Ep 804: Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? (Start Here Series Vol 29)

**Jordan Wilson** (0:01)
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.

**Jordan Wilson** (0:16)
There's three important things happening right now that make me think open source AI might be having its ChatGPT moment. Number one, the models are actually pretty good. With the 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 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. Alright, 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 source has 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 shake up and you have to be paying attention to GL 5.2 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 DeepSeek 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 close 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 starthereseries.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. 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 its light 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 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 in 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. 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 to make DeepSeek as a viable alternative.