Join theCUBE and NYSE Wired as we delve into the rapidly evolving world of artificial intelligence with our esteemed guest, Lin Qiao, Chief Executive Officer and Co-Founder of Fireworks AI. In this insightful discussion, we explore the "Mixture of Experts" series, highlighting the dramatic shifts in AI and open-source development.
In this engaging video, John Furrier, Co-Founder and Co-Chief Executive Officer of SiliconANGLE Media, hosts Qiao as they share expertise and perspectives on the latest trends in AI development. The discussion centers on the role of open-source models and the importance of customization in AI applications, with a focus on strategies for developer engagement and innovation.
The conversation offers critical insights into the convergence of open and closed AI models and the strategic significance of private data for enterprise advancement. Qiao discusses Fireworks AI's mission to enhance speed and cost efficiency through advanced customization, providing actionable insights into the AI native transformation across enterprises. They stress the shift to targeted workflows and reinforce the value of real-world application development.
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Lin Qiao, Fireworks AI
In this theCUBE + NYSE Wired: Mixture of Experts segment from the New York Stock Exchange, theCUBE’s John Furrier sits down with Raj Verma, CEO of SingleStore, to unpack how the intersection of technology and finance is shaping enterprise strategy. Verma shares why SingleStore is “on course” for the public markets, reflects on brand-building through the company’s partnership with golf Hall of Famer Padraig Harrington and connects that ethos to how SingleStore helps organizations fix struggling data “swings.” The discussion zeroes in on what’s next as Wall Street watches the AI infrastructure buildout: after chips and systems, the software and data layers set the pace for value creation.
Verma outlines why enterprises must modernize “brown” data estates into “green” ones to safely bring corporate context, governance and compliance into LLM workflows via RAG – and why commoditized data-at-rest puts the advantage at the query layer that unifies data in motion with data at rest. He predicts agentic AI will gain reasoning capabilities in roughly 18 months, cites industry indicators like Google reporting ~25% of its software now built by AI and argues that high switching costs will give way to disruption as buyers reassess legacy vendors. The conversation closes with concrete momentum: ~33% YoY growth, ARR in the ~$135M range, gross dollar retention ~98%, cloud NDR ~130, ~50% of business now in the cloud, landing ~3 new customers per day, a path to cash-flow breakeven in the next two quarters and a teaser for AI-related announcements in the next two months. Listeners will find notable stats, real-world use cases and forward-looking views on how databases power reliable AI at enterprise scale.
>> Welcome back. I run to theCUBE here at our NYSE Studios. I'm John Furrier, your host of theCUBE. It's a mixture of expert series. We bring in leaders who are experts who are making the AI wave happen, as well as other accelerated disruption enablement that generative AI is certainly making happen. Obviously, open source and the rise of developers now taking advantage of the infrastructure, it's always great to have the leaders share their thoughts. Lin Qiao, CEO and co-founder of Fireworks AI. Lin, thanks for coming in remotely to theCUBE here at the NYSE Studios, part of our NYSE Wired program and community. Thanks for joining us.
Lin Qiao
>> Thanks for having me, John.
John Furrier
>> First of all, we covered on siliconangle.com my site, your recent funding 250 million at over a $4 billion valuation. Quite a nice run over the past two years or year and a half for you guys. Congratulations.
Lin Qiao
>> Yeah. Thank you. This is a big moment for us. Life speed and index collect around with the past round led by Sequoia and Benchmark, I think there's a lot of trust from our customer partners and investors for us to continue to grow very fast, and there's a lot of momentum that we have accumulated and it's a lot more of us to build into.
John Furrier
>> Yeah. The thing that I love right now and I want to get your thoughts on is the market around open source and how open source developers are starting to lean in and look at the models, and how AI native companies are thinking around building companies. Obviously, having a data mode is a huge advantage. You're starting to see the enterprise open up again. You see the big large scale hyperscalers and the neoclouds doing well, but it's going to be the applications that are dealing with these models is AI native. What's your thoughts on that? Because there's the new school of AI native and then there's like the sass moving into AI enabled and then transitioning. Talk about those two dynamics in context to how developers and builders are thinking about the models themselves.
Lin Qiao
>> I would say open model is strategically very important for developers to build the state of art application. The fundamental reasons are the open model providing a huge variety of choices. At the same time, open model quality is converging with closed model. The fundamental reason is model quality is decided by two factors, data and the training algorithm. Data is pretty much the whole entire public internet and data provided by labeling company, which all various different model providers by the open and close are using, so there's not much differentiation. On training algorithm side, the researchers are moving around so there's not much secret sauce remaining. That's explaining why the closed model are all converging, but at the same time we are depleting all possible data that is in public domain or that can be generated by labeling company. However, at the same time, there is much bigger data that we're not tapping into today that is the private data locked inside application, locked inside enterprise. The foundation model developers or researchers have no access to. I would say the next era of model improvement and model development should heavily index on customization and that's where Fireworks when we started, we have set our mission to inter-customization. Our long-term mission is called autonomous intelligence, where we deliver application customized inference. We have already delivered application customized inference for speed and cost efficiency, and these two are extremely important factors that get back to it, and we just launched a new product called Fireworks Reinforcement Fine-Tuning, it's based on reinforcement learning to customizable quality using applications as private data. These three combined, we are giving agentic developers or application developers a tool to activate the data, the user engagement data, user preference data, user intent data to build much better model, much faster model, much more cost-efficient model to run their business, and extremely important for multiple reason. One is the agentic developers need to have full control. Full control of the quality of the version of the predictability of their mode. The second didn't. You have full control of cost and many companies, they have great product and it is funny in today's stage, having a product market fit and having a viable business are completely two separate things. You can have a brilliant product that your customer is willing to pay, but that doesn't mean you're going to have a valuable business because many company come to us saying they cannot scale, they have 10X more people, they hold in the waiting list and they cannot open up the floodgate because they're going to scale into bankruptcy. The way they burn money is so fast, on top of GenAI, is not sustainable business at all for them and that's why we provide these three-dimensional customization across quality, speed and cost efficiency. Agentic application developers can leverage the tools we provide from Fireworks to have the best product experience where they build on top of their customized model with the best quality and interactiveness to the best speed and build a viable business with the best cost efficiency.
John Furrier
>> Lin, I love that description because you just explained the enterprise problem, which is they have all this data that they want to unlock value out of, and it's proprietary to them, they also have workflows. They want to apply agents, they got to come in and understand how to I guess to distill from the best but also apply targeted generative AI capabilities to that data, and we saw a lot of POCs die on the vine because they were too hard to wrangle. This is an interesting point. What's your thoughts on that? Because when you start to see the enterprise get it right, they're going small or not bigger, they're going targeted on workflows where they know the data. This highlights that point. There's a lot of data that has not yet been trained or can be inferred on. What's your reaction to that?
Lin Qiao
>> I would say this is not just the enterprise problem, it is also the problem for AI native startups. Most of the AI native startups are actually consumer or developer facing and there are a lot of product data for them to understand what is their consumers or developers preference, intent, engagement metrics. Almost like all these application developers, I think all application developers are very familiar with A-B testing these days and the product analytics, there's no different. With GenAI, with various different models, with various different customized models, they need to have that telemetry and also, continue to evolve their product by understanding user. From that point there are a lot of data being generated from a user-interested point of view and those data are not activated. I would say there's a huge loss on their side if they're not activate those data to make the underlying model they use to be smarter, faster and more cost-efficient. To answer your question about what enterprise probably has a lot of data as well already a lot of data sitting and a small model or large model, here's typical flow. First, there's no question. There are a lot of interest to customize. It's just customization is not easy and we derive a lot of best practices in addition to the tool chain we provide from our offering. First is, leverage your data to customize model and initially, usually you want to customize a small model to see if your data is actually valuable, is the right data for model customization and small model can help you iterate really fast and for you to see if there's a good trajectory. If there's a good trajectory, then next step is actually to customize a large model. The reason is large model has much stronger logical reasoning capability. It can absorb the knowledge much better and then can learn much faster. But you use a small model to iron out your data issue and create a fast loop to curate a high quality data set and then start to the largest MOE model. Usually, those tuning is very complicated because the model is very complex. Our underlying infrastructure requirement is very high. That's where we offer, we just completely take that concern away from the researchers or developer. Then you have the largest, and the most smartest customized big model and then you go small from that point. From there, you can distill into a small model and teach a student to solve a specific problem from the biggest teacher model. It doesn't go straight line into small model. It goes multiple hops and that's the best practice we've seen and very effective that way.
John Furrier
>> On your success, obviously the funding is great validation, it's good to have fresh money in the bank and get great investors. What's the success formula for you guys? Was it on the adoption side? Is it developers looking for that observability for AI native or is it enterprise? Where is your sweet spot right now in terms of the momentum? Could you share some data on that?
Lin Qiao
>> When we just started, in my mind, I was thinking about sequential adoption that a startup with most tech embracing will adopt that first and then digital native, which is tech forward then traditional enterprise and usually, they want to wait until the whole entire tool chain is ready and so on. That's my initial assumption, but right now it's happening all across the board. It's reflection of how explosive this market is, and we have seen many traditional enterprise also move really fast. With that said, we have adoption across the board, the best AI native startups and digital native companies and the traditional enterprise as well. To us, it's very eye-opening to see cross board explosion of creativity using GenAI. People are re-imagining what would be the new user experiences. They are redefining, what does recruiting look like? What does outbound marketing look like? What does education look like? What does medical look like? Healthcare look like? What does coding productivity look like? But coding is not just coding. If coding is not about generating code, it's also about generating business workflow, generating SQL, generating design and generating a website. The variety of use case is off the chart and that's where we shine the most, is we shine the most where the biggest amount of sparkle creativity lies and they need the best state of art tools to help them move really fast.
John Furrier
>> I love this market because the classic segmentation, okay, we'll start here, we'll sequence to the next position, we'll get to the enterprise. You're a horizontal value proposition because people are transforming every part of their business, whether you're an enterprise or a startup. A startup could say, "Let's re-imagine what recruiting is like. Let's build an AI native app. Let's work with the models," and then put the entrepreneurial energy into doing that, and you change the category. The enterprises are sitting there saying the same thing saying, "Hey, I want to change how I do business with my customer." Hence, it's a reinvention, it's a transformation, so you're kind of horizontal. You're enabling that app transformation. You're transforming old to new. Now, entrepreneurs are new but they don't have the old. They're going to crack the code and disrupt the old categories and their applications, and we're seeing successes out there on theCUBE and SiliconANGLE where these narrow verticals are completely transforming that wouldn't have probably been venture-backed, but you can make a great feature with AI and actually change the makeup and the economics. That's the magic.
Lin Qiao
>> That's the magic. This is the once-in-a-lifetime industrial transformation we're living through and that's extremely exciting. I think I've been going through various different fireside chat. One of the question I got asked is, what does next year's startup look like? My prediction is next year, the startups are going to build on top of many startups, because that's where the maximum amount of creativity is going to derive and it will change our day-to-day life. We already see that happening.
John Furrier
>> It's a mosaic of startups that's together our generative. Can you imagine starting a startup and going, "Hey, build me a solution code assisted that then integrates in with another application startup and then sells to the enterprise?"
Lin Qiao
>> It's already happening in our company and we have so many startup built on top of us and we are also using their next GenAI powered product as well. In this fast moving time, we are building on top of each other. We compound each other's results, and this is something I've never seen in the past and this part that's-
John Furrier
>> It's an entrepreneurial shift. Most startups have a formula, you get an idea and they've changed how they do it obviously accelerates it, but you're getting at how people are working together. Jensen Wong at Nvidia, I know was an investor in your company, he calls it extreme co-design. I think this is a phenomenon that is this generation because co-designing is not just API calls. There's actual things happening between two different companies or platforms. You're teasing that out, but this is a feature, not a bug. What's your reaction to this co-design thinking or systems thinking? Because if I'm going to work on top of Fireworks AI, I got to know your system. It's not just an SDK or an API or an agent interface. There's benefits to co-designing. What's your thoughts as an entrepreneur on this new phenomenon? Do you agree and if you don't, what would you add?
Lin Qiao
>> This is a very interesting point. We have seen so many startups or enterprise build on top of us, and there are many of them took off explosively. There's one common pattern among those companies, is they do not consider model as a commodity or as a utility or as just as an API statically. They are actually co-designed their product and model very closely together. The fundamental reason is model and the product are misaligned by design. Model is developed by researchers in lab, and product is developed by this application product company and they optimize for completely different things. The misalignment is indirectly result in lack of quality, speed and cost efficiency. The successful ones figure out a way to leverage their product data and use that data to align the model they use. They basically boost up the model quality even cost for their product specifically, through and we call it product model co-design, and the end result is model become their IP and model is not static. Model continues to alarm for their new product data, so they create a data flying well. This approach is extremely effective. However, this approach is very complicated right now because the whole entire space is void and they have to figure out a lot of things by themselves. That's where we build our product. We want to make it so easy, such a no-brainer to plug their product data and build this product model co-design in a very straightforward, quick to start way. Time-to-value will be within a couple of minutes if you show the result. That's where we're heading towards.
John Furrier
>> You get the fine-tuning thing. This is interesting because I've been having conversations about... This has started, we go back months ago DeepSeek made the big impression, but the distillation, what they did was interesting. You're starting to see if you really get into the models and tie it in to your point, this value there. It's like coding software down to the chip level and the semis, so you get better access to the raw kernel, whatever word we want to use, the asset that could be there. Okay. Now, that's an overgeneralization to compare that, but what you're getting at is that the more you lean into the model, you do it. That sounds hard. What's-
Lin Qiao
>> It's hard actually, and that's why we're coming to solve the hard problem to make that a reality. The end result is every single application company, every single product team, they will have their own foundation model, so think about that. It's a very powerful position to be in.
John Furrier
>> Yeah. Well, we'd love theCUBE. We have our own CUBE foundation model. This interview will be converted into linguistics, into data. We'll capture that. We'll have our little language model, but we're just a small little island. But now I can connect it with other things to add value. It's a multiplier effect. How are people reacting to that? Are people getting this? How would you articulate the uptake? Then where is the gap? I know you guys are doing a lot more research with your use of funds. How do you build on that? How do people understand this? Do they, and how do they build on top of it faster and keep up with the changes on all the models?
Lin Qiao
>> Actually, that's a huge challenge. On one hand, the benefit of open models, there's a lot of optionality and the application developer, they don't get locked in. They have a lot of choices. On the other hand, there are too many choices. Almost every two weeks there's a new step art model topping the leaderboard, and people constantly ask us as their trusted advisor, "What do we do here? Do we keep chasing them?" Then it feels like a lot of work, so that's where we come and solve all the problems. If you build a way to customize model, you can easily just... Don't to change anything. You can port to try any new models.
John Furrier
>> It's like horse betting. You bet on one horse and you lose all in if they get superseded by the next horse, the next model, you got to pivot. You're saying you can swap out almost port the customization to any model. Vertex, Gemini hits this, OpenAI comes out, Anthropic, and also there's different use cases. The models actually have different applications too.
Lin Qiao
>> Exactly. You're absolutely right. Just to ask people, today's prompt based application building, people are very used to swap out all the Frontier apps. Like they want to have optionality and the plugin, all different offerings, API offerings, for customization, same thing. We want people to have optionality to test and try all different open models they can tune and integrate with their agents.
John Furrier
>> Basically what you're saying is build an IP around your model, work with whatever model, whatever tool's best for the job, and then keep focused on that piece of it? That's the engineering product integration with the models. Did I get that right?
Lin Qiao
>> Exactly.
John Furrier
>> Okay, great. First of all, how do I get Fireworks going right away?
Lin Qiao
>> Very simple. Yeah. Go to fireworks.ai and we have a public platform that's a completely self-serve, so it's a very easy sign up. Then you can use all the features from our platform and we also have support. We have a chatbot, you can ask questions. We also have a Discord channel, free to ping me if you have any feedback as well, lqioa@fireworks.ai.
John Furrier
>> Well, I will get on this immediately with my team, with theCUBE AI model, we'll get on Fireworks. Great stuff. Final question for you. What's the plan for you on the use of funds? I saw in the release and in the news you're going to do some research expansion. The role of research has become less academic, more academic targeted towards real use cases and more engineering applied. What's your plan?
Lin Qiao
>> Yeah. We are absolutely more focused on applied research. In that sense, there are actually a wide spectrum of academic research and we want to adapt them into the real world use cases. In addition to that, we are a very innovative group of people. We create new algorithms in the new way to run the tuning process. Tuning is mostly post-training. Post-training inference alignment and the inference speed and cost-efficient research. We have own innovation. Many of our own innovation along the way as well. On that front, we also are hiring the best talent across the industry who are the expert in post-training as well as numerics and inference optimization and building extreme large cluster and operate cloud infrastructure as we plan to significant grow our fleet size, so we're hiring across the board.
John Furrier
>> Lin, thank you so much for being an expert in our mixture of experts series on theCUBE. We really appreciate you and congratulations and hey, make things simpler and easier to use and reduce the steps it takes to build is always a great formula. Congratulations.
Lin Qiao
>> Thank you so much.
John Furrier
>> All right. I'm John Furrier with theCUBE here at the NYSE CUBE Studios. Thanks for watching.
>> Welcome back. I run to theCUBE here at our NYSE Studios. I'm John Furrier, your host of theCUBE. It's a mixture of expert series. We bring in leaders who are experts who are making the AI wave happen, as well as other accelerated disruption enablement that generative AI is certainly making happen. Obviously, open source and the rise of developers now taking advantage of the infrastructure, it's always great to have the leaders share their thoughts. Lin Qiao, CEO and co-founder of Fireworks AI. Lin, thanks for coming in remotely to theCUBE here at the NYSE Studios, part of our NYSE Wired program and community. Thanks for joining us.
Lin Qiao
>> Thanks for having me, John.
John Furrier
>> First of all, we covered on siliconangle.com my site, your recent funding 250 million at over a $4 billion valuation. Quite a nice run over the past two years or year and a half for you guys. Congratulations.
Lin Qiao
>> Yeah. Thank you. This is a big moment for us. Life speed and index collect around with the past round led by Sequoia and Benchmark, I think there's a lot of trust from our customer partners and investors for us to continue to grow very fast, and there's a lot of momentum that we have accumulated and it's a lot more of us to build into.
John Furrier
>> Yeah. The thing that I love right now and I want to get your thoughts on is the market around open source and how open source developers are starting to lean in and look at the models, and how AI native companies are thinking around building companies. Obviously, having a data mode is a huge advantage. You're starting to see the enterprise open up again. You see the big large scale hyperscalers and the neoclouds doing well, but it's going to be the applications that are dealing with these models is AI native. What's your thoughts on that? Because there's the new school of AI native and then there's like the sass moving into AI enabled and then transitioning. Talk about those two dynamics in context to how developers and builders are thinking about the models themselves.
Lin Qiao
>> I would say open model is strategically very important for developers to build the state of art application. The fundamental reasons are the open model providing a huge variety of choices. At the same time, open model quality is converging with closed model. The fundamental reason is model quality is decided by two factors, data and the training algorithm. Data is pretty much the whole entire public internet and data provided by labeling company, which all various different model providers by the open and close are using, so there's not much differentiation. On training algorithm side, the researchers are moving around so there's not much secret sauce remaining. That's explaining why the closed model are all converging, but at the same time we are depleting all possible data that is in public domain or that can be generated by labeling company. However, at the same time, there is much bigger data that we're not tapping into today that is the private data locked inside application, locked inside enterprise. The foundation model developers or researchers have no access to. I would say the next era of model improvement and model development should heavily index on customization and that's where Fireworks when we started, we have set our mission to inter-customization. Our long-term mission is called autonomous intelligence, where we deliver application customized inference. We have already delivered application customized inference for speed and cost efficiency, and these two are extremely important factors that get back to it, and we just launched a new product called Fireworks Reinforcement Fine-Tuning, it's based on reinforcement learning to customizable quality using applications as private data. These three combined, we are giving agentic developers or application developers a tool to activate the data, the user engagement data, user preference data, user intent data to build much better model, much faster model, much more cost-efficient model to run their business, and extremely important for multiple reason. One is the agentic developers need to have full control. Full control of the quality of the version of the predictability of their mode. The second didn't. You have full control of cost and many companies, they have great product and it is funny in today's stage, having a product market fit and having a viable business are completely two separate things. You can have a brilliant product that your customer is willing to pay, but that doesn't mean you're going to have a valuable business because many company come to us saying they cannot scale, they have 10X more people, they hold in the waiting list and they cannot open up the floodgate because they're going to scale into bankruptcy. The way they burn money is so fast, on top of GenAI, is not sustainable business at all for them and that's why we provide these three-dimensional customization across quality, speed and cost efficiency. Agentic application developers can leverage the tools we provide from Fireworks to have the best product experience where they build on top of their customized model with the best quality and interactiveness to the best speed and build a viable business with the best cost efficiency.
John Furrier
>> Lin, I love that description because you just explained the enterprise problem, which is they have all this data that they want to unlock value out of, and it's proprietary to them, they also have workflows. They want to apply agents, they got to come in and understand how to I guess to distill from the best but also apply targeted generative AI capabilities to that data, and we saw a lot of POCs die on the vine because they were too hard to wrangle. This is an interesting point. What's your thoughts on that? Because when you start to see the enterprise get it right, they're going small or not bigger, they're going targeted on workflows where they know the data. This highlights that point. There's a lot of data that has not yet been trained or can be inferred on. What's your reaction to that?
Lin Qiao
>> I would say this is not just the enterprise problem, it is also the problem for AI native startups. Most of the AI native startups are actually consumer or developer facing and there are a lot of product data for them to understand what is their consumers or developers preference, intent, engagement metrics. Almost like all these application developers, I think all application developers are very familiar with A-B testing these days and the product analytics, there's no different. With GenAI, with various different models, with various different customized models, they need to have that telemetry and also, continue to evolve their product by understanding user. From that point there are a lot of data being generated from a user-interested point of view and those data are not activated. I would say there's a huge loss on their side if they're not activate those data to make the underlying model they use to be smarter, faster and more cost-efficient. To answer your question about what enterprise probably has a lot of data as well already a lot of data sitting and a small model or large model, here's typical flow. First, there's no question. There are a lot of interest to customize. It's just customization is not easy and we derive a lot of best practices in addition to the tool chain we provide from our offering. First is, leverage your data to customize model and initially, usually you want to customize a small model to see if your data is actually valuable, is the right data for model customization and small model can help you iterate really fast and for you to see if there's a good trajectory. If there's a good trajectory, then next step is actually to customize a large model. The reason is large model has much stronger logical reasoning capability. It can absorb the knowledge much better and then can learn much faster. But you use a small model to iron out your data issue and create a fast loop to curate a high quality data set and then start to the largest MOE model. Usually, those tuning is very complicated because the model is very complex. Our underlying infrastructure requirement is very high. That's where we offer, we just completely take that concern away from the researchers or developer. Then you have the largest, and the most smartest customized big model and then you go small from that point. From there, you can distill into a small model and teach a student to solve a specific problem from the biggest teacher model. It doesn't go straight line into small model. It goes multiple hops and that's the best practice we've seen and very effective that way.
John Furrier
>> On your success, obviously the funding is great validation, it's good to have fresh money in the bank and get great investors. What's the success formula for you guys? Was it on the adoption side? Is it developers looking for that observability for AI native or is it enterprise? Where is your sweet spot right now in terms of the momentum? Could you share some data on that?
Lin Qiao
>> When we just started, in my mind, I was thinking about sequential adoption that a startup with most tech embracing will adopt that first and then digital native, which is tech forward then traditional enterprise and usually, they want to wait until the whole entire tool chain is ready and so on. That's my initial assumption, but right now it's happening all across the board. It's reflection of how explosive this market is, and we have seen many traditional enterprise also move really fast. With that said, we have adoption across the board, the best AI native startups and digital native companies and the traditional enterprise as well. To us, it's very eye-opening to see cross board explosion of creativity using GenAI. People are re-imagining what would be the new user experiences. They are redefining, what does recruiting look like? What does outbound marketing look like? What does education look like? What does medical look like? Healthcare look like? What does coding productivity look like? But coding is not just coding. If coding is not about generating code, it's also about generating business workflow, generating SQL, generating design and generating a website. The variety of use case is off the chart and that's where we shine the most, is we shine the most where the biggest amount of sparkle creativity lies and they need the best state of art tools to help them move really fast.
John Furrier
>> I love this market because the classic segmentation, okay, we'll start here, we'll sequence to the next position, we'll get to the enterprise. You're a horizontal value proposition because people are transforming every part of their business, whether you're an enterprise or a startup. A startup could say, "Let's re-imagine what recruiting is like. Let's build an AI native app. Let's work with the models," and then put the entrepreneurial energy into doing that, and you change the category. The enterprises are sitting there saying the same thing saying, "Hey, I want to change how I do business with my customer." Hence, it's a reinvention, it's a transformation, so you're kind of horizontal. You're enabling that app transformation. You're transforming old to new. Now, entrepreneurs are new but they don't have the old. They're going to crack the code and disrupt the old categories and their applications, and we're seeing successes out there on theCUBE and SiliconANGLE where these narrow verticals are completely transforming that wouldn't have probably been venture-backed, but you can make a great feature with AI and actually change the makeup and the economics. That's the magic.
Lin Qiao
>> That's the magic. This is the once-in-a-lifetime industrial transformation we're living through and that's extremely exciting. I think I've been going through various different fireside chat. One of the question I got asked is, what does next year's startup look like? My prediction is next year, the startups are going to build on top of many startups, because that's where the maximum amount of creativity is going to derive and it will change our day-to-day life. We already see that happening.
John Furrier
>> It's a mosaic of startups that's together our generative. Can you imagine starting a startup and going, "Hey, build me a solution code assisted that then integrates in with another application startup and then sells to the enterprise?"
Lin Qiao
>> It's already happening in our company and we have so many startup built on top of us and we are also using their next GenAI powered product as well. In this fast moving time, we are building on top of each other. We compound each other's results, and this is something I've never seen in the past and this part that's-
John Furrier
>> It's an entrepreneurial shift. Most startups have a formula, you get an idea and they've changed how they do it obviously accelerates it, but you're getting at how people are working together. Jensen Wong at Nvidia, I know was an investor in your company, he calls it extreme co-design. I think this is a phenomenon that is this generation because co-designing is not just API calls. There's actual things happening between two different companies or platforms. You're teasing that out, but this is a feature, not a bug. What's your reaction to this co-design thinking or systems thinking? Because if I'm going to work on top of Fireworks AI, I got to know your system. It's not just an SDK or an API or an agent interface. There's benefits to co-designing. What's your thoughts as an entrepreneur on this new phenomenon? Do you agree and if you don't, what would you add?
Lin Qiao
>> This is a very interesting point. We have seen so many startups or enterprise build on top of us, and there are many of them took off explosively. There's one common pattern among those companies, is they do not consider model as a commodity or as a utility or as just as an API statically. They are actually co-designed their product and model very closely together. The fundamental reason is model and the product are misaligned by design. Model is developed by researchers in lab, and product is developed by this application product company and they optimize for completely different things. The misalignment is indirectly result in lack of quality, speed and cost efficiency. The successful ones figure out a way to leverage their product data and use that data to align the model they use. They basically boost up the model quality even cost for their product specifically, through and we call it product model co-design, and the end result is model become their IP and model is not static. Model continues to alarm for their new product data, so they create a data flying well. This approach is extremely effective. However, this approach is very complicated right now because the whole entire space is void and they have to figure out a lot of things by themselves. That's where we build our product. We want to make it so easy, such a no-brainer to plug their product data and build this product model co-design in a very straightforward, quick to start way. Time-to-value will be within a couple of minutes if you show the result. That's where we're heading towards.
John Furrier
>> You get the fine-tuning thing. This is interesting because I've been having conversations about... This has started, we go back months ago DeepSeek made the big impression, but the distillation, what they did was interesting. You're starting to see if you really get into the models and tie it in to your point, this value there. It's like coding software down to the chip level and the semis, so you get better access to the raw kernel, whatever word we want to use, the asset that could be there. Okay. Now, that's an overgeneralization to compare that, but what you're getting at is that the more you lean into the model, you do it. That sounds hard. What's-
Lin Qiao
>> It's hard actually, and that's why we're coming to solve the hard problem to make that a reality. The end result is every single application company, every single product team, they will have their own foundation model, so think about that. It's a very powerful position to be in.
John Furrier
>> Yeah. Well, we'd love theCUBE. We have our own CUBE foundation model. This interview will be converted into linguistics, into data. We'll capture that. We'll have our little language model, but we're just a small little island. But now I can connect it with other things to add value. It's a multiplier effect. How are people reacting to that? Are people getting this? How would you articulate the uptake? Then where is the gap? I know you guys are doing a lot more research with your use of funds. How do you build on that? How do people understand this? Do they, and how do they build on top of it faster and keep up with the changes on all the models?
Lin Qiao
>> Actually, that's a huge challenge. On one hand, the benefit of open models, there's a lot of optionality and the application developer, they don't get locked in. They have a lot of choices. On the other hand, there are too many choices. Almost every two weeks there's a new step art model topping the leaderboard, and people constantly ask us as their trusted advisor, "What do we do here? Do we keep chasing them?" Then it feels like a lot of work, so that's where we come and solve all the problems. If you build a way to customize model, you can easily just... Don't to change anything. You can port to try any new models.
John Furrier
>> It's like horse betting. You bet on one horse and you lose all in if they get superseded by the next horse, the next model, you got to pivot. You're saying you can swap out almost port the customization to any model. Vertex, Gemini hits this, OpenAI comes out, Anthropic, and also there's different use cases. The models actually have different applications too.
Lin Qiao
>> Exactly. You're absolutely right. Just to ask people, today's prompt based application building, people are very used to swap out all the Frontier apps. Like they want to have optionality and the plugin, all different offerings, API offerings, for customization, same thing. We want people to have optionality to test and try all different open models they can tune and integrate with their agents.
John Furrier
>> Basically what you're saying is build an IP around your model, work with whatever model, whatever tool's best for the job, and then keep focused on that piece of it? That's the engineering product integration with the models. Did I get that right?
Lin Qiao
>> Exactly.
John Furrier
>> Okay, great. First of all, how do I get Fireworks going right away?
Lin Qiao
>> Very simple. Yeah. Go to fireworks.ai and we have a public platform that's a completely self-serve, so it's a very easy sign up. Then you can use all the features from our platform and we also have support. We have a chatbot, you can ask questions. We also have a Discord channel, free to ping me if you have any feedback as well, lqioa@fireworks.ai.
John Furrier
>> Well, I will get on this immediately with my team, with theCUBE AI model, we'll get on Fireworks. Great stuff. Final question for you. What's the plan for you on the use of funds? I saw in the release and in the news you're going to do some research expansion. The role of research has become less academic, more academic targeted towards real use cases and more engineering applied. What's your plan?
Lin Qiao
>> Yeah. We are absolutely more focused on applied research. In that sense, there are actually a wide spectrum of academic research and we want to adapt them into the real world use cases. In addition to that, we are a very innovative group of people. We create new algorithms in the new way to run the tuning process. Tuning is mostly post-training. Post-training inference alignment and the inference speed and cost-efficient research. We have own innovation. Many of our own innovation along the way as well. On that front, we also are hiring the best talent across the industry who are the expert in post-training as well as numerics and inference optimization and building extreme large cluster and operate cloud infrastructure as we plan to significant grow our fleet size, so we're hiring across the board.
John Furrier
>> Lin, thank you so much for being an expert in our mixture of experts series on theCUBE. We really appreciate you and congratulations and hey, make things simpler and easier to use and reduce the steps it takes to build is always a great formula. Congratulations.
Lin Qiao
>> Thank you so much.
John Furrier
>> All right. I'm John Furrier with theCUBE here at the NYSE CUBE Studios. Thanks for watching.
John Furrier