How to Apply Learning Design for Success in Business and Life | Stephen D. Torres | Glasp Talk #20
This is the twentieth session of Glasp Talk!
Glasp Talk delves deep into intimate interviews with luminaries from various fields, unraveling their genuine emotions, experiences, and the stories behind them.
Today's guest is Stephen D. Torres, an accomplished educator, learning scientist, entrepreneur, and venture capitalist. Stephen is a mentor at StartX, an accelerator from Stanford University, and a partner at several venture capital firms. He holds a bachelor's degree from UC Berkeley, an MBA from Cornell University, and a master's degree from Stanford, where he specializes in Learning Design and Technology.
In this interview, Stephen shares his unique journey from starting as an account and sales manager to becoming a learning scientist and educator. He discusses the turning points in his career, his passion for understanding how entrepreneurs learn, and the intricacies of learning design. Stephen also provides deep insights into the science of learning, the impact of AI on education, and the nuances of knowledge transfer. Join us as we explore Stephen D. Torres' inspiring path and his contributions to the fields of education, entrepreneurship, and learning sciences.
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Transcripts
Glasp: Hi everyone, welcome back to another episode of Glasp Talk. Today we are very excited to have Stefan Torres with us. Stefan is an accomplished educator, learning scientist, entrepreneur, and venture capitalist. He’s also a mentor at STX, which is an accelerator from Stanford University, and he’s a partner at several venture capitals. He has a bachelor’s degree from UC Berkeley and later earned an MBA from Cornell University and a master’s degree from Stanford, where he majored in Learning Design and Technology.
Stephen has a deep passion for inspiring innovators to become great readers and has made significant contributions as a speaker, trainer, and venture capitalist. Today, we are very excited to dive into insights and experiences of, you know, learning and knowledge transfer. So, thank you for joining us, Stephen.
Stephen: No, very excited to be here. Happy to share, thank you.
Glasp: So first of all, we’re interested in your career because you started as an account manager and sales manager, and then later you became a learning scientist. We wonder what happened and what made you go in this direction. Could you share a little bit about your background and, you know, was there any turning point in your career?
Stephen: Yeah, absolutely. So, I was born and raised in California. I’m actually from the Central Valley of California, a place called Fresno. Lots of agriculture, you know, growing up there you certainly have a lot of exciting things when it comes to family and being in the community, etc.
I started my career actually in sales and was very fortunate to do okay at that. I had a great time in consumer products, selling kitchen housewares and cookware, and things like that. I did pretty well and got up into management. I was one of the youngest managers in the company and learned so much by doing that. It’s a really exciting thing when you learn how to create a business.
I think that’s when someone starts in sales, what people don’t realize is that sales create a lot of business. There’s a saying one of my mentors used to say: "Nothing happens until someone sells something." Whether it’s an entrepreneur starting with an idea and recruiting a co-founder or getting investment, it’s all sales. So, I did that for a while and then continued working and moving up. I got into management and was very fortunate to have just some great people around me who helped.
From there, I got into software. So, I started in consumer products, then got into software. It was a SaaS company here in the Bay Area. After growing up in Fresno, I did a stint in—where was my stint?—Modesto. Well, first it was Stockton, Modesto, and then eventually got out here to the Bay Area. You know, being in the Bay Area, Silicon Valley, whatever you want to call it, I think that certainly had a lot of influence. I started working in technology. From technology, again, that was another sales position. I moved on from there into renewable energy. That was a sales and marketing director position.
I really wanted to get my MBA at that point. I had already graduated from Berkeley and decided to do that, so I went to Cornell. That was an amazing experience. After that, I really got the bug for entrepreneurship. I actually launched my first company when I was an MBA student, and it’s a very interesting story. It was a New Venture class—New Venture Management, I believe was the name of the class—and you had to come up with an idea and create a business plan, etc. So, I did that, and it just so happened that business worked. It was a data analytics company in the renewable energy sector.
After that, I had another startup, also in renewable energy. It was a customer acquisition platform. That was acquired, as was the first one, and then I worked for a large investor who had a portfolio of companies. I started working with one of his companies after we were acquired, and that was an amazing experience because he was just a wealth of knowledge.
It was about that time that I started teaching. One of my old professors from Berkeley asked, "What do you want to do next now that you’ve had a couple of exits?" I was like, "You know, I’d love to teach." He asked, "What would you want to teach?" and I said, "Entrepreneurship and Leadership." He replied, "Well, we have this class," and that’s how I started teaching.
It was through teaching that one of the things I do for all of my students is give them lifetime office hours, meaning any one of my students who I've had can reach out to me anytime to talk about life, business, career, whatever. Usually, every week I have one to two, sometimes more, students who will reach out to me and say, "Hey, I have this idea," or "Hey, I’m thinking about switching jobs," and so forth. What I was really enamored with when teaching entrepreneurship was the part of learning that entrepreneurs go through. I was really fascinated by this because the information, you know, yeah, there was some information asymmetry, but for the most part, especially as the internet came around and all of these things—you have Twitter, you could literally access all the information—so why is it that some people are still way more successful at entrepreneurship than others?
So, I started going down that rabbit hole of learning, like what is it? What does it take to actually learn something? What does it actually mean? That just fascinated me to no end. So, as I was teaching, I decided, you know what, I want to go back and actually learn how people learn, and that’s kind of what started me on the path of this learning scientist transition. Of course, I love entrepreneurship; I’ve been working with entrepreneurs, whether it was myself or other people, for decades. So, that’s not changing, but that’s really where it started was, you know, how do entrepreneurs learn, and why is it that some entrepreneurs are more successful?
It turns out that a lot of the things that people do, anybody could do, it’s just a matter of being able to execute them, being able to do things, and that comes down to learning.
Glasp: Very, very interesting topic and path, and yeah, thank you for sharing. At the same time, I’m curious, you know, learning is a really broad topic, as you mentioned, and knowing doesn’t necessarily allow you to do something. Sometimes execution matters. But you studied learning design, right?
Stephen: Yeah, so think of it as how do you create systems, environments, curricula, and experiences that people actually learn from? Now, I should define learning because there’s something different between Did you learn it or do you know it, right? I think when we spoke the first time, I mentioned this, Kazuki. Knowing or having the information isn’t always enough, right? What I like to say is, if information was enough, everybody in the United States would be skinny, rich, and happy because all the information to be skinny, rich, and happy can find. People still aren’t skinny, rich, and happy, right? Why not? Well, it’s not that they don’t know. I used to be really large, overweight, and obese, and I knew that I shouldn’t eat a whole half-liter of ice cream every day, but I did it anyway, right? So, I had the information. Back then, we didn’t have ChatGPT, but I’m sure ChatGPT would have said, "Hey, don’t eat the ice cream." I still ate the ice cream, right? So, there was this thing. And so, this is where learning comes in, right? One of my professors at Stanford, the way that she defined learning, there was an element of not only having the information but acting differently because of the information you had.
This was interesting, right? This is, okay, so if you know something but you don’t actually do what you know, did you learn it, or do you just know the information? So, that’s kind of, I think, the things that I was thinking about, and then, okay, so if you need to act differently, you have to be able to, at least in your brain, recall or do something different at a future state. So, how do you do that better? And this is where the design comes in. You can actually design learning experiences, curricula, and so forth that make it easier for someone to recall the information down the road. That’s learning design. One of the things that shocked me was that, particularly in universities, you have a lot of amazing people who know the subject—they really understand X, whether it’s geology, sciences, literature, whatever it happens to be—they know it cold. They did their PhD, they researched it for years, they’re constantly working at it, and they know the subject, but they’ve never learned how to teach. They don’t know how students actually learn. They don’t understand the cognitive process, the experiential elements, you know, all of these other things that go into someone being able to learn, which, you know, if I use my old Stanford professor’s thing, being able to act differently because of the information that they have in the future. So, that’s where the design piece comes in, that you can actually design learning experiences, you can design learning pedagogy, you can design different things. There’s what we call mechanics of learning, things that teachers, leaders, anybody can do that actually help people learn, help people remember or recall, technically, so that they are, quote-unquote, acting differently because of the information that they have.
This is, I think, the key insight that I had, was like, wow, every entrepreneur understands what product-market fit is, they hear it, but they may not understand how to actually get to it. Well, getting to it is a different thing, right? And then how to actually do the mechanics so they get there, those are the things that really resonated with me. And then, you know, to further build on that experiential piece, what I start looking at is, well, gosh, if you could do that, and let’s say that you’re working with entrepreneurs as I work with entrepreneurs if you could use these mechanics to help more entrepreneurs, you actually help them create better companies, they do it quicker, they employ more people, they have greater impact. You know, what else would I want to do, right? And so, that’s kind of how I got here. But that was a long, roundabout way, Kazuki, for me to explain the design aspect of learning is learning should be designed, right? It shouldn’t just be, you know, you throw stuff out there, which is how, frankly, most teachers do it, right? They do what they see because that’s what teachers did as they were coming up.
One of the startup founders I work with here at Stanford, has created a whole platform that basically helps teachers create some of these learning experiences and do it better, have pedagogy, experiment, and do all this fun stuff to make learning better for students. So again, that’s the design element I see.
Glasp: Yeah, and also you tapped into the know-do gap, right? Knowing and doing are different, and there’s a gap. I think all audiences, people want to know how to apply things we learn to daily work, as you mentioned, you know, knowing as a founder, knowing product-market doesn’t let us reach product-market, right?
Stephen: That’s exactly right, yeah. You know, there is what I call the know-do gap, the difference between what you know and what you actually do, and it’s a huge gap, right? We see it all over the place. How many times has someone told us something, "Oh, just do this," and you’re like, "Yeah, I know that I’ve heard it all the time, I just don’t do it," right? And so, there are a couple of things that you could do. One is learning how to create good habits, right? How do you affect your own behavior? One of the professors I studied with here at Stanford, his name is Dr. BJ Fogg, and he’s an expert, probably the world expert on behavior design. I actually have, I’ll plug you should get this book, it’s amazing, but in it, he actually gives you the recipe, right? And I’ll share it with you, but I’d encourage you to go to his website. It’s Tiny Habits. The recipe is very simple: Behavior equals motivation times ability times prompt. So, it’s B=MAP: Behavior equals your motivation, your ability, and some type of stimulus or prompt.
And if you can understand this concept, you can actually influence your own behavior. You can also influence the behaviors of others. There are some caveats around this and how to apply it, and when to apply it, right? Because you don’t want to do it in a bad way; you could manipulate people, and that’s not what we want. You know, BJ has these two maxims that I think are amazing, which is helping people do what they already want to do, and helping people feel successful, right? And I really love that idea. But so, getting to how do you actually do what you know? Well, the one is you have to have the motivation, right? And BJ calls it the motivation monkey, right? And this motivation, it can be high, it can be low, it can go all over the place. But the higher the motivation, the easier it is for you to do something. And on the other axis, you have the ability. So, the harder it is to do something, the less likely you are to do it; the easier, the more likely, right?
So, if you think of those two things on an XY axis, right, your motivation to do it, high or low, and your ability to do it, high or low, wherever you are, what you want to do is there’s a certain threshold that you have to either be above to do it or below, it doesn’t happen. And then you have these things called prompts. These are, you know, the little reminders, if you will, alerts, they’re little things that get your attention. So, think of it this way: If you’re getting a phone call from your best friend or your mom, right, she’s calling you on your cell phone, but your cell phone’s in the other room, and you didn’t hear it, there was no prompt, or you didn’t feel the buzzer, do you answer the phone? You can’t because there was no prompt, right? And so, this is, again, another one of those factors.
In his book Tiny Habits, BJ really outlines this. He goes over the research that underpins all of these things to learn how you cross the chasm of the know-do gap, the difference between what you know and what you do. There’s a whole system that he has. Again, I highly recommend it; I’m very biased because BJ is amazing, and I actually worked in his Behavior Design Lab at Stanford, so, you know, full disclosure there. But that’s how you do it, is you have to figure out how to impact your behavior, and there’s a way to do that.
Glasp: I see. Interesting. Just curious, like, you know, how behavior is different from custom or habits, right? So, how to make habit behavior into customs or habits?
Stephen: So, think of them as, yeah, they’re technically different, but habits are behaviors, right? Habits are essentially repeated behaviors, they’re things that you do because of certain things, right? Hence the habit. And where do they come from? They typically come from prompts, right? So again, think of this: Behavior actually stems from some of these habits or habits from behaviors. So, as you have a prompt—let’s say it’s a—and a prompt could be some type of outside thing, it could be an inside-your-head thing, it could be a biochemical reaction for wanting nicotine if you smoke, right? There are all types of prompts that are out there, but those prompts will essentially push you on a path toward what behaviors you do. This is in turn what forms your habits. Does that make sense? So, I wouldn’t look at them as mutually exclusive, right, behaviors and habits. Instead, I would look at them as part of this ongoing organism or life, you know, that is Stephen or Kazuki or Kai, right? It’s what is happening there.
Glasp: I see. Makes sense. Yeah, because a prompt is, like, internally happening or externally happening, but if it’s internally happening, motivation is related to emotions or meaning, so it’s related to each other, I think.
Stephen: Yeah, and here’s the interesting part: You can affect them, right? This is what most people don’t understand about these things, they think the world happens to them, when actually, you know, the world happens to them. And if you understand this, okay, here’s how I think. And then, if you take that thinking to the next one, which is how I feel, and you can focus down what happens—how you think, how you feel, and then how you act—so your thinking actually influences how you’re feeling, how your feeling influences what you actually do. Again, this is one of those things that people get it, as they understand it just intuition-wise, but what they don’t know is, okay, how do I actually engineer this for myself, right? How do I design it for myself? How do I learn how to focus, for example, on specific emotions? Because as those emotions change, you can actually change your actions, you can do different things. It’s a very fascinating thing, and this is all part of what got me into the learning sciences, is when you start to understand these and learn this, okay, how do you now teach this so that other people can have the same success?
Glasp: I see. Interesting. Could you repeat the name of the book again?
Stephen: Wait, say that again?
Glasp: Could you repeat the name of the book, the title?
Stephen: Oh, the book? Yeah, the book, it’s a New York Times bestseller, it’s called Tiny Habits by Dr. BJ Fogg. He’s awesome, by the way. I think the website, I believe, is tinyhabits.com if I’m not mistaken, but it’s really awesome. He also has some free stuff that you can go through at no cost; they’re really phenomenal. So, I highly recommend it. Again, I’m biased because I was in his class, I worked in his lab, I think he’s an amazing, amazing person.
Glasp: Okay, thank you. Thanks for that. I’ve actually watched his video and lecture on the internet before.
Stephen: Yeah, yeah, he’s really popular.
Glasp: That’s exactly right.
Stephen: Yeah, you know, it’s good stuff.
Glasp: Yeah, and also, you mentioned that teaching enables us to remember more, right?
Stephen: Right.
Glasp: So, some people say it’s called the Feynman Technique, you know, teaching, learning by teaching, learning by doing, or something.
Stephen: Yeah, that’s one mechanic.
Glasp: At the same time, I know another one is spaced repetition, it’s also a popular learning method or technique.
Stephen: Oh, yes, absolutely.
Glasp: Does that work effectively?
Stephen: Oh, yeah. Here’s the thing about learning, and I think we talked about this last time. There are people who’ve been studying this for decades. One of my advisors, Roy P., I think he’s probably the world’s best encyclopedia on learning sciences. The dude knows more than anybody I can possibly think of, and whenever I need, "Hey, I’m thinking about this," he’s like, "Oh, you should read this, this, this, this, this." He’s absolutely amazing.
But the thing is, the studies of how people learn really give you insight into what’s happening both cognitively and physically. You have to understand that humans are all-encompassing organisms, right? We are animals as far as being able to take in these inputs, whether they’re sensory, cognitive, verbal, hearing, or sight, it all comes into this one CPU, right? Our CPU is made up of several parts, you know, we have a GPU, right? We have all these things in this little brain. It’s funny because right now there’s this huge push to how do we make software and hardware that can do things that this little several-pound mass in between—like I’m looking at you guys—in between your ears up here, how can we create, you know, things that replicate or are similar, right? Because we’re not going to necessarily recreate a microorganism, at least right now, maybe in the future, who knows, right? All, everything’s fair in the future.
But the thing is, with the brain, these stimulations that come as inputs actually change, like they physically change, right? If you understand how brain elasticity works with neurons and synapses, I’m not going to get into all that because we could talk about that for like 10 days. But your brain is constantly changing, right? And this is a very unique thing. So, because your brain is constantly changing, there are different things that help it over time.
So, when you think of just overall learning, you have something that’s called encoding, right? Information or knowledge that comes into your brain, and then it has to be stored there in some way. The brain is not like a CPU where there’s ones and zeros that are written somewhere; it’s not like that, right? What happens is we create these electrochemical reactions, and the way I like to think of it is to think of them as lightning bolts, right? They’re lightning bolts from one position in your brain to another position, and that’s a memory, right? That’s something. But it doesn’t stay there; there’s just a path that it went down. And if we want to recall that, we want to remember something, we’re not going back to the actual memory that happened. It’s not like ones and zeros were encoded. What we’re trying to do is get back to the same lightning strike. Make sense? And it’s hard to get back to the same lightning strike, because your brain has to recreate it, and it doesn’t always recreate it perfectly, right?
This is why you—again, there are tons of studies on this with, you know, people remembering things after the fact, like car accidents. People could see the same thing, but they remember two different things, sometimes it’s not always correct. Most of the time, it’s actually not always correct, and the reason is because what they’re doing is they’re trying to recreate lightning bolts in their brain. So, all of that to say is, okay, so now if we know that these lightning bolts, they happen, they have to be recreated, what’s the best way to recreate those? Well, it turns out that if you give it a little bit of time and then remember it or come back to it, we create a stronger lightning bolt, and, you know, maybe the lightning bolt breaks down and the next thing there’s something called myelination, which is actually things forming around that make the neuro path—anyway, it gets really complicated, you can look all this stuff up, right? There are great YouTube videos on this.
So, this spaced practice, interleaving, you know, doing by teaching, learning by teaching, all of these mechanics, what it turns out is they’re helping to shape the brain, right? That’s what they’re doing, it’s helping to shape the brain. And so, you know, it’s very interesting now because now we have tools that we never had before, right? You look at all the stuff that’s coming in AI, which is fantastic. Now, how is that going to impact learning? Well, you know, nobody knows. I think there are lots of people now who are going to start studying this because we know that it’s different, and we know that it’s different because the environment is different, right? When you have a different environment, brains change differently. This is why someone growing up in Japan and someone growing up in the United States of America, this is why they’re different because this external environment that they’re in created the brain. You take someone out of one environment, pop them to another, different learning scenarios, different learning environments, all of these things.
So, I think that’s, you know when you’re talking about the spaced repetition, the interleaving, you know, all of these things, the reason that they work is because there’s a better way than others that helps shape the brain so that you can do eventually what you want.
Glasp: I see, yeah. Very interesting topic. And then also the next question comes to me, how to strengthen the path to create that lightning in the brain? So, some people take notes digitally, and some people take notes physically, right? Are there any differences in performance?
Stephen: So, there’s actually a lot, right? We talked about this last time, right? Here’s the deal, we have to understand that the brain takes all types of inputs, right? Whether it’s physical inputs, whether it’s cognitive inputs, and it has to formulate these lightning bolts, right? Again, I’m just using this as an analogy; they’re not real lightning bolts in your brain. But there are certain things that work better than others at consolidating these lightning bolts into specific things so that you can recall them later. It turns out, according to studies, that when you physically write notes, whether it’s on a piece of paper, maybe it’s on a digital iPad, but when you physically do this, the learning is better. You will learn more, you’ll be better at it, you’ll be able to recall more. Now, the question is why, right? You would think that, well, gosh, if I’m just taking notes on my computer, I get way more notes, right? I can essentially transcribe almost word-for-word as someone is talking, but when I have to write notes, I’m not getting all the notes. Turns out, this is what we, in learning science, call a desired difficulty, where what’s happening is your brain is now taking all of this information that’s coming into it, and what it has to do is cognitively process it. It’s a heavy, what we call cognitive load. Turns out, this is one of the keys to more learning because you have to take this heavy, dense, all information that’s coming at once, you have to understand it, you have to figure out what’s the best way to summarize this, and then write it physically out in words, right? Symbolic systems. Turns out, that’s wonderful for the brain. It’s wonderful for recall, and remembering, and it’s wonderful for getting deep conceptual learning and what we call understanding, right? Where you really understand the concepts. Versus, when you’re just basically taking notes on a computer, what ends up happening, is you end up transcribing, meaning you’re no longer thinking cognitively about everything that is said. What you’re doing is you’re matching words into your fingertips to type those letters. That is a very different cognitive process than actually summarizing it, getting key concepts, and physically writing them out because you’re not writing the same thing word-for-word, you’re writing bits and pieces that have larger meaning. Does that make sense?
Glasp: Yeah.
Stephen: So, you know, this is, I would tell you—and I think I said this last time—I have the perfect way for you to get worse grades, remember less, and actually be a terrible student. Take all of your notes by computer, and transcribe them. It’s brilliant, right? That is a wonderful path for doing really poorly in the long term. Short term, you may get some stuff, really good way to do bad long term. To do good long term, now, it’s going to be painful. It’s a desired difficulty. Take notes physically, whether it’s in your iPad and you’re physically writing them out, or a notepad and paper, because what’s happening is your brain is working harder to take in the information, cognitively process it, and repurpose it. You’re actually teaching yourself, right? Because you have to write down, hey, he said five different things, but I can only write down one in a time, so how do I put those in this one little thing? Maybe I make a little drawing, and I do a little thing with five little sprockets, which again turns out the system good spatial—anyway, we’ll get into all that. So, that’s what I would say about note-taking right now. Having been a professor at Berkeley for many years and teaching classes at Berkeley, Stanford, Santa Clara, UC Davis, you name it, I always see students, and I almost feel bad for them because they don’t know this. No one actually says, "Hey guys, if you want to be a worse student, use your computer to take notes." Nobody says that. They’re just like, "Do it." And in fact, when I tried to do that at Berkeley, students would complain, like, "I want to take notes on my computer because I get more information." Information’s not enough; you’ve got to actually learn something. Anyway, so yes, I hope that answered your question there because I think that was a lot.
Glasp: Interesting, yeah. And yeah, thank you for the great advice on becoming the worst student in class. But just curious, even if we take notes on the computer, if we at the same time summarize it in our brain, not just transcribe what the teacher says, but if we summarize cognitively, processing what the teacher says and summarizing, taking notes on the computer, is that the same thing?
Stephen: No, it’s not. You have to understand why because think about—do you have your keyboard in front of you?
Glasp: Yes.
Stephen: Okay, put your fingers over the keyboard and then think about when you type, what do you do? What’s the actual physical motion of typing?
Glasp: I just listen to what the professor says.
Stephen: Yes, but what’s happening with your fingers?
Glasp: They just automatically, you know, move. Not looking at them.
Stephen: Yes, your fingertips are going up and down, right? Now, do you have a pen?
Glasp: I can grab it, yes.
Stephen: Okay, take a pen and write out your name in the air. What happens to your fingers?
Glasp: Like, moves like drawing.
Stephen: Yeah, do you see the difference between this and this, right? It’s an actually a different physical movement, right? This is what we call embodiment because you’re doing something physically different. It’s not the same. It turns out in learning, the way that we learn with speech and symbols, there is something that happens when we actually write something out that’s different in our brain than simply doing this. Now, of course, who knows? We’re in a very different time, and all of these studies were predicated on past research, and there’s new research that’s going to be happening, and maybe it will change. But right now, all the studies actually show that there is a difference when you physically write out words than when you type them. And it makes sense because you actually see it. Doing this is a lot different than doing this, right? Your body works differently. Your head, your shoulders, there’s a whole physical embodiment. Again, most people don’t realize this; they just think, "Oh, I’m writing it. I’m typing. It’s the same thing. The words are the same." But they’re not, right? And this is the nuance. This is the nuance that most people, like—there’s a really good book here, another one, Thinking, Fast and Slow by Daniel Kahneman. I don’t know if you guys have read that, it’s amazing. But he uses the analogy of heuristics and the thinking fast and thinking slow. Most people think really fast. I do it all the time, we all do it because it’s a shortcut. But when you actually start to dig in and actually see what’s happening, it’s that slow piece. And so, this is, you know, I would say one of the reasons why typing is very different than writing.
Glasp: Interesting. It is fun when you start to know this because most people don’t think about it. And again, this is all learning science, right? Most people don’t actually think about learning and all the things that go into learning.
Stephen: Yeah.
Glasp: So, it’s probably a mean question, but if we take notes with an Apple Pencil on an iPad, is that different?
Stephen: Well, right now if you’re—I don’t have my iPad in the other room, sorry. I have my Apple Pencil, but it would be similar, and the reason is that you’re doing the same motions. Think about as a kid, right? As a kid, you had to learn to do the motions, and so there’s this—what we call embodiment. I mentioned it earlier—where the learning and everything is part of that, that the word, you know, think about your—I like using your name as an example because the way you write your name, means something. There’s actually something there when you physically write it out, when you make your signature, there are all of these things, and you’re going through the same motion, which means you’re using the same parts of your brain, which again tie into the learning. And right now, what the learning sciences say is that physically writing notes is better than typing. Again, who knows? Maybe in 10 years, as more research is done, it’ll shift. So far, it hasn’t. All of the studies indicate that.
Glasp: I see. And also, we talked last time, but there’s a preference, right? I prefer to read on Kindle, and also physical books, but does it matter your preference?
Stephen: Yeah, so here’s the thing, right? I’ll use this in the context—there are a lot of people who say, "Well, I’m an auditory learner," meaning I learn best by hearing, right? Or, "I’m a visual learner, if I see it, I actually learn better." What the science actually says, and what the research shows, is that learning happens in all different ways, and actually sometimes the harder one, to slow down and read something even though you would prefer to hear it, is actually the best way to learn something that is super dense or technical, or vice versa, right? I’m using these as analogies, but every one of us has a preference where we prefer to take in stuff that we hear, we prefer to see it, we prefer to read it, we prefer to actually go and play with stuff with our hands. Those are preferences. They’re not actual learning styles that you learn best by. That’s a myth, right? And it’s a myth that is still out there. Teachers say it all the time, "Oh, I try and teach students in their specific learning style." There is no one best learning style. What it is, is students have different preferences, and there are different things to learn things, right? If you’re learning geospatial things, sometimes being in the maze is going to help you, but for some people, that’s not the best condition for them to learn, right? So that’s what I mean by the styles and the preferences. We all have our own preferences, everybody—you have them, you have them, I have them, everybody has their preferences. But learning styles that we learn best by, it’s not necessarily true because there are different ways to learn different things, and different mechanics that help you learn specific things in better ways. That’s what I mean there. And I just realized I cussed. I hope you can cut out the cussing. I hope that’s not one of the banned words.
Glasp: No, no. By the way, I’m curious about how you manage or keep your knowledge, and do you use any tools? How do you take notes? Because I think you have less of a know-do gap, I think.
Stephen: I don’t know about that.
Glasp: You know what it is.
Stephen: Everybody has it. There are certain things that I do that have helped me, right? One is, I think I mentioned this to you last time, I have here, I have these little 3x5 cards when I read where I kind of do my own little summary. You can’t see this—this is actually from Dr. Bridget Barron, another one of my Stanford professors. It was interest in self-sustaining learning as a catalyst for development, a learning ecology perspective. So, this is a research paper that she wrote, and I put, you know, after I read it, I put all my stuff in here, and then I review these. That’s why it’s on my desk here, I come and I just kind of review them, I forget it, come back to it. That’s how you kind of build on those types of things. That’s what works for me.
These ones, I don’t know—there’s another one. Which one was this one? This one was Packard and Cole, institutional foundations of human evolution, autogenesis, and learning. So, this is just some of my resources. And I actually, you know, you could see some of the drawings that I make and different things that I’m thinking, different structures, little graphs that I write in here. This is, again, just to kind of help me remember, and learn. And then the key is I go back and I review these things. That’s why they’re right here, is I’ll reflect and go see. And what that does is it creates the memories, I’m like, "Oh yeah, I remember that. Oh yeah, there was something else. Oh yeah." And that’s how you kind of ideally expand your melon, your brain.
Glasp: But over time, your notes will be bigger, right? And sometimes you cannot carry them every time. So, have you digitized your notes?
Stephen: I haven’t. It’s funny, I have—you can’t see it down here, but down here, I have every year—well, I’ll just show you this. So, I actually do take notes—sorry, can you still hear me?
Glasp: Yes.
Stephen: Okay. I use a physical thing where I write in my days and everything that’s happening and kind of plan it out. So, what I do at the end of the year, and then this is my actual schedule here. What I do at the end of each year is I’ll spend a week between Christmas and New Year’s, and I’ll go through every day of the year. And each day I’ll reflect on what I did, what I was doing, what I was thinking, my thoughts, and everything, little graphs and things that—so that’s why they’re right here. It gives me a chance to reflect, but then what I can do is I can then go in and I can look here at the past. I have about 20 of them down here. So, the past 20 years, I have had these physical books. So yeah, it’s kind of like a diary, a reflection. I can go and tell you what I was doing eight years ago on August 14th because it’s in my little thing here. I can tell you who I was with, and what I was thinking, it’s all here. But have I digitized it? No.
Glasp: But how do you search for information, let’s say, you know, do you use a custom method or, you know, like to index your personal notes, especially for books?
Stephen: No, no. The assumption that you’re making is that, well, if you could just find the information quickly, you would remember it better, right? That’s a faulty assumption because as I was taking—as I was explaining, the hard part actually is helpful, right? So, when you can’t remember or you can’t find it, that searching, "Oh, I was trying to remember, what was I doing? There was something that I said, it was about this." There’s this difficulty to get it, and then eventually you get it, you’re like, "Ah, that’s it." That actually tends to help you learn better, right? And so that’s because you forgot it, it went out, you came back, you reignited the lightning rod. You did it again. For some people, yeah, they may like that. Is it actually—I shouldn’t say it’s not helpful because it is, it is helpful, right? To get it quicker, and I’m not trying to say that you don’t want to be able to access it quicker and so forth. That’s not what I’m saying at all. What I’m saying is that it doesn’t matter whether it’s digital or whether it’s physical. The idea of remembering and trying to go back to when it was and doing that search, whether it’s a digital search or whatever, it’s a very—it’s a similar thing. So, it doesn’t matter whether it’s digital or physical, right? Now, it may be easier if it’s digital. Okay, maybe it is, but because it’s physical and I have to go back to the day, that doesn’t actually make it worse. Does that make sense? I don’t know if that makes sense.
Glasp: Makes sense, yes. And sometimes, easy come, easy go, right?
Stephen: Yes, and that’s exactly—that’s a great way to put it, really great way, Kazuki, is because, you know, we have a short-term memory and a long-term memory. The short-term memory is fleeting, right? And what happens, so like when you take notes with your computer, typically what that is, is you’re getting stuff into your short-term memory, but you’re not going to have it long term to use. This is why I said, if you want over the long term to be a worse student and to get less from your education, whether it’s college, high school, or whatever, take notes on your computer, right? Because you’ll get through the class, you’ll get enough in short-term memory to pass your classes, maybe even get good grades, but that’s not going to be there for your long-term knowledge that you’re going to use to transfer and so forth. You guys have all seen this, right? Have you ever met people who are like, "Wow, how do they know so much? It’s like they’re walking encyclopedias." Typically, it’s because they had to work really hard at actually learning it. And usually, that was, you know, physical learning, forgetting, repeating, coming back. Now, don’t get me wrong, there are corner cases where people have, you know, memories off the charts, right? There are some of these types of things, but for the most part, they’ve worked really hard on learning.
Glasp: So, like you, right? You’re walking—
Stephen: Yeah, I do. I work hard. My bookshelf here, a lot of people just think, oh, they’re books. These are books I’ve actually read. I have another bookshelf over here of stuff that I’m reading, and I have other things over here that I’m reading, but these ones I’ve actually read. I used to have another one, but I got rid of all the books. Those are the books I’ve actually read. Now, can I remember every word on every page? Absolutely not. But do I remember different ideas that came, you know, in certain books that I still utilize? Absolutely, right? And that’s, you know, that’s the key to learning, I think.
Glasp: Nice, interesting. But in the future, yeah, I understand, you know, the whole concept, but, you know, in the future we cannot, like, how do I say, you know, the next generation, future generations need to—will certainly use computers and PCs to do something, right? And also, we need to collaborate with AI. I think AI is a huge topic in learning, I guess, nowadays for in classrooms also, you know, like learning for work and so on. So, how should we use AI, like, you know, in the future? Because some people say, you know, because of AI, our intelligence will get worse because of AI, because we rely on everything on AI, so we let AI do everything so that we have less time to think about something. So, what’s the source of AI, and how should we deal with that?
Stephen: Look, I’m super bullish on AI. I think it’s one of those things that, you know, I remember when ChatGPT launched—now, by the way, AI is not new. I want to make sure—you know, I know right now everybody’s on the hype train because of ChatGPT and the actual accessibility, but the stuff that underpins that, that research has been going on for decades, right? Back when I was at Berkeley in the 2014, ’15 era, people were working on AI. In fact, it was kind of a bad word, and they started calling it machine learning because you couldn’t use the word AI, it was in what did they call it, an AI winter or whatever, something like that.
But I think it’s going to change, but we have to understand what AI actually is, right? What is artificial intelligence? Well, it’s software with statistics. Oh, perfect. What does that mean? So, let’s be clear, the machine isn’t intelligent. The software running on the hardware is giving us humans information that we perceive as intelligent. Okay, so I think that’s one of the first things to understand. It’s like, look, the software and the thing isn’t—it’s not like biological intelligence; this is software intelligence. So, we really got to make sure we understand that.
I think the other thing we have to understand is the way this works is you’re taking previous knowledge, and like in regular learning, we have prior knowledge, which is data, and we’re processing it in a way using really fun algorithms, math, probabilities, statistics, to give us what a reasonable human would classify as meaningful information, which is wonderful. I think that’s great, and I think that it’s only going to get better. Now, will it be able to do what humans actually do, right? And when I say what humans do, like, look at kids. I recently had a barbecue, and a friend of mine had her kids over here, and, you know, they were young, four or five. And you see what they’re able to do and learn with no data, right? Is software going to be able to do that? Right now, it doesn’t look like it. Right now. And I know there’s a lot of people—I was talking with a computer scientist from Berkeley yesterday—there’s a lot of people working on how do we actually, you know, improve this? How do we have machine learning learn without prior data? How do we have it learn with, like, fewer data points? Like, there’s a lot of people doing this. But right now, the software isn’t there. Will it be able to be in the future? Maybe.
Okay, so now we’ve classified, okay, what’s really going on here? I used to in Berkeley, I had a—I don’t want to call it a debate, but I was talking with a friend who was into machine learning. I was like, look, this is math. It’s math. I don’t know any other way to put it. This isn’t like intelligence; it’s math. And he would argue, well, but it’s—okay, tell me, it’s math. He’s like, yeah, you’re right, it’s all math. Okay, it’s taking words, using equations, algorithms, and math, to give you other words, you know? So anyway, okay.
How does this affect humans? Well, I think it affects humans in a very fun way in that we now are going to be able to have tools—and I look at it more as a tool right now—that enable us to get different perspectives based on larger data sets, right? You know, I can come up with my own stuff, but I think it’s cool to see what—you know, put a whole bunch of potions together and see what it comes up with because some of the stuff I like, some of it I don’t like. Now, how does this affect learning, right? This is an interesting question, because if you no longer have to recall stuff, right, that’s what learning actually is, is being able to recall something and then act differently because of what you know. Will it actually affect humans’ ability then for recall? We don’t know yet. It could, it could not, right? Will humans be stupider? Maybe, maybe not. I don’t think anybody has the answer yet. You know, we have lots of speculation, you know. But here’s what we do know. We do know that our education system has certain outputs. We also know that human beings have mental things that happen to them as they develop that enable them to, you know, process things cognitively to—we talked about knowledge transfer—to take one thing in this domain and apply it to something that’s completely different, unrelated, and use that same stuff. And we haven’t been able to see software intelligence do that as of yet. That doesn’t mean it’s not going to, but as of yet, it hasn’t been able to. And the reason is because of the data constraint, right? There’s this data constraint because you’ve trained it on this; it needs to be in there.
There’s an old episode of Silicon Valley, it was a TV show, I don’t know if you guys have ever seen it, it was on HBO. One of my favorite episodes was "Hot Dog, Not Hot Dog." And I don’t know if you recall this, but the main character, you know, creates an artificial intelligence system, and because he didn’t have enough data, he didn’t have all the things, it could do one thing: it could tell you if it’s a hot dog or not a hot dog. And right now, unless we have training data, we can’t get to anything beyond hot dog or not hot dog. People are working on this; I hope they solve it.
How does this affect development, right? And now this is the key, right? So, we have learned, right? I can recall this, I know it, I’m going to do something different because I know that, you know, in history, this happens, and why, why, why. But how does AI impact the development of the brain, right? Will neuro processes be different if children are using digital things? We don’t know yet. This is all ongoing. Now, the optimist in me says that look, humans are probably the most adaptable species, you know, in the universe—big claim—and because we’re so adaptable, we’re going to, you know, ideally evolve in a way that is going to be using these technology tools. But I think we have a long way to go, right? There are, what, 8 billion people roughly now on this planet Earth. There are a lot of people that aren’t going to have access to AI tools, right? I don’t know if you guys have been to some countries that have really large populations, let’s say India, or China. There are a lot of people that aren’t, you know, they’re not buying iPhones or even Huawei phones or whatever because of the means. And so, when you think about how AI impacts learning, for some people, it’s definitely going to impact it. For others, at least for the next decade or two, it’s going to be a while. You know, when we look out 100, 200 years, then I think it gets really interesting, right? Because you have a lot more time, and, you know, who knows what happens in the next 200, 300, 400, 500 years.
I always like to think of it—you know, I don’t know if you guys have traveled to ancient cities, like when you go to Rome or you go to Greece or—had a chance to go to Amman, Jordan—and you look at these cities that are centuries old, right, and you think back, what were those people thinking, you know, 2,000 years ago, right? Could they have ever envisioned a scenario where people would be sitting in different parts of the planet, talking over airwaves with little things in their ears so that we could hear each other? Like, they could have probably never even imagined that they were like, "Man, imagine, you know, the next Parthenon." So, who knows how it’s all going to shape out? We won’t be around for it 2,000 years from now, unless, you know, maybe you guys know something I don’t know, but 2,000 years from now, we’re probably not going to be here, right? And the planet will be here, humans—we’ll see, right? You know, how will societies look? We don’t know. We can look at 2,000 years ago what societies looked like, you know, we have the ruins, but what will, you know, we don’t know.
Is AI going to impact learning? Yes. Is AI going to impact humans’ abilities? Yes. Sometimes in good ways, sometimes in not-so-good ways. Yeah, so that was a long way of giving my thoughts on AI.
Glasp: Thank you. Yeah, that’s really interesting. And also, you tapped into knowledge transfer, which is another of our main topics today. And then, since Glasp is a platform where people learn and share what they’re learning and reading, it’s kind of, you know, like a knowledge transfer in that sense. But what is the better way to transfer knowledge among people in a better way?
Stephen: That’s an interesting question. What is a better way to transfer knowledge?
Glasp: Or like, you know, how people can learn better from other people on a platform?
Stephen: Yeah, well, there are all different ways to learn, you know. Again, the challenge with what you said is "better," right? Because what’s better for me and better for you may not be the same thing, right? This is one thing, it’s almost like the research studies. All of them have a way, but it’s not the way because no matter how statistics work out, there’s always some probability or some people that don’t, you know, aren’t affected. Drugs are like this, right? Some people take aspirin, it works for 80, 90% of the time, but there’s 10% of the time it doesn’t work. You still have a headache, whatever.
And so, in what you’re talking about, knowledge transfer specifically, it’s how do you take what I know and my view of the world and share it so that someone else can learn from that view? And the challenge with that is that view may not be right for that person, right? I say this a lot when I mentor founders, is everything that I say is a way, it’s not the way. You have to figure out what’s best for you. And there’s this conception that just because it worked for someone else in a particular way at a particular time doesn’t necessarily mean it’s going to work for you. This is one of the big challenges I have with all the advice people give on Twitter or some of the social platforms. They make this false assumption that because it worked for that person at that time, that person knows different things, they act in different ways, you’re never going to be able to replicate that specifically because you’re not that person, you don’t have the same thoughts, you don’t have the same knowledge base, you’re not in a specific situation, you’re not at the specific time with these specific actors. What you’re trying to do is, okay, what can I take from that that applies specifically to me in this case with everything that I know and my circumstance? And this is where it comes up to me and my ability to judge things. And for some people, they judge them correctly more often than others. Some people, judge them worse off. Sometimes AI can help with this; sometimes AI doesn’t help with it.
Now, all of that being said—right, that was my disclaimer—knowledge transfer is a very complex yet simple idea. It’s being able to take the building blocks or essential learning, essential knowledge in one domain and applying it to another domain with positive outcomes. An example: one example could be, let’s say that you’re learning about biology, and there’s a specific biological system that works, and you take that and you figure out a way to model it in computer science using code, and it happens to work. That’s knowledge transfer. You took this biological system, you used some type of coding based on that system, and now it worked, right? That’s knowledge transfer. The challenge with this—and remember I said AI hasn’t been able to do this—the challenge with this is what humans are really good at doing—well, I shouldn’t say all—some humans are really good at doing is being able to see this biological system and how it can be used in code to do something else, right? Humans do this. We do this all the time. Software is not able to do it.
So, how do you help humans see those patterns more often? Well, it comes down to practice. You actually have to practice. You have to—you know, school is a great example. School is nothing more than practicing a lot of random stuff. A lot of it sticks, some of it doesn’t, but it’s all practice. There was many years ago in the United States, there was a basketball player who got fined for not going to practice, and there’s a famous rant, you could probably find it on YouTube, of him going, "Practice? We’re talking about practice, not the game, practice." And it’s this practice, this ability for humans to do stuff, experiment is what we would call it, in novel ways that enable transfer. And sometimes that practice is mental practice, where like, "Oh, I wonder if this would work. Oh, I wonder"—right? That’s mental practice. Sometimes it’s actually writing it out, right? But it’s this practice that enables humans, at least, to be able to take knowledge in one domain and apply it to another.
And so, how do you do that as a platform? Well, I think one is it’s dependent on who comes to the platform, right? Because you have people that are closed and not interested in practicing or not interested in even doing it. Well, one, they’re probably not going to come to the platform. And if you’re spending money to get people there, that’d be bad. But, you know, in sales, we talk about knowing your audience, right? And so, by knowing the audience and making sure that whatever is being shared with them is intriguing, is interesting to them—I should say intriguing to them and has these specific elements—I think you’re going to get more knowledge transfer. There are also certain things you can do mechanics-wise, and, you know, questions and all these types of things, but we don’t have time to get into all of that. That was a really long, convoluted answer to how do you help transfer more skills to people.
Glasp: Yeah, thanks. Interesting. It’s kind of a different perspective question, but do you have any favorite product, software, or organization that helps people learn? So, for example, Khan Academy or just, you know, online courses at universities, do you have any favorites?
Stephen: Yeah, so Khan is really good. You know, I think it depends on what you’re learning, right? There are different types of learning environments, one learning environment is a formal learning environment, right? This is you actually going to class. There are also things that are structured learning environments, so these are online courses, MOOCs, etc. There’s another type of learning that is happening just in the environment or what we call informal learning, and this is a really—this is actually bigger than formal learning, right? When we think of learning, most people think of you going to school. You actually do most of your learning outside of school, and learning comes down to a couple of elements. When it comes to environment, Bridget, who I was just talking about that paper, it’s kind of funny that was up there.
So, learning environments can be anything. One of my favorite learning environments actually is conferences and summits, where you get people sharing ideas and bantering about, and the reason is that one, I like to kind of hear how people talk and think, and so forth, but it stimulates me and my ideation. In fact, sometimes some of my best work is when I’m at a conference and there are speakers up there speaking, but I’m actually in the back writing my own stuff for some reason. That’s a great learning environment for me. The other learning environment that I love the most probably is on an airplane on a long-distance flight between San Francisco and Japan—it’s like 11, or 12 hours—or San Francisco and Europe. I will bring a book, I will write stuff, I will think, I will digest things that happen. That to me is an amazing learning environment for me. Now, by the way, for—I recognize for many people that’s a terrible environment for them to learn. There’s the babies crying, there are interruptions, it’s a small, cramped space—like, it’s a terrible learning environment. But for me, that’s one of my favorite learning environments.
And so, what I want you guys to take away from this is that learning is not just someone coming to a platform and listening to something. Learning can also be someone coming to a platform, listening to something while they’re on a subway, and a street performer is dancing, and it sparks an idea for a new business. That can also be learning, right? But we don’t think about that as learning because our contextual base is the learning environment of actually sitting and watching something to intake information, ideally for quote-unquote learning. But that’s not the only way learning happens. So, this kind of goes back to the first question of transfer. We make this faulty assumption that in order to learn, I have to watch a YouTube video. In order to learn, I have to take a class. In order to learn, I have to read this book. It’s not true. In fact, apprenticeship, which is probably one of the most tried and true elements of learning, has been done for centuries. And now, a lot of things are more of a cognitive apprenticeship—entrepreneurship certainly is. So yeah, that was a long, convoluted explanation. I hope it’s not too much there. But you should think of learning and specifically learning environments and contexts. You also have role models; role models are super important to learning. The environment you’re in and the role models that you have will certainly influence how much you learn. This is why you’ll see when political figures are in power, what you tend to find—and there was some research on this—that people start, you know, imitating them. They start acting the way they do. The reason—and it’s both conscious and subconscious—is that role models play a really big impact on our learning, how we process things, and how we actually end up manifesting in the world. So, anyway, sorry, I know that was a long response. Anything else?
Glasp: Yeah, we are just recording, but so is your time okay?
Stephen: Yeah, absolutely. I’ll have to go here in a little minute. I think I have 10:30, so, okay.
Glasp: In that sense, I was curious, you know, like, you know, that makes what you said make sense, you know, being exposed to many certain circumstances or environments. And also, like, in that sense, you know, working in the street, you know, sometimes helps us, you know, bring new ideas and so on. But what’s the source on, like, social media, like Twitter or LinkedIn, by the way, as a knowledge transfer platform? Is that a good environment?
Stephen: Yeah, in some cases, yes. In some cases, no, right? Realize that it’s a curated and cultivated environment by people who are not you. So, that’s one thing to understand. Whenever you’re using social media, make no mistake, you are not in control of what gets shown to you. Someone else is, right? And so, remember that. I mean, you know, it’s bad, good, you know, everybody has their own opinion on it, I’m agnostic. But just realize it’s a cultivated environment, you know, it’s kind of like you as a person, right? You as a person come down to three things. Learning is like this, you know, certainly our—you know, the way we show up in the world, and it’s one, biolog—excuse me, biological, right? There are certain things that happen, chemical processes in your brain, there’s biology. Two is your environment, the environment that you’re in. And three is your role models, whether they’re conscious or subconscious role models. These are the things that actually are going to influence your life more than anything else: biology, you know, your gene expression, certain environments kick things on, take things off, whether you develop disease—it's biology.
The environment you’re in, at least until you—in the United States until you’re 18—is pretty much determined by whoever your elders are, right? Because your parents, your family, they put you in that environment, and you can’t change it. Once you get to a certain age, you can change it, right? You can just get up, move, leave, whatever. People won’t like it, some people will like it. What, you know, most people do it in the United States for university, for college. But the environment is a very important aspect. And then the role models—and I talked about this earlier—there are conscious role models, like I look up to this person. There are other role models that just come in subconsciously. So, let’s talk about one right now in tech that a lot of people talk about: Elon Musk. He could be a good role model in some sense and a terrible role model in others, right? It depends. It really depends. And so, if you go onto a social media platform and you’re seeing—I don’t know what they call them, sheets? I don’t know what they’re called now instead of tweets—but you’re seeing this, and he’s influencing, right? Whether he realizes it or not, and whether you realize it or not, he’s influencing you because he’s a role model. And he may be one that you don’t like; he may be a role model you hate, but that influence is still going to seep into things that you do. It’ll seep into the way you think.
This is why the biggest thing that you can do for yourself is your ability to focus and ignore, right? There are some things that you want to focus on and ignore. I’ll give you an example. As a kid growing up, I loved television and movies and things, and I still do love movies, I go all the time. But now I realize that that’s such a huge time suck. And like, I could spend hours—like, same with social media—hours, and I realize it’s not really doing anything good. Now, here’s the thing. I also love sports, and particularly, I love college football and college basketball. And, you know, and so now I’m about to enter the time where I actually watch television again. I actually cancel my subscriptions at the end of football and basketball season, and I don’t watch anything. And then when it comes back, I’ll watch it. But I realize that there’s influence, that there are role models, whether they’re conscious or subconscious, that are coming into play.
Whenever I get on LinkedIn, right, I think LinkedIn is probably one of the more benign social media platforms. Now, I’m not getting a lot of political stuff. Typically, I don’t see a lot of hate stuff, you know, stuff that conjures negativity. So, I tend to like that one a little bit more. Some of the other ones, you know, I want to particularly ignore the stuff that I don’t want to influence me. And I think this is—you know, I think of my nieces, who are now in middle school and one going into high school, you know, I feel for them because they have no idea how much they’re influenced by people who have one motive in mind, whether that’s power, money, something else. And, you know, I was lucky, I grew up before some of this social media, so I was influenced in other ways: television ads, right? Same thing. So, we all have these things, but until they start to realize that, it can certainly be detrimental.
So, that’s what I think. You know, I think there are some social media platforms that are better than others. You know, again, just realize consuming and producing are very different things, and I think it’s better for young people to do different projects and create rather than consume 100% of the time. That to me is probably the more interesting thing on social media is it now as a platform that people can be creators and explorers and experimenters and documentarians more so than just consuming what everybody else is doing, because that’s not a very fulfilling thing in the long term, I think, for many people.
Glasp: Thanks so much. And yeah, since, you know, our time is running up, and, you know, we have—sorry—two more questions. One is, like, you know, do you have any advice—our audience is like aspiring founders, writers, and people who want to learn better—do you have, you know, you shared already many pieces of advice and, you know, academic research or so, but do you have some advice for those people to learn better?
Stephen: And to be a founder, is that the question?
Glasp: To learn better, I think, yeah.
Stephen: Okay, to learn better. One is be open to learning, right? A lot of times, people are—they think they’re open to learning, but they’re really not. They come in biased. And I think it’s important, you know, one of the challenges I have with social media is they have this thing, followers, right? I follow this person. Like, don’t ever be a follower, be a student, right? Learn from them, and realize you can learn from anyone. You can learn from people who suck, right, because they’ll show you what not to do. Sometimes I think failure should give seminars: here’s how I messed everything up. Because if you did, you’d be like, "Oh, okay, I’m not gonna do that." So, I think that’s one thing. And the other thing is to be very careful who you’re learning from. Who you learn from matters. There are a lot of people who will give advice. There are a lot of people who will share things, and it’s really terrible advice. It’s bad advice. It’s biased, and they don’t realize it. And so, this is probably one of the things is cultivate who you are a student of. And it’s not just who’s the richest, right? It’s not just who has the biggest following, it’s who has values, who has, you know, relationships that you want to have, who are people that connect with others in ways that are meaningful and add value, right? I think those are some of the things that—those are the people you want to learn from, not necessarily the most, you know, loud, not necessarily the most quote-unquote popular. I think many of the most popular people are probably the worst people to learn from. Yeah, so that would be my advice.
Glasp: Thank you so much. Yeah, that’s really great advice. Thank you. Also, this is the last question, and since Glasp is a platform where people share what they’re reading and learning as a digital legacy, we want to know, what kind of legacy do you want to leave, or legacy or impact do you want to leave behind for the future?
Stephen: Impact? You know, I’m smart enough to realize that, you know, 100 years from now or a thousand years from now, it’s not going to matter, right? There are some fundamental things, right, that matter and don’t matter. I think the legacy that I want is the people who I worked with, I want them to feel better for having worked with me. The people who I worked with, I want them to feel that you know, they were valued and that we had a good time, that we had fun, that we enjoyed our time together. That there is more to life than just monetary gains or power, these types of things. That the relationships were so much fun. I think that would be the legacy that I would want to leave, is just making sure people felt better when they were around me. Because at the end of the day, we’re all here for a limited time, you, me, everyone, and we’re not going to be here in a couple thousand years, unless, you know, who knows, maybe there is a breakthrough and we are. But, you know, right now it doesn’t look like that’s going to happen. And so, because of that, you know, how do we make sure in these moments that we have—I always like to tell my students is we have a finite past, right? Everything that we’ve done up until this point is finite. We can’t change it. We can’t go back to yesterday and change things. So, we have a finite past, but we have an infinite future, right? And this is a really wonderful thing, right? We have finite—every one of us has a finite past that we can’t change, but from this moment forward, it’s infinite. We could do all—I could jump off here and go outside and dance around the street if I wanted to, right? It’s infinite. The possibilities abound, right? And so, how do we as humans make the most of our infinite futures? How do we, whether that’s in working, whether that’s in relationships or what have you—and to me, it’s how do you have joy and fun and really, you know, be around folks that, you know, inspire you, that make you feel good, that, you know, help you be successful, help you do things you want to do, like make you just really fired up to live life? I think that would be the legacy.
Glasp: Awesome.
Stephen: Well, hey gentlemen, thank you so much. I really appreciate this. Hopefully, you got something and it helps you. I need to run here to my next call, but it really was a pleasure.
Glasp: Thank you so much.
Stephen: Thank you guys. Bye.
