00:04:38 · Rebecca Balan, Irene Solaiman
[00:04:38] S1: Yeah. Okay. So revenue... Like, for the for the open model providers, like, sharing built into the model license? Or... I know this is something that you've talked about kind of wanting to spearhead it at Hugging Face. [00:04:48] S2: Yes. So licenses are generally drafted in legalese, which is why they don't get a lot of active readers. Right. And there are a couple standard ones that you see most commonly with open weights, MIT and Apache two point o. They're quite permissive, as opposed to the latest iteration of flagship open weight models that are using custom that require some level of revenue sharing if you make some threshold of revenue. [00:05:19] S1: So so we're we're talking revenue sharing. There's there's potential for cloud providers to to get a piece of this pie. Maybe the companies that are, you know, building the apps off these open weight models. Are we... Like, could we possibly be living in a world where open models are creating enormous value for the AI industry while the companies actually developing them are struggling to capture much of it?
00:05:19 · Rebecca Balan, Irene Solaiman
[00:05:19] S1: So so we're we're talking revenue sharing. There's there's potential for cloud providers to to get a piece of this pie. Maybe the companies that are, you know, building the apps off these open weight models. Are we... Like, could we possibly be living in a world where open models are creating enormous value for the AI industry while the companies actually developing them are struggling to capture much of it? [00:05:38] S2: It's hard to say without seeing the the explicit financials and not everything is a public company, so it'll be quite an interesting next few months. The level of value from the deployers and from the developers, I think, also depends on the makeup of the the business organization. So for Alibaba Cloud, they're just such an enormous cloud company. It could be considered a sort of feature to bring folks into Alibaba Cloud and Qwen is used really worldwide. So it's... They're both their smaller model, the 27 b, which is Apache two point o and runnable on a laptop, and their larger model, which is frontier level. The latest is Qwen 3.8 Max, which is open weight, can be used on prem, or it's easier to use inference. So a lot of folks in those kind of scenarios do make a substantial amount, but we're not able to view those exact metrics.
00:05:38 · Irene Solaiman, Rebecca Balan
[00:05:38] S2: It's hard to say without seeing the the explicit financials and not everything is a public company, so it'll be quite an interesting next few months. The level of value from the deployers and from the developers, I think, also depends on the makeup of the the business organization. So for Alibaba Cloud, they're just such an enormous cloud company. It could be considered a sort of feature to bring folks into Alibaba Cloud and Qwen is used really worldwide. So it's... They're both their smaller model, the 27 b, which is Apache two point o and runnable on a laptop, and their larger model, which is frontier level. The latest is Qwen 3.8 Max, which is open weight, can be used on prem, or it's easier to use inference. So a lot of folks in those kind of scenarios do make a substantial amount, but we're not able to view those exact metrics. [00:06:37] S1: Right. So... So some of the bigger companies, specifically, we're talking China here, like Alibaba, they're offering open source models, but they're also offering the cloud and the compute. Right? Exactly. So they're able to offer this as kind of a package deal. Whereas if we look at maybe the American ecosystem, that's not necessarily the case, right? A lot of the open model providers are just open model providers. They don't have compute that they can... I mean, maybe they have deals with Neo Clouds that they're trying to, like, sell a package deal with, but they're not the ones getting revenue from the compute. So I I kinda I kinda worry for them a little bit more in this in this case.
00:06:37 · Rebecca Balan, Irene Solaiman
[00:06:37] S1: Right. So... So some of the bigger companies, specifically, we're talking China here, like Alibaba, they're offering open source models, but they're also offering the cloud and the compute. Right? Exactly. So they're able to offer this as kind of a package deal. Whereas if we look at maybe the American ecosystem, that's not necessarily the case, right? A lot of the open model providers are just open model providers. They don't have compute that they can... I mean, maybe they have deals with Neo Clouds that they're trying to, like, sell a package deal with, but they're not the ones getting revenue from the compute. So I I kinda I kinda worry for them a little bit more in this in this case. [00:07:12] S2: It's a tricky decision on if you're releasing an open model that doesn't quite reach or beat Frontier level. A lot of folks love using Frontier. I mean, they're great products. They're easy. Then how do you get those kinds of users interested in deploying? And this is where these customized licenses come in for The US model providers on the open weight side. Some of the more visible ones who have done a great job are, I mean, Meta historically has been an open weight champion, and RC, Reflection have been releasing with permissive licenses, but they also do enterprise contracts. So you'll see with Reflection, they did a enterprise agreement with South Korea and have a partnership to build a sovereign AI cloud. So there are other ways to do these kinds of custom solutions.
00:07:12 · Irene Solaiman, Rebecca Balan
[00:07:12] S2: It's a tricky decision on if you're releasing an open model that doesn't quite reach or beat Frontier level. A lot of folks love using Frontier. I mean, they're great products. They're easy. Then how do you get those kinds of users interested in deploying? And this is where these customized licenses come in for The US model providers on the open weight side. Some of the more visible ones who have done a great job are, I mean, Meta historically has been an open weight champion, and RC, Reflection have been releasing with permissive licenses, but they also do enterprise contracts. So you'll see with Reflection, they did a enterprise agreement with South Korea and have a partnership to build a sovereign AI cloud. So there are other ways to do these kinds of custom solutions. [00:08:03] S1: Okay. So so almost like a white glove service. Will, like, you know, you wanna build your own sovereign or enterprise solution and you wanna use open source model school, we'll build it for you kind of thing. [00:08:12] S2: Yeah. It's... For how much interest there is in AI, there are very few people that really know how to customize this relatively.
00:08:12 · Irene Solaiman, Rebecca Balan
[00:08:12] S2: Yeah. It's... For how much interest there is in AI, there are very few people that really know how to customize this relatively. [00:08:19] S1: Yeah. I mean, we're seeing that with, know, the whole... I mean, it's kind of side note, but with the whole FTE trend that we're seeing, everyone's... You know, this is becoming the job in AI as a Exactly. For an employed engineer because there's so few people that actually, you know, know enough about business on the one side, and then how to, you know, work with AI tools on the other in order to build anything that's bespoke for enterprises or governments. [00:08:39] S2: Exactly. You [00:08:40] S1: know, a lot of our listeners are founders. So why should a founder care? Like, what do they need to check before trying to build a product on an open model?