Why This Ex-Meta Leader is Rethinking AI Infrastructure | Lin Qiao, CEO, Fireworks AI
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What is the main topic discussed in this episode?
Welcome back to the Matt Podcast.
What is Fireworks AI and how does it help developers build generative AI applications?
I'm your host, Matt Turk from Firstmark.
What is PyTorch and why was it such a pivotal project at Meta?
Today my guest is Lynn Shao, the CEO of Fireworks AI, an LLM inference platform that enables companies like Cursor, Uber, and DoorDash to build AI product experiences on top of hundreds of open source models like DeepSeek, Quen, Lama, and Mistral.
How do traditional machine‑learning pipelines differ from modern GenAI workflows?
Prior to co-founding Fireworks, Lina had an illustrious career at places like iBooks.
Why is AI transforming enterprise products and operations today?
Research, LinkedIn, and Meta.
How did Lin Qiao’s experience at Meta shape the founding of Fireworks AI?
We talked about how her experiences with PyTorch and Meta led to the creation of Fireworks.
What strategies does Fireworks use to simplify AI infrastructure for developers?
One of the biggest success we saw from the PyTorch experience is simplicity scales.
How are Fireworks’ customers leveraging generative AI in real‑world use cases?
People don't want to spend time figuring out how to make things work. They just want it to work.
And also covered a bunch of other topics from the continued drop in inference prices.
I believe this overall AI infrastructure would go down by order of magnitude in terms of cost. And they should.
To Lynn's thoughts on the future of open source AI.
Despite Deep Sea model extremely hard to tune and optimize, more than 500 variants published on Hugging Face, optimizing for local device, optimizing for cloud infrastructure.
Please enjoy this great conversation with a CEO who's very much at the forefront of generative AI infrastructure today. Helene, welcome.
Hi, thanks for having me here.
We are going to talk about all things Fireworks AI today, but as a level set, what is the elevator pitch for Fireworks AI?
Um Fireworks AI provides a developer platform. for application developers to build on top of Gen I technology. They need to hypothesize what kind of product is interesting. And they need to use certain models to integrate and build this great uh innovative user experience. Oftentimes they are they have to solve multiple problems. One is quality. Um is the model delivering towards a quality one? And also interactive real time speech. Because many of those products are consumer personal facing. Um and then when um they hit the product market failure, when they scale the business, they have to have a sustainable way to scale. So cost efficiency is very important. So oftentimes we see application developer heavily focused on three dimension optimization, quality, speed, and cost.
And we want to take away all this complexity from them. So they focus on thinking about what is the best user experience, what is best product idea.
So infrastructure as a service, you abstract away the whole complexity of uh what needs to happen behind the scenes to deploy those models. Exactly. Exactly. Exactly. Maybe a few words on your story, your background. In particular, uh you were one of the key people uh on PyTorch, uh at uh Meta or F Facebook at the time. Meta yeah, Facebook at the time. I guess what is Pi PyTorch for anybody that doesn't follow the things uh, you know, in great detail, and then um how did it all come come about? What was your role there?
Yeah, that that was a that was a amazing journey. When PyTorch happened, what was the kind of industry context here? So I joined Meta uh two thousand fifteen. At that time Meta It was called Facebook at the time. was going through the transition of finishing mobile first to starting AI first. Like the fundamental reason why it's sequenced that way, because after um meta transitions application from desktop to mobile first and it's widely accessible everywhere using your phone you can connect with people. And that drives a lot of user engagement and there's a lot of data being uh created. And then we all know data field AI. So quickly after mobile first transition, we started AI First. At that time, there's no software, no hardware, there's no GPU, no people building all this infrastructure.
So we bootstrapped the whole thing from ground up. And it was an interesting time. Feels like right now, there's so many companies building their own AI framework. There's a gazillion number of AI frame. frameworks even within Meta there are three different flavors, one for mobile, one for research, one for production. So it's very confusing, and we decided to unify all of that and have one framework.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:01
2
What is Fireworks AI and how does it help developers build generative AI applications?
0:01–0:04
3
What is PyTorch and why was it such a pivotal project at Meta?
0:04–0:20
4
How do traditional machine‑learning pipelines differ from modern GenAI workflows?
0:20–0:25
5
Why is AI transforming enterprise products and operations today?
0:25–0:27
6
How did Lin Qiao’s experience at Meta shape the founding of Fireworks AI?
0:27–0:32
7
What strategies does Fireworks use to simplify AI infrastructure for developers?
0:32–0:40
8
How are Fireworks’ customers leveraging generative AI in real‑world use cases?
0:40–59:14
Speakers
1 identifiedMore from The MAD Podcast with Matt Turck
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