The Exchanges

Q kind of iterating and testing and, and designing the product, uh, is, is often a sticky point For founders of hardware products and most hardware founders like manufacturing as the biggest pain point. Um, and so, and so actually Mamoon at Social, who's, uh, Chamath's partner, obviously, uh, wants to know how was the experience for you and how do you approach iteration and testing within product development for Athos?

A Yeah. So for what we're doing, there is a ton of different challenges. One of the things that's like very unique to us is that no one has ever integrated technology. Into clothing the way we've done it. Um, there was no manufacturing process when we started doing it. We work with one of the best manufacturers in the world. They call it MAS based in Sri Lanka, and they do work for Nike, Lululemon. Even at a facility like that, there was just no way of manufacturing this technology and how to do it. So we spent a lot of time actually developing the manufacturing process, the tools that go into it, how to, how to integrate technology and apparel in a seamless way that can be Mass produce. And then developing testing standards, and how to create testing, and how to do, how to do washability testing, because, you know, standard ways of doing these things weren't good enough, or weren't able to cover all the things that we need to cover with our product. When we think about designing and iterating on these things, like, speed is very crucial, because otherwise, you kind of lose your ability to iterate. We optimize a lot of the The details of how we manufacture, we, we fly to different places a lot. Um, we understand, and we work really collaboratively with all of our manufacturing partners to really optimize our development cycles to be very fast and very iterative, um, while we keep…

AI assessment note: “we spent a lot of time actually developing the manufacturing process, the tools that go”

Q kind of iterating and testing and, and designing the product, uh, is, is often a sticky point For founders of hardware products and most hardware founders like manufacturing as the biggest pain point. Um, and so, and so actually Mamoon at Social, who's, uh, Chamath's partner, obviously, uh, wants to know how was the experience for you and how do you approach iteration and testing within product development for Athos?

A Yeah. So for what we're doing, there is a ton of different challenges. One of the things that's like very unique to us is that no one has ever integrated technology. Into clothing the way we've done it. Um, there was no manufacturing process when we started doing it. We work with one of the best manufacturers in the world. They call it MAS based in Sri Lanka, and they do work for Nike, Lululemon. Even at a facility like that, there was just no way of manufacturing this technology and how to do it. So we spent a lot of time actually developing the manufacturing process, the tools that go into it, how to, how to integrate technology and apparel in a seamless way that can be Mass produce. And then developing testing standards, and how to create testing, and how to do, how to do washability testing, because, you know, standard ways of doing these things weren't good enough, or weren't able to cover all the things that we need to cover with our product. When we think about designing and iterating on these things, like, speed is very crucial, because otherwise, you kind of lose your ability to iterate. We optimize a lot of the The details of how we manufacture, we, we fly to different places a lot. Um, we understand, and we work really collaboratively with all of our manufacturing partners to really optimize our development cycles to be very fast and very iterative, um, while we keep…

AI assessment note: “optimize our development cycles to be very fast and very iterative”

Q Now, I'd like to get started by, by hearing about you and the founding story of how you started Athos from university and, and turning down a job at Apple, and so what's the founding story for you and the aha moment?

A Yeah. So it's been an incredible journey. Um, just thinking about it from the history of things. The reason we started was because Chris, my co-founder and I went to the gym a lot and we wanted to find a way to get more of the time we spent at the gym, um, how to get better. Are we doing things right? Are we doing the right things for ourselves? There was really no way of doing that. The technology out there wasn't really helping us be able to get better and we couldn't afford a personal trainer. So we wanted to make something that could give us that experience where some, like something could tell us how to do things right, how to get, get better, how to get more of that time we spend at the gym. We started looking around, uh, to figure out a way to capture information that could tell us all these things to make that possible. We kind of started looking at like, what are the existing ways people were doing this accelerometers, gyroscopes. They weren't really very good at understanding exactly what's going on. And for us, it was really important to be able to give feedback that actually we could take away and do something immediately. Be meaningful and actually affect how we worked out. There was nothing that was accurate enough or gave us valid enough data that we would come to valid conclusions that would result in us being able to do, provide this information. So we started …

AI assessment note: “The reason we started was because Chris, my co-founder and I went to the gym”

Q want to discuss the alternatives available to you in terms of business models. So, so how do you approach the business model conundrum and are you entering into the hardware enabled services where you have a similar form to Fitbit with the paid product? Upfront and then the optionality for a premium software to accompany it. Is that the business model that you're going to optimize for in the future?

A Honestly, I think everything we do is, um, iteration. Let's figure out like what are all the different things we can try and let's figure out what are the best things to be, uh, for us to try and take an approach and like test and iterate on those things. Um, and I think business model is also one of those things. There are so many different, um, opportunities available, um, With the product we're doing, there is, there are almost three different aspects. There is the apparel itself. There's the hardware components, as well as the software. And there are very different combinations of ways you can think about generating revenue on that. We're, we're really just focused on like, right now it's like, let's sell, let's go through a very traditional model where the clothing and the hardware you purchase, the physical product and the software is completely free. But over time, based on how people are using it on what value they're getting and how they generate value, We'll optimize to, um, get the pricing model in a way that as many athletes as possible can be using our product, and at the same time, everybody can be successful.

AI assessment note: “right now it's like, let's sell, let's go through a very traditional model”

Q speaking of fast iteration there, I actually had, uh, Ben Einstein, a Bolt, a hardware investor on the show the other day, and he said that speed's great for software companies. But it will cripple hard by companies if not managed with caution. To what extent do you agree with this statement? I'm, I'm intrigued. Obviously you said that about rapid product iteration. So what do you think of this?

A I think the key point in there is that it's very important to manage it properly, which is making sure you capture the right metrics and you do enough testing around things. Definitely things that we've learned over time. You need to test everything and keep structured logs about like what, what's going on, what's breaking, what are you iterating, What's the next version? Why are you making that? But I think it's also very important to iterate really fast. And like the speed is actually, uh, developing a manufacturing and a hardware development cycle that is very rapid and making the right trade-offs to make that fast is actually a crucial, um, advantage. I think a good hardware company needs to have because you need to develop things. Like there's a lot of things that, um, help software companies By name, because they are able to iterate fast. If you can take all the good things about being able to iterate fast, which means that you can test and try things out and get feedback and make things better without waiting at three year cycle or one year cycle to make something new and try it out. You actually have a higher probability of being successful that you have to do to fail a bunch of times before you can get it right. The faster you can fail and the faster you can learn from those failures, the better, um, the faster you can make an amazing product.

AI assessment note: “I think the key point in there is that it's very important to manage it properly”

Q Now, I'd like to get started by, by hearing about you and the founding story of how you started Athos from university and, and turning down a job at Apple, and so what's the founding story for you and the aha moment?

A Yeah. So it's been an incredible journey. Um, just thinking about it from the history of things. The reason we started was because Chris, my co-founder and I went to the gym a lot and we wanted to find a way to get more of the time we spent at the gym, um, how to get better. Are we doing things right? Are we doing the right things for ourselves? There was really no way of doing that. The technology out there wasn't really helping us be able to get better and we couldn't afford a personal trainer. So we wanted to make something that could give us that experience where some, like something could tell us how to do things right, how to get, get better, how to get more of that time we spend at the gym. We started looking around, uh, to figure out a way to capture information that could tell us all these things to make that possible. We kind of started looking at like, what are the existing ways people were doing this accelerometers, gyroscopes. They weren't really very good at understanding exactly what's going on. And for us, it was really important to be able to give feedback that actually we could take away and do something immediately. Be meaningful and actually affect how we worked out. There was nothing that was accurate enough or gave us valid enough data that we would come to valid conclusions that would result in us being able to do, provide this information. So we started …

AI assessment note: “we came across the science of electromyography”

Q want to discuss the alternatives available to you in terms of business models. So, so how do you approach the business model conundrum and are you entering into the hardware enabled services where you have a similar form to Fitbit with the paid product? Upfront and then the optionality for a premium software to accompany it. Is that the business model that you're going to optimize for in the future?

A Honestly, I think everything we do is, um, iteration. Let's figure out like what are all the different things we can try and let's figure out what are the best things to be, uh, for us to try and take an approach and like test and iterate on those things. Um, and I think business model is also one of those things. There are so many different, um, opportunities available, um, With the product we're doing, there is, there are almost three different aspects. There is the apparel itself. There's the hardware components, as well as the software. And there are very different combinations of ways you can think about generating revenue on that. We're, we're really just focused on like, right now it's like, let's sell, let's go through a very traditional model where the clothing and the hardware you purchase, the physical product and the software is completely free. But over time, based on how people are using it on what value they're getting and how they generate value, We'll optimize to, um, get the pricing model in a way that as many athletes as possible can be using our product, and at the same time, everybody can be successful.

AI assessment note: “right now it's like, let's go through a very traditional model”

Q speaking of fast iteration there, I actually had, uh, Ben Einstein, a Bolt, a hardware investor on the show the other day, and he said that speed's great for software companies. But it will cripple hard by companies if not managed with caution. To what extent do you agree with this statement? I'm, I'm intrigued. Obviously you said that about rapid product iteration. So what do you think of this?

A I think the key point in there is that it's very important to manage it properly, which is making sure you capture the right metrics and you do enough testing around things. Definitely things that we've learned over time. You need to test everything and keep structured logs about like what, what's going on, what's breaking, what are you iterating, What's the next version? Why are you making that? But I think it's also very important to iterate really fast. And like the speed is actually, uh, developing a manufacturing and a hardware development cycle that is very rapid and making the right trade-offs to make that fast is actually a crucial, um, advantage. I think a good hardware company needs to have because you need to develop things. Like there's a lot of things that, um, help software companies By name, because they are able to iterate fast. If you can take all the good things about being able to iterate fast, which means that you can test and try things out and get feedback and make things better without waiting at three year cycle or one year cycle to make something new and try it out. You actually have a higher probability of being successful that you have to do to fail a bunch of times before you can get it right. The faster you can fail and the faster you can learn from those failures, the better, um, the faster you can make an amazing product.

AI assessment note: “I think the key point in there is that it's very important to manage it properly”

Q And I have another question from an investor on similarly on that kind of vibe in terms of user testing. And I'm intrigued to hear, and so is Frederick Court at Felix, as to, to your biggest learnings from watching users and consumers, uh, really play and, and consume Athos as a product. So what's those learnings been?

A Yeah, I mean, there's, there's just been a ton of things from, from, I think one of the earliest ones is that it's very important to disambigrate what you want. For the product versus what the users want. Because I think one of the things that we definitely made the mistake of earlier was to think about what, what are the worst is that what, what do we want from the product and just build that versus really listening to and looking through how our customers using it and then really addressing those needs and making the product really work for those customers. And then I think the other one is just simpler is always better. Like make it as simple and as targeted as possible to deliver the value they're expecting.

AI assessment note: “one of the earliest ones is that it's very important to disambigrate what you want”

Q And I have another question from an investor on similarly on that kind of vibe in terms of user testing. And I'm intrigued to hear, and so is Frederick Court at Felix, as to, to your biggest learnings from watching users and consumers, uh, really play and, and consume Athos as a product. So what's those learnings been?

A Yeah, I mean, there's, there's just been a ton of things from, from, I think one of the earliest ones is that it's very important to disambigrate what you want. For the product versus what the users want. Because I think one of the things that we definitely made the mistake of earlier was to think about what, what are the worst is that what, what do we want from the product and just build that versus really listening to and looking through how our customers using it and then really addressing those needs and making the product really work for those customers. And then I think the other one is just simpler is always better. Like make it as simple and as targeted as possible to deliver the value they're expecting.

AI assessment note: “one of the earliest ones is that it's very important to disambigrate what you want”

Q And what do you think you did well at them? And what would you like to improve on for the next funding round?

A Um, looking back through the history of things, I, I, I always feel like, wow, I can't believe somebody funded on that because we've gotten so much better over time. Um, so I think for me, it's like always, let's make sure we're getting much better at demonstrating real progress. For us, I think it's, it's just been how far we've come on the technology, the customer validation. Those things have been, um, really useful, and like for the next round, I want to make sure that we, we articulate that and then capture the, the vision and where that, that really expands to and how massive an opportunity this is, uh, better and better as we get better and better at understanding and discovering and validating that pathway.

AI assessment note: “for the next round, I want to make sure that we, we articulate that”

Q So what do you do then to build that brand in this space with the kind of rise of millennials and, and potentially their lack of consumer loyalty?

A Um, I think lack of consumer loyalty is sort of a hard work to say. I think it like, if, if, if there's a lot of undifferentiated products that have no reasoning for brand loyalty, um, Then it's completely understandable when the switching cost is low. But at the same time, if you build a product experience that delivers value and you trust, I think that's where the, the most value of a brand comes in, where you can capture somebody's trust, at which point I think, especially with things like health and fitness, you want to be able to trust the thing that's giving you advice on your health and fitness to be, to be associated with the brand that trusts you. And then that, that is what kind of retains you in there because You're not going to go to a system that you don't have trust in, um, just because it's lower quality or, sorry, lower price.

AI assessment note: “if you build a product experience that delivers value and you trust”

Q And what do you think you did well at them? And what would you like to improve on for the next funding round?

A Um, looking back through the history of things, I, I, I always feel like, wow, I can't believe somebody funded on that because we've gotten so much better over time. Um, so I think for me, it's like always, let's make sure we're getting much better at demonstrating real progress. For us, I think it's, it's just been how far we've come on the technology, the customer validation. Those things have been, um, really useful, and like for the next round, I want to make sure that we, we articulate that and then capture the, the vision and where that, that really expands to and how massive an opportunity this is, uh, better and better as we get better and better at understanding and discovering and validating that pathway.

AI assessment note: “and like for the next round, I want to make sure that we, we articulate”

Q So what do you do then to build that brand in this space with the kind of rise of millennials and, and potentially their lack of consumer loyalty?

A Um, I think lack of consumer loyalty is sort of a hard work to say. I think it like, if, if, if there's a lot of undifferentiated products that have no reasoning for brand loyalty, um, Then it's completely understandable when the switching cost is low. But at the same time, if you build a product experience that delivers value and you trust, I think that's where the, the most value of a brand comes in, where you can capture somebody's trust, at which point I think, especially with things like health and fitness, you want to be able to trust the thing that's giving you advice on your health and fitness to be, to be associated with the brand that trusts you. And then that, that is what kind of retains you in there because You're not going to go to a system that you don't have trust in, um, just because it's lower quality or, sorry, lower price.

AI assessment note: “if you build a product experience that delivers value and you trust”