00:00:00 · Rebecca Balan, Rebecca Balan, Irene Solaiman
[00:00:00] S0: This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed Sponsored Jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs. [00:00:30] S1: Hello, and welcome back to Equity TechCrunch's flagship podcast about the business of startups. I'm Rebecca Balan, and this is the episode where we bring on industry experts to help us explore a trend in the tech world and dive deep. Open source models are becoming more mainstream as enterprises, governments, and startups alike rely on the promise of lower costs, more control, and more flexibility. But as increasingly capable models become more freely available, how do companies actually capture value? And with Chinese models gaining ground, can American players keep up? Today, we're joined by Irene Solaiman, chief policy officer and SVP of strategy at Hugging Face. Irene has spent her career researching how AI models are released, evaluated, and potentially misused. We're gonna talk about what startups need to understand before building on open models, from the economics and global competition to the cybersecurity threats that could put openness itself at risk. [00:01:24] S2: Irene, welcome to the show. Thanks, Rebecca. I'm so glad to be here.
00:01:28 · Rebecca Balan
[00:01:28] S1: Yeah. I'm so glad to talk to you again after we just recently had you at Disrupt a few weeks ago. You were on stage talking to us about evals. And I'm really excited to talk to you because you can just riff, as we both know from our many conversations we've had, you can just riff on so many things when it comes to open source. So just to give our listeners a bit of background, you join... And stop me if I get anything wrong. You joined Hugging Face as a researcher in April 2022. You became the policy director, head of global policy, chief policy officer, and SVP of strategy in February 2025. Quite a litany of titles there. So in that role, you're running strategy, geopolitics, research, and policy. You founded eval eval, an open source research community focused on AI evaluations. And before that, you were at OpenAI working on bias, social impact, public policy. So stop me. Did I get anything wrong? You know me better than I know myself, Rebecca. Oh my gosh. Yes. Okay. So one thing I wanted to kick off the conversation with, when we talk about open models, you know, we had Clem Delangue, your CEO, on in July. He talked about companies moving away from expensive AI services and toward open models that they can run themselves as a way to cut costs. We've also got investors pouring billions into training, you know, closed models. But if these models themselves can be commoditized and more freely available, how... Who's making money? How are we making money here? Where are the threats, and where are the opportunities?
00:02:55 · Irene Solaiman
[00:02:55] S2: Well, first on the switches, the conversation you had with Clem, it's funny how prescient you've continuously been. I realized it was before the incident that we endured in July. And when we were seeing already a trend of movement towards open models, it's only expedited since then. So as of, I believe, today Europe time, I'm not sure if you saw Revolut, the kind of neobank for our American listeners that was founded in London and operates largely in Europe, announced that they are switching to open weight models and using only clotting codex for exceptions. We are seeing this overall trend, particularly since July, of more established and startup companies announcing their shift to open weights with the explicit purpose of cutting cost. To your question about what does this mean for the model developers, there are a few ways that people earn money and it is harder for the developers if everything's hosted on prem, but preferable from an enterprise perspective, both for the cost, but also for the data privacy and data residency, especially if you want that in a certain jurisdiction. But particularly for making money, a lot of that is going to be inference costs. It could be a sort of cloud lock in if folks are using inference via, for example, Alibaba Cloud, which is the parent of Qwen, Alibaba's excellent flagship model, or it's a sort of revenue sharing system, which is the kind of boring area of licenses that we can get into if our reader... If our listeners don't get too snoozy.
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.