Inspired with Alexa von Tobel
00:18:11 · Daniel First
[00:18:11] S0: 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 I can solve them? But there's actually many different functions that want to use axi... Axion for lots of different workflows, and people increasingly wanna 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, that were... Is gonna be used actually for creating more of a ecosystem of products all around these product issues. I'll give you an example. You know, SharkNinja, they released a, you know, an ice cream machine. And, there were a lot of, initially, there were... It was received very, very well, but then there were a lot of issues back in 2023 where suddenly people were were complaining about it. And when 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 were talking about, you know, how to 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 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 in... And 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 across the entire product life cycle is gonna be a huge part of our product going forward.
00:19:45 · Alexa von Tobel, Daniel First, Cameron
[00:19:45] S2: 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. [00:19:55] S0: Exactly. Yeah. I think that that the really the next generation of manufacturing is gonna be much more customer centric. Today, engineering teams, they create hardware, they toss it over the fence. They actually then move on to to 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 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. [00:20:26] S2: That's awesome. [00:20:27] S3: So, Daniel, on the the product road map, 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... How... What what have you done intentionally to design a product that can work so extensively?
00:20:27 · Cameron, Daniel First
[00:20:27] S3: So, Daniel, on the the product road map, 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... How... What what have you done intentionally to design a product that can work so extensively? [00:20:49] S0: 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, you know, software edged 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, you know, ultimately, you know, the same the same insight and and, you know, algorithm that can flag, you know, an issue about, you know, a home appliance is the same thing that can flag that there are, you know, 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 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 and and, you know, have algorithms can flag when there's new emerging problems.
00:20:49 · Daniel First, Alexa von Tobel
[00:20:49] S0: 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, you know, software edged 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, you know, ultimately, you know, the same the same insight and and, you know, algorithm that can flag, you know, an issue about, you know, a home appliance is the same thing that can flag that there are, you know, 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 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 and and, you know, have algorithms can flag when there's new emerging problems. [00:21:48] S2: 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 wanna talk a little bit about the future and maybe we'll just start with, why don't you tell us from your perch as 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.
00:22:18 · Daniel First, Alexa von Tobel
[00:22:18] S0: I think that people really get wrong the way that AI is gonna impact manufacturing. I think that a lot of the discussion today is all about how AI is gonna be... 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 they're less looking to to copy lower cost regions at a lower cost, that... You know, 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 customers' needs and iterate your product in response to that. [00:23:33] S2: 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?
00:23:33 · Alexa von Tobel, Daniel First
[00:23:33] S2: 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? [00:23:44] S0: I think that, you know, right now, I think that, you know, many companies, they have, you know, portfolios with hundreds to tens of thousands of SKUs. I think that the public doesn't understand the degree to which most of these, you know, hard, manufacturers or consumer brands, because solving... 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 SKUs that are most important to that manufacturer. So imagine instead a world where every SKU that a manufacturer creates is constantly iterating and evolving in response to customer needs. I I think that that world is gonna look really different, that I think it's gonna be actually a much more empowering world for for people who are using products to solve their problems because you can give feedback and then see the next version that that things are solved.