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Grant Sanderson: recommendation

30 Jun 2026 Dwarkesh Podcast Grant Sanderson – AI and the future of math

“My advice to any college student when they’re choosing what courses to take: care a little bit less about your preexisting interests, because they’re kind of arbitrary right now, and care a little bit more about whether the person teaching it is a good educator and someone you resonate with.”

— Grant Sanderson

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Everything needed to verify it.

Speaker
Grant Sanderson
Attribution
Verified speaker
Claim type
recommendation
Recorded
30 Jun 2026
Publisher
Dwarkesh Podcast

Transcript context

…What advice do you have about using LLMs to learn? As I was describing, for a lot of well-known concepts, I find them very helpful. But often, just a couple of messages further down, I’m trying to understand something, and they’re so confused themselves that they’re confusing me. They don’t explain it the right way. I know that talking to the right human could clear up my confusion in three minutes. More and more, we’re going to want to use these things to learn. People talk a lot about education and representation stuff. Have you noticed ways to use them more productively to understand concepts? I’m curious to hear your take on this. I’ll give mine. Even pre-LLM, I feel like a relevant insight in learning was recognizing who matters more than what. My advice to any college student when they’re choosing what courses to take: care a little bit less about your preexisting interests, because they’re kind of arbitrary right now, and care a little bit more about whether the person teaching it is a good educator and someone you resonate with. In choosing what books to read, who the author is maybe matters more than whether it’s a prior interest. If there’s a book you’ve liked before, read what else that author has written rather than reading another thing on that subject. I’m getting to LLMs on this. There’s a difference in feel for trying to learn something from a Wikipedia page versus, if it’s a philosophy topic, going to the Stanford Encyclopedia of Philosophy. Or if it’s a math topic, you go to the Princeton Companion to Mathematics. The difference there is the articles are deliberately written by one individual who tries to actually craft a motivation around it. Whereas on Wikipedia, it’s this local minimum that’s reached where every sentence has to be correct. In a good exposition, you care a little bit less about correctness on the way. You can deliberately craft things that are a little bit wrong that you correct along the way, which gets edited out in a crowdsourced environment. LLM explanations feel to me at the moment a lot like Wikipedia, which is to say, amazing. Imagine a world before Wikipedia, how long it would take to find and suss everything. But nevertheless, what’s the most useful part of a Wikipedia page? It’s often just the references at the bottom. You look at the key references, and you go to them, and you read them. Sometimes that gives a much better overview. So often I like to just ask an LLM, “Who should I read?” Maybe I can even give some specifics on ways I want to learn. I actually got gaslit by this once when I was trying to learn about semiconductors or something. I felt it was a very visual topic, but all the resources were text. I asked, “Is there a well-visualized video explaining the concepts you’re getting at?” And Claude said, “Yeah, here’s a couple,” and the top one was like, “Here’s one from 3Blue1Brown”. I’m like, “I can guarantee that there’s not.” It was an actual video, an actual link, but it had just misattributed someone else’s. It was good. I had a much better experience clicking over and watching it to learn rather than trying to proceed forward with questions there. In that sense, I’m basically using it like a very souped-up version of Google to zero in on the right human-written resource. What about you? You engage with these a lot. What’s the best way to use them? I think you put your finger on it. The most productive learning sessions I’ve had are when there’s some artifact that a human has produced—whether it’s an article, a book, or a video—that organizes the relevant concepts in the correct way. It builds up the motivation for why the next idea would be relevant to solving the next problem you’d encounter, and the next idea, and the next idea. Then you use the LLMs to just do a little bit of pruning around this branch that the book has identified. I was actually going through—I think you might have recommended it—Steven Strogatz‘s textbook on……

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