Axion Founder Daniel First on Solving the $4 Trillion Manufacturing Problem with AI

We actually had a medical device manufacturer and the surgery equipment was malfunctioning during surgery. And I would read reports about people losing blood when these devices would malfunction. And there were actually two distinct root causes that the AI picked up. And so we were able to flag that. And I think that that was an example where we were very clearly impacted people's lives. So Axion is an observability platform that links together all these data sets and helps manufacturer understand what the issues are, why are they happening and how do we solve them? - Welcome to Inspired, I'm Alex von Topel. What if I told you there is a two to three trillion dollar opportunity hiding inside the 30 trillion dollar American manufacturing industry? Quality failures, recalls, medical devices that bail during surgery and planes with faulty parts. For decades, manufacturers have been blind to these issues until it is too late. And for Axion, an AI command center that helps manufacturers detect and solve product problems before they become disasters. And joined today by Daniel First, founder and CEO of Axion. Four years ago, inspired capital co-led Axion on Sea Drowns. And since then, we've watched Daniel build what is genuinely the future of American manufacturing. Today, companies like Boeing, Pratt and Whitney, Baxter, and others rely on Axion to catch catastrophic issues before they happen. In this conversation, you'll hear all about Daniel's journey building Axion, how AI is transforming manufacturing from playing defense to playing offense. And his vision for a future where every product learns and evolves based on real customer needs. Let's dive right in to this very ambitious way. If you're running a startup, we know things like managing equity and communicating with investors are always top of mind. Garda has built a connected suite of tools that over 40,000 companies used to do just that. Their platform of software and services helps you lay the groundwork so you can focus on building. To learn more, or get started, visit carda.com. And with that, let's welcome Daniel. Daniel, first of all, I'm so honored to have you here today. So excited to talk about your journey, everything that you're building at Axion and Cameron and I have been really fortunate to get to work with you. So I want to go back to the beginning. Let's go to your childhood. I like to ask one really simple question, which is if you go back to your childhood and look to where you are in the world today, you've accomplished so much in a short period of time, what from your childhood really stands out as something that was a real gift to helping you get here? Yeah, I think that my childhood really had absolutely nothing to do with computer science or technology. I think that I was brought up in a very religious, very intense orthodox Jewish community where we were debating medieval texts all day. But I do think that what it taught me how to do is really think for myself. And I think that thinking for yourself and forming your own views is really something that is essential to survive as an entrepreneur. So Daniel, you have an impressive and unconventional background from studying cognitive science and neuroscience during undergrad at Yale, then a master's in philosophy from Cambridge, and then also a master's in data science from Columbia. So can you walk us through that journey? Was there a through line that connected all of those experiences? And maybe to start, why don't you start with what they do interested in neuroscience initially? So when I was in my teens, I was really interested in these thinkers who would connect religion and philosophy and what at that time was science. And they would try to form ideas about the human mind. For example, my monodies, who is this 12th century thinker. And so I thought that if I studied neuroscience, maybe I could come to some sort of deep scientific understanding of human nature, about what human flourishing looks like, why we desire, what we desire. And in philosophy, there's a lot of philosophy ultimately is derivative from people's ideas about human nature. So that really is what drew me to neuroscience in the first place. By the way, Daniel, you and I never put that together. We had that in common. I also started studying mind-brain behavior out of such interest. So you did something that I kind of want to call out, because it's pretty fascinating. When you were at Yale, you founded the society for the interdisciplinary human flourishing, which is a mouthful. What does human flourishing mean in your mind? Maybe just start there. I became a little bit less religious in my late teens. And after I got deeper in neuroscience, I wanted to really think about-- I just felt like there were a lot of different ways that I could live life if I wasn't just going to blindly follow religion. And so I started looking, and a lot of my friends were studying these questions about what it meant to live a good life, what human happiness is. Really an entirely different discipline. So I had one friend who was a philosophy major, and he was studying virtue ethics. And then I had another friend who was an economist. She was studying Bhutan and how they used gross national happiness instead of GDP to measure success of their economy. That's what I wrote my senior thesis on. No worry. Yeah, we never connected the dots. Sorry, keep going. No, interesting. I want to hear more about that. But yeah, and so I had other friends, obviously, who were studying psychology. And so it seemed odd to me that all these disciplines were being thought about completely independently. And so I made a salon series, dinner series, we would bring different people together and say, hey, if you're studying the economics about human flourishing, obviously, you'd be interested in talking to people in philosophy or psychology thinking about the same thing. I just, it really stands out as such a young person for you to be thinking so profoundly about human flourishing and sort of society as a whole. Really struck me. And so thanks for sharing that with us. I want to fast forward to kind of your early career. What this do would happen after college? After college, I got pretty dissolutioned with neuroscience. And I first studied philosophy for a couple of years because I saw that neuroscience would try to come to these big conclusions about the human mind. By doing, for example, I was working in a cocaine addiction lab where they were studying cocaine addicts and what was going in their mind when they had an FMRI. But I saw that when these professors who were doing these things would try to make conclusions about the human mind from that, they're often where a lot of philosophical assumptions embedded in it about things like what is our true self? What do we truly want? So I studied philosophy for a couple of years. And when I was there, I just saw this pattern keep on happening that whenever there would be a scientific breakthrough, whether it's a neuroscience, whether it's Darwinism, whether it's the furnace, people then just try to explain everything through the lens of that scientific breakthrough. People thought the mind was a furnace, people thought the love could be explained by Darwin. And so I was always kind of interested in what would be the next, what would be the next mania. And then I attended, really on a whim, this talk that McKinsey gave in 2016 when I was a grad student at Columbia and I was studying philosophy of science at the time. And it was all about big data and how big data is going to solve all problems. And McKinsey was going to be at the forefront. And I thought, oh gosh, this is really going to be, this is going to be the next mania. So speaking of the next mania, AI has been around for a while. But in 2022, with the rise of generative AI, that's when it truly went mainstream. And so in 2016, you were already deep in the space doing graduate work and machine learning. How did you end up focusing on AI? Really, after I attended that talk, I thought that what was obviously going to happen over the next couple of years was that everybody would start making every decision, using big data and eventually AI. So I started writing philosophy papers about whether AI was going to be the next religion. But soon I realized that if you actually don't have any experience building these models in the wild, your opinions are detached from the actual reality of what's happening. So I actually went and I joined McKinsey to try to see how these Fortune 500 enterprises were actually building AI. And so that's what made me interested in the first place. I want to transition to you starting Axion. Obviously, we met you years ago. Where was the idea? What was the aha moment? And in plain English for everybody, let people know, what is Axion? In plain English, Axion really, it's a command center that American manufacturers use across aerospace, medical devices, consumer electronics. Really all to try to understand what are the top issues that are impacting their customers. So you can imagine a company like Boeing's trying to understand why are planes having issues in the hands of airlines, medical device manufacturers like MedTronic are trying to get ahead of recalls. And companies like Shark Ninja will release slushy machines and try to understand what issues are customers facing so they can also make the next version even more compelling. It turns out that there's just tons and tons of data that comes from IoT and telematics, from other types of customer feedback, from technician investigations. And so Axion is an observability platform that links together all these data sets and helps the different groups at the manufacturer understand what the issues are, think issue 360. What are the top issues impacting customers? Why are they happening and how do we solve them? - I want to ask a really simple question 'cause it's fascinating. But so right now, you work with some of the biggest names in manufacturing, Boeing, Baxter, New World, Pratt and Whitney, I won't name them all. In clean English, what are you doing for them? You're an observability later of tons of data set. But practically, what are you doing? - We'll go to a manufacturer and they're often solving a lot of issues impacting their customers. And our challenge to them is we bet that there's tons of issues driving billions of dollars of cost often that you don't even know about. And so where this all started is that we were working with a Japanese OEM a couple of years ago. And initially, the idea for Axion was slightly different. But what we basically did a prototype and found and discovered that they had tons of issues costing them contributing to their billions of dollars of warranty cost that they were just totally unaware of. So that's really what the platform does it today. A lot of the customers, they used it to try to understand what are the issues impacting our customers and how do we solve them so we can stop the bleeding of bad customer experience and warranty cost. Tell us a bit more about the problem of quality. How big of a problem is this to manufacturers? Why are they so focused on it? - So I think that there's a couple different lenses about why manufacturers are so focused on this today. So number one, you have this very obvious reason which is that many manufacturers are just getting totally debilitated by things like recalls or warranty cost, a company like Ford might have $5 billion of warranty cost every year. So I think that's well recognized. You also have a lot of these booming industries like data centers where uptime is really critical. And you need uptime from power systems, from HVAC. And when you don't have that, that's obviously a huge problem. I think that there's a much deeper point though about why quality matters so much to manufacturing right now. Especially in America, a lot of American manufacturing, a lot of leaders of American manufacturers are trying to position themselves against more commodity markets and commodity manufacturers by positioning their products as high quality. And you can't do that if you don't understand what issues your customers are facing. - Daniel, let's talk a little bit more about recalls. I mean, I think when I first met you and you talked a little bit about just how massive of a problem recalls are for US American manufacturing companies. We'll just start there. Give us a sense of the scale of the problem. - If manufacturing is 30 trillion of revenue every year, quality probably costs anywhere from two to four trillion depending on what you count. I think recalls are the most pernicious and most, why they recognize example of a quality issue. But the reality is that when you release any product, there's actually usually hundreds to thousands of death by a thousand cuts, quality issues that customers experience every time. So I think that that's really what makes us such a big problem is customers, their experience of products is really that they buy version five and they have a lot of issues and then they buy version six and all those same issues are still there. - Are you in a position where let's say you have three of the biggest companies on axions platform where you find an issue with company one and you can actually now alert and get ahead of an issue with company two into the there's true network effects. Talk about that. - We never share private data from one manufacturer to another where I think that this can happen is very often a lot of these OEMs, the systems aggregators and manufacturers who assemble all the parts, they might find an issue that gets localized to a supplier and then that supplier might be supplying that same part to many other manufacturers or there might be lots of other models that it's included in. So that can often happen. - So Daniel, when we first met back in 2022, you had just signed your first contract and fast forwarding to today, you now serve many of the largest manufacturers in the world. And Alex and I and our team have had a front row to your growth, but in your words, what has surprised you about the journey so far as you've scaled? What has changed and what have you learned? - I think that one of the biggest things that's been surprising to us is that when we started off, we thought quality was really for one department for the quality department. And then it was really about defense. You release a product, you're suddenly attacked with all this warranty cost and you're trying to defend it away. But at Harley Davidson, for example, within the first couple of months of us working with him, they had 12 different departments on the platform. Everybody from marketing to CX, to supply chain, to the software engineering team and product strategy. And when we picked at it, it turned out that people were using quality to learn more about what their customers wanted. And the quality issue is just a situation where a customer's needs are mismatched with what your product is. And so they were actually using this to inform not just the messaging, not just the CX of the existing product, but actually to inform the next generation of products. So I think that was a very big shift that we saw. - What else has surprised you? - Just like being a CEO, building this journey, what else has been harder than you expected? And then what's been easier than you expected? - I think definitely what's been easier than expected is I think that when I started out, everybody said that there's no way that manufacturers would ever buy software. And I think that actually people are pretty rational. And we're in an exciting time that people are willing to try out technologies and if they actually work, then they're pretty happy to scale it. I think that something that I didn't realize would be so difficult is that I think that Axion were actually a very different type of company from many other startups that have been created over the last 20 years. Axion really is about going from data to detecting a signal in the data, to trend, to insight, to root cause, down to action. And how you can do that at scale as these companies will solve tens of thousands of problems every year on their products. And I think that there actually aren't a lot of companies that are trying to drive automation in that loop. And I think it's required us to build our organization in a very different way than most organizations are built. - I wanted an example or a story. Can you think of a story that stands out when you saw firsthand just how effective and impactful Axion has been for your customers? - We actually had a medical device manufacturer that had surgery equipment. And the surgery equipment was malfunctioning during surgery. And I would actually read reports about people losing lots of blood when these devices would malfunction. And the manufacturer, they'd actually thought that they had solved the problem, but there were actually two distinct root causes that the AI picked up. And as a result, people were still being impacted by this. And I think that was an example where we were very clearly impacted people's lives. - By the way, that's incredible. What like an amazing practical example of AI playing offense. That's wonderful. - Daniel, you mentioned just now how your organization looks different based on what you've learned. Can you tell us a bit about what that means and then what the organization looks like? - Yeah, I think that it looks different in a lot of ways from traditional enterprise SaaS startup. So one of the ways is that, you know, again, we're trying to automate, you know, if a company has to solve 10,000 problems every year and maybe currently they're solving only 1,000 of them, we're trying to automate going from data to trend to, you know, issue to root cause to insight, to insight to root cause to action. So to do this, we actually have many, many aerospace engineers, medical device engineers that both work at Axion and obviously at our clients. But what we'll do is we'll map out how these people think, how they think about scoping an issue. They'll actually go and, you know, their work behind the scenes on the platform and try to scope issues or root cause them. And then our product team, they look at what steps these engineers are taking when they're root causing an issue. And then one by one, we automate the least complex, most manual steps of those, of that problem solving. But I actually see very few companies that are actually built that way. But it's a key part about how we're built. - Talk about the future. What do you see ahead of Axion for the next 24 months? What are you excited about right now and how are you thinking about investing those dollars? - One of the big ways that our customers have been pulling us is that, you know, today, a lot of the way that the platform is being used is, you know, after products are released, how do I learn about the issues as rapidly as possible so that I can solve them? But there's actually many different functions that want to use Axion for lots of different workflows. And people increasingly want to pull the insights that are coming out of the platform earlier and earlier into the product development life cycle. And so a lot of the funding that, you know, is going to be used actually for creating more of a ecosystem of products all around these product issues. I'll give you an example. You know, Shark Ninja, they released a, you know, an ice cream machine. And there were a lot of, initially there was, it was received very, very well. But then there were a lot of issues back in 2023 where suddenly people were complaining about it. And when they looked into it, it turned out that one of the biggest use cases for an at home ice cream machine was protein ice cream. So fitness influencers were talking about, you know, how to make high protein ice cream. But the machine wasn't made for that. It was really, it was really, you know, not made to kind of take very, very thick protein powder. So on the one hand, that's a quality issue with the existing model. But on the other hand, that obviously can inform the next generation of innovation. And so the product strategy team can then use that insight and they actually made, you know, a creamier machine that's actually dedicated for protein. So I think that really building out an ecosystem that can be used by across the entire product life cycle is gonna be a huge part of our product going forward. - So now you can play more offense into feedback from the products, give those back to the design teams on the manufacturing side and continue to fill that loop. - Exactly, yeah. I think that really the next generation of manufacturing is gonna be much more customer-centric. Today, engineering teams, they create hardware, they tossed it over the fence. They actually then move on to designing the next generation of products because historically, they didn't get a ton of rich information about customers' needs from the field. But I think that in the next couple of years, as AI can use this information to make sense of it, what we're gonna see is that overall product development is just gonna become a lot more customer-centric and about solving customer issues. - It's awesome. So Daniel, on the product roadmap, one thing that's always been fascinating about your business is that you can solve, to your point, product quality issues in an ice cream machine as well as identify issues in a jet engine. Can you talk about just those are so different? How do you, what have you done intentionally to design a product that can work so extensively? - I think that we think of all these different industries as different, but that's like our own human category. Ultimately, in all industries, you release software-rich products into the field, you get fault codes, IoT and telematics, technicians inspect them. It's actually the same data schema across all of these different industries. And so I think something that was really smart that we did was early on building our data schema in a way that it would scale across all these different industries. But ultimately, the same insight and algorithm that can flag an issue about a home appliance is the same thing that can flag that there are miners trying to use a power system that's too noisy and so they put a blanket over it, and now it's overheating. I mean, it's all the same thing. You kind of have to put together IoT and telematics with what technicians are seeing, with what's happening in the factory, and have algorithms can flag when there's new emerging problems. Daniel, as somebody who loves America and is extremely excited about the manufacturing boom that I think is very much happening here in America, I want to talk a little bit about the future and maybe we'll just start with, why don't you tell us from your purchase somebody who is really at the forefront of how AI will intersect with the future of American manufacturing? What are your predictions? And I want the weirdest and the wildest. I think that people really get wrong the way that AI is going to impact manufacturing. I think that a lot of the discussion today is all about how AI is going to bring robotics and automation, and that's true, but that all really is about reducing costs. And I think that that's not actually how, you know, the CEOs that I talk with across motorcycles, medical devices, or consumer electronics, none of them are looking to compete that way. The way that they're less looking to copy lower cost regions at a lower cost, that's not really how they're looking to compete. I think that the future of American manufacturing is one where people are, they think about empathy with their customers at scale, and understanding customer needs that nobody else understands. And I think that AI is enabling us to have much more rapid feedback loops between customers experiencing issues and iteration, where I think that it used to be people would try to be first to market. I think what actually is going to define American manufacturing going forward is the speed at which you can learn about your customer's needs and iterate your product and response to that. - That sounds amazing. What else can you see? What do you feel like you can see maybe speeding up, happening more frequently? And also what problems do you think go away entirely? - I think that right now, I think that many companies they have portfolios with hundreds to tens of thousands of skews, I think that the public doesn't understand the degree to which most of these hard manufacturers or consumer brands, because learning about a problem and solving it is so arduous and time-consuming, usually the humans who are working on this are only solving problems on the top 10% of the skews that are most important to that manufacturer. So imagine instead a world where every skew that a manufacturer creates is constantly iterating and evolving in response to customer needs. I think that that world is going to look really different, that I think it's going to be actually a much more empowering world for people who are using products to solve their problems, because you can give feedback and then see the next version that things are solved. That's so interesting, what you're basically saying is right now, if you're a big manufacturing company, your all of your brain effort focus goes to the top 10%, wherever the biggest headache, pain point, et cetera. And because of the proactive nature of what Axion's doing for your customers, they're actually going to have a lot more surface area. So sorry, it's the plunge kind of that, just like far better products for all of us in every skew on every sort of on every dimension. - Yeah, absolutely. I think it's going to be much better for customers. I think innovation is going to happen much faster. I think also that the humans who work at these manufacturers instead of spending all day reading line by line through data, AI is going to take care of all that. AI agents are going to be learning about customer needs, proposing iterations to products. And I think it's going to be much more exciting to work at a manufacturer, which today it is for some people, but it may not be for others, because they have to spend all day slaving away at the data. I think in the future, humans will be presented with complex decisions about customer needs. And I think they're going to decide how to react to that. I think it's going to be much more exciting future to work at a manufacturer. - I'm pumped about when my stroller no longer breaks, and I have to deal with trying to figure out how to get a wheel replaced. That sounds like a beautiful future for people here. - So Daniel, there's studies out there that show that the vast majority of enterprise AI pilots fail, yet Axion has been quite successful in converting real contracts in industries that have historically been very slow to adopt technology. What do you think people are missing or misunderstanding most about AI? - Yeah, how did you tell us more? Like what did you get right there? - I joined McKinsey in 2016. And it's really funny reading this study that came out this year about the 95% of AI pilots feeling. I mean, I didn't need the paper. I watched myself and my friends at companies like C3 or BCG Gamma that were building all these enterprise AI pilots. We watched them fail in real time. And it was very easy to make a flashy demo, but then when it came to actually bringing things to production, it often wouldn't make it. And it was really soul crushing, bringing all these really brilliant colleagues to enterprises having this big flashy project and then seeing time after time it didn't make it to production. The projects that failed most consistently were ones that I think of as cross-functional intelligence workflows, which is a bit of a mouthful. But basically people would hire McKinsey or Quantum Black pretty much always with the same problem formulation. It's a very unique one. It's hey, we have a lot of different groups with different data across the enterprise. Everybody, if they could just link up all their data sets into this omniscient pool, then suddenly everybody would be able to do their work much better. So I worked with a professional sports team that had 26 different data sets. They're trying to make decisions about who to play and game strategy and injury. Could we link up the 26 data sets? I worked with a clinical trials study. Can we link up all the data about clinical trial audits? Ultimately, the reason that these projects were failing it wasn't because the AI didn't work. It was because there were these very big gaps between the prototypes that were created and what you actually needed. So there was a knowledge gap, which is that when you showed the results of the machine learning to experts, they would say my expert knowledge is not incorporated. There was a process gap, which is that people had very legacy processes. And so there was no way for them to really fit the AI into a new process. And number three, you might have AI models, but humans would need to dig through and find insights and some do need to take action. So Axion was really born to close these three gaps and do it in a way that scaled as a product. So really just seeing all these enterprise AI projects failed to make its production is really what gave me the chip on my shoulder to found the company. - I do love that you sort of describe your own chip on your shoulder with very much seen kind of your ambition and hustle and it's very real. Where do you think Axion's future intersects with robotics? Just trying to like get a sense of, you're at the forefront of manufacturing, I very clearly understand that you're going to, I say, literally trillions of dollars, you're gonna save injuries and problems. So that's just an incredible human benefit. You're gonna be able to speed up these response times and cycle times of catching problems to allow people to now spend significantly more time and building better products across most skews. So that's awesome. Where does that intersect with robotics in your mind? - People get very excited about new technologies, whether it's electric vehicles or robotics or software rich strollers, which are coming. And actually the more innovative the technology, the more quality issues there are and that's why you see recalls happening about electric vehicles all the time the news. So I think that robots already and will continue to have tons of quality issues because of how difficult hardware, software, interaction is an engineering and how new a lot of the technology is. And I think that the faster that we can learn about what those issues are, the faster we're able to build the next generation of the robot that can overcome it. So I think that robots will be an increasing, we have some customers who are using Axion for robots, but I think as that market grows, we'll become more and more of a focus. - What else do you worry about these days? - Like what can you see that maybe we should just be more aware of? - I think that right now there's a tremendous amount of investment in data centers and in the entire supply chain all with the expectation that this is eventually going to show up in GDP. But if the enterprise AI pilots keep failing and we don't learn from that and figure out how to do enterprise AI in a way that actually works that makes it to production, then you could see that the investment may not lead to as much of the GDP lift off that people expect. And that could lead to a big pullback in investment in data centers and associated things in the supply chain. So I'm quite hopeful that we don't just keep on trying over and over again to chuck AI models over the fence and hope that they stick. - You're currently one of the founders to kind of deepest in what I'm going to call incumbent corporate America, right? These huge massive companies that have been around for in some cases even hundreds of years. What from the inside out, how do they think about AI? Meaning how forward leaning are they? Are you know, you've been wildly successful and getting very quick adoption, which has been so impressive, but give us a sense from what you hear and see given your vantage point of the last three years. Leaders at American Enterprises on the one hand, they all feel like they need to be using AI. On the other hand, I think that the secret that they maybe don't say out loud is that they're worried that their organization may not be ready for AI or that the pilots are gonna fail and it's gonna cause people to lose trust for innovation. And the reality is that that's true. I mean, many, many American Enterprises, even if we show them what the issues are, they might not even have healthy processes in place to go act on those issues. So in order for the AI to have impact, you really need to have healthy processes for it to be embedded into. And so, one of the views that I have is that, I think that process redesign so that you can take an enterprise and enable it to design processes that AI agents can work well and is gonna be a hugely important skill over the next couple of years that I think is very, very underappreciated. But I think that's the anxiety that a lot of leaders have. - Related to that, Daniel, you, can you talk a bit about what you're seeing broadly with American manufacturing and just all of the tailwinds currently? - Oh, I think there's a bunch of tailwinds here. I mean, you think that are related to some of this. I think, you know, number one, you know, there's obviously this huge build out of data centers, but that this is really causing many other related, you know, industries to spike, whether that's HVAC, you know, energy electrical components. And I think that what we're seeing is that all of these manufacturers are being asked to scale up, build and deliver more innovative products than ever before. But as a result, what we're seeing is, you know, tons of product failures and tons of shortages, which can also be because things are being scrapped on the line. And so I think that that's something that's underlying a lot of the, you know, a lot of the focus on customer quality. I think another big shift is obviously that tons of these companies have been shifting where their manufacturing is happening, what supplier they're using. And whenever you have new suppliers or you have new suppliers that have never worked with each other before, what you end up with is that often their parts don't integrate. It's very hard to figure out why. So even though there's a lot of trends that are happening that are making customer quality more top of mind than ever. Daniel, I want to shift a little bit to your personal sort of leadership philosophy. You've been running a company for five plus years, you've learned a lot. How have you thought about keeping the team moving at the exceptional pace that you are and balancing speed and innovation, sort of culturally speaking, how are you doing that so well? We run a lot of experiments. So very often we'll look out at the next six months and we'll say, here are some of the things that we don't know. We don't know whether this use case is going to have an ROI. We don't know if this other idea that we have is really going to work across industries or it would only be used in one industry. And so we really just ruthlessly prioritize what experiments we run and just keep a very quick eye to kill the experiment that don't end up working, but to really invest in the ones that really end up flourishing. And so I think just continuing to have an experimental mindset, continuing to move the company at hundreds of miles an hour and then keep with you the people who want to move at that pace. I think these are some of the really critical things that we've learned to continue the pace of innovation. So speed is very much, I know, top of mind for you. How do you keep the team going faster? What if, and again, when you've accomplished in such a short period of time, he's been really special, how do you have a culture around speed and teach all of us? What are you doing? - We also try to be responsive to our customer's issues. And we measure how responsive we are when there's new needs that our customers have, how fast is it until that's incorporated in our product? And I think that my own experiences, initially going to market four years ago with something that manufacturers, maybe at a high level we're interested in, but didn't want. I think that that really is what taught me that it's less about who's first to market. And it's more about how fast can you learn about what your customers want, what your customers are trying to do? And how do you always improve the velocity with which you're incorporating that into your product? - I love that. I always say CEOs are engines, and it's probably good analogy for you to be an engine, given what you do. And I think you set the pace, you set the tone. You know, I always love the companies that think in hours, not weeks, especially when you're at the forefront of a category that is evolving so quickly. Any other big lessons that you don't want to share with us? - You know, a big lesson that I just had to learn as a founder is that there's a tremendous amount of advice out there that was really relevant in the 2010s, but it's really no longer relevant in the age of AI. And I think that there's such an industry around producing YouTube videos with advice for founders, but you know, and that's great. But ultimately, you have to be able to think from first principles and decide what advice applies to you. And I think that building a company that's dealing with a messy, hairy problem, like how do you go from emerging issues at airplanes and medical devices and figure out how to manufacture that intelligence at scale? A lot of advice that somebody used to make a database in 2010 is just not relevant to us. - That is so well said, which is, you know, sitting care as a founder who helped build companies in 2007 and in 2010, there's some things that are very similar and there's other elements of, I mean, we're in a break neck pace of innovation that in some ways is so profound and so fast that it blurs in what's possible. And I actually think in some ways, we underestimate what's possible. The bigger risk is actually, of course, it'll be bumps, but I think sometimes we all underestimate what's really happening. And I think, I agree with you. I think that's really well said, which is like, this is a very different age to be building a company and in some ways, some things are much easier. In other cases, things are much harder because you're trying to run a marathon while literally sprinting every millisecond of it. But I also think that's what makes us such an exciting moment in time. When you think about maybe the companies that you're paying attention to, I don't mean like your competitors, I mean, the pace setters, who are those in your mind? - In different industries, there are companies that are, you know, taking the latest in AI and LLMs, but then applying them, you know, in the wild to specific verticals or sets of verticals, you know, you have, of course, in law, you have a company like Harvey in healthcare, you have a company like a bridge. And I think that, you know, obviously in CX and like, you know, responding to L1s, you have the decogons and CRs of the world. I think that ultimately, you know, all of this becomes a bit of a cohort of those of us who are trying to figure out, you know, how do you take all these advances in agents and AI and LLMs and then really figure out really complex enterprise workflows and how we can, you know, apply these emerging technologies as rapidly as possible in a way that, you know, because of our focus becomes really specialized. - I agree with that. I want to move to the quick fire round. If Cameron doesn't have anything else that he's burning to ask, Daniel, again, it's been such a pleasure to get to know you on a personal level, what's one habit that you've formed since you started Axion that has really served you well that you want to pay forward to other people? - I have a longstanding habit that I think you're really unique, but it started way before I started Axion. I've actually been on airplane mode since I was 17. I think I've got the last sacred space for deep thought out here. You know, I just, for me, when I have my phone on and it's just buzzing all the time, I just can't think about anything in any deep way. So it's a habit that's always been with me for the last, you know, 15, 20 years. - Wait, same more about that. Your phone's on airplane mode from when to when? - Pretty much always. So I'll check it at like very specific points in the day. You know, sometimes I'll allow like important calls to go through, but I think that this way that a lot of people live that their just phone is just constantly buzzing with notifications, it just doesn't work for me. - Wait, do you know, this is a really profound thing. I just need to like roll this back. So since 2017, your phone has been in-- - Since I was 17. - Sorry, you owe me that. Even, since you were 17, your phone has been in airplane mode and you very thoughtfully decide to check in. Say more, when like how many check-ins a day, morning and night, just give us a quick sense of how the, by the way, I'm gonna do this immediately. The camera's gonna be like, I can never reach Alexa. - God damn you, Daniel. But how many, how many times a day do you think you actually sort of purposefully check in? - I would say, you know, maybe, maybe like, you know, I mean, you get a lot of what you need through the computer. I think, you know, maybe every, you know, every hour or two, I might kind of take out my phone and kind of check what I've got on WhatsApp. But I think where you actually see the biggest difference is, you know, when I'm just like, you know, walking around or traveling, I mean, I really use that time to think and just get deep in my thoughts. And I think that it's just something that is being lost today. I think a lot of ideas that I have about things we should do differently as a company, they come really when I'm, you know, walking around or just, you know, just taking walks around Williamsburg. So yeah, I think it's a sacred space you've got to get back. Now I'm just wondering if Alexa and I are on the allowed collars list, I know. (laughing) - I hope so. Daniel, last few questions. What is a book that's really had a massive impact on your life that you would pay it for? It doesn't have to be a business book, any book. But a book that's like, how do you really lasting impact on how you think? - Yeah, there's this book by Charles Taylor, the Ethics of Authenticity. He's a Hegel philosopher, but it's all about the history of how we make decisions and how, you know, in some time periods, people would consult their religious mentor. In other time periods, people would look deep inside of them. And but the framework that we use to make decisions has kind of changed every couple of decades. And so whenever I'm making a decision, it just made me very thoughtful about what framework I'm using to make that decision and think about whether it's the right one. I love that. My last question is, is there a mantra you live by? Having gotten to know you pretty well, you move at a speed, you're extremely hard-charging, you have some of the highest bars for excellence of founders out there. What is a mantra that kind of speaks to you? - At Axiom, we talk a lot about high agency customer obsession, which is that the customer is that we work with, they have tons of needs. And sometimes the product, as it is today, can meet those needs. But sometimes they can't. And the people who we've brought on that I'm really proud of that really thrive, they're folks who, you know, when there's a gap between what the product can do and what our customers needs are, they're first instinct is to figure out what creative thing can I do that won't cause a lot of tech debt, that can really meet my customers needs. So I think that's a huge part of our culture that I'm really proud of. - Well, Daniel, first of all, this has been such a pleasure. It has been so fun to think about kind of your front row seat to the way things are being made here and in the future. We are so lucky to be on your journey. And I think just most importantly, we're really rooting for you because when Axiom wins, what happens is the world gets safer, the world gets better innovation and better products, which make all of us much happier. Lives are saved. So just again, it's a great company to get out of bed every day to go build and we're rooting for you. Thank you. - Thanks, it's been a great discussion. - Thank you all so much for tuning in today. If you enjoyed the episode, please re-review, subscribe. And we'll be back again with another episode of Inspire in the Luncheon Table. Thank you. [BLANK_AUDIO]