The future of the technical book looks a lot like what already happened to cookbooks. I say this as someone who has written two technical books for O’Reilly, runs live Excel and AI training for finance teams, and has watched the ground move under all three of those things at once.
There was a time when a cookbook packed with hundreds of recipes was incredibly useful. The collection itself was the value. That role has mostly disappeared. If I want a chicken pot pie recipe, I can get twenty in thirty seconds, tailored to what’s in my fridge and how much time I have. A big, generic collection does not add much anymore.
What still matters, and what people will pay for, is a point of view. A cookbook that shows how someone thinks. Why they braise a certain way. Why they choose one cut over another. What changed after the twentieth time they made the same tart. I’m not looking for instructions. I’m looking for judgment and story. I want to see how someone actually works.
Endless technique drills are not the draw anymore.
The same shift is arriving for technical books
I used to think about technical books very differently. When I was writing my first book, my instinct was that a good technical book should introduce every concept, define every term, and build everything from the ground up. You did not want a reader to be lost.
I am less sure about that now. A lot of what I used to pack into foundational chapters is the exact kind of thing AI is very good at delivering on demand. If a reader does not know what a PivotTable is, they can ask Claude and get a solid, personalized explanation in about ten seconds. A chapter that exists mostly to define things has a harder job to do than it used to.
End-of-chapter questions are another piece I’ve thought twice about. They always had a slightly perfunctory feel, but they had a real function. An author cannot quiz every reader individually, so a handful of review questions at least created some structure for self-testing. That job also turns out to be pretty easy for an AI assistant to do, and to do in a way that is more responsive to the specific reader than any static list of questions can be.
What is not easy to spin up is judgment. You cannot ask a model to produce the “why this matters” and the “here is how I actually think about it” from nothing. Those still have to come from a person with real experience and a point of view.
What readers actually want now
If I zoom out on the technical books I have seen people get excited about recently, the pattern is pretty consistent. Readers are looking for the big picture, told well, by someone they trust. They want to understand why the author made the choices they did, where the author sees the field going, and what the author would skip or de-emphasize. They want to leave the book feeling like they know how to think about a topic, not like they just finished a long drill.
| The old model | Where things are heading |
|---|---|
| Comprehensive coverage of every concept | Judgment about what actually matters |
| Definitions, syntax, step-by-step drills | Big-picture framing and worked examples |
| End-of-chapter review questions | Inspiration and a clear point of view |
| The book as a reference | The book as a guide |
Technical detail still has value. The bar is just higher, since the detail now has to earn its space against an AI that can explain any concept on demand.
The other problem: the tools keep moving
There is a related issue, which is that technical books are harder to keep current than they used to be. I have had books get genuinely derailed by feature changes during the writing process. One chapter I wrote ended up needing a near-total rewrite because the workflow it was teaching got restructured between drafts. That is not a comfortable experience, and it is not a rare one anymore. I wrote about the dynamics driving this in more detail here:
The short version is that interfaces and features are moving faster than a publishing cycle can comfortably absorb. A book tied tightly to a specific UI flow can be out of date before it reaches the shelf. That is a real cost for authors, publishers, and readers, and everyone involved should be thinking about it.
One response is to lean harder into evergreen content. Write about how to think about a problem rather than which exact buttons to click this month. The judgment layer has a much longer shelf life than the screenshot layer, and it also happens to be the part AI is worst at replicating.
What this means for you
If you are a reader trying to figure out which books to buy or which to spend real time with, weight the ones that give you a point of view from a practitioner you respect. Discount the ones that read like a very long reference manual. The reference role is mostly covered now.
If you are thinking about where to invest your own learning effort, I would steer toward the same thing. Spend time on the big picture and on judgment. Let AI assist with the drills. The drills compound much less than they used to, because the baseline has shifted.
If you are a team lead deciding where to put a training budget, the same principle applies. A canned e-learning library is competing with a free AI assistant. A live program with an experienced guide is not.
Conclusion
If you take nothing else away from this post:
- The reference role of technical books is shrinking, the same way it shrank for cookbooks.
- Judgment, story, and point of view are the parts that hold up, and the parts readers actually want.
- The moving-target problem is real, and the evergreen parts of any book or training program are the ones that compound.
This is the bet I am making with my own work. Finance teams do not need another reference manual, and they do not need another generic AI-in-Excel demo. They need a guide who has seen how this plays out across real finance workflows and can help them make judgment calls about their own.
If you want to see how I put that into practice in my training, take a look here: