How writers are reacting to Substack’s AI transparency tools

Earlier this week, we announced a feature that allows readers to scan text published to Substack to see how much of it is estimated to be written by hand or with AI assistance. We also launched tools for publishers, including the ability to run the program—Pangram—on drafts prior to publication, disable scanning on a post-by-post basis, and add a “How I make this” statement explaining their creative process to set expectations for their readers. These features do not use publisher content, either through Substack or Pangram, to train generative AI models and are now available on web, iOS, and Android.

Norms around AI use and its disclosure are unsettled. But we have made the assessment that while the use of AI isn’t necessarily a problem, a lack of transparency around it definitely is. Our aim is to help writers get credit for the work that’s theirs and help their audiences make more informed choices about what they spend their time on.

Substack has long been home to brilliant writers and thinkers exploring questions about AI disclosure, making a livelihood through writing, and whether technology can replace human effort in art.

Archie Hall, a columnist at The Economist, is highly bullish on AI’s economic upside but recoiled from AI-written prose: “We should shun AI writing even if it is perfectly bearable to read,” he wrote. Science fiction writer Lincoln Michel evangelized intention, and said that AI output can’t be art because it isn’t the product of choices. The Hinternet Editorial Board proposed a Mohs-style scale of AI reliance, from 0 to 10, and Sam Kriss menaced, “If you let AI do your writing, I will come to your house and kill you.”

Writers have methodically tested what AI can and can’t generate. Freddie deBoer ran an experiment to track if an LLM could write the parts of a novel that aren’t the prose itself. Engineer, founder, and former venture capitalist Rohit Krishnan tried to measure the statistical signature of literary style, interested to see if a model could eventually learn to write with it. Literature professor Hollis Robbins examined two very different approaches to LLM-generated poetry and asked whether either gets anywhere close to greatness: “For poets, edge cases are the poem.” Elle Griffin wrote a fictional day-in-the-life in a future AI utopia, and Andrea Bartz, the named plaintiff in Bartz v. Anthropic, the case that resulted in a $1.5 billion settlement for writers, wrote her own detailed argument against generative AI today.

Writer Teddy (T.M.) Brown named an economic reality rather than a spiritual one—that most commercial writing was never really creative expression to begin with, so what’s disappearing is a livelihood, not a soul: “I am less threatened by the presence of bad writing created by robot-equipped dullards than I am by the specter of there being no way to make a living as a writer at all.”

The common thread? We believe that human perspective is an essential ingredient of culture, and this will be true no matter how powerful AI becomes.

In the days since the AI scan feature was announced, we’ve seen many publishers putting the tools to use, and initial responses suggest that transparency is the right first step toward protecting writers and improving the experience for their readers.