Shaan Bassi 0:00
You can have a relationship that's really strong. But if you're not able to self repair, it's ultimately a brittle relationship. You basically want to meet as many of them as you can. Because like you have like, as an entrepreneur, you have a worldview, you have a way in which you're like, I can change the future, very early on, adopt the mindset of like, this is this is a volume game, I need to meet lots of different people to find someone who shares my worldview 1000s of other applications that I had never thought of someone could think of and could do and could change the world with if we created that platform for everyone. Even if they love your product, you probably How often would you answer a survey for a company maybe once every two, three months? Maybe if you don't have people dying at the same rate that you have today? sort of somewhat balancing that through, you know, age that that presents a very interesting question around like, do we suddenly have to old control, you know, how many kids you can have if you can have kids maybe you can't keep extending your life if you're gonna have kids like walk, and.
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Max Matson 1:11
Everyone, welcome back to future of product. today. My guest is Shawn, bossy co founder at Cool. Shawn, would you mind introducing yourself to everyone just telling us a little bit about who you are?
Shaan Bassi 1:21
Yeah, and thanks for having me. I'm Max. I'm John. I'm the CEO and one of the cofounders of Kuo. I come originally from a biotech and neuroscience background from Imperial College London, I spent time there developing out a cutting edge emotion recognition platform. It was cool. It was fun at the time, I used to plug it into my Alexa to make literal mood lighting, plug it into my Spotify to have it tailor the music that it was recommending to me. But I couldn't think of like the commercial applications right away. I spent time. So I moved into working into a bunch of different health and wellness startups before I found my way into something called Venture building. I spend a bunch of time basically being an entrepreneur for hire as part of a collective of entrepreneurs called pre hype. And there I was building digital products over and over again. And I I saw some problems that were related to understanding how customers interact with products, how they feel. And that led me to feel good. So it's a little bit a little bit about me.
Max Matson 2:32
Perfect. Yeah, let's, let's get right into it. Let's talk about that that initial kind of build, right? What was what was it that motivated you to kind of put that together? And what was that process like?
Shaan Bassi 2:44
So I mean, I like with the venture building stuff, we've spent a lot of time building digital products for Fortune 500 companies, and whether we were doing things small scale or integrated fully into those companies, and in a much larger scale. I kept seeing two challenges. One was the when you're trying to understand why users behave as they do, you have a couple of different sets of tools. One is like your product analytics, where you can basically look at what users are doing. But then you have like a lot of questions about why users are doing those things that you might see someone drops off at a certain point, or they give your product a little review after they have some interactions, but it's hard to actually pinpoint where the problems are and what's driving those problems. And so we would spend a lot of time doing surveys, interviews, focus groups, trying to get people to explain to us why issues were occurring. But what I noticed was that, basically, every time we would do an interview, every member of the team would hear the same interview but have different interpretations. I think it's because they were frustrated here. I think this was confusing, I think, and all those different perspectives when we were building out many different hypotheses, which we had to build many different features for, in order to be able to test and only about 20% of the features we developed wherever used again.
So there was just a lot of time and energy spent chasing down hypotheses that what we were really unable to do is just understand how users were feeling as they're interacting with the product and what was driving what they were saying and what they were doing. So that insight was one of the major things that led me to build kua, which is literally analyzing people's emotional states as they interact with products so that we can understand their behavior better. And the second sort of thing was that almost every customer was expecting personalization from their digital products, but it was really hard to get good personalization going. I think what I realized was that the way we do personalization today is quite limited. It basically will go, Max, you listen to some content that Charles listened to, I'm going to recommend you all the content that Shawn listens to and hope that some of it sticks, right. But yesterday, maybe you were stressed, the content is very different to today, when you relax, you want to go to the gym, you need a very different type of interaction. Without that context, it's really hard to be consistently relevant and reliable. And again, that was underpinned by understanding how users feel as they interact with our products, so we can make more interactive and better products. And so that was, that was what led me to cool.
Max Matson 5:38
Perfect, no, that that makes a ton of sense. Taking a step back, though, when you were first building, you know, kind of the idea for COA before Kubo. Right, when you were using it to personalize your on Spotify recommendation, what was kind of the thought process there, right? Like, what, what led you to kind of think, hey, what if my, you know, my computer systems were able to actually recognize how I'm feeling.
Shaan Bassi 6:01
So I think for me, there was, is I started off a little bit before thinking about what if my technology could identify, it started off with me going, looking at my physical health, where I was getting loads of measures, you know, from how much weight I could live to how far I could run to all of these different things. I looked at my mental health and I was going, I basically have maybe a traffic light system, I'm good, I feel a bit tired, or I'm burnout, like, those are my like, you have a red Amber green type system. And I just found that it's very difficult to manage, because you tend to find that people go through burnout cycles, like, and I was going through that a lot where I was kind of, I was fine, I was working really hard. I burn out, it took me a little bit of time to realize it. And now I have to take a big rascal, you know, like, do a bunch of things to remediate that. Whereas you don't get that if you have some kind of like data that's able to go, Hey, you're like, you're getting close, let's, let's take the rest. And so my interest originally started around just being able to give myself the data. Then once you get the data, you realize you're a little bit lazy on it. And it'd be great if technology could just do things for you. And that was kind of how it led into me going, well, maybe I could help myself mellow out with like some music that was worth, like responding to me and all of those types of things.
Max Matson 7:27
Awesome. And it seems like wearables are the natural kind of answer there. Right? So what was kind of that initial process? Like? Did you kind of pick a specific kind of wearable and build out from there? Or were you like, I had the idea that this is going to apply to different types.
Shaan Bassi 7:43
We actually started off with not wearables, we started off with brain imaging data. Yeah, like, basically, there's a bunch of brain imaging technologies, where you can map out brain activity, and use that to identify different states that people are in. So we built out one of the big challenges is that you can't really do that outside of lab settings traditionally. And so we built out a advanced de noising process to be able to make that technology work from headphones whilst being portable. And whilst people were out and about. And that was the starting point. We use that to gather out large datasets of brain imaging and wearables data, and then train models that were able to work off of just wearables. And that was kind of to make it so that because we never wanted to be doing a hardware business, like hardware is challenging on many fronts, you'd have to like, from our implementation would have to convince people to give up their favorite Bose headphones, Sony headphones, whatever it is, to get these headphones that have an additional feature, it's very tricky. Audio quality is always going to be hard things like that. So we never really wanted to well, we at some point we did, but we very quickly moved away from that. And so yeah, like it was about how can we scale these insights with existing data and existing hardware? And that was what led us to wearables.
Max Matson 9:11
Gotcha. Yeah. I mean, it's, it's a cool sci fi premise, at least, right? Like the headphones that read your mind to some extent. But I would imagine that with wearables, it's a little bit easier to actually get into the market. Right. Got it. So can we talk a little bit about how you actually tangibly utilize, you know, AI to interpret these emotions? Like, what kind of data is actually being fed in and then how is that made sense of?
Shaan Bassi 9:38
Yeah, um, well, I'll ask you a counter question and to answer that. The last time you were stressed, can you think of some ways in which your body physiologically responded to that state?
Max Matson 9:50
Yeah, I think increased blood pressure, right. Probably increased heart rate to some extent.
Shaan Bassi 9:55
Yeah, those are two great ones. Some people often notice that they start a sweat little bit, that can be driven by increases in body temperature. You know, when you think about, like meditation instructors, they'll tell you to like breathe really deep and really slow to counteract stress, that's because your breathing gets more shallow, it gets more rapid. And these are the types of well, and some people feel things like butterflies in their stomach. There are all of these different ways in which our body responds to emotional states. And wearables are gathering lots of data points and all of that, from your heart rate to your heart rate variability to your blood, oxygenation to your breathing rate, etc. And we take taken all of these different data points, and feed them into our algorithms that use that data to identify emotional states. And we're looking at things like a lot of different mathematical abstractions, that data, you know, what's the, what's your baseline? How far from your baseline? Are you? How much variation is there in your signal? Like, what are the high and low frequency components of the signal streams that are coming in? And we take all of that? And use that to identify emotional states?
Max Matson 11:05
I see. Interesting. So how difficult is it? Because I know that you have a background in in bio, right? Yeah. How difficult is it to map kind of a physical manifestation of something like stress to it actually being that signal?
Shaan Bassi 11:23
It's relatively difficult. Like you need to have a way to generate good labels. We did that both through the brain imaging data, and through asking people a series of questions that would allow us to figure out their emotional states, because you can't ask people. So you end up with this difficult problem around like, what truth is when you're trying to tell your algorithm what to look for. And so that part was really difficult. Because basically, being able to gather the brain imaging data requires to build a whole bunch of sense tech that just didn't exist before. So yeah, like I it's pretty difficult to get that baseline of truth, you can't just be, you know, asking people all the time, all of these different questions to figure out how they feel. And so yeah, you know, we spent at least two, two and a half years, like building out the fundamental technology, and IP that allowed us to even be able to do this in the first place.
Max Matson 12:25
Very cool. Very cool. So let's talk a little bit about how co actually came together. Right? So you've got a couple of founders, co founders. How did you guys meet? What What was that process? Like? Was it just kind of immediate that you knew that these were the folks?
Shaan Bassi 12:42
Actually, we've known each other? Sorry, we've known each other for over a decade, we've been, like, we've been to college and university together. We live together, we've worked together, like it was a long time. And yeah, like, I kind of knew that these were guys who I knew how to work with and you had to fight with, I knew how to, you know, we knew how to disagree and like resolve those kinds of things in a way that works really well. And I think that like the way that I see, like, really strong, healthy relationships, which is very important when you have co founders is that it's not about how often you align it. I mean, it is kind of about how often you align and agree, but I think one of the most important things is how well you can handle disagreement and handle conflict. Because that basically, you can have a relationship that's really strong. But if you're not able to self repair, it's ultimately a brittle relationship. And so I think these were some of the people who I had the best foundations for, like, relationships that can take impact, and then like, resolve and heal themselves. So that was kind of how I, how we decided that we want it to work together.
Max Matson 13:56
That's very smart. Yeah, yeah. It's very easy to to be in alignment with somebody until you fundamentally disagree on something right.
Shaan Bassi 14:04
And you just never know when that that thing that you've never thought about before you've never discussed before. What if we apply it this way, someone's like, I will never like I'll never do that. That leads to like, really interesting disagreements. And if you don't have a good protocol, to like, fall back on and ways of communicating about disagreement. It just makes things feel fragile. Yeah. Despite however, however, often you might align.
Max Matson 14:33
Totally, no, that's great advice for any, any entrepreneur that's looking for co founders. So all that being said, taking a step forward. How did you guys get involved with TechStars?
Shaan Bassi 14:46
Um, so actually, we met one of the MDS, the managing directors of the London program, a guy called the AME on carry and we met him at a time I called Web Summit, which is a big tech conference. And basically, we just left thinking he was crazy smart. Like he very quickly got to like the challenges we were facing, you know, gave us some really insightful thoughts on how we could tackle those challenges. And I just remember thinking, I've never met someone who got to the point, so quickly. And around that time, we've been starting to think about how, you know, we would be wanting to sell into US companies. So we went, well, actually, you know, if the other like, managing directors are as smart as this guy, let's apply to one of the programs, you know, we want to be out in the States, let's apply to the LA program that was run by, that's run by a guy called Bob Meyers, who's just, again, he was insanely smart. And he had this almost superpower of within three questions, he would get to the heart of the challenge that we haven't solved yet. Like, every time I've gone, oh, we've made this great progress, etc, etc.
And he'd be three questions away from finding the thing that I was like, I have no idea how to solve it. And it's like, my main focus. And I just love that because you will always get straight to the point. And so yeah, we applied to his program. We narrowly missed it when we got to the final stages. But they didn't think that we were going to be able to get a project going with one of their corporate sponsors within the three months length of that program. But, you know, I guess, Bob, Bob, like does. And so he referred us on to the Austin program, run by a guy called Amos. Schwartz. And, yeah, like we we immediately clicked with the whole Austin textiles team. And that was that was kind of the journey. So we bounced around a little bit, a little bit of perseverance needed. And, yeah, honestly, TechStars was just incredible. And that's actually why I'm in San Fran, at the moment, I'm, like, we're here for something called founder comm where all the TechStars founders get together, yeah, get to like, share stories and meet investors and lots of different things.
Max Matson 17:13
Awesome. That's great. Let's talk a little bit more of that, about that. Right, like, as an individual, right, beyond just a founder, what is it? What is that process? Like going through kind of the Valley of, will they won't they with investors?
Shaan Bassi 17:28
You mean? Like, will they make it to raise investment? Or how do you mean,
Max Matson 17:33
more, so just when you're trying to court investment, when you're trying to, you know, create the scaffolding to take something to market, and it's your baby. And it's it's very real to you, but you've got to, you know, make that vision clear to others kind of what what are the tips or kind of, you know, things that you've learned along the way,
Shaan Bassi 17:50
I think one of the things that we learned was the, you want to think about just like how when you think about selling something, you don't think about finding just three or four people you want to sell to you kind of like, you want to always look at like volume, and how you can meet lots of different people, there are actually lots of investors out there. And you basically want to meet as many of them as you can. Because like you have, like, as an entrepreneur, you have a worldview, you have a way in which you're like, I can change the future. And like, this is going to change it for the better, you need to find those investors that are out there who see that same worldview, who like resonate with you.
And so, I think, you know, fundraising is a journey where you get far, far more nose than you get yeses. And I think that one of the things you can do is most like very early on, adopt the mindset of like, this is this is a volume game, I need to meet lots of different people to find someone who shares my worldview. Rather than trying to go like, you know, we've got this really great company, you know, I'm going to find investors like really quickly, with a small number of meetings that can make it feel much more personal when you get knows, it also means that like, you know, investors like to invest in things that are going to, clearly going to do well kind of with or without them. That's like the ultimate you know, if we're, if you're investing your money, you don't want to invest in a place where you're not sure if it's gonna like pan out. Which you kind of never know what startups but there's definitely a sense in which you're like, if these guys are going somewhere with or without me, I would much rather be on the journey. And it's much easier for that to be true. If you're meeting lots of investors and you're kind of Yeah, like kind of on that, like with or without any individual investor, you're gonna make it across that line.
Max Matson 19:45
Perfect. No great advice. Great advice. It's all about. I like that. You kind of mentioned the personal aspect, right? How do you kind of make sure that you when you're kind of doing these things that you're divorcing the poor There's no aspect from the kind of business aspect.
Shaan Bassi 20:03
I think it realistically it takes time for you to just you get a lot of like nose and a lot of people asking questions where you're like you haven't understood, like, what we're trying to do here, you're asking, you're like looking at the floor while I'm looking at the sky type thing. And I think that, you know, the thing is like, they're all smart people, they're just focused on different different types of innovation or different, you know, they don't quite see the work the future that you see. But yeah, I think basically, it's a process of you kind of feel that hike a bunch of times by getting those noes, and slowly over time, you start to divorce, like the personal wholeness, because in the beginning, it's very much like your baby. And also you kind of go, this is obvious, there's no way that this doesn't exist in the future. And so when people start telling you that it doesn't, in their view, that can feel quite that can that can Rocky, but it's just kind of one of those things that you just keep going through it. And as you encounter that more and more, you kind of kind of get used to it.
Max Matson 21:06
Yeah, yeah, totally. No, that makes a ton of sense. So let's talk a little bit more about emotional data kind of as a field, right. So as a field, it is relatively new, right? Our ability to actually measure these things has has kind of gone from zero to to 100. Really quickly. What kind of sparked your interest in the topic was it was it kind of going from these multiple multidisciplinary fields of you know, bio focus to Tech was that kind of what, you know, instigated that thinking for you.
Shaan Bassi 21:38
I think, you know, as I said, originally, I was like using the technology to enhance my everyday, I kind of actually wanted to go b2c Originally, but it was by speaking to one of our investors and advisors, a guy called Phil Green, who was one of the ex CFOs, of Amazon. And he was, basically, he helped us to see the bigger picture, he was like, you can try and make some content, that's a bit better. But you're going to this got this emotional component. And that can respond to how users are feeling more, you could be a platform that enables every interaction with technology to better and to have that layer of digital empathy. And I think that once we started to see that big picture, where we were like, oh, instead of trying to like make our own content, make that compete with everyone else's content you're in, you're essentially in like that kind of, you have a unique selling point around this ability to understand and interpret emotions.
But you're basically in a like content game, which is a very crowded space, being an enabler for anyone creating apps, content experiences, that suddenly sparked my interest much more, because that was originally kind of what I always saw as being so interesting, where you can create this human machine interface where technology is smarter, more intuitive, but right now, it lacks the ability to understand how, you know, one of the things that makes our interactions so great with each other, is the ability to understand each other states to understand how you can look confused, maybe I'm going to re explain that. He was excited. I'm going to like, you know, I can share that excitement with you. technology's always like that. And yeah, I think the sort of point that Phil pointed out to me was the just like, how I was enjoying plugging it into my Alexa enjoying plug it into my Spotify, there were 1000s of other applications that I had never thought of, that someone could think of and could do and could change the world with if we created that platform for everyone.
Max Matson 23:57
Interesting. Yeah. I mean, the implications on you know, retention, right are kind of obvious, I think. But you gave an example before we actually started recording that I thought was just super apt. Would you mind kind of reiterating that?
Shaan Bassi 24:13
Yeah, can you? Can you remind me
Max Matson 24:15
of? So I believe it was the person who who views your application daily, right. But every time that they view it,
Shaan Bassi 24:24
got it? Yes. So basically, I think one of the things that you find is that a lot of product teams today are working off of what we call behavioral data. So you know, your Google Analytics or Mixpanel, where you're trying to look at what people are doing, and understand how you need to improve your product and what you need to change. But one of the challenges that you have is that behavioral data is kind of one dimensional. And what I mean by that is you can have a person who opens your app or product every single day and Every day, they're getting more and more frustrated, they're getting closer and closer to churning. But from that behaviors, you're not actually able to pick up on that, you know, they open the app every day, they go in, they do the same like five minute session, and they leave. But they're actually getting more frustrated, you're missing that whole dimension of the experiential data. But you can also have users who are opening your product once a week, and they look like they're less engaged. But actually, it's the highlight of the week, they love every session, they're never gonna leave. Without having data on experience, as well as data on what they do. It's very hard to holistically understand why your users do what they do, and how to build and respond to them. And that's, you know, one of the big ways in which adding in emotional data just levels up how companies understand their users and build for them. Beyond then actually being able to respond to those states as well not just build for the audience, but make it personalized to those individuals.
Max Matson 25:58
Totally No, at such a good example, right? Because I can completely see even like in some of my own workflows, that if we're benchmarking on somebody coming in every day, right, we're actually taking the opposite learning from what we should be, which is that this person who's actually incredibly frustrated, and is probably going to churn, we should make sure that our product is tailored the exact way that it is to them because they love it so much. Right?
Shaan Bassi 26:20
Exactly, exactly. There's so many like false signals out that because like, essentially, when you just look at what people do, it's like trying to watch a play, but you're looking at the shadows of the actors, you're not watching them, you're not seeing their actual expressions and how they're interacting. And it just makes it a bit of a mess. It's a little bit blurry. So yeah, like I, when you add in the experiential side, it's like having a person in the room there who can just go, oh, actually, that's really interesting. They, they're really not enjoying this bit, doing this differently. Which when you think about it, that way, I always find that it feels a lot more obvious that you kind of want to have someone there. Like if you could, in an ideal world, you would have someone that understanding how people were interacting with your product all the time. So you could really just fix it for them every time. But you know, when we're doing like apps and things that scale, you can't do that. So the way to get around that is to actually have intelligence that's able to understand that at scale automatically. And that's, that's kind of what we're
Max Matson 27:26
totally. And that's kind of the the value of a focus group. But across your entire customer cohort.
Shaan Bassi 27:32
Exactly. Well, with focus groups, you're still limited to how like how well people can articulate themselves. We have recency bias, when we report things, we have intensity bias, you know, we'll remember the best and worst things and the things that happen at the end. That's kind of how our brain chops up memories. The problem with that is, you know, I could have a user journey with 30 different touch points. And you know, maybe a lot of my content is music content that's playing in the background of, say, an exercise class. The challenge that you have is that like, well, you know, how do I understand how much music is contributing towards the experience? We kind of know it is intuitively you know, like, if you did an exercise class without music, it's harder, feels less enjoyable, but like, how do I understand which songs are pushing you in the right or the wrong direction, but I don't have any way of understanding that I kind of know you turn to the end, but I probably played you 1000 tracks before you turn, like you have all these complex questions. When you're going, how do I curate content? What content do I give? When do I give it? That's also on top of you know, all of those things? Like, I think actually from their behavior, this is good, or this is bad, but I don't I don't have a good sense of it. So there are many different layers, I think to how having an understanding of your users experience quantified, really changes how you see them and how you understand them, moving them away from numbers that you looked at, to like people that are experiencing your product in a certain way.
Max Matson 29:02
Interesting. Okay, I see what you're saying. So it's, you know, we kind of went through this voice of customer revolution these last few years, I would say and, and it seems like your product and some others that I've seen that that do different things, but somewhat similar, trying to get to the actual core of that experience, are actually going a step further than that.
Shaan Bassi 29:21
Yeah, it's the difference between you know, there's what people do is what people say, but then there's how they think and feel. And even if people want to tell you the truth, they can struggle to like articulate it because of the way our brain chops up memories. But also, like, you have to be asking the right questions, it's really hard to figure out what questions you need to ask. Whereas if you just can measure my users who turn they have this emotional profile, my users are retained, they have this emotional profile. This is how every piece of content is pushing users towards or away from that experience is how every interaction we have is doing that. You don't need to know what questions to ask, you don't need to sort of take as many guesses, you're actually just able to understand how your interactions are influencing your user and how to build for them.
Max Matson 30:13
Interesting. I see. Yeah, I would imagine, you know, somebody who's on the cusp of turning is also not exactly. Super excited to fill out your survey. Right? Exactly.
Shaan Bassi 30:22
That's another great point. Like, you know, there are total limitations on how often you can ask people these questions. Even if they love your product, you probably, how often would you answer a survey for a company maybe once every two, three months? Maybe? And so how many interactions are you going to have with that product? They're just not going to get any insight on like, well, you know, like, you could go over three months, you can very quickly go from loving a product to hating it. Right? How do I pinpoint exactly what how that happened from, it's pretty rough, it's pretty difficult to like, get that layer of understanding is much easier, if you could just be understanding them all the way through every interaction they were having. And then you can start to separate out like, some people might exhibit some behaviors and churn because, you know, their financial situation changes dramatically, and they love your product, but they have to leave, you can at least discount that you can be like, Look, this is this person was having an experience where they were, like really loving what we were doing, they turned anyway, I probably can't actually fix that group of people, you know, I can't change the way the world works. But this is a whole other group of people who are having a really bad experience future, like this is something I want to like focus on this is you can separate out noise from signal much, much better. When you understand experiences,
Max Matson 31:43
makes a ton of sense. Makes a ton of sense. So all this being said, How are you actually Who are you actually be selling to I should say, who kind of is your ideal customer profile? And what do they look like? What are their needs.
Shaan Bassi 31:59
So we are selling to, like within? Basically, we sell to be to see Midmark and enterprise level content companies typically asked. That's your, you know, basically, wherever you're creating and curating an experience for your users. So you know, an example of a client that we would love to work with, is a company like Spotify, where you're curating music for your users, all of the time, music is incredibly emotive experience. And, you know, literally, like I said, you know, this started off with me in in my house, building playlists that were responding to my states, I always just thought that that was such a fun application. But it's those types of apps and companies. Where basically, your user experience is really important. And ideally, you'd love to personalize to your users in like a more impactful way.
Max Matson 33:02
Interesting, it's funny that you've already done kind of the proof of concept for them, Spotify, where yet
Shaan Bassil 33:11
if anyone's listening from Spotify, happy to chat anytime,
Max Matson 33:16
after game of Spotify. So let's talk about kind of the difference between let's let's keep using Spotify as an example. Right? So the way that they're curating music now, like you said, is kind of looking at, you're connected to this person, kind of that social networking problem that we all talk about in linear algebra, right? Yeah,
Shaan Bassi 33:34
I don't know if it even though I know that Spotify doesn't have it's not necessarily people you're connected to, it's basically looking at songs that you've listened to. And then taking an approach. It's kind of like collaborative filtering, where we go, Well, who else listens to all of these, this type of songs, we can create different profiles of people who are similar to you based on things that you've listened to across, you know, a period of time, and then doing recommendations based on you know, like taking so there's all the music you've listened to, there's people who've listened to those songs, and they've listened to other songs. So you kind of go well, hopefully, if you've had this big overlap, these other songs will also be relevant to you type thing.
Max Matson 34:16
Interesting. So if we were talking, you know, just wholesale, recreating that algorithm around emotional insights, what would that kind of look like? Would there would that be a layer on top that's saying now out of this bunch, here's the ones that align with this kind of emotional state?
Shaan Bassi 34:32
Yeah, well, it's things like being able to go who evil listen to that music, but actually, there's a bunch of music where one person listens to it to relax, the other person listens to it to get excited, the other person listens to it to like, you know, help them focus like these are all very different motivations. And without being able to understand your motivation for listening to it. When I build you your workout playlist. I'm going to chuck in a whole bunch of stuff for you like this is you know how you know sometimes you have Have those plays and you're just skipping three things be like, this isn't? This isn't? Like, it's because like, there's just this huge shifting context like you might even like, I often find that there are lots of songs that I'm skipping in a play. That's where I'm like, yeah, like I, I would listen to this, I do listen to this in different settings. It's not what I wanted. Right now in the gym. It's not what I wanted, the focus. You lack? Because you lack context, over when and why and how people are listening to it, or really like, what is motivating them to do that? You can't. You can't build for them currently.
Max Matson 35:37
Right on. Let's let's talk a little bit about data privacy, right? Because emotional data is kind of like we talked about this, this newer kind of data point that we're able to collect now, what are some of the precautions that you see as being important within the market? And how can people kind of rest assure that their data is being acted upon in a way that they would favor?
Shaan Bassi 36:00
Yeah, I think this is something that we think a lot about Akuo. You know, there's a bunch of types of companies that we don't work with never will work with things like gambling companies, etc. Anyone with addiction is involved, because you don't want to like play on people's emotions against them. But I think basically the way the the ways that well, already, you know, most wearables, companies are focused on being very like data first, in terms of keep putting our users at the heart of how their data can be used. So users always have to opt in to share data already. And you have to specify how you're going to use that data. And that's something that we think is very important. Because you know, if you aren't going to be taking that data and using it to build experiences that are curated to people, they should be something that they want. And so I think the really just one of the things that's really important here is making it clear how your data is going to be used to make a better experience for you and for everyone else. And making it clear, like what that trade off is. So you know, if it is your Spotify, like how you're getting better recommendations. If it's a mindfulness company, being like, well, we can now see when you're stressed and interact with you, when you need us most, you know, if it's there, we can see what when you're focused, what's helping you to learn faster. And we can give you more that content helps you learn those skills that you were trying to learn faster, if it's a game, like I can see when you're excited when you're enjoying it. And I can give you an experience, it's more immersive and Taylor's better to you. So it's that type of interaction that we can build in the that users want to share the data for, you know, like, if I can share some totally anonymized data on how I'm feeling. And then I can learn a language faster. That's, you know, if if that's something I wanted to do originally, that's helpful, you know, just better.
Max Matson 38:00
Yeah, absolutely. I, your entire answer is great. Kind of what you mentioned about like gambling companies, for instance, right. I think that's super prescient. Can I ask, do you, so I really appreciate close focus on not kind of exploring those types of opportunities. Do you see it as a foregone conclusion that that market still will have somebody serving it? Kind of without some type of regulation?
Shaan Bassi 38:28
I? Well, one thing that I think I've heard about the gambling industry is that they're already have like, polygraph type machines, they set up to people during, like testing phases of how they lay out like casinos and how they like do machines, like a lot of these things are already kind of tested. You know, it's just the difference is that it's kind of tested on a general setting rather than on a really personalized setting. Yeah, like, that's, like, I think it already is happening. I think it will continue to happen. But the difference is, it won't happen in a way that where they're actually able to sense how you're feeling. It's kind of done in a more generalized setting.
Max Matson 39:10
I see. Okay, gotcha, gotcha. They're still kind of doing that gamification, but more so as a Yeah, as in the build process. So well, kind of a non sequitur here. Once what's one piece of sci fi tech that you think will exist within your lifetime?
Shaan Bassi 39:30
This is something I talk about a bunch with my friends. Basically, there when I was studying Imperial, there's this paper that I read that stuck with me where they basically figured out how to reverse age mice. They took an old mouse and they stitched all of its blood vessels to a young mouse and they were basically sharing a circulatory system and the old mouse got, like it's muscle. Will Scott denser is like to be younger, it lived for I think, like 60% longer than a regular mouse. And I seeing that we figured it like, I think the conclusion was that there's some kind of Bloodborne agent that controls aging to some degree. And I was like, Well, if we know that now, by the time I'm 70, the speed at which we like, build technology, I would assume that reversing aging is going to be a thing that we can be. By the time I'm old, which is, which opens up all sorts of interesting, ethical and other conundrums, sustainability rights, resource wise, etc,
Max Matson 40:37
that modally I, not to go too far off track. But let's talk a little bit about some of those, right, because I think you're right, I've also seen the same study. It's very fascinating. I have seen, you know, on the far end, the early adopter kind of cohort in tech, right, people who are already using younger people's blood. But you know, that notwithstanding, let's talk about some of those kinds of consequences that you were you were mentioning earlier.
Shaan Bassi 41:05
I think that there, you know, if people start to be able to live much longer, or even potentially indefinitely, depending on how far you can take that. You have a lot of really interesting questions around, well, what happens to people's wealth, someone becomes a millionaire or a billionaire, they never have to work again, then you start to have this like, bigger, economic discrepancy. But then you also have things like, well, how can we all keep having kids at the same rate, if we can all live a much longer life because the world will become exponentially more crowded, you know, like, the world is already. Some people would say overpopulated. We already have, like, a population that's hard to sustain and, you know, causing a lot of different climate issues, etc. But if you don't have people dying at the same rate that you have today, sort of somewhat balancing that through, you know, age. The that presents a very interesting question around like, do we suddenly have to all control, you know, how many kids you can have, if you can have kids, maybe you can't keep extending your life if you're going to have kids like work? And that type of reasoning is just something that we haven't done before. You know, like, it's very difficult to to have those even to have those conversations, but to figure out what you can do in a way that's, that's ethical.
Max Matson 42:29
No, totally. Yeah. That's really interesting. I, I wonder about that, right? Because it does seem that we're on the intersection of a lot of these sci fi technologies kind of coming together. So, Shawn, thank you so much for joining me. This has been super fun. Where can people find you follow you?
Shaan Bassi 42:48
Follow me, follow me on LinkedIn or you know, check out WWE www.koco.io Perfect. Awesome. Thanks again, Shaan. Thanks for having me on max. Have a good day.