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Everyday AI Podcast – An AI and ChatGPT Podcast
Ep 804: Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? (Start Here Series Vol 29)
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This is the Everyday AI show, the Everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.
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There's three important things happening right now that make me think open source AI might be having its chat GPT moment. Number one, the models are actually pretty good, with a recent splash from Zai's GLM5 2 leading the way. Number two, the era of token maxing is over as companies cut AI spend. And number three, one of the biggest and most influential companies in the world is looking at open source as a viable option. Granted, this doesn't mean that you'll have a frontier level model operating 24. 7 on your computer. That's not how any of this works. But for large enterprises, they will and do have that option today. But even if you're not a Fortune 100 company with GPUs to spare, you too are going to have to start paying very close attention to open Source models in 2026. Yes, the Chinese companies are distilling from American labs and there's privacy considerations, but that doesn't change the fact that US tech companies are using these models in production as AI costs are starting to skyrocket. So will models like GLM 5.2 thrust open models onto the streets of mainstream AI America? Or will this just be another drop in the bucket until the next wave of US lab models make the current open contenders look archaic in comparison? Well, let's find out on today's edition of Everyday AI as part of our Start Here series. All right, if you're new here, welcome. But let's talk about the big picture here. Open source AI has nearly caught the top proprietary models. So I think most, even people who are bullish on open models would have admitted that for the most part, open source or open weight models are about six months behind. And I'd say now that gap is maybe only two months, two or three months, which is pretty incredible to see. And then a lot of benchmarks which we're going to look at. Open sources kind of caught the closed proprietary models, so they are now finally credible enough and powerful enough for serious enterprise evaluation. Also, Microsoft is reportedly looking at Deepseek as it looks to lower its costs in Copilot Cowork. So that's huge. And the model pushing all of this I think right now is Zai's GLM 5.2. I know that's a mouthful, but we're going to look at some of the charts that show that this is now a Big picture model. This is a big shakeup and you have to be paying attention to GL52 and what comes after this. And as companies now shift from token maxing to token efficiency, open models may finally be having their chat GPT moment. So on today's show, here's what you're going to learn. You're going to learn why Microsoft reportedly looked at Deep Seek for lower cost copilot agents. You're going to see why GLM52 is an enterprise infrastructure play not exactly a business laptop AI. You're going to know why autonomous workflow overshoot blocks adoption more than model quality. And I'm going to tell you about that secret issue that I think people aren't paying attention to when it comes to closed proprietary and open source AI, that autonomous workflow overshoot. All right, let's get into it. Welcome to the Start Here series. This is the Everyday AI Essential podcast series to both learn the AI basics and to double down on your OR knowledge. So if that's what you're trying to do, sweet. Me too. So this is an ongoing series. We're actually on volume like 29 now, so make sure you go to start hereseries.com that's going to give you free access to our exclusive inner circle community. And there, there's a playlist that has all of the Start Here series all in one spot on a Spotify playlist, all of the newsletters, everything, all in one spot. And you can go connect with other business leaders that are trying to do the same. If you missed our last Start Here series episode, I think it was actually a really important one. We talked about AI super apps and why every company is racing to create one and what they are. That was volume 28 or episode 799. And today let's talk about the open source surge. So let's quickly recap these three different things happening all at once. So number one, Chinese open source models have kind of closed the gap. They haven't closed the gap completely, but they've closed the gap to. It's like teeny. All right, so obviously I'm not going to talk a whole lot on model distillation in this episode, but if you don't know what that is, essentially Chinese companies steal more or less from American companies, right? We've seen the US Government is working, you know, on this with the top labs, but Google, OpenAI and Anthropic have all but said, yes, the Chinese companies are stealing all of our work and making open source models. So we're going to look past that. And the reason why we're actually looking past that is, well, Microsoft, right, Microsoft, being one of anthropic and OpenAI's biggest investors, is reportedly looking at Deep Seek as a viable alternative to using the Anthropic and OpenAI models. And that's one of the reasons why I think it's now finally time for enterprises to take a serious look. So, yes, there's obviously a lot of privacy data considerations when it comes to using open source models, all right, because you don't always know the weights. So you know, you might be getting an output and blindly copying and pasting that, knowing there might be a geopolitical reason, you're getting a certain answer. So there's obviously a lot of considerations to take into account. But I think the fact that we're seeing, number one, the models are good enough, number two, token maxing is going away. It's no longer about, oh, you know, everyone go burn 5 billion tokens so you can go climb up the internal company token leaderboard. That's over. Companies are cutting AI spend. And number three, Microsoft looking at open source models like Deep Seek as a viable alternative. So let's talk a little bit more about this new model that is catching everyone's attention. This is from Zaid, I believe they're formally called Zifu AI, but they are called Zai, based in China. And GLM 5.2 is a 744 billion parameter MIT License open weight mixture of experts model. So it was developed by Zai and It has a 1 million token context window and it is designed specifically for complex long horizon coding in autonomous Asian workflows. And it targets coding tool use, long context and agentic work. And here's the reason why we're talking about this, y'.
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All.
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It is incredibly good. Okay, I've used it a little bit. I've been impressed. I haven't had as much time as other people and I'm going to be reading some thoughts from other kind of leaders in the AI space. But when you look at artificial, the Artificial Analysis Intelligence Index, which was actually just updated to 4.1, a little detail there. But essentially this takes about a dozen or so widely used and widely respected benchmarks, puts them all together and gives all of these models a score. So this is a good way to look at all the different kinds of benchmarks and to know about how smart or good a model is. So right now, obviously we don't have Claude Fable, right? That could change at any minute. But the best model Right now it is 1a and 1b at least what's generally commercially available. Claude Obus 4.8 max and GPT 5.5x high from OpenAI and then not too far behind now you have GLM 5.2, which surprisingly scores higher than any Google model. Again, that could also change because, you know, Google did say in June they're coming out with their new model. So you know, that could change later today or later this week or next week. But regardless, this is the first time that, you know, in recent memory at least, you know, since the AI race became more than just open AI, this is the first time that an open source or open weights company has cracked a top three company. Right. So it's now Anthropic OpenAI and Zai. Yeah. So coming in ahead of Google, coming in ahead of Grok, coming in ahead of, you know, Deep Seek, Meta, the Quinn models, all of these other, you know, even the other Chinese open source models, incredibly, incredibly well benchmarked. So a lot of accusations out there that maybe they're a little benchmax or overfitting, you know, to make sure that they score really well on these benchmarks. But I, I mean, let me just read for you all some reaction from some respected people in the AI community. So this one from Chris Som, who's a partner at Active Capital Capital, who said, I'm starting to feel like GLM5.2 might be better than Opus. I was spending $300 a day on Claude, switched to GLM, switched to GLM, spent $3 and 82 cents a day and it found and fixed a bug, a clawed bug from yesterday. I honestly can't tell which is better anymore. All right, Jeremy Howard, very well known name. He was the founding president at Kaggle. So he said, wow, Zai's GLM 5.2 is a marvel. It is at least as good as Opus 4.8 in GPT55. It is super fast, inexpensive and not too verbose. It responds with nuance and judgment and handles long context very well. I've never experienced an open weight model like this before. And then Guillermo Roche, who is the Vercel CEO. So yeah, these are big, big names, right? You know, Vercel, they're one of the leaders in the AI space. So he tweeted out, genuinely impressed, almost, almost shocked at how good GLM52 by Zai is at coding. This changes things. And then last but definitely not least, another prominent name in the AI space, this is Matt Velaso, who's a VP at Google DeepMind also VP at Meta and worked at Microsoft as well. So he said all day using GLM 5.2 didn't miss much. All right, so saying compared to using other models. So all day using GLM 5.2 didn't miss much. First open model that passes the bar as a daily driver, things are not going to be the same. And then talking about how we need to get some serious hardware. All right, so now let's talk about this. Because when people think open source models, what they default to is, well, oh, that means I can download this thing on my computer and I can run it 24, 7 and I can plug it, plug it into Openclaw or Hermes agent and all of a sudden I have a, you know, a model that's the level of, you know, GPT55 and Opus4.8 running for me 24, 7 and I don't have to pay any API bills, I don't have any subscriptions, etc. No, not at all. Not at all. No matter what anyone tells you. Because you're going to need at least 15, $20,000 in hardware to get anywhere near that type of performance. Right. And I'm just saying the outputs. So if you're just comparing outputs to outputs, yeah, you're going to need a machine that's at least $15,000, probably a little bit more and it is going to be slow as a snail. So it's is not economically feasible or economically reasonable. Right. To you know, run a model like GL952 locally. You will also be running a quant version. So you'll be running like a two bit quant. So it's going to be a dumbed down, very slow version. So yeah, good luck doing that. You know, there's people that argue out there like oh yeah, I'm doing this. So unless you have some very rare special reason, you know that you have to be able to run things locally. Right, because that's the other obviously aside from cost, you know, once models do become more powerful and smaller and efficient enough to run on consumer hardware, obviously the privacy, right, being able to run things locally and not having to send things to the cloud. But that's not where you're at with GLM 5.2, right. There's maybe, I don't know, a couple people in the world that have a powerful enough machine to actually do that, but it's for most people. This is not going to replace your 200 subscription to Claude or ChatGPT, but this is a legitimate option for large enterprises who have Access to compute. All right, but small teams, skip this. All right, I'm not saying skip GLM 5.2 because obviously you can still use it on the API side. You know, there's plenty of hosts from, you know, AWS to hugging face every everyone in between that you can actually go in and run this model using their servers, their GPUs. Right. But this is not something you need to overlook. All right, and what do I mean by that? GLM5.2 might not be a model that you're going to run locally. It may not be even something that you run on the API, even though it's a fraction of the cost. Right. That's the other big thing. It depends on what level you're using. Right. But if you're comparing the Highest output of GLM5.2 to the highest, you know, GPT5.5 or Opus4.8 or Fable 5, whenever it comes back, it is a fraction of the cost to run it on the API side. So yes, there still is, you know, if you're building modularly, which is smart to do and can, you know, swap out a set of API keys or if you're using something like Open Router, that makes it even easier to do that. Sure, you know, maybe you're running this model in the cloud, but this is not something, you know, I think, think most people think, oh, open weights, you know, I can, you know, download it, fine tune it. You're not going to be doing this on a consumer piece of hardware. But so that's factor one. Factor one is, well, the model is actually good enough. Why this might mean open source is having its ChatGPT moment. Number two, the Microsoft move. So according to Axios, and this is just not even a week old, we covered this on our Monday show. So Axios reported that Microsoft is actually looking at lower cost open source alternatives to Anthropic in GPT models. So right now, both in Copilot Cowork, but specifically Copilot, you know, they mainly have always relied on GPT models from OpenAI. Over the past year they've started to integrate and offer other models specifically from Anthropics Claude models as well. But Copilot co work is obviously an agentic offering. This is based on Anthropic's very popular co work technology. It's kind of Microsoft's version. Microsoft is obviously a big financial backer in both Anthropic and OpenAI and they also well serve those models. So they make money on the cloud when their customers use these models. So the, the Fact that, and I do have to, you know, break this down and I won't go too much into it because we just talked about it on yesterday's show, but the fact that Microsoft is potentially looking at Deep Seek of all companies, right, because all the US companies have called out Deep Seek by name for distilling their models. So I don't know if I'm, if I'm in leadership at OpenAI and anthropic, not super happy with my, my biggest financial backer by potentially, even though this is just a report, the report could be all wrong. Maybe it's true, maybe it's not. But the fact that this report is out there from a reliable source in Axios, that Microsoft, one of the biggest companies in the world and one of the most trusted and respected companies when it comes to AI deployments, Right. The fact that they're looking at a company like Deep Seek to potentially be an option under the hood for running Copilot Cowork to make it more affordable to customers is big. Right? Because now that Copilot Cowork is generally available. That's new as well. Before it was just in beta, but now they're charging usage, so they're having to, instead of subsidizing this like so many custom companies are, right. They're charging end users for usage and, and, well, what they're going to see very far is Copilot Cowork. No one's going to use it if they have to pay the APIs for, you know, Opus 4.8, which is one of the most, Aside from Fable from anthropic, it's opus 4.8 is one of the most, you know, expensive models out there. You know, depending on the task, it's actually twice more than twice as expensive as GVD 5.5. So pretty big here. But the reported goal was just cheaper agents inside of Azure security protections. And that pressure exists because of the agentic work behaves just like it's, it's nonstop. Right. I think when we go back to the chatbot era, we didn't have to worry as much about, you know, token usage. But now when these agents run in loops, right, and it's, it's easier to set off loops now in Codex and Claude code, it's easier to, you know, put into this goal mode and, you know, have an agent work for not just hours, but days. Right. So now all of a sudden we have to start thinking about things like token efficiency and, you know, the cost of compute. All right. And that leads us to, well, Factor number three. And that's the, the shift that we've gone. Well, now companies are not just saying, let's burn tokens, they're saying let's save tokens. So there's been a lot of recent reporting over the last week or two. You know, it's New York Times article from this week that read, tech workers max out their AI use. Now they're trying to minimize it. Business Insider story titled, silicon Valley's AI token craze is facing a reality check. And then a TechCrunch article that said the token bill comes due inside the industry scramble to manage AI's runaway costs. And I did cover this in depth on episode 789, also in the Start Here series when we talked about token maxing, the shift from token maxing to token efficiency. But in short, you had meta in other companies that had these internal leaderboards where they were reportedly just rewarding employees for using tokens. And I think it started maybe with good intentions, right? Because AI leaders thought, well, hey, if our people are using AI a lot, that means that the business is going to grow and we're going to be saving time. But that backfired because people were just burning tokens. You know, a reported example is One engineer used 281 billion tokens in a month just to climb the rankings, right? Which is pretty, pretty crazy number. You know, you had reports that employees were just running agentic loops intentionally to keep running, even if they're, you know, doing personal projects, right? But they were just burning tokens, just to burn tokens because, well, there was a thought for a brief period of time, I'll say, from the end of 2025 to early 2026. Well, people looked at employees all like, for a brief period of time, right? They saw token usage as like a KPI, as a key thing to evaluate employees on, right? Like, hey, if you're burning tokens, thumbs up, you're doing a great job in our book. And then there was the report that a company reportedly spent $500 million on Claude on accident in one month because they forgot to set limits. And that shows you just the amount of tokens, right? So a Stanford study found that agentic task can use up to a thousand thousand times more tokens than a single chat. All right? Because chatbots answer once. Agents can call tools endlessly in a loop, right? I actually, for fun, I have my Claude code, ultra code going on a simple task just to see how much usage it's going to burn when it shouldn't burn. Anything, Right. But I'm looking at the chain of thought and I'm seeing Opus 4.8 going in some silly loops, just burning tokens, you know, like, I don't know, burning marshmallows on accident. But this is going to a broader capability gap that companies are already struggling to close. And that capability gap is half the problem. And I'm going to go through this one quickly because I have covered this one in depth before on episodes 7:35 and episodes 755. So if you want to know more about this, I did cover it in both a those episodes. But great study from Anthropic that talked a little bit about the capability gap and this does get to the kind of closed source versus open source and GLM52, stick with me here. But in that study, essentially Anthropic looked at anonymized chats and they hit a ceiling for these different categories of work. And they said here is this ceiling of a model's capabilities. And according to all these anonymized chats, here's what people are actually using it for. Right. So one of the best categories that had the best usage was only 33% and that was computer math tasks. But many tasks was not even 10%. So you know, you could have the best, as an example, business strategist. And people were only using 10%. Right. And that's the baseline kind of model capability gap. And that's the first bottleneck. But there's kind of a new, this is kind of my, my, my secret term. I teased in the beginning. I've been trying to put a name and a face on this. So I'm trying this out. Maybe I'll rename it down the line. Right. There's always these, these common, common concepts that come up over and over. And it takes me, you know, sometimes a month or two to put, you know, to put a, put a label on it. So you're not going to see this anywhere. This is something I'm tried on for size here. But I think aside from the model capability gap, I think the bigger problem maybe is autonomous workflow overshoot. All right. And I think that's the next gap that you need to prepare for. And that might, you might see, stick with me here, you might see how that might actually make some of your day to day tasks. Looking at a model like GLM52, even though it's text only, it's not multimodal, it still might make it a feasible option in the future. So let me talk about this concept of Autonomous workflow overshoot. All right, so models right now, right? Models by default, GPT5.5 Opus 4.8 Gemini 3535 flash. They carry your company's context. They can plan, they can act, they can call tools, they can spin sub agents. Right? So essentially I ask companies, what would you do if every single employee had at least one or many 24. Seven agents? AI moves too fast to follow, but you're expected to keep up. Otherwise your career or company might lag behind while AI native competitors leap ahead. But you don't have 10 hours a day to understand it all. That's what I do for you. But after 700 plus episodes of everyday AI, the most common questions I get is, where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one in episode 691. Number two, tap the link in your show notes at any time for the Start Here series. Or you can just go to start here series.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. And it's not a rhetorical question, because those capabilities are here now, right? I've had both Claude agents and Codex agents run for more than 24 hours. That's the autonomous workflow overshoot. Because the capability gap is humans are not fully understanding the model's capabilities or using the models to their fullest extent. Right? This is a human not taking advantage of what's there. But this is different. This is overshoot, right? So essentially, I'll argue that 99, 99% of bleeding edge capabilities go unused. All right? And that's from a combination of the capability gap, but also workflow overshoots. So what is autonomous workflow overshoots? That's essentially that the capabilities of the agentic models are uncharted in your standard enterprise workflows. Because I will say that most workflows still need that human handoff, right? If, if, if, if. I told you at your company, hey, everyone has a 24. 7 agent that can carry your company's context. It can plan, it can act, it can research, it can call Tools, it can create PowerPoints, Excel, anything, websites, right? Hardly anyone, hardly any business leader would be prepared to implement that. That is the overshoot, right? Today's models are more than most companies can handle, not more than most humans can take advantage of the capabilities. That's two different things, right? They can't handle this. That is the workflow overshoot. They, these models are capable of just too much because I think that even AI forward companies right now need multiple quarters a year or even more to completely rebuild job descriptions, their inputs and outputs, approvals and even the type of work that you do, right? These things all have to change. This is also this concept of yes, there is going to be all these new AI, there's going to be all these new jobs that AI is going to create. I ultimately think AI will take away more traditional full time roles than it will create. But AI is obviously going to create millions of jobs that we just don't know what they look like. Because it's going to take businesses a while to understand where all these autonomous agents capabilities are headed. So we can start creating both the context that the agents need, the expert driven, the expert driven loops that they need to do these workflows, right. But also the lines of business, the streams of revenue that go along with those things, those things are all going to change and that is the autonomous workflow overshoot. So thank you for sticking with me because now I can tie this together and answer the question, well, should open source be an enterprise priority? All right, And I'll say yes in three different scenarios. So let me lay those out for you. Number one scenario, if your company has access to compute and they also have a high API bill, right? They're shifting away, you know, from token maxing to efficiency. I think a general purpose model like GLM 5.2 makes sense. Okay. Again, this is a very small sliver of companies, right? This is essentially your Fortune 100 companies. Not everyone has, you know, a rack of GPUs sitting on the shelf and can, you know, if they want to, you know, roll out the type of compute that their enterprise needs. Like I, like I'm saying GLM 5.2, you can't download this on your every employee's laptop. It's not feasible, doesn't make sense, not going to work. But if you have the compute, you can, right? Yeah. And what's crazy is I, I've talked to and I've worked with plenty of companies that fit into this category. I've seen their, you know, server racks and all the Nvidia chips blazing and all the cooling, you know, water going underneath it. It's all above my head. But there's companies out there. Yeah, they can, you know, find, they can download GLM5. 2. It's open weights, they can fine tune it and they can, you know, make a, a portal or a way that their employees can access this model. And in theory they can use it 24. 7 around the clock. Right. Obviously there's still bandwidth and other issues that you have. It's not as easy as, you know, click download and you know, click deploy. But there are companies, number one. Well, maybe because of that, what I said, the autonomous workflow overshoot, I'm literally thinking of one company in particular that's spending millions of dollars a year on Anthropic as an example. They could probably do this because I would say less than 1% of people using this. Their current AI systems that they're paying multiple seven figures for are less than 1% are using it to its full capabilities. So in Most cases the 99% of people, a GLM5.2 would probably be enough. Aside from the fact that it's not multimodal, that, that, that is a huge downside. Right. But for the most part, number one, the companies that should be using it are those that have access to compute and for the most part they're not needing 24. 7 or can't take advantage of 24. 7 autonomous coding agents. Number two, those for non agentic tasks. All right, and this is just a different chunk, a different segment of work. So this is the non agentic tasks that can and should be chunked for future open models. All right, so like I said, so few people right now, their workflows require something like a Fable 5. Yeah, it's fun to go in there and you know, oh, let me make this 3js world view game and oh, let me go code up this web. Right, yes, there's obviously people on, in, in, in software development and dev roles that need that 24,7 but most people even using this, you know, you don't need Fable to write better emails. Right. So I think when you start chunking your large enterprise companies start chunking your non agentic tasks, your non frontier tasks. I think that that's a big group of people that can start looking at an open source model like this, maybe using it via the API. And then last but not least, it's preparing for the future because I hope that intelligence just, you know, I hope we truly do get the intelligence to cheap to meter promise at Some point soon. But there's a reality that as the capabilities increase, right? At least for now, for the most part, just. Well, that Stanford study showed that, well, agents just can burn a thousand times more tokens than a simple chatbot query. Right? And that is one downside of GLM 5.2. It is, it is token inefficient. It burns through a lot of tokens to get that level of intelligence. Although the level of intelligence is extremely high, the highest we've literally ever seen for an open model. But I do think that we're going to see small, smaller task soda models in early 2027. Let me tell you what I mean by that. You know, task soda. So, you know, task specific state of the art models, right? So right now when we talk about models, they're just general large language models, right? They're one model and people use it for everything. I've been a huge advocate and believer in the future, right. There's going to be a mixture of models, technology. Thank you. Certain companies that made my crazy 2023 prediction a reality in 2026. I was only a couple years too early. Right. But we've seen that, you know, open router just had their fusion technology, you know, perplexity with their computer, right. You put a prompt out and it will route it to whichever model it thinks is best. It might put it through multiple models. Right. But I think we're going to see that on a small, small language model platform here in 2027. Probably not this year. But I essentially what's going to happen, right? The model distillation is both, you know, when you, I think most people think about model distillation, they think, oh, you know, Chinese models distilling a big frontier trillion parameter model. Right. Which is what we're seeing here with a lot of the, you know, the deep seats and the quns, right. All these accusations flying around. But what about, well, just when, you know, frontier companies distill their own models, right? The legal and you know, teacher student model scenario or you know, we're obviously going to see a lot of these Chinese companies come out with small language models or task specific models, but I think those are going to be state of the art, right? So as an example, I think that whether it's through, you know, quote unquote, not exactly legal distillation or intentional legal distillation, I think we're going to see state of the art open models for things like, you know, specific tasks, you know, front end coding, web search text summarization, PDF parsing, You know, copywriting, I think we're eventually going to see dozens after that, probably hundreds of open models that are state of the art at one task. Because when you can create a model around one certain task, it can be smaller, it can be better, it can be more token efficient, which in in turn makes it cost efficient. Right? Which is what people are wanting. So those are, I think the three scenarios where enterprises should be considering open source models, whether it's GLM 5.2 or something else. So to quickly recap, number one, if your company has access to compute and a high API bill, number two, if you're an enterprise company that can start chunking all of these different AI tasks into agentic versus non agentic and maybe you keep your current agentic options that are maybe a little bit more expensive and then you take your non agentic options to maybe on the API side something like a GLM 5.2. And then last but not least, it's more, I think for everyone else preparing for the future and it is starting to categorize those tasks because not every task needs a Fable 5 right? Not every task is going to need a GPT5.6 Pro, although I'll still use it for every task, right? Like that's how you also have to start thinking because eventually these subsidies are going to go away. We are going to have to become a token efficiency mindset. That is the future. And I think is this the chat GPT moment for open source? Is GLM5.2 that thing? I don't know if it is, it's still too early to tell. However, I do think this is, if nothing else, the foundation for open source to have its Chat GPT moment. All right. I hope this one was helpful. If so, please let me know about it. Go to starthereseries.com there you can sign up for free access to our Start Here series kind of space inside of our inner circle community where every single episode is there ready for you to gobble up. You can listen to it on 2x. I'm not going to be mad at you. All right. I hope this was helpful. Thanks for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'.
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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 your everydayai.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.