What happened
On July 21, Substack CEO Chris Best published a post announcing a new Pangram integration inside the Substack app. The feature lets readers scan text and see an estimate of how much was likely written by hand or with AI assistance. Best framed the problem as a mismatch between what readers expect and how something was actually made.
Substack's July 23 help documentation gives the operational details. The Scan for AI text tool works on posts and notes published on or after July 21, 2026, and uses Pangram to assign percentages for human-written and AI-assisted text. The help page says it is available in the Substack Reader on the web, the iOS app, notes, comments, and replies, with Android coming soon.
There are clear limits. Substack says the tool is unavailable for video or audio posts, posts viewed on standalone Substack sites or custom domains, and emails. The same help page says creators can add a How I make this statement, scan drafts before publishing, disable detection on a post or note, and report a detection error.
Axios covered the rollout on July 23 and corroborated the core product move: Substack is partnering with Pangram so users can estimate how much text on the platform was made with AI. Pangram's own explainer, published July 17, describes its detector as a classifier that estimates whether a segment of writing is AI-generated based on learned text patterns.
What the detector cannot see
Substack allows AI-assisted writing when authors use it thoughtfully and stand behind the work.
Pangram estimates whether text is AI-generated. Substack says the detector cannot tell whether human care went into a piece or whether AI was used as a source.
A detector looks at the finished text. It does not see the interview, the notes, the draft history, the source material, the editorial judgment, the rejected claims, or the approval conversation. In expert communication, those are often the parts that matter most.
Explain how the piece was made
Substack's How I make this field may prove more useful than the detection percentage.
Use the field to explain what AI did, what the named person contributed, and who is responsible for the final claim.
For a researcher, founder, consultant, educator, or creator, a useful process note can be simple: AI helped gather background, organize questions, transcribe the recording, suggest clips, or tighten a draft. The human supplied the examples, caveats, interpretation, approval, and responsibility for the published claim.
Role clarity is stronger than either pretending AI was absent or treating any AI assistance as disqualifying. Did AI prepare and organize the work, or did it impersonate the author's judgment?
Use the interview to check later drafts
Substack's detector currently scans text only. Teams publishing across other formats still need to keep the sources behind each claim.
A research-guided video interview creates that source of record. The person answers on camera. The transcript captures the words. The editor can trace a clip, article paragraph, caption, or newsletter section back to the original answer. If AI helped prepare the question or organize the result, that role can be described without making AI the author.
A clip may travel without the full article, or a quote may be rewritten for another channel. Keep the original answer available so editors can check the meaning after those changes.
A recorded answer gives the team a primary source to inspect, approve, and revisit. Video can still be performed or edited badly, so its advantage comes from that inspectable source rather than any automatic claim to authenticity. That is harder to get from a polished text draft that arrived with no visible path behind it.
A practical process disclosure
If your team uses AI in expert publishing, write the process note before you need one. Keep it factual and boring.
Start with source material. What did the human actually provide: a recorded answer, a transcript, a document, a product note, a research link, a firsthand example, or an approved position? If the central claim cannot be traced to any of those, it is not ready for publication.
Then name the AI role. Did AI research context, draft questions, summarize notes, suggest structure, identify clips, create captions, or help with editing? The more specific the role, the easier it is for a reader, editor, or stakeholder to understand what happened.
Finally, name the human review. Who checked the claim, the implication, the sources, and the final wording? Approval should cover what the audience is likely to believe, not only whether a sentence sounds acceptable.