Ep 870: Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? (Start Here Series Vol 29)

Everyday AI Podcast – An AI and ChatGPT Podcast

The episode explores the potential 'ChatGPT moment' for open-source AI, driven by the high performance of Chinese models like GLM-5.2. Jorda

Key takeaways

• GLM-5.2, developed by ZAI, is performing at levels comparable to top-tier models like Claude Opus and GPT-4.
• Enterprises are moving away from 'token maxing' toward 'token efficiency' to manage skyrocketing AI costs.

Main topics

• Open Source AI performance
• GLM-5.2 and ZAI

Notable quotes

The era of token maxing is over as companies cut AI spend.

Conclusion

Open source models are becoming a legitimate enterprise priority for n

Transcript preview

Speaker 2 (0:00) Welcome to the Everyday AI Podcast. My name is Jordan Wilson, and for the past three and a half years, we've put out more than 800 episodes. Yet, one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI Podcast? And that's why we started the Start Here series. And with the fall now back in full swing, the Everyday AI Podcast is going back to school and playing back the entire Start Here series. From front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights day by day. The series is a culmination of spending more than 10,000 hours covering generative AI over the past three and a half years. So you don't want to miss a Speaker 1 (1:12) single episode of the Start Here series. Let's get into it. Speaker 2 (1:17) 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 GLM 5.2 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 seven 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 U.S. 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? Speaker 1 (2:39) Well, Speaker 2 (2:40) 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. Speaker 2 (3:16) 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 co-pilot co-work. 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 GL five, two 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 GLM 5.2 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 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 star here, series.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 search. 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? seen the US government is working 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 DeepSeek 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 DeepSeek 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 ZAI. I believe they were formerly 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'all. It is. Speaker 2 (8:16) 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 OBIS 4.8