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When Microsoft design executive Jon Friedman and his son Judah began making Max & The Purple Anxiety Monster, they expected to print a few copies for family. Instead, the project became a published children’s book—and a revealing example of what generative AI can and cannot do in a creative workflow. Judah supplied the personal story; AI helped explore its structure and generate illustration ideas, while people made the editorial, artistic and production decisions.
A school assignment became a family story
Judah Friedman was 13 and in seventh grade when his family moved from Seattle to Chicago. A school writing assignment gave him a way to describe the nerves that came with leaving familiar surroundings and facing the prospect of making new friends. In the account published by GeekWire on March 12, 2024, Judah said writing about his feelings helped get them “out of my system.” That is a report of his experience, not evidence that writing served as clinical treatment.
The story became Max & The Purple Anxiety Monster. Its central character, Max, faces the upheaval of a move to Chicago. A purple monster gives visible form to his anxious thoughts, following him through scenes such as an airport trip and his arrival in a new bedroom. The monster begins to recede when Max befriends a neighbor, Ethan, and feels accepted.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThat plot offers children a way to talk about fear and connection. It is a story, not a diagnostic tool or a substitute for professional mental-health support.
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Two AI tools, two different jobs
Jon Friedman, whom GeekWire described at the time as a Microsoft corporate vice president leading design and research, saw a way to help turn Judah’s classroom story into a picture book. The family initially intended to make only a few copies—one for home and two for the grandparents. Illustrating a whole book by hand felt daunting, so Friedman used generative AI to make the project more approachable.
The reported workflow separated text exploration from image-making:
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- ChatGPT helped explore story structure. The original story was run through the chatbot to consider how it might work as a children’s book, including questions of pacing and page count. Judah and his family then edited and refined the language; the report says Judah’s mother and therapist also took part in that process.
- DALL-E generated candidate illustrations. Friedman directed the system toward a watercolor look and used it to explore scenes and compositions.
- Friedman selected and corrected the artwork. He generated more than 1,000 images and chose roughly 20 for the finished book. He also used Photoshop to clean up details, including some skin-tone adjustments.
Those numbers are a useful reminder: generating an image is not the same as finishing a book’s illustrations. The AI provided material to review, not a ready-made sequence of final art.
The hard part: making Max look like Max
Across a picture book, readers need to recognize the same character from one scene to the next. In the process GeekWire reported, DALL-E did not reliably preserve Max’s appearance. His apparent age, hairstyle, clothes, skin tone, eye color and facial features could change between images. Friedman tried iterative prompts—for example, asking the system to make Max “a little older”—but the next result could overshoot and make him look too old.
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For young readers, those inconsistencies can break emotional continuity as well as visual flow. If a child’s face, age or clothes keep changing, the pictures can distract from the story or make the book feel unfinished. A pile of attractive standalone images is not yet a coherent illustrated book: someone still has to establish a visual direction, compare every scene, reject mismatches and repair what can be repaired.
That is where the human contribution becomes clearest. Judah brought the experience and initial story. Friedman directed the images, tested prompts, reviewed a large volume of output and made manual edits. Family members and a therapist helped refine the text. The family chose what belonged in the book. It is more accurate to call the result human-authored and human-directed, with AI used for structural experimentation and image generation, than to call it an AI-created book.
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From private keepsake to publication
The project’s public life came about unexpectedly. Greg Shaw, an author associated with Microsoft CEO Satya Nadella’s writing and founder of Clyde Hill Publishing, became interested in Judah’s story and the AI-assisted illustrations. His involvement helped turn the family’s small private project into a published title.
GeekWire reported plans at the time for distribution through booksellers, school and neighborhood libraries, and bookstores in the Chicago and Seattle areas. Those were plans reported in March 2024, not confirmation of current availability or distribution. The book was a family project; Friedman’s Microsoft role does not make it a Microsoft publication or an official company project.
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What this case shows about AI-assisted creativity
For this family, image generation lowered the barrier to trying a project that might otherwise have felt too large. It made it possible to explore many visual possibilities without first hiring an illustrator or mastering painting techniques. ChatGPT offered another way to experiment with shaping a classroom story into picture-book form.
But lowering the barrier is not the same as eliminating the work. The high image count, the character-continuity problems and the Photoshop corrections show the trade-off: rapid variation can come with significant selection, consistency and retouching demands. The process suited a motivated family project led by someone with design experience and a willingness to iterate. It does not prove that image generators can reliably produce polished children’s books at scale, or that they are a replacement for illustrators.
Nor does one 2024 account settle questions about commercial rights, disclosure, publisher policies or the terms of any image-generation service today. Those details depend on current terms and the specific publishing route; they should be checked separately rather than inferred from the tools used for this book.
A practical checklist for creators
- Begin with the human story. Decide what experience, point of view and meaning the book needs to preserve before asking a model to reshape it.
- Use AI for exploration, not automatic approval. Treat suggested structures and generated pictures as drafts to assess, not finished editorial or artistic decisions.
- Keep a character reference. Record a character’s age, appearance, clothing and other essential features, then compare each candidate image against it.
- Budget for iteration. Expect to generate, reject and revise many images. Make sure the project can absorb that time.
- Review every scene carefully. Check consistency, anatomy, identity and whether the image suits the emotion and age group of the story; correct or replace images that do not work.
- Check rules before publication. Verify current tool terms, publisher and retailer policies, disclosure expectations and any rights questions relevant to the project.
- Protect a child’s privacy and agency. When a child’s personal experience becomes public, consider their consent, comfort and control over sensitive details.
- Keep mental-health claims modest. A story can open a conversation, but it should not be presented as treatment or as a substitute for appropriate support.
Jon and Judah Friedman’s book is most useful as a grounded example of AI as an assistant to a human creative process. It helped make experimentation feasible; people supplied the lived experience, judgment, editing and care that gave the project its shape.
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