EP 584: ChatGPT's New Open Source Model gpt-oss: What it means, the risks, and more

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Everyone, rightfully so, has their eyes on OpenAI's GPT5, which is going to be released in hours. Or if you're listening to this podcast a little later, it was just released, but there was actually a different OpenAI release this week that I think is actually bigger than GPT5. Hardly anyone aside from us AI dorks talked about it, and I think it's actually going to be the pivot point in the AI race when we look back many years from now. So that's why today we're going to be talking about ChatGPT's new open open source model, GPT O S S. We're going to talk about what it means, the risks, and who the winners and losers are. I'm excited for this one. We're going to be uncovering and unpacking a ton in a very short amount of time. All right, let's get into it. What's going on, y'? All? My name is Jordan Wilson and welcome to Everyday AI. This is your daily live stream podcast and free daily newsletter helping everyday business leaders like you and me. I just keep up with all this news, but how we can use it all and leverage it to get ahead to grow our companies and our careers starts here in the podcast, unedited, unscripted, but where you actually go and be the smartest person in AI, that's on our website. Your everyday AI dot com. Go sign up for the free daily newsletter. We're going to be recapping not just what's happening in the world of AI news, but the most important takeaways from today's show or maybe some stuff that we couldn't get to as well as you can go listen to all. Almost 600 episodes now for free on our website. And if you do want the video version ever, you can always go find that on our website. Click the Episodes tab. And if you want the AI news, like I said, go check that out in the newsletter. But let's talk open source. Great name from OpenAI here, GPT O S S. As if the thing to help the Alphabet soup of model naming was to throw more Alphabet letters into the soup. Anyways, this is pretty big. OpenAI just released a free open source model and it is on par with the model that most of US were using nine months ago, which is GPT4O. It's on par with it and it has reasoning. So let's talk about this new models and there's actually two of them. So this is their first open source model since GPT2 in 2019, before many of us were even using this technology, many years before ChatGPT came out. Also, it does have a lot of higher tier capabilities that you wouldn't necessarily think would be in an open source model such as reasoning. And it's trained on techniques from OpenAI's Frontier O3 model. And this is a direct response to the Chinese open source movement. Whether OpenAI is going to admit that or not, that's the truth. As the AI labs here in the US were taking the proprietary approach, aside from meta, the Chinese AI firms went all open source and that's really forced OpenAI's hand here and it is available for free for anyone today to download and use. That is why I am saying this is going to be a pivotal point when we look forward. Yes, GPT that's being released today, it's, it's going to get all the headlines right, but it's just a upgrade in a model. This is a completely different direction that the business world is now going to travel in, not hyperbole. I'm going to tell you why. And this, like I said, it does completely change the trajectory of both open and closed source model development. There's no going back now. This thing is out in the wild. People are going to download it, they're going to fork it, they're going to distill it. It's, it's got to get wild on the AI development front. And this is going to force all the proprietary AI companies, the big AI labs, to shift their strategy as well. They're either going to have to release back better models faster or they're going to have to cut prices significantly. This is really going to cut out, I think the mid tier market and kind of the 1C kind of tier of model makers as well. So on today's show, here's what we're going to talk about. We're going to go over how OpenAI's first open model since 2019 ended their proprietary only strategy and how their internal business model has shifted. Why businesses can now download GPT4 Level Reasoning and run it locally without APIs. We're going to talk about the winners and losers. One of those winners is Nvidia. While API only providers could face an existential threat. And we're going to talk about why powerful AI becomes uncontrollable once it's released globally and why I think this will be bigger than GPT5. All right, let's get into it. So here's open source. If you're confused, maybe you're just, just go to chatgpt.com and you're like, all right, I don't really understand this whole API open source thing. Why is it a big deal? So Right now there's two different versions of this model. There's a 21 billion parameter model and then there's 120 billion parameter model as well. They're completely free, right? So normally if you're using ChatGPT, you're paying, you know, on the front end paying 20 or $200 a month for the Pro or the plus plan, or you're on a free plan. But when you're on a free plan, obviously the companies are training on your data, right? And training on how you interact with the model. This is different, right? With an open source model, you download it, you can cut off the Internet and use it. It is local, it is secure, it's relatively fast. As long as you have a souped up computer, right? You do need a fairly powerful computer to run the 21 billion parameter model. You do need at least 16 gigabytes of RAM and a newer GPU chip as. So here's some other things that I think are gonna change, right? Because this is you can download, own this and control it essentially forever. And we're gonna talk about the open source licensing that OpenAI went with as well, which I think is pretty interesting, especially if you're sitting in meta's seat. So now I think small companies and startups are gonna be able to gain access to high level AI without restrictions, right? And without having to raise capital or without having to run up, you know, tens of thousands of dollars in API costs, which a lot of companies do. You can run this now completely offline with configurable reasoning as well. That's great. You can run it on low, medium or high reasoning. So this is a reasoning model, an open source reasoning model. Crazy to say this out loud from OpenAI, right? All of their critics have been calling them closed AI for many years. You know, the CEO Sam Altman said that they maybe took the wrong approach on the open here, but rectified that fairly quickly here. And so you can modify, fine tune and integrate this open model into products without even asking OpenAI permission, or without paying a dime, without having any ongoing costs, right? If you have the hardware, you can run this locally. It doesn't require a ton of skills either. And I'M going to tell you the different ways that you can download it and run it today. So like I said, two different versions. The larger version it is according to OpenAI, this is their large open model designed to run in data centers and on high end desktops and laptops. I believe as an example, the highest souped up model, MacBook Pro M4, I think it's like 64 gigs, which is like how do you even fit that much RAM in a laptop that can run the 120 billion parameter model and then the GPT OSS20B described by OpenAI as a medium sized open model that can run on most desktops and laptops. So yeah, you do have to have a newer laptop with at least 16 gigabytes of RAM. And I do think in the future, you know, an iPhone will be able to run this. Not today's iPhone, maybe the iPhone 17 will be able to run this version or if OpenAI updates it in the future. And that's I think one of the big plays here that we're going to get to later. So let's talk about some of the capabilities. So again, live stream audience, you can see it on my screen. Podcast audience. I'm just reading these couple of parts straight from OpenAI's website. So kind of the four big capabilities that they are talking about here is the permissive license. It's designed for agentic tasks. It's deeply customizable in the full chain of thought. So on the license side the models are Apache 2.0. So you can build freely without worrying about copy, copy left restrictions or patent risk, whether you're experimenting, customizing or deploying commercial. On the agentic side you can leverage powerful instruction following and tool use. That's the other thing. Yes, there is tool use, python, web search, etc when you are using this on your local machine. And then you can also follow along with the chain of thought. It's deeply customizable. So you can adjust the reasoning effort to low, medium or high. Plus you can customize the models to adapt to your use case with full parameter fine tuning. Yeah, so you can literally download this, fine tune it, run it locally, run it on prem, run it securely, run it without WI fi. Right. And then full chain of thought. That's the other thing. This big a big jump ahead in capabilities when you're talking about a reasoning model. Right. Kind of quote unquote old school transformer models. Right. They're essentially very advanced autocomplete models. Reasoning models are a lot different that you can kind of see their chain of thought. So these are models that kind of take time to think and they plan ahead like a human would. And obviously the results that you get from these reasoning models are much better than you get from transformer models almost in every single case. But then you can see the chain of thought. You can see. Right, which is so important. If you're a company using an open source model, you need to be able to kind of audit how it gets from, you know, question a ton to answer a. Which is pretty important there. Benchmarks, pretty good. We're not going to go through them all. We'll leave a link to them in the newsletter, one I like to talk about. Even though it's kind of an older benchmark by now mmlu, there's a lot newer ones including like MMLU Pro. But this is essentially, I say it's like an old school ACT for large language models. Right? But they're not trained on the data set. So the 120B version got a 90 on the MMLU, which is crazy. The GPT 20B version, so the small version that can in theory run on a phone, not on an iPhone, but it can run on other phones technically locally, y', all, this got an 85.3 MMLU. So that's essentially the same score as GPT4O. Let's think about that. You have a model as capable as GPT4O that you can go download fine tune.

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Use commercially without paying a single penny. You're not running API Costs, Right, and let me explain that for our non technical audience, right? Many of us, we go to chatgpt.com pay, don't pay, right? But you're using a front end chatbot, then you can use it on the back end. So maybe your business builds on top of this technology and you're essentially paying a certain price for every million tokens, right? So you're paying your usage and sometimes that's a couple hundred dollars a month if you're a smaller business, if you're a bigger business, you know, could be hundreds of thousands of dollars, right? But you're paying for usage on the back end and maybe, you know, you have hundreds or thousands of customers that are actually using OpenAI's technology through the lens or through the API kind of pipeline that your company is connecting it with your data. This is free, this is nothing, you're paying nothing. That's why open source, it's really powerful and it does change the game. So let's even talk about the open source itself because it's different, right? When we're talking about large language models, we don't talk open source a lot because for the most part companies aren't playing there because the monetization strategy on open source, as you can imagine, it's hard. How do you make money when you give everything away for free? I think OpenAI is going the scorched earth approach, but we'll talk about that here in a minute. It just so happens there released the GPT OSS two days before they're releasing GPT 5, which will not be free. Right. So my thought is they're just trying to make every other model obsolete or essentially obsolete and just force people, hey, if you want the best of the best, you go to GPT5 and we're going to knock out mid tier competitors almost completely by putting a model as good as almost any of them out there. Right. And the top, you know, I'm guessing it's going to be the top five top 3% of models available worldwide. Maybe even better than that. We'll see once all the third party benchmarks come in. It could be a top two, top 1% model. Why are companies going to pay? Right? Anyways, let's talk about the open Source license and it's much different than metas. All right, so OpenAI is using the Apache 2.0, a very common open source software license. So this essentially permits unlimited commercial use without user caps. There's no geographical restrictions, there's no fees. It is truly free and open Metaslama a lot of people don't understand it's not like that. So it blocks the biggest tech companies from commercializing on it. There's geographical restrictions. As an example, right now you can't use multimodal capabilities of Meta's LAMA in the eu and there's a lot of other kind of fine tuned restrictions on Meta Islama. So a lot of people, especially in the open source community, say Llama's not technically open source. You know, Meta is trying to essentially define open source, which I guess they can do that. They're one of the biggest companies ever in the history of technology to get behind the open source technology the way they have. So essentially Meta's trying to redefine open source technology. But Here you go, OpenAI just came in with way better licensing. There's hardly no restrictions. Also, Apache 2.0 includes patent protections, making enterprise adoption legally safer. Right. Which is something that Apache 2.0 provides. And metal Llama is not on there. So right away you can see how this is not going to be good for Meta. And we've also seen reports and rumors over the last couple of weeks that Meta's new MSL team, their Meta super intelligence team, is thinking about maybe going to proprietary models and going to paid models or making certain models paid. Right. So the open and free kind of Meta might be starting to go down the paid route. And then the open AI, which has been dragged through the mud and saying they're only closed and proprietary. Why are you called OpenAI? Yeah, now they're taking the opposite route. And I do want to highlight this a little bit more about how big this Apache 2.0 open source license is. So like I said, that is unlimited commercial use. There are literally thousands of amazing businesses that are now possible that just weren't possible. Right. There was no open source reasoning models that at least people here in the US could safely use and safely build upon. This is it, right? This is the, this is the gold rush, right? Because I think a lot of times when we talk about AI development, if you wanted top tier model, if you wanted that top two, top 3% model in the world, you could have the best business idea in the world. If it takes off, you're still paying for it. This is different, this is free. This opens up so many, so many new business opportunities. If you are an entrepreneur, you have to understand what's on the table here. There's no user limits, there's no revenue caps, there's no licensing fees. It's, it's literally truly open Source. The only requirement is you have to include OpenAI's copyright notice in your code files. You don't have to attribute anything else to them. And then like I said, with the model itself, you can modify, fine tune and sell products without even asking permission or applying for any program. So as an example, if you're a huge bank, you can deploy this for customer service. You can fine tune it according to your needs, never have to spend a penny, right? So maybe you've racked up a monthly six figure bill building something on the API side. Well, you're saving money now. So I want to talk about three ways you can actually use this. Because you're probably thinking, okay, how can I use this? I have a computer with 16 gigs of RAM, right? I have a new enough computer, how do I do this? Right? There's a lot of different ways, but I'm going to talk about, well, technically four, but three and a half different ways, right? So one is local tools. You can download certain programs, right? So I do this on airplanes, right? Sometimes I got to get a newer laptop with a little bit more power. But you know, I have some of the meta models that, hey, in a crunch, if I'm on an airplane, no WI fi, I can open up different local tools that you can download. So you have as an example, Olama LM Studio, things like that that you can download. So essentially their software program, so you can download them and save them to your computer and then at the same time you download the model itself, either the 20B or the 120B, you can download that from Hugging Face, which is the leading open platform to download models in the world. Okay, and then that's it. You connect the two, you're good to go. You can literally turn off the Internet. All right, Then there's cloud platforms as well. So obviously AWS from Amazon, their bedrock platform, Microsoft Azure, Hugging face fireworks together, AI, etc. But then you can also do a hybrid strategy for your company. So you can do cloud training with edge deployment for regulated data compliance requirements. If you need certain things to be processed on prem, you can do that as well. So you can do a hybrid strategy. Or if you're just like, all right, well, maybe you don't have that powerful of a computer, or maybe if you just want to play around with it a little bit to see if this is for you or for your company. I love that OpenAI made this. They made a playground. So you can go check it out right now. We'll have this in the newsletter as well. So it's GPT-o s s.com very simple. It is a chat GPT ask interface. You don't have to have an account, you don't even have to log in. You choose which model you want. Do you want the 20B, the 120B? Do you want high, medium or low Reasoning? That's it. And then you can go really try this thing out. Go code with it, go have it write something, go have it be your brainstorming partner, have it do research on emerging markets, right? This is where you really have to have your company's internal benchmarks in play because this is a huge opportunity for cost savings. And I still know there's a lot of companies out there that are using just one model. So as an example, I know there's a lot of companies out there that are using like, you know, Claude 35 Sonnet or Claude 4 Sonnet because someone on the development side needed it. But then they use it on the marketing side, which is terrible if you're using on the API because it's so expensive. Right? There's so many use cases that a model like this is just going to be better than a paid model. Right? But you have to have all of your benchmarks ready, right? Maybe for software development, a model like GPT OSS isn't going to do the trick. Maybe it will, maybe it won't, but maybe for customer service this will do the trick, right? So it's important you have your internal benchmarks ready to go because this is something that could work. Okay, here's how this works, all right? The actual open source nature. Because a lot of companies are wondering from a competitive perspective, like, how does this add up? So let me explain. So OpenAI released the weights, they released the mixture of experts architecture and they released the inference code. But because a lot of people are like, okay, well now what advantage does OpenAI have? If they just release this open source thing, isn't everyone else just going to copy and paste it and, you know, essentially put out a better open model? Well, yes, so you can modify this, but it is still part of their model. But there are certain things on how they got to this point that's protected, right? So the training data sets, you don't get that when you are using downloading, fine tuning on an open source model. Their data curation methods, their reinforcement learning with human feedback alignment techniques, right? So there's still a lot of things that are under the covers. So if you're wondering from a competitive standpoint, oh, if this is Open source and people can download it. Don't they just see every single thing? No, you don't. But competitors get the final product essentially. But they can't reverse engineer OpenAI's competitive moat, which is their people. This is. Their engineers are the best in the world. Right. Obviously Google is giving them a run for their money now, but largely a smaller team from OpenAI. But Google has been shipping faster so we'll actually. I'm excited to see how Google responds. Google's been dropping some cryptic tweets this week that they have a big week as well and we've already seen a couple releases from them. So it's been fun to watch them again go back and forth. So what is OpenAI doing? Right. They're making billions of dollars by charging people on the API end and now they're essentially giving away. It's not the most powerful model, right? It's not as powerful as their O3 or their O3 Pro. But I would say in, in my use so far, I would peg this somewhere and I know this is confusing Alphabet soup. I would pig this somewhere depending on your use cases, but you can think of it as a GPT4O level to an 04 Mini, right? Because it's reasoning. So I think for a lot of use cases it falls kind of between there, which is a really good model. Right. But why again, OpenAI is going scorched earth. This is fun. It's fun for me as someone that covers AI every single day to see this happen because there's going to be so like Anthropic is going to be in the hot seat. Meta is going to be in the hot seat. All these mid tier providers, they're going to be in the hot seat. Right? But reports have said that OpenAI's enterprise market share on the API side has gone down as Chinese alternatives have gained steam. Right. So about Starting in early 2025, a lot of the Chinese AI companies came out with some very powerful, very capable open source models. I wouldn't use them if you're a US company, FYI. I've done plenty of shows on that, but I wouldn't touch them with a ten foot pole anyways. I mean companies like very capable models. Not good to be sharing your data, especially on the website. So Kimi, Deepseek, Quen very good models that have been pushing into that global market share. So I think what OpenAI is doing here, it's a strategic choice. They are commoditizing the mid tier market rather than starting to lose that to the competitors, right? A lot of these. I think there's so many companies, especially here in the US that have been on the fence, right, because they see these open source models, but they're from China and there's a lot of data privacy and security issues with that that they, they want to, but they're not quite sure. And some have, right. And some of the global share has gone, especially on the developer side. Small developers, solo developer, which, you know, they're making great, great pieces of software. So I think this is just OpenAI saying, yeah, they feel confident, essentially. I think they only see Google as their only competitor and they're like, we're going to put a powerful open source model that is going to start to bankrupt some of our smaller competitors. Maybe this is a move and they'll eventually acquire or Aqua hire some of them because this is going to bleed some of them out. Because some of the AI labs cannot make a model, even proprietary, as good as this one. Right. It's one of the benefits of being the company with the most users. You have the most user data, which also helps you build better models. So it is a strategic choice here. They're saying, all right, we'll be fine losing, you know, a couple billion dollars this year. Because what we're going to do is we're going to bleed out the small guys, we're going to get more people using our platform. And I like to think of it, it's like a sample, right? Does anyone like me like to go to Costco on a, on a Saturday or Sunday? And man, I can get a whole, whole meal on a Saturday or Sunday at Costco. Just crush it, right? But I mean, I end up buying a bunch of stuff that I didn't go there to buy because I, I eat it and I'm like, oh, this is better than, you know, this, I don't know, this brownie is better than the brownie I've been buying. Or this, you know, energy bar is way better than what I normally eat, right? And then you switch over, right? You get a sample and you're like, oh, this is great. I love this. So that's, that's also, right, Kind of a freemium offering or a freemium kind of onboarding ramp for, you know, enterprise clients as well, right. I think a lot of people are going to be jumping off anthropic. A lot of people are going to be jumping off cohere. A lot of people are going to be jumping off Mistral Meta as well. I mean, they're going to Start and they're going to get in here and they're going to say, hey, for six of our eight use cases, this new open source GPT OSS works. For the other two, well, since we're already on the OpenAI platform and we've kind of worked our processes around them, we, we're going to get GPT5 or we're going to get 03 on the API side, right? So it's, it's a smart move. But they're essentially saying, yeah, we're going to burn out, we're going to lose some money, but we're going to squash everyone. And in the long term we're going to gain a lot. So in the short term, scorching the earth, medium long term, I think a huge gain. I think the only company that is going to be able to compete with this in the long term is Google. Winners and losers, I've already talked about them, but let's talk about a huge winner, by gosh, Nvidia, like just wait. And this is going to be a gradual realization, right? It's not like people are going to wake up in three days and be like, oh my gosh, this is huge for Nvidia. It's just going to be a gradual thing over the next year or two as we see that the type of Edge AI, the type of on device AI that's now possible because of this new model, right? Think of this model in the future being on an iPhone. Think of this model, you know, being on every single Windows laptop, right? In Microsoft did announce that they are going to be incorporating this into some of their hardware. It's amazing, right? This opens up so many capabilities, but you need the Nvidia chips, right? There's very few chips in the world that can handle this at scale. Nvidia, huge winner here, Huge winner. I'll actually go, you know, I'm sure at some point in the coming weeks I'll go look at Google search trends for certain consumer, you know, Nvidia GPUs. I'm sure they're spiking. The H100 searches are going to be spiking, right? That's more for the enterprise side. Here's a weird one. I think Google's a winner here. Oh yeah. Here's why I think this gets the mainstream conversation going about putting capable large language reasoning models on a phone. We're probably a year or two off. Apple's too slow to do anything competent in the AI world, right? So I think this actually plays to Google's advantage. Because now all of a sudden consumers, you know, businesses who need edge AI, maybe they weren't thinking about it, but this is going to drive, I think this is going to drive the conversation. This is going to be that, that pivot point. But guess who's already there? Google is. Google has a very capable Gemma 3 open model. So it's a little different, it's a bigger drop off between, you know, Gemini 2.5 Pro and Gemma 3. It's, I, I'd say it's more of a mini Gemini where I'd say GPT OSS is a capable, right. It's a medium, it's a medium size. Right. Like I said, it's probably in some instances between GPT4O and GPT04 or sorry, 04 Mini. So it's a very capable model where Gemma is, it's a step down, but it's still a great model. But you have Gemma 3 already on smartphones, right. And I think that Google's Gemma 4 could be at the same level as today's GPT OSS. Why does that matter? Yeah, I think Google's going to be cashing in on the hardware side and I think that this is actually going to push and popularize their Gemma series, which I think it doesn't get enough love. I've gushed on it plenty from the time that it was first released. I, I, I, I'll have to go back and see, but I believe when it was released, right, Like I know in boxing people say, you know, it's pound for pound the best fighter, right? And it's usually, I don't know, some dude that weighs like 120 pounds that no one's ever heard of, right? But pound for pound, when Gemma 3 was released, it was the best model by parameter and it wasn't even close, right? So it's a very lightweight model that punches way above its weight class. So I'm excited to see what happens with Gemma 4 and I think Gemma 4 could be the first mainstream edge, edge AI kind of push losers. I kind of already talked about some of them. I think mid tier API providers, I think, you know, when we talk about kind of these tiers of the AI labs, AI companies, right, you have OpenAI and Google heads and shoulders above everyone else. They're like, you know, flip flopping. We'll just say they're number one. We say, I like to say Microsoft and Anthropic. Again, you know, people, we can argue about this all day but they're in the conversation, they're like the one B tier anthropic might be in trouble, right? So you're already hitting that, the 1B tier and then the 1C tier. They're in trouble. They're in trouble, right? So the mistrals, the coheres, right? There's so many of these labs that a lot of people maybe haven't heard of. Meta, right? This is going to hit Meta's user base as well. This is going to be a pretty big shakeup because again, you have to think it's not just the model that they're putting out. There's going to be thousands, there probably already is thousands of fine tunes. Even though the model's only been out for, I don't know, 36 or 72 hours or something like that, there's thousands of fine tunes. There's going to be so many industry specific, domain specific versions of that 20B GPT OSS popping up overnight and they're going to be really, really good. So let's talk a little bit more about the edge computing timeline and I think that's big here and I don't want to overlook that. There's so many things that, you know, I want to talk about with GPT oss, but edge computing has to be one of them, right? I think that's the end game. The end game is we're not using the cloud. The end game is the world's most powerful models are going to live on our laptops, they're going to live on our phones, they're going to live on our tablets. And there's. That changes things, right? Security becomes less of an issue because your sensitive data doesn't leave your device, it stays on device, right? That's, you know, on device AI, Edge AI, kind of the same thing. But I do truly think that this, this in theory could save Apple. It could save Apple. If Apple gets their act together and if their iPhone 17 or iPhone 18 can handle this model, they could actually have AI, right? And they could avoid all these class action lawsuits for promoting AI and then not actually getting AI. Did you, did you guys see the, the Google Ad? Oh, so good. They didn't even mention Apple by name. It was something about like, hey, if you want a phone with AI, but you know, not just a promise, like something that actually delivers. Great, great, great ad, by the way. But this is actually good news for Apple and Apple could get their act together, right? It's, I'm sure the iPhone 17 is probably too late to change the specs, but the iPhone 18, Apple could actually have meaningful edge AI by 2030, which before this I wouldn't have thought possible, right? Even giving Apple five years, no, again, this can change how we all live and work, right? Think of the best ever large language model experience you had, right? You're using a cloud provider, you're uploading all your data. It might take 2, 3, 4, 5, 10 minutes. Now think of that without the cloud. Think of that on your phone, right? Think of it like, like, hey, what was that picture I took? And you know, who was I texting about that and what did the email say? And it just knows instantly, right? And I know, you know, Google is a little ahead there and on, on some of the Samsung phones. A couple other things that we need to talk about safety because what does this mean, right? We can think about all the good things, but when you released a model like this, without restrictions into the wild, it's great risk, great reward, right? We might be able to solve certain diseases, discover new medicines. There's a lot of downside, there's bad actors, right? So OpenAI did say they conducted the first of its kind safety analysis intentionally maximizing bio and cyber capabilities just for this open source model. Also malicious fine tuning removes safety guardrails for disinformation, cyber attacks, bioweapons research successfully. So a lot of work went in. OpenAI did say a lot of work went in and they recognized that releasing a model this powerful can create a lot of bad and it can, bad actors will be able to bypass those things. These open source models will get jail broke, jailbroken. Bad things will happen, right? But it's, I like to tell people it's the same thing with the Internet. The Internet came out, you know, people, you know, bad, bad actors connected on the Internet. They do bad things on the Internet. It's going to be the same thing with large language models in the cloud. It's going to be the same thing with offline, but especially offline because you can't trace it, right? Once someone downloads it, that's it, you can't, you have no clue what they're doing with it anymore. You have no control. So there's zero ability to recall, update or monitor usage once it's downloaded globally. But the big picture that I want to leave everyone with is this is the pivot point. I talked about this. This is bigger than GPT5. I think this is the strategic pivot going from closed AI, proprietary AI to open models. I think this is going to reshape the international AI race in this right now. Open source capable Open source models from the big boys. It forces all providers to accelerate innovation or lose relevance. If you thought AI innovation was fast in 2025, wait until the last four months. Here it's going to go warp speed. Right now, it's us and China competing who's going to get to AGI first. And I think one of those things, once you go open source, there's no going back. It makes other open source models better. It makes proprietary models faster. No longer can companies just sit on a proprietary model for a year, which we've had reports of. Oh, this model's been done for nine months and they're just waiting to release it. They're waiting to see what their competitors do. That's not going to happen anymore. The pace is going to be even faster as we race toward AGI. We've talked about it first. AGI is the new, you know, gold plus power plus electricity plus currency times a million. If you get to AGI first, you can control everything, right? You become the global superpower. And actually, open source is a big step to get there because it pushes innovation on both sides. All sides. Right? Because now all of a sudden, you're going to have researchers from OpenAI and other labs see what thousands of independent developers are going to do with this open source model to make it better, to make it more useful, literally. I do think we're going to see it. We're going to see these two models, these two open source models from OpenAI. Like I said, bad things are going to happen, but they're going to cure diseases, they're going to discover new proteins, they're going to help with drug discovery. Literally, open source models. Right. So this is going to accelerate everything and be bigger than GPT5. It is. We're going to hear today or you'll hear tomorrow. If you're listening on the podcast, I'll. I'll do a show on GPT5. GPT5 will be a major model improvement. But this is bigger. GPT OSS is a step change in the future of technology and how business works. Because like I said, AI labs can't sit on models anymore. They'll go out of business. Innovation is going to overdrive, and that changes what's possible for me, you and business leaders everywhere. So now more than ever, you got to pay attention, you got to keep up to date, and you have to put it all into practice. All right? So if you miss anything, we're going to be recapping it in the newsletter. If you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. Join us Tomorrow, we're going to be going over the GPT5 release, so thank you for tuning in. Hope to see you back tomorrow for that and every day for more Everyday AI. Thanks y'. All.

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