Why Reading Still Matters in the Age of AI
Read What You Love Until You Love to Read
AI can now summarize any book in seconds. It can pull key insights from a 400-page research paper before you’ve had your morning coffee. So here’s an honest question: why read at all?
We’ve been thinking about this a lot at Glasp — a company built on the belief that what you highlight reveals how you think. And the more AI can do for us, the more we keep coming back to the same answer.
Reading isn’t just about getting information. It’s about building the mind that can use it.
“The Means of Learning Are Abundant. The Desire to Learn Is Scarce.”
Naval Ravikant said this before AI made it even more true.
In one of the most widely shared conversations on reading ever recorded — his dialogue with Shane Parrish on The Knowledge Project — Naval argued that the real problem isn’t access to knowledge. It’s the habit of genuinely wanting it.
“The foundation of learning is reading,” he said. “I don’t know a smart person who doesn’t read and read all the time. I probably read one to two hours a day. I think that alone accounts for any material success I’ve had in my life.”
But Naval’s deeper insight isn’t about hours logged. It’s about how to build a reading habit that actually sticks — and it’s simpler than most people expect.
“Read what you love until you love to read.”
Don’t force yourself through books that feel like assignments. Don’t read what you think you should read. Start wherever your genuine curiosity points, follow it, and let it take you somewhere unexpected. Fiction to science fiction. Science fiction to nonfiction. Nonfiction to philosophy. The path doesn’t matter — the motion does.
→ Explore Naval’s full reading philosophy on Glasp
Curious what others found most memorable from Naval’s writing? The Glasp community has highlighted 96 pages from nav.al — you can browse what resonated with them here: nav.al highlights on Glasp
Your Highlights Are a Map of What You Love
Here’s where Glasp fits into this philosophy — and it’s something we’ve come to believe deeply.
Most people don’t know what they love to read. Not really. They know what they think they should love. They know what’s on the bestseller list. They know what their colleagues are reading.
But the sentence that stopped them mid-paragraph last Tuesday? The paragraph they reread three times before moving on? The passage they screenshot and sent to a friend at midnight? That’s the real signal.
Your highlights don’t lie.
When you highlight something — on the web, in a PDF, in a Kindle book — you’re recording a moment of genuine contact with an idea. Not what you thought was impressive. Not what you wanted to seem interested in. What actually caught you.
Over time, your Glasp library becomes something more than a collection of saved text. It becomes a portrait of your intellectual curiosity. A map of what you actually love to read — which, as Naval would argue, is exactly where your learning should begin.
What AI Changes — and What It Doesn’t
AI is genuinely good at surfaces: summaries, key points, quick overviews. If you need to know what a book argues, AI can tell you in thirty seconds.
But Naval’s most important point isn’t about information retrieval. It’s about the compounding effect of a reading habit on how your brain works over years.
When you read deeply — when you follow an argument, sit with a difficult idea, disagree with a paragraph and keep reading anyway — you’re not just consuming content. You’re building mental models that become part of how you see the world. That process can’t be shortcutted.
AI can summarize what Nassim Taleb wrote about risk. It cannot give you the intuition that develops from twenty hours of slow, annotated reading, where you argued back at the margins.
This is the gap AI doesn’t close.
Naval puts it simply: “Reading is faster than listening. Doing is faster than watching.” The act itself is the point.
One Practical Shift
If you’re thinking about how to protect your reading habit in an AI-saturated world, we’d suggest one small change:
Stop treating AI summaries as a starting point, and start treating your highlights as one.
Before reaching for a summary of a book you’ve already read, pull up your own highlights first. What did you find important? What questions were you asking when you underlined that sentence? That’s your thinking — not an average of everyone else’s.
This is what Daily Highlight Review is designed to do. And it’s why we believe the future of reading isn’t AI replacing books — it’s AI helping you go deeper into them.
The Glasp Take: Read What You Highlight
In the age of AI summaries, we’d offer one reframe on Naval’s advice.
“Read what you love” is right. But if you’re not sure what you love yet — or if your reading has become mechanical, a list to get through — try this:
Read what you highlight.
Go back to your Glasp library. Look at the sentences you actually saved. Ask yourself: what was I thinking when I underlined that? What question was I trying to answer? What idea made me stop?
That’s your map. Follow it.
The highlights you make today become the thread you can pull tomorrow — across books, articles, YouTube videos, and conversations. Glasp exists to make that thread visible, and to make sure it doesn’t get lost.
Because in a world where AI can give anyone the same summary, what you actually found interesting is the only thing that’s uniquely yours.
What’s a book or article you’ve highlighted recently that surprised you — something you didn’t expect to love? Leave a comment, we’re genuinely curious.
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