Transcript & Summary: How To Become Dangerously Self Educated (with AI)

[00:00] Everyone is learning about AI these days. If you don't know what's going on with AI, it feels like you're leaving yourself in the dark. But the thing is, you do not need to become an AI expert to remain competitive. You just need to be good at what AI is not good at. So, in this video, I want to make that clear for you. I'll show you what AI truly struggles with and what you can do to learn so fast in your own domain that AI cannot keep up with you. And so, for the first part, it's just understanding what [00:33] AI is actually bad at and therefore where your opportunity is. So, let's start with a few basics, not just about AI, but also about understanding your role and basically why people pay you to do stuff. One way of thinking about why someone would pay you money is that you bring to this role a set of skills and knowledge. You know, a set of competencies that you can bring to this business. That's not an highly inaccurate way of thinking about it. A business has certain problems that it needs to overcome. And these problems [01:08] need to be overcome because this is the way that it's going to create solutions and these solutions are what generate that business money. And so, if someone's going to hire you, they're going to hire you because they believe you will be helpful in helping them solve this problem. And only part of that is the skills and the knowledge that you bring. The ultimate truth is that a business will hire and retain you because they have a belief that you can solve future expected problems. And the important part about [01:48] this is understanding that these future problems are partially undefined. It isn't clear exactly what these future problems are going to be. And so, a business has to have the belief that regardless of what these future problems look like exactly, you will be able to solve them nonetheless. Now, I want to hit you with a pretty interesting, maybe shocking statistic. Over the last few years, a couple of pretty interesting reports have come out uh done by huge companies. One of them was done by Deloitte and LinkedIn, and I [02:21] think another one was done by Gallup. And what these reports have indicated is that across the next 5 years, they're anticipating between 30 to 50% of the skills that you need to be useful, valuable, competitive in the workforce to change. That means if you think about the reasons, the skills, the competencies that you bring to your work to keep your job, to get promoted those things that are the most valuable for you right now, 30 to 50% of those will change in the next few years. And the crazy thing is that [02:55] I'm I'm pretty sure these surveys actually came out before the huge AI hype bubble. So, that number is probably going up. And this is really crazy if you think about it from a university perspective. The time scale of change is so rapid that by the time you start a university degree when you get your first job after graduating, a third of everything you learned in university will be outdated. Now, obviously that's not the case for every single profession, but it's a general trend. But, the important thing [03:28] here is that people will still get hired. There are people who will still get a job, that will still be promoted, that will still be retained. And this comes back to the idea that the business has to have a belief that you can solve future problems even when those problems are undefined, when we don't even know what skills and knowledge specifically you're going to need. In other words, they need to have the belief that even if you do not have the skills and knowledge right now, you will be able to [03:59] learn the skills and knowledge that you need to solve the problem when it comes your way. And one of your biggest competitors in the next few years is going to be, can you learn to solve this problem faster than AI can learn to solve that problem? And so, at least on the surface, what it looks like is that there is this race between humans learning to solve problems and AI learning to solve problems. Because AI is definitely going to be cheaper. And once AI is able to solve that problem, that's not going to be a very secure [04:30] place to be in career-wise. But the thing I think that's important to really understand about the situation right now is to know that humans are not in the same race as AI. Okay? This race is different. A human learning faster is not the same thing. You can't compare that to an AI learning to do something. AI learning to be better at something is about the underlying technology evolving. The capability of the AI has to reach a certain level for it to be able to solve certain problems. With the human, it's about using the brain you [05:08] already have and applying it in the right direction. This seems really obvious, but there's some important implications to really being clear about this. One of the biggest implications is that if you have a job right now and you're trying to learn about AI to keep up with it, the questions that I see a lot of professionals engaging in are, how can I use AI to speed up my current workflow? How can I increase my personal individual productivity and output by using AI solutions and tools? And yes, there's a direct efficiency benefit [05:41] there. Feel free to explore that. But that does not make your long-term career security significantly better. Because at the end of the day if what someone was paying you 40 hours to do, you can now do that in 10 or 20 hours using AI, why would someone still pay you 40 hours to do that thing? And when AI gets better, why wouldn't they just replace you completely with the AI? The way you are thinking about using AI in your own personal workflow is the exact same reasoning that the business will have to replace you. And [06:19] so what naturally occurs, and and this is the way that you should be thinking about this, is that okay, well, when the work that I'm doing now is replaced by AI, when parts of that are being replaced by AI, and it's not as tedious and some of the easier, you know, low-level, simple execution stuff is being done by AI, and my time is now freed up, what am I going to be doing with that free time? You're not probably going to be paid the same and then spending the rest of your time just twiddling your thumbs. And in [06:48] that situation, when it is normal and commonplace for most professionals in your domain to be using AI to augment their own workflows, which is a pretty much already becoming standard as we speak, what additional value do you bring? That additional value is the thing that makes you competitive. And usually that additional value is going to be in the things that the AI can't do very well by itself. In the real-world professional context, what type of work is this? Well, we know at this point it's it's pretty well [07:22] established. The things that AI really struggles with doing therefore the additional value that you can bring by being really good at this is working with things that are deeply contextual, very multifactorial, and with a high high of conditionality and complexity. These are situations where there isn't a right answer, where the information is potentially really new, very nuanced. It's that critical problem-solving. It's that high-stakes decision-making. It's about seeing how all the pieces fit together. And that for AI is very [08:03] difficult. And the thing is that for humans is also very difficult. Humans get easily overwhelmed when there's too many pieces and those pieces connect with and they influence each other and the quality of your decision or your problem-solving depends on understanding how it all fits together. That becomes very overwhelming. That becomes complicated. People are generally not very good at that. And this is the point about understanding that humans and AI are not running the same race here. For AI to get really good at that, there [08:33] is a very significant difference in the underlying technology that needs to develop. And when we say AI, really what we're talking about here in the big hype around all of this, unless you're an artist and we're talking about generative AI, a lot of the hype around what we're talking about is an LLM, large language models. ChatGPT, NotebookLM, Claude, Gemini, things like that. LLMs, which fundamentally run on a transformer architecture, that underlying technology and architecture has certain limitations. And there are [08:58] some substantial technological breakthroughs that need to happen in order for it to be good at this stuff to the level of an expert human. Now, do I think that one day it will get there? Probably. Do I think that's going to be soon? I don't think so. And what I do know, and this is what I know for sure, is that the people who maintain their jobs and have that retention and still get promoted and stay competitive are going to be the people that can do this stuff very well. And maybe when AI gets good [09:30] enough, they're going to lose their jobs, too. But they're going to be the last people to lose their jobs, at least in the knowledge worker space. So, for AI, it's it's a difficult thing to win that race because for a human, for them to get good at this, they don't need to develop any additional underlying technology. Humans already come stock, default, you know, the existing hardware of your brain already has the capability of doing this level of operation. It's just that for some of us, uh we haven't [10:01] released that feature in our brain yet. And so, this is what I want to help you to do. I want you to be able to see very clearly where your goal needs to be. When you think about learning and upskilling to stay competitive and outpace AI, I want you to be thinking about it mostly in terms of getting good at this level of thinking. When you learn and upskill fast enough that you put yourself in a position where you can work with deeply contextual information and situations and do complex problem-solving, the business has belief [10:36] that you can solve future problems using AI or not. On the other hand, if you learn and upskill learning whatever other skills that are out there, but you are not able to do this deeper level of problem-solving and thinking contextual application, then you are now running the same race that AI is racing. And so, to anchor this in, I want to add a few terms here. This level of thinking that we've talked about here, contextual, multifactorial, uh high levels of complexity, lots of things relating to each other. This is [11:11] higher order thinking. When you learn at a higher order, we call it higher order learning. When you solve problems at a higher order, we say it's higher order problem-solving, but it's all wrapped up in the overall term higher order thinking or higher order thinking skills. And one of the prerequisite of higher order thinking, and like you need to be good at this to be good at higher-order thinking, is you need to see everything is connected. Instead of seeing each thing as an individual point, you need to see [11:48] everything in a pattern. Higher-order thinking is about understanding that each point of information, each fact, concept, factor, variable in your problem-solving, decision-making, or your learning only has value and importance because of the influence and relationship it has on another point. Things are not valuable and important in isolation. And so what that means is that when you think about things, when you learn things, you spend most of your time, your attention, your effort on unraveling those [12:24] relationships. Understanding, okay, here's a point. What does that mean for all the other points? What's the influence? And this is a very different way of thinking than how a lot of us were kind of trained and conditioned to think. Because the way that we're trained and conditioned to think is largely what's called lower order. Even still to modern day in a lot of curriculums, it's changing, but a lot of the emphasis used to be and is still on that isolated memorization of content. So lower order is about seeing it as [12:57] just points in isolation. This is about memorizing things. Just understanding something. And you you're tested on that. You read a certain concept, you understand a certain concept, you're tested. Do you understand it? Can you recite it? Can you regurgitate it? And so a lot of the way that we kind of grew up thinking about the value of knowledge and information was based on can you replicate that knowledge? But in the real world professional context, replicating knowledge is very limited value. The most value in a professional [13:31] context comes from how you apply what you know, which you can usually look up, and you don't even need to be able to recall it and regurgitate it from memory. It's just about how you can apply that in these deeply contextual multifactorial ways. And the more difficult the problem you can solve, the more responsibility that you're given, the more you're paid for it. But unfortunately, we have these habits of lower-order thinking. There's a new paper or a new announcement or something that you need to learn about to solve a [13:58] certain problem, to tackle a certain task or a project at work. You read this paper, no one cares whether you've understood it. The questions that matter are, do you see how this impacts the decisions you make? How you solve problems? How does that interface with everything else that you're working with and that you know? It doesn't matter if you understand it so deeply you could write a PhD on it. If you can't do the connecting and the contextual application, that knowledge is valueless. And as AI can do [14:28] all the lower-order stuff, like AI is getting really good at doing things in isolation. You're not going to be able to win a race against AI if you're still trying to get good at things in isolation. And at a certain point, when AI can do just do all of this better than any human can, which honestly is coming very soon, all the value is going to concentrate on people that can do this. Because as someone who literally teaches this for a living, let me tell you, there are very few people, a small percentage of the [14:57] population, is good at higher-order thinking. But a very large percentage of the population can be good at higher-order thinking if they practice, if that becomes the primary goal. And so that's my focus. I want you to see that that needs to be the primary goal. And to help you with that, I want to give you a few practical tips and insights to kickstart that journey. Now, FYI, I won't be able to go into everything here cuz this is a deep and complex topic. Please check out some of my other videos [15:27] where I go into different elements of this in more detail. Another place that you can check out is also my weekly newsletter. This concept of getting better at higher-order thinking and higher-order learning is a central part of the stuff that I teach. So, a lot of my newsletters distill some of those insights and perspectives and give you certain practical takeaways to get better at this. I try to keep them relatively quick, takes a few minutes to read. I try to make them relatively practical so that each week you've got [15:53] something to practice on. And it's completely free as well. So, if you're interested in that, check it out. I'll leave a link in the description for you to sign up if you want. And you can always unsubscribe if you don't like it. So, let me give you a few pro tips, some practical takeaways to get better at this higher-order thinking uh based on my years of experience coaching people to get good at this skill. So, the first practical understanding that you need to have, and actually this is uh I I think this is one of the most [16:22] important overall perspectives that you need to have to get good at higher-order thinking. And even in research, like when you read through the research articles, I think they get this wrong a lot of the time. It's called lower-order and higher-order thinking. And if you've seen some of my other videos where I talk about um higher-order learning, you might have noticed that I draw this triangle uh a lot. And what I normally say is that, you know, these top levels, this is this is what higher-order learning [16:51] is. And then these these bottom levels, this is what lower-order learning is. So, you want to be more in this top space of thinking than you, you know, than the the bottom space. But, actually, the issue with the this representation and talking about it as lower-order and higher-order is that there is this implication that you start at the bottom and then you go up towards the top. And some of the earlier papers that were talking about this topic talked about it in this way, like you kind of have to start at the bottom and [17:23] then as you get better and better you work your way towards the top. At this point I've taught this skill to thou like tens of thousands of people. I have a lot of data points on what makes someone actually successful at learning to do higher order thinking. And I found that this is just not a productive way of thinking about this. And the reason is that this lower order pattern of thinking, right? So if we say that thinking in this lower order has a certain pattern or a certain set of habits of thinking. [17:58] And these patterns and habits of thinking tend to keep information in isolation. Then higher order thinking involves different habits and different patterns of thinking. And these habits and patterns create integration. These are the patterns that make you see that each point fits inside a bigger network. It's all connected to something. It's very interrelated. These two habits and patterns they they don't connect with each other. When you get good at doing this type of thinking, it does there's no natural [18:32] progression to getting good at this type of thinking. This this connection does not exist. It doesn't matter how good you are at this, it does not lead to this. They are completely different ways of thinking. And in reality, what I see is that when people commit to just getting really good at thinking in this way, it puts them on a losing trajectory. So if we have time on the x-axis here, and this is kind of the let's say level of something here. If you get better and better at lower order thinking, lower order [19:09] thinking, the value this creates caps out at a certain point here. So, this is the actual value created. Beyond a certain point, it doesn't matter how much you can memorize. It doesn't matter how good you can memorize things in isolation. Just the practical utility of that skill is very low. Like, I've spent time mastering certain memorization skills. Like, I can sit there and in 20 minutes, I can memorize like 150 different things. Right? You can use like a memory palace or a story method or a link [19:43] method or a bin system or whatever it is. Like, there's lots of these different memory hacks. And so, if it comes to rote memorizing an enormous volume of facts in a short period of time, I'm very confident in that. That is a skill that I use like never. Less than 0.1% of my time and what actually produces value in my life comes from that skill. At most, it's an impressive party trick. And when I was going through school and going through uni, it was a little bit more handy to have that. Probably 20% of my academic [20:20] performance I was able to attribute to having that skill. But even then, it's it was a minority. And the deeper, sort of more problematic implication of this at a career level is that this is also the value that you create as a employee or as a professional, which means that sort of becomes the ceiling of your career progression. If you're only good at lower order thinking and lower order learning, I mean, why is someone going to pay you more if you're not able to solve more important problems? And to [20:48] make it worse, this value created line is actually going down. So, the amount of value that this brings you is going to get lower and lower. Why? Because AI is filling this. AI is getting much better at this lower order stuff, and it's already surpassing basically what most humans are capable of doing. Now, compare this with a higher-order thinking graph, where as you get better at higher-order thinking, what also tracks along with it is the value that you can create. So, your value is increasing as this ability for [21:23] higher-order thinking increases, because you can now deal with increasingly more and more complicated situations. You can tackle bigger responsibilities that most other people can't tackle. You can solve problems that other people can't solve and don't know how to approach. Where other people feel overwhelmed, you know how to navigate that. And so, to no surprise, this also represents an increase in how much people are willing to pay you. And frankly, to be pretty candid with you guys, people are willing to pay me a lot of [21:52] money to sit down one-on-one and coach them through their higher-order thinking skills. Because if I can increase their higher-order thinking skills by 50%, that means they get paid 50% more. That's a salary bump. That's a promotion. That's a new job. And the reason that I'm spending so long on on this particular point here is because there is a way to get good at higher-order thinking. There are things that you can do, like steps that you can take. The reason people don't get good at it is not because the step doesn't [22:21] exist or the path doesn't exist. It's because people don't stay on the path. They don't realize that it's valuable to actually get good at this. It is much easier to not develop higher-order thinking. Like, it takes time, effort, diligence, like concentration, mental effort. You know, it's a real commitment. It's much easier just to be where you're comfortable, which if that is at this lower order, what I want you to understand is that I don't think that's a winning game. And I don't want you to have the assumption [22:51] that eventually you will just get good at higher-order thinking. It is a completely different set of skills, and you will not get better at higher-order thinking unless you deliberately and actively push yourself to do higher-order thinking. So, with that being said and out of the way, here are three things that you can do starting from today, starting from tomorrow, that can put you into this higher-order thinking state more often and help you develop this skill a little bit faster. Number one, apply things from day zero. [23:29] So, you remember how I said that we have these habits of lower-order thinking built up from, you know, years of schooling and conditioning. And one of those habits is to say, "Hey, we're going to open up this new book, webinar, whatever we're learning from, and focus on trying to understand things first." Which is very intuitive, feels normal. Like, what's the alternative? Read it and just not understand? What I want you to realize here is that the understanding is not something that you have to try to do. That shouldn't be your [24:01] primary outcome. Your goal when you consume any new information, when you're trying to learn anything new, anything complex, your goal should be to immediately try and apply that into your context. Forget about trying to remember it. Forget about even understanding it. And what will happen is that the understanding will happen by itself. If you try to take new information and apply it immediately, you you're forced to understand it as part of that process. But, there are some subtle and important differences [24:33] between immediately trying to apply it as your primary outcome versus deliberately saying, "I'm going to understand it first and then apply it." One of the big differences is your cognitive framing. When you frame new learning or problem or a scenario, just new information that your brain needs to work with, when you give yourself the context of saying, "Hey, we're going to do a bunch of stuff with this. We're going to put it all together and we're going to do whatever it is, but the reason we are doing that is to solve [25:05] this problem. Is to achieve this outcome. Is to reach this goal." Then your brain knows how to think about it. And so automatically, when it sees each individual piece, it's not only just going to understand it, it's going to understand it in the context of how you need to use that. And naturally, it makes it easier for you to say, "Okay, well, here's this piece of information. Okay, how does that relate to the outcome I'm trying to reach?" And then, "Okay, well, I've learned this thing and I've also learned this thing. How do [25:34] these two things combine together or influence each other in my ability to reach this overall outcome?" And what you notice is that it puts your brain in a more active, relational method of thinking. The alternative, when we're not doing this and we are stuck in that lower-order method of processing, is that we say, "Cool, here's a bunch of stuff I need to learn. Let me just understand it first. Let me just wrap my head around it. And once I've understood it, I'm going to see how it all fits together." [26:05] And so we spend a lot of time, probably hours, maybe days, maybe weeks, going through high volumes of information, often in way too much detail, not knowing what we need to know, how much we need to know, how we're going to use this, hoping that eventually, with enough time, we're going to figure it out. But here's what actually happens. Your brain doesn't know what to do with that information. You're just throwing it things and saying, "Hey, hold onto this. Understand it." Your brain doesn't know how to understand it. There's no [26:34] value to this information because it doesn't influence anything else. And because there's no way for this new information to fit, there's no reason for your brain to hold onto it anymore. So it will forget it. It will just prune that information out. And this is the reason why you can spend a week learning about new things and then you're like, "Cool, I've covered all the material I need to. Now let's try to apply that, build my solution architecture, create my strategy, think about my plan. Now you don't even know [27:00] what you don't know where to start. And you'll go back to the old notes that you wrote and you'll read it and you just think, I don't even know what to do with this information. The fact that you've understood it doesn't put you in a better position to bring it together because they're two different skills. And counter-intuitively, trying to just understand and remember something when you're first going through it is an incredibly efficient way to forget it. The purpose of understanding it or remembering it in [27:29] the first place is so that you can put it together. So just start there. If the gap between information coming in and first thinking about how it's going to be applied is more than like 5 or 10 minutes, your brain is already going to remove it. It's a very, very short window of time, which means your brain has to constantly be coming back to how do I apply this? How does it connect? What does this mean in terms of my ability to solve a problem and reach a certain goal? There are other frameworks for activating this [28:00] type of thinking that I teach in other videos, uh but I'm focusing on this one right now because for a lot of professionals, the reason you're learning something is to achieve a certain task in the first place. So it's a it's a effective way to bring it together. Uh whereas if you were a student or you're learning something a bit more abstract, there isn't that clear real-world relevance. There are other techniques that you can use to force your brain into this state as well. But for now I'm focusing on [28:23] this technique for this video. The second practical tip is to create mental organization. And the word mental is important here because we're saying mental organization, which is not the same thing as physical organization. Over the last, I don't know, like several years of me learning about all sorts of things. Business, learning science, psychology, education, employability. You know, I've studied lots of different topics over the years. Medicine, you know. I forgot about that. I went through medical school. If you look at my notes [28:59] and how they're all organized, it's very, very, very rudimentary. I do not have excellently color-coded notes inside folders and tags connecting everything and a perfect notion template with all these different things on it. I don't have any of that. I don't want any of that stuff because it's just noise. It's just a distraction. It's taking me out of just focusing on creating mental organization. A lot of people create physical organization, which is very easy to do as a substitute for creating mental [29:30] organization, which is harder but more meaningful. Mental organization is basically about creating a way to categorize and group and relate the items and the points together so that you can see the overall big picture. Mental organization is about examining the jigsaw pieces and putting the jigsaw puzzle pieces together so it actually completes the picture. It's organized because each piece is in a particular place because that's where it belongs. Each piece has value and purpose and position because of where it [30:09] is relative to the pieces around it. When applied to decision making, problem solving, learning new things, this means that when you think about the topic or you think about the problem or you think about the project or the task, you're able to say here at a big picture, these are the steps that need to be taken. These are the things that are the most important that we need to be able to absolutely nail. These are the small little details and how they fit around it. Here are some of the contexts and [30:33] nuances. It means you're able to explain it. It means you're able to explain it simply and communicate that to relevant stakeholders. You see how a plan could change. You see the implication of something new being added into your network. This is very different to creating physical organization. Physical organization would be like taking all the pieces of the jigsaw puzzle and then putting them in different boxes depending on their size or their shape or their color or whatever else you want to use to [31:05] categorize them. Yes, they're organized, but the organization is meaningless and actually help you in any way other than feeling good because they're organized. Physical organization for learning is when you're writing notes and all your headings are the same style and all of your bullet points look a certain way and you've arranged everything in the perfect flash cards in the little boxes and you've created the little reference links here and there and you've added everything to a little citation software and you know where to [31:32] find that information. Great. You know where to find the information, but the the point was for you to actually do something with that information and you are no closer to reaching that goal after potentially hours of just physical organization. This is basically productive procrastination. We don't want to do real meaningful organization using higher order thinking, so we substitute that with the feeling of organization through just putting things in space. And so one of the, you know, quick tips that you can [32:01] use to try to create mental organization is do a level of mind mapping. Try to connect the ideas together in a way that makes sense. As you do that, see if you're able to create clear structure and connections between all the points that you think are important. Some people will try this and they'll say, "It's too complicated. Like there's too many factors that all influence each other in different ways. It's just too much to try to put it into a mind map." Okay, well, someone who really understands how it's connected together, [32:32] they can create a mind map out of it. The only difference is that they can create many different mind maps with things are related in many different ways depending on how they want to perceive the situation or the topic. Whereas someone who doesn't have it mentally organized and therefore is going to find it hard to actually use that information in a meaningful way and solve complex problems. That person is going to feel just oh there's too much and I can't do it. They're not capable of expressing [32:59] how everything is connected because they actually don't know. Or a really common one and this is more of a misconception is that people will do a mind map and then as they're doing it they'll realize hey there's these new factors and these new angles and perspectives. Halfway through the mind map they realize this is not the right way to do it. I need to change this. And they feel like this is not working for them. It's the opposite. It is working. And the fact that you found new factors and variables and new perspectives that [33:30] you need to consider is actually the goal. That's great. Now you want to review the map that you've created. You want to generate it. You want to revise it. You should do that. It's a mind map. It is a living document that reflects how you are thinking and creating mental organization. So when you previously when you were first learning it you thought it you know A connected to B and then that connected to C and then as you learn more you realize oh D is actually really important as well. And then you [33:59] learn more and you realize actually all of this is wrong. It needs to have been A and B together leading to C you know and that all leading to D. You know this might be the new and updated understanding that you have after going through that topic and really exploring and pushing yourself to see how it comes together. That's the goal. You want to revise your thinking. And if you realize this is wrong and you have to redo it again that's also good. As the way you think about the topic becomes more [34:30] sophisticated the map starts becoming more sophisticated. You realize you misunderstood things earlier on that you then revise. And so, the big thing here is that when you try to do this and you try to do some mapping, if you find this really challenging, you need to do it more. Higher-order thinkers, they can map. And practical tip number three, this will help you if you're not used to mapping, is to forget about perfection. If you're learning something new, something complex, you're going to be [35:01] wrong. You're going to be wrong about that topic many, many more times than you're going to be right. If you could be right about seeing how everything fits together to begin with, you wouldn't find it complicated. When I do workshops uh in front of a group of professionals and I get them to map something within their domain. When I see someone who really, really struggles to map things together, it's not usually because they are the dumbest in the group or that they have the least amount of knowledge. [35:30] Often, the reason is that there are so many ways that it could connect together, they don't know which one to choose. I could connect A to B, but I could also connect A to C or D or E or F. There are so many options, they don't know where to start. And this is perfectly normal. And this is actually a good thing. It means that you're thinking actively. So, the way that you make this easier is just think on paper. If you think you've got, you know, like A, B, C, D, E, F. And you're trying to see how these [36:03] things connect together. And you think, "Okay, well, A could lead to B, and maybe, you know, it could be something that looks like this. But then also, it could be something that looks like this. Or maybe it could be something that looks like this." You don't need to know what's the right answer. The process of mapping it out like this is just helping you think. So, instead of wondering about it in your head, just write down and draw out what you're thinking on paper. And now that's on paper, you can literally look at it. [36:32] And then see which one you feel like makes more sense. You might look at this and you might think, "Hmm, I wonder what would happen if I grouped these things together and then actually I fit E as part of D. What would that look like?" Don't wonder. Just draw it out. Okay, if I did that, it would look like A leading to this kind of bigger group that is composed of both B and C. And then D is leading to that, but actually, you know, that's a bigger group composed of D and E. Okay, this is what it would look like [37:06] if I took that idea and fleshed it out. Okay, well, what do I think about this? Do I like that? Does that make sense to me? Once you write it out, you might realize, "Oh, actually, this doesn't make sense at all." Because, you know, this is actually also part of A. So, does it mean A needs to go here? Okay, draw it out. Think through it. It is much faster and easier to stay in this productive thinking flow when we get used to thinking on paper. So, don't think about the notes that you write as a source of truth. This is your [37:33] assistant. This is the process you're using to create that mental organization, right? So, this leads to this. It makes it much easier and faster. And this final practical point here about just thinking on paper is probably the biggest unlock for statistically the largest number of people. If I take 100 people in a workshop, and I've done this, I have like 100, 200 people in a workshop and I'll get them to do the same activity of mapping things out. 70% of the people will struggle with doing this. Now, have a [38:07] list of those kind of like main points that they're thinking about and they'll just stare at this list and they'll wonder, "How could it maybe connect together?" They're not really sure. Most of those people can go from struggling and not being able to even start their first set of connections to getting something that actually looks really meaningful and productive just by thinking on paper and not trying to get it perfect straight away. This is the part I was talking about all the way at the beginning is [38:35] that it's a different race for you as a human. Your brain already has the capability of thinking in this way. The only difference is whether you are letting it do this process and overcoming maybe previous less productive habits of lower order thinking that you may have. Try these tips out and really push yourself to get good at this higher order thinking because I promise you you can learn to think and solve problems at a higher order faster than AI will be able to. This is an incredibly valuable skill. It's [39:09] always been a valuable skill and people have always struggled with higher order thinking. The only difference is that you used to be able to get away with a job where you're doing mostly lower order thinking. Higher order thinking was a luxury but now it's not just about making you more competitive, more valuable. It's getting to a point where it's becoming what you need for job safety. So push yourself to that higher level. It's not necessarily going to be easy but I believe you can do it. Believe in [39:41] yourself. If you want to go a little bit deeper into how you can do higher order learning, then you might want to check out this video here where I go through learning to learn in much more detail and how you can actually build an entire learning system. I hope this has been helpful. Thank you so much for watching and I'll see you in the next one.