I wrote an AI textbook — how long until AI can do it better?

the author: 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).

the author: Today, the models seem genuinely horrible at long-form technical writing. They can get a sentence right, but if you try and get them to write an entire chapter it’ll be a mix of sprinkled with confusing wording, muddled in its organization, and generally a bit off. They try to be too cute where they don’t need to be and in the process make random conceptual errors. The models in the near future will get much better at the small errors, especially as models get bigger — which allows them to hold more world knowledge — but I do not expect their ability to utilize it to transform. For example, the GPT models have been incredible at finding typos and minor issues for a long time. I passed a near-final draft of my book as a PDF to GPT 5.5 Pro and it found deep, surprising minor typos across the manuscript that is 200-300 pages. On the other hand, the Claude models have been much more useful as an editor. They have a lot more taste, tend to understand the mental model of the task better, and have more interesting suggestions to unstick the different forms of writer’s block. The examples I’ve given above all have a sort of consistent theme. The models know how to check every unit of content, in this case usually a sentence or equation or figure, or make one, specific section where you are caught. With these skills, they don’t do a good job revisiting components and stringing them together as they make many additions on top of each other. It feels like a sort of irreducible compounding errors.

the author: I would’ve expected way more progress on non-fiction writing from the models. I almost thought I would look dumb publishing a non-fiction book in 2026, given how things looked in 2024. Today, some of the most famous models on writing ability are pretty old, examples include OpenAI’s big GPT 4.5 and Moonshot’s Kimi K2. In and around these releases, the models have gone from okay to superhuman at other tasks like coding and mathematics. Maybe a closer, but still imperfect, comparison is how the models went from incapable to decent at search and research tasks. The pace of progress on most other skills is steep, but writing well feels orthogonal to most of them. I do not think writing is just ignored, but rather it’s challenging and lacks good training data to specifically intervene on it. There is certainly some low-hanging fruit for making AI models better at writing — such as specialized harnesses like Claude Code, prompts, and training environments that make models spend a lot more inference tokens on the output, but I don’t think these will have a multiplicative impact on ability. Writing well is a very hard task! It’s a shame that we haven’t unlocked inference-time scaling for one of the great intellectual pursuits. Regardless, writing seems very different than what the models are good at.