AI Ate Its Own Tail, and I Learned Something About Writing (www.nibzard.com)

A recent experiment explored using AI—in this case, Claude Code in “YOLO mode”—to analyze and narrate the entire development history of an AI-driven game project by processing every commit and file change. The AI-generated article offered a compelling, human-readable timeline of iterative algorithmic improvements, blending technical insights with a storytelling approach. While the output contained typical AI redundancies and noise, it highlighted the potential for AI to document complex coding processes transparently and accessibly, though better prompting could sharpen the results. Beyond documentation, the experiment sparked deeper reflections on authorship and provenance in the emerging era of AI-assisted writing. The author proposes a verifiable proof-of-work system that logs each creative step—from initial ideas and AI drafts to human edits and final publication—with cryptographic timestamps. This approach, embodied in projects like MindMark, aims to make AI-human collaboration visible, traceable, and trustworthy, addressing the blurred line between machine and human contribution. As AI tools become integral to writing—just as spellcheck and autocomplete once did—the focus shifts from insisting on “pure” human work to embracing and transparently crediting this collaborative creative process. This experiment underscores a broader cultural shift: writing has always been iterative, involving feedback loops whether from readers or now AI. As illustrated by Andy Weir’s evolution of *The Martian*, where crowd input shaped the story before publication, today’s AI acts as a new type of collaborator. The challenge and opportunity lie in developing systems that reveal, validate, and honor these new creative workflows, heralding a future where human and AI co-authors work openly and seamlessly together.