In this interview, Lin Qiao discusses Fireworks AI’s rapid growth and substantial funding—over $300 million total, including a $250 million Series C round. She explains the company’s mission of “autonomous intelligence,” positioning it as a complement to AGI. While AGI aims to concentrate vast intelligence into a single, general-purpose model, Lin argues that most valuable data (>90%) resides in private, enterprise‑specific sources that are inaccessible to foundation models. Autonomous intelligence seeks to continuously and automatically customize models for individual applications, leveraging this private data. This approach envisions a future of millions of specialized models—one per use case—rather than a single universal model, with ongoing automated updates to keep models aligned with evolving enterprise needs.
Chapters
So we're here to talk about Fireworks AI, your business, which has done incredibly well. I mean, you've just grown so quickly. I believe you've now raised over $300 million in venture capital, including a recent $250 million Series C, if I got that correctly.
Right.
And so it's a platform built around open-source model deployment at scale. And the Fireworks AI platform is built around open-source model deployment at scale and this idea of autonomous intelligence. Tell us what that means, Lin.
Yeah, sure. You're right. We raised our last round last year, and we are growing really fast. So our mission is autonomous intelligence. This mission is very complementary to AGI. Where the direction of AGI focuses on investing in directing a lot of intelligence into this one model and have this model be able to solve various different kinds of tasks in a, in a great way. So the idea is you just build your application on top of the AGI model as a utility, right? So this is a great— AGI is a great direction. It's very scalable. If it's successful. But the reality is only a very small fraction of data goes into the foundation models for AGI. If you look at the world's data, majority of the data, by majority I really mean like more than 90% of the data is actually not in the public domain. It's not in public, internet. It's not labeled by labeling companies, which goes into the foundation models. And majority of those data are private data locked inside applications and enterprises. And data, we all know data is intelligence, data is knowledge. And those application-specific, enterprise-specific data is not accessible by the AGI labs. And we just leave a lot of intelligence on the table. My prediction, and that's where we're betting on the future, is to be able to activate those private data and let the model absorb additional application-specific intelligence and bring the model to the next level. And this kind of motion is more like customization, right? It's kind of— it is the model and the inference deployment were customized towards applications in their specific pattern. And this customization should not be just one time, right? Our application enterprise product, it keeps evolving. So this customization should be continuous. And ideally, this continuous customization should be fully automated. That's autonomous intelligence. We are making great progress towards that direction. And we believe the future is not one model rules all. It's going to be millions of models, one per application, per use case. All right.
So the term autonomous intelligence, it sounds kind of vaguely to me like the buzzword of 2025 in our field, probably the buzzword for 2026, which is agentic AI. But it sounds like it's quite different from agentic AI, this idea of autonomous intelligence.
It's heavily connected with agentic AI. So think about agent as a way to automate many of our day-to-day tasks. Right, so we have been living in a world that many expert-intense tasks have been gradually automated so we can free up our time. Eventually, some of the even professions will be redefined. So for example, I think there are interview agents or hiring agents where you give a job listing, it will source the candidates and even do the first rounds of filtering and interview for you. And there's marketing agent, you give your ICP list and we'll source the right company, right stakeholders, and start to drive customized outbound emails and reaches. And there's customer service agent, just give the human agents some really good assistant, assistants who kind of be smart. And there are so many like agents to doctors and so on. So this is happening, is transforming our day-to-day life. But similarly, another big transformation that's happening is in my domain is software development is being disrupted. And today, a coding agent can really start to behave like a junior engineer. And I'm not kidding, this is kind of really happening. And it's actually changed, it changed our interview process. And the fundamental question we are asking ourselves is the coding, is coding, coding interview important anymore, right? So coding interview in the past is gonna replace by, you know, how good you are at using coding agents. So it is actually happening across our day-to-day life. Now let's go back to, to this autonomous intelligence era, right? So without that, currently this work of continuously adapting the model and changing and customizing inference setup is done by a very, very small set of experts. So those experts are like, they have been doing AI system for a long time. They have been researcher for a long time. Accumulated their knowledge over years. So only a few companies who has those strong density talent pool are able to do that. And the question is, can that part be automated, right? And similar to other areas that has been disrupted and has been reshaped, and can this part of doing product model co-design And, you know, infusing more intelligence to the model and making the inference serving tier much faster and much more efficient. You know, can that part be accessible by a wide range of application developers without them putting in a lot of work and carrying the burden of learning all the deep knowledges? So that's what that means. So if we heard a lot about AI is going to free up a lot of human labor, right? So, and this wave is interesting because it will start from different angle. It will actually free up a human from the high intelligence level, not from the physical level, right? The robotics is going to disrupt the physical level. Of engagement, but AI is going to free up a lot of kind of high intelligence level of the tasks work. So that's kind of interesting change, and we are also innovating and disrupting in that space from a baseline platform space.
