Ep 842: Generative AI: How it works and why it matters in 2026 more than ever (Start Here Series Vol 1)

Welcome to the Everyday AI Podcast. My name is Jordan Wilson, and for the past 3 and a half years, we've put out more than 800 episodes. Yet one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI Podcast? And that's why we started the Start Here series. And with fall now back in full swing, the Everyday AI Podcast is going back to school and playing back the entire Start Here series. From front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights. Day by Day, the series is a culmination of spending more than 10,000 hours covering generative AI over the past 3 and a half years. So you don't want to miss a single episode of the Start Here series. Let's get into it. Have you ever felt overwhelmed by AI? Like there's certain aspects of artificial intelligence that you barely understand to begin with, yet you're expected to use it And it's changing every day. I understand where you're coming from. Chances are, if you're listening to this, you probably have a full-time job where you're expected to leverage AI, but you haven't had much formal training, if any at all, and you definitely don't have extra hours in your day to learn. Oh, and yeah, I mentioned that thing where AI is changing literally every single day. And when I say I understand the challenge, I mean it. I mean, literally, it's my only job to use, to build with, and to teach AI every single day for 10 to 12 hours. And that's all I've been doing for the past 3 years. And even I find it hard to keep up. But don't worry. That's where this new series comes into play. It's called the Start Here series, and whether you're a beginner, extremely confused, or you're someone that uses AI every single day, yet you're looking to double down, this new series is for you. Because one of the most common questions I get asked all the time is, where do I start? Or someone saying, hey, I know a lot about large language models, but I want to know more about the creative side. Where do I start? To tell you the truth, I haven't had a good answer. Until now. That's why we're kicking off this Start Here series. So for those keeping up, keeping up live, right, January is a time when I think most business leaders are setting new goals or trying to double down on good habits. So I know a lot of you are really trying to put in that extra effort here at the beginning, at the beginning of the year to improve your understanding of AI. So that's why we're going to be releasing probably 2 of these episodes a week for the next 5 or so weeks. So not necessarily abandon Abandoning our normal daily schedule. If you're an avid listener to the program, don't worry. We're just flexing a little bit here in the beginning of the year to try to help both beginners and advanced users alike get started and also get caught up. So we're going to be going over the basics like generative AI today, like what the heck is it, to simplifying more advanced techniques like AI agents and explaining explaining things like the model context protocol or what the hell. Is a Ralph Wiggum loop, right? So whether it's concepts that we're zooming out and going back in time or things that are happening literally today and helping you put them in perspective, the Start Here series is gonna be for you. So we're gonna be going over, you know, the, from the correct way to launch a successful AI pilot to how to measure ROI to how to read and understand benchmarks and choose which AI system is best for you or your company. We're gonna be tackling it all here in the Start Here series. You ready? All right, let's get into it. What's going on, y'all? If you're new here, my name's Jordan Wilson and Everyday AI, it's for you and we've been doing it for a very long time. It is an unedited, unscripted daily livestream podcast and free daily newsletter helping everyday business leaders like you and me not just keep up, but how we can leverage all the good stuff, make sense of the nonstop updates, and get ahead to grow our company and our career. So. If that sounds like what you're trying to do, maybe this is episode number 1 for you. Maybe it's episode 700. It doesn't matter. We have something new and fresh for all of you. Yeah. A new URL to throw out there. Ready? So if you're interested, go to the— go to starthearseries.com. That is starthearseries.com. So if you want to keep up with this particular series, go do that. You're going to get an invite link to sign up for our free community, and you will be inserted directly into the Start Here series, kind of onboarding flow. So as we add to this, there's gonna be more and more shows in there in that space, the dedicated space in our community. So if you wanna keep up with all the shows in this series, connect with other leaders. I mean, literally we have industry leaders in our free community, right? That you can learn from, go connect with, make sure you go to that website. All right. Now we have that out of the way, just to let you know, These shows are going to be a little quicker. All right, this one's, you know, might be 30 minutes, but most of these Start Here shows are going to be about 20 minutes. I want to keep them very fast, very factual, which, you know, is going to be a little hard for me. All right, so without further ado, let's get into it. Let's talk about generative AI, the basics, how it works, and why it matters in 2026 more than ever. So Let's start with the pace and the reality. Well, nothing has spread this fast ever. Uh, nearly 900 million people, the last confirmed was 800 million, but I know the stat, it's nearly 900 million people are using ChatGPT weekly. I mean, that's more than the entire population of Europe. And ChatGPT, let's talk about an explosion. It reached 100 million users in its first 2 months to get to that same number. The internet took 8 years. So if you wanna talk about a technology that you can't ignore, think about how commonplace the internet is in our day-to-day lives. You can't do too much without it, right? Especially if you're a knowledge worker, uh, you can't do too much without it. In just 2 years after launch, 40% of working-age Americans are using generative AI. And the internet took 7 years to reach that same level. So the generative AI technology, which is what large language models are kind of under the umbrella on, it is the most explosive growth of any technology ever. And companies are using it, whether you have realized this or not, right? This conversation has changed a whole lot over the past few years, but nowadays using AI is table stakes. Like you have to, you don't have a choice, right? Maybe 3 years ago it was a competitive advantage. You know, it was kind of novel, you know, to be using generative AI and large language models back in, you know, '21 or in 2022 when ChatGPT came out. It's not anything special today. You have to be using it today. Right now, about 80% of companies are even deploying AI Agents that take actions, not just answer questions. So, uh, not only are 92% of Fortune 500 companies, you know, as an example, using OpenAI's technology, that's just one company, right? But 80% of companies are even deploying AI agents. All right. So yes, the space moves quickly and yes, we are gonna zoom out, but I first just wanted to set the table, so to speak, and let you know where we are. Just about, right? And depending on where you're tuning in from, right? Yeah, we have listeners from all over the world, so thank you for that. But here in the US, that's usually the lens I'm talking through. I'm from Chicago. Hey, good to meet you. Right? A lot of you are starting here with this episode. Everyone's using AI here in the US, every single company, right? You don't see a company anymore. I haven't met a company in a very long time that's not using, whether officially or unofficially, right? Yeah, there's the whole shadow AI, you know, in some of those things, but every company is using AI. So it's changed though. And I think as strange as it is, I think a lot of people still have a very 2022 view of AI, right? Let's just use ChatGPT because that is the most widely used AI tool in the world, right? Obviously Microsoft Copilot, very popular in the enterprise. Google Gemini, Anthropic Claude, right? Those are what I refer to as the Big 4. So if you ever hear me reference the Big 4, I'm not talking about consulting. I'm talking about those 4 companies. But now they're not just these friendly chatbots anymore. These AI systems are operating systems in and of themselves. I've been saying this for years. Companies need to make a decision. You need to move all your operating, your day-to-day operations Into a large language model sooner rather than later, right? We'll probably explore that more in a later Start Here series—the exact process of choosing and setting up an AI operating system. It's not an official term; it's something I think I made it up a couple of years ago, and I've been running with it ever since—the AI OS, right? But in the same way, companies, you know, in the '90s or '80s or early 2000s depends, right? They gave all their employees computers at some point, and they had to make a choice, right? Are we a Windows organization? Are we a Mac organization? Are we a Linux organization? You have to make the same choice. What is your company going to be? But now in a few clicks, your entire organization's data can be accessed instantly in these AI operating systems, right? ChatGPT, they have a Teams and an Enterprise plan. Gemini has a Business and an Enterprise plan. Obviously Microsoft 365, uh, Copilot has an Enterprise plan. Claude has an Enterprise plan. These are for teams now, and they bring your data in instantly, and teams can collaborate. Right? Seamlessly. So with no knowledge even right now, right? Here's, here's some stuff. So some new stuff, right? Yes, I know this is going to age, right? If you're listening to this in June or July, but, you know, Anthropic just came out with a tool for their desktop program called Claude Cowork. So with no knowledge at all, no tech know-how, you can use a desktop program. Let me say this again. You can use a desktop program. Using their Opus 4.5 model, one of the most powerful models in the world. It can control your computer. It can access your file system. It can browse the internet in your browser that's logged into everything. A large language model can do that. It's not just a friendly chatbot. That is the work us humans do. It can access my computer. It can access the terminal. It can code. It can access all my files on my local machine. It can go out and log into my email, log into any system. and access any data that I tell it to. That's where we are now. Today's generative AI is much more than friendly chatbots. But let's zoom out to understand, because I think for whatever reason, people think of AI as a very new technology, which I think is one of the reasons why there's a high distrust. The other reason why is most people don't know how to use it. And you see someone share some, you know, someone puts a screenshot of something from ChatGPT on Facebook or LinkedIn, and someone usually that has no clue what they're doing. And it's an example example of a large language model getting something very simple, very wrong. FYI, anytime you see that, I've been doing this for a long time, 99 times out of 100, it is human error. That human has no clue what they're doing. Right? But AI's not new. AI has been around in some way, shape, or form for decades, up to 70 years. So the term artificial intelligence was actually coined in the '50s, and we've been kind of chasing this, you know, dream ever since. So there were early systems, the expert systems in the '70s and the '80s, Right? Used in a lot of different sectors. I think banking, mortgages were some of the bigger ones, obviously in healthcare, but even early systems in the '70s were shown to be able to diagnose infections better than most doctors, right? And we obviously, with the advent of large language models, we have a lot more studies that show that. So what has changed? It's not the ambition, right? It's finally there's The technology has evolved a lot. You know, there are a couple breakthroughs in the 2010s, but obviously the big one that most people are familiar with is kind of the advent and the proliferation of the transformer that has led to ChatGPT. So the biggest thing that's changed is, well, the technology and the compute needed, right? In the same way, how to, you know, access the internet, you know, in the early days, You had to have a computer bigger than a car, right? And connected up to all these cables. And now your cell phone obviously can. So the same thing is true with AI, right? These systems that used to be very slow and very big and very expensive, right? It's not like that anymore. The technology has changed. It's gotten exponentially faster, more affordable, and more powerful. But probably the biggest breakthrough that led us to where we are today Is in 2017. So that's when Google researchers published the very popular research paper, Attention Is All You Need. And it quietly changed the trajectory, technically not just of the artificial intelligence technology, but I would say of the world, right? The other thing I didn't really touch on artificial intelligence right now, and maybe we'll tackle this in another Start Here series. It has become, I'd say, probably more important than oil. It's probably become more important than countries' military, right? Whether you realize it or not, right? Maybe if you come back and listen to the show in 2027 or 2028, you'll be like, oh, okay, this weird guy was right. It is more important, right? That's why you're seeing a lot of geopolitical tensions heightened. Right now around the technology itself, right? Essentially, you know, there's this thing called AI, there's this thing called AGI or artificial general intelligence, and then there's this thing called ASI, artificial superintelligence, right? That's when you start to get into the Terminator thing. But we'll tackle this in a later Start Here series show. But essentially, the first, you know, country or company to develop AGI, artificial general intelligence, That is big, right? Anyways, it started with this paper and that has kind of changed the course of the world and the business world especially. But that paper essentially introduced the T in GPT, right? Generative Preformed Transformers. But the T in that paper was introducing the transformer architecture, which is the engine behind almost every single AI model today. And OpenAI was kind of the first to run with it, right? You know, a lot can be said with the current race between OpenAI and Google. They're definitely the front runners in the AI race. You know, Claude, Anthropic's Claude is right there as well. But OpenAI was the first to take this research and really productize it. So they built GPT-1 in 2018, GPT-2 in 2019, GPT-3 in 2020. which is actually when they started releasing it to the public with a lot of earlier software programs. That's when, you know, myself and my team, we started using it daily in 2020, 2 years before ChatGPT came out. But most of the world kind of figured out about this big breakthrough that no one saw coming that originated in the 2017 Google Attention Is All You Need paper. It really came to life in November 2022. That was the ChatGPT moment. That is the line in the sand that I think has changed the course of Not just the business world, but also the information world. So how the heck do large language models work? All right. So you're like, artificial intelligence has been around for a long time, right? And then there's this important thing called the transformer. Well, and then the transformer led to large language models. So here's kind of what they are and how they work, right? So they predict essentially these large language models have been trained on the history of the internet. Right. There's a lot of lawsuits that are going to come from that, right? Because people are like, well, what about copyrighted information? Yeah, it's going to play out in the courts eventually. But these large language models essentially scrape not just everything on the internet that we use, the open web, but they scrape everything on the closed web, offline datasets, right? Essentially, the entirety of human history and human knowledge has been scraped by these large language models. Then you have very smart humans at these big companies that then train the models and they go through a process. I'm not going to get too dorky. It's called reinforcement learning with human feedback, but they train these models, right? So they say, hey, when, you know, someone asks a question about, you know, topic A, here's what a good output would be. Here's answer A is good, answer B is bad, right? And so they go through this reinforcement learning process to train the models. And these models are trained to be a helpful assistant, essentially. With this, their training data. All right. And this is how the earlier models worked. Today's models, much more sophisticated. Right now, if you ever hear a term like scaffolding or agentic, right, today's large language models are night and day difference than the large language models that set off the ChatGPT craze. Right. Those were kind of just I call them old school transformers, right? Today's models, they're technically still transformers, right? But I call them reasoners, right? A lot of people call them reasoners or logic-based models. So they're much different now. There's a process that they can kind of mimic human logic. So yes, they are still technically next token prediction machines, but there's essentially a step-by-step problem solving on top of that prediction engine that mimics human logic, right? And you can see it think step by step and go through and, you know, use these different tools it has at its disposal. It might run code, it might go, you know, before it responds to you, these models that think now, right? They used to just spit out very quickly an answer, right? The earlier models, the GPT-2, the GPT-3 were really bad, right? The GPT-3.5 that launched with, you know, ChatGPT a little bit better. It was at least coherent and usually accurate, right? But today's models, Extremely impressive, right? They tackle problems like experienced humans. Today's models score better on offline IQ tests than 99% of humans. They are literally in the top 1%, right? They are at genius-level scores taking offline IQ tests that they have not been trained on. All right, so that's where kind of large language models are today. How they kind of work, right? So scrape data from data sources. Humans train them to be helpful assistants for users. A user asks a model a query. The model might just pull that out from its training data, or it might decide, hey, I need to use some tools at my disposal. I should probably go fetch something on the web. It seems like the user is asking about something very current. Maybe I should use a data analysis. Maybe I should run some code under the hood to get to this, to get to this answer. So yes, this kind of this scaffolding or the tools that models use help it hallucinate less and just provide much more robust and impressive outputs. So the scale is hard to comprehend, right? We're talking about billions and trillions of parameters. So a parameter is essentially a learned pattern that these models have gone through in their training data. So more parameters just means that there's more patterns that the models can recognize and use. I like to think of it's, it's a connection in its big neural network brain. So GPT-3 had 175 billion parameters. GPT-4 reportedly had over about almost 2 trillion parameters. And a lot of today's newer models, we don't know how many, but we've seen reports that they're anywhere from 2 to 4 trillion parameters. All right. So it's just an insane amount of data. And then there's also something called the context window. And even that has exploded. And that's essentially how much a large language model can remember before it starts to forget. Right? And we do in our, in our free, you know, Prime Prompt Polish course that you can access in our community, by the way. So you can just go to starthearseries.com, sign up for our community, and you will also get instant access to that free prompt engineering, context engineering course that's been taken by more than 15,000 people. Anyways, a context window is extremely important, and that's another thing that has scaled alongside with models because the early versions, they would forget things almost right away, right? Today's, you can work with them sometimes depending on how you have it set up. You can work with them for hours or days until they start to forget things, right? And there's obviously some auto-compaction. You know, these models are starting to work kind of automatically compacting this information. So it keeps a longer kind of memory going. So generative AI isn't just text anymore, right? So large language models started as mainly text-based models, but now they're multimodal by default. And there's a lot of different kind of techniques, but everything kind of now falls under this generative AI umbrella, right? Where traditional artificial intelligence was more deterministic, right? It was based on more decision trees, right? If-else or if-this-then logic, right? So traditional AI, right? The expert systems in the '80s and used through the '90s, et cetera. A lot of it was more deterministic, right? It was very rule-based. That's not large language models, right? You know, people say hallucinations or creativity is a feature, not a bug, right? That's with this next token prediction. And what that means is, right, like if, think if a little kid goes to to touch a stove, what do you say? You say, oh, be careful, that's hot. Oh, that's hot, don't touch that. Be careful, right? That's what you would naturally say, right? Humans, I think, very much are like large language models. So when people say, oh, large language models, they can't, they can't reason. Well, they, they kind of can because it's based on the reasoning of millions of humans in theory, right? But the same thing can be said for different type of On the image side, you have very popular, you know, AI image generators as well. You know, 3, 4 years ago, they were very bad. They didn't even look like, you couldn't even tell what something was. You know, some of the earlier versions, like, you know, DALL-E 2 or some of the earlier versions of Midjourney. Today's AI models, you can't tell the difference, right? I've mentioned this before. I used to do a lot of photography. I've taken more than a million photos. I've owned like, I don't know, 8 different DSLRs, you know, professional cameras. I can't tell the difference. And if anyone tells you today, I think maybe 3 months ago, you could tell the difference. Today, no one can, right? So yes, there's different types of AI. It's not just text. There's, you know, text to music. That's really good. You know, Suno V5. There's text to video. Great companies out there, Runway, their Gen 4 or 5, Google VO3.1, Sora 2, a lot of Chinese models, right? So it's not just text, it is multimodal. These models are multimodal by default. So, you know, text, images, music, soundscapes, sound effects, writing code, it is all over the place. And it works. Right? Yeah. Maybe you read a bad headline recently that said, you know, 95% of AI pilots failed. They don't. That was marketing. The ROI is true. If you look at real studies, right, that talk to thousands or tens of thousands of business leaders, almost every single reputable study in the world shows that the ROI, return on investment of AI is exponential. So as an example, the International Data Corporation found that companies get $3.70 back for every $1 invested in generative AI, and top performers are seeing a $10+ return per dollar invested. Snowflake and ESG Enterprise Strategy Group survey of more than almost 2,000 business leaders show that 92% of early adopters say their AI investments are already paying for themselves. So yeah, already positive ROI. And then similarly, other studies show that up to 98% Of the same companies that have previously invested in AI are looking to increase their investment. 98% are increasing their investment, right? So it's no longer experimentation like it was in 2023. Now it's all about scale. Now it's all about, oh wait, we can crush our competitors with AI. Unfortunately, it's about reducing headcount. We'll get to that in a little bit. But The returns are there and they are undeniable. And the economic stakes as well are massive, right? Like I said, never trust small-scale studies or studies that have an agenda. But when you talk about does AI work, I mean, look at Anthropic's November study, 100,000 real conversations. They found that AI reduces task completion time by 80%. There was a McKinsey Digital study a couple years ago, same thing, said between 75 to 80% time savings on standard knowledge work tasks. PwC study of 50,000 workers globally said 92% of daily AI users report productivity gains, right? 92% are reporting productivity gains. And I want to talk to the other 8% and teach them a couple of things. But these stats are undeniable. These large language models, do the tasks that used to, right? I remember tasks I used to do 15 years ago, a lot of researching, right? Looking, having 20 tabs open, you know, reading these PDFs, grabbing information out, personalizing it, synthesizing it, putting it in spreadsheets, maybe then eventually turning those spreadsheets into a PowerPoint, something like that. Those are projects that would take 40, 50, 60, 100 hours. I literally timed this the other day. Uh, takes like 10 minutes now. One model, one prompt, if you know what you're doing, can do that entire process in 10 minutes. And it's about 99.7% factual if you know what you're doing, right? Today's models are completely unrecognizable from the ChatGPT of 2022. And you might be thinking, wait, if all these AI models are that good, What's happening to jobs, right? Let me just tell you this, if you're brand new to the show, I'm a realist, right? I'm not someone drinking this AI Kool-Aid and being like, oh, you know, none of us are going to have to work and we're all going to live, you know, this utopian AI dream. No, I don't think so. I think it might get a little bad, right? And we're already starting to see that. Talk to recent grads. How many recent grads do It's hard to get a job because companies just aren't hiring anymore, especially the companies that have figured out AI, right? So some stats here. So only 30% of graduates in 2025 secured a job in their field, and that's down from 41% the year before. That is a huge year-over-year drop. And entry-level hiring is down 44% from its peak 3 years ago. So entry-level hiring going down nearly 50% is catastrophic. And just the number of Graduates not being able to secure a job in their field. That's worrisome as well. Right now, 62% of employers say that candidates should have AI knowledge, but 55% of graduates say their degree programs didn't prepare them. So yeah, everyone wants AI experience, but unfortunately, a lot of colleges and universities, you know, from 2022 to 2024 or 2025, Just banned AI. So it has created, especially in the US, kind of a crisis, right? Companies can't find the experience that they need. So instead they're just doubling, tripling down their AI investments as these models get more and more smart, or as these models get smarter and they're like, wait, maybe we don't need all these people. Also, 51% of recent grads are second-guessing their career choice due to AI. Up from 33% the year prior. That is a huge jump, right? Anyone, if you study statistics and you see something like that, this is from a study I covered this in an earlier episode. It had always been around that like 25 to 30% mark, and then it just skyrocketed to more than 50%. And I do assume probably in 3 years that number is going to be more than 3/4. I would assume that the overwhelming majority, probably 3 in 4, College grads are going to be like, what did I just go to school for? Right? Unless you're in something related to AI, it's like, what did I go to school for? Yeah. All right. And companies are betting big on it. So global AI spending on AI has already— is expected to hit $2 trillion this year, and that's a 37% jump from 2025. And Gartner predicts that 40% of enterprise apps will include AI agents by year end. So essentially everything, age, sorry, companies and enterprises are spending more and more money on AI in the common software that most companies use, right? I'll just throw some basic examples, right? Salesforce is a very You know, popular CRM, the most popular CRM in the world. And then, you know, ClickUp, let's use that as an example. Smaller company, but they're a, you know, pretty big CRM company. You know, you could say the same thing for, you know, HubSpot, right? Those companies, basic software that tens of millions of businesses use. Everything's an agent now, right? Everything is being agentified before our very eyes. The very tasks that humans would do inside of this software. It's all just becoming an agent now. So as we wrap up here, what does all this mean? All right. So now, you know, a little bit the history of generative AI, right? AI is not new, but the transformer technology that led to large language models is relatively new. And the adoption of large language models has been unlike Anything we've ever seen, and it's not even close. So why does it matter this year more than ever? Well, I've been the crazy guy yelling at you all for many years to not wait any longer to train your employees, uh, to invest in the future of work, right? Just using AI is not gonna do anything. It's not gonna do anything for your department. It's not gonna do anything for your career. It's not gonna do anything for your company, right? I think a lot of, you know, a lot of enterprises thought like, okay, well, yeah, we'll just buy some Copilot seats. We'll buy some, you know, ChatGPT enterprise seats and, you know, that'll make the board happy. And, you know, maybe our people are a little more productive and we'll be able to quietly reduce headcount and everything will be good. It's not like that anymore. Companies that adopted AI early are now 3 times more likely to see operating profit impact, uh, up to 5% than those companies that are still in the experimentation phase, right? You can't be experimenting anymore, both as individuals, uh, as departments, as organizations. You can't treat this as, you know, we're gonna pilot this AI thing. No, you have to hit the ground running. You have to hit the ground learning. You have to hit the ground experimenting. You have to hit the ground measuring. You have to hit the ground scoping. You have to hit the ground running your own internal benchmarks, but you have to hit the ground running as fast as you can, right? You have to go slow, but you have to go fast, right? You have to be methodical. You have to measure. You have to, uh, know, you have to be able to quantify, but you have to do it as fast as possible because the gap between AI fluent workers and AI fluent companies and everyone else is widening every single month, every single week, every single day. So by companies sitting there and saying, yeah, we're gonna do a year-long pilot, you know, we're gonna make sure we get this AI thing right. We're gonna do a slow rollout. It's not gonna work, right? Even Fortune 500 companies that have that mentality, they're gonna get eaten up. You're already starting to see stories of it, right? Companies that thought they were too big for AI, right? You saw a lot of companies, FYI, doing about-face, right? Some of the consulting companies, you know, now investing billions of dollars. You know, some of the banks said, oh no, We're large language models, never touching that. Let's laugh at that. No, now they're investing billions of dollars. You can't not play the game. You don't have a choice. The future of work is generative AI, is large language models. So the window is closing, y'all, and that is why generative AI matters. In 2026 more than ever. This is the year to make your move. This is the year to level up. And guess what? It starts here with the Start Here series. All right. Thank you for tuning into the first volume of the Start Here series. Like I said, future ones are going to be much, much faster, about 20 to 25 minutes. And make sure if you're still listening, check the show notes of this episode. So if you're listening on January 15th, right, that's when, you know, this show is debuting. Nothing's going to be there, but in the future, we're going to go and update the show notes. So if you're listening on the podcast, as we release new episodes, we will make sure to link them there. But more importantly, just go to starthearseries.com. There you can sign up for our community for free. You can go follow. It's going to send you straight to the space that we have set up that's gonna have just the Start Here series in there, nothing else, so you can focus. So whether you're hearing this in mid-January, mid-February, late 2027, it doesn't matter. It's gonna be there. You can get caught up and instantly level up. All right. So here's the final take. AI isn't new, but generative AI's compounding impact is. And today's large language models move faster than anyone can track, and they can even outperform humans in blind tests. Producing economically viable work and valuable work. So don't think about AI upskilling or AI reskilling. If you do that, you're gonna fail. That is the wrong way to approach AI. You have to unlearn, you have to unlearn good habits, and you have to build a solid foundation from scratch. AI first, AI native. You don't get to sprinkle AI on the top. It's not gonna work. All right. Thank you for tuning in. Like I said, please go to Start Here series. If this was helpful, tell someone about it. Please subscribe to the podcast. Share this if you're listening on social media, on LinkedIn. Tag someone who needs to hear this. We all need to start somewhere. All right? Don't let the rapid pace of AI confuse you. Don't let it slow yourself or your company down. That is my job. I work for you. You don't have to spend hours every single day. I do it. I cut it to you straight. All right? So thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.