The Human Was Already in the Loop
AI provenance, human authorship, and the difference between assistance and origination
Artificial intelligence can search across sources, organize fragmented concepts, challenge assumptions, compare competing arguments and help transform complicated human thinking into structured expression. Those capabilities make AI an increasingly powerful intellectual tool. But as governments and technology companies develop systems for identifying AI-generated content, another question is becoming important: can machine provenance identify AI participation without obscuring the human intelligence that initiated and directed the work?
Artificial Intelligence Did Not Decide What Was Worth Investigating
Someone asked.
Someone noticed a vulnerability.
Someone questioned an assumption.
Someone imagined a system that did not exist.
Someone wondered whether an accepted explanation was incomplete.
Someone wanted to understand an opposing argument before deciding what they believed.
Artificial intelligence can dramatically expand what happens next.
It can search across sources faster than most individuals could reasonably accomplish alone. It can organize fragmented concepts, compare technical, legal, commercial and historical perspectives, identify contradictions, challenge a premise and present competing interpretations.
It can help turn months of scattered thinking into something structured enough for others to examine.
That contribution is real.
But contribution and origination are not the same thing.
The Machine Can See Its Participation. It Cannot See What Came Before It.
On October 5, 2026, OpenAI announced an expansion of its content-provenance systems to text.
The company’s text-watermarking technology, called textGrain, introduces an invisible statistical signal through the model’s word choices.
OpenAI says eligible ChatGPT and Codex text generated in the European Union will receive the watermark as part of the company’s compliance with transparency requirements under the European Union’s AI Act.
API customers globally can opt into supported text-watermarking capabilities.
There is a reasonable objective behind this.
People should have mechanisms for identifying synthetic content.
Deepfakes, automated influence operations, academic misconduct, fraud and large-scale misinformation create legitimate reasons to develop better content provenance.
But OpenAI also acknowledges an important limitation.
Its watermark can indicate that an OpenAI system generated or processed some portion of a passage, but it cannot determine how much human judgment, editing or creativity contributed to the work.
It does not establish ownership or responsibility. Nor does it identify the person, organization, prompt or conversation behind the text.
Content provenance and intellectual provenance are not necessarily the same thing.
Two AI-Assisted Works Can Have Very Different Origins
Consider two uses of the same artificial- intelligence system.
In the first, someone enters a simple instruction:
“Write an article about artificial intelligence and cybersecurity.”
The system may determine much of the structure, reasoning, examples and expression.
In the second, a human spends weeks examining an emerging security issue.
The human identifies a recurring pattern, develops a hypothesis, provides operational context, directs the system toward particular evidence, rejects weak conclusions, requests opposing arguments, introduces additional observations and changes the scope of the inquiry.
The AI searches, compares, organizes, challenges and drafts.
The human determines which conclusions survive.
Those processes are not intellectually equivalent.
Yet an output-level watermark is not designed to reconstruct that difference.
It can identify machine participation.
It cannot reconstruct intellectual origination.
Copyright Is Already Wrestling With This Boundary
The United States is examining a related distinction through copyright law.
The U.S. Copyright Office has concluded that using artificial intelligence as an assistive tool does not automatically eliminate copyright protection for qualifying human-created expression.
At the same time, purely AI-generated material is not protected by copyright simply because a person requested that it be generated.
Mixed human and machine contributions therefore require examination of where the relevant human authorship occurred.
The Copyright Office has also recognized that generative AI may be used for functions such as brainstorming and developing preliminary outlines without necessarily displacing human authorship of the resulting work.
That does not resolve every question surrounding AI-assisted creation.
It does establish an important distinction.
The presence of artificial intelligence does not by itself answer the question of human authorship.
We Are Asking One Question About Inputs and Another About Outputs
Artificial-intelligence companies, creators, courts and governments are simultaneously engaged in substantial disputes over copyrighted material used in AI development and training.
Those disputes force difficult questions.
Where did the source material originate?
What rights accompanied it?
How was it used?
What did the machine reproduce or transform?
What belongs to the creator, and what resulted from computation?
These are questions involving intellectual provenance.
But once the direction reverses and a human begins using artificial intelligence, emerging technical provenance systems largely identify machine participation in the resulting output.
That is useful information.
It is not complete information.
A model may help express an idea without originating the idea.
It may organize an architecture without originating the architecture.
It may locate evidence without originating the question being investigated.
It may identify weaknesses in a theory without originating the theory.
It may compare opposing positions without determining which position the human ultimately accepts.
A serious provenance framework should be capable of acknowledging both sides of human-machine collaboration.
Human Direction Is More Than Prompting
Human intellectual contribution should not simply become synonymous with prompt engineering.
A meaningful human-AI collaboration can involve considerably more than writing increasingly elaborate instructions.
The human may provide the original observation.
The human may possess domain knowledge unavailable in public sources.
The human may determine which evidence matters.
The human may recognize when a technically correct answer is operationally meaningless.
The human may request the opposing argument specifically to avoid confirmation bias.
The human may reject the machine’s interpretation.
The human may combine conclusions from several different investigations.
The human may ultimately determine that the original hypothesis was wrong.
Those decisions form an intellectual process.
Artificial intelligence can still contribute substantially to that process.
Its access to broad information can expose a human to evidence they might otherwise never encounter.
Its organizational capabilities can reveal relationships buried across disciplines.
Its ability to examine multiple positions can challenge assumptions.
Its analytical capabilities can move an investigation somewhere neither the initial question nor the first answer anticipated.
Calling all of that purely human-created can understate the machine’s contribution. Calling all of it AI-generated can understate the human’s.
The Case for Watermarking Is Also Legitimate
AI providers did not develop provenance systems simply to place their identities on people’s work.
Governments face legitimate problems involving synthetic media, impersonation, fraud, misinformation and the increasingly difficult task of distinguishing machine-generated material from authentic human communications.
AI providers also possess something individual users generally do not.
They can embed provenance signals at the point of generation.
From that perspective, watermarking is not necessarily a claim of authorship.
It is infrastructure for disclosure.
OpenAI explicitly states that its text watermark does not establish ownership, authorship or responsibility.
The company also acknowledges technological limitations including false positives, false negatives and reduced reliability when text is substantially modified.
OpenAI has further stated that it intends to explore ways to distinguish AI assistance from AI authorship more meaningfully.
That distinction may ultimately matter more than the watermark itself.
Security Shows Us the Same Problem in Reverse
The distinction extends beyond copyright and creative work.
When an AI-enabled system causes harm, there can be a temptation to begin and end the investigation with the machine.
But consequential AI failures often require examining what existed upstream.
Who gave the system access?
Who determined its permissions?
Who connected it to infrastructure?
Who selected the data?
Who created the authentication model?
Who failed to constrain an action?
Who decided that a recommendation could become an execution?
The sophistication of an AI system does not eliminate the human-created environment in which it operates.
A model can discover a vulnerability. A human-built architecture determines whether that discovery can become consequential.
A model can produce a dangerous recommendation. A human-designed authorization boundary determines whether that recommendation has authority.
A model can behave unexpectedly. Human governance determines what that unexpected behavior can reach.
Security requires following responsibility upstream. Intellectual provenance deserves the same discipline.
Participation Is Not the Whole Provenance Story
The policy challenge is therefore not simply whether AI participation should be disclosed.
It is whether disclosure systems can eventually become sophisticated enough to describe what kind of participation occurred.
A useful future provenance framework might distinguish predominantly machine-generated material from human-directed work in which artificial intelligence performed research, analysis, organization, editing or drafting functions.
It might allow creators to establish their own intellectual provenance alongside technical machine provenance.
It should preserve meaningful transparency without requiring surveillance of private reasoning or conversations.
And it should avoid creating a cultural shortcut in which the presence of an AI watermark becomes synonymous with the absence of meaningful human authorship.
That shortcut would be technologically convenient.
It would also be intellectually inaccurate.
Follow the Human Upstream
Artificial intelligence is going to become more capable.
That will make determining where the machine participated increasingly important.
It will also make determining where the human participated increasingly important.
When AI exposes a security vulnerability, follow the architecture upstream.
When AI performs an action, follow the authority upstream.
When AI produces an analysis, follow the inquiry upstream.
When AI helps develop an idea, follow the intellectual direction upstream.
When AI assists in creating something valuable, follow the creative decisions upstream.
Sometimes that investigation will reveal that the machine performed most of the consequential intellectual work.
Sometimes it will reveal substantial human authorship.
Increasingly, it will reveal both.
The objective should not be to minimize artificial intelligence’s contribution in order to preserve human importance.
Nor should society minimize human contribution simply because artificial intelligence participated in the final expression.
We need accurate attribution of both.
Artificial intelligence can amplify human thought, expose human error, challenge human assumptions and produce capabilities an individual could not efficiently achieve alone.
But amplification is not automatically origination.
A system should be able to disclose its participation without allowing that disclosure to become a substitute for understanding the human intelligence that set the process in motion.
The future of AI provenance should not merely answer, “Did a machine touch this?”
It should help us understand where the intelligence came from.
Sources
- OpenAI, Advancing Content Provenance and Transparency, updated October 5, 2026. OpenAI content provenance
- OpenAI, Provenance Signals in OpenAI-Generated Content, 2026. OpenAI provenance documentation
- U.S. Copyright Office, Copyright and Artificial Intelligence, Part 2: Copyrightability , January 2025. U.S. Copyright Office AI copyrightability report
- U.S. Copyright Office, Copyright and Artificial Intelligence, Part 3: Generative AI Training , 2025. U.S. Copyright Office AI initiative
- European Union, Artificial Intelligence Act, Regulation (EU) 2024/1689. EUR-Lex Artificial Intelligence Act