Why Your AI Learning Projects Keep Fizzling Out

LLMs have made it absurdly easy to go deep on almost any topic. So why haven’t we all used ChatGPT to earn college degrees we wished we had majored in or pursued a niche interest, like learning how to name the trees in our neighborhood? I know I’m not the only one to feel guilty for well-intentioned attempts at autodidactism that inevitably peter out. Entrepreneur Nir Zicherman has a reason for this disconnect: LLMs can answer most of your questions, but they won’t notice when you’re lost or pull you back in when your motivation starts to fade. As the CEO and cofounder of Oboe, a platform that generates personalized courses about everything from the history of snowboarding to JavaScript fundamentals using AI, Zicherman has thought deeply about why the ability to access information does not automatically lead to understanding a concept. In this episode of AI & I, he talks to Dan Shipper about everything he’s learned about learning with LLMs. They get into Zicherman’s counterintuitive belief that learning is a more passive process than you’d think, the biggest blocker for most people who want to learn something new, and where AI agents currently fall short in providing a meaningful learning experience. If you found this episode interesting, please like, subscribe, comment, and share! Want even more? Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free. To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:00 - Start 00:00:36 - Introduction 00:01:49 - Why you need a dedicated AI learning app 00:04:32 - The process of learning is more passive than you might think 00:10:21 - Live demo of Oboe to create a course about philosopher Ludwig Wittgenstein 00:16:52 - Learning works best when it comes in many formats 00:28:21 - Where AI agents currently fall short in the learning experience 00:34:10 - The importance of making learning feel accessible 00:35:56 - How Zicherman uses Oboe to learn quantum physics 00:40:54 - How embeddings spaces remind Dan of quantum mechanics Links to resources mentioned in the episode: Nir Zicherman: @NirZicherman Learn something new with Oboe: https://oboe.com/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  The grand vision is you can learn anything with OBO. Anything that you ever thought was too hard to learn, we could at least get you started and you can feel like it is achievable.

• 0:09Dan Shipper?

  Why does it need to be a separate app? Like, why isn't ChatGPT just like the ideal way to learn new things?

• 0:13Speaker 00

  A real learning platform has to be built as a learning platform. LLMs are built to be a general tool, but given that they were not built as learning tools at their core, they're missing a lot.

• 0:36Dan Shipper?

  Nir, welcome to the show.

• 0:38Speaker 00

  Thanks for having me.

• 0:40Dan Shipper?

  So for people who don't know, you are the co-founder and CEO of OBO, which I don't know what your one liner is, but from my perspective, it's like a, it's an AI learning app that makes like one-off courses for you, like on demand, basically. It's pretty cool. You can tell me where I'm wrong there, but it's a really, it's a really good app. I think it fits with a lot of stuff you've been thinking about. Prior to, prior to doing OBO, you were most well-known for being an every writer sometimes. And, and you're the VP and Global Head of Audiobooks at Spotify. And before that, you were the co-founder of Anchor. Excited to have you on the show.

• 1:24Speaker 00

  Thanks for having me. And yeah, I was thinking back on the writing that I had done for every, I actually do think it's very on-brand for OBO because a lot of what I had written was about trying to make people realize that they can learn and understand things that they probably were intimidated by and thought were too hard to learn.

• 1:41Dan Shipper?

  I love that you're doing this because I think it's, I think AI is just so good for learning, like it has expanded my mind in so many different ways. And I want to ask the like, the tough question upfront, which is like, why does it need to be a separate app? Like, why isn't ChatGPT just like the ideal way to learn new things?

• 1:57Speaker 00

  Look, LLMs are incredible. I use them.

• 2:02Speaker 00

  Now, honestly, in every aspect of my life, as I know you do too, and I think it's becoming more and more the case for everybody who realizes how powerful it is. But LLMs are built to be a general tool, like the most universally general tool that you could possibly have. I've spent a long time thinking about learning. I've spent a long time thinking about it, both from the perspective of an entrepreneur who wants to build products in the space, but also as a person who uses these products day in and day out to actually teach myself things. I teach myself things a lot and I have for years. And what I fundamentally believe is that while chat is extremely powerful for learning, it is not the primary way that people do learn.

• 2:48Speaker 00

  Most learning is passive. It's not active. It doesn't require leaning participation. Most of the learning that you've done in your life has been through the consumption of content passively with active engagement every once in a while. Most of the learning that you've done in your life has been multimodal, right? It has not been sitting and consuming and engaging with content in one particular way. It's been done through piecing together different formats online, for instance, right? Every single day, I'm willing to bet that you do this and everybody listening does this. You get curious about something. You read about something in the news. You identify something that you don't know too much about, and you go down rabbit holes on the internet to try and learn those things. And you don't just default to ChatGPT or other LLMs, despite the fact that they're really, really powerful.

• 3:32Speaker 00

  You may use them as one tool in the arsenal of tools that you use to learn, but they are just one modality. People learn through multimodality, right? So you'll start with ChatGPT and then you'll Google some stuff and you'll end up on Wikipedia and you'll go to YouTube. And you're not alone in doing that because billions of people do that every single day. Like, there are billions of people who use the internet to learn every day and to piece together a learning experience using these different formats and these different platforms. And so back to your question, I think LLMs are a piece of the puzzle, right? And I think they they enable an important piece of the puzzle. But given that they were not built as learning tools at their core, they're missing a lot.

• 4:15Speaker 00

  And I think that a real learning platform has to be built as a learning platform. As I believe that a real fill in the blank platform that many of your guests are probably building on the on the show, those have to be built by people who are focused on a particular use case.

• 4:30Dan Shipper?

  That's really interesting. I want to go back to something you said, which sort of shocked me, which is that most learning is passive, not active, or like basically good learning is passive, not active. Did I get you right? Because I would have assumed the opposite. And so like my my mental model of like how learning works is you like, at least for me, it's very much learning in context. I'm like, I need to do a specific thing. And so I want to like, understand how to do that thing and how how it might fit into all the other things that I know and want to do, which feels very active to me. I do spend a lot of time reading, which I guess is passive, and I am learning stuff. But I just hadn't really, if I had to bet what you would say, I would have said, learning happens as an active thing.

• 5:09Speaker 00

  Well, I think what we're talking about are probably two different dimensions, right? What you're talking about is intentionality and objective. Where does the objective come into learning? I do believe that the best learning happens through high intent, high agency, and significant objective driven motivation. And that's actually a thing that we talk about a lot as far as the product goes. Most of the use cases that people today already are using Ovo for are objective oriented. People come in and they say, I know what I want to accomplish. I want to be good at X, or I want to be able to, you know, take out a mortgage to buy a house. I have no idea what that is. Or I want to be able to do, gain this particular skill so that I can upskill at work. Or I want to pass this particular test.

• 5:54Speaker 00

  All of those fall under this umbrella of like, high intent, objective driven motivation.

• 6:02Speaker 00

  That I think is separate from the dimensionality of how you actually go about learning the thing. So I do think that high intent is very important. It is what motivates people the most, and that's why it's important to build a product that taps into whatever their objective is and tries to embrace a path that gets them to their objective as quickly as possible and lets them see that it is feasible for them to hit that goal. But then the question of how do you actually present the material to them? Goes to the heart of a different dimension, which is what I think is passive versus active or single modal versus multimodal. If you think back to school and you think about the best teachers that you had, most of the time that you were learning, you were learning from them, teaching to you with an opinion about how to teach you.

• 6:43Speaker 00

  You were sitting and consuming them talking or consuming the reading or whatever it was. You were not actively participating in the conversation. That's not to say that active participation is not very important. It's one of the modalities that I think reinforces a lot of the learning that people are able to achieve. But it's not the primary mechanism, right? And what L L M's do is they basically put the onus on the user, on you as a learner, to be very explicit around what you want to achieve and how you want to achieve it and giving a constant feedback. That's not how people learn. Like when you were in school, you didn't give constant feedback to the teacher to get them to adjust their curriculum. The teacher was not asking you questions about how best to structure the course and where to go next. That's what I mean by passive versus active.

• 7:25Dan Shipper?

  That makes sense. That makes total sense. I think here's maybe one, one thing where I'm sort of slotting OBO into my, into my model of the world. And maybe one way to put it, I'm curious what you, what you, what you would say is L L M's as they are, are incredible learning machines and people do in this like very low intent way, pick up new things all the time, like every single day, or at least I do, from them. And sometimes there are categories of your life where you're like, I actually just want to like really learn this thing and I want to get the equivalent of a degree from it or, you know, a diploma or certificate or whatever. And in those cases, you actually need a real teacher that is like thinking about this as a course, not just like a, I'm one off, like

• 8:09Dan Shipper?

  reading this book and I like get curious about this particular character and I'm like going down with, going down the rabbit hole with chat G P T on it. Is that, is that, is that sort of what you're, what you're thinking about?

• 8:17Speaker 00

  Yeah, I don't, I don't think OBO is a quick answer platform. There are many, many ways to get your quick answer to things, right? The internet, I mean, if you think about, there are many ways to think about L L M's and I think, I know. You know, you've written about this and explored a lot of, a lot of these different perspectives. But like at their core, what an LLM, what an LLM is at its core is a information compression machine that just takes the massive breadth of the internet and gives you the information in a much more personalized, specific, fast, you know, compressed way. That's what it does. It just takes all human knowledge in the form of content on the internet and compresses it down to to what could be distilled into, into a back and forth conversation.

• 9:00Speaker 00

  And so there's an assumption there that the way that people should use LLMs is for quick succinct information, right? It's not intended to be long form. And yet, learning, true learning, the thing that people really wanna, whatever it might be, that objective that people want to achieve requires a commitment and it requires you to follow through and it requires you to take multiple steps. And anybody who has spent more than a few minutes in an LLM conversation trying to do anything longer than the quick information thing has probably learned LLMs are not great at that, right? They lose context very quickly. They don't stay focused. It's really easy for them to deviate from the path that you originally set out. And then that I think is problematic. That's actually a thing that we talk about at OBO a lot is

• 9:43Speaker 00

  how do you allow users to engage with our platform and deviate and ask questions and make it personalized and yet continue to provide the scaffolding that'll always bring them back to the core objective that they set out for themselves.

• 9:58Speaker 00

  And that I think is something that LLMs do not do very well.

• 10:02Dan Shipper?

  I want to show everyone the product. I think it's really cool and I think that'll give us a lot more, a lot more concrete stuff to talk about.

• 10:07Speaker 00

  Sounds good.

• 10:08Dan Shipper?

  And is, can I pick the course topic?

• 10:11Speaker 00

  Yeah, of course. Live demo. This, this has never gone wrong. This is.

• 10:16Dan Shipper?

  We're doing it live, folks. Okay.

• 10:20Dan Shipper?

  Okay, what, the course that I, the course that I want to take is,

• 10:25Dan Shipper?

  I want to take a course in Wittgenstein's Philosophical Investigations. And I'm sure that it can do this, but the thing that I'm not sure about, and you tell me, is what I, my ideal version of this course is.

• 10:39Dan Shipper?

  It uses the full text, which is like available for free online. And every unit of the course is just taking it's it's written in aphorisms, basically, or like paragraphs, like subsections, and each each unit of the course is like it takes one of the subsections and then explains it and talks about it, talks about what you need to know to understand it, and then moves on to the next one. And maybe maybe that's a horrible way to learn this book, but that's my eye. That's like my vision.

• 11:11Speaker 00

  Interesting. I two things. First of all, you're gonna have to help me spell it.

• 11:20Speaker 00

  But that specific use case, I actually think is it'll be interesting to see what it produces. And maybe it's not the best example, because one of the things that we built into the engine that powers this is we want things to feel lightweight and achievable from, you know, hitting a milestone standpoint. And so one of the things that will probably happen is it'll reduce. It sounds like you envision a pretty substantial scope to this course. It'll by default try to reduce that to the key points, because it wants to make you feel like you're making progress along the way and hitting important milestones, rather than giving you something that's like an unachievable thousand chapter long.

• 11:59Dan Shipper?

  I see.

• 12:00Speaker 00

  Course.

• 12:01Dan Shipper?

  I'm also super happy to like have it be a section of it, like the first 30, the first 30 things or the first 30 sections or something like that. Would that be better?

• 12:10Speaker 00

  Let's let's see what it does. Let's see what it does. I don't know. Let's say, all right, how do you spell it?

• 12:15Dan Shipper?

  So Wittgenstein, Wittgenstein, W I T T G E N Stein, Wittgenstein.

• 12:28Speaker 00

  Okay.

• 12:29Dan Shipper?

  And Wittgenstein's philosophical investigations.

• 12:36Speaker 00

  Philosophical investigations, I'm gonna say, give, um, you know, pull out the first part and give me, uh,

• 12:46Dan Shipper?

  I would say part is not gonna be clear enough. I would say first thirty subsections.

• 12:51Speaker 00

  Okay.

• 12:54Speaker 00

  Uh, and give me the context I'll need to

• 13:00Speaker 00

  understand them.

• 13:01Dan Shipper?

  Understand each one. Yeah.

• 13:03Speaker 00

  Okay.

• 13:03Dan Shipper?

  Great. Understand them.

• 13:04Speaker 00

  Interesting.

• 13:05Dan Shipper?

  Perfect.

• 13:06Speaker 00

  Yeah, you know, one of the interesting, well, we'll generate this. One of the interesting things that is a tough balancing act that we've had to strike is because we're building this, the, the grand vision is you can learn anything with OBO.

• 13:22Speaker 00

  And building a platform that is able to focus on Wittgenstein, uh, while also teaching people all of the other things that they want to learn. It really interesting, like it's really interesting balancing act to figure out how do you empower a platform to, uh, to have autonomy and to have the breadth to cover everything while also being opinionated about what it does because, uh, like I said, the true learning platform has to be built with learning use case in mind. And so we have to have opinions about pedagogical methods and how it is we should be presenting information and what is the balance of passive versus active, uh, engagement and things like that. So, all right. So here's our course. So OBO creates a course.

• 14:00Dan Shipper?

  Let me just, let me just stop you right there. So

• 14:02Speaker 00

  Yeah.

• 14:02Dan Shipper?

  This is sick. It's super cool. Um, like for people who are listening, it's a, it's now, it's now a page with a bunch of different sections. There's a headline that says Wittgenstein's philosophical investigations, first thirty subsections. There's an introduction. It has like an explanation. It has a podcast. Um, and then it has a bunch of like subsections. And one of the things I think is cool about how you did this is it's obviously difficult to, uh, make a course, uh, all in one shot, um, all in like a, a very short timeframe. And it looks like you're in parallel, generating a lot of different things and, and, um, You are showing me the first thing I want to read so I can get started immediately while you're then also filling in the rest of it, which I think is like really, really smart

• 14:46Dan Shipper?

  rather than making me wait. Yeah, that's how you're thinking about it, right?

• 14:51Speaker 00

  Yeah, I mean, look, speed is a critical piece of any AI product, I think. We did not want to be one of those products and there are other products on the market that do this. We didn't want to be one of those products that requires you to submit something and then wait around for a while in order to get started.

• 15:08Dan Shipper?

  Yeah.

• 15:08Speaker 00

  A big part of our value proposition here and our positioning for the product is you can learn anything, anything that you ever thought was too hard to learn, we could at least get you started and you can feel like it is achievable. It's very hard to do that if you give us something and then we kind of like immediately violate your trust by taking forever to give you something like we're there to be the guide, right? And so we want to take you on that first step as quickly as possible.

• 15:34Dan Shipper?

  I have a question, like, is there, so it says there are two sources. Did it go and pull the full text?

• 15:41Speaker 00

  In this case, it looks like it may have from these sources. And also, it's possible that it will do that if it feels that it needs to go get an external source. Also, each of the chapters that we have here will have different sources. And so it's possible that, you know, it may have pulled them into particular ones, but not into the specific one.

• 16:06Dan Shipper?

  Interesting. Okay.

• 16:08Speaker 00

  You know, this is the beauty of working with AI is like you don't always know what's happening under the hood. I mean, we've, obviously, we can dig into it behind the scenes. But when you're looking at the product, a lot of times, even as the person who built this, a lot of times I'm looking at the product and I'm like, wow, it's pretty incredible how much agency, you know, you give it the right guardrails and you give it the right amount of autonomy, it's able to use that agency to do things that you had never expected. I've never, this is a live demo, I've never looked at this example before. I've never seen the content that we're about to see before. So it's always very cool seeing what comes up.

• 16:38Dan Shipper?

  I love it. So take us through it from the perspective, I mean, and talk about it as, because there are people who are going to be watching and there's also people who are going to be listening. So take us through what's there.

• 16:47Speaker 00

  For sure. Yeah. So.

• 16:50Speaker 00

  A bunch of different things going on here. Multimodality is a big piece of what we believe in. So what that means is you can't just be getting a bunch of text in the way that an LLM would give it to you. You have to be given a variety of information, a variety of different ways that the way that makes is most suitable at any given time. And so a big part of the pipeline that powers this is kind of figuring out what is the right thing to show you also, what is the right format to show you at any given time. Now, the one thing that is an exception to that is our podcast format because podcast listening is a very different type of experience. The times during your day when you would want to listen to a podcast are very different than the times that you would want to, you know, engage with something that looks like this. And that's why the podcasts sit separately. This is a generated podcast.

• 17:34Speaker 00

  It's a conversation between two people talking about the topic. And one of the things that we've recently added is you can think about this particular chapter as the first episode in a podcast. And all the chapters that you see here on the side are additional episodes of this long form podcast. And so the course that you created actually also created a podcast with multiple episodes. In this case, it's six episodes because there are six chapters here, each of which kind of builds on each other and reference things that came before, but you always have the same two hosts. If I were to play this, I don't know if this would work in our recording, but you tell me if you can hear this.

• 18:08Speaker 02

  Have you ever tried to explain a really specific feeling or maybe a dream you had and the words just fail? Like the more you talk, the further away you get from the actual thing?

• 18:17Speaker 03

  All the time. It's that frustrating moment where you feel like language itself is the barrier. And it's funny you bring that up because that exact problem obsessed a philosopher named Ludwig Wittgenstein for his entire life.

• 18:30Speaker 00

  She pronounces Wittgenstein. Interesting.

• 18:32Speaker 02

  Yeah, I know. Now, now I'm unsure.

• 18:35Speaker 00

  You probably know more than the synthetic voice that we're using there. The, you know, the experience of consuming this is it's supposed to be rich. It's supposed to be.

• 18:51Speaker 00

  As I said, it's supposed to make you use of a variety of different formats. Now, depending on what type of content you're creating, you're going to see a different mix of things. If I were, we should look at a STEM example because it'll look completely different than this, right? If I'm looking at something that's very philosophy based, it'll be heavily about the language that you're seeing, a lot less about the visuals, despite the fact that we have a nice photo of Ludwig here.

• 19:14Speaker 00

  We have, you know, you get taken through an experience that, as I mentioned, is meant to feel very piecemeal and achievable because I think one of the biggest issues, the biggest blockers that prevents people from learning is feeling like it's overwhelming to be able to get to the end state that they want to get to. And so, you know, this case here, I'll show you a different example, just as a point of comparison. If I were to go here and here, so this is a recent course that somebody generated on the platform, Quantum Tunneling Explained, very different, obviously. And so as you're, in this case, you're getting a course about quantum mechanics and you'll see examples of visuals and content that's pulled in

• 19:58Speaker 00

  and content that's generated that looks very different because it's built for the specific use case. As we scale the team, as we scale the product, we want to add support for more and more formats and more different, not only formats, but what we call embedded formats, which means the formats that show up in line at the appropriate moment for you to learn the things you want to learn. Well, what happened if I uploaded a full book? We do have size limits on the uploads, however, I guess it depends how big your file is. But if you were to do that, if you were to take Wittgenstein's book, for instance, you could upload it, you could drag in any attachment here and we see people doing that and we see people using it for, for work for that purpose or, you know, help me understand this article or this document or whatever it is. That's what this plus button is right here.

• 20:40Speaker 00

  And in the case of you uploading a book, I think without giving it direction, it would have to infer a lot about what your intent was by uploading that. But if you uploaded the book and then gave it specific intention about, you know, I want you to analyze this fiction book and do a whole character analysis with plot. Graphs and things like that, it should be able to do it versus, you know, giving it a completely different set of directions with that uploaded book.

• 21:05Dan Shipper?

  Okay, cool. That's really interesting. Well, can we go back to the to Wittgenstein real quick? I don't want to get too far away from Ludwig. I'll have attachment anxiety. Okay, so if we if I want to scroll down. So it looks like in these in these chapters, there's like in the chapters that you that you have.

• 21:25Dan Shipper?

  It has like introduction to Wittgenstein, transition to philosophical investigation, understanding language gains, meaning as use, which are it's going through all the major concepts of the book. But I'm wondering if we go to analyzing the first 30 sections, I just want to see what it did there.

• 21:42Dan Shipper?

  Okay, interesting. Let's keep going. So it's it's basically talking about it is actually in the beginning of the book. It is actually breaking that down. Let's keep going. Scroll to the bottom. I just want to see like where it where it got to.

• 21:58Dan Shipper?

  Oh, interesting. And so

• 21:59Speaker 00

  It looks like it's some more summarized.

• 22:03Speaker 00

  The first 30 sections as opposed to doing what you what you suggested.

• 22:08Dan Shipper?

  Yeah, yeah, that's interesting. I do like I did notice this. You have this like quiz thing where every once in a while, there's a format where it like asks you stuff about the whatever it's teaching you to like kind of help you do. I guess that's the more like one of the more active things you have. Tell me about that.

• 22:22Speaker 00

  Yeah. So as I mentioned, a lot of our philosophy here is around we refer to as embedded formats, which is let's put the right thing in at the right time. And so when it makes a decision that this is an appropriate point for me to reinforce the material that I've already covered, it'll throw in a quiz or it'll throw in flashcards or, you know, a game or different formats as we as we add support for more and more embeddable formats. What we're doing here is we're giving this pipeline that generates all this content more of a toolkit, if you want to think about it like that, to use just like a teacher has a variety of different tools at their disposal and they're able to use the right one at the right time that they determine is is fitting.

• 23:05Speaker 00

  But you'll notice in this case, you know, back to our question about passive passive versus active. It is objective oriented, right? It is intentional in the sense that you can see that the course was built in a way that built up to the thing that you asked it to do. In this case, the end result, I guess, was not as detailed as we were hoping that it would be. But it builds up from basics to get you to the end result. But on the active versus passive thing, it tries to strike the right balance there, right? So in this case,

• 23:31Dan Shipper?

  That's interesting.

• 23:32Speaker 00

  It's mostly passive.

• 23:33Dan Shipper?

  Is there a way for me to be like, hey, like, no, no, like, you know, do more. I want a bigger, deeper course. Can I say that?

• 23:38Speaker 00

  So we talk about that a lot currently in the product. We don't have the ability to do that. Well, the way to do that would be to continue to refine your prompts and you could generate as many of these as you want for free. There's no limit on how many you can generate these. However, one of the things that we talk about a lot and users ask for this a lot is how can we take content that we've already generated and give the user the ability to continually refine the course so that you could say, actually, here, I want you to double down. I want you to split this into multiple chapters.

• 24:03Dan Shipper?

  Yeah, yeah, yeah. That's really interesting.

• 24:05Speaker 00

  And then giving more granular control to the user to be able to also say, I want it more tonally in a particular format, right? I'm using this to teach my kid, you know, fifth grade math or whatever it is. And so we want it to be presented to them in a slightly different way. And given those type of giving those granular controls is going to become an important part of the product as well.

• 24:25Dan Shipper?

  Got it. Awesome. How's it going so far? Like, business-wise?

• 24:29Speaker 00

  It's great. So we launched the product officially in September of 2025. And the three months or so following that were all about addressing some fundamental things that came from our users, came from the launch, the realization that we could be much more objective oriented in our content. We could have a much more enriched format, enriched presentation of the material, which is the version that you're seeing right now. And so we recently launched a substantial.

• 25:04Speaker 00

  Change in terms of how the product is presented to the user and what they get out of it, as well as some changes like allowing users to create unlimited courses

• 25:14Speaker 00

  for free. And so that's relatively new. And then the other thing that happened recently was we announced our Series A, which we raised after after launching the product. And so that was just announced as well. And so we're now entering the phase where we're able to grow the team and do a lot more and hopefully build out a lot of these features that you and I are talking about.

• 25:32Dan Shipper?

  That's awesome. Congrats.

• 25:35Dan Shipper?

  What has surprised you so far about like who's using it and what they're using it for and maybe what they're not using it for?

• 25:44Speaker 00

  I wasn't expecting the extent to which what people put in is objective oriented, as I mentioned. And that's informed our roadmap a lot. And so if you look at the the prompts that people put in, more than two thirds of them are under this category that we refer to as objective based learning goals. And it kind of reinforces, it probably should have been obvious to us, but I think actually seeing the real world data changes your perspective on what a product like this is for. You know, most people today struggle, I think, with this gap that exists in in their minds, in my mind, for a lot of things that I want to learn, and I'm sure in yours as well. I know what I want to achieve. I just have no idea how to get there.

• 26:29Speaker 00

  Right? I know that I need to understand X. I know that I want to do Y for my job. I know that I want to start learning about this topic that I know is I'm theoretically interested in it. I have no idea how to even begin, right? And I have no idea what the steps are to get there. And so I think creating, you know, learning to me is primarily about that. It's about allowing someone to specify what their end objective is and getting them. To that path. Now, if you think about the way that most people historically have learned things with online platforms, it's actually the exact opposite of that. They have no control, no agency over where I'm going. It's just, I'm generally interested in a topic. I'm going to find some resources for beginners to get me on the first step. And then I have to do a bunch of research to figure out how to get to the second step. But you're not personalizing the

• 27:14Speaker 00

  journey. I mean, even LLMs, to the point that we mentioned earlier, if you were to specify to an LLM what your long-term objective was, and you were to spend more than a few minutes, or a long context window, trying to talk to it to get you there, it'll very quickly lose context, right? And it'll, it'll lose track of what it is that you're trying to achieve. And so ultimately, I think a real learning platform that's, that's successful at teaching, has to be able to simultaneously be always oriented towards the objective the user is trying to get to, and also allow for a lot of freedom along the way, and, and a lot of quick wins, and a lot of keeping the learner motivated so that they continue to go.

• 27:55Dan Shipper?

  That's interesting. Yeah, I have not found it that, you know, like ChatGPT, especially with like now, like let's say GPT 5.2 and Opus 4.5, that it really gets that off track with long context. Opus 4.5 definitely like in longer chats, it'll do this compaction stuff, and it's just like, the way they've done that is like stupid. But more or less, like, it seems in my experience that it keeps going, like, these chats keep going. The thing that I find, because I've used, I use these tools to learn all the time. So when 03 came out, was it 03? Yeah, it was 03. They added the ability for it to set reminders. And so what I started to do was, I took a couple of things that I wanted to learn about. So one was, I

• 28:37Dan Shipper?

  wanted to go through Andre Karpathy's Building a Language Model course on YouTube. And I said, like, every day, I want you to, like, take me through a new section of that course on YouTube. So like, get the whole video, figure out what he does, and then like, walk me through it step by step, and stop when I'm, when I don't get something, and like, let's just keep working on that until I get it. And it actually worked really well for like, a couple of days. And it, it was like, actually really good overall, because like, 200 days in, it was still like, beeping me and being like, hey, like, there's this piece of this thing that you haven't done. It was like, pretty good. But the problem is,

• 29:18Dan Shipper?

  Basically, like splitting it up into those little pieces at some point, I got to a piece where it was like a little hard. And then I was like, I don't have time for this right now. And I let a couple of days go by. And then that piece, like looking at it again, three days later, I was like, I don't really have the context anymore for even where we were. And I have to do now even more work to like build back up to this. And that's even less appealing. And so it just like ended up just there, which kept reminding me, hey, like, we're right here at this, like, you know, we're learning about how key value stores and L L Ms work or whatever. And, um, uh, and I would just feel guilty about it every morning. And then I just like dropped it, basically.

• 30:01Dan Shipper?

  So that's, that's been my experience, actually, is that, um, one, I often have a like motivation to learn something hard, but that motivation is, um, it passes, like, especially if it's not fully related to like my job or like something. And this is actually kind of related to my job, but like I can get by my job without having done this course, right?

• 30:25Dan Shipper?

  And then, so there's the passive stuff. And then there's the like, I, we're building up to something and I like sort of lose context of where we are on the path. And then it doesn't adjust to be like, hey, I saw you haven't responded in three days. Like, let's try to reignite your interest here and like get you caught up in a way that like gets you psyched again in the way that, you know, maybe a friend would or something like that, doesn't have that like little, that in that sort of intelligence, which maybe at this point it could do it, I just like haven't prompted it for that. But those, I think those are my two big problems.

• 30:59Speaker 00

  I think that touches on the interesting, uh, double edged sword of creating L L Ms and building them to be generalized tools, right? Especially once you get into the agentic stuff, which is what you're talking about. In order to have it deliver the value, you really let this thing run on its own more than an LLM in a regular conversation, right? And so in order to have it deliver value over a long period of time, over multiple sessions, you have to be pretty constraining, I think, in terms of what this thing can do. You don't want it to go off the rails, which agents, if you set them off on their own, easily could. And so there's this really interesting balancing act that that has to happen under the hood

• 31:42Speaker 00

  for any of these AI platforms, especially for LLMs, where it's like, as you're building agents, you know, how do you, how do you set the guardrails, give them some level of autonomy, but set it in a way that's flexible enough? And to your point, it's clearly not flexible enough to say, hold on, I need to reassess the entire approach here because Dan's kind of lost track or he's lost interest and I need to come at this from a different angle. Clearly, we are in the very early days, I think, of the technology that is able to successfully do that, right? It's an agent sort of reassessing its own, its own objective and its own approach to things and allowing the system to rewrite the rules, redefine the guardrails and move

• 32:23Speaker 00

  them so that, so that it becomes more valuable without requiring you as the user to actually come in and be very explicit with, actually, no, I want you to do this. Right? So my point from earlier, you would never do that with a teacher. A teacher would be able to read the room. A teacher would be able to know when their students were confused. A teacher would be able to know, hold on a second, it's been a week since we last covered this, I need to reinforce certain material. They don't put the onus on the user, or in that case, sorry, the student, to come to them and say, hey, actually, teacher, you know, you need to do it this particular way. And I think that's where the current landscape breaks down and doesn't fulfill the promise of being the type of learning experience that we all had in school growing up.

• 33:02Dan Shipper?

  And how do you think about, like, so, you know, this experience with ChatGPT, it's like, I can sort of set this thing and then I forget it. And, but it continues popping up, so it feels like it's now part of my life and it's in the way of my normal routine. Whereas my experience with OBO, just using it a little bit a couple of days ago in preparation for this, and honestly, just, I like saw a tweet about it and I was like, I gotta, like, I honestly forgot we were, I knew we were recording at some point, but I was like, this actually sounds sick, I should just try this. And my experience with it is I asked it to make a course and I can't remember what it was, and I was looking at it, I was like, oh, this is cool. But then I was like, and this is, I was on my phone, I was in the airport. And then I was like, okay, like, this is cool. There's a lot here. I read a little bit of it.

• 33:47Dan Shipper?

  And then I just forgot about it, basically.

• 33:51Dan Shipper?

  And it feels like there's enough material in any course that I could spend probably hours over a couple of days on it. But the consumption format feels very ephemeral. How do you think about that?

• 34:08Speaker 00

  It's a, you know, talking about balancing acts again, it's a tough balance to create content that feels lightweight, but not ephemeral. And I think we are continuing to work on that is how do you make this thing feel like a

• 34:20Speaker 00

  an asset that has longevity to it that you can come back to, but not so much so that it intimidates you and makes you feel like it's heavy. I think one of the single biggest issues that exists with any learning platform historically, especially like formal education platforms is the content feels incredibly overwhelming. Right? You're presented with this massive amount of information. It feels heavy. You don't want to even get started. So that I think is an interesting balance. Now that we have a model where users can create as many courses as they want for free and put in as many prompts. I actually think that ephemerality is totally fine, but at any given time, we should be able to retain context about what you've asked before. If you want to pick up where you left, you don't need to go into a pre-existing course.

• 35:03Speaker 00

  We can make a new one picking up where you left off and you should be able to to prompt Oba to let you do that. I will say, you know, given how early we are,

• 35:14Speaker 00

  we don't yet have the re-engagement hooks that a product like this should have. We don't have a mobile product yet, a native mobile product. It's a web-based product right now. And that's all coming. And obviously, learning requires notifications, re-engagement. Learning requires you to learn on your phone and on your desktop and have a native app.

• 35:33Speaker 00

  Where we decided to focus our energy with a small team that we have in the early days was on hyper focusing on the utility that this product would deliver because if we can nail the value proposition, if we can really find product market fit with the use cases that that people would come with, then the sky's the limit in terms of where you could take that in terms of re engagement. What are you using it for? What are you learning with it? So I'm a big nerd and I I for years have been very interested in all of these like advanced math and science topics that I never learned in school. I did not major in math and physics and I wish I had. And you know, this kind of speaks to the mission of the company also is it took me a very long time

• 36:16Speaker 00

  to realize that despite the fact that I was fascinated by all these topics, that I could actually teach myself. And it's becoming increasingly easy for me to teach myself, especially with tools like OBO. And yeah, so a bunch of physics topics primarily that that I had never gotten a chance to learn formally in an academic setting. And now I realize like I don't need the academic setting, right? I'm able to actually go through and teach myself and have an enjoyable lightweight experience where I can jump in and jump out as easily as I want to. What kind of physics stuff are you thinking about?

• 36:52Speaker 00

  In the case of OBO here, I'll let me see, let me see what I've made recently.

• 37:01Speaker 00

  I was reading the other day about the history of quantum physics, like how it was first discovered in the early experiments.

• 37:08Speaker 00

  You know, how did they discover? Like black body radiation and stuff? More the I I forget how to pronounce it's have you ever heard of the? We're going to get very

• 37:22Speaker 00

  yeah Stern-Gerlock, I think it's called. Do you know Stern? It's an excellent follow up to Wittgenstein. I don't know Stern-Gerlock. Okay, so we'll get very nerdy here for a second. So Stern-Gerlock experiment was the experiment that basically proved that.

• 37:39Speaker 00

  Spin, quantum spin, which is the, you know, if you take a particle, any particle, any atom or electron or whatever, it has some inherent spin property where it spins. And, and there's this challenge of determining what direction it actually spins in. And so they built this apparatus, these two experimenters Stern and Verloc or Garloc, I don't know how it's pronounced, and they built this apparatus that basically determines spin, it measures spin and determines it as either being up or down, right? And that's to be expected, like if you throw a particle and you measure the spin of a particle as up or down, then it's, it's one or the other and probabilistically in quantum physics,

• 38:22Speaker 00

  it kind of expects that, you know, it's 50% of particles will be up, 50% will be down. But there are all these really weird variations on the experiment that they did to determine that quantum states are like totally inherently probabilistic and completely unpredictable. And so to give you an example of this, if you were to take a particle and measure its spin as either being up or down, and then you were to measure its spin along a different dimension, so like instead of measuring how much it's spinning on the z-axis, you measure it on its x-axis, and then you measure it again back on the original axis that you had, so you go from z to measuring it on x back to z, there is once again a 50% probability that it's either up or down on this new axis. So this experiment basically proved, and this was, had been theorized for a while,

• 39:04Speaker 00

  but they actually proved it through this experiment, like 100 years ago, that the particle somehow almost forgets the state that it's in, it almost forgets which way it's spinning when you measure it the second time, and when you go back to orienting it,

• 39:19Speaker 00

  my computer, I don't know if you saw that, it sounds like fireworks. I guess I did a hand gesture. Mind blown. When you go back to measuring it on the original axis that, that you measured on, it, it totally like disregards the original measurement, which makes absolutely no sense, right? That's almost like, as if I were to say, I spin a top, or I spin a ball or something like that, I measure it in one way, I measure it in a different way, and then when I go back to measuring it the first way, it's actually spinning in a different direction 50% of the time.

• 39:50Speaker 00

  And the thing that blows my mind, learning about this is not only the underlying physics itself is fascinating, but also this happened 100 years ago. Like, they were able to build successful experiments to determine that this actually was true. Something like 100 years ago. I can look up when this experiment was done. 1922. This experiment was done more than 100 years ago, and it just boggles my mind. It's like, think about the technology that was available to them at the time and that they were able to figure this out.

• 40:16Dan Shipper?

  Totally. It's, it's really interesting. It makes me feel like we haven't really, I feel like Newtonian physics has made its way through all of culture and society, but like quantum physics has not. And we're still stuck in sort of like a Newtonian world in a lot of ways.

• 40:31Speaker 00

  Yeah. Well, it's the one we experience day to day, right?

• 40:35Dan Shipper?

  Well, it wasn't before Newton, you know? Like, we were like, this, this Newton shiz, that's crazy. That's definitely not how it works. So, but it is, I think it is, it is sort of unintuitive. The thing that I've been, because I've been, I guess everyone, every nerd just sort of likes quantum mechanics. So I've been like, reading a little bit about like quantum mechanics and philosophy. And, and so, one of the things I was thinking about, and I'm the furthest thing from being good at physics. So I'm curious, as another non-expert, but someone who I seems to be a lot smarter and more grounded in it than me. And maybe we can make an Oboe course about this to explain it to us live on the show. So you tell me, you tell me.

• 41:20Dan Shipper?

  But I've been wondering about, you know, I think of language models as this interesting discontinuity in the way that we think about how knowledge works and what knowledge is. And when we first started trying to build artificial intelligence, we started from like symbolic AI, which is essentially like, we're going to reduce intelligence down into.

• 41:46Dan Shipper?

  A set of rules that Yep. Are human understandable, are formal, are explicit, and we're going to build our way up to something that can learn anything. And then we just ran into this like big problem, which is it would take more computation than is available in the universe to actually, actually do that. And then we flipped to language models, which I think of as being a little bit more of a postmodern technology that learns like countless

• 42:15Dan Shipper?

  implicit

• 42:18Dan Shipper?

  patterns from

• 42:22Dan Shipper?

  like tiny, tiny correlations and like long pieces of text to figure out what comes next. And that ended up like working really well in this way. That's like, it works, but we don't quite understand it. And we can't reduce it down into that symbolic thing that we, that, that makes it understandable and almost makes it feel a little bit more Newtonian.

• 42:40Dan Shipper?

  And the way that we did that is we like invented embedding spaces. And

• 42:49Dan Shipper?

  and there's something about that whole thing that reminds me a little bit of quantum mechanics. And the specific thing that I think of with quantum mechanics is the, the double slit experiment where I can't, I cannot do the double slit experiment off the top of my head, but more or less it's very similar to what you, what you just explained where depending on how you look at a photon, it shows up as either a particle or a wave. And

• 43:19Dan Shipper?

  there's all these different weird variations of it, but effectively the way that you measure it at the time that you measure it like determines whether it becomes a particle or wave. And before that, it's in like some probabilistic in-between state. And what it, what that reminds me of is sort of embedding spaces somehow. It's like, if, if at different times you measure the same thing and it's a different depending on when you measure it and how you measure it, then there's probably some like high, it exists in this high dimensional way that you're reducing its dimensions down to something that, that you can actually measure in the same way that. You're sort of, you're taking a point in the embedding space and like reducing it down into

• 44:01Dan Shipper?

  the next letter. I'm saying this very inelegantly, but I think you probably get what I'm saying. Does that make any sense? Or am I just like being crazy?

• 44:08Speaker 00

  Well, for a few reactions. First of all, I'll tell you, you just reminded me, there was maybe 10 years ago or something, I was, I had always been a huge fan of the double slit experiment. If anybody listening doesn't know what the double slit experiment is, I probably, I don't think I can think of anything I've ever learned in my life that has blown my mind as much as the fact that this is real. And, and I remember I had a conversation with somebody about 10 years ago where the double slit experiment came up. And we were both just totally nerding out about how fascinating it was. And we had this moment where we were like, to your point earlier around how the world hasn't come around to thinking about this, how is not everybody talking about the double slit experiment all the time? The fact that that actually is a real thing, and we don't just constantly talk about it,

• 44:53Speaker 00

  is crazy. Because it's probably as incredible a like fact of the universe as can be imagined. But, but you're right, there is another, there's definitely a lot of similarities to the fact that there's this other incredible, totally inexplicable thing about the universe, which is that if you take a massive amount of information created by humans, the internet, and you pass it into one of these massive neural networks, it's able to identify patterns that we humans don't even know exist. And it's able to do it, in a way, you know, the massive high dimensional space of these embedding spaces that you're talking about, means that there are dimensions of human output of what it is that we create that

• 45:34Speaker 00

  we're completely blind to. And it's not like the AI goes about, or the, you know, training one of these embedding spaces, it's not like it goes about and labels these axes for us. And so these, these dimensions exist in this high dimensional space that basically totally define patterns and how it is that we behave that we have no idea how to make sense of. And that's certainly a thing that is incredibly weird as a human being to try and wrap your head around like philosophically. How is it possible that we as humans were able to create machines that are able to understand our output? And predict it so much better than we ever would be. And not only that, but do it in a way that we have absolutely no sense of how they actually work under the hood.

• 46:14Speaker 00

  Like, we know the technical reason, way that they work, but we have no understanding of why they find certain patterns? And what those patterns actually represent, because they're just numbers and we can't make sense of them. But you did say something that I thought was interesting, which is, you know, you talked about the probabilistic nature of the L L M's. It's worth pointing out, L L M's inherently are not probabilistic. Actually, the underlying models that power them are 100% deterministic. We put in the probabilistic variance to try and make it sound more human-like and unpredictable. But the truth is, and I don't know if anybody's done this type of experiment, but I would love to see this is like, what does an L L M look like? Where you reduce the variability of its output to zero, which I think it would be the temperature setting, right?

• 46:57Speaker 00

  You basically reduce the temperature of any L L M down to zero, so that the thing that it's determined it's outputting is 100% mapped to the to the patterns that it had found, right? And to the weights that it's giving to each one of the tokens.

• 47:10Speaker 00

  How good or bad would that output be? It probably wouldn't feel very human, but it would weirdly actually be more accurately representative of the output that humans are creating on the internet.

• 47:19Dan Shipper?

  That is interesting. I think the reason why that doesn't work currently, there's, there's really good research from Thinking Machines, which is Miramirati's company. Where they're looking at why, even if you set it to zero, it's actually still not deterministic. And it turns out that it's because when you add floating point numbers of different precision in different orders, you get slightly different results. And those floating point additions are happening in parallel on GPUs. And depending on the order in which it, like the, the work is batched,

• 48:02Dan Shipper?

  it will, it will end up changing the, the end result. And so they've figured out how to fix that so that when you set it to zero, it is actually purely deterministic. But it's like any production L L M is not, except for Thinking Machines, is not actually deterministic, even at, even at temperature zero.

• 48:19Speaker 00

  And I guess the reason is, just so I understand it, because it's parallelizing across many GPUs, I guess there's a race condition where you don't know which computation is going to get completed.

• 48:28Dan Shipper?

  I think it's something like that.

• 48:31Speaker 00

  That's interesting.

• 48:32Dan Shipper?

  It's definitely about the order of operations of float addition being parallelized in batches on GPUs.

• 48:38Speaker 00

  But you know what? I've never thought about this before, but I actually wonder if the human brain works exactly the same way. You know, you could think about the fact that the human brain is a computational neural network with gazillions of parameters. And so this question arises of take out any consideration of quantum mechanics or anything like that, right? In a purely Newtonian world, if I were to get exactly the same input into my brain, would it produce the exact same output? And it sounds like based on this research in the real world, because it's noisy and because, you know, different neurons are firing off at different speeds, you actually have the same race conditions happening in your brain. And so it's totally possible that you would have a totally non-deterministic answer or output, given the same input in the brain as well.

• 49:21Dan Shipper?

  I would, I would guess that it's not the same race condition because I don't think we're doing floating point arithmetic. But, but if you're around old people, they tend to repeat themselves. And in the same, in the same situation, in the same context, they'll say the exact same things. And it gets worse and worse as you get older. And I bet there's something, there's something there and it's, it's something about

• 49:51Dan Shipper?

  the flexibility of your neural pathways and which ones get activated. And you just end up activating more and more of the same ones instead of new ones. There's something like that, I think is going on.

• 50:00Speaker 00

  There's probably a reinforcement thing there too, where as you keep activating the same ones, it reinforces that those are the ones that your brain should be activating, right? And so it ends up getting worse over time.

• 50:10Dan Shipper?

  Yeah, exactly. And I think you also, like as a child, you have way more connections than you do as an adult. So you're constantly like pruning connections. And I think that that process, maybe, I don't know the biology of it, but there's something about that process that I think continues. And then just you ossify it a little bit as you get older.

• 50:26Speaker 00

  Right.

• 50:28Dan Shipper?

  Anyway, lots to learn. I want to make some MOBOS about about all these topics. But I think the one thing that you did actually make me think of is language models know things that we don't know, but that's because we think of knowledge as something that we need to be able to explicitly talk about. And language models are able to do things that we actually know a lot about implicitly. We just have not been able to articulate in an explicit way how it works. So we've been able to write for a very long time. And we have some idea of like how how language is formed from linguistics. But that has not enabled us to generate language in the way that language models do.

• 51:09Dan Shipper?

  But I do think that there's some broadening of our idea of like what it means to know something from looking at language models and looking at how even if we can't explicitly say how they work, they are actually able to embody a corpus of knowledge that it's not new knowledge because it's all generated from us. So it's just knowledge that exists in us in a different way.

• 51:30Speaker 00

  Wait, say that last part again, knowledge that exists exists in us in a different way because of the fact that we can't.

• 51:34Dan Shipper?

  In a different way, it's intuitive. It's it's not something that we can we can talk about, which is actually Wittgenstein's whole point.

• 51:45Speaker 00

  Back to Wittgenstein.

• 51:51Speaker 00

  Yeah, look, I think there is the mind and the internal way that the mind works. And then there's the way that the mind projects out into the real world and the way that we look at it. And I think if anything, LLMs have probably forced us to realize that those two things are massively disconnected. Right? Like you can't just because a mind is conscious and self-aware and aware of its own output, doesn't mean that it understands any of the mechanics of how it works under the hood.

• 52:19Speaker 00

  Yeah, I mean, I.

• 52:23Speaker 00

  I think there are, we are in the, we're very much scratching the very surface from a physics stamp of the implications on physics and philosophy and things like that of all these questions around like, what is the mind and what is consciousness and all that? And I think, I often think, try to reflect on like, are we ever going to have some kind of breakthrough that actually answers some kind, these fundamental philosophical questions that you're talking about? I don't know that I'm convinced that that actually could happen, you know? I think the human mind biologically developed in a way that, evolutionarily developed in a way that probably intentionally obscures all this from us and makes it so that we,

• 53:07Speaker 00

  we probably can't do the things that LLMs can do and we can't identify the patterns that LLMs can identify. I don't know, I, I think I've convinced myself of that, that like, there is a limit to how much we can actually know about this, but you might feel differently.

• 53:19Dan Shipper?

  I, I think that I've just broadened the definition of knowledge because anything that an LLM can do, a human has done. And I think that should count as a form of knowledge and if we can't explain it.

• 53:31Speaker 00

  That's fair. That's fair. Although what an LLM has access to in terms of its input and its ability to train on all these different, you know, corpuses of.

• 53:40Dan Shipper?

  Any individual person can't, but humans have.

• 53:43Speaker 00

  Humans collectively can, yes, correct. Yeah, that's right.

• 53:46Dan Shipper?

  Yeah. Well, that is, I think, a great way to end it. Nir, thank you so much for coming on the show. People want to try OBO or find you on the internet, where can they find you?

• 53:57Speaker 00

  OBO.com. Go create as many courses as you want, you know, let us know what you think of us, feedback, we're always welcome to it, really appreciate it. Yeah, I'm Nir Zikraman, you can find me with all, my name is my handle on all the various platforms, so we'd love to hear from everybody and if you have any product feedback, let us know.

• 54:18Dan Shipper?

  Awesome, thanks for coming on.

• 54:19Speaker 00

  Thank you, Dan, appreciate it.

• 54:28Speaker 04

  Oh my gosh, folks. You absolutely, positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard. But instead of gold, it's filled with pure, unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.