Is the traditional notion of what it means to be a published "author" due for a redefinition in the age of AI writing tools? Many argue AI undermines the credibility and effort implied by authorship, while others believe good ideas will rise above the tools used to develop them.
In this thought-provoking episode of The AI-Powered Thought Leader podcast, host Alastair McDermott talks to business leader and author Jonathan Stark about how AI could disrupt our perception of authorship. The fundamental debate centers around whether the increasing use of AI writing tools could redefine or disrupt the traditional concept and credibility associated with being a published "author."
On one side of the argument, it is posited that leveraging powerful AI assistance for tasks like research, outlining, drafting, etc. could make the historically arduous process of book writing seem too easy. This undermines the mystique and effort traditionally expected of authors - the struggle and "marathon" of personally doing all the writing through one's own toil and creative exertion. Having one's name on the cover of a published book has long implied living up to this romantic notion of authorial struggle. But if AI allows circumventing that process, it could erode the very credibility signaled by being a published author.
The counterargument, however, is that the quality and value of the ideas in a book ultimately matter more than the tools or process used to develop them. Even if AI makes the writing process more efficient, good books containing insightful and resonant ideas will still gain traction and spread based on their merits, not the "how" behind their creation. Following this view, while AI could disrupt long-held perceptions of authorship, the value a book provides to its audience should be the true determinant of an author's credibility.
Key Insights:
- AI should be viewed as just another tool, like a word processor, to create more value with less work
- Using AI for menial tasks like data conversion, transcription, and formatting frees up time
- Training AI on your own previous work allows it to assist in a voice/style you can validate
- The notion of being an "author" may evolve, but good ideas rise above the tool used
- AI excels at research and knowledge synthesis, automating tedious pre-work
Strategies:
- Use AI for tedious pre-work tasks you wouldn't normally do, like transcripts and show notes
- Train a custom AI model on your previous books/blogs to get AI assistance in your voice
- Use AI to transform data between formats like HTML to Markdown
- Use AI for open-ended research and let it propose novel framings or perspectives
- For recurring tasks with checklists, input the checklist to AI and let it identify steps it can automate
- When using AI for research, "interrogate" it for hours, fact-check, and iterate to refine understanding
Jonathan shares candid insights on why he's embraced AI wholeheartedly for his writing process, despite potential concerns around authorial credibility.
Tune in for a fascinating dialogue on AI's impact on one of society's most revered creative roles.
Watch this episode on YouTube: https://www.youtube.com/watch?v=eR2hW7S8cuM
"If the ideas are good and they're novel and they're opening people's eyes I kind of don't care who did it or got credit for it." - Jonathan Stark [9:43] (The AI Powered Thought Leader)
🎙️+📺 SHOW: The AI-Powered Thought Leader podcast explores the intersection of artificial intelligence and thought leadership in the digital age. Join us as we have insightful conversations with thought leaders and AI experts, and providing fresh perspectives on navigating the evolving landscape of AI.
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