Companies rushed into generative AI asking one question: What can we create?
Far fewer asked the question that eventually matters more: What can we actually own?
That distinction becomes important the moment an AI-assisted asset becomes valuable.
Imagine spending millions developing a brand campaign, character, product design, software asset, soundtrack, or proprietary content library. The work succeeds. Customers recognize it. The company starts treating it as intellectual property.
Then a competitor copies it.
Only at that point does someone ask the question that should have been asked at the beginning. Can we prove what the humans actually created?
That is the business risk I think many executives are underestimating.
Companies are creating assets faster than they are creating ownership.
In the AI economy, creation is cheap. Ownership is expensive.
The Legal Risk Runs in Both Directions
The legal ground around generative AI is moving quickly.
In March 2026, the U.S. Supreme Court declined to hear Thaler v. Perlmutter. That left standing the lower court ruling requiring human authorship for copyright protection without the Supreme Court itself deciding the merits.
The U.S. Copyright Office takes a similar approach. AI-assisted work can receive protection for sufficiently human-authored elements, while prompting alone generally does not establish authorship.
The other side of the problem concerns what goes into the models.
On July 20, Sony filed another lawsuit against Udio covering 30,117 additional sound recordings it says were copied for AI training. Eleven days later, the Munich Regional Court ruled largely in favor of GEMA in its case against Suno. The decision addressed copyrighted works used during training, memorization inside the model, and generated outputs.
That creates exposure at both ends.
On the input side: did we have appropriate rights to the material involved?
On the output side: did humans contribute enough original authorship for the resulting asset to receive meaningful protection?
A company can potentially face claims involving what went into an AI system while struggling to protect what came out.
The AI copyright problem cuts both ways: someone may claim what went in while you struggle to protect what came out.
The Rise of Phantom IP
I call the emerging problem Phantom IP.
Phantom IP is something a company creates, uses, and treats as proprietary even though its legal defensibility may be far weaker than management realizes.
Marketing copy is only the obvious example.
Think about brand characters, product designs, software, training materials, research, music, images, advertising concepts, and other assets that could become commercially important.
The danger rarely appears when the asset is created.
It appears after the asset becomes valuable enough for someone else to copy.
That is why I see Phantom IP as a board issue.
Executives need to understand which assets the company genuinely controls and which ones merely sit inside a folder marked proprietary.
AI can create something valuable in seconds. Proving that it is yours may take years.
The most dangerous Phantom IP is great content that becomes valuable before anyone asks who owns it.
Provenance Is Becoming Part of the Product
For the first phase of generative AI, scale and speed were enormous competitive advantages.
I would not rewrite that history and argue that AI companies should never have moved aggressively. Rapid experimentation produced extraordinary innovation.
The competitive environment is changing.
The next phase will put much more value on traceability and defensibility.
Companies spent years asking how capable a model was, how much it cost, how secure it was, and how quickly it could generate output. Provenance often received much less attention.
Now businesses have to ask where the intelligence came from.
Was training material licensed?
Can the vendor explain its data lineage?
Can a company identify where human contribution entered an important output?
Can it document which model was used and what happened after the initial generation?
This is why I think clean provenance will become a premium feature.
Vendors that can offer licensed data, transparent lineage, and clearer evidence around outputs will have a meaningful advantage with enterprise buyers.
Yesterday, provenance was paperwork. Tomorrow, provenance will be product.
The distinction matters more than simply labeling something as AI-generated. I made a similar point in Why AI Labeling in Video Games Is Quickly Losing Its Value. A label tells you that AI participated. Provenance tells you what participated, where the material came from, and who contributed what.
Companies Need to Measure IP Materiality
The answer is to use stronger controls where the asset matters most.
An AI-generated meeting summary and a global brand identity carry very different levels of commercial risk.
So do an internal brainstorming document and a core product design.
I would evaluate AI-generated or AI-assisted assets according to IP materiality.
The more strategically valuable, reusable, and distinctive an asset could become, the stronger the evidence trail around it should be.
That trail may include the source material, model used, human contributions, edits, approvals, and the decisions that turned machine output into a finished asset.
A human approval box in a workflow proves very little by itself. That connects to the problem I discussed in The Human in the Loop Is Legal Fiction. Meaningful human involvement has to exist in practice and leave evidence behind.
There is one question I would put in front of every CEO and board:
Which AI-assisted assets would hurt us most if we discovered tomorrow that we could not stop a competitor from copying them?
Start there.
Creation Is Becoming Abundant
Generative AI gives almost everyone the ability to produce more.
More images. More copy. More software. More designs. More music. More concepts.
As creation becomes abundant, the sources of competitive advantage move elsewhere.
Brand becomes more important. Distribution becomes more important. Proprietary data, relationships, and trust become more important. Human originality becomes more important.
Legally defensible ownership becomes more important.
The next AI advantage is proving that competitors cannot simply take what you created.
I see the same underlying issue in Your Voice Is No Longer Yours. Once technology can reproduce something cheaply, identity and ownership become harder to establish and more valuable to protect.
This is another example of AI moving value.
The ability to generate content becomes less scarce. The ability to build an asset that is distinctive, trusted, and defensible becomes more scarce.
Boards Are Asking the Wrong IP Question
Executives often ask how much intellectual property the company owns.
I would change the question.
What do we own that we could successfully defend?
Those two portfolios may already be very different sizes.
As generative AI spreads through marketing, product development, software, research, and design, many organizations will see their creative inventory grow much faster than their defensible IP portfolio.
That gap is the exposure.
A company may have thousands of AI-assisted assets and very little evidence showing where important human contribution occurred, what source material was involved, or what rights attach to the final output.
By the time someone challenges the asset, reconstructing that history may be difficult or impossible.
Your company may own less intellectual property than its executives think it does.
That does not make generative AI less valuable. It changes what responsible adoption looks like.
Creation can happen almost instantly now.
Ownership still has to be built.
