In 30 seconds
Nat Eliason shares how he uses AI in writing, reflects on personal crypto losses and describes the small wins that sustain long creative projects. He also discusses the incentives and trade-offs of creator businesses.
- 1Use AI feedback with judgmentEliason asks for feedback on clarity, repetition and fairness, then decides which suggestions fit his voice.
- 2Experiment with fictionHe describes drafting a novel himself, then using tools for research, character details and editorial prompts.
- 3Distinguish taste from recurring feedbackConflicting reader responses can reflect preference; repeated criticism of the same passage may signal a problem.
- 4Recount a risky crypto periodHe describes a rapid rise in paper value and a major unauthorized wallet loss, with personal and financial consequences.
- 5Build confidence through small winsHe argues that manageable self-directed projects can provide evidence of progress.
- 6Track inputs on long projectsDaily word counts helped him recognize work completed before a book was published.
- 7Choose a sustainable creator pathHe warns that algorithm and audience incentives can pull creators toward repetitive content they no longer value.
Takeaways
- Use AI feedback selectively and verify technical claims.
- Look for patterns across reader responses.
- Understand that personal crypto experiences are not typical outcomes.
- Start learning through manageable projects.
- Track progress on work with delayed rewards.
- Choose creative incentives that support the work you value.
Read the edited text by chapter
Writing with AI feedback
Editing, fiction and reader responses.
Nat Eliason uses AI primarily as a feedback tool for nonfiction: he asks about unclear passages, repetition and whether he has represented an opposing view fairly. He reviews the suggestions rather than accepting every rating or stylistic change, because a model can be inaccurate or flatten a writer’s voice.
For his fiction project, he says he wrote the first draft without AI and later experimented with models to brainstorm details and organize material. He describes using a fictional medical scenario for story research. Generated medical claims and other technical details still require independent verification; fiction prompts are not medical guidance.
Reader feedback can conflict because people have different tastes. Eliason looks for patterns: when multiple readers identify the same passage as slow, that is a stronger signal to investigate. He also uses tools to suggest missing sensory details or possible edits, selecting and rewriting what fits.
A personal crypto experience
Risk, losses and the story behind the book.
Eliason recounts becoming deeply involved in crypto and seeing a large paper valuation that he says was not realized at that level. He also recalls losing about $35,000 through unauthorized transactions after launching an early app. The discussion is a personal account of risk and emotional consequences, not evidence of typical returns or investment advice.
Small wins and long projects
Self-directed learning and tracking progress.
He argues that confidence in learning grows through trying manageable challenges and accumulating small wins. His examples are personal, and readers can choose tasks suited to their circumstances rather than assuming that risky physical practices are necessary.
Long projects do not provide the immediate feedback of social posts. Eliason tracked daily word counts and built a small writing tracker to make progress visible before publication. The aim was to reward the input he controlled while continuing to work toward a longer-term project.
A sustainable creative career
Algorithms, audience incentives and durable work.
He worries that creator-economy incentives can reward increasingly repetitive advice or content designed for an algorithm. For his own career, he wants more time for books and family and fewer obligations to maintain a content business. He suggests thinking about durable work and what the audience is ultimately meant to do with it.
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