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Memory Models, Technical Leaps, and AI Predictions for 2025: Shaping Your Business Future
Artificial General Intelligence (AGI) - a term thrown around in boardrooms and tech conferences, often hailed as the future of AI. But will it really come to fruition in 2025? Many experts believe so.
AGI is expected to surpass humans in executing any knowledge-based task, raising the bar for what an AI system can do. Its evolution, from defeating chess grandmasters to performing complicated, mission-critical tasks, has the potential to turn the tide for businesses.
Despite its high stakes, the achievement of AGI may go unnoticed by many. As technology progresses rapidly, the definition of AGI continually adjusts. What would have been considered AGI a decade ago, today is simply the norm.
The Rise of Narrow AI Agents
As AGI continues to develop, another emerging trend is the rise of Narrow AI agents. Presently, AI systems have conquered narrow tasks, such as playing chess or answering trivia. As technology continues to evolve, these Narrow AI agents are expected to become infinitely smarter.
While there is a common expectation for one singular AI agent to handle all tasks (like a Jarvis), it's likely that companies will have a suite of differently specialized AI agents. This shift will demand new positions, like Agent Orchestrators, to manage and direct these AI entities effectively.
The Memory Models Revolution
In the near future, businesses can expect memory models to become a significant focus of AI development. This doesn't refer merely to the context window (which will indeed continue to grow), but rather to the AI's ability to recall information over extended contexts, far beyond the immediate interaction.
Innovations in memory models will eliminate the need for a data centre or an expensive infrastructure to operate advanced AI. Lowering costs and increasing accessibility for businesses across the spectrum, this progress will create more equality in the AI sphere.
The Era of Small Language Models
An interesting development in the AI landscape is the transition from Large Language Models (LLMs) to Small Language Models.
Topics Covered in This Episode
1. Narrow AI Agents
2. LLM Memory
3. LLMs Becoming Small Language Models
4. Mixture of Models
5. AGI is Achieved
Podcast Transcript
Jordan Wilson [00:00:17]:
In 2025, we're going to achieve AGI. Large language models may not really exist at least how they do today. They're gonna get infinitely smarter, and dumb AI is going to be a little less dumb. Alright. You've made it. If you've joined us all week, thank you. Today, we are wrapping up our 2025 AI predictions as we are literally laying out the road map for you and your company to succeed at least when it comes to AI in 2025. Today, we're gonna be tackling AI's technical leaps, memory models, and major changes.
Jordan Wilson [00:01:09]:
That's right. Spicy. It's it's gonna get extra spicy today y'all because because I'm feeling it. I'm feeling in my feels. I've been talking AI all week, giving you the predictions, and I'm excited to cap it all off with the last, the last show here. So if you're new here, welcome. My name is Jordan. I'm the host of everyday AI, and we do this thing every day.
Jordan Wilson [00:01:33]:
So, it's a daily livestream podcast and free daily newsletter helping us all learn and leverage generative AI to grow our companies and careers. I want you to be the smartest person in AI at your company, and we are your cheat code. And your website is where you do it. Our website, your everydayai.com. So make sure to sign up for our free daily newsletter. We're gonna be recapping today's show there. If you miss anything, don't worry. As well as we have more than now, like, 450, back podcast episodes, sorted by categories, no matter what you care about.
Jordan Wilson [00:02:07]:
Right? HR, legal, tech, ethics, advertising, whatever. Midjourney. It's all on our website sorted by category, so you can learn from the world's leading experts for free. You can listen to all of the podcast videos. It's all there. Alright. So, if maybe you just stumbled upon this show and you're listening for the first time ever, and you're like, what's going on here? Well, I I'm super lucky. I get to talk to 100 of the smartest people in the world, on AI.
Jordan Wilson [00:02:37]:
And usually what happens, right, because this is a live stream. This is unedited, unscripted. Right? And a lot of times I go back and I reflect because I write the newsletter with my fingers. Right? I don't hand that off to an AI model. And then later, I'm connecting all these dots. And throughout the full year, I'm writing down big trends that I'm seeing that no one else is spotting. Right? Because guess what? I yeah. I get to talk to all the people from Microsoft and Google and OpenAI and IBM and all these other companies, but they probably don't talk to each other a whole lot.
Jordan Wilson [00:03:07]:
I'm lucky enough. I get to. And, you know, I'm helping, you know, people hire us, to help them, you know, learn, ChatGPT or copilot or whatever it is. So, I'm in a very unique position where I get to talk to a lot of the world's smartest people. Soak it all up, but I spot these trends, And that's what this week's series is all about. Our 2025 AI predictions, and we are on volume 5 of 5. So if you are tuning in for the first time, you missed a lot, but I'm gonna give you a very quick update on what you did miss. Alright.
Jordan Wilson [00:03:39]:
Ready? And you can go back and listen to all of these episodes in the theming of things preparing you for 2025. Trying to keep all these episodes to 25 minutes or less, which if you listen a lot, you know, that's a miracle. So here's what you missed. Ready? Volume 1, I'm just gonna give you the predictions. If you wanna go listen to this, if something in volume 1 catches your ear, it's gonna be in the show notes. It's gonna be on our website. Just go look for it. Alright? It's 4 episodes ago.
Jordan Wilson [00:04:08]:
So volume 1, number prediction 25. Agent orchestrators will be a growing position. 24, public companies will post jobs for AI agents. 23, company reasoning data collection. That's gonna be huge. 22, high end professional services will go through pricing crisis. 21, UBI becomes a household conversation. And then in volume 2, start with number 20, open source surges, open large language models will temporarily overtake proprietary models.
Jordan Wilson [00:04:37]:
19, Chinese AI will dominate and cause confusion. 18, perplexity will pivot, get acquired, or get squashed. 17, API prices are gonna drop like they're hot. 16, embodied AI will be an exploding sector. Then in volume 3, here's what we covered. Number 15, AI video tools will one shot 5 plus minute HD videos and will have advanced personalized media. 14, the future of traditional Internet comes into question. 13, social media makes deep fake AI problems way worse.
Jordan Wilson [00:05:08]:
Number 12, first big copyright case is decided. Number 11, AI influencers are gonna start killing off human UGC content. Here we go with the top 10. This was volume 4 from yesterday. Go back and listen if you want to. Number 10, non techies will build on the fly software. 9, reasoner rappers will hit the scene. 8, virtual machines become all the rage.
Jordan Wilson [00:05:31]:
Number 7, AI becomes overly political. And number 6, global regulation around AI titans, but not in the US. And here we go without further ado. Here's volume 5, our last set of predictions, AI's technical leaps, memory models, and major changes. Here we go. Whoo. I'm out of breath. Number 5, narrow AI agents will be achieved.
Jordan Wilson [00:05:55]:
Number 4, LLM memory becomes a major focus. Number 3, LLMs become small language models, and SLMs dominate. Number 2, mixture of models becomes a thing. And number 1, AGI is achieved, but no one notices. Alright? Same as always, whether it's livestream audience, you let me know. Podcast audience, I leave my email and my LinkedIn. Just tell me you're from the podcast. Just connect with me.
Jordan Wilson [00:06:25]:
But let me know which one of these is the most likely to happen and which one is the least likely to happen. Maybe I'll put it in the newsletter, but let me know. You can just put, like, if you think number 3 is the most likely, just put 3 most or 5 least or one most, whatever you think. Alright? Because I wanna hear from you. These things it's not just me yelling, at the camera in my home office. I'd love to hear from you. We'd love to feature, you know, some of your great comments in our newsletter as well. So, let's get into it.
Jordan Wilson [00:06:59]:
Number 5. Narrow AI agents will be achieved. Let me tell you what that means. 1st, I have to quickly explain a little bit of the difference between AGI and ANI. So, artificial general intelligence versus artificial narrow intelligence. So, artificial narrow intelligence has been achieved for quite some time. Right? Depending on, you you know, your perspective, your background, it could have been for, you know, more than 10 years, maybe even longer. Right? That's when a certain AI system beats the world's leading person in a certain task or a certain field.
Jordan Wilson [00:07:44]:
Right? You can go back to, you know, go on Jeopardy or, you know, AI beating chess grandmasters, whatever. ANI, we've been past that, but, you know, that's not really what we're about. You know, everyone's always talking about now agents and AGI. Right? 2 things that I think we are on the cusp of. But I don't think we're going to have a genteq AGI anytime soon, and I think it's actually, I'm not gonna say it's a problem. It's more of a people are talking about it, like that's the thing that's going to happen first. Right? Where we're going to have agentic AGI and what that means or, like, let's just start at agentic AI. Right? We'll break one piece of alphabet soup off the the the spoon at a time.
Jordan Wilson [00:08:28]:
Alright. Agentic AI is when an AI system can go and make and execute decisions on your behalf. It has access to tools. It can access generally, it can access the Internet and your data. Right? So think of it like, you know, like a very new intern. Right? You give it some directions, and then it can go play with multiple programs, and it can make decisions on your behalf, access your company's data. You can give it guidelines, etcetera. Right? AGI, that's that's more of a this this ever changing threshold.
Jordan Wilson [00:09:00]:
More on that later. Right? But I think when we think of agents, everyone's thinking of agentic AGI. No. No. It's going to be narrow AI agents. So everyone thinks that they're gonna have one agent that just becomes their personal, does everything. No. I think the the rise of agentic a AI in 2025, that's a lot of rhyming.
Jordan Wilson [00:09:25]:
Right? It's it's on narrow applications. Right? I don't think you're gonna have a super agent like a Jarvis that does literally every single thing. I think you're gonna have ten different narrow Jarvis's. Right? That are just good at one very specific thing, and there's going to be an agentic router of sorts. More on that here in a minute as well. A lot of my predictions today actually, kind of bleed into each other, and I did that on purpose, because I'm the most excited about this one. It's gonna be very hard for me to wrap this show in 15 minutes. Alright.
Jordan Wilson [00:09:59]:
But I don't think in in in to keep it short, I don't think we're gonna get these general AI agents, which is what most people think we're gonna get. We're have narrow AI agents because plenty of people, the big companies as well, they're going to try to release general AI agents. They're not gonna do too well. Alright? The same way that early large language models, you really had to tune them, and work with them a lot to get them to be good at one thing. Right? That's why in our, you know, prime prompt polish course, which I know y'all been hitting me up. It's gonna be back on soon. I promise. Right? But that's why, you know, we teach you.
Jordan Wilson [00:10:35]:
You really have to work, with a chat to get it good at one thing. Right? Same same same thing applies true, at least right now until we kind of, quote, unquote, get to AGI, or, Agentic AI that uses Reasoner models. But until then, I think you gotta focus on one agent, and you're gonna have many agents. One of our first, predictions was actually Agentic Orchestrators, and that's why. Because you're gonna have a ton of them, and they're gonna be narrow, not general. Alright. Number 4, LLM memory, large language model memory becomes a major focus. So I'm not talking about context window.
Jordan Wilson [00:11:11]:
These are kind of 2 different things. I'm talking about memory. So that's in AI's ability to be able to recall events almost infinitely, across the entire, organization or across an entire account and not just in a singular context window. So I know that there's, kind of some some nuances there, and, you know, unless you're a dork, it might be a little difficult. But think of it like this. I think, context windows, and, you know, that's kind of the amount that you can throw at a large language model in a single chat and it can kinda remember it all, right, until it starts forgetting things. Context windows are going to continue to grow, but I think memory is actually going to become just as big of a factor. Right? OpenAI's early memory feature was actually very novel.
Jordan Wilson [00:12:01]:
It just it's not executed well, because you can't kind of toggle when you want it to remember something or not. So it's a little finicky. I think some simple UI UX interfaces are really going to take this memory kind of concept and and run with it. Also, we've seen reports that OpenAI and Microsoft are working together on AI advancements scheduled for this year where they're focusing on developing AI models with near infinite memory and also expanded context capabilities. Right? That's one of the biggest problems, you know, aside from hallucinations and people not even knowing how a freaking large language model works. Right? They They don't know what a transformer is. They don't know, you know, tokenization. They don't know weights.
Jordan Wilson [00:12:42]:
They know nothing. Right? They're just like, ah, look at look at this prompt that I put into AI, and it gave me this garbage. AI is not taking my job. No. AI is definitely taking your job because you don't know how it works. So, yeah, you're 1st on the chopping block, Bill. Alright. I don't know.
Jordan Wilson [00:12:56]:
Sorry if your name's Bill in listening. I just picked a name. I'm sorry. But no. It's it's most people don't understand the basics of generative AI of large language models. But as context window grows, as memory grows, outputs are going to be better, then you become, more trustworthy. Organizations have more trust, in these models as hallucinations, hopefully start to become less and less frequent. Yes.
Jordan Wilson [00:13:28]:
Hallucinations are a feature, not a bug. I get it. We're talking about nondeterministic, you know, technology here. So the whole point is it's supposed to be able to, give you something different every time, but not at the, at the expense of being wrong or incorrect. Alright. Number 3. Here's a sneaky one. Large language models, as we know them today, they're gone.
Jordan Wilson [00:14:00]:
Large language models are becoming small language models, believe it or not. We're gonna do some math here y'all. Alright. Let's do some mathing. Let's talk about GPT 4. That I think is the epitome of large language model. Right? When we're looking at frontier models, you know, you can look at the early or maybe even go go further back early days of the transformers, late 20 tens, seeping into the early 20 twenties. You know, these these models kept getting bigger and bigger.
Jordan Wilson [00:14:35]:
And at first, you know, small language models have been around for a long time as well, you know, and and generally, they're they're for more specialized purposes and not these general models. So these general models, we call them more large language models. Right? And then you had your smaller models, and it's was judged by the size of of in measured in parameters. So not all companies release and say, hey. Here's how big these parameters are or how many parameters are in the model. Right? But it was reported, that GPT 4, which at the time when it came out, smoked everyone. It's disgustingly good. Right? That it was 1.7 or 1.8 trillion parameters.
Jordan Wilson [00:15:13]:
Alright. So nearly 2,000,000,000,000 parameters. Think of that like like a hard drive. Right? Like 2 terabyte hard drive. Right? For a comparison. 2 trillion parameters. Huge. Okay.
Jordan Wilson [00:15:25]:
So Microsoft just came out with some research recently that apparently cracked the code on the size of some of the newer proprietary, models. Guess what? Its successor, GPT 4 0, 200,000,000,000 parameters. So a tenth of the size and much more powerful. Still a fairly large model. But when you look at GPT 4 0 Mini, this is some of the biggest news, I think, of the last couple of months that literally no one's talking about. GPT 4 o Mini, which is a very capable model. Right? And people generally who are using the API, right, who are using OpenAI's API, many of them are using GPT 4 o, not just sorry. GPT 4 o Mini.
Jordan Wilson [00:16:11]:
Not just for chunking, but for being the workhorse because it's an extremely capable model. Right? It is near the capabilities of the original GPT 4 model. That was 2 trillion parameters. I'm not great at math, but let me just go ahead and compute. But that's that's not even 1% of the size. That's a half percent of the size. Okay. What do you get in that weird guy? Well, the definition of large language model used to be you know, it was it was always, you know, a moving definition, but it was like, oh, it's when it's 100 of, you know, 100 of billions of parameters, and then it's like, oh, well, no.
Jordan Wilson [00:16:48]:
It's actually when it's trillions trillions trillions of parameters. Okay? What happens when even frontier models, the big ones? Right? What happens when there's a GPT 5 o that's, like, a couple billion parameters? Right? And can fit I'm just gonna grab something random on my desk here. Right? And can fit inside of these headphones as an example. Right? I need obviously proprietary models. You can't download them, but I'm I'm I'm putting out a point. Right? Think of Meta's Llama. Right? They haven't released the large version yet of 3.2, but the 3.1 version was 405,000,000,000 parameters. So I assume the if they do come out with the large variation of llama Meta's Llama 3.3.2, which you can download and run locally and, fork it and all that good stuff.
Jordan Wilson [00:17:45]:
Right? Probably fit it on, you know, NVIDIA's new, digits computer. Right? Large language models are becoming small language models. And I think we are going to see, speaking of llama, right? Here's a little fun fact for you. NVIDIA has fine tuned Lara, Meta's llama, and this was their 3.1. And it out benchmarked Meta's actual model. Right? So I think what we're gonna see is more of those types of builds. Right? I'm very excited for either Llama 3.3, or you know, when they do if they do come out with their 3.2 large, to see what happens. I think some of these other big tech companies like NVIDIA did are gonna start to tune models.
Jordan Wilson [00:18:32]:
And I see I don't see this yet happening in 2025, maybe toward the end. But early 2026, I see proprietary models even going domain specific. Right? And that's gonna set me up into prediction 2 here real nice, but bear with me. Think. If these models, llama. Right? And, I know there's gotta be some other open, even truly open source models, that are going to be benchmarking as, like, a top 10, frontier model in the world. So when that happens, and then when you eventually when they're small enough that you don't need a a a research institution and a team of, essentially data scientists to fork them, to build new versions of them. Right? Pretty soon, anyone with, yeah, $3,000, could, you know, go and download the world's most powerful open source model and play with it on your computer.
Jordan Wilson [00:19:37]:
Right? That's nutty. Because it's like, you know, when you think of, like, 3, 4, 5 years ago, you had to have a freaking data center. Right? You had to spend probably 1,000,000 of dollars to be able to actually run and use and and and run inference on these models. Like, it's crazy. So I think this whole concept of large language models, you know, for for a point there, I was calling them jumbo, jumbo models. I think they're just gonna be small models now. Right? I don't think, you know, in in 3, 4 years from now, I don't think we're gonna have any models that are 100 of of billions of parameters. Right? I think even the biggest models in theory are gonna be able to fit on a device.
Jordan Wilson [00:20:19]:
Right? Which is big. That that that cuts down energy consumption. Right? So we don't have to have, you know, 500 nuclear plants, you know, here in the US producing nuclear energy because we're out of energy. Right? Because when you send your all your queries or all your company's queries, to the cloud requires a lot of energy. Right? Also, data and privacy concerns. You know, everyone wants edge models. Everyone wants on device AI. And I think we're gonna get there.
Jordan Wilson [00:20:47]:
Right? I mean, think of the fact that GPT 4 o Mini is reportedly 8,000,000,000 parameters, and it's out benchmarking, you know, so many models that were more than a 100 times that size. It's wild. And then I think, like I said, in the future, I think we're gonna start to see 100 of small language models that are very capable. NVIDIA's, llama, Nematron esque type models. There's gonna be 100 of them. Right? State of the art models, 100 of them. Not just, you know, a dozen or so that are, at today's capabilities. Alright.
Jordan Wilson [00:21:29]:
Number 2, I'm gonna get a little technical, but I think I just set you up there with number 3. So number 2, we have mixture of models become a thing. You might be thinking, oh, Jordan, you're a dweeb. We already have that. No. We don't. I made it up. No, we don't.
Jordan Wilson [00:21:48]:
See, we don't have it. We have something called mixture of experts. Right? So let me just break it down super simply. That mixture of experts is essentially it activates one expert at a time. So there's a essentially, there's a gatekeeper. Right? So in this in this system, there's a gatekeeper. So you give a you give a prompt, there's a gatekeeper model, and then, you know, let's just say there's, you know, a 100 different, smaller models, and the gatekeeper model's like, oh, okay. Let's send it to to this model.
Jordan Wilson [00:22:20]:
Alright? And then for the most part, it activates one expert at a time. It might give it to multiple experts. Right? There's different MOE setups. I don't see that happening anymore. I see something different even within the same, system. I see something called a mixture of models. That's when it just you can run a prompt, and the same system will be able to run multiple specialized models in parallel. Not a gatekeeper handing it off to, the best model or handing it off sequentially, and it runs one at a time.
Jordan Wilson [00:22:58]:
But running it through multiple specialized models in parallel, each working on different parts of the task simultaneously. Right? So the the it's it's kind of similar to how mixture of experts works, but also completely different. So this is what I see. This is what I see. I see as an example, on the front end, you send a very advanced query, to ChatGPT. Right? I have the, the pro plan. It's pricey. $200 a month, but I think it's definitely worth it.
Jordan Wilson [00:23:33]:
Alright? And I can choose right now. I can choose o one pro. Right? I I love to handle. I like to see how long I can make o one pro think. Right? Sometimes 10, 12 minutes. I see when I give those very hard tasks off, it's gonna give some of it to o one minutei right away. It's gonna give some of it to to GPT 4 o maybe because, you know, the o one models yet don't have the web. Right? So in parallel, I see this is how it's going to be happening in the future.
Jordan Wilson [00:24:01]:
Right? Or think of like Google. Right? A centralized place where, hey. Here's here's my company. Here's the issues that we're working at. You have all my data, and then Google, in this mixture of model scenario, it might send some of your things to its, to its imagine AI image generator. It might send something from there in parallel at the same time, to its, VO. It might send something to a specialized, large language model. It might send something to, you know, flash thinking.
Jordan Wilson [00:24:35]:
It might then send send something to deep research. Right? But keep it all in the same interface. Right? And, essentially, similarly to how a mixture of expert scenario works out, but this is a mixture of models. It runs them all simultaneously, and it just chunks off the different pieces, does it all at the same time, and then gives you their output. So similar, but a little different. Because studies show that right now, organizations, are deploying on average 3 or more foundational models in their AI stacks, and oftentimes, it's more than a dozen. Alright. Here we are at the last one.
Jordan Wilson [00:25:13]:
I would love to spend 30 minutes alone on this, but I'm trying to keep these at, like, 25 minutes, and I just hit the 25 minute mark. So it's gonna be quick. Prediction number 1. AGI is achieved. No one notices. No one notices. Right? So there's already been an argument with OpenAI's 03 model, which is the successor to 01. Right? Some people are like, oh, well, you know, hey.
Jordan Wilson [00:25:37]:
It passed the Arc AGI challenge. So AGI is achieved. Right? And there's a lot of now internal talk, especially at, at OpenAI that, you know, oh, now they're focusing on superintelligence, you know, because they've said, hey. They've quote, unquote kind of figured out AGI. So if you don't know what AGI is, it's artificial general intelligence. So that is when one AI system can essentially do any task, any knowledge based task that a human could do, but it can be better than all humans at all tasks. Right? So, you know, I gave the example earlier like, oh, an AI can beat someone at chess, right? So think of any knowledge based task that you could do, well, right away one model is better than every single other human on the planet at just about anything. The definition of AGI constantly changes.
Jordan Wilson [00:26:25]:
I'm a dork y'all. I did a show on this about 3 or 4 months ago. I went back and I used archive.org, and I looked all the way back to, like, 2,005. Right? Because you can go look at the web from, like, 2,005. And I was reading the definitions of artificial general intelligence from 20, I think I looked every 5 years, up through 2015, and then I looked every single year from 2015 to 2024. Right? Guess what? By definitions of 10 to 15 years ago, we've already achieved AGI. I think AGI is constantly it's the the definition of what it means is changing just as fast as all the models. Right? Now Microsoft came out and said, oh, well, AGI is achieved once, you know, one AI system is capable to generate a $100,000,000,000 in profits.
Jordan Wilson [00:27:10]:
Right? That's part partly because of their current kind of relationship, with OpenAI is a reported 49% equity holder in the company, and there's kind of this AGI clause. So they're trying to define it one way. But here's the thing y'all. We can keep defining it every single day. Let me ask you this. Let me ask you this. And and and once as an example, once o one gets tools. Right? When we see agentic AI and the combination of a reasoning model that has tools, when those things happen, all the individual pieces are there.
Jordan Wilson [00:27:46]:
When that happens, that's AGI. Right? It doesn't have to be this big moment. Right? Like OpenAI CEO Sam Altman kind of said the same thing. He's like, it's gonna happen, and then everyone's just gonna go on about their lives. I feel the same way. I think all the individual pieces are there. It's agentic AI plus reasoning model, but also GPT model. So it's the combination of those two models working at the same time, plus tool use.
Jordan Wilson [00:28:10]:
Right? So being able to access the Internet, access, you know, data, analysis modes, all these other things. AGI is definitely going to be achieved in 2025, whether people admit it or not. I don't know because I think the definition is gonna keep changing. But do me a favor. Go go look at archive.org. Look at the definition of AGI from 2015. I did a full hour episode. We've already achieved it.
Jordan Wilson [00:28:35]:
But I think the common consensus in 2025 will finally be, alright. We've achieved AGI, and then we're all gonna find out nothing happens. Right? I don't think there's this huge life shift, business shift. I think we're gonna see a lot more layoffs, and I think things are gonna get weird, when it comes to traditional jobs. I don't think 9 to 5 jobs are gonna be a thing in like 3 to 5 years, right? But I don't think it's gonna be this this mine in the sand and the whole world shakes. It's not gonna be like that, but it's gonna happen in 2025. Alright. I hope this was helpful, y'all.
Jordan Wilson [00:29:09]:
We made it. This is volume 5, our last set. So this is more than just a show of predictions. Right? I probably should have named it the trends ahead because that's what this is. That's what this series has been. This has been the culmination. Y'all, certain days, I spend 10, 12, 15 hours reading about AI, learning about AI, talking to people about AI. I don't make these predictions lightly.
Jordan Wilson [00:29:41]:
Right? These are your blueprints. Alright? You need to be learning from what I said. Because and and I'm not trying to say that in a way of, like, oh, listen to what I say. No. Because I just steal all the information from all the smartest people in the world, but I'm giving you all that platform to say this is what's happening. This these are the trends ahead. Alright? Some of y'all know, like, my background actually, I have a couple different backgrounds, but I spent 10 years working in a nonprofit. I love helping people.
Jordan Wilson [00:30:20]:
That's why I do this every day. Right? I could probably be making a lot more money doing something else, than teaching you all about AI every day. Right? But I feel the need to. Right? I was a journalist also before that. Right? So it's it's it's kind of this culmination in what I've been doing for the last two and a half years doing this every day. I honestly want to help. I want to help people. I wanna help companies not just survive this AI, but I want them to thrive.
Jordan Wilson [00:30:49]:
I wanna help companies thrive. It's not gonna be easy. Things are only gonna get weirder. They're gonna get harder. Job loss, I'm telling you, I don't wanna end on this note. It's gonna pile up. Right? But if you're here, if you're listening, if you are actively taking a part in using AI every day and learning every single day, putting it into practice every single day, you're gonna be fine. Alright? So please go listen to all of these episodes.
Jordan Wilson [00:31:24]:
We put a lot of work in. I hope they're helpful. If so, let me know. Please, reach out. I always, like I said, put my email in the show notes. If you're listening on the podcast or my LinkedIn, just let me know you're from the podcast. Otherwise, I don't know who you are. Alright? If you're listening on the live stream, let me know.
Jordan Wilson [00:31:40]:
I hope this is helpful. We got a lot planned, for the rest of this year, 2025. It's gonna be exciting, and I can't wait to be able to show you guys a lot of what we have come for you. So thank you for tuning in. I hope to see you back next week and every day for more everyday AI. Thanks, y'all.