Today, I’m joined by Noosheen Hashemi, founder & CEO of January AI, a precision health company using AI to treat metabolic issues.
Tackling unmet needs for people with or at risk for diabetes, January AI’s foundational team of computational, food, translational, and medical scientists use a multi-omic approach to synthesize CGM, wearable, food, and microbiome data, delivering personalized recommendations and insights for better health.
In this episode, we talk about the data behind predictive glucose monitoring. Noosheen also explains how January leverages digital twins to create personalized health insights, and she warns against deceptive marketing practices in the digital health space.
In this episode, you’ll learn:
- How January balances product development & refinement with product availability
- Noosheen’s tips for getting and staying far ahead of your competition
- How January is expanding their customer base and making it easier for people to adopt a healthier lifestyle
Links & Resources
- Subscribe to the Fitt Insider newsletter
- Visit the Fitt Insider jobs board
- View current Fitt Insider investment and get in touch
Noosheen’s Links
- Follow Noosheen on Twitter
- January AI’s website
- Follow January AI on Twitter
- January AI is on Instagram
- Connect with January AI on Facebook
- January AI’s LinkedIn page
Episode Transcript
This is a machine-generated transcript. Please excuse any errors.
[00:00:00] Noosheen:
Continuous glucose monitors are reading inside your body, which means we can apply machine learning to this data to collect longitudinal data on people, track their health over a period of time, and inform them of small things they could make changes around to have positive outcomes.
So our company is an AI company that predicts your glucose.
[00:00:34] Joe:
Welcome back to the Fitt Insider podcast. I’m your host, Joe Vennare.
Today I’m joined by Noosheen Hashemi, Founder and CEO of January AI, a precision health company.
In this episode we talk about using glucose monitoring to improve wellbeing. Noosheen explains how January leverages digital twins to create personalized health insights, and she cautions against deceptive marketing practices in the digital health space.
Let’s get into it.
Hi, Noosheen, welcome to Fitt Insider. Thanks for joining us.
[00:00:59] Noosheen:
Thanks for having me.
[00:01:00] Joe:
I’m looking forward to the conversation today. I think there are a lot of exciting things to talk about as it relates to both what you’re working on and the broader metabolic health category.
To get started can you introduce yourself and tell us about January AI?
[00:01:15] Noosheen:
Absolutely. My background has been in software for decades. I was at Oracle for about a decade and then a startup, and then a family office where we’ve invested in over 140 companies. I was essentially going into some of the portfolio companies in operating roles in 2013.
In 2016/2017 I decided to start January AI. I teamed up with Mike Snyder to start the company. I can tell you about that if you’re interested, but essentially our interest was in bringing multi-omics to the masses, starting with very inexpensive wearables.
So, rather than start with whole genome sequence and the whole microbiome, and all of those things, we thought, “What can inexpensively tell us a lot about health?”
That was an inflection point. Up to that point people were using fitness trackers really for fitness. Wearables had been used for just counting steps and that sort of thing. So, continuous glucose monitors, they were reading inside your body. For the first time they were being considered for health.
And, now of course, so, so our heart rate monitors are now health trackers as well. so, we felt that they were accessible inexpensive and continuous, which meant that we could apply machine learning to this kind of data to collect longitudinal data on people and to be able to, track their health over a period of.
And be able to inform them of, you know, small things they could make, you know, changes around to be able to have positive outcomes in terms of their markers and everything else. So we were really excited about that and we wanted to start there and then add, add Omix as we go. the company has been around, for, about four and a half years. we started, we hired our first, technical person in November of 2017 and we spent the next three years in research. so, we were looking at, what heart rate and CGM data together mean.
we are revolutionary in that sense we were pioneering and we, we then kind of realized that we wanted to know what people were actually eating. and so we committed ourselves to years of research around nutritional intelligence. So we built a big database of foods We created labels for foods that did not have labels. So nutritional label. So right now, only groceries and chain restaurants have labels and nothing else has labels.
So we spent an enormous amount of time at, in a machine learning, effort to develop labels for foods. And we then also, spent an enormous amount of time, coming up with glycemic index and glycemic load of foods. so enhanced labeling, labeling that doesn’t exist today for foods because essentially glycemic response is better associated with, glycemic load of foods, as opposed to with carbs alone, cuz you often don’t eat carbs by themselves and not all carbs are created.
You eat carbs with maybe the foods you eat. They have protein, fat, you know, water, fiber. So, these were like foundational work that we did to enable turning food into an assay. So if you’re basically, building mathematical models of someone’s metabolic health, What is the role of their heart rate and activity?
What is the role of food? What is the role of their glucose? I wish there was a continuous insulin, tracker in the world because that would provide a very, very valuable piece of information that is now missing from everyone’s calculations. but our company is an AI company that predicts your.
[00:05:07] Joe:
When you talk about that, it’s, there’s so much that, you know, when you think about starting the company, what, just from a hardware perspective, that enabled how you leverage that with software and AI, then. It looks like in terms of considering not only just what people are eating, but how they exercise, how they sleep, all these other factors that go into it. and you mentioned, you know, being an, an AI company, how does the consumer experience that from the outside?
So there getting prescribed a continuous glucose monitor. You have the app, but thinking about that experience in terms of how they are then given the data, the insights, in a way that’s, that’s actionable, easy for them to understand beyond just like big glycemic load beyond the foods that are getting kind of calculated behind the scenes. what are they experiencing? And then how has that kind of user experience evolved as you’ve gone through this research process?
[00:06:01] Noosheen:
Okay. Great. So the way that AI benefits people, it, creates a digital twin for them. So rather than them having to Eat something to see its impact. they can feed it to the twin to see the impact. So the experience that you have today with a lot of products out there is you go on their website and you order a, a CGM, you go through a telehealth, service, you hook up your CGM to your app and they give you a, a score or some kind of a feedback about the foods you that you’re eating.
Maybe they. Take a picture of that. Maybe they have text with that and things like that. Well, the information that’s gathered today about those things has no, nutritional intelligence. When you do that in January, it does. so you know, how much, GI it has, or it knows how much macros it had in terms of carbs, proteins, fats, and you know, how much fiber you’re eating, which is really important to metabolic health fiber.
It’s central, to lowering your blood sugar. and so I can talk about fiber for the whole hour. but it is a, a really critical factor. So we track that. So what this means is, usually with those products, you will have to consume something and retro actively see its response. But with January after a period of training, you can now not have to eat something.
You can simply look up a food and see how you might respond to that food. So it’s a dramatically different user experience. It’s vastly different. It’s not the same user experience at all because it enables mindful eating. It enables for you to do mindful grocery shopping. You can look up, you can go to the grocery store and look up, you know, scan a product and get your curve immediately for that.
Instead of buying it and taking it home, you can experiment to see what would happen. If you ate half of it, you ate one fourth of it, one eighth of it. You can do. What if analysis? What if I ate half of it and walked in then is what would happen, which none of these things you’re able to do with, other products in the marketplace.
So AI allows you. to essentially, experientially learn what works for you and what doesn’t work for you. And it’s highly personalized to you.
[00:08:16] Joe:
Yeah, I think when you talk about it, there’s just from the kind of standard user experience, maybe with other companies, not even just metabolic health, but certainly with like wearables in general, it’s just creating a feedback loop. And so you’re.
[00:08:30] Noosheen:
Exactly.
[00:08:30] Joe:
Wearing it, you’re doing this thing. And then it’s telling you, here’s what happened after you did this thing, and then it’s kind of on you as the user to be like, okay, now I have to make a change.
Hopefully at best case scenario, I’m adapting based on this feedback and some apps. Different wearables for whatever the kind of modality is like now they’re starting to prescribe hopefully, suggestions, right? Do this take a walk after you eat, or maybe don’t eat this thing or eat at a different time, so on and so forth.
But in this case of just kind of making sure I’m understanding it correctly, you’re saying we you’ve created a digital twin after a period of time where you actually don’t have to do these things. You can. Create a situation in which what if I did this thing? And then you see what happens, see that it’s a bad result and say, oh, I’m actually not going to do that thing.
And if anybody’s kind of new to the concept of a digital twin it’s, but we could probably talk about that for the entire conversation as well. But this idea of replicating based on a ton of different software and data, like essentially your body and how it’s gonna respond to different adaptations inputs, so on and so forth.
Can other people create this similar digital twin concept? if they’re collecting the same data, could they do similar modeling to say this is more predictive than it is reactionary? And if so, maybe like why not in terms of what you’ve already put into that research?
[00:09:57] Noosheen:
Yeah. So, I mean, if people, we took a very, very, my God, an extremely rigorous, process, which, which is why it took us so long and we’ve been perfecting it over the last two years, even since we put our poster out at the American diabetes association conference in 2020, when we presented our prediction model after our clinical trial, which took a year and a half.
To perform so people could go through a rigorous process and end up with that. Of course. but it really depends on how accurate it’s going to be. There are, there are prediction models today that. Glucose for type one, folks. And they are a calculus between essentially carbs and insulin, and we are more accurate than those.
And we are in the league of our own. We are able to predict your glucose more time in the future. So those papers are usually for 30 minutes in the future, we can predict your glucose two hours in the future. We can predict your glucose with food. They cannot, they only predict your glucose without food.
Like if you ate nothing in the next 30 minutes, they can produce your glucose and we can predict your glucose if the CGM is not even on you. And they cannot do that, they predict if the CGM is continually on you. So we are in a league of our own. I think we’re a decade ahead. And I think, the approach that we’ve taken has been very rigorous. we’ve had, you know, a series of very dedicated people that, that were very committed to doing this pioneering work. So, I think a lot of machine learning people essentially think, well, you just throw a lot of data out there. Eventually you’re figured out and. That’s by and large. True. You can throw a bunch of, things out there.
Yes. Once you, if, if Google decided to do this, and, if Amazon decided to do this and Amazon knows, maybe what drugs you’re taking, Amazon knows Amazon knows what kind of exercise you’re doing. If you’re, if you’re a camper or not. If you’re buying tents and by hiking shoes, I mean, yes. I think people, like Amazon could put together an unbelievable picture of, of a person’s life. but there is nuance to building these models and, there is work that’s gone to them. It’s very interesting. You show it to normal, like any kind of machine learning people. Their first reaction is like, oh, so you did it like this and you go, no, I did not. I did that years ago. I tried that, but, That did not have good results.
And anybody can do that. No, I that’s not what I did. and then you did this, no, actually, and we have a tremendous amount of IP also around our work. We have IP around our nutritional inference work. Where we, where we can detect nutrients from text. right now, when you log a salad with, with another company, you don’t know what’s in that salad.
You don’t know, you don’t know anything about that salad. You don’t know if you can. There’s another similar salad to that salad somewhere else. You’re getting a salad trader Joe’s. Do you wanna know there’s another trader Joe’s salad. That’s better for you or you’re having this Dan and yogurt. But if you’re having this blueberry Dan yogurt, do you know that there’s another Dan and yogurt that’s actually has lower GL for whatever reason that you can have, or maybe you’re gonna have, you know, over time, maybe there’s a vanilla yogurt you’re willing to have with fresh blueberries.
You know, I’ve written this massive manual on hacking foods that, I know one of your questions was going to be like, what’s next? Like, I’m really excited about bringing AI to the hacking process because. It’s not so hard for people to, you know, instead of eating that almond cost song in the morning, if you could eat half of that almond cross on save half of it and just have a hard boil egg.
And if AI can tell you things like that, For you specifically, cuz they know how you respond to things. it’s really phenomenal. So can it be replicated? I think anything can be replicated ultimately by, it depends by whom and how much does it take and how much nuance and fortunately for the rigorous scientific world, we have ways of evaluating accuracy and evaluating things.
So there is. We live in a scientific world where things are compared rigorously. And it’s not like my word against your word, which is, which is challenging because I think that you see the winners are marketers. And so they tend to have a louder voice, but marketing is not the science of the body. And they are just different things. We all have our own DNA. We are coming at it from different perspectives and we have come at it from a crazy. geeky scientific place.
[00:14:22] Joe:
Yeah, I think it’s, it’s really interesting as you’re talking about that, just fundamentally the perspective being, predictive versus. Reactive, even as it relates to the data and what you’re telling people and how you’re making those recommendations. I do wanna talk more about that, but just since you mentioned the marketing piece, you, you talked about starting out doing all this kind of behind the scenes research, even clinical studies, it sounds like.
And then moving into getting this in the hands of customers. Well, at the same time, Other companies have taken the approach of, we’re going to talk about weight loss. We’re going to talk about performance. We’re gonna talk about optimization of these biomarkers. We’re gonna build. Kind of a massive wait list and leverage influencers and different kind of high profile people, especially in kind of like the tech investor world.
And then we’ll slowly launch, roll it out to them and get these various kinda like, data points as we go are in a beta phase. How have you approached this marketing piece, going to market, launching to people and actually, you know, acquiring customers to this.
[00:15:30] Noosheen:
Yeah, I would like to come to that in one, one moment. I wanna say that, I, I sincerely mean this as a public service announcement. I worry about marketing, companies, going to health. I worry about people. Minimizing and simplifying and characterizing. Metabolic health, something as complex as metabolic health as keeping your glucose to 110, which is what I’ve heard from people doing when they use some products that scares the hell out of me.
When someone says, a reporter asked me, are you having trouble with people being, getting obsessive with your product and like not eating anymore? I said, sorry, I don’t understand what you’re, what you’re saying. They said, well, we were interviewing. Somebody else. And, and people are not, they don’t wanna eat anymore because they don’t want their glucose to ever rise.
That is not metabolic health. That is not metabolic flexibility. and so I think there is a, a lot of disservice that marketing companies can do to people By simplifying things in this way that are just, that are just insane. the goal is not for you to keep your, your glucose at a certain level.
You should exercise you should, you are a thinking person. Your glucose is gonna go up. you are, if you are metabolically healthy, you’re able to eat something and produce the right amount. You have the right balance in your body in terms of how much insulin gets produced, how quickly it gets produced to get to that glucose.
And you maybe just fine being able to eat certain things, metabolically unhealthy. If you’re metabolic unhealthy, we can start talking about how to not exasperate. Your unhealthiness and how we want to then try to eliminate or hack your high spiking foods, but not all foods I think we exist to help people actually eat intelligently and to eat differently and to eat smartly, but not to not eat. so you could simplify things and you could say, well, You get a spike when you have carb. if you have no carb, you’ll lose weight, hence Everything’s cool.
We disagree. we are proud that we have started from science. We are proud that We have spent so many years in understanding deeply how people’s bodies work and building the models that we have, that we are proud of. Simplifying and, dumbing down, if you will, for people or trying to show something for what is not. So how have we gone to market? So we went to market in November of 2020, after we put out our poster in the summer of 2020, and we started selling our product and, we got to a decent amount of revenue and there was, there was a lot of buzz. in early 20, 21 around metabolic health.
So we got to a decent amount of revenue and, we decided we wanted a better different user experience that really. Showcased our AI and made things possible for people they couldn’t do with other products. Essentially other products were just copying what we were doing, the tracker we had built, in our clinical trial to collect data, and to put heart rate and glucose on the same, graph.
So, so they were copying that, and, and throwing on, you know, a dish on it. So tracker we created in 2018 actually, And they were throwing a score, like scoring this dish or versus scoring that dish. so we decided to invest in the next version of our product. So we began developing, we brought on someone who, was a key executive at IDO in design for a decade. and she has. Guiding product design for the version two of our product.
And that version two is coming out, in alpha, in August, and then in beta, in, in September and October. We got to, a number of users we’ve actually published a, white paper, based on the results that the first 2000 people had our program lowered weight. we weren’t trying to get people to lower weight, but they, it lowered weight. It increased the amount of fiber intake increase the amount of protein intake, which helps with satiety as you know, Reduced overall carbs, overall calories. And it increased also time and range. So it was very successful program.
And people have told us anecdotally that they’ve lost 10, 20 pounds, but that was not the goal of, that’s not how we’ve marketed ourselves and that’s not how we put ourselves out. We are truly about getting metabolically healthy. We’re really working on people’s underlying physiology. And the levers we use for that is one is intermittent fasting. So with our product, you are able passively to track your fasting period and your eating period. and we slowly increase your fasting period over time. that along with calorie restriction, which is our second lever has been shown, in literature to, to be beneficial for improving insulin sensitivity. Based on your activity levels and lots of other features about you. We choose, we, we determine how many calories you should be consuming.
So if you go and look at, someone like me, on the internet, it will tell me I can eat 2,500 calories. Actually. I can only eat about a thousand calories. Believe it or not. without gaining a lot of weight every day. So, January Cal figures out your, your caloric intake, ideal CalWork intake. And it’s dynamically calculated. So it’s reporting back to you. If you do a 10 mile run, then it figures those things out. We also encourage more fiber intake, which is, as I mentioned there in literature, there’s quite a bit of evidence that having fiber in your body.
In fact, like I said, you know, ancestral diets used, you know, had 150 grams of fiber a day. And right now Amer average Americans taking 10 or 15 grams of fiber a day versus what they should be having. For women, 20 to 25 grams a day for men, 30 to 38 grams of fiber a day. So we encourage more fiber, which actually makes you feel more full.
It lowers your blood sugar. and we also, we are the ones who came up with, activity immediately after food, and popularize that, over years. and we showed evidence of that. by doing it, we used to eat something and walk in our office. We called it sizing. and we’ve been talking about it for the last four years, four and a half years. so we talk about activity and our AI does something very cool with activity insights. it. does counter factuals. So it. says you ate this, Joe, you had this, you had this food, you had steak and eggs. had you, walked after your steak and eggs for 10 minutes. This would be your curve. Had you walked 25 minutes.
This would be your curve. So you can actually see, oh, I can eat that. Okay. That’s cool. That’s fine. I have. No problem. I’ll eat it. I’ll have the mashed potatoes, but I’m gonna get right on it. I’m gonna get up and start moving. So we do activity insights. And the fifth thing is we look at your spiking foods specifically, and we give you recommendations of alternative foods. So you can have the same food, but with lower GL, you can have a similar food with lower GL. You can have the same food with less quantity. You can have the same food, but take, some parts of it out. So I think the company has, invested tremendously in nutritional intelligence and it will continue to invest heavily into nutritional intelligence. We wanna make precision nutrition possible for. whether you’re eating at McDonald’s or buying your groceries, or you’re picking a pizza for your kid or whatever you’re doing. Be able to compare foods and be able to see what you should have.
[00:23:05] Joe:
Yeah, for sure. When you talk about kind of initially going to market and then wanting to then refocus on the next version. So people currently using the first version, are you onboarding people to the second version yet?
[00:23:19] Noosheen:
Yeah, we’re not marketing it We haven’t been marketing it since last December. and we have been building the new, yes, people can go to our website and sign up for our alpha right now. and yes, we are continuing to sell the V1 because it was, it was successful in terms of producing.
Outcomes, in terms of increasing time and range and things that are clinically validated like weight. it does lots of other things as well. But, yes, our V2 is gonna be coming out. We will market our V2 and we would love for people to go out and try our V2 and give us feedback and help us, help us dial that product.
It’s going to be the initial version of V2. That’s gonna come out in September and then we’re going to have this rolling thunder of other features that are added. each, you know, each release is going to add features to it as we.
[00:24:07] Joe:
Yeah. And I think you, you said it really well of being concerned about the marketing, right. And, and people kind of pushing whether it’s these different. Weight loss performance. What have you, or kind of overly simplifying or promising these outcomes when really it’s not necessarily scientifically backed or proven that that is gonna be the outcome?
You know, we talk often about how it’s, especially in like the health tech and fitness tech space, like it’s way easier to make money than it is to deliver an. And that’s, it’s way easier to raise money than it is to deliver an outcome. And I think we see a lot of that and sometimes get caught up in this kind of growth at all costs.
But at the same time, you were kind of talking a little bit about, you know, some folks copied putting the, you know, XYZ feature, putting heart rate in this, and you kind of initially publicized or, or pushed the idea of, you know, being physically active after the meal. And some of the other things that you’ve.
I guess my question is, and this for other founders who are working on this, like, how do you balance wanting to grow and put, put your flag or stake in the ground and pioneer some of these things with the idea of like, oh, we have to have a product out there. It’s like, can we go through the process of clinical trials or getting this thing scientifically back doing the research, developing the features while also kind of.Getting maybe surpassed in a way or getting copied or the risk of being copied. How do you balance the patients that it takes to like deliver that outcome with having a product out there in market?
[00:25:40] Noosheen:
I think that’s a really good question. it takes focus to do certain things and I don’t know, apple computer was behind Microsoft in so many years, in terms of market share and, I think you’re building a DNA for your company. You’re building a DNA that is scientifically rigorous, as opposed to, you know, on show and on display and in marketing all the time.
I think you have to decide what kind of DNA you wanna build for the company. And you know, I think that is a, Hard decision to make, of course, and you know, we’ve taken our approach. There are other valid approaches, you know, we applaud everyone That’s trying to help people in metabolic health.
And we think there’s room for everyone. I mean, let me tell you this in metabolic health, you have, let’s say 15 companies. they have fewer than 2 million users. So count Livongo that went public or got sold for 18 and half billion count everyone count AADA Livongo, Berta Vita on duo level two, count levels, nutrition signals, January supers, sapien, everyone steady health, everyone, and everyone, you don’t have 2 million users.
But you have 96 million people who are believed to have prediabetes. I don’t mean they just wanna be fit. I mean, they actually, on, on their way to, you know, they are Metabo unhealthy, you have 37 and a half million people with diabetes, in this country. And 22% of them don’t even know they have that.
So you have hundred and 33 million people with diabetes or, prediabetes. And the 15 companies that are supposed to address them have 2 million, 2 million people. What’s wrong with that picture. Do we need more marketing for that? Is it just marketing? Is that it will marketing take care of it and we’ll just like own more and more of that, of that people or do we need a combination? I think marketing is important. It’s important for creating awareness. It’s important for education, and it’s important for increasing access, but I also think that, science is critically important and You you must remain committed first And foremost in imparting information. That’s scientifically accurate.
And, scientifically correct. And not misleading sorts of population to think their glucose should not be over 110 that’s that’s insane. so how do you do it? It’s hard. I don’t think we’ve nailed it. I think that, it’s almost like a false choice right now. You can either be good at this or good at that, but can you be good at both? And I think over time you can be good at both. I think we each come at it from a different perspective. we have, you know, our strength, what’s rooted in our DNA and then we try to, you know, get better at the other game. So, I do think though that there is so much room for solutions right now, there’s so much, and I do think the power of AI, to really transform health, you don’t need to wear a CGM all year.
I think some business models are based on you wearing a. CGM all year long or wearing a CGM for several months. You don’t need to do that. So if we can build a model of you and then you can essentially know, how you, how you react to different things without wearing a CGM, then more people can do this.
More people can afford this CGM don’t cost that much. So we imagine a world where 130 million people would wear a CGM, at least once a. That’s our dream. if everybody with diabetes or prediabetes could throw on a CGM once a year, that’d be amazing. And I think what, we are able to do for consumers with AI, we’re able to enable, mindful shopping at the grocery store, mindful, choosing of, of foods at a restaurant, you know, mindful recipe, picking to cook for your family.
You’re able to. Essentially see the, the role of activity And everything else in, in your blood glucose for payers, employers, and providers, providers more and more, they’re consolidating their becoming bearing organizations for these folks. We can bring the cost of diabetes, management way, way, way, way down.
And so, you know, you can bring it down to, to a few dollars instead of, you know, hundreds of, and hundreds And thousands of dollars. And that has huge ramifications so if they could throw, you know, two CGMs a year or four CGMs a year on the whole type two population, instead of, just the three and a half million, people with diabetes that are intense insulin users, we could benefit tremendously and doesn’t cost that much. They’re also ramifications for other industries. You know, I think food companies are trying to develop more healthy foods and we’re able with our digital twins to give them the kind of intelligence that they would, they normally don’t have in their product development process.
So we’re able to help them see how people with diabetes or prediabetes would do on their various foods and decide how to, reformulate their foods to be healthier for the population. I think food companies are trying to produce better foods. I think big food wants to be on the right side of history here.
So I think there’s gonna be a lot of interesting partnerships coming about.
[00:30:42] Joe:
Yeah, I think a key thing to think about. And as you were talking about that, kind of mindful eating and helping people make better choices, grocery shopping, giving them an idea of, you know, the impact that this is having on their body and hopefully in a way that leads them to. Make changes.
Right. And I think that’s a key piece of the Puzzle. And as we get towards the end of the conversation, wanted to pivot to that a little bit. Like there is a lot of talk about, you know, whether it’s the hardware. The software piece of it. Yeah. But getting to the punchline of it, it’s like, we may know all these things and maybe we do get a glucose monitor on everybody once a year, or maybe even multiple times a year, but how do we get that next step?
Because even now, right. I would venture to guess that people who don’t even know what a glucose monitor is and will never wear one, have. Decent understanding that they probably shouldn’t eat that ice cream or the bag of chips. They probably should be walking or more physically active. Like they probably should be going to bed or, you know, trying to get sufficient sleep, but they’re not doing it.
How do we nudge people or force people, right. To take that next step of actually implementing this stuff that we’re giving them all this really great data about.
[00:31:52] Noosheen:
Well, we can’t force them. The literature has shown that. So literature has shown that you can’t tell people not to eat something, which is why it’s hard to, to commit 100% to a keto diet, or back a keto diet. Cuz it’s really hard. As soon as you tell people don’t eat something, that’s all they wanna do.
So literature says basically the best you can do is to. Here are some things that are healthy for you Eat more of these, and here are some things that are not so healthy for you. So let eat less of these. and I that’s the approach we’ve taken from day one. the small nudges atomic habits, tiny habits, all of those work. So apple watch is a prime example. It’s not overly gamified. If you want to know if you want to geek out on data with apple, you know, watch, you can it can tell you all sorts of things about you. for example, I.
Learned that the average amount of time I stand is seven minutes. Like, so when it tells me to stand, I. I take the AI’s recommendation, but then I sit down fairly quickly. So I now understand, oh, my goal is to try to take it from seven minutes to 10 minutes and then like keep improving it so that I can be standing up more so you can geek out on that. So apple watch is not overly gamified, but it’s gamified a bit.
You’re trying to close those three rings. There’s small frictionless nudging, frictionless nudging. So I think user experience is ultimately what’s going to create mass adoption and user experience. I think is, is being able to do those small nudges to people, which we can. We are able to tell you your last meal, sleep gap. We can tell you passively, when you last ate and if you ate too close to your sleep, whether that’s going to, you know, we can share with you how close to your bedtime you’re eating So that you can try to maintain a three hour, timeframe. We can slowly nudge you. To get you closer To that. So if you’re eating right before your sleep, we can slowly, slowly just like your URA ring, which I’m wearing right now tells you ideal bedtime for you is between this and this.
Tonight, we can tell you the ideal eating time for you is between this and this to have the last thing that you’re going to eat. So we can do by small nudges and user experience is the way to deal with the, the human. There are some structural issue. Going on in our society, which no hardware or software is going to really change.
And that’s the kind of food that we’re eating right now. the kind of, you know, subsidies that, that certain. Commodities get, and those commodities on their own are not dangerous, but when they are sold in such abundance, so cheaply and they are become the source of nutrition for tens of millions of people, hundreds of million of people, then it becomes a real problem.
So I think this, this structural, the structural lack of movement in our, in our society, structural foods that we eat, the standard American diet, the low fiber, high carb foods that we’re eating are also, causing problems. But, but you know, there’s at least a hundred million optimizers out there.
People that are wearing a fitness tracker belong to a gym or on a diet, et cetera. There are people who want to move in the right direction. The question is how do we empower them? How do we help them? And we strongly believe that we want to help them understand, you know, onboard them onto a health journey.
If they’re not already on a health journey. Help them stay on that health journey, help them have a nuanced view of their health as opposed to a single measure. So it used to be A1C or cholesterol. Now it’s your weight now it’s your time. And you know, not, not having your glucose go, no. We want them to understand holistically the levers that are available to them and they will adopt some of those levers.
They will not adopt all of those levers. So it is true that the healthiest people. Share the same disciplines. Like if you take some very healthy people, they’re more or less, a lot of ‘em are doing similar things, but not everyone can adopt all of those things. People can adopt one or two of those, those things.
And as their interest level goes up, you can introduce more things to them. So that’s what, that’s why we provide calorie restriction, restricted, eating time, restricted, eating fiber, all that. So like can’t pick up all five. Okay. Can you pick up. That’s easy for you to do, just add fiber. so I think that the there’s no silver bullet, I think it’s about, nudges.
It’s about valuable data. You can’t get people to do everything differently. You can get them to do some things differently. Do a little bit better in some things, a little bit less of the harmful things over.
[00:36:12] Joe:
Yeah, I think that’s really well said.
[00:36:13] Noosheen:
And of course, community. I think community is also really important cuz no one changes by themselves.
Everyone is, you know, we’re social animals. So we need to change with people around us.
[00:36:25] Joe:
There’s there’s so many factors. And I think as you frame it, even this. Simplicity, small nudges consistency over time, making those changes, making sure that they’re implemented, having support system around you. I mean, I think so many times it gets overlooked. We talk about the power of the technology and some of the thing, the hardware software, you name it, but it’s like, What about food deserts and what about access to parks and green space?
And what about people that, you know, don’t have no community or support system it’s like, that’s not going to be solved, with technology necessarily. So yeah, it’s, it’s trying to think about then, well, what are the things that we can impact, right? As founders, entrepreneurs, people, investors who are starting and backing companies like to move that needle. And I think that that kind of small nudge over time is.
[00:37:17] Noosheen:
Yeah. And I think as people we have, multiple levers for change. So for myself, one lever of change for me is January. So I spent, you know, 14 hours a day on that. that’s very important, but also there are, I’m also supporting white house conference on food and nutrition. That’s coming up in September.
So I spend my time understanding what are the social issues around food? what is the role of government. which can be massively positive or massively negative? I mean the us government. Outweighs everything else you can do in society’s just so massive and so powerful in the way that it funds, subsidies to farmers in the way that it funds school lunches in the way that it funds, for example, snap program, et cetera.
And so I’m extremely passionate about, the role of, policy and, government. So I work in that as well. I. spend my time on a, on a innovation council with food companies and trying to understand how big food can come along on this journey, because that is part of the solution. So I think all of us. can make impact from through the private sector. We can make impact through government and policy. We can, in terms of policies we support and, people that we support and as well as through philanthropy, as well as through, nonprofits, private sector and governments. and you know, communities and, and local communities. So, I have a portfolio strategy to change. My passion is getting everyone to eat a little bit more mindfully, and consider what’s right for them. Like I’ve done my whole genome sequence. and like, I’m not supposed to really take Advil. it has to do with something, some protein that your kidney produces and that sort of.
That’s great. I love knowing that. I now also know that, Tylenol, I don’t metabolize that very, fast. So I know if I get a headache, it’s gonna be two hours before I have help. So I now don’t do things that trigger, a headache. I know if I have caffeine or sugar in the middle of the afternoon, I’m gonna get a headache.
So I don’t do that. So I don’t have those things. I also gave up like cow dairy and I don’t have sinus infections anymore, which I used to have all the time. so, I mean, you just find out and you go, okay. It takes a while. It really does take a while. I mean, I used to chug like half and half as a child. So like for me to give up certain things was really hard and I.
Do still, of course, absolutely enjoy it from time, to time, but I’m not chugging it every day. It’s not, it’s not my regular food. It’s my joy. It’s I eat it for fun, not for, for sustenance. So I think if we, empower people with more information about what works for them and what doesn’t work for them, and we give them tools to kind of do this frictionlessly as opposed to, with a lot of friction and we have this small nudges and we have the community support.
And at the same time we work on policy and we work on big food in, in conjunction with food. I think we, we can make forward progress.
[00:40:17] Joe:
Yeah, to your point, it’s all the above. I think it’s an “all the above” approach.
[00:40:23] Noosheen:
That’s exactly right.
[00:40:25] Joe:
Given the chronic conditions, obesity, and lack of physical activity, and big food and government subsidies, it’s gonna take that “all hands on deck” approach to move us forward in the right direction. So, definitely with you on that one.
In wrapping up, and we’ll get you outta here on this, it sounds like a lot to look forward to this fall talking about the alpha and then the beta launch.
So for folks that want to follow along and keep up to date as that rolls out, and are interested in trying the product where it is today, where would you point them and what should they look out for?
[00:41:00] Noosheen:
Come to January.AI and join the alpha. Alpha is gonna be pretty rough, but come and give us your feedback. Hopefully, by the time beta rolls rolls around, things are gonna be more wonderful.
So yeah, I think the best thing is if you feel passionately that we need more solutions out there, and you feel that January speaks to you, come help us develop an amazing product; just give us your feedback, that’s what we really want.
[00:41:30] Joe:
Fantastic. I definitely hope folks take you up on that, and I’m super grateful that you made some time to join us today.
[00:41:35] Noosheen:
Thank you so much, Joe.