The Watermark Debate: Why AI Provenance Matters More

Anthropic’s decision to add invisible watermarks to Claude-generated text has brought a long-running AI debate into sharper focus: How should businesses establish the origin of AI-assisted content, and what should they do with that information once they have it?

The question matters as generative AI becomes embedded in everyday knowledge work, from drafting and research to marketing, analysis and customer communications. The European Union’s AI Act is also pushing the industry toward machine-readable disclosure of AI-generated content, making provenance an increasingly important part of enterprise AI strategy.

But provenance is not the same as authorship, quality or accountability. A watermark may establish that an AI system was involved without explaining how extensively it was used, what a human changed or who ultimately stands behind the work.

Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, offer a range of perspectives on what should come next. In the discussion that follows, they examine whether provenance should be standard or optional, where watermarking fits, how companies can protect privacy and human judgment, and what AI leaders should do to build greater trust and accountability around AI-generated content.

Treat Watermarking as a Product and Ethics Question

For Piyush Lakhawat, Senior Member of Technical Staff at Salesforce, the basic technology behind watermarking is not the difficult part.

“Watermarking is not a technically challenging problem,” Lakhawat says. “When it is done at the current scale of GenAI, there should be public open-source benchmarks that capture performance and any inherent bias to maintain transparency.”

He also expects different content types to require different approaches.

“There will be some innovation needed around tailored watermarking for different types of content, like code versus natural language,” he says.

For leaders, however, the bigger issue is not implementation. 

“The more important question is around the goals and intention behind watermarking and how it should be done,” Lakhawat says. “So it becomes more of a product and consumer ethics discussion rather than a technical one.”

“Watermarks answer ‘Was AI involved?’ The more important question is ‘Who reviewed it, and who stands behind it?’”

– Mo Ezderman, Director of AI at Mindgrub Technologies

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Ask Who Stands Behind the Work

Mo Ezderman, Director of AI at Mindgrub Technologies, believes the industry is framing the debate too narrowly.

“I think the debate around Anthropic’s watermarking is asking the wrong question,” he says. “We’re focused on whether text was AI-generated, when AI-assisted writing is quickly becoming the default for knowledge work.”

His alternative is fundamentally human-centered: “Detection isn’t the real trust problem. The better question is: Did a human review this and put their name behind it?”

That question is key as AI moves from generating finished drafts to assisting with research, editing, translation, analysis and routine business communication. 

“Watermarks answer ‘Was AI involved?’” Ezderman says. “The more important question is ‘Who reviewed it, and who stands behind it?’”

For enterprise leaders, that suggests a practical complement to provenance: a documented human-review step for consequential work.

Build Interoperable Provenance, Not One Perfect Signal

Aishwarya Shah, an independent researcher, argues that provenance deserves a permanent place in the AI stack—but watermarking should not become synonymous with provenance.

“AI provenance should become a standard layer of trust, but not necessarily through a single watermarking mechanism,” Shah says. “The lesson from Anthropic’s approach is that transparency needs to be designed into AI systems from the beginning, while recognizing that no technical signal is perfect.”

Her preferred model is interoperability.

“I would favor interoperable provenance standards that can communicate how content was generated or modified, while giving users appropriate visibility and control,” she says.

Shah cautions that provenance should inform decisions rather than become an unquestioned verdict. 

“Provenance should strengthen trust, not become another invisible layer users are expected to accept without understanding,” she says.

“The Claude watermark only shows that Claude was involved; it shouldn’t imply that AI ‘authored’ the finished work or diminish human judgment.”

– Brock Murray, Co-Founder of seoplus+

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Separate AI Involvement From Authorship

Brock Murray, Co-Founder of seoplus+, brings a digital marketing and search perspective to the issue.

“Anthropic’s move is directionally right because people should have context about where content comes from,” Murray says. “With that said, the Claude watermark only shows that Claude was involved; it shouldn’t imply that AI ‘authored’ the finished work or diminish human judgment.”

He favors an industrywide standard that identifies the technology without exposing the customer.

“The better industry approach is a common, machine-readable standard that identifies the tool without identifying the individual user or organization,” he says. 

Murray points to Anthropic’s statement that its watermark carries no user-identifying information as an important privacy principle.

He advises AI leaders to “protect trust before optimizing technology.” That means clear disclosure, customer-data protection, experimentation with safeguards and collaboration around open standards.

Don’t Mistake the Mark for the Thinking

Jim Liddle, Entrepreneur, Investor, Advisor and Enterprise AI Strategist, offers a practical enterprise perspective on what gets lost when AI is treated as a binary label.

“The best AI outputs are the result of hours of framing, prompting, iteration, critique and interaction,” Liddle says. “Watermarking ignores all of that human effort and it infers AI generated as being equal to low effort.”

He sees a potential behavioral consequence for both creators and readers.

“In my opinion, watermarking enables intellectual laziness on the reader side (‘It’s AI-generated so I won’t read it’) while punishing intellectual rigor on the creator side,” he says.

That is a warning for executives considering how provenance will be used internally. A signal designed for transparency can become harmful if employees, customers or decision-makers treat it as a proxy for quality.

“Watermarking marks the text, but it doesn’t mark the thinking behind the text,” Liddle says. “In a world drowning in content, thinking is the only thing worth knowing.”

Make Provenance a Default Capability

Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft, brings a standards-oriented cloud and AI perspective to the debate.

“The real lesson from Anthropic’s watermarking effort is that trust cannot depend on a hidden signal,” Muthukamatchi says. “Watermarks may help, but determined actors can remove, rewrite or obscure them.”

He recommends changing the industry’s vocabulary and architecture.

“The industry should stop asking, ‘Can we detect AI content?’ and start asking, ‘Can we verify its origin?’” he says.

For Muthukamatchi, that means provenance should be built into AI infrastructure rather than added as an optional feature. 

“Provenance should not be optional,” he says. “It should be a built-in capability supported by open, interoperable standards that track how content was created, modified and distributed.”

His strongest analogy is infrastructure itself: “Treat provenance the way the internet treats encryption. Users should not have to think about it, but it should be there by default.”

Govern What the Mark Is Allowed to Do

Mani Padisetti, Founder of Almost Magic Tech Lab, believes the industry is asking the wrong question about governance.

“The missing governance question is not whether content should be marked, but what decisions the mark is allowed to influence,” Padisetti says.

This is critical for executives. A provenance marker might be useful for a newsroom or records-management system but harmful if a recruiter treats it as a reason to reject a candidate.

“An invisible signal could become a shortcut in hiring, education or disciplinary action long before its accuracy and meaning are understood,” he says.

Padisetti recommends stronger provenance requirements for high-stakes content, coupled with explicit rules about how the information can be used.

“I would require stronger provenance for consequential content, but every mark should state exactly what it proves, who may read it, how long it persists and how a false attribution can be challenged,” he says.

His bottom line: “Provenance without rules for its use is not transparency. It is an ungoverned decision input.”

Match Provenance Requirements to Consequence

Andre Shojaie, Founder of HumanLearn, argues that the industry risks making provenance too broad.

“The mistake is treating provenance as a property of content,” he says. “Almost everything will eventually have some machine involvement, whether drafted, translated, summarized, corrected, researched or reformatted. Marking every sentence ‘AI touched this’ may become as informative as labeling documents ‘made with software.’”

His solution is a risk-based model.

“I would make provenance contextual, not universal,” Shojaie says. “The higher the consequence of the content—medical guidance, financial decisions, journalism, legal evidence, public communications—the stronger and more durable the provenance requirements should be.”

For executives, the implication is to stop asking whether every piece of content needs the same treatment.

“AI leaders should build an auditable chain of creation rather than a hidden scarlet letter inside the text,” he says.

Give Users Control Over Transparency

Hastimal Jangid, Co-Founder of RankRabbit AI, offers an engineering and digital-visibility point of view.

“The lesson isn’t about watermarking; it’s who decides what transparency means,” Jangid says. “Anthropic built invisible marks for EU compliance, then applied them globally, one region’s rule becoming the default for everyone.”

His concern is not merely geographic scope but how quickly compliance decisions can become permanent product architecture.

“That should worry us less because of the marks themselves and more because irreversible product decisions are being made on regulatory deadlines, not consensus,” he says.

Jangid rejects a simple standard-versus-optional choice. 

“Provenance should be standard at the infrastructure level, but visible and controllable to users, not invisible by default,” he says.

For other AI leaders, his recommendation is to move before regulation dictates every detail: “Don’t wait for a regulator to force this. Build disclosure that feels like user control, not surveillance, and publish your methodology openly.”

“A hiring manager sees it on a cover letter, an editor on a submission, a teacher on an essay. What should any of them do differently the moment it turns up?”

– Divya Parekh, Founder of executive coaching brand DivyaParekh.com

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Make Disclosure Actionable, Not Merely Visible

Divya Parekh, Founder of executive coaching brand DivyaParekh.com, describes what happens after a provenance signal reaches a human decision-maker.

“Picture the flag arriving,” Parekh says. “A hiring manager sees it on a cover letter, an editor on a submission, a teacher on an essay. What should any of them do differently the moment it turns up?”

The problem, she argues, is that organizations often fail to define the operational meaning of the signal.

“Nobody has told them, so the mark gets read as a verdict, the calls come out inconsistent, and the people who disclosed honestly take the hit,” she says.

Her prescription is simple: “Make provenance standard for consequential content, and publish the receiving policy alongside it.”

That policy should explain “what disclosure is expected, what it changes about review, and what it never changes about the decision,” Parekh says.

She connects the issue back to organizational accountability: “The deeper issue is that detection keeps standing in for accountability.”

Building a Trustworthy AI Provenance Strategy

• Make provenance standard infrastructure, not a single technical solution. Open benchmarks and testing should measure watermark performance, bias and limitations across content types.
• Put human accountability alongside AI detection. A provenance signal should identify AI involvement without replacing the more important question of who reviewed and approved the work.
• Build interoperable provenance standards. AI systems should communicate how content was created and modified without forcing the industry to rely on one vendor’s proprietary mechanism.
• Separate AI involvement from authorship. A watermark should not automatically imply that AI created, authored or deserves credit for the finished work.
• Measure the thinking, not just the tool. Enterprises should avoid treating AI involvement as a proxy for low effort, poor quality or weak intellectual contribution.
• Make provenance a default capability. Users should not have to opt in to basic origin information, particularly when AI-generated content enters consequential workflows.
• Govern the use of provenance signals. Define who can access provenance information, what decisions it may influence, how long it persists and how false attributions can be challenged.
• Use a risk-based model. Medical, financial, legal, journalistic and public-sector content warrants stronger provenance and auditability than routine everyday writing.
• Give users meaningful control. Transparency should not become surveillance; explain what the mark means and give users appropriate visibility into the information attached to their work.
• Publish the receiving policy. Every organization using provenance information should tell employees and customers exactly how a mark changes—or does not change—the review process.

Provenance Is the Beginning, Not the Verdict

The industry should not choose between “watermark everything” and “watermark nothing.” The stronger path is to make provenance a standard infrastructure capability while treating the resulting signal as evidence—not a verdict. Watermarks can be one layer in that system, but they cannot establish authorship, human judgment, quality or intent on their own.

The future of AI trust will therefore depend less on whether companies can detect AI involvement and more on whether they can establish origin, preserve context, document transformation and assign accountability. For AI leaders, that means building provenance into products now, using open standards where possible and defining clear rules for how provenance information can affect real people. That is how transparency becomes trust rather than another invisible layer of complexity.