In a March 23, 2026 episode of Decoder, host Nilay Patel confronted Shishir Mehrotra, CEO of Superhuman, over Grammarly’s now-removed “Expert Review” feature. It presented AI-generated writing advice through named experts, including Patel, without first asking those people to participate. Mehrotra apologized, but the interview left a harder question open: when does crediting someone’s published ideas become using that person’s identity to sell a product?
What happened in the interview
Patel’s interview with Mehrotra focused on a feature Grammarly had launched under the name “Expert Review.” The episode was published March 23, 2026, and identifies Mehrotra as CEO of Superhuman, the company formerly known as Grammarly. Apple Podcasts lists the episode and its publication date; the episode summary describes the feature and the company’s response.
Patel’s name was among those used as an AI “expert.” According to Patel’s account, he and other writers whose names appeared had not been asked to authorize the use. The summaries available for the episode describe the system broadly, but do not establish every interface detail, the complete roster, or the technology behind its outputs. It is therefore more precise to say that the feature presented advice under named experts’ identities than to claim it cloned their voices or trained a model on each person’s work.
The conversation was tense at points. Mehrotra apologized, while the underlying disagreement persisted: the company described the feature in terms of synthesizing and attributing ideas from public writing; Patel challenged whether making a person’s name a selectable source of AI advice crossed into impersonation. A transcript and episode-insights page summarizes the exchange, though it is a secondary source rather than a substitute for the recording.
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What “Expert Review” did—and what is not established
At the level established by episode descriptions, Expert Review generated writing suggestions framed as coming from named experts. Patel and other reporters and writers were among the people whose names were used. That is enough to explain the controversy, but not enough to confidently describe precisely what every user saw or how the system assembled an answer.
- Established: The feature used real people’s names to frame AI-generated advice, and the people identified had not given permission according to Patel’s account.
- Not established by the available descriptions: The exact screen wording, whether users chose a named expert or encountered one in a recommendation, what badges or disclaimers appeared, the complete list of names, and whether the system relied on retrieval, prompts, a curated database, model training, or a combination.
- Do not assume: The use of a person’s name proves the product cloned their voice, copied a particular passage, or reproduced a specific opinion. Those claims require evidence about the interface and outputs.
These distinctions matter because “AI based on published work” can describe very different products. A search tool that links to an article, a summary attributed to its author, a synthetic persona that answers in the author’s name, and a human-like voice clone are not interchangeable.
Why critics called it impersonation
Attribution tells users where an idea came from. Impersonation concerns whether a product makes a person seem to be speaking, advising, participating, or endorsing something when they are not. Between those poles are style imitation and persona simulation: a system may imitate recognizable writing traits or offer an “AI version” of someone without literally claiming the person generated the output. The presentation and the user’s likely understanding matter.
Critics’ strongest objection was not simply that Grammarly might have consulted public writing. It was that a real person’s name functioned as part of the product: a signal of expertise attached to newly generated advice. That can lend an answer authority and commercial value while leaving the named person without approval over what the system says in their name. The episode’s discussion of names and check-mark-like signals is summarized by AudioScrape’s transcript page; exact UI claims should be checked against the original interface before being stated as definitive.
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The company’s counterargument, as reflected in the interview summaries, is that synthesis of public ideas with attribution is different from claiming that the person personally wrote or approved the new advice. That distinction is meaningful: citing a source is not automatically impersonation. But attribution alone does not answer whether a named person agreed to become a product feature, whether users could mistake the output for authorized advice, or who bears the reputational risk if the system gets that person wrong.
Consent, opt-out, and the company’s response
The reported sequence is that Grammarly launched Expert Review, initially offered affected people an email-based opt-out after objections, and later removed the feature. Episode summaries place the launch “back in August”; because the interview aired in March 2026, that appears to mean August 2025, but the year is an inference from the chronology rather than a separately verified launch announcement. The summaries do not establish how long the feature remained available, the precise opt-out procedure, or what data or outputs were deleted when it was removed.
An opt-out is not equivalent to opt-in. Under opt-out, people may need to discover an unwanted use, identify the responsible company, and request removal after the feature is already live. Opt-in puts the choice before launch. Removal of a feature also does not by itself show whether names, cached outputs, prompts, training material, or other associated data were deleted.
The episode description reports that journalist Julia Angwin filed a class-action lawsuit related to the feature. That is a reported legal challenge, not a finding that the company broke the law. The available material does not establish the complaint’s specific claims, court, class definition, requested relief, or current procedural status, so none should be inferred from the existence of the suit.
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The legal questions are separate, not one “AI law” issue
Whether a company may use a person’s work and whether it may commercialize that person’s identity are different questions. The relevant legal theories depend on the product’s exact presentation, the material used, the jurisdiction, and the facts alleged.
- Name and likeness rights: Some jurisdictions recognize rights that can limit commercial use of identity without consent. The rules and exceptions vary, and the facts here would matter.
- False endorsement or association: A claim could turn on whether users might reasonably think the person sponsored, approved, or participated in the feature.
- Copyright: Questions about copying articles, summarizing them, or using them in model development are distinct from whether a person’s name appeared in the interface. A name’s use does not prove that particular works were copied.
- Consumer protection and unfair competition: These theories may be relevant if the product presentation misled users or created confusion, but the applicable elements are fact- and jurisdiction-specific.
- Expression and public interest: Companies may argue that discussing public figures or their published ideas is protected expression. A commercial product that turns identity into a branded feature may raise different considerations from ordinary reporting or citation.
- Contracts: A creator’s agreements with an employer, publisher, or platform could create separate obligations; the episode summaries do not establish whether any such contracts apply.
These are possible lines of analysis, not conclusions about Angwin’s case or the feature’s legality. A court’s ruling, if any, would depend on the actual pleadings and evidence.
Public writing is not blanket permission to market a persona
Making an article, book, or interview publicly accessible lets people read and discuss it. It does not, by itself, settle whether a company may turn its author into a synthetic adviser, use the author’s name as a product hook, imply endorsement, or generate advice that the author would reject. At the same time, that distinction does not mean every commercial use of public text is unlawful. Access to information, use of copyrighted expression, and commercial exploitation of identity are related but separate issues.
That separation is especially important when an AI answer is presented under a person’s name. A user may treat the identity as a guarantee of expertise or as evidence the person approved the advice. If the output is inaccurate or simplistic, the company may capture the product benefit while the named individual absorbs the reputational cost.
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Who should be paid—and what payment would not solve
Patel pressed Mehrotra on how much a company should pay people to use their names; a social post about the episode also highlights that compensation question. That post is commentary on the exchange, not evidence of a compensation proposal. The available episode summaries do not establish that Superhuman offered a payment framework.
Possible models range from a one-time license to recurring royalties, subscription-revenue sharing, collective licensing, or payment specifically for use of a name and persona. Another model could require permission and editorial control without payment. Each allocates value differently, but money alone cannot answer whether the person may veto a use, approve the description of their views, correct generated advice, withdraw later, or prevent the product from implying endorsement.
For an identity-based AI feature, consent and control are not merely add-ons to a price. A responsible arrangement would make clear what the person is licensing, how outputs are labeled, how they can be reviewed or corrected, whether the person can withdraw, and what happens to existing material after withdrawal.
A practical standard for AI products using real people
For companies, a defensible approach to named synthetic advisers would start with affirmative permission before launch, not an email opt-out after discovery. The permission should cover the person’s name and representation, the product context, and the kinds of outputs users may receive. The interface should clearly distinguish authorized participation from a system that merely draws on published work.
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- Obtain explicit opt-in for using a living person’s identity as a feature or authority signal.
- Let the person review and approve their biography, descriptors, and representative examples.
- Label synthesis clearly; do not use first-person speech, badges, or wording that implies personal review unless authorized.
- Provide a workable correction and withdrawal process, including a clear account of what removal does and does not delete.
- Keep provenance that distinguishes direct quotation, paraphrase, and generated synthesis.
- Consider compensation where a person’s identity is part of the product’s commercial value, while treating payment as separate from consent.
For writers and other creators who discover their identity in an AI product, useful first questions are concrete: Is the name displayed to users? Does the product imply participation or endorsement? Can the company show what sources or outputs are associated with the name? Is there a written removal and deletion process? Those answers clarify the issue without assuming that every use of published work has the same legal or ethical status.
Why the corporate context matters
At the time of the interview, Superhuman was the corporate name formerly associated with Grammarly; Grammarly remained its flagship product, and the company also operated Coda and a Mail product, according to the episode description. The rebrand places the incident within a broader productivity-platform strategy, but it is not evidence that the name change was intended to avoid responsibility. The relevant accountability question is whether the company’s approach to identity, consent, and attribution changes across products—not what its branding implies.
The interview is useful because it makes a difficult boundary visible. Citing a person’s ideas can help users find expertise; turning the person’s name into a synthetic product interface can also suggest a relationship that does not exist. The unresolved test is whether users can tell the difference, and whether the person whose identity supplies the feature’s authority had a meaningful choice.
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