971: 90% of The World’s Data is Private; Lin Qiao’s Fireworks AI is Unlocking It

Over 90% of the world's intelligence is locked inside private enterprise data that no foundation model has ever seen. Today's guest is on admission to unlock it. Welcome to episode number 971 of the Super Data Science Podcast. I'm your host, John Crone. Today's guest, Lin Chao, is the CEO of Fireworks AI, a Bay Area startup that has raised over $300 million to unlock the world's vast quantities of enterprise data for LLM training and inference, revolutionizing capabilities and performance. With a PhD in computer science from UC Santa Barbara and years of experience as a director of engineering at Meta, Lin is now a highly successful technical founder with a rich perspective on AI today and what the future holds for all of us. Enjoy this one. This episode of Super Data Science is made possible by Dell, Intel, Cisco and Excel data. Lin, welcome to the Super Data Science Podcast. It's an honor to have you take time out of your busy schedule to be on the show. How are you doing today? I'm doing great. Thanks for having me, John. Of course. And where are you calling in from? I'm calling from Portola Valley. That's where I live. Nice. It's Porta Bay Area. Yeah, yeah, yeah. Palo Alto. Yeah, very close to Stanford. Nice, nice, nice. 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 complimentary to a GI where the direction of a GI focused on investing in directing a lot of intelligence into this one model. And have this model be able to solve very difficult tasks 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, a GI 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 a GI. If you look at the worst 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 applications 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 planning 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. And this 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 that keeps evolving. So this customization should be continuous. And ideally, these continuous customization should be fully automated. So this is the way we are going to be able to do that. And we're going to be able to do that. But it sounds like it's quite different from Agent AI, this idea of autonomous intelligence. It's heavily connected with Agent AI. So think about Agent is a way to automate many of our day-to-day tasks. Right. So we have been living the world that many expert-intense tasks has been gradually automated. So we can free up our time. And 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 that still will source the candidates and into even do the first rounds of a future interview for you. And there's marketing agent you give your ICP list and will source the right company, holders, and start to drive customized outbound emails and riches. And there's customer service agent, just give the human agents some really good assistant, assistants who will kind of be smart. And there are so many agents to doctors and so on. So this is happening to transform our day-to-day life. But similarly, another big transformation that's happening is in my domain, 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 actually changed our interview process. And the fundamental question we're asking ourselves is the coding, is coding interview important anymore? So coding interview in the past is going to replace by 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 these autonomous intelligence era. 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? Similar to other words that has been disrupted and has been re-shaped and can this part of doing product model co-design. And infusing more intelligence in the model and making the inference serving tier much faster and much more efficient, can that part be accessible by wide range of application developers without them putting in a lot of work and carrying the burden of learning all the deep knowledge. So that's what that means. So we heard a lot about AI is going to free up a lot of human labor. And this wave is interesting because it will start from a different angle. You actually free up a human from the high intelligence level, not from the physical level. The robotics is going to disrupt the first level of engagement. But AI is going to free up a lot of kind of high intelligence level of the task of work. So that's kind of interesting change. And we are also innovating and disrupting in that space from a baseline platform space. Really cool. So it sounds like yeah, autonomous intelligence builds on agent things, but also involves lots of things not associated with agent like systems like models automatically retraining and the whole kind of system humming along nicely. It seems like a key part of that working for you, especially in an earlier answer you mentioned how you see the future as millions of different models. These could be like LLMs, but millions of different AI models that are tuned to specific tasks within specific enterprises. And it sounds like fireworks offers a reinforcement fine tuning product that allows your customers to beat frontier closed models. In under a month on specific tasks that that relatively small, tuned open model is fine tuned for. Right. So we are very bullish in this direction. So think about AI has been following a lot of full step of human intelligence. Right. So it's artificial intelligence has been following that full step even the model architecture is called neural networks. It's kind of emulating like human human's brain. Right. So for reinforcement learning, it is actually very similar to we how we human learn knowledge is. So we learn by by various different angles. So one of the angle is we get positive feedback that we know, oh, this is the correct thing to do by learning the principles. Or we get negative feedback. And it's kind of we get penalized for doing something bad from our behavior point of view. Then we learn, no, don't do it anymore. And we kind of change toward different direction. So this also happened to how we do agriculture, for example, as you know, I'm a foot lover. And you know, today we all like to eat like sweet sweet fruit, very, the fruit also grows much bigger. But this is not how originate it became right. It gone through multiple generations of selection process where we collect the seeds of the sweeter and bigger fruits and the planet and among those and collect another. So, so this is kind of another way of reinforcement learning reinforcement, seed collection selection process. So a lot of a lot of things we do whether for our self our own learning process or we have applied in other domains has following the same principle. And this is also similar to how model is is you you teach model what is a party reward you teach model what is a negative reward. And the model is going to automatically scan through a search space of possibilities and do these feedbacks and find a path to specialize self in solving certain kind of problems really, really well. With that said, it's not or like only benefits right it's a trade off because if you let the model specializing certain direction area, it's going to be less specializing other areas. So it's like us exactly as you're specializing in driving this park cars in your Greek holes, you have very knowledgeable how to engage with guests. And I specialize in building the best air flow for platform and which can customize towards application specific patterns and and so on. Right. So, but we then I'm not a good good chef cook and I don't know actually how to you know how to do gardening very well. So see, so that's kind of a natural selection. We have direct our attention to similar to the models. So that's kind of where we are. And then we have our background is very, very accessible to all application developers. So they can basically have a model in tune to their product all the time. Imagine that imagine a model is just constantly learning the intelligence from your application and then you have your private model and and then you have your mode where nobody else built on top of existing API would have. So this is special thing that we want to kind of have every application developers to get hold of. In data science, the right hardware is essential for performance. 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Yeah, and this is not another new thing actually. For example, doing the mobile first move so before AI at the biggest shift is mobile. So the application moves from desktop to mobile devices and that actually opens up a whole new domain of doing part of development. And which is similar to autonomous intelligence we're hiding towards in terms of thinking. Our mobile first I think one thing that opens up is the access of end consumers to an application right because the people who owns desktop and versus people who owns a phone. And that just means now your product could have reached to orders of magnitude higher group of people and it will go global much quickly and you will be able to access very different demographics of the cohort much wider. Change how app developer think about product. Product evaluation because now it's much broader and you cannot just deploy a group of pms to understand what the customer want and the product no longer become monopoly just one design because for different cohort coverage you may want to highlight one feature versus the other. So so then it becomes all how do we even to product development how do we incorporate that feedback it has to be customized. And that customization is being done through a new technology called a testing right so the idea here is to use the insights from your product to compare a versus B of the product feature and make statistical. And decision based on statistical result make the decision where your product should look like within certain code of groups so. The idea is the basic similar like product has a lot of intelligence and we need to leverage that intelligence to make the product better and here similarly. Product has a lot of challenges we need to leverage intelligence to make the model better for your product therefore your product build on top of specialized model will be better so right now it's a completely open space it is a vacuum and there's no existing solution that has been solving this problem really well i'm pretty sure industry will move towards. And looking at this space very closely and I firmly believe there's a lot of value in there awesome i love it yeah really exciting you're kind of defining a new category. Which is an exciting thing to be doing so when people are trying to figure out these you know these relatively small say LLM's for a particular task does model selection really matter i mean do you are your clients like picking oh i'm going to use this llama model of this size or when or. Do they make those decisions or is this something that's kind of like handled automatically by the autonomous intelligence system so model selection matters but it's also exhausting. So for anything there are two. Interesting phenomena that's so unique to a. One is the model depreciation is very fast as you can observe every couple weeks there's a new model launched when it's cost open someone top the leader board and then couple weeks later someone else top the leader board and we often they all are strong in different ways so and we start to see the researcher focus start to diverge. Right it's clear some labs are really good at chat based models some labs are really good at coding to use agentic models some labs are really good at multi modality models some are really good at long context is they all start to diverging to focus on specializing different areas become a specialization so so different use case will suit different kind of model specialty very well but it's really hard for people to figure out which one is the best for my use case in on my use case will also evolve all time and the benchmark result public benchmark result is fully saturated and we need to figure out how to pick and choose continuously and which is exhausting so we do help our customer figure that out. But at the same time there's another level of complexity is hardware depreciation is also very fast this is something new before this wave usually every three years there's a new hardware skill and now last year by self in video launches three skills three new skills and and twenty six there will be a lot more new skills for all different vendors whether GPU or customer is sick. So then how to manage hardware becomes a very hard problem in these two combined is causing so much headache to to the application developer who want to stay on top of these all different kind of wave it just too much deep knowledge and expertise into game in order to kind of pick the best for them so we are the platform that we abstract out. We eventually want to abstract out hardware we want to abstract hardware selection and we want to kind of provide the best model for various different use cases. And so basically there are various different kind of mapping from specific use case specific work load patterns to to the model to the hardware to the inference setup to the flea design so all of that requires a big team in house where deep experts really hard to find and we want to kind of make it super easy for our customers. Right so instead of needing to find the deep experts they can just come to fireworks and work with you guys work with your solutions. Absolutely and interestingly there are so last year by self I haven't counted there are every month there are few new models get deployed and launched and it has been very exciting but also I will say early on we better open models and that was a big debate in the company that we we debate how many what ifs right but because our background in PyTorch which is open source project. PyTorch is now and we built PyTorch from ground up it is now the dominant air framework we firmly believe in the power of open science and it will in the long run we believe that is going to really shaped industry in an unprecedented way so that's why we better open models from very early on. And it's great to see that open model performance is converging with close model I think 2026 is the year that that convergence will become more prominent and super excited about that. So something that I haven't mentioned on air yet is that for seven years before before founding and being CEO fireworks AI you were a meta as a senior director of engineering where you led over 300 engineers and a big part of what you were doing there based on what I could find online is developing PyTorch so thank you for that. This is definitely a very big team effort and I have a very talent team at a time and yeah so I am very thrilled about the industry wide impact of PyTorch there are many great researchers and leaders engineers I'm lucky to work very closely with and some of them start a company with me fireworks. Really great crew and we continue to going down the same path of democratize AI to the whole industry really cool alright so back to fireworks and what you were saying about open source not just PyTorch but open source models and how you at fireworks have made this bet to embrace open source AI models. Do you think that a lot of organizations a lot of enterprises under estimate what's possible with open models I don't think it's about under estimation that much is just about. Not being familiar with with open models I think especially I think I will put simplify this answer there's two groups and there's a native startups there is enterprises obviously enterprise has like digital natives and traditional enterprise and various different categories but let's just take a look at these group two groups there are reactions. And the start ups are they just want to try the best option and they do not have any baggage they're very very brave in testing open models they're always very curious and at the same time there's a very interesting phenomenon that's also unique to AI right so as you know there are many companies whether startups or or incumbents they're trying and experimenting with new user experiences that our day to day is interacting with and that is those kind of experiment has been. I have seen a lot of success in terms of product market fit but product market fit doesn't at a time doesn't mean a viable business there could be a big 10 that people are willing to pay the product pay for the product but the cocks of running the business could be much higher than the revenue get and then people just cannot scale their business after hit the product market fit and it's funny literally they're going to scale into bankruptcy so obviously startups are like limited funding and even in community incumbents they cannot they have the distribution and they have to be so careful they say I was happy so careful in controlling the cost so the kind of budget and this R.I. totally makes sense and because of that they have to hold a lot of people on the waiting list and cannot open up the gate so while the startup going back to a native startups they are very brave in embracing open models but part of motivation is open models are they have a lot more control they can customize however they want but also at the same time open models are much more economical because we as a provider we do not have pre training cost billions of dollar preaching cost amortize over and we only focus on bringing the best quality speed and cost like from post training which is much cheaper and inference customization of the deployment. So the unit of economics of fireworks operating open models very different from frontal apps so that's kind of where we see the native startups are very brave in adopting and going down a path of customization with open models the second group of enterprises obviously they're much more careful because many of those are public companies. And they are under certain kind of obligation especially the legal team to understand what that implication of using open models license what that license limitation just kind of trying to understand what is this beast so I but I I'm from my observation enterprise start to open up and they start to understand much better oh this model actually is not going to send data to other countries is just a it's just a model and and depend if the model provider is hosting a model in US and then and then they can provide privacy and security around around the inns and outs. So so there but anyway there are other concerns as in the kind of content generated this a follow certain kind of God real and so on so but overall I've seen the whole industry start to understand better and start to kind of understand the bounds of operating open models and I only see an upper trend of embracing open models. Quick reality check for anyone building with AI agents your agents can discover each other they can pass messages they can coordinate on tasks but here's what they can't do they can't think together when your agent figures out how to handle a complex workflow that knowledge stays isolated the industry has focused on scaling AI vertically bigger models more compute those breakthroughs matter but intelligence also scales horizontally agents sharing knowledge across a network coordinating on common intent reasoning together the infrastructure for that second horizontal axis doesn't exist yet outshift by Cisco is formalizing if they call it the internet of cognition they're publishing the architecture and building reference implementations read scaling out super intelligence we've got a link to that in the show notes then check out episode number 961 in it dr. And the joy panda the head of outshift by Cisco walks through how horizontal scaling of intelligence works and why it matters. Nice that was a very cool explanation of those kind of two customer types the AI native start up the enterprise it sounds like regardless of which category a customer of yours falls into a huge advantage of fireworks must be that they can not only are running open source models cheaper like you said. But by running them through you it means that they don't need to themselves right be buying the GPUs be managing all the ops around that physical ops software ops they just can rely on fireworks to get things up and running and then you know having the right GPUs running behind the scenes. And controlling those kinds of costs is handled automatically so at the beginning of this conversation I mentioned AI is changing a lot of things in our day today I think one fundamental thing is changing is the velocity of product development on AI is extremely fast including the enterprises right so because of that. It's really hard for enterprise internally to forecast how many know how much traffic this product is going to generate it may generate no traffic because the experiment doesn't go like to the face they can go into production or it can generate massive traffic it's kind of the variation is very broad in this fast evolution of product development. So then it calls a big problem because with without like steady prediction of how to do capacity like from finance one view and and then what does that even mean to to procure hardware and you either over provision or you either or you under provision it's kind of the ranges of our broad. So that's where we can help because we're aggregator we aggregate demand across all different customers and we take that risk off the table from being you know you want to worry about that and in a very flexible being accommodating by being request because we can kind of add them together and drive the adoption so I think that's kind of a unique. Unique nature of AI but it's not that unique but think about that right so in the early days of cloud first. Before cloud first everyone every big enterprise they have may frame so data center locked in with keys in a cage of machines right this is the most rigid way of doing capacity planning and the cloud when cloud comes into the picture. And usually people think you're crazy like why would you move on the most secure. In close like deployment of of your infrastructure into renting someone else infrastructure and you run your most important application there. But guess what that cloud infrastructure provides so much flexibility that over the long term it's much more efficient to run so similar things is happening in the space and that's why it's very fascinating the velocity of air development is shifting and changing and even disrupting how we do business in the traditional way really cool answer again from you there thank you so much I learned I've learned so much from every response. And so it seems like you might have an interesting and informative perspective on reasoning models or slow thinking models so that's been a big thing last year open AI and through a big Google there's a big fuss around the slow thinking models that they released. And particularly their performance on complex tasks like math Olympia and writing academic papers and these kinds of things require you know more processing before just outputting some kind of train of thought. I noticed on the fireworks website that you mentioned very fast latency like sub two second or even sub 500 millisecond latency in a lot of real world deployments. Do you think that the like slower reasoning models that take time before they output something do you think that there's a lot of enterprise need for that. So I will classify again extremely simplified way of looking at application so one type of application is real time response so it's either human facing interactive response latency or it's kind of fast transaction facing for example for detection right so. So in aggregate ways kind of very fast going the entire propagation is offline so for example I have a case study legal case study I need my legal assistant to go off and analyze similar cases and come back in three days give me a report now agent legal agent can go off and do this study for a couple hours and come back right so. So those are two very big categories of agents or agent applications that has different latency requirements and so typically for the real time extremely low latency requirement you cannot afford to think. So you have to basically react and usually the customization the go back goes back to customization right usually the customization process is very interesting also is you go small big and small for real time use cases. So the thing is when you customize a lot of time it's about your data is a data high quality keep you mind is kind of garbage in garbage and I'll say same fly here if your data is not high quality then your end model is not high quality so for you to test data quality and the fixed quality issue you start with the model to see if the model is going the right trajectory. So you're going not going to you go back to fix our data and then you know small model help you either it fast but that small model is not important the trajectory of the model quality changes important when you like to trajectory and then you move to the biggest model and usually those are biggest MOE model really hard to to get it running so obviously that's where we will definitely help you. And use the clean data to tune the model get the best special model to solve your real time problem but usually those largest model is not as fast right because they're very big that trillion trillion or parameters very big but very good in terms of quality and then you go small again but they still find the biggest model you have tuned into small model because you need to fast response. And and then you launch the small model model you can get to high quality of our process and can go from there so small big small right and now we move to offline asynchronous agents and usually those agents are doing some deep research in certain kind of domain whether it's about legal was about finance whether it's about software engineering or whether it's about something else right so. But usually takes time to think like us human being we're going to do some homework go hiding a cave figured out so similarly and those model requires the highest level of intelligence highest level so so then the kind of slow thinking mode becomes extremely important but not just that those those big models for offline research also requires a lot more context a lot more context to make the right like thinking process like get the right design of the flow and but at the same time lot of time those model also into get specialized in solving certain kind of problem really well the legal process and the finance process the process of working building PowerPoint and the process of building a executive overview of pitch to investors and the process of doing customer service those are all very very different so so we have also seen a lot of the application agent double or first they start to customize offline agent where that just means they need to figure out how to tune with the thinking tokens so so that's kind of a very different classes of occasion over simplified here. So I mean over simplifying but making it very easy to understand as you have with all of your responses in this episode I love particularly the small big small approach to finding the right model for use case and having that be performant in real time as you're discussing that something that came to mind for me is how one of the most difficult things with deploying such complex models that have stochastic outputs is evals So even like when you're doing that small big small like how do you ensure that when you go say from the big to the final distilled small model you're still getting the same kind of performance evels can be so difficult do you have thoughts on that or does fireworks have any tooling that helps out. So for evels we do not offer evil product we instead partner with our with the companies whose specialty is doing evals so here again we really respect specialty and the customization and the focus so you're right evels is where people get started right so but it's interesting if you talk to the startup or people building an native. Agents there are so many different way to do evels first and foremost people do vibe evolving. I feel a bit application the first thing you it's very different from soft soft soft soft soft development practices usually when we write piece of software we start from unit test first right so and they will build different kind of guard will to make sure the quality we have to do. We have guarantees on quality right for various different unit test to integration test to then testing production you kind of call some metrics and so on. But a gentied development people usually start with some hypothesis and just look at result and do vibe testing. It's very interesting because they don't they are they do not want to bound their creativity and imagination by forming small test to big test the test big ideas. So so then but those kind of vibe testing is really hard to to harden and the drive further model customization because it's very subjective and the then the question is how you turn vibe testing into something more conclusive. That model can understand or other tuning platform can understand. So so I think it is it is a very important area and where we have been we have been contributing is to solve another complexity. So today they are are various different tuning platforms and the various different evaluation platform as you know the if you are a boss are directly feeding into tuning to drive the tuning process without you basically don't have a guide. Hey this direction is correct not right so but there's so many different evil systems there are different tuning systems and we're part of the tuning systems. And there's no standard across them the integration. Of cross product complexity is very high so we open source a project called evil protocol. The idea here is to standard as a format so any evil system can talk with any tuning system so for our developers they if they can pick any combination as they want. But because of evil protocol it kind of bridge the gap across and they can they have optionalities on both sides. So that's our intention to help bring more commonality across these two sides because these two sides are deeply integrated. This episode is brought to you by Excel data the leader in a genetic data management. If you've ever wished your data pipelines could think diagnose and even fix themselves you'll want to check out studio lab Excel data is new fully interactive sandbox. For 15 days you get hands-on access to an AI powered environment where you can explore data reliability governance and automation with real agent workflows no setup no overhead just instant impact experience how AI can transform your data operations start your free studio lab trial at excel data.io/free trial that's ACC EL data.io/free hyphen trial we've got a link for you in the show notes. Cool so as you were talking there evil protocol from fireworks hadn't showed up actually in our research but I quickly found the GitHub repo for it so I'll be sure to include that GitHub repo in the show notes. A different offering of yours that did show up in our research with something called 3D fire optimizer and so this seems like it's in a way it's a way it goes over 100,000 possible ways of optimizing an LLM stack do you want to tell us about that. Yeah definitely so when it comes to customizing goes back to our mission of autonomous intelligence we're customizing across three dimensions across model quality model speed and the model efficiency which is cost. So when we started our journey people were sure hey they're really concerned about cost or hey they're really concerned about speed because they are very interactive or hey they're very concerned about quality. Wherever they start their pain points at the end they are concerned about all three dimensions there's no time we're like hey the cost is really good here we are like 10 times 10 times cheaper compare alternatives and they're like go for it they always like oh we also need the better quality to be to launch this. So we need to be faster to launch this is always always all three dimensions has to be much better. So we're like hey this is actually not a linear problem. It's a complex problem is with the expense exponential search space because the three dimensions are evolved but it's actually more than three dimensions if we decompose this problem then it becomes so many different building blocks to. To stack onto each other and each building block has no five to ten different options to pick and choose from so that's where in combination there are more than 100,000 options in this search space and becomes such problem again that's a new not a new problem in across our you know system research. For example databases databases has query optimizer where a data engineer or analyst write a SQL query and but the execution so SQL query is a semantic description of what they want to achieve. But in reality based on whether you have an index where the how the data is being laid out how data is sorted or how data is being partitioned there are different way to retrieve that data and process data in the most efficient manner. In the query optimizer basic converts is query at the same preserving the same semantic level of meaning and turn that into specific query plan customized towards the current state of database. And in query optimizer basically is the brain of a database engine and it's massive there has been massive innovation in kind of the first decade of this man and man and I was part of the research group building query optimizers. But this in in the space we are operating we are optimizing and customizing across quality speed and cost and in cost and that that is much more complicated and query optimizer which only optimize for for efficiency right. So so that's kind of what we build is to free up our customer those app developers from carrying the burden of learning how to do this three dimensional optimization and find the sweet spot best best spot among all these candidates the best suited for their requirements. So so yeah so that's kind of one of our innovations as we build our platform but it is directly fitting to our mission of autonomous intelligence very cool and love it that's the end of my technical questions for today but I suspect that we have a lot of listeners out there who having heard one amazing speaker and thought leader you are with all the capital that fireworks has raised with the impact that they're making the challenging problems that they're solving. I bet we have a lot of listeners that would love to work for you do you have any open roles and what do you look for people that you hire. Absolutely. The reason we raise your C last year is to massively accelerate our growth. So we're actually having a cross the board from all the way from the GTIM side we have a lot of openings from sales marketing as well as product engineering and in the finance business business operations across the board I really mean it because we we just have so much demand we cannot manage and we love and we love we love to work with very creative and high aptitude people who are willing to join us take a big chunk of ownership and drive a lot of impact. So yeah if you're interested I love to please send please contact us and send your application wonderful. Alright so now we're just in my last two questions that I ask every guest but I think I'm going to get an interesting answer based on our conversation before we started recording I always ask my guest for a book recommendation I think you have something else. Yeah so I think this is an interesting time of AI and everything has changed and how we learn how we learn has changed. In the past I've been reading books from mostly around I love to read books around business and when I know leadership and I really like to read books about so to kind of individuals I'm very curious about. But nowadays I listen to a lot of podcasts I think John your podcast will be very impactful as well. I particularly for mental reason is the following right so the this AI innovation is changing how how we do business how we create technology. All these agents that is emerging is going to change our day to day but at the same time fundamentally it's also changing how we learn how we learn what's happening in a fast moving world and particularly I think we we have a lot of people including my funding team including my top tier engineers they learn a lot from. For a lot of post we were always on the cutting edge we read a lot of paper and kind of the paper reading velocity is very high and paper generating velocity is very high but we read a lot of best practices from social media and there are a lot of creative ideas a different way of thinking approaching problems and we have to like you have to think differently to be very innovative in the AI time because a lot of fundamental. So fundamental assumptions has been disrupted for example we were rethinking our interview process and because now agent is code agents are very good at general code almost as a fresh graduate junior engineer. So then the question is hey is writing code quickly and correctly a important area to test or not right so we're question that because with a system of coding agent that's no longer problem and it's more problem to have the eyes and the mental framework to judge how good is the code. And have a strong way to steer a coding agent to design very well like coming from the source of design and system architecting to drive the implementation so it's start to shift kind of the focus in bottleneck of the software development just give you an example of recruiting in the hard process that has changed. So I will say the whole community is a book the whole community is writing a book of AI I think that's fascinating to me and that's kind of it's I just feel like I'm very lucky to live in this time of this world to fully embrace fast velocity of changes and so much there's so much to learn by each other. Cool. What an answer. Yeah certainly we've never had an answer like that before on the show but I love it you're you're really progressive thinker and I have personally learned tons from this episode I'm sure a lot of our listeners have as well. Lynn how can people follow you were like you know where on social media or how can people be getting your thoughts after this episode yeah I'm I'm learning also to transition my interaction more towards social media so you can follow me on a L Q I O my handler on tutor X AI X.com but also you can follow my LinkedIn I sure my thoughts I will do more in the future but also I talk about our product and the product launches and direction why I need to words. I love engagement so if you have for all the audience here you have thoughts feedback ideas I would love to talk with you to kind of exchange notes and go from there awesome thank you so much for making for opening up your inbox to our listeners Lynn really appreciate it and yeah that's the end of the episode thank you so much for joining us I can only imagine how crazy your schedule is and so take to take this time out and speak with me and share your thoughts with our audience we really appreciate it thank you Lynn glad to be here thanks y'all super episode today with the exceptional engineer and entrepreneur Lynn Chao in it she covered how over 90% of the world's data live in private enterprise systems and never make it into foundation models representing a massive on tapped source of intelligence how autonomous intelligence is about continuously and automatically customizing models with private enterprise data resulting in millions of specialized models rather than one AGI to rule the mall. She talked about her small big small approach which means starting with a small model to iterate on data quality moving to the largest model for best quality tuning then distilling back down to a small model for fast real time inference. How coding agents now perform at the level of junior engineers fundamentally changing what matters in technical interviews from writing code quickly to having the judgment to steer and evaluate AI generated code as always you can get all the show notes including the transcript for this episode the video recording any materials mentioned on the show the URLs for lens social media profiles as well as my own at super data science dot com slash 971 thanks to everyone on the super data science podcast team our podcast manager Sony Bravich media editor Mario Pombo partnerships manager Natalie Jaiski researchers surge mcees writer doctors R. K. Che in founder cure Larry Manko thanks to all of them for producing another fantastic episode for us today for enabling that super team to create this free super data science podcast for you we are deeply grateful to our sponsors you can support the show by checking out our sponsors links which are in the show notes and if you'd ever like to sponsor an episode you can get the details on how to do that by making your way to john chrome dot com slash podcast otherwise help us out by sharing this episode with anyone that would like to listen to it review the show on your favorite podcasting app or on YouTube subscribe but most importantly just keep on tuning in I'm so grateful to have you listening and I hope I can continue to make episodes you love for years and years to come until next time keep on rockin it out there looking forward to enjoying another round of the super data science podcast with you very soon