When AI Makes Assets, Who Owns Them? Lessons from OpenAI Docs and Transmedia IP Deals

When AI Makes Assets, Who Owns Them? Lessons from OpenAI Docs and Transmedia IP Deals

Hook: Your team is under pressure to produce more visuals, faster, and cheaper. AI generation can satisfy demand, but it raises immediate questions: who owns the image, who can license it, and what legal risks follow when a studio or influencer uses AI-assisted art in a global transmedia deal? If your workflows tie into CMS, DAMs and commercial partnerships, these aren’t academic questions — they’re business-critical.

The current moment (2026): why ownership questions are urgent

In early 2026 the debate about AI ownership and open-source models is no longer theoretical. Unsealed documents from high-profile litigation (reported by The Verge) exposed internal tensions at leading AI labs over open-source vs proprietary trajectories — including comments that open-source AI had been treated as a "side show." At the same time, traditional media players like The Orangery are executing transmedia licensing deals (The Orangery signed with WME in January 2026) that assume clearly defined chains of title and exclusivity. That collision — rapid AI innovation against legacy IP deal structures — is why content teams and rights managers must act now.

Two worlds colliding: open-source AI debate vs. transmedia IP practice

Open-source AI: transparency, velocity, and uncertain licensing

Open-source models accelerated research and lowered entry barriers for creators. By late 2025 many open-source weights and checkpoints proliferated across communities. The upside: fast iteration, community validation, and modular licensing. The downside: inconsistent license terms, ambiguous third-party data provenance, and differing commercial-use permissions.

Key risks with open-source models:

• License compatibility — Model weights or associated code may carry copyleft or restrictive clauses that make commercial use complex.
• Training data provenance — Community models often lack rigorous datasets records; that increases exposure to claims about unauthorized use of copyrighted content.
• Unclear output rights — Some licenses grant no explicit assignment for outputs, creating downstream ambiguity when outputs are repurposed at scale.

Transmedia IP deals: clarity, exclusivity, and legacy structures

Transmedia outfits — like The Orangery, which holds rights to graphic IP and just inked representation with WME — operate in a world built on clearly assigned rights, exclusives, and revenue share mechanics. These deals routinely specify who owns character art, derivative works, merchandising rights, and sublicensing permissions. Traditionally, parties expected human authorship and traceable chain-of-title documentation; contracts contained representations, warranties, and indemnities tied to that assurance.

When AI is introduced into this model, the usual deal language breaks down unless adapted:

• Does the IP owner permit AI-generated derivative works?
• Is the AI output treated as original or derivative under the agreement?
• How are moral rights, attribution, and authenticity handled for AI assistance?

Legal reality in 2026: evolving rules, persistent ambiguity

Legal frameworks have changed little in principle: copyright still favors human authorship in many jurisdictions. The U.S. Copyright Office’s 2023 guidance reaffirmed that works produced without human authorship are not registerable; courts continue to grapple with how to treat AI-assisted works when a human exercises significant creative control.

At the regulatory level, 2025–2026 saw heightened scrutiny. The EU AI Act (implementation phases rolling through 2024–2026) and similar national rules are increasing transparency and risk management requirements for high-impact AI systems. That regulatory pressure amplifies commercial caution: brands, platforms, and agencies now demand more documentation about training data, model provenance, and usage rights.

Practical takeaways about legal risk

• Ownership can be contractually defined — Even if statute is ambiguous, parties can assign or license outputs by contract. The best practice is a specific clause assigning ownership of AI outputs or granting an exclusive license.
• Indemnities matter — Contracts should allocate risk: who will defend and pay for third-party infringement claims related to AI outputs?
• Document human contribution — If claiming human authorship, keep logs showing creative decisions, prompt iterations, and human edits.
• Track model provenance — Record which model, weights, and datasets (where available) produced an asset. That aids both defense and compliance.

How to manage ownership & licensing in practice — playbooks for teams

Below are tactical approaches that studios, influencers, and publishers can adopt immediately. They balance operational speed with rights safety.

1) Define ownership of AI outputs in all supplier and contributor contracts

Clause to include:

"All outputs generated using AI tools under this Agreement shall be deemed 'Works for Hire' and assigned to [Client], or, if not work-for-hire under applicable law, the Contractor hereby assigns to [Client] all right, title and interest in such outputs."

Why it works: clear assignment eliminates ambiguity for downstream licensing, especially in transmedia deals where exclusivity and merchandising matter.

2) Require model and data provenance disclosures

Ask partners and vendors to provide:

• Model name, version and license
• Whether the model was fine-tuned on third-party copyrighted material
• Logs of prompts, seed images and key human edits

How to use it: attach provenance metadata to assets in your DAM (XMP fields, JSON sidecars). This simplifies audits and rights clearance.

3) Use layered licensing for transmedia projects

For projects that span books, games, and merchandising, build a layered license approach:

1. Core IP ownership: who owns characters and universe elements?
2. Art & assets license: specify AI usage allowances and any exclusivity.
3. Sublicense terms: limit or permit sublicensing to production partners.

Example clause for The Orangery-style deals: "Licensee may use AI to generate derivative visual assets only with Licensor's prior written approval and subject to quality control and attribution standards set in Exhibit A."

4) Capture human creative intent and edits

Maintain a creative log that documents the author’s role — prompts, adjustments, compositing, and post‑process editing. If you will claim copyright or exclusive rights, this evidence is often decisive.

5) Choose your model strategy based on risk tolerance

Options:

• Public API with strong provider terms: Vendors like major cloud AI providers often include explicit ownership grants and indemnities for outputs — lower operational friction but rely on provider’s policy.
• Self-hosted/fine-tuned private model: Higher control and traceability; better for exclusive IP work but requires investment and a legal review of the base model license.
• Open-source models: Fast and flexible but demand rigorous license audits and provenance processes.

Case study: imagine The Orangery licensing a new AI-assisted art line

Scenario: The Orangery licenses a new merchandising deal for "Traveling to Mars" and wants to produce a 500-image pack for a global apparel partner, some images generated with AI and finished by in-house illustrators.

Steps to protect rights and reduce risk:

1. Define the output ownership: The contract assigns all outputs to The Orangery, with sublicensing rights to the merch partner.
2. Prescribe permitted AI tools: Require that any AI model used either be company-hosted or a vetted third-party API that provides commercial-use rights and indemnity.
3. Require provenance records: Each image submitted must include a JSON manifest listing model, prompt, seed images, and human editor signatures.
4. Quality control & approval gates: Final assets must be approved by an IP representative to screen for inadvertent inclusion of third-party protected content.
5. Escalation & reversion clauses: If a rights claim emerges, the contract includes reversion triggers and a process for replacement assets and financial remedies.

Result: The Orangery keeps commercial control, preserves exclusivity for merchandising, and reduces exposure to infringement claims while using AI efficiencies.

Operational controls: how to make this repeatable across teams

Integrate rights metadata into your DAM/CMS

Store the model manifest, license text, and assignment docs alongside each asset. Enable search by license and model so marketers and product teams can filter assets by commercial safety.

Implement prompt & generation versioning

Treat prompt engineering like code. Version prompts, model versions, and final edits. Your creative team will thank you when a partner asks for proof of authorship.

Enforce an approval workflow for external licensing

Any asset intended for an exclusive or monetized deal should automatically enter a legal review queue. Use gating in your DAM to prevent accidental license breaches.

Insurance, indemnities and contingency planning

Traditional E&O (errors & omissions) insurance is adapting to AI risks. By 2026, several insurers offer endorsements for AI-generated content, but terms vary. Two practical steps:

• Negotiate indemnities with vendors that train or host your model whenever possible.
• Purchase E&O coverage with clear AI-related clauses. Expect higher premiums for large-scale, consumer-facing releases.

Open-source models: governance checklist for safe commercial use

1. Audit the model license and any third-party content licenses bundled with it.
2. Document training-data provenance where possible; flag unknown sources and accept higher risk for unknowns.
3. Isolate outputs used commercially behind a review workflow.
4. For high-value IP, prefer self-hosted fine-tuning on proprietarily cleared datasets.

What buyers should demand from AI vendors in 2026

If you’re evaluating an AI provider, include the following in procurement:

• Explicit output ownership language and commercial use grants.
• Training data attestation and audit rights.
• Indemnity for third-party IP claims arising from the model or its outputs.
• Operational SLAs for provenance logs and data retention.

Future-facing predictions: what will change next?

Based on trends through early 2026, expect these shifts:

• Contractual standardization: Industries will converge on model-output assignment templates for transmedia and merchandising deals.
• Model provenance registries: Third-party registries and standardized model cards will gain traction to prove training-data lineage.
• Regulatory disclosure mandates: Laws like the EU AI Act will force more transparency from model providers, making provenance easier to obtain.
• Insurance products evolve: More granular AI E&O coverage will emerge alongside underwriting standards tied to model provenance.

Actionable checklist: 10 steps to close the ownership gap (start today)

1. Audit current AI tools and list their model licenses and provider terms.
2. Update contributor and vendor agreements to include explicit AI-output assignment or licensing clauses.
3. Embed model and prompt metadata into your DAM for every generated asset.
4. Require provenance manifests for external partners and freelancers.
5. Implement an approval gate for high-value and exclusive usage.
6. Negotiate indemnities with cloud or model providers when possible.
7. Purchase or update E&O insurance with AI considerations.
8. Prefer self-hosted/fine-tuned models for strategic IP generation if budget allows.
9. Train legal, product and creative teams on AI risks and required documentation.
10. Establish a response playbook for takedown or infringement claims, including replacement asset processes and financial reserves.

Conclusion — make ownership a business process, not a hope

AI image generation is a powerful tool for creators and publishers — but it won’t replace the need for clear rights architecture in transmedia and commercial licensing. The recent unsealed internal discussions at major AI labs and the ongoing sophistication of transmedia deals like The Orangery’s show both sides of the coin: rapid innovation and established IP discipline. To operate confidently in 2026, treat AI outputs as you would any high-value creative work: document provenance, contractually fix ownership, and integrate rights metadata into your production systems.

"Design your contracts, processes, and systems to reflect the realities of AI — not the other way around."

Next steps (call to action)

If you’re evaluating AI tools for a transmedia release or need to retrofit rights controls into existing workflows, start with a rights audit. imago.cloud helps teams centralize asset provenance, embed license metadata at creation, and design approval gates that integrate with CMS and design tools. Book a quick consultation and we’ll map a practical rights-governance plan tailored to your IP and commercial goals.

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