AI can provide substantial value in nonfiction writing when an expert supervises it closely, but it should mainly handle repetitive, verifiable, or editorial tasks rather than the parts that convey a work's story and intellectual soul.

The author used models for formatting, copyediting, diagrams, manuscript review, synchronization, and other repetitive tasks, occasionally accepting suggestions under expert review. In scientific papers, models are useful for routine background or related-work sections, but the expert should retain the abstract, introduction, experiments, and conclusion. ✦ AI generated

the author · Interconnects · 2026-08-12 · original ↗

I am working through similar balances in my scientific work too. AI models are great for repetitive pieces of the paper, like drafting a related work or background section that you know by heart, but using them for the abstract, introduction, experiments, or conclusion is a shame. Those are where the story and soul of the work is communicated — it’s where you learn what your research is really about. I am confident I created a lot more net value by being able to have AI models create and check my non-fiction writing work. They make writing equations trivial, can help refactor the repository, port between languages, and many other things. At the beginning, it was very fun, until I was a bit worn down by the length of the publishing process, watching the field move on. For an example of why AI was crucial in this case, I had to maintain Markdown and LaTeX versions of my book simultaneously in two spots, as readers gave feedback on the web version and my Manning editorial team reviewed a forked copy. Without AI agents, syncing between the two of them would’ve easily taken me five times as long (and this task took tens of hours already).

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