Building the Open Source AI Revolution (with Hugging Face CEO, Clem Delangue)

**Ben Gilbert** (0:00)
Clem Delangue, welcome to ACQ2.

**Clem Delangue** (0:03)
Thanks for having me.

**Ben Gilbert** (0:04)
It's a pleasure to have you here. We have heard so much about Hugging Face over the last few years. It just feels appropriate in this moment to talk to you about the company directly.

**Clem Delangue** (0:14)
I feel like it's a very critical time for AI, and with Hugging Face, we have the pleasure and the honor to be at the center of it. So excited to be able to share some of the things that we're seeing.

**Ben Gilbert** (0:27)
I think the listeners who are tuning into this and saying, what is this episode going to be about? We want to frame it as you should come in and you don't need to know anything about AI, and you should walk out with a pretty clear understanding of open source AI, the more closed ecosystem, what is the difference between the two, what are the trade-offs, what are the virtues of each one, and we're going to tell it through the hugging face story. So what role do you play in the ecosystem? Who do you work with? Who do you not? How did this thing spring up out of quite an unlikely place given the name of your company? We'll kind of work our way backwards. At this moment in time today, how do you describe what Hugging Face is?

**Clem Delangue** (1:05)
So Hugging Face has been lucky to become the number one platform for AI builders. So AI builders are kind of like the new software engineers in a way, right? Like in the previous paradigm of technology, the way you would build technology was by writing code. You would write like a million lines of code, and that would create a product like a Facebook, like a Google, or all the products that we use in our day-to-day life. Now today, the way that you create technology is by training models, using data sets, and building AI apps. So most of the people that do that today are using the Hugging Face platform to find models, find data sets, and build apps. So we have over five million AI builders that are using the platform every day to do that.

**Ben Gilbert** (1:53)
The ecosystem around Hugging Face in many ways reminds me of the 2008 to 2010 era of the Web 2 sort of restful APIs that everybody was publishing, and you could suddenly daisy chain together a million different companies' services. Yeah, this sort of API mashups. It kind of feels like there's a loose analogy to, at least the movement that you're on is similar to that one. What can we create with a bunch of these sort of more open, flexible building blocks?

**Clem Delangue** (2:22)
Yeah, it's super exciting because it's replacing some of the previous capabilities. Now, you're starting to see search being built with AI. You're starting to see social networks being built with AI. But at the same time, it's empowering new use cases. It's unlocking new capabilities that weren't possible before. To some extremes, some people are talking about super intelligence, AGI, completely new things that we weren't even thinking about in the past. So we're at this very interesting time where the technology is starting to catch up to the use cases, and we're seeing the emergence of a million new things that weren't possible before.

**Ben Gilbert** (3:05)
It's cool. And just so listeners understand the scale at which you're operating, Hugging Face is currently valued as a recording at $4.5 billion. Investors include NVIDIA, Salesforce, Google, Amazon, Intel, AMD, qualcomm, IBM. It's a pretty wild set. What are some metrics that you care about as a company that you can sort of use to describe the scale at which developers are using it today?

**Clem Delangue** (3:26)
So I was saying that we have 5 million AI builders using the platform. But more interestingly, I think it's the frequency and volume of usage that they have on the platform. So collectively, they shared over 3 million models, datasets and apps on the platform. So some of these models, you might know them, might have heard of them like Lama 3.1. Maybe you've heard of stable diffusion for image. Maybe you've heard of whisper for audio, or flux for image. We're going to cross soon 1 million public models that have been shared on the platform and almost as many that have not been shared and that companies are using internally, privately for their use cases.

**David Rosenthal** (4:13)
So the analogy and model for you guys really is just like GitHub except for AI models, right? You can public, open source, open to everybody and companies can also use internal closed source repositories for their own use, right?