Chief AI Architect, capMedia Inc.
AI Fellow, R42 Institute
Before launching The Internet Letter in October 1993 — the first newsletter dedicated to the commercial internet — Jayne Lytel worked as a copy editor at The Washington Post, where she wrote the first piece on the internet to appear in a major national newspaper. The Internet Letter broke news about the web before Google existed, when Gopher still rivaled HTTP. The Internet Companion named her a “Legend of the Internet.” She also published The Federal Internet Source, a director of federal government websites that was later acquired by the National Journal, and wrote the Internet911 column, syndicated by United Media for five years. Lytel went on to advise on cybersecurity and privacy governance as a federal contractor at NASA, the National Science Foundation, and the U.S. Department of Homeland Security. She holds an M.S. in Cybersecurity Risk and Strategy from New York University and an M.A. in Human Development from Pacific Oaks College. Her certifications include (ISC)² HCISPP, ITIL 4, and Licensed FAA Remote Pilot.
A member of the National Press Club, Lytel serves on the advisory board of its Press Freedom Foundation and is a 2026 Vivian Awardee. She is the author of Act Early Against Autism (Perigee, 2008) and a two-time USRowing Masters National Champion in the single scull. Her psychological eco-thriller Run from Sunday is forthcoming from Bold Story Press.
The publishing industry is making consequential decisions about authors and books without a standard to judge them against. No industry‑wide definition distinguishes AI‑assisted from AI‑generated content. No threshold separates permissible use from prohibited use. And no governance framework exists comparable to what other industries have built when technology outpaced their rules. The consequences for publishing are now on the record.
The New Malleus examines that standards vacuum and the forces that created it.
Part I documents the culture and the fractures in the author community — the hardliners, the pragmatists, and AI myths that are being mistaken for facts that can damage an author’s reputation.
Part II covers U.S. copyright law and the human‑authorship requirement, from the evolution of U.S. copyright law since the landmark Burrow‑Giles Supreme Court decision to wins and losses by authors seeking recognition for their AI works under the law.
Part III examines the AI‑detection tools at the center of the controversy: how they work, what they measure, and where they fail.
Part IV identifies the standards vacuum and what it costs everyone operating inside it.
Part V is a narrative chronicle of active and decided AI copyright infringement lawsuits (as of May 15) now defining the legal fate of human creativity.
Appendices provides a high-concept glossary of technical terms (A), a guide to the major AI detection services and their tools (B), and a copyright registration quick reference for authors navigating disclosure requirements (C).
Fig. I This 1574 edition of the “Hammer of Witches,” printed by the Giolito de’ Ferrari family in Venice, remains one of the most significant historical documents regarding the legal and theological framework of the European witch hunts. Courtesy of Washington University Libraries, Special Collections.
A new Malleus for a new century — how AI detectors, viral suspicion, and a Big Five publisher's panic converged after it acquired self-published novelist Mia Ballard.
In the 15th century, Malleus Maleficarum gave inquisitors a manual, a method, and a mandate. Today the manual is Malleus Auctorum — the Hammer of Authors.
In the 15th century, Malleus Maleficarum — translated into English as the Hammer of Witches — gave witch hunters in Europe a manual to follow, a method to apply, and a mandate from the Catholic Church.
In the 21st century, the manual is Malleus Auctorum, the “Hammer of Authors,” and the method is using an AI detector to judge a book’s authenticity. What hasn’t changed is the mandate — fear. Just as the inquisitors invoked the absolute biblical command, “Thou shalt not suffer a witch to live,” to justify eradicating suspected witches, modern purists treat AI detection scores as evidence to justify cancelling further promotion and publication of an author and her book.
Author42’s The New Malleus explores what happens when a disruptive technology collides with a culture primed to weaponize it. The pattern is not new. In the late 1400s, the Gutenberg printing press spread the Malleus Maleficarum, which advanced the mission of the Church to hunt suspected witches by codifying rigid, arbitrary tests, such as scrutinizing a suspect’s inability to weep under torture, during a time of social unrest, and spiritual and moral decline.
Today, AI detectors question authorship and deliver verdicts, handing publishers a weapon to purge books suspected of being machine authored in a climate of cultural anxiety and AI backlash. That backlash has pushed the debate past rational discourse, leaving authors in a conundrum to either revise their manuscripts to break the statistical patterns AI classifiers are trained to detect, keep meticulous forensic-type notes on AI use, or avoid AI altogether and condemn those who don’t.
By the end of March, AI viral panic turned Mia Ballard of Sacramento, California, into a pariah after her femgore horror novel Shy Girl got axed by the third largest publishing company in the world — Hachette Book Group — over allegations that it was AI generated. That move marked the first time a Big Five publisher had remaindered a book after such strong initial sales, reportedly 1,800 copies since its U.K. release last fall. Public reaction was like a dog pausing in the middle of relieving itself, then kicking dirt over the pile when it realizes the whole park is watching. And watching they were but not until The New York Times blew its whistle on March 19 to amplify what social media had already flagged as AI slop back in early January. In Shy Girl, a desperate and broke Gia obeys a controlling man she met on a sugar-daddy site and acts like his pet pooch on the promise he'll pay off her debts.
Let’s step back to the 15th century, when the Little Ice Age — the coldest stretch of climate change — brought severe frosts and extreme weather that killed crops and sparked societal upheaval across Europe, from bread riots and famines to advancing glaciers in the Alps that crushed villages and farms that had existed for decades. In this climate of fear and uncertainty, people desperately searched for explanations and scapegoats.
They found them through Dominican clergyman Heinrich Kramer. When the Bishop of Brixon halted Kramer’s crusade against witches in Innsbruck, now in modern-day Austria, he expelled him from the diocese. Kramer, 56, relocated to Cologne, then a major intellectual and ecclesiastical center, and began writing the Malleus Maleficarum, one of the most controversial book in history.
The manual instituted a regulated legal process to identify suspected witches and obtain confessions from them through torture. It also offered the panicked public a direct explanation for the era’s climate disasters, detailing how witches invoked the power of devils, acting with God’s permission, to summon hailstorms. In one example, the book claimed a witch would sacrifice a black rooster at a crossroads, hurling it into the air for the devil, who would then stir up the skies and send down destructive weather.
Aided by the printing press, which had debuted three decades earlier, the treatise spread across Western Europe. From 1487 and 1520, it went through fourteen editions and became the ultimate authority that dictated witch-trial jurisprudence in France, Italy, and England for nearly three centuries.
In modern times, generative AI (gen AI) is disrupting the sanctity of authorship amid the industry's eroding gate-keeping authority — a long-held monopoly that has been fracturing since the early-to-mid 1990s, when the internet made anyone a journalist or an author, regardless of training, degree, or prior byline. The accused are no longer witches with wrinkled faces and moles. They are writers, and the tribunal is social media: Reddit, Facebook, X, Goodreads, LinkedIn, YouTube, TikTok — you name it. And the evidence is no longer an unusual birthmark; it is an AI-detector score that lands in the red zone, much as it did for Ballard after the Times' expose. Social media chatter that Shy Girl exhibited the fingerprints of AI-generated text surfaced in early January, about three months before the Times validated their suspicions. In a January 9 post on the subreddit r/horrorlit, a book editor raised allegations about AI, saying “… it's so repetitive. Ugh.” The editor identified tropes and clichés. She was not the only one. Ten days later, the creator of frankie's shelf on YouTube blasted the novel as “ai slop” in a 2-hour, 40-minute video. The video went viral, surpassing 1.6 million views.
“My mental health is at an all-time low, and my name is ruined for something I didn't even personally do,” she wrote the Times in an email. In a post to frankie's shelf, now deleted, she wrote, “At the time, I didn't have the money for a professional editor or formatter; those services are expensive, and I was working on a broken laptop, doing the best I could with what I had.” Ballard added, “Being a Black woman in the publishing space has been exhausting. I get attacked constantly when all I'm trying to do is write.”
After the AI allegations surfaced, Ballard said in the post that she removed her third novel, a 97-page novella titled We All Rot Eventually, from Amazon. Ballard disclosed that she had used the same editor for the novella as she had for Shy Girl. The Amazon publication date listed under Ballard's profile for We All Rot Eventually is 31 days before the publication date for Shy Girl.
Ballard indicated in the post that she is pursuing legal action. As of late April, however, no court records had verified rumors circulating on platforms such as YouTube that Ballard had filed a $1 million lawsuit against Hachette Book Group.
The same algorithm that elevated Ballard also pulled her under.
In February 2025, Ballard published Shy Girl under Galaxy Press, her self-publishing imprint. She soon became a “buzzy BookTok sensation” on TikTok. As reviews raised her visibility, readers called her out over the book's cover art, Dreamer — an oil and wood panel that she admitted on Instagram to using without permission from the Scottish artist Whyn Lewis.
Lewis said in an email that she reached a settlement with Ballard over her unauthorized use of Dreamer in January. She added that Ballard is now in default under the terms of that agreement that she had reached with her. She declined further comment.
Despite the criticism, Shy Girl received praise across Amazon, Bookshop.org, and other reader platforms. The Bookshop.org listing, now marked “unavailable,” still carries blurbs from authors including Olivia Blake, a New York Times best-selling author. On Goodreads, the book has 4,985 ratings and a 3.45-star average.
Other details complicate the narrative. A search of the U.S. Copyright Office's database shows that Ballard did not seek federal copyright protection for Shy Girl, or for her other books: Sugar, We All Rot Eventually and Delicate Thoughts, a poetry collection she co-authored under the name M. Ballard. That book was published by the now-defunct Jeanius Publishing LLC in Lehigh Acres, Fla., on Oct. 15, 2017, according to its Amazon listing. The domain jeaniuspublishing.com expired on April 8.
Before miaballard.com expired on Oct. 3, 2025, Ballard had promoted the site on social media. WHOIS records on Whoxy show that the domain was acquired after lapsing from its previous owner, Georgia real estate agent Mellania “Mia” Ballard.
It is unclear why Ballard — an author on the verge of a huge U.S. release with the third-largest publishing company in the world — would allow miaballard.com, her author website she'd promoted on Instagram, to lapse. Ballard did not respond to multiple requests for comment. The reasons may reflect oversight or other factors. Regardless, they are now part of the record but do not, on their own, establish intent.
The allegations against Ballard emerged from a forensic investigation first driven by social media, which combined quantitative, stylometric software detection with qualitative linguistic analysis to identify the stylistic markers associated with large language models (LLMs). The process revealed how collective intelligence can surface synthetic patterns.
The most compelling review came from YouTuber frankie’s shelf. (See Part III, The AI Detectors, to understand why detectors don't prove anything without further scrutiny.)
Ballard, 33, lived the dream literary agents and other authors say never happens: a Big Five house directly acquires the novel of a self-published author and plans a global rollout. Author bios describe Ballard as an American poet and fiction writer living in Northern California with her partner and springer spaniel.
Some note her African American and Native American heritage and that she “loves all things horror and is passionate about writing stories focused on feminine rage.” In her Amazon author profile under M. Ballard, she wears a custom Danburite pendant she commissioned from a crystal seller. In a 2022 Instagram post, the creator, moldavitemani, described Ballard as a “dear friend” who had ordered the pendant while “going through a whirlwind of transformation, grief, and growth.” In the top-row, second image from the left, the gold pendant is visible against her neckline.
In an Aug. 25, 2025 Bookstr interview, Ballard disclosed that, like Gia, Shy Girl's protagonist, she also suffers from obsessive-compulsive disorder. The article quotes her as saying, “It's living with a mind that's always scanning for danger.”
Ballard self-published three books in the span of four months and eight days, with Shy Girl arriving 31 days after the novella that preceded it. Ballard pulled We All Rot Eventually from Amazon amid the Shy Girl backlash.
Sourced from public records: U.S. Copyright Office research, domain registration data (Whoxy), public-records aggregators (Nuwber, Family Tree Now), YouTube, Reddit, the Wayback Machine, TikTok, LinkedIn, Pinterest, Instagram, and Hachette.
Viral Justice and the New Age of Spectral Evidence
From the Innsbruck witch trials to the comments section: how a crowd that has not read the manuscript is, once again, deciding whether the author is a witch.
Hans Baldung Grien · 1510, chiaroscuro woodcut.
The 1970 film Tora! Tora! Tora! gave Japanese admiral Isoroku Yamamoto the line, “I fear all we have done is awaken a sleeping giant and fill him with a terrible resolve.” Yamamoto never said it. But the fictionalized quote captures the sentiment among authors and publishers since the rapid, aggressive rollout of generative AI.
Generative AI is not like the TCP/IP technology that evolved today’s internet from its origins, beginning with the military’s Advanced Research Projects Agency Network (ARPANET, launched in 1969,) to the National Science Foundation’s NSFNET, which went live in 1985. That’s when two men — Vint Cerf, one of the “fathers” of the internet, and Robert Kahn — designed the TCP/IP protocol in 1974, then plugged it into ARPANET’s packet-switching infrastructure on Jan. 1, 1983, making it possible for the protocol to spread across the globe, connecting everything and nearly everyone.
Once that happened, it did what technology does: it automated at scale and moved information at speed. For more than five decades, the internet behaved like that until the next technology advanced again; this time it was the next evolution of artificial intelligence since Alan Turing asked, in 1950, whether machines could think — and then built the test to find out. The first serious answer came in 1966, when MIT’s Joseph Weizenbaum built ELIZA, the first conversational AI. And that was something no other technology had done before: it created.
ELIZA could mimic a therapist well enough to unsettle Weizenbaum himself. But she couldn’t learn. For the next five decades, AI advanced in bursts — Deep Blue beating Garry Kasparov at chess in 1997, IBM’s Watson winning Jeopardy in 2011, Google’s transformer paper in 2017 quietly laying the architecture for everything that followed. Then, on Nov. 30, 2022, OpenAI released ChatGPT. One million users signed up in five days. The machine didn’t just answer questions. It wrote your cover letter, your novel synopsis, your child’s book report. It wrote like you, if you wrote the right prompts.
Each decade before that had its own apocalypse. In the late 1990s, travel agents were among the first casualties, as Expedia and Travelocity gutted their livelihood. In the 2000s, streaming media helped kill Blockbuster, which filed for bankruptcy in 2010, while Craigslist stripped an estimated $5 billion in classified ad revenue from U.S. newspapers. In the 2010s, Uber and Lyft hurt taxi drivers, and online retailers took the Saturday afternoon at the mall away from American families.
Generative AI is different. It targets the source. The capacity to make language — symbolic thought, the architecture of every story ever told — comes from the same human cognition that produced shell beads in the Middle Paleolithic, 82,000 years ago. A model creates a phrase from a vector database. A writer creates one from hundreds of thousands of years of evolution. By contrast, the new and synthetic brain, a large language model, or LLM, survives on processors and training datasets that generate language from somebody else’s material, never tiring, never sleeping, unless the power goes out. But LLM’s parents aren’t going to let that happen, not with the billions of dollars they feed them and the megawatt datacenters they live in year-round, drawing power like rockets idling at low throttle, day and night.
For writers, the reckoning arrived from two directions: they could experiment with AI and become suspect, or turn away from it and fall behind. By late March, anxiety had shifted from unauthorized data sources used to train LLMs to publishers' ability to detect AI-generated content. While publishers viewed AI detector scores as a proxy for truth, what they got was an illusion of clarity. In reality, the scores simply handed publishers a new tool to reject authors by using the very technology that writing communities had already feared online for months.
The giant in this story is not the size Yamamoto imagined. The tech industry told itself it had “smitten a sleeping enemy” — the phrase attributed to him after Pearl Harbor. But the giant was awake the whole time. Now publishing trembles, and the rest of the creative class is on watch. An industry that once ran on trust now runs on suspicion. The cancellation of Shy Girl further validates that you can’t always believe what you read. It has also left writers constantly questioning whether they will someday need to prove that their words came from the soul.
One is winning by force of authority. The other is rebuilding the craft argument on first principles.
proof by the machine.
the tool, not the master.The pragmatist’s answer.
For hardliners among authors and publishers, transparency and disclosure are the only acceptable terms for working with generative AI. Suspicion runs through the entire supply chain: editors use the technology, authors use the technology, and no one trusts anyone else to say so, though the Shy Girl case is beginning to change that.
This duck-and-cover environment of paranoia is not unlike the climate that sustained the Malleus Maleficarum. Just as the inquisitors hunting witches believed they were eviscerating “hideous dangers” in an “eternal conflict of good and evil,” the publishing industry has framed the infiltration of AI not as a technological improvement, but as a moral contagion.
Desperate to fight back, some in the industry — especially literary agents — give any use of AI a hard “no,” while publishers are beginning to consider, or use, AI detectors as their own “standard text-book” and “supremely authoritative practice.” In their zeal, they have chosen to overlook the “bias” and “plain faults” inherent in these tools. Even the suspicion that an author’s manuscript is AI-generated makes authors fear immediate rejection.
Protecting their reputation has become their raison d’être. Some authors run their own manuscripts through detectors out of curiosity; others do it out of fear. The latter are more likely to rewrite until they break the statistical patterns AI classifiers detect. Authors who lack a technical understanding of how detection works are the least equipped to defend themselves. Their arguments rest on moral grounds, or they redirect the conversation to the environmental cost of AI-powered data centers. Others disengage entirely.
In the O.J. Simpson trial, the football star tried on a glove that the defense knew wouldn’t fit. When publishers confront authors about their use of AI, authors are not applying the same logic as Simpson’s defense team. They are not asking publishers to try on the glove. They are not asking how their AI detector works, what it measures, or what score counts as proof that a manuscript was not
written by a human. By and large, they do not understand the technology well enough to formulate a coherent question.
That is understandable; try explaining temperature or few‑shot prompting to someone who's never used AI. Their argument has emotional power nonetheless. AI is a crutch, they assert; any generative use forfeits authorship. True writing lives in the struggle of creation. Wrestling with plot, character, pacing, and subtext in dialogue is what makes a writer a writer, and outsourcing any part of that struggle to a machine means the work was not done.
One author framed it as “skull sweat,” the cognitive labor of solving creative problems is where authorship lives. If a machine solved the problem, “the book is not yours.” Another author split writers into two groups — those who study the craft, versus “content generators,” the ones who optimize and produce for volume. He said the “generators” operate in a separate profession and market, and the public is taking notice.
The position resonates: it defends writing as an art form. Storytelling originates in Aristotle’s Poetics, a foundational work of literary theory written about 335 years before the birth of Jesus. All fiction frameworks inherit Aristotelian theory: he gave us the elements of a story — plot, or mythos — and the principle that stories have a beginning, middle, and end.
In the 19th century, Germany’s most popular author and critic Gustav Freytag formalized dramatic structure into a five-part pyramid: exposition, rising action, climax, falling action, and denouement. Modern fiction how-to books, such as Jessica Brody’s Save the Cat! Writes a Novel, repackage Freytag’s pyramid as fifteen story “beats” borrowed from Blake Snyder’s screenwriting framework. Nonfiction craft books, such as William Zinsser’s On Writing Well, descend from the rhetorical tradition, which begins with Aristotle’s Rhetoric and emphasizes clarity, voice, thesis, and the writer’s relationship to truth and audience rather than dramatic structure.
Understanding theory, however, is different than applying it. Writing effective scenes with action and dialogue with subtext, knowing when too much backstory becomes an information dump, and developing characters that readers care about are difficult skills to master. The fear that a machine could replace a human brain’s creativity threatens their identity and livelihood. The hardliner position is not cynical; it is protective. It defends a value system. But the technical claims that underpin it are wrong.
Outnumbered, outranked, and out-platformed.
On the other side of the divide are authors who use AI as a tool. One described using it for brainstorming at 2 a.m., when no human collaborator was available. He used the word “mask” in his prompt, more as a seed than as a story conceit, and built a story around what came back. He acknowledged that AI-generated ideas tend to gravitate to the statistical center of a language model’s training data. He acknowledged that the writer’s task is to take that raw material and push it somewhere the model wouldn’t go on its own.
Another author occupied a distinct position — a rare participant who both understood the technology at a technical level and was traditionally published. She explained AI detection concepts like perplexity and burstiness, and posted an AI copy edit of her own writing alongside the original, making the process visible rather than obscuring it. Her contributions were instructional rather than polemical. They did not gain the traction that posts dismissing AI-generated text as AI slop did.
The pragmatists are not seeking permission to use AI without accountability. They only want a standard that doesn’t berate them for using AI — so long as they stay within the de minimis threshold — versus those who generate books and use the output verbatim just to make a buck. For now, the pragmatists are outnumbered, outranked, and out-platformed.
A consistent pattern emerges across the debate: authors who reject AI most categorically are often the most likely to describe it inaccurately. Those who describe it with greater technical precision are more likely to use it — or at least to engage with it directly.
The pattern is directional, not absolute: engagement does not guarantee understanding, and rejection does not imply ignorance. However, the correlation is strong enough to matter, particularly because those with the least technical knowledge are often advancing the most expansive policy claims. The concern is that professional organizations may adopt these positions because they are emotionally resonant and culturally legible, even when they are technically unsound.
The hardliner case rests on three claims about how AI works. The data and the institutional record refute each one.
The claim that AI models are trained on “the slop of the internet” misrepresents how they are built. Stanford’s 2025 AI Index estimates the open web at roughly 3,100 trillion tokens, while curated training datasets, such as those derived from Common Crawl, are far smaller, with a median of about 130 trillion. In practice, training data is filtered and curated, not simply scraped wholesale from the web.
The claim that AI produces generic content oversimplifies how the latest models operate. Advances in systems such as OpenAI’s o1 and o3 have raised the baseline quality of generated output by using additional compute to reason through responses more thoroughly before delivering them. Even so, prompt precision remains a key factor in achieving nuanced, non-generic results.
The zero‑tolerance stance of some traditionalists departs from established norms. The Committee on Publication Ethics (COPE), a leading authority in academic publishing, permits AI use with full disclosure of the tools and methods involved. Under this framework, the ethical burden shifts from abstinence to accountability. Mainstream publishing has yet to adopt a comparable standard.
From Sarony's No. 18 to the U.S. Copyright Office's AI guidance — a tour of how U.S. law is now re-answering, the question: Who counts as an author?
How a velvet‑coated aesthete, a theatrical photographer, and a pirated lithograph led to authorship under U.S. copyright law.
After that, Sarony discovered that Ehrich Bros., a nearby dry goods store, was giving away No. 18 as a trade card to sell hats. When he traced the unauthorized reproductions to Burrow-Giles Lithographic Co., he sued for copyright infringement in the U.S. Circuit Court for the Southern District of New York. By that time, Burrow-Giles had already reproduced and sold around 85,000 copies to a Chicago department store.
In court, Burrow-Giles challenged the constitutional power of Congress to extend copyright protection to photographs when lawmakers amended the copyright law in 1865. The company also argued that taking a photograph was a button-pushing exercise, not creative expression. The court rejected both arguments and entered a $610 judgment for Sarony. The image qualified for federal copyright protection, the court held, because Sarony created it “entirely from his own original mental conception.”
Burrow-Giles took the case to the Supreme Court on a writ of error. The Court rejected the company’s arguments and held that copyright could protect a photograph when it embodied the author’s “original intellectual conception.” The decision handed Sarony a landmark victory on March 17, 1884. Later commentary often connects the Court’s reasoning to the “master mind” principle associated with the 1883 English case Nottage v. Jackson.
The Burrow-Giles decision distinguished copyrightable intellectual creations from patentable inventions and rejected the argument that photographs were mechanical reproductions. The Court concluded that the constitutional term “writings” is not limited to books or manuscripts but also includes visual forms such as prints and engravings. Photographs, the Court reasoned, can fall within that category when they embody the author’s “original intellectual conception,” as they did with Sarony’s No. 18.
Two Supreme Court cases in 1884 and 1991 set the floor for AI copyright arguments.
An 8-year fight to register a machine’s output ends where it began — with the human authorship requirement intact.
Thaler’s “Creativity Machine” produced an entrance to paradise — but the U.S. Copyright Office requires a human soul to enter.
A graphic novel, a Midjourney prompt, and the Copyright Office's first major line in the sand on AI-generated art.
Knowing the magic words is not the same as holding the brush.
In The Sorcerer’s Apprentice, a young magician’s assistant bewitches brooms to do his chores and fetch his water, only to discover he has unleashed a force he cannot stop. As the room floods with water, the frantic apprentice learns that initiating magic is not the same as mastering it.
Zarya of the Dawn is a modern, legal parallel. Like the apprentice, New York artist Kristina Kashtanova conjured something she couldn’t fully control when she generated the sci-fi book’s images with Midjourney, using what she described as “hundreds or thousands” of prompts. The AI did the labor, but the results emerged from a process beyond her mastery.
When the U.S. Copyright Office — acting as the returning sorcerer — learned of this from a Wall Street Journal reporter’s inquiry, it canceled her Sept. 15, 2022 copyright registration and applied the law to exclude the Midjourney images. It protected the compilation, the arrangement, and the text she wrote about Zarya, a non-binary protagonist transported to another world who reflected her own grief after losing her best friend and grandmother.
Her law firm, Taylor English Duma LLP, mounted a robust defense in an 11‑page letter to the copyright office, dated Nov. 21, 2022.
The firm defended Kashtanova’s authorship claim by documenting her iterative creative process, including examples of the “hundreds of versions” of images she produced, and argued that she satisfied copyright protection by meeting the “modicum of creativity” standard under Feist Publications, Inc. v. Rural Telephone Service Co.
The Copyright Office rejected the argument. Because Midjourney generates images from noise in a mathematically unpredictable manner, it ruled that the human user lacked the “master mind” control copyright law requires — the principle that “the author is the person who has actually formed the picture — the inventive or master mind.” The office concluded the work did not contain enough original human authorship to sustain the claim.
A blue‑ribbon image, a refused copyright, and the question of whether help has become authorship.
Plate XIII. Théâtre D’opéra Spatial — The report author's reinterpretation of Jason Allen’s Midjourney‑assisted image as a video, generated in Google's VEO 3.1. The author also produced the image that overlays on top of the video with OpenAI's DALL·E 3 model. Allen's image (see pag 27) won first place at the 2022 Colorado State Fair digital‑art category.
Once upon a time, in the kingdom of Pueblo West, Colorado, a game designer named Jason Allen sought to enter a new kind of magic when he submitted an entry in the digital art category of the annual Colorado State Fair Fine Art Show in 2022. He summoned a mysterious, non‑human helper named Midjourney, an AI image‑generation tool.
In Rumpelstiltskin, the miller’s daughter is locked in a room and commanded to spin gold from straw. Allen faced the same fairy tale problem: transformation. But in Allen’s case, the helper was also the loom, the spell, and the disputed hand, responding to his prompts until the image emerged: Théâtre D’opéra Spatial, French for “Space Opera Theater.”
The State Fair judges were enchanted. They awarded Allen a blue ribbon and a $300 prize. His win infuriated traditional artists. In The New York Times, Allen defended his work. “I’m not going to apologize for it,” he said. “I won, and I didn’t break any rules.” He said he titled his submission “Jason M. Allen via Midjourney.”
“Mr. Allen did not ‘exercise any control over the actual creation, development, or execution of the image that Midjourney rendered on his screen.’”— U.S. Copyright Office
Victorious, Allen sought the ultimate royal decree to protect his gold. He file for federal copyright protection on on Sept. 21, 2022. He identified himself as the sole author and didn't disclose that he'd used artificial intelligence.
The High Magistrates of the Copyright Review Board knew of the invisible helper because of news reports. When the examiner him asked about it, Allen described his process, writing at least 624 prompts, to revise the image.
Photographed by Kevin J. Beaty/Denverite. Jason Allen and his AI‑assisted illustration, “Théâtre D’opéra Spatial,” in Denver’s Pugasus Studio. Sept. 2, 2023.
He also said he used Photoshop to remove flaws and create “new visual content.” The examiner asked Allen to exclude the Midjourney‑generated features. Allen refused. In response, the Copyright Office refused the registration. And that's where Zarya of the Dawn and part ways. In Zarya, the artist accepted the copyright office's ask to exclude Midjourney's AI-generated image. Kashtanova left it at that.
The “Traditional Elements” Principle. In Allen's case, the copyright office invoked the “traditional elements of authorship” standard from its 1965 annual report. The office developed the standard in response to a “technological blitz” of new storage media, including magnetic tape and punched cards used to program computers.
The 1965 report framed the central question this way: Is the work human authorship, with the computer acting as an “assisting instrument,” or were the “traditional elements of authorship” — literary, artistic, or musical expression, along with elements of selection and arrangement — actually conceived and executed by a machine?
Four unique Midjourney outputs generated from the identical prompt: "white bunny with low ears, rainbow background, love, cute, happy." The U.S. Copyright Office submitted these images in federal court as visual evidence that AI expresses the idea, not a human.
Allen’s legal team criticized the office’s reliance on this standard when it rejected Théâtre D’opéra Spatial. They argued that it carries no real legal weight because the test appears nowhere in the Copyright Act, the Constitution, or any binding Supreme Court decision. Instead, they said, it exists merely as the 1965 “musings” of a former register of copyrights.
While Allen’s team fights to prove the “magic” was his own, the story remains caught in the spindle of the federal court system, with no ruling as of May 7.
hen the kingdom’s courts refused to crown Théâtre D’opéra Spatial with copyright, some petitioners proposed a different decree: sui generis protection, a specialized intellectual property right built for AI-generated works, with its own term, its own rules, and its own royal ledger.
If Rumpelstiltskin’s gold fails the test for traditional treasure, perhaps it deserves its own seal. A sui generis regime could set shorter protection terms, name a rightsholder, and draw a line between human direction and machine execution.
The High Magistrates were not persuaded. After a broad public inquiry, the U.S. Copyright Office concluded that “the case has not been made” for either copyright expansion or sui generis protection for AI-generated content. The kingdom’s existing law remained adequate. What it lacked was a petitioner who had done the spinning.
History offered its own warning. Congress passed the Semiconductor Chip Protection Act of 1984, a sui generis decree for a specific technology, and within two decades the technology had outrun the law. What remained was a set of rules for a market that had already moved on.
Most public commenters told the Office not to crown the straw. In response to the Copyright Office’s August 2023 Notice of Inquiry, more than 10,000 voices entered the record, and roughly half addressed copyrightability directly. They considered whether Congress should clarify the human authorship requirement or create a separate sui generis right for AI outputs. The overwhelming answer was no: existing law was adequate, human authorship remained the constitutional line, and machine-made gold should not receive its own royal seal.
For now, the spindle keeps turning, and the question remains in motion: what is the gold worth, and who can claim it?
As of May 2026, no published, high-visibility U.S. court case directly parallels Thaler or Allen in the context of AI-generated or AI-assisted prose, whether fiction or nonfiction.
A look at the technologies, how they work, what they measure, and where they fail, and why a probability score has come to function as a verdict.
Two forces collide. Generative AI becomes the breach too big for a finger to hold back.
A Princeton computer science major coded the first AI detector app in late 2022. The next year, an emerging authenticity economy forced publishing to redefine its rules.
Founder, GPTZero · Princeton, computer science.
Photo: X Profile
The public release of OpenAI’s ChatGPT on Nov. 30, 2022, marked a cultural and technological turning point comparable to the iPhone’s debut in 2007. ChatGPT handed a baton to anyone connected to the Internet — not to conduct an orchestra, but to extend their thinking with a large language model that could write.
One month after ChatGPT’s release, Edward Tian, a 22-year-old Princeton computer science student, built GPTZero while on winter break in Toronto. When it went live on January 2, 2023, Tian was not prepared for the response. Neither was the server that hosted it. The server buckled under the load of 30,000 users in a single afternoon.
Today, the AI detector market has become an essential layer of the “authenticity economy,” now led by a dozen-plus firms, including GPTZero, Turnitin, and Originality AI.
AI firms positioned themselves as arbiters of authorship amid a growing online backlash in which authors condemned any use of AI. At the same time, many of these firms developed “humanizers” that helped users evade the statistical patterns their own detectors were trained to identify in AI-generated text, allowing the companies to profit from both sides of the market.
The message is sticky because “AI-generated” functions the way “witch” once did: it reduces a complex question to a verdict. Once the label attaches to a book as AI generated, the author’s defense becomes the story, not the work.
For three decades, the publishing industry had been ceding ground to ebooks, digital platforms like Substack, and a web increasingly saturated with free content. The arrival of gen AI in November 2022 sharpened this pressure to a breaking point.
The publishing industry now faces its own crisis of abundance: a tsunami of AI-generated books. In his January 7 eNeuro editorial, “AI-Generated Scientific Papers: Crisis? What Crisis?,” Christophe Bernard borrows his title from Supertramp’s 1975 album and warns that fake, AI-generated scientific papers — mass-produced by paper mills — may constitute “arguably the largest science crisis of all time.” The threat is not merely volume, but trust. When counterfeit research floods the record, public confidence in science begins to collapse. Bernard calls for a structural overhaul of the scientific ecosystem to confront a catastrophe hiding in plain sight.
Publishers and distribution platforms are tightening their rules. In April, Draft2Digital announced a $20 fee to activate new accounts and a $12 annual maintenance fee, citing a surge in “automated and low-quality” content and a rejection rate of 40%–75%. Barnes & Noble Press also updated its policies, setting a $14.99 minimum base cost for paperbacks to deter low-margin spam, limiting accounts to 100 titles, banning public-domain works, and reserving the right to remove books at any time.
A taxonomy of the four technologies behind the score.
A 2008 nonfiction work from a then-Big Six publisher got wildly different scores in 2026: one test said 100% human, the other 86% AI.
The Malleus Maleficarum was was like a standard operating procedure for prosecuting suspected witches, setting the rules to try, convict, and condemn the accused. With AI detectors, there is no single manual — just five main methods.
Statistical Intuition (Zero‑Shot): These tools spot machine writing “on sight” by looking for mathematical patterns. Without needing prior training on a specific AI model, they measure how predictable the word choices are (perplexity) and whether the sentence lengths change enough to feel human (burstiness).
Style Analysis (Stylometric Analysis): The method looks beyond raw numbers and focuses on a text’s “accent.” It analyzes how words, grammar, and sentence structures are put together, checking whether the patterns match the repetitive transitions and stylistic habits AI models favor.
Adaptive Classifiers (Few‑Shot): These quick‑learners study the writing style of new or updated models from just a “few” samples, then take a “shot” at spotting that specific model’s AI‑written “accent” in the wild.
Hidden Signals (Watermarking): Some AI companies “watermark” their output by giving the model a secret rule or mathematical key to follow when it picks words. To a human, the writing isn't bland, but a detector with the matching key can often spot it as AI‑generated.
LLM‑as‑a‑Judge (The Sniffer): The newest method uses a powerful AI to detect a weaker one. It essentially asks a model like GPT‑4, “Does this sound like something a machine would say?” and relies on that model’s internal sense of what typical AI‑style writing looks like.
AI detectors look for mathematical patterns associated with the way LLMs generate language. The two signals that do most of the work are perplexity and burstiness.
A ParableSnow White wrote with care and clarity. Yet the Evil Queen’s magic mirror, trained on patterns in human and AI content, declared her scroll 73% AI-generated, even though it didn’t have a clue how Snow White wrote it or know anything about the scroll’s history. The mirror only saw statistical patterns in sentences and word choices. The high score was just an educated guess. Nonetheless, the Queen thought otherwise and mistook it for truth, revealing a fundamental flaw in how humans understand AI detectors. Unless users learn how the technology works, they may not realize that detecting patterns doesn't detect authorship.
— WHAT DETECTORS ARE TRAINED TO SEE
Variation in sentence length and rhythm.
How surprising the next word is to a model.
Two more detector families round out the field. Understanding how they work explains why the false‑positive problem is structural — built into the measurement itself.
When a detector flags academic writing as AI, it is not broken. It is working exactly as designed — on the wrong question. The tool identifies patterns.
No industry definition. No threshold. No governance. Part IV names the vacuum at the center of publishing’s AI moment — and what it costs everyone operating inside it.
A 1968 demonstration in San Francisco previewed the dual model of human and machine intelligence. Publishing hasn't really accepted it yet.
Twenty-five days after Martin Luther King Jr.’s assassination in Memphis ignited riots in more than 100 U.S. cities, the revolutionary rock musical Hair premiered on Broadway at the Biltmore Theatre on April 29, 1968. It pronounced a new age in American culture, even as the country was convulsed by political violence, protest, and social unrest.
Seven months later, on December 9, Dr. Douglas Engelbart of SRI International stepped onto a different kind of stage. Before 1,000 professionals at the Fall Joint Computer Conference in San Francisco, he unveiled technologies from his Augmentation Research Center, a project devoted to expanding human intellect through computers. The demonstration would later be remembered as a turning point in how humans interact with machines.
On a 22-foot-high screen borrowed from NASA, Engelbart projected a new device: the computer mouse, so named because its cord resembled a tail. He showed the audience how clicking on hypertext links could jump directly to source documents. He also demonstrated screen sharing, using the system to collaborate in real time with a colleague in Menlo Park.
After the 90‑minute presentation, Engelbart and his team received a standing ovation. The Redwood City Tribune called it an "unusual demonstration." A 1994 book gave it a larger name: “The Mother of All Demos.” Engelbart had not only introduced new tools but also staged a new model of intelligence, one that paired human thought with computing power at a time when the IBM Selectric typewriter remained the height of office technology.
Despite the audience’s applause, the mainstream computer science community largely shrugged it off. While Engelbart imagined a symbiotic relationship between humans and computers, the field still viewed computers as punched-card number crunchers.
Engelbart was not alone in seeing computers differently. In 1960, J. C. R. Licklider proposed “man-computer symbiosis,” a framework for thinking about humans and computers as collaborators. Two years later, Engelbart extended that idea in his work on augmenting human intellect, arguing that computers could help people think, organize, and solve problems in ways they could not manage alone.
But even before Licklider and Engelbart, W. Ross Ashby had helped define the problem. Ashby, an English psychiatrist and early figure in cybernetics, studied systems, control, and complexity. In Design for a Brain (1952) and An Introduction to Cybernetics (1956), he argued that machines could help people handle problems too complex for humans to solve on their own.
He also introduced the Law of Requisite Variety, which holds that controlling a complex system requires tools complex enough to match it. In other words, you can't manage complexity with something too simple.
That idea sits at the center of hybrid intelligence. Humans and machines bring different strengths. Humans supply judgment, context, values, imagination, and lived experience. Machines supply scale, speed, pattern recognition, and computational reach. Put together, they create something neither can achieve as effectively alone.
The concept has continued to evolve. Dr. Cornelia C. Walther, a Wharton visiting scholar, has applied hybrid intelligence to the A-Frame, a model that integrates AI into organizations. In a March 11, 2025, article in Knowledge at Wharton, Dr. Walther describes its four dimensions: aspirations (what we want); emotions (how we feel); thoughts (how we reason); and sensations (what we experience through the body and senses). These dimensions operate at both the individual and group levels.
The larger aim remains augmentation: using human and machine intelligence together to make people more capable. Combining the dimensions is meant to make human beings more capable. Ashby gave the field a logic of complexity, while Licklider gave it a model of partnership. But it was Engelbart combined their views in his demonstration. Today’s AI is revisiting that vision, forcing the same question in a new form: Will we use machines to replace human judgment, or to extend it?
Value Human Complexity: Recognize the emotional intelligence, stylistic variation, and deliberate linguistic choices that distinguish human authorship. Writers bend syntax for pacing, rhythm, and voice; AI tends toward more uniform prose. That human complexity is central to copyright’s idea of an “original work of authorship.”
Respect Collective Goals: Respect intellectual property, acknowledge concerns about AI training on copyrighted work, and follow AI use and disclosure rules.
Map Natural Intelligence (NI) Dimensions: Systematically analyze how human intelligence manifests in the creative writing process. At the individual level, the process includes recognizing the author’s unique voice, lived experiences, emotional depth, and distinct aesthetic vision. At the collective level, it involves understanding publishing industry norms, reader expectations, and ongoing ethical debates involving AI.
Embrace Hybrid Systems: Treat AI as a tool that augments, rather than replaces, human authorship. Use it for feedback, perspective, or structure, while keeping any AI‑generated final expression de minimis.
Promote Iterative Learning: Refine AI use through continuous feedback while preserving the core creative process. Maintain version histories and drafting artifacts to document creative evolution and help substantiate authorship if questioned.
Establish Ethical Governance: Take responsibility for AI-assisted work, following publisher disclosure policies, and disclosing AI-generated content when it exceeds de minimis thresholds. Identify the specific AI models and services used, such as Claude Opus Adaptive 4.7, and explain their purpose. Maintain transparency with readers while avoiding unnecessary technical detail.
The court is called to order. A narrative chronicle of the AI infringement copyright wars — and the landmark cases now defining the legal fate of human creativity.
The tale began in the quiet Houses of Creation, where the human mind’s creative spark filled libraries and bookstores across the land. These libraries of human creativity were the Creators’ birthright, guarded by the old magic known as federal copyright law.
Soon, the AI Empires rose, driven by a relentless hunger to capture subscribers of every ilk. Each Empire had its own Master Plan. Though proprietary, they worked similar ways. The Empires wrote Great Algorithms to define the logic — the hidden rules — that guided the Fairies in their Data Centers to steal millions of human treasures, using scrapers.
The scrapers moved like hands, reaching deep into vast Shadow Libraries at their master's command. Then the Master Plans summoned another set of instructions, directing the Pirated Torrent protocol to siphon away millions of human treasures — books of every conceivable genre, journals, lyrics, and other creative works.
A core legal issue split: It’s no longer about what the AI creates, but whether the data used to train it was legally obtained.
These were gathered and sealed within the internal vaults of the Unseen Folk of the Cloud. Within each vault, the stolen treasures lay cradled, gently rocking in the dark — until the Empires made their next move, the placement of a Changeling, a large language model, an LLM, that would eat them.
The Changeling was large and strange, with a computational head and eyes that could see all the world’s text. It could mimic the voices of stolen human creative works, so long as a user’s prompt was written just so. The Changeling never tired, and it answered nearly any query, depending on how its AI Empire adjusted its temperature and weighted the millions of parameters that
The $1.5 billion resolution in Bartz v. Anthropic PBC shattered the perception that tech giants could absorb data-scraping suits as a routine cost of doing business — and reshaped courtroom strategy across every open case.
From “Training” to “Torrenting & Retention”
Judge Alsup’s summary judgment bifurcated the defense. Training on lawfully acquired text may clear the fair-use hurdle — but the standalone act of torrenting, compiling, and permanently retaining pirated files in a corporate library is not fair use. Plaintiffs now use aggressive discovery to demand internal dataset logs proving acquisition from shadow libraries like LibGen, Sci-Hub, and BookCorpus.
A Concrete Financial Benchmark
Anthropic’s settlement set a brutal, mathematically sound baseline: roughly $3,000 per copyrighted work. Because rival models trained on far larger scraped corpora, plaintiffs now leverage that figure in closed-door mediation — forcing defendants to weigh catastrophic, multi-billion-dollar exposure at a jury trial.
The Collapse of the Omnibus Class Action
A massive payout did not cure the legal headache. Because the framework offered only fixed, standardized amounts, dozens of authors opted out to launch high-leverage individual jury suits seeking the statutory maximum of $150,000 per willful violation. Rightsholders realized they wield more power by threatening fragmented, highly specific trials than by joining one clean class.
Accelerating B2B Content Licensing
With unlicensed, pirated repositories now legally radioactive, the developer’s safe harbor has vanished. The immediate risk has pushed AI firms to pivot toward legitimate licensing pipelines — securing multi-million-dollar data partnerships with legacy publishers, media houses, and financial entities to insulate future models.
On May 20, Judge Araceli Martínez-Olguín, handling the $1.5 billion Bartz class settlement, ruled that two newer copyright suits against Anthropic — Chicken Soup for the Soul v. Anthropic and Cognella v. Anthropic — are not related to Bartz. Each case will be reassigned to a different judge and proceed as a separate dispute rather than as an extension of the settled class action. She has not yet formally signed off on the $1.5 billion settlement, but final approval is expected.
Cruz v. Anthropic
Filed May 13, 2026Novelist Angie Cruz, joined by 27 Bay Area authors including Pulitzer finalist Dave Eggers. Same factual foundation — books scraped from pirated shadow libraries like LibGen — but they bypass the class structure entirely, arguing the ~$3,000-per-book settlement grants Anthropic a cheap pass to keep exploiting their work. They pursue direct, vicarious, and contributory infringement, seeking up to $150,000 per willful violation by jury.
Shakespeare v. Anthropic
Filed June 17, 2026London author Thomas William Shakespeare and 99 others sue Anthropic, CEO Dario Amodei, and co-founder Benjamin Mann in the N.D. Cal. — the 10th major copyright suit against the company. Its technical pivot builds on Alsup’s ruling: rather than relitigate whether training is fair use, it targets the discrete step of acquisition and retention — arguing that torrenting and compiling unlicensed files into a “central library” is standalone infringement, regardless of any training that follows.
The complaint characterized Anthropic as an “AI Colossus” and part of an “arms race” to build “generative intelligence at superhuman scale.”
De minimis — short for de minimis non curat lex, a common law principle federal courts have applied for more than 150 years — holds that the law does not concern itself with trivial matters. The term does not appear in the Copyright Act of 1976 or in any congressional amendment enacted through December 2025. It enters AI and publishing through case law and U.S. Copyright Office guidance.
In the second of its three reports on AI, the copyright office explained de minimis use in the context of copyright registration. Its guidance instructs applicants to disclose the presence of AI-generated material and the extent of their human-authored contribution, though it explicitly does not require a detailed accounting of the exact proportion or amount. The office identifies AI-generated material as de minimis if the contribution is so trivial that it would not independently support a copyright claim had a human created it.
The Authors Guild relies on this same de minimis standard for its “Human Authored” self-certification program. The Guild treats AI-powered grammar and spelling edits, brainstorming, and outlining as permissible. More extensive AI editing is harder to classify. What disqualifies a work from certification is using AI to generate the text itself “beyond a de minimis amount.” The Guild sets a de minimis threshold of 5%. For that reason, the Guild advises authors to consult a lawyer.
Model clauses offered by the Authors Guild address four areas: barring AI use of an author's work without consent; treating specific AI uses as subsidiary rights subject to licensing and compensation; protecting audiobook and translation rights from unapproved AI use; and setting terms for both the author's use of AI in the manuscript and the publisher's use of AI in connection with the work. Authors and agents can request the clauses; publishers can adopt them.
No industry standard defines AI-assisted versus AI-generated content, or the threshold at which a copy edit becomes substantive revision. The Authors Guild’s 5% threshold is the only attempt to operationalize the line. The Guild’s certification relies entirely on an honor system. No forensic audit exists for books, nothing comparable to the governance, risk, and compliance framework used to verify security and privacy controls in, say, a federal information system. Even if it did, keeping track of every edit and revision beyond what Microsoft Word offers in tracked changes would be burdensome.
Added AI clause to model contracts for trade books and translations, covering LLM training rights.
Updated contracts with four new clauses — AI translations, narration, cover art, and author disclosure. Introduced the 5 percent cap on AI-generated text.
Launched fee-based “Human Authored” self-certification program for members.
Human Authored program expanded to include any author with a book published in the United States.
The Authors Guild’s “Human Authored” self-certification seal, available since January 2025 to Guild members and expanded to all U.S. authors in March 2026.
Author's Use of AI
Author shall not be required to use generative AI or to work from AI-generated text. Author shall disclose to Publisher if any AI-generated text is included in the submitted manuscript, and may not include more than [a de minimis/5%] AI-generated text.
Three references for the working author, publisher, and editor: a glossary of technical terms, a survey of detection tools, and a quick reference for copyright registration.
Read this before you trust a score. Every tool here models surface patterns and guesses; none reads intent. On independent 2026 benchmarks most cluster near 80% accuracy, with Pangram the research-backed outlier. False positives remain the rule, not the exception — Scribbr’s free tier flags roughly one human passage in eleven, and plain, formal, or edited prose draws the most false flags. Read any score as one signal, not a sentence.
The U.S. Copyright Office requires applicants to disclose AI-generated content when it exceeds the de minimis threshold. AI-assisted works (where a human author used AI as a tool but made the creative decisions) may be registrable for the human-authored portions.
The Authors Guild characterizes AI content below 5% as de minimis for purposes of copyright registration disclosure. This figure also appears in the Guild’s model contract clause capping AI-generated text. These are two distinct instruments with different legal consequences.
Register at copyright.gov using Form CO (online) or Form TX (text works). For works containing AI-generated material, use the “Limitation of Claim” section to exclude AI-generated portions and identify what human-authored content you are claiming.
To register an AI-assisted work, you must be able to identify and describe the human creative expression you contributed. Selection, arrangement, editing choices, and original additions all qualify. A prompt alone does not.
How the Author Wrote the New Malleus
The author leveraged AI extensively in the creation of the special report. The three AI categories involved design, artwork, and written content. The author also used her judgment to decide whether to use or edit the output in each category.
1. DESIGN: Claude Design created the layout in HTML. Using HTML was a deliberate design choice. Its performance varied from a rating of 1 to 5 stars. Sometimes, Claude Design made expert choices; at other times design execution was poor. Claude Design is also token‑intensive, and its usage is billed apart from using Claude in a chat window.
2. ARTWORK: The artwork included generating images in DALL·E 3, which was spot on for consistency. Midjourney generated two images. Other images were either public domain or attributed. Tools included HeyGen to create talking avatars, ElevenLabs to generate voices, Claude Opus 4.7 to write text‑to‑image prompts, Veo and Magnific for videos, and CapCut to edit and crop them.
3. WRITTEN CONTENT: AI services included Claude Opus 4.7 and Sonnet 4.6, Gemini Pro, Perplexity, and the latest ChatGPT, custom GPT engines. Purpose: deep research, outlining streamlining overflowing content, and copy editing. The author did not use AI to fact‑check; she either located the primary sources or asked subject‑matter experts. AI‑generated content was reserved mainly for definitions, and even in those cases the author revised the text.
