When anyone can write a book, more people will. More topics, more perspectives, more niche knowledge that was previously locked inside practitioners’ heads. But what about the top — the works that change how we think? AI has had access to virtually all of human knowledge for three years now. It has not produced a single original explanation for any unsolved problem. What does that tell us about the future of books?

For five thousand years, books have done something no other technology has managed: let you enter another person’s mind and reconstruct their thinking in your own. In 2026, the most powerful technology we’ve ever built communicates the same way — as a chain of words on a screen. But in a world racing toward interfaces that bypass text entirely, will the book survive?

There are two ways to know a book. One changes how you think. The other gives you answers to concrete problems. Most people drift toward one or the other. Combining the two is where you stop consuming knowledge — and start creating it.

There are two ways to know a book. One changes how you think. The other gives you answers to concrete problems. Most people drift toward one or the other. Combining the two is where you stop consuming knowledge — and start creating it.

A new AI tool exploded this week – 100,000 GitHub stars, Mac Minis selling out, three name changes in seven days. But unlike most tech hype, OpenClaw might actually deliver on a promise we’ve been waiting for: AI that gives you time back instead of stealing it.

My books should work for me. Not sit passively in highlights waiting to be searched, but actively remind me of what I’ve read in the right moment, suggest connections I haven’t seen, and help me apply what I’ve learned to real problems. Here’s how I built that system with Claude Code.

What if an AI could search every book you’ve ever read and find the exact insight you need — condense years of reading on a topic, or compare how different authors approach the same question? I’m experimenting with Claude Code to make this real: teaching an LLM to navigate my entire Kindle highlight collection and become an intellectual sparring partner.

Your reading history contains years of insights, but they’re scattered across dozens of books. When you need to pull together everything you know about a topic, those highlights feel unreachable.
We’re building new cross-library AI workflows for DeepRead that transform this scattered knowledge into structured outputs. Through guided conversations, the AI helps you generate topic memos or mind maps that synthesize insights from across your entire library. The key difference from traditional AI chat: the system has persistent access to your complete reading history, not just what you upload. Every interaction creates something tangible and makes your knowledge system more valuable.
We’re developing these workflows now and looking for early users to test features and share ideas.

Systematic highlighting is hard work—but it unlocks four powerful ways to engage with books through AI.

Each workflow creates a tangible artifact—not just another chat that disappears. This article shows you these workflows: building mind maps, generating meaningful flashcards, challenging the author’s arguments, and capturing insights with visual connections. I’ll explain why comprehensive highlighting—the Composer strategy from my previous article—makes all of this possible. We’re also exploring how to bring these workflows into DeepRead.

Most readers highlight reactively—marking whatever seems important as they read. But effective highlighting starts before you open the book: by choosing your strategy and defining your desired outcome. The difference between collecting treasures and composing arguments determines everything that follows.

Some books function as learning systems rather than mere content delivery vehicles. They do this by using design elements such as quotes at the start of chapters, highlighted questions within the text, or chapter summaries at the end. These elements create a reading architecture that guides you through three phases: preparation, exploration, and synthesis. The real power lies in actively using these elements to transform any book into your personal learning laboratory. This way every book becomes an expedition where you decide what to discover and take home.

The best book discussions aren’t happening in traditional book clubs—they’re scattered across Reddit threads, X debates, and comment sections, but we’re missing them because we don’t know how to connect our reading insights with ongoing conversations. This article explores how to create “book clubs of the future”: time-independent, globally scaled discussions where your specific book insights connect with current debates happening across the internet. You’ll discover existing platforms where these conversations are already taking place, see real examples of authors and readers successfully applying book knowledge to current events, and learn practical steps to start participating today.