0:00 So, I spent the last 7 years building and operating AI businesses all the way back from GPT-2 in 2019 to intermediate image generators in 2021 and 2022 to modern cloud code and Codex based systems. This has been very financially lucrative and quite interesting, but with AI now moving into real-world knowledge-based roles, I know a lot of people are wondering, how do I actually learn AI? And not just an entry-level understanding of AI, not just how to use the tools, but how to gain a broader appreciation and a more foundational kind of comprehension of what exactly is going on and why. So, in this video, I wanted to put together a straightforward one-week curriculum designed to take you from somebody that knows very little about AI or maybe you've just experimented with one or two tools to actually having a very strong understanding of AI, both its underpinnings and more broadly how you can use AI in a variety of ways, both for knowledge work, your business, your hobbies, and your personal life. Ultimately, by the end of this video, you'll feel a lot more confident about how to use AI, and you'll know more about artificial intelligence than likely 99% of all humans that have ever 0:58 lived. This is a broad road map, and so I'm going to link a bunch of resources down in the description below that you guys could use in order to, you know, actually watch the videos and read the references that I am going to be including. I'm also going to provide a brief, high-level description of all the various concepts that I wish that I had started on. Would have saved me so much time, probably years of my life, um back at the beginning of my own journey. So, in order to really get this, we're going to start back in the 1950s with AI history, uh where Frank Rosenblatt initially invented the idea of a modern neural network, what he called a perceptron. It's important to understand the history behind artificial neural networks and later on, you know, machine learning and deep learning and now modern artificial intelligence because a lot of what we're dealing with today seems really complicated. And if you just air-dropped into it, you're going to think, why the hell did they do this? And what the hell does that term mean? And what was the motivation behind this? The reality is, anytime anything looks really complicated, if you just go back to when it was invented and put yourself 1:53 in the minds of the people that were dealing with the the that necessitated those inventions, it often becomes really straightforward. And so, with Frank Rosenblatt back in the 1950s, this whole idea of a perceptron, when you understand the history behind it, sort of what he was trying to solve, a lot of the modern terms of, you know, artificial neural networks and regression models and weighted functions and so on and so forth, all that stuff becomes really clear. At a high level, what ended up happening was the invention of this artificial neural network led to a tremendous amount of excitement around artificial intelligence in general. So, people started talking about how, "Hey, in the next 5 to 10 years, to have, you know, AI models that are going to run everything for us. Machines will think and they'll govern our economy and they'll do really, really crazy things." But, obviously, at the time, they were pretty compute bottlenecks. They didn't really have anything in anywhere near close to what we have now. And so, you know, most of these things did not come to pass. And that led to a massive, widespread disillusionment with AI, known as an AI winter. Um after the AI 2:46 winter, okay, there became renewed interest in designing what are called expert systems. These expert systems, at a high level, are just like computer programs like we're dealing with now. They were really complicated if-then or, you know, binary search tree-based systems that tried to deal with problems on a procedural and deterministic basis. And they worked pretty well for some problems in their fields. Not at all based on neural networks and really nowhere near what we consider AI today. Uh they were still quite useful. Nowadays, that's just our modern conception of a computer program. But, back then, these expert systems were still called artificial intelligence. People used expert systems all the way up until maybe 2011, 2012, when AlexNet broke onto the deep learning scene, um absolutely crushing on an image classification task at the time. Um it performed so much better than virtually all other approaches that people started to say, "Wait a second. Are these guys using that crazy architecture back from like the 1950s and 1960s? Why the hell is it working now?" It led to renewed interest in artificial neural networks and sort of what they were capable of. 3:44 And really what had happened there, if you think about it, is the technology had just finally caught up to the theory. You know, we we've had the theoretical underpinnings behind the technology we're using today, ChatGPT, Codex, for the most part for the last 70 years or so, but we needed the actual hardware and computes to catch up to, you know, where theoreticians were back then in order for us to actually be able to use them to do cool things like image classification and so on. That takes us to 2017 where the transformer was first released. This is the same architecture that underlies ChatGPT, Claude, Gemini, and really most successful large language models today. That basis became the foundation of the generative pre-trained transformer GPT-1, GPT-2, GPT-3, later ChatGPT, which is based on GPT-3's architecture, and now the explosion and sort of, you know, extraordinary diversity of LLMs currently available on the scene. Then 2024 hit, we invented reasoning models that also added significantly more complexity to how, uh, you know, large language models solve tasks. And really the rest is history, kind of like our current date. 4:45 So, that's a really broad overview, but there's actually way more underneath the scenes. Some fantastic resources for this are 3Blue1Brown's What Is a Neural Network series. This actually walks you through the foundational mathematics behind how a neural network processes and computes. I know that can be kind of scary to people that aren't big fans of math, myself included, but um, it is really cool to just sort of like grok it and sort of understand that flash of insight behind, wait a second, this is something really similar to what my brain is doing. Aside from that, a really good resource is by Welch Labs, The Moment We Stopped Understanding AI. That details what occurred around 2012 or so with, um, you know, AlexNet and sort of the deep learning revolution. And then finally, The Art of the Problem has an incredible video called The 35-Year History of ChatGPT. Once you're done with those three videos, again, which you can find down below in the description, I'd highly recommend reading The Bitter Lesson by Rich Sutton. Um, fantastic work that I think really grounds you in what most researchers believe to be the path to artificial general intelligence and 5:41 ultimately superintelligence. Next, you need to understand how large language models actually work. And so, there are a lot of resources that I'm going to throw at you here, uh, and it is a very broad topic, but I'm going to try, you know, just giving you the 80/20 up front right now. Hopefully, this sparks some of your neurons, which then can make you a little more excited to learn about the resources that I'm linking below. Uh the first is there eight key ideas to understand in how modern AI works. First is tokens. Now, token is essentially a portion of a word. You know, word might be seven characters long. A token might be four or five characters. And so, you know, what large language models will do is they will split text into these chunks of tokens, and then the model will read them one token at a time, or write them one token at a time. And so, this is a little bit alien for people that like to think in words. For all intents and purposes, most people believe, you know, an artificial intelligence model just generates the most likely next word, but it's a lot more complicated than that because of this token concept. It's not really a word, it's sort of like a 6:38 a fraction of a word, so to speak. And the reason why is because there are just a lot of different fractions of words that you can rinse, repeat, recombine in a variety of different ways to make different words. There's also next token prediction, which is the idea that at its core, what's going on with these neural networks is we're running a bunch of like very complicated matrix math using the same things you will have learned in the three blue three brown one blue video, um in order to, you know, generate the most statistically likely next token. That's not exactly true. It's a little bit more complicated than that. Really, what you're doing is you're generating a distribution of the most likely next tokens, and then you're sampling from, or you're taking from that distribution, maybe like the top five or whatever, and then iterating on that randomly. Cuz if you always selected the statistically most likely next token, you know, you'd basically always know what you were going to say ahead of time. You need a little bit of variance. You need a little bit of jitter. And that's a very important concept to understand in next token prediction for large language models. 7:32 Afterwards, this concept of pre-training is also crucial. And pre-training is the initial kind of steps involved in getting a model off the the and having it be able to reason over things. Uh pre-training is basically what like GPT-3 was. It was basically a purely pre-trained model. I mean, I actually still used that model and sort of its fledgling alien-like intelligence uh to build a business that did over a million dollars. And so, that's how I made my first million with GPT-3, which most people today would look at and be like, "That thing, you know, that thing has less neurons than a freaking beetle. How the hell are you going to make a million-dollar business with that?" Well, the way I was able to do so was really understanding just how much data and information was compressed via pre-training into the model's intelligence and ultimately what patterns I could extract from that to do things like, you know, write really high-quality articles by pretending that I was actually an award-winning journalist, feeding that in as my system prompt, and then having it iterate over and over and over again. Nowadays, models go a little further than just 8:26 pre-training. We actually fine-tune them through human feedback. That's another important concept you guys are going to learn a lot about with these resources where basically not only do we pre-train and sort of like instantiate this brain, but then we we coax and shape and evolve this brain into uh something that can actively produce productive conversations with human beings. And so, this is where human beings are shown a bunch of different outputs and they're asked to rate the the best output according to them. And usually what ends up happening is the human beings rate the output that sort of glazes them the most, makes them feel the best. And because of that, you also end up with model sycophancy and a bunch of other things. Again, nothing that you'd really understand on a deep level unless you did this foundational sort of learning. This might only take you a few hours, but very, very important. After that, with reasoning models and chain of thought, you actually feed an output back in as input and then simulate the steps required in order to make a decision. Chain of thought is why models can do extraordinary things today, and it's also a good monitor tool. It's a 9:22 good observability tool for human beings to sort of understand what exactly is going on in this model's weird alien-like brain when I give it a task and ask it to complete it. A really important concept you're going to learn a lot about is this idea of a context window, which is the total number of tokens that you can actually fit into a model at one time. You know, the longer the token window, the context window rather, the more tokens you can fit in. And ultimately speaking, the more a model can reason over a bunch of different things to kind of stick together. Very analogous to human working memory, but you can go up super deep there, too. Hallucination is another really important bit. You know, because the statistical nature of how models think and you know, they sample from that distribution, they are also from time to time going to fall prey to biases and errors like coming up with things that don't actually make any sense. Why? Because the way that the model initially sort of gained its ability to to talk was not through, you know, grounding it in real-world facts. The way it gained the ability to talk and to reason and think was just looking 10:17 at a bunch of text and then analyzing the statistical correlations between them. And it turns out text isn't always the real. We don't always just talk about things that are real. And so because we don't always talk about things that are actually real and grounded in the real world, the model will, like that, learn that, "Oh, okay, I can write about things that don't necessarily exist." Finally, the concept of it being grown and not built. You know, a lot of people talk about AI researchers building these really smart intelligence and and useful models. The reality is they don't actually really build them. What we're doing is more akin to dropping a bunch of different types of solutions in a Petri dish and then slowly monitoring as it grows. We're just doing it in silica, not biology, so you don't have Lovecraftian horror novels written about it. Um but really, like we are growing alien-style intelligence as through mathematics. And I think that's important to get. Couple of the resources I'm going to show you are going to go really deep into that and then you'll learn a lot more about what it means to be grown and not built. So, some really good ones. Large 11:08 language models explained briefly, again by Three Blue One Brown. That's uh 5 or 10 minutes and it's super straightforward. It walks you through essentially how large language models work on a foundational levels. Then two really good video resources from Andrej Karpathy, who's widely considered to be one of the best educators in AI. I look up to that guy quite a bit. Um first is intro to large language models. It's a 1-hour explanation for a general audience. It occurred before reasoning models have sort of hit the scene back in 2023. Because of that, there a bunch of things that he says that now I look at and I say, "Wow, if I were a researcher back in 2023, that's probably what I'd believe as well." And you also see how we're solving the problems that he talks about in that video through things like, you know, chain of thought and reasoning, which were developed immediately after. So, it's very cool because this is in effect also a short history. After that, he has a great video called deep dive into LLMs like ChatGPT. It's about 3 or 4 hours or so, and it's sort of it's almost like a novel, essentially. But, it gives you everything from pre-training to 12:04 fine-tuning and reasoning models. And ultimately, what you learn in that video is going to be most of today. Probably going to take you a couple sittings to make through, but still worth it. Then, in terms of reading, would highly recommend you guys check out a primary source from Anthropic. They wrote a wonderful article called Tracing the Thoughts of a Large Language Model. This goes deep into what the inside of a model's brain looks like. Ultimately, why it does things like hallucinate. It's also fairly accessible to a modern audience. If you wanted to go a little bit deeper and learn a little bit more about how transformers, the specific architecture that underlies ChatGPT, Codex, other models, and stuff like that work, um I'd also recommend watching Transformers: The Tech Behind LLMs by 3Blue1Brown. This is about half an hour or so. It's more technical, for sure, but it is also extraordinarily powerful. Once you're done with that, it's important to understand the stack, okay? A lot of people talk about artificial intelligence as if it's some abstraction, just like a software tool or even a mathematical idea. But, it's a lot more than that. You know, there's 13:00 hardware that has to run these models. There are chips that have to run, you know, the the the mathematical operations that are occurring. And they also have to be fabricated as well, and there are very few fabricators. And so, you know, this sort of model production also starts impacting politics and sort of what various government leaders are doing in various regions of the world. And ultimately speaking, like why certain pressures have changed models, let's say, from the East and from China uh in a slightly different direction than models from the West because of computer availability. You know, this stuff is not obviously crucial to the utility there, but again, gaining a strong foundational understanding of the context behind this will make you a far better operator later on. And so, first, you're going to start with the chain. And so, there are a variety of layers kind of stacked like an onion that ultimately result in the artificial intelligences that we know and love today. They start with chips, then they move to data centers, then they move to energy, then they move to model labs, then they move to APIs, then apps and harnesses, and finally actual 13:53 implementation. So, on the chip level, uh you have big companies like Nvidia, some of the most value companies on planet Earth that actually design said chip architectures. You know, they don't actually manufacture them. They send them off to company like TSMC or the Taiwan Semiconductor Manufacturing Company. Um they they make the chips. And you know, what's interesting is TSMC itself, the company that, you know, makes the chips, well, somebody needs to make the machines that make the chips. Turns out that's another company, um ASML, which is in the Netherlands. And so, now you're starting to get into like supply chains of the things that manufacture this stuff and the hardware and the infrastructure that ultimately, you know, most modern machine learning artificial intelligence is built off of. Most people don't understand that. Most people don't get that there's actually a complicated supply chain at the very base of AI, and that might be even more valuable and more powerful than the stuff that model labs like Anthropic and, you know, OpenAI are doing. On the data center level, you know, you have Microsoft, you have Google, you have Amazon, you have Meta, you have, uh I 14:53 don't know, Oracle, you have X. You have these large companies that are now pouring billions, sometimes even on the planned order of trillions of dollars, into building plants where you will just line the walls with the chips that are, you know, invented by Nvidia, made by TSMC, and then, um you know, actually one step up the chain, machines produced by ASML. And so, the idea is to have rows and rows and rows of these, you know, chips basically running in perfect unison. Um, you know, the the the outputs of TSMC, for instance, um, to do the the processing in such ways that Nvidia prescribes. And so, you know, big companies are pouring, again, hundreds of billions of dollars into this because they know that that is where the calculations are actually going to be occurring. But those big data centers, they also don't exist in isolation, right? Because they need something to power them. Every single one of these operations requires an extraordinary amount of power in order to ultimately work at the scale and scope of which we want them to. And so, you actually have 15:53 utilities and power grids. You have, you know, municipal governments that are responsible for saying yes or no to where these big data centers are going to go. And so, you know, we're just working our way through that onion right now, but hopefully you understand that's a little bit more complicated than just like, "Oh, Anthropic and OpenAI, they do everything." Speaking of, then you have the model apps. Okay, at that layer, you have companies like OpenAI, Anthropic. You have Google. You have X as well, you know, some of these companies are vertically integrated. So, they're trying to work at multiple levels of this layer. Um, you have DeepMind. You also have a couple of open-weight labs. Now, these sorts of companies, they're actually responsible for the software implementation. They're responsible for coming up with almost like the the the mathematics behind what computations will be done on the chips. Then, one step up from them, you also have, you know, the labs themselves, some of which offer APIs, but now tools like OpenRouter, which are basically intermediary layers where, you know, you make a request to them and then they dole out the requests to whatever 16:46 provider makes the most financial sense at that moment in time. But what's ultimately sending most of those requests? It's not human beings just at our computers anymore. Really, it's one level up to harnesses, to automated agent frameworks that do these sorts of requests on mass. And so, these are, you know, Codexes. These are your Claude codes. These are your Cursors. And then finally, you actually have implementation, which is the people, like myself, probably like most of you, that use all of this technology, okay, in the in the AI vertical, and then take it and then apply it to other verticals out there. You know, like your your your local plumber, or like some big consulting company whose operations you are optimizing, or something like that. Maybe, you know, you're implementing into your own business, or your own life, or your hobbies, your personal, like whatever. Ultimately speaking, the implementation layer is the one where we sort of go, "So what?" And take all of the transformative outputs of these industries, and then apply it to something different. So, as somebody that, you know, has probably recently been introduced to artificial intelligence technology, you are 17:44 starting at the implementation layer, which is all the way at the very top of that onion. But, you have to realize just how deep everything goes if you really want to gain an appreciation for what exactly is going on and why. Some great resources here to watch. Um on the AI harnesses specifically, I have a great video called AI Harness: An In-depth Explanation, um what a harness is, why it matters just as much as the actual underlying model. There's a wonderful, um uh video resources by Andrej Karpathy as well, called How I Use LLMs. It's basically like a deep walk-through of how a power user, like him, you know, somebody that now works at least, um as of the time of this recording, at Anthropic, uh uses actual underlying artificial intelligence. Um I'd also read a little bit about the State of AI Report. There's a lovely one called the State of AI Report 2026 by a company called Air Street Capital. It's about 45 minutes or so, almost 250 slides. So, you're going to have to spend a fair amount of time on this. But, if you skim that, you'll understand a lot about what I talked about here at the deeper levels of the onion. There's also, um a great piece by Epoch, Epoch 18:40 AI Trends. These are numbers on compute, how much money it costs to do things like, you know, train models, how much money is in data centers. After this, I'd spend some time learning about the specific players in the game. I mean, I touched on a few of them earlier, right? You need to know who's building frontier AI, and how their philosophies are a little bit different. You know, every one of these companies has a different philosophy. That's why they have different companies. The two main ones in the lead, at at least at the time of this recording, um OpenAI and Anthropic, themselves sort of spun off of a bunch of work that other researchers are doing at Google. You You they sort of had like ideological issues with some of the way that the research was occurring at Google. And so, that spun OpenAI. Uh a few generations into the models of OpenAI, Anthropic had some ideological issues that was going on with OpenAI, so then they spun off. And so, everything is still connected sort of on a foundational underpinning sort of level. You have to know that. And so, you know, the labs as of right now, uh you have OpenAI, massive, massive consumer company, probably one of the biggest and 19:35 most lucrative artificial intelligence companies ever. And obviously the ones to first bring to light ChatGPT and make it a consumer product. I'd still say they own a fair amount of the market, and I don't want to date this video's, you know, education potential by putting specific numbers on it. But next to OpenAI, you have Anthropic. The whole idea about OpenAI is, you know, they want to get to an automated research intern. However, along the way, they've taken some detours. They've made consumer apps and consumer products, things like Sora. They've made, you know, image models. They've made like GPT image, for instance. They've um done a bunch of things in both video, now audio, voice transcription. this is sort of like your general purpose AI company. They were the first to the scene, and they obviously know that there's a large financial incentive to getting this right. Um but, you know, they're not exactly like super all in on any one thing. Speaking of companies that are all in on one thing, Anthropic. The key differentiator between Anthropic and OpenAI that most people don't realize is Anthropic is 1,000,000% all in on computer programming as a 20:30 means to get to AGI and ultimately ASI. Whereas OpenAI was releasing all these cool consumer apps, they were dabbling in video, they're dabbling in photo, Anthropic was literally like, "We are text in and text out, baby. The only thing that matters is we're going to make our models the smartest programmers on planet Earth. Then we're going to make them program the next generation of the the models themselves. If wasting even 1% of those resources that, you know, we're we're spending on video takes away from our ability to reach that AGI or ASI, it will all have not have been worth it." And so, their whole sort of modus operandi is to build this better future by racing ahead to, you know, recursive self-improvement, a concept we'll talk about later, and then using that, and once they're at RSI, they'll kind of do everything else. There's also Google DeepMind. Now, this is probably one of the oldest labs out there. Um, you know, this is the one that made AlphaGo way back in the day. It's the one that, you know, sort of took the world by storm, I think, probably 7, 8, or 9 years ago, before OpenAI or anything like that was even created or invented. It was really the 21:29 thing that sort of defined a generation of AI researchers, and obviously Google DeepMind is part of Google. DeepMind was its own separate lab before its acquisition. Google obviously had a lot of financial incentives to acquiring it, but um, these guys are sort of like vertically integrated into everything. They own the full stack, cuz Google also manufactures chips. It also manufactures, or rather runs, um, cloud infrastructure, so think Google Search. It has probably more data than any other one of these companies, simply because it's been a search engine for the better part of the last 15 or 20 years. A lot of people refer to them as sort of a slow giant, where, you know, when all this AI stuff initially kicked off, um, it was Google DeepMind that people were looking at and being like, "Oh my god, these guys are probably going to take over, right?" But then they just didn't do anything for a really long time. And the reality is, the bigger a company you are, the more bureaucratic your processes are, but also the more things that you have to move around in order to like actually kick-start momentum. That said, the bigger a company is, the more 22:21 inertia it has. Just like a really heavy freight train, when it starts moving in a direction, although it can take some time to pick up, once it does start moving and eventually keep moving, it's very difficult to stop. There's also Meta, okay? And Meta, by Mark Zuckerberg, has been well behind the model race for quite a while, only very recently caught up. But they've championed themselves essentially being like open-source, you know, models for the people. We want everybody to have their own personal superintelligence, and so they dropped a bunch of products to that end, like Muse. And they're not afraid to say why. They want to make a ton of money with this stuff, and they think that by empowering all humans with their own superintelligences, they can create markets that have never been created before. You also have xAI with Elon Musk. Now, their big bet really is compute. Their huge bet is we have the most money ever. We also have what we need for fabrication of large data centers and compute farms um because of SpaceX, because of Tesla, and the fact that we've essentially been heading, you know, a robotics manufacturing, um specifically spaceship uh manufacturing, 23:19 but also things like solar panels for the better part of the last like 10 or 15 years. And so, despite the fact that they weren't really capable of competing in the model race initially, they just didn't have the, you know, model talent or the know-how, um they're now making massive strides in compute build-outs. And they're gotten to the point now where their data centers, like Colossus 2 in Memphis, are literally being rented out to other model labs that don't have anywhere near that much capacity. So, they're performing strategic partnerships where it matters and ultimately leading the model race in in that hardware space, at least as of the time of this recording. Now, all these labs are racing towards this idea of recursive self-improvement. In plain words, that just means that an AI is doing the research and development of AI. And it does so completely independently and autonomously without any human involvement. Whether or not it's slower or faster than human beings' own approaches, the benefit to RSI, at least from an AI research perspective, is when you have AI that is capable of actually figuring this stuff out, you can just run it arbitrarily fast and 24:14 then arbitrarily scoped. So, you can spawn, you know, 100,000 of these researchers, a million of these researchers. All you really need is compute and energy. Those two are the main bottlenecks. The idea is once you have RSI, the models can begin actually optimizing their own inference and also optimizing their own training. And then the next generation of the models will be, you know, an exponentially uh greater degree faster and then cheaper to run. So, those models will then optimize the next generation of exponentially faster and cheaper to run until, I mean, honestly, people are talking about these models essentially attaining omniscience, you know, knowing everything about everything, simply because, you know, they can compute and they can think so many orders of magnitude faster than people can. And we're getting pretty close. I'm not going to date this, but suffice to say, recursive self-improvement is something that I think is probably starting to occur already, if not is in full swing right now, which is one of the main motivations behind me making this video, by the way. I thought it was important everybody understand just what the hell's going on and how quickly all of 25:07 it is happening. This means that in 1 year, you might see approximately 10 to 20 years of progress. Try and wrap your mind around how much progress you've had in the last year, and then how much progress we would have in an exponentially increasing situation. We could be at the point where models don't drop once a month. We could be at the point where models drop literally every 5 minutes. And I don't say that facetiously. Anyway, some great resources to read here. The Gentle Singularity by Sam Altman. Uh it's maybe 15 or 20 minutes or so. It's a very optimistic case, and ultimately speaking, it's probably a little bit biased for investment reasons, but it's an awesome piece. Um We Must Pace the Frontier by Dario Amodei. He's the CEO of um Anthropic. Uh you know, this is a cautious sort of case, whereas OpenAI is like, "Hey, everything's going to be awesome. Everything's going to be amazing." You know, Anthropic's like, "No, no, no, we got to be a little more careful." And that's the point that he's making in that piece. If you read that piece start to finish, and it can be kind of a mouthful, but it is quite important and valuable. Um alongside his 26:00 other piece, Machines of Loving Grace, I think you start to understand just how maniacally focused on achieving that RSI a lot of these companies are. Time Horizon 1.1 by METR. It's a company that's heavily involved in tracking what is going on in these AI models and how quickly they are progressing. I also think it's important to understand the skeptic's case. Um here's one from MIT called AI's Recursive Self-Improvement Might Not Come So Quickly After All. And then, if you really want to understand not just the model dynamics, but the geopolitical dynamics, like I was telling you guys about earlier, read AI 2027. It's a detailed forecast of what is occurring over the course of the last few years and will occur over the course of the next few years, maybe into the 2030s or so with artificial intelligence technology. Next up, I wish I had done all of this ahead of time before I learned the tools, because I think this would have had quite the impact on, you know, what I did and why. But, eventually, when push comes to shove, you can't spend all your time in a la-la land obsessing over geopolitics or whatever, you actually got to, you know, when push comes to shove, learn a damn 26:58 tool. And so, this is all about the tools. And as of the time of this recording, you have a few major ones. You have Claude, you have ChatGPT, um you have Gemini, you know, you have X's Grok, you have a few open weights models coming out of China that are competing as well. Uh but these are really the main contenders, and these are probably going to be the ones that are going to proceed. I guess you could have Muse as well, depending on how things go over the course of the next few months. Reality is, like, most of these are going to be quite similar, but you do just have to pick one and then start talking to it. In addition to the chat assistant layer, again, this is kind of like an onion, one level up, you have the agent layer, you have the harness layer. And so, the variety of different harnesses, you have Claude Code for instance, you have Codex, you have Kimmy Code, right? There are a variety of these different harnesses, which are sort of software implementations and approaches to uh these these models that sort of constrain them and control them in various directions. You'll know a lot more about this than what I'm explaining right now, because you'll have actually 27:50 gone through the whole harness section of our curriculum. Finally, from there, I'd recommend learning some sort of automation tool. Um I don't mean automation tool in the traditional sense, but I think it's important to understand that the way that most people are going to be applying these models is not really just to ask them how much protein is in their yogurt, as my co-host Jack Roberts typically says. It is to ultimately make money or do some sort of economic end, you know, achieve something um in a business-type scenario. And so, the way that you do that typically is by automating processes that currently produce revenue or produce some sort of positive value. And so, automation tools typically hook up artificial intelligence models and then empower them with access to other tools to allow them to do things within a business in a reliable and predictable way. And so, personally, what I would do is I would pick a chat assistant, so maybe Claude, um a harness or an agent an agentic layer on top of that, something like Claude Code, and then finally, I'd pick an automation tool, something like, I don't know, N8N. Although these automation tools as is 28:46 may not really be required over the course of next years depending on how quickly AI moves to to take over all that stuff. Some simple and easy ways to wrap your mind around prompting, which is the process of putting, you know, a request into an AI model to kick off some sort of prompt or action chain, um is delegate. Don't prompt. At the end of the day, these models actually work better if you anthropomorphize them. For those of you guys that don't know, that word just means treat them like you would treat a person. And so typically when delegating to other people, you always want to have four things. Um first is context. So who you are, what the work is for, you know, why you want to do it and so on. The second is goal, which is what the result that you want out of this. The third is what great looks like. So some sort of example or criteria that you are explicitly stating means the job is done. And then finally, how to verify that the job has been done to your, uh you know, specifications. And so maybe you verify it with like a screenshot, or maybe you verify it by comparing the pixel-perfect accuracy of the thing that it Claude generated versus the thing that, you know, you 29:42 generated. Something like that. This is most of what I teach on my channel and also inside of Maker School, which is my 90-day accountability program that guarantees you your first customer for a paying AI or automation service in 90 days, or I'll give you all your money back. The reason why is because we walk you through how to do things like prompt engineer, how to give agents strong tasks, and so on. Anyway, ad over, you should check it out. Some things that you guys could learn or uh uh read or watch to really embody, you know, what it is to be effective with these models. First, AI Fluency uh Framework and Foundations. This is a brief 4-hour mini course by Anthropic Academy that actually walks you through everything to do with their version of their large language model, Claude, but also, you know, Claude code, what all these different tools are for, and ultimately how you can apply them to business use cases. Next up, uh 2.4 million dollars of prompt engineering hacks in 53 minutes. That's by me. I spent 53 minutes basically just ringing out my brain like a wet towel trying to give you guys as much value as possible. So definitely check that out below, too. 30:40 And I know I'm probably going to come across as you know Anthropic uh uh a script kitty or super biased or something. But if you wanted to go a little bit deeper into this, I would also read through the interactive prompt engineering tutorial by Anthropic. It's hands-on. They actually have agents just built in directly to your your structure. You sort of like quizzes. All this stuff was built by AI. And so it's sort of like a meta example of what AI can be used for. Both education, both upskilling, and both teaching people and onboarding them to your product. All things that you can learn how to do with AI as well. Finally, how to turn all of this stuff into money. I mean it's one thing to learn how to use a tool, but how do you actually apply it to real businesses? The whole idea here behind this part of the roadmap is that AI hasn't made anything new possible. What it has done it is it has made old processes much more profitable. And it is in the making of those old processes more profitable that some new things have seemed like they become possible. But really these things were always possible. The whole idea about artificial general intelligence is that 31:37 it is the same intelligence as a human being approximately in all domains, right? And so, you know, all the things an AGI can do a human being can do. It's just about the time scale. And so when people talk about AGI and like artificial intelligence producing all these new incredible things, I'm always like, "Well, the question is more was that thing profitable before AI?" You know, was it profitable before the next cool new model drop? If the answer to that question was no, it's probably still not profitable now. But if it is profitable now and you apply AI to it, now we're talking. And so, the ability to turn these wonderful developments into some sort of monetary outcome typically relies on your ability to understand what a good monetary process actually looks like. And in general, for most business, that looks like a very structured step-by-step repeatable and procedural workflow that, you know, takes some raw good. Might be, I don't know, like a person that just doesn't know anything about whatever you're selling. If you're selling education, which might be, you know, I don't know, some sort of like raw thing, maybe an iron ingot or something. And then it 32:34 goes through a pipeline filled with, I don't know, a bunch of processes to turn that iron ingot into something else. And then, you know, outcomes, I don't know, some sort of like working hammer or working tool that you can use on a farm. It is in that pipeline, it is in that process, that ultimately value is delivered and you are paid. And so, you know, what you need to do in order to become really good at turning these wonderful technological advances into money, is you just need to understand what pre-existing processes made businesses money and then how can we make that a 100x faster in order to generate much more leverage with artificial intelligence. And so, the simplest and easiest way to really understand all this stuff is actually just to go out there and and try selling it to people. Ask them, "So, like, what exactly keeps you up at night? So, what exactly is your business currently doing in order to make revenue?" Wow, really? Hmm, that's interesting. I wonder if that's something an AI could do faster. Let me think. It's not just to like, you know, come up with a bunch of great ideas using artificial intelligence for the sake of great ideas, but it's 33:27 actually to look at like pre-existing processes and then just implement artificial intelligence to iterate on them and make them a little bit better, a little bit faster. The cool thing is, you know, if you grow 1% better every day, you grow, I don't know, 2 or 300% better by the end of the year. AI can compress that whole year-long process into one that that legitimately only takes a few days. And as somebody that's now made, you know, over $10 million using artificial intelligence tools, it is shocking what that difference can do for, you know, a company's wallet. Some very simple and straightforward guides to this, how I would start AI consulting in 2026 if I could start over, that is by yours truly, took about half an hour for that video start to finish. Uh, if you guys like that sort of thing, then another really good one is what I'd learn instead of AI automation in 2027. That's about 20 to 25 minutes. Um, on the reading side, probably my favorite piece ever throughout this entire video is probably this one, Do things that don't scale by Paul Graham. Uh, it's about, I don't know, 10 or 20 minutes or so. Basically, it's why your first customers always come from doing 34:23 unscalable things by hand, aka the sorts of things that you were always doing whether or not AI was around. And then ultimately speaking, when you identify what those things are, how you can convert that into some sort of highly leveraged AI process. Next is request for startups. This will take you just a few minutes to read, but basically it's like a place where people make requests for companies to build solutions to problems other companies are having. So, this can help put you in the mindset of somebody that's like looking for ways that you can apply artificial intelligence to things that people are currently seeing detract from their ability to make money or ultimately speaking just solve problems for them. Okay, ultimately AI opportunities are coming. In general, they're getting a lot bigger. They're also getting a lot more frequent, but the time window that you can capitalize on them is a lot shorter. And so, I wanted to make this video because I feel like by understanding the context behind all this, and really by just spending, you know, a few days if not a week, you can achieve such a well-rounded understanding of what exactly is going 35:15 on here that, you know, you ultimately can actually be the person that knows whether or not an opportunity is good, whether or not these new developments are real or they're total fluff, whether or not you need to be concerned about certain impacts geopolitically, whether or not you need to worry about the job market or what have you. This is what I wish I'd learned when I had started this AI journey forever ago. And hopefully, my video alongside all the resources that I've linked down below can help take you guys to that level as well. If you guys want these resources, um just click the second link in the description to head over to Maker Zero. It's where I give everything away completely for free. This will be up at the top of the recently uploaded module inside of classroom. If you guys also want to learn how to do this for a business purpose, as mentioned, check out Maker School. That's the first link in the description. Aside from that, guys, thank you very much for watching this video all the way to the end. I really appreciate every single one of you, especially the ones that do make it this far. If you guys have any suggestions for future videos for me to make, just 36:03 let me know. I'd be more than happy to. I get most of my ideas from my audience at this point. Um please do me a solid, like, subscribe, and I love every single one of you. Have a lovely rest of the day, and I'll catch all y'all in the next one.
