
• 2. From Automation to Co-Creation: What Changes When AI Becomes Collaborator
• 3. Creative Work as Governance Laboratory
• 4. Knowledge Work: Where Responsibility Becomes Critical
• 5. Governance Models for AI Collaboration: What Organizations Need
• 6. The Regulatory Horizon
• 7. Governance as Infrastructure

Who Owns Content Created Using AI?

This is not a hypothetical concern. It is an operational problem already surfacing in creative studios, policy research teams and knowledge work environments where AI agents increasingly function as co-authors rather than assistants.

The infrastructure layer — the chips, compute clusters and foundation models powering these agents — creates a misleading sense of neutrality. Technology feels deterministic: better chips enable faster inference; larger models produce more sophisticated outputs. But infrastructure investments encode governance decisions.

They determine who can deploy AI agents, under what conditions and, critically, who captures value when those agents generate economically or culturally significant work.

Productivity gains from AI agents cannot be separated from governance models defining responsibility, ownership and value distribution. For creative and policy sectors in particular, the shift from tools to co-authors requires institutional frameworks that current AI governance approaches do not provide.

1. The Infrastructure Illusion: Why Chips Matter for Authorship

When NVIDIA announced its latest data center GPUs optimized for agentic AI workloads, the discourse focused on performance metrics: inference speed, energy efficiency, cost per token. What went largely unexamined was the governance architecture these infrastructure choices enable.

Specialized AI chips do not merely accelerate computation — they determine which organizations can afford to deploy sophisticated AI agents and under what economic models. A creative agency running local inference on high-end GPUs operates under different ownership dynamics than a policy research team relying on cloud-based API access to foundation models hosted by platform companies.

The infrastructure layer shapes three governance dimensions often treated as separate:

Access Asymmetry

Organizations with capital to invest in on-premise AI infrastructure maintain control over model outputs and training data. Smaller organizations dependent on third-party APIs cede this control, often without recognizing the authorship implications. When a policy analyst uses Claude or GPT-4 to co-draft legislation, who owns the intellectual property in that draft? The analyst? The employing institution? The platform providing the model?

Current terms of service from major AI providers remain deliberately vague on this point, deferring to user agreements that most organizations have not carefully examined for collaborative authorship scenarios.

Compute as Gatekeeping

The economics of AI inference create de facto governance through resource allocation. High-quality agentic AI — models capable of multi-step reasoning, contextual memory and autonomous decision-making — requires compute resources that privilege well-funded organizations. This is not merely an equity issue; it is a governance design problem. If only certain actors can deploy sophisticated AI agents, those actors unilaterally shape norms around AI-human collaboration without input from communities most affected by these systems.

In South Korea's cultural sector, this asymmetry is already visible. Large entertainment conglomerates like HYBE and SM Entertainment invest heavily in proprietary AI systems for music composition and visual content generation, while independent artists and smaller studios rely on commercial APIs with restrictive licensing terms. The result: divergent authorship frameworks emerging not from policy debate but from infrastructure access.

Training Data as Governance Precedent

The datasets used to train foundation models — and by extension, the AI agents built on them — embed assumptions about authorship, creativity, and value. When these models are trained on copyrighted works scraped without consent or compensation, the infrastructure itself normalizes extractive relationships between human creators and AI systems.

This is not a technical problem amenable to better data curation. It is a governance failure: the decision to build AI infrastructure on unlicensed creative labor reflects institutional choices about whose work is valued and whose can be appropriated.

For organizations deploying AI agents in creative and knowledge work, ignoring these infrastructure-level governance questions means accepting by default the power relations encoded in existing systems. The choice is not whether to govern AI agents, but whether to do so intentionally or allow infrastructure providers to govern on your behalf.

Related Article: How AI-Generated Code Puts Your Company at Risk

2. From Automation to Co-Creation: What Changes When AI Becomes Collaborator

Traditional productivity tools — word processors, spreadsheets, design software — augment human work without claiming authorship. AI agents disrupt this model.

When an AI agent drafts a policy memo, composes a musical motif or generates visual concepts, it is not merely executing predefined functions. It is making creative and strategic decisions previously reserved for human judgment: which arguments to emphasize, which aesthetic directions to pursue, how to structure information for maximum impact.

This shift from tool to collaborator creates three governance challenges:

1. Authorship Ambiguity

In traditional creative workflows, authorship maps cleanly onto human contributors.

A policy analyst who writes a brief is its author; a designer who creates a logo owns that creative output (subject to employment agreements). AI agents destabilize this clarity.

Consider a scenario increasingly common in policy research: an analyst uses an AI agent to synthesize hundreds of legislative documents, identify patterns and draft recommendations. The agent does not merely compile information — it makes analytical choices about relevance, framing and argumentation. Who is the author of the resulting brief? The analyst who prompted the system? The institution employing both? The AI company whose model performed the synthesis? All three? None?

Current intellectual property law offers no clear answer. Copyright doctrine in most jurisdictions requires human authorship, but this principle was developed for contexts where tools did not generate substantive content autonomously. As AI agents become more sophisticated, this doctrinal gap widens.

2. Responsibility Diffusion

Collaborative authorship with AI agents creates accountability vacuums. When an AI-generated policy recommendation proves flawed or a creative output infringes on existing work, who bears responsibility?

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Organizations deploying AI agents often assume human oversight resolves this problem — "the analyst reviewed the AI's output, so they're responsible." But this framing misunderstands the nature of AI collaboration. If an agent generates a 30-page policy analysis incorporating hundreds of sources, meaningful human review becomes impossible within realistic time constraints. The analyst becomes a ratifier rather than author, yet organizational accountability structures still assign them full responsibility.

This mismatch between collaborative reality and accountability frameworks creates legal and ethical risk. More fundamentally, it undermines the productive potential of AI agents: if humans cannot meaningfully review AI-generated work, collaboration devolves into blind acceptance or rejection rather than genuine co-creation.

3. Value Capture Misalignment

Perhaps the most consequential governance question: when AI agents and humans collaborate to produce valuable outputs, how should value be distributed?

The current default — human labor captures wages, AI providers capture platform fees, organizations capture output value — emerged not from deliberate governance design but from existing employment and licensing structures. Yet AI collaboration fundamentally changes value creation dynamics.

In creative industries, this tension is already visible. Musicians using AI to generate backing tracks, visual artists incorporating AI-generated elements, writers co-authoring with language models — all face the same question: what portion of the final work's value derives from human creativity versus AI contribution, and how should compensation reflect this?

Organizations treating AI agents as costless collaborators (beyond infrastructure and licensing fees) may achieve short-term productivity gains while destabilizing the creative labor markets they depend on. If AI can "co-author" without compensation or recognition, why employ additional human collaborators?

This is not hypothetical. South Korea's webtoon industry — a global leader in digital comics — is already experiencing this shift. Publishers increasingly use AI agents to generate background art, color palettes and even narrative suggestions, reducing demand for junior artists who previously performed these tasks. The productivity gains are real; so is the value transfer from human labor to AI systems and the organizations deploying them.

3. Creative Work as Governance Laboratory

Creative industries offer particularly revealing case studies for AI agent governance because they make visible dynamics that remain latent in other sectors.

Seoul's Digital Art Institutions: Governance Through Practice

South Korea's cultural institutions are experimenting with AI agents in ways that illuminate governance challenges. The Seoul Museum of Art's digital curation initiatives and Seoul Design Foundation's Open Curating program incorporate AI systems not as automation tools but as curatorial collaborators — systems that recommend exhibition layouts, suggest thematic connections and even generate interpretive text.

These experiments reveal a critical insight: AI agent governance cannot be outsourced to terms of service. Effective governance requires institutional frameworks defining:

• Decision authority: Which curatorial choices can AI systems make autonomously, which require human review and which must remain exclusively human?
• Attribution norms: How should exhibition materials credit AI contribution without overstating machine "creativity" or erasing human curatorial labor?
• Value recognition: When AI-assisted curation increases audience engagement or generates revenue, how should this success be allocated between human curators, AI system providers and institutions?

Seoul's cultural sector has not fully resolved these questions, but the governance frameworks emerging from practice offer replicable insights: multi-stakeholder governance committees including AI developers, curators and artists; explicit attribution policies distinguishing AI-generated from AI-assisted work; and revenue-sharing models that recognize AI's contribution without eliminating human compensation. These are governance innovations, not technical solutions. They demonstrate that AI agent collaboration requires institutional design, not just better algorithms.

• K-Pop's AI collaboration shows productivity masking extraction: South Korea's music industry provides a contrasting case where productivity gains through AI agents obscure governance failures.
• Major entertainment companies deploy AI extensively: Generating melody variations, producing mix suggestions, even composing entire tracks that human producers then refine.
• Industry narratives emphasize efficiency: AI agents enable faster production cycles, more experimental sound design, global market responsiveness.

Governance Questions Still Linger

Yet beneath this productivity narrative, governance questions fester. When AI agents are trained on decades of Korean pop music — often without explicit consent from original composers and performers — who owns the resulting AI-generated variations? When AI systems suggest melodies similar to copyrighted works, is this inspiration or infringement? When human producers rely increasingly on AI-generated starting points, does their creative contribution diminish in value?

Korea's music copyright collective, KOMCA, is grappling with these questions through what I observed during my time as Legislative Researcher at the National Assembly: the organization has no clear framework for AI-generated music, leading to ad hoc decisions that privilege established industry players while disadvantaging independent artists who lack resources to navigate ambiguous authorship claims.

This governance vacuum is not neutral — it allows extraction. Platform companies and large entertainment conglomerates capture value from AI-human musical collaboration while original creators whose work trained these systems receive neither compensation nor recognition.

Training Data Justice as Governance Framework

The concept I have developed through research and policy work — training data justice — provides a governance lens for AI agent collaboration in creative work.

Training data justice rests on five principles:

1. Collective stewardship (cultural data governed collectively, not atomized through individual copyright)
2. Tiered access (commercial AI requiring licensing, research uses permitted with attribution)
3. Transparency (training data composition disclosed)
4. Benefit sharing (economic value from AI training flows back to contributing creators)
5. Artist agency (individual creators retain meaningful control).

Applied to AI agents in creative and knowledge work, these principles translate to operational governance requirements:

• Organizations deploying AI agents must disclose what training data their systems were built on
• Commercial outputs from AI-human collaboration trigger compensation obligations to training data contributors
• Individual creators maintain opt-out rights over their work being used to train agents they collaborate with
• Attribution systems distinguish AI-generated from AI-assisted from human-created elements

These are not theoretical principles. They are governance mechanisms being implemented in South Korea's Seoul Cultural Data Trust pilot — a project I direct — where 50 artists collectively license their work for AI training under terms that ensure ongoing compensation, transparent usage and maintained attribution.

The trust model demonstrates feasibility: AI agents can be deployed productively while respecting creator rights and distributing value equitably. What it requires is governance infrastructure, not just technical infrastructure.

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4. Knowledge Work: Where Responsibility Becomes Critical

If creative industries make AI agent governance visible through authorship disputes, knowledge work environments reveal governance failures through responsibility gaps.

Policy Research and Legislative Analysis

During my tenure at Korea's National Assembly, I observed the early adoption of AI agents in legislative research — systems that synthesize case law, draft bill language and analyze policy impacts. The productivity gains were undeniable: research that previously required weeks could be completed in days.

Yet these gains came with unexamined governance risks. When AI agents draft legislative language, who verifies accuracy? When they synthesize legal precedents, who ensures no relevant cases were overlooked? When they recommend policy positions, who accounts for embedded biases in training data?

The implicit answer — "the analyst who uses the system" — breaks down under scrutiny. Legislative researchers are subject matter experts, not AI auditors. They can evaluate an AI agent's output for legal soundness but cannot assess whether the system's training data included representative case law or whether its recommendations reflect structural biases in the legal corpus it was trained on.

This creates a dangerous accountability gap: AI agents make consequential decisions (which precedents to emphasize, how to frame policy trade-offs), human analysts ratify these decisions without full visibility into the decision-making process and, when errors occur, only humans face professional consequences.

Governance frameworks must close this gap. This requires:

• Explainability standard: AI agents used in policy research must document their reasoning process — which sources they considered, why certain arguments were prioritized, what alternatives were rejected.
• Audit trails: Organizations deploying AI agents in knowledge work need systems to reconstruct decisions post-facto, identifying where AI contribution influenced outcomes.
• Shared accountability: Responsibility frameworks should recognize AI agents as collaborators whose developers and deployers share accountability for outputs, rather than treating human users as sole responsible parties.

The Productivity Paradox

Knowledge work sectors face a paradox: AI agents genuinely increase individual productivity while simultaneously creating organizational risk through unclear responsibility allocation.

A policy analyst using AI agents can produce more research, a legal researcher can review more cases, a strategic planner can evaluate more scenarios. But if organizational accountability structures do not adapt, this productivity becomes liability — more outputs produced under governance frameworks designed for human-only authorship.

The solution is not rejecting AI agents but redesigning accountability architecture.

This means acknowledging AI collaboration explicitly in organizational policies, creating review processes appropriate to AI-assisted outputs (different from human-only work) and establishing clear protocols for when AI agent decisions require human override.

5. Governance Models for AI Collaboration: What Organizations Need

Organizations deploying AI agents in creative and knowledge work need governance frameworks addressing authorship, responsibility and value distribution. Based on research, policy analysis and operational experience with Seoul Cultural Data Trust, I propose four governance components:

1. Authorship Transparency Protocols

Every organization using AI agents should implement clear attribution systems distinguishing:

• Human-created content
• AI-assisted content (human-directed AI contribution)
• AI-generated content (human-reviewed AI output)
• AI-autonomous content (machine-generated, minimally reviewed)

This is not about legal compliance — current IP law does not require such granularity — but about institutional integrity. Stakeholders deserve to know when outputs reflect AI collaboration, and at what level.

2. Shared Responsibility Frameworks

Accountability for AI-human collaborative outputs should be explicitly shared:

• Human collaborators responsible for output quality and alignment with organizational goals
• AI system providers responsible for training data provenance, bias mitigation and system reliability
• Deploying organizations responsible for appropriate use cases, review processes and oversight mechanisms

This shared framework prevents the current pattern where humans bear all responsibility despite having limited visibility into AI decision-making.

3. Value Distribution Mechanisms

When AI-human collaboration produces commercially or strategically valuable outputs, governance frameworks should address:

• Compensation for training data contributors (creators whose work enabled the AI agent's capabilities)
• Recognition for human collaborators (ensuring AI productivity gains do not eliminate employment or devalue human expertise)
• Institutional benefits (organizations deploying AI agents capture efficiency gains but within frameworks ensuring sustainable creative and knowledge labor markets)

4. Governance Institutions

Effective AI agent governance requires dedicated institutional structures:

• Cross-functional teams including legal, technical and domain experts reviewing AI collaboration frameworks
• Ethics committees with authority to restrict AI agent use cases where governance remains unclear
• Stakeholder representation from creators, knowledge workers and affected communities in governance decision-making

These are not aspirational recommendations. They are operational necessities for organizations seeking to deploy AI agents responsibly at scale.

6. The Regulatory Horizon

Regulation is beginning to address AI agent governance, though unevenly.

The EU AI Act classifies certain AI systems as "high-risk" based on application domain, which could encompass AI agents in policy analysis, legal research and creative production depending on implementation. South Korea's AI Basic Act establishes principles for trustworthy AI but remainslight on operational guidance for collaborative authorship scenarios.

More consequential may be sector-specific developments. Hollywood's recent labor negotiations established precedents for AI use in creative production, explicitly requiring human authorship acknowledgment and limiting AI-autonomous content creation. These are governance innovations from practice, not legislation — and they point toward what effective regulation might resemble.

Organizations waiting for regulatory clarity before implementing governance frameworks misunderstand the policy trajectory. Regulation will likely codify practices emerging from early adopters rather than imposing wholly novel requirements. This means governance leadership today shapes regulatory expectations tomorrow.

For organizations in creative and knowledge work, the strategic implication is clear: implementing robust AI agent governance now provides competitive advantage by demonstrating operational maturity, positions you to influence emerging regulatory standards and builds stakeholder trust that productivity-focused competitors without governance frameworks will struggle to achieve.

Related Article: How to Form an AI Council: Lessons From Those Who've Done It Right

7. Governance as Infrastructure

The infrastructure powering AI agents — from specialized chips to foundation models — creates an illusion of technological determinism. Better hardware enables more capable agents; larger models produce more sophisticated outputs. This narrative suggests governance is secondary to technical capability.

The opposite is true. Infrastructure investments without governance frameworks generate systemic risk: authorship disputes that undermine creative markets, responsibility gaps that expose organizations to legal and reputational liability, value extraction that destabilizes the knowledge labor AI systems depend on.

For creative and knowledge work, where AI agents increasingly function as collaborators rather than tools, governance is not overhead — it is productive infrastructure. Organizations that treat governance as integral to AI deployment will achieve sustainable productivity gains. Those treating it as compliance burden will discover that ungoverned AI collaboration creates more problems than it solves.

The question facing organizations is not whether AI agents will transform creative and knowledge work — they already are. The question is whether this transformation happens through deliberate governance design or ad hoc extraction.

Infrastructure alone does not determine outcomes. Governance does.

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