Kyriakos Eleftheriou (00:00):
One of the things I always see for myself is I go to the gym. Whenever I’m listening to specific types of songs, I’m a metal guy, so every time I hear a metal song, my heart rate is increasing drastically. So my activity and my performance is much better. So at the moment that the consumer apps are going to use that information, for example, if my Spotify is using heart rate, they could play the right song at the right moment. If my Netflix is using my stress levels, my HRV, they can play the right movie at the right time. So it’s those two categories I guess is how do you move from reaction to prediction, and then how do you enable all the consumer apps out there to be using the insights so much better.
Ben Grynol (00:58):
I’m Ben Grynol, part of the early startup team here at Levels. We’re building tech that helps people to understand their metabolic health, and this is your front row seat to everything we do. This is a whole new level.
Growing up on the island of Cyprus, Kyriakos Eleftheriou was very much surrounded by a different food system than we have in North America. The things were fresh, the things were very much bought that day, often from smaller markets. They were real, unprocessed whole foods, things like vegetables, things like fresh fish and meat. When Kyriakos came to the US, when he went over to London, things started to change. Things evolved in even some of the things that he saw. A lot of packaged foods, things were fast, things were convenient, things were not always fresh. Food was very much a part of the culture growing up in Cyprus, and it’s something that Kyriakos thinks about. And so fast forward to when Kyriakos was a young adult, he ended up spending time in the army, and there he got exposed to wearables. It was his first foray into this world of seeing data feedback loops into his own biometrics.
So he had this, he thought, hey, what would it look like if you actually were able to integrate some of these biometrics, some of this data, this feedback that you get into one centralized hub? Fast forward many years later, well, he ended up coming to the US, specifically to San Francisco. There he was part of Y Combinator’s 2021 Winter Batch where he founded a company called Terra. Terra aggregates all this data, the biometric data that you get from different wearables, things like WHOOP, things like Aura, things like Eight Sleep, and it’s all integrated into one place so that you can see what happens when you do X and how does it relate to Y when you start to think about this feedback that we get from different systems.
And so following his path into YC, Kyriakos hit the streets, raised some capital, and he’s still very much a big part of this movement in the wearable space. We had a great conversation. It’s one of the member stories. And here’s where Kyriakos tells his story.
Kyriakos Eleftheriou (03:21):
So when you come from a small place, you get to see things happening in a very different way, meaning that you don’t see what’s happening in London or New York and all of that. I grew up in Cyprus, and in Cyprus, first of all, it’s a very small island. And if I speak more specifically about food that you mentioned, it’s a celebration, and it’s all about quality and quantity. So most of the people are eating a lot of food, and wherever you go, all the celebrations that you do, you meet people. And it’s all about, let’s have a meal together, let’s sit down, let’s start working, let’s start speaking. And it’s a lot about the community, a lot about having a lot of friends, and a lot about being happy at the moment. And I was growing in such a place that is a very small place. And it’s indeed very, very different than what you expect from tech hubs. I would say it’s exactly the opposite from a tech hub.
While growing up I had to go to the military, and at that point I chose to go to the Special Forces, which is basically the Mountain Commandos. And once I went to the special forces, what happened is that they just start very, very rigorously to throw you into what I call chaos. And for a few weeks you get a lot of intense activity, a lot of intense requirements from your body, from yourself, from your mindset and all that. It was just so difficult to make it in that environment. So the special forces was very, very intense, and it’s what actually got me into the space of nutrition, what got me into the space of health, what got me to this world in the first place.
When I was there, those polar heart rate monitors, the chest wraps, those Nike bands that they had back in the day, which was measuring your steps, you are wearing that sensor in your shoe. Those are the first two things I bought when I was in the special forces. One, to measure how many runs we are doing every day and the distance, and then I was using the heart rate monitor to see my calories that I burned, and then I was trying to educate myself all the time on how can I eat more properly so I can make it to the next day, to the next activity, to the next performance event that we had in the military.
I came to school to study in the UK, but because of my special forces background, I got obsessed with health in general. So I was that person. I was trying to get into body building competitions for a while, and then into power lifting competitions. So I was that person that was scheduling every single meal every single day. So I had my six meals every day. I was going to the university, I was cooking every night all of my meals, I was preparing all of my meals, I was going to the university. I started studying and I was eating the meals one by one, and then I was going to the gym. So it was a hundred percent optimized in eating in order to be able to achieve the goal of body building at the day. So it was very different from what I was used to in Cyprus, in a way that I had a big goal of achieving that physique, if you’d like, that better body composition. So I changed so much the way I was eating altogether.
I came with the idea of what I wanted to do as a startu,p and I started speaking with investors in the community in London, and I realized it would be really, really difficult to actually raise funds and start doing the right things for the business. So what got me to get into San Francisco is basically me understanding that if I want to build a business that’s going to influence people on scale, it needs to be in the location that most people are doing something similar. So I spent a couple of months here in London and I realized honestly that it will be impossible to raise any funding in London. So then I said, what are my best options? Let’s just head to San Francisco. Because you are listening to all those rumors back in the day. So I grabbed my stuff and went to SF just because of that roughly four years ago.
The first time I went to San Francisco, I learned a few things. I learned that the investment community is very different and I also learned that the founders are very different, and I also learned about Y Combinator, which I didn’t know from before. So I came back to London and I started building what Terra is today. I started speaking again with investors here in London. I realized it would be impossible to raise from them. And then while speaking with my co-founder, then we are thinking, well, we don’t have any other options. It’s either we go to San Francisco and we get into Y Combinator, or it’s just going to be very difficult to raise funds and it’s just going to be very difficult to build what Terra is today. So we started doing all that preparation. YC has a lot of videos online, they have a lot of content, so we took it as if it was our last option.
So we’re literally doing a preparation. We had to apply with an application and then we are preparing on a daily basis on the interview style and all of that. Then we got the interview and did the interview with Jared and team from Y Combinator. There was a lot of back and forth there, and then we made it into YC for the Winter ’21 Batch.
So I guess there was a lot of preparation, and it was basically our last option of getting an opportunity from an investor there. But this is where it really started because in Y Combinator, once we got into YC, we had some other investors coming into their own [inaudible 00:10:32] catalyst and this really propelled us to become very relevant in the San Francisco community, which really, really enabled us to start building what we have today.
That point I got the polar heart rate monitor and then I got my Nike and then I started buying more and more wearables, I bought a Withings scale and then I got a Garmin watch. I could see all the time the same issue. It’s basically, yes, I have all these wearables. Yes, I’m measuring all of my data. But then it’s just impossible to access that data within other apps. So for example, if I want to connect my Levels account to my Withings scale, or if I want to connect my Levels accounts to my Garmin activities, impossible. So I had that idea in mind all the time. And then I was speaking with my co-founder who’s a swimmer as well. He had a similar problem. And then we came down and said, well, we can either build something that aggregates that data, but we can also build something that enables everybody else to access that information. That enables every app in the world to access that information. And that’s how we came down to this.
What we are seeing at Terra all the times is you get more and more hardware, which hardware is measuring a lot of information that we haven’t accessed before. For example, you’re going to get a sensor from [inaudible 00:12:14] Levels or the folks at Dexcom that is measuring your glucose 24/7. You are going to get, there is a startup called Vena Vitals. They are measuring your blood pressure 24/7. You are getting, there are companies that are measuring your sweat analytics, for example. So we’re going to have more and more biomarkers over time from a hardware perspective, which is very, very interesting. We get to see our bodies more and more and understand 24/7 what’s going on, which is something we didn’t have before.
To correlate this, is if you go back 10 years, you’re going to see that, or even today you go to a doctor, they do a blood analysis, that blood analysis shows you a static sample, shows you a static result and a static representation of your glucose, static representation of your testosterone, and all that. So these sensors, because they get to the point that they’re measuring 24/7 information, then what you can do with that information, it’s much, much more useful. Which gets me to my second point, which is software.
So now we go to the point that we have more sensors, more devices in the market. Now, the next question is how do we use software to predict what is going to happen, or give specific recommendations and educate the population regarding certain things. So for example, what I really like is Levels is creating content and it’s educating people how they should be in sync. So getting the hardware data and educating exactly the consumers on how they should eat to optimize their glucose, optimize their nutrition, and performing their day. There are other startups that are using software such as the folks at [inaudible 00:14:16], for example. They reward you based on your activities. So they’re going to take your wearable data, reward you based on your wearable data, and that’s interesting too. Then you have folks like Eight Sleep. So they’re taking your wearable information and they optimize the temperature of the mattress based on your HRV, based on your deep sleep levels. Which gets to the next point, which is closing the loop between hardware, software, and recommendations.
What I’m really excited to see is where the consumer tech industry is going to go, and also where the medical space is going to go. So if I speak about the medical space first, what’s the problem for me for all those years is you go to a doctor after a certain event. So someone had a disease, they go to the doctor and then doctor needs to basically react to some sort of disease. When you have all this information, you can detect patterns before they happen. So if you are looking at let’s just say a thousand people, and you’re looking at them all the times, and you’re looking at their heart rate for 20 years time and you detect a certain difference, certain variation in the pattern, you can predict if a certain event is going to happen.
So for example, if we can see the heart rate of a lot of people and we can detect and predict before an event is going to happen, that would enable us to give the recommendation to these individuals, you know what, if you continue doing what you are doing, if you continue being inactive, and if you’re not training in A, B and C ways, then you are going to statistically have a heart attack in 10 years time.
So one is exactly this point, which is moving from reaction to prediction. There are many solutions being built on top of this logic, and I’m very optimistic about it. And then the second point to touch is the consumer space. One of the things I always see for myself is I go to the gym, whenever I’m listening to specific types of songs, I’m a metal guy, so every time I hear a metal song, my heart rate is increasing drastically. So my activity and my performance is much better. So at the moment that the consumer apps are going to use that information, so for example, if my Spotify is using my heart rate, they could play the right song at the right moment. If my Netflix is using my stress levels, my HRV, they can play the right movie at the right time.
So it’s those two categories, I guess. How do you move from reaction to prediction, and then how do you enable all the consumer apps out there to be using the insights so much better? I got one of those sensors to my dad, and I remembered how impressed he was by looking at the information himself. Because yes, I was informed for, yes, I was learning about my nutrition and all that, but for someone that is a bit older, when they see that information, it’s even more surprising.
At the time when I first used Levels, I learned more and more about nutrition and I was making plans for everyone. So everybody that was about to go to the gym, I was sitting down looking at their schedule and I was making programs to them, their nutritional recommendations and all that in how they need to train, how they need to be doing their activities, how they need to be thinking about their days. And once you see the data themselves, and once someone sees the data themselves, they cannot argue with you. So you can use it as a mechanism of a forcing function if you’d like. And you help a bit older people in a way that you see exactly what this is doing to your body. You are very informed about what this is doing. So it’s all about you actually being very careful about your nutrition.
If someone wants to avoid having type two diabetes, versus someone that wants to compete in a body building event or versus someone that wants to compete in a power lifting event, the recommendations are very, very different. So I would just start from that. What is your goal really? What do you want to achieve? If you are an athlete, let’s say, one of the things that really work is aggregating your carbohydrate intake around your workouts. And one of the things that really works as well is making sure that your intake of sugars in the morning is very minimal. Your intake of sugars around your workouts, as I mentioned, is maximal. And then in the night, it depends how you want to sleep, and adding the carbohydrates based on how you want to sleep.
Now, if someone is considering longevity, for example, it’s much different than this. So someone that wants to live healthier and prolong their lifespan, I guess a lot of fasting works better, a lot of even reducing much more the carbohydrate intake works, and being very, very careful about the amount of proteins that you’re having at what times as well. But it’s all about the goal. If there is a case that you are an athlete, for example, and you’re doing a nutrition that is very low in carbohydrates, for some people is not good. So you have to be considering what do they want to achieve, and based on what they want to achieve you cater that.
When I was optimizing my meals, I was optimizing to the gram, every single gram. So to give you an example, when I was trying to grow a lot of muscles, grow up in weight and all that, I was optimizing all the meals and I was taking zero sugar, for example. I was taking carbohydrates at the very, very specific moments in the day, which was around my workout. And then throughout the day I was waking up, it was steak and salad, for example. And then my second meal would be eggs with something that has protein and something that has fats. Before my workout, I would surround it with a lot of carbohydrates, a post workout as well, which is going to be surrounded by carbohydrates and protein. And then my dinner would have something that did not have carbohydrates, which would be, again, something with fats and proteins.
By doing that for a number of years, when you go out of it, the carbohydrate increases much, much bigger. Because if you optimize your diet for so prolonged amounts of times, what happens is that your body becomes much more sensitive. So after a few years that I stopped being so excessive with my nutrition and I started thinking about longevity as well and other things, then I started seeing that if I was not careful with pasta, for example, and I have pasta during lunch, it’s just impossible to work for the whole day. So now the way I do it is that I optimize my day and my meals around my work. I work out in the morning, I work since very early until very late, so I have to be optimizing for focus. And this means that I cannot have a lot of carbohydrates during my lunch break, for example, because this is going to give me all that headache, it’s going to give me all that sleepiness, it’s going to give me all these effects that cannot help my productivity.
By getting to that level of understanding and by looking at hormonal level changes, then you get to understand the human body so much better and you get so much closer to the first principles of the human body. And then you can certainly predict behavior instead of looking at it from the outside as a black box. So in physics and in sciences in general, you can take the first principles of physics and be very, very accurate on your prediction of something because the rule set is there. Whereas if you do the same exercise on what we know about human physiology and about endocrinology today, you will never deduce the same results. So different medications yield different results to people, because we are looking from the outside in. And if we get to the point that we can measure the prolactin levels, the testosterone levels, the estrogen levels, and you can look at that depth, I think is going to be such a game changer.