The Failure and the Future of the American Healthcare System w/ Kian Sadeghi

Hi everyone. Welcome to the latest and greatest episode of Failing Up. I'm your host, Claire, and today I'm sitting with Kian, who is the founder and CEO of Nucleus. Nucleus is creating a doctor in your pocket, starting with genetic testing. In this episode, we cover everything regarding the pitfalls and the failures of the existing American healthcare system and infrastructure, as well as what the future will look like for American healthcare in 100 years. Will we have AI doctors? Will we have humanoid doctors? We dive into it in this episode. Kian, how are you? It's so nice of you to make it out here.

Absolutely.

We're actually rolling now, right?

Okay. Okay, great.

How are you? How's everything?

I'm great. I'm doing really great. I'm a lot going on. It's the end of the year. It's kind of a sprint for me, the company, but we're doing great, honestly.

Okay, amazing. So I just want to start by asking what got you so passionate about DNA testing? I know you came from a 2 parents who were doctors. Tell me more about what that experience was like growing up.

Yeah, so I'm a first-generation American. Both my parents are Persian. They actually escaped Iran during the revolution. The government of Iran was trying to literally murder my dad, and so he escaped the country, eventually ended up in America. He kind of has the proverbial story of someone flipping hamburgers in McDonald's, literally, to becoming a doctor. He's actually an ophthalmologist, an eye surgeon. And eventually they moved to Brooklyn. I grew up in Brooklyn, not too far. And I think the story of Nucleus begins very unfortunately with a personal tragedy. My cousin actually went to sleep one night, she didn't wake up the next day. She died in her sleep. She had a condition that basically can cause irregular heartbeat and sudden death, and there's actually no phenotypic signal. If you know you have this genetic marker, you can give someone something called a beta blocker, which can reduce the risk 10x. But we didn't know. This kind of got me very interested in genetics, and I started naturally kind of diving deeper and deeper into it. One of the first kind of forays into it was me thinking about how actually disease isn't really an inherent property of human beings, but can be more viewed as a problem to be solved. If you could actually correct for an error in someone's DNA, you could well cure a disease. So, I actually joined one of the first do-it-yourself gene laboratories in the country. And there we engineered yeast to make the lemonades, we edited bacteria to make them resistant to antibiotics. And I started thinking about, well, I love the beauty and depthness of biology, but I want to combine it with the iterability of programming and work at that intersection of bits and atoms. Went to Penn, so studied computational biology, which is kind of genetics, programming, statistics. And I was literally in Bio 221 class and the chairman of the biology department brought up what is my favorite chart in the world, which is the decreasing cost of sequencing an entire human genome. Right. you know, around $100 million, even $1 billion at some point per sample in 2020 was literally around $1,000 per sample, an exponential decrease on a logarithmic scale. And I thought to myself, how is it possible? You know, like, you know, first off, how did this even happen? Second off, wow, this price is obviously going to go towards zero, right? It's inevitability. $100 million to $1,000. It's going to go to probably negligible, you know, a couple of dollars per sample to sequencing an entire human genome, which, you know, just for reference, everyone has DNA. DNA is the blueprint of who we are. If you can imagine your DNA, DNA is like a 1,000-page book. Old genetics has to look at 1 page, 2 pages, maybe 10 pages. We look at all 1,000 pages. That's what's now possible. So, I thought to myself, okay, I have to go build this kind of application layer for DNA. Dropped out of school, moved back to Brooklyn, and let's just say I got to work.

Amazing. Wow, that's an incredible story. And so, you said your dad was—

Ophthalmologist, eye doctor.

Yes, but he came from Iran, correct?

Yes, both my mom and dad are from Iran.

So what are some of the key lessons that you've learned from the relationship with him and what he's taught you based on his experiences that you've applied to your life today?

Yeah, well, it's a lot of adversity, right? I mean, when you're immigrants, this is kind of shared whether you're from Iran or India or any other country, it doesn't really matter. The reality is that life is very hard and sort of, Even the thing— there's so many things today that we take for granted. Freedom of speech is obviously one. The fact that we can go on Twitter and say literally whatever we want, that's actually a supreme luxury. There's very much a topic of that in the United States today on freedom of speech. Obviously, it's important. We should continue to invest in freedom of speech, but it's very much the case that if you even speak badly about the government in other countries, I mean, it can put you in a very extremely risky position. That's how it was with my father, for example. So, I think what happens is I like to say you combine the Almost immigrant energy and kind of passion and persistence and relentlessness with the opportunity in America, right? So you have someone who escapes a country, comes to America by himself. This is someone who— I like to say that there's little microcosms of life. So if you play a video game with someone, or if you play some sort of tennis match or whatever, soccer match, whatever it is with someone, you can get almost a sense of who they are in their kind of every in every sense. It's like the old adage, the way you do one thing is the way you do everything. And one thing I always think about my father, one of my most fond memories is playing any sport with him. And I think it's a perfect encapsulation of his spirit. He just does not freaking quit. I mean, the man will throw his entire body on the line to win a point in tennis. And I still beat him for the record. Oh, yeah, yeah. You know what I'm saying? And so I feel like, and then I take that and I imagine that, except the difference is you're in a country that actually encourages innovation, encourages speaking your mind, encourages following your bliss. You combine those 2 things and it's magic.

That's a beautiful response. So speaking of America, I'd like to dive into a really hot topic, especially right now at the moment, the American healthcare system.

Yeah.

So I believe it was back in the '70s, the expenditure to life expectancy was similar across like Western countries. And since then, the US has progressed this way and all the other countries have progressed this way. And our spending is now what, like $12,444, I believe, as of today I checked. But why, why do you think the trend for the US is progressing in the wrong direction while all the other countries it seems to be progressing in the right one?

Yeah, I mean, you make a lot of salient points. Well, you know, we, we basically have the highest per capita spending in healthcare. and the worst outcomes out of any other kind of developed nation, which is extremely concerning. We spend around $4 trillion a year on healthcare, which is— I mean, I always like to say these numbers, I think it's good just to stop and be like, $4 trillion? Where is this money going? And the reality is $1 trillion of it, literally 25% of it estimate, is going toward admin expense. So, it would make a lot of sense if the GDP per capita— That's pathetic. It's crazy. The GDP per capita is increasing so dramatically compared to life expectancy. Well, it would make a lot of sense if the money is not even going to healthcare, if it's going toward admin. Yeah, you're not going to get any good outcomes. I think insurance is a huge problem. I mean, I think recently on the UnitedHealthcare CEO killing, the fact that we are even in a world where something like that happens and then you see whole swaths of people who come and I wouldn't necessarily say defend. I think we all agree it's a tragedy. Someone didn't deserve to die. But the fact that people can even kind of see and understand how an insurance company has caused so much harm, maybe in some world it's justified. The fact that's even a conversation. That's something that's even deliberated over, I think is an entire encapsulation of the current state of US healthcare. There's a dramatic distrust where because of this per capita expenditure and people feel as if insurance companies are frankly just playing games with them. I always like to say also the most valuable healthcare company in the world today, in the world today, is UnitedHealthcare. I don't think that's going to be true 10 years from now. I mean, how is the fact that the most valuable healthcare company in the world is an insurance company? Are we serious? Where's the health innovation? So there's a kind of complex between insurance Kind of the paternalism and kind of regulatory complex, almost like these 3 things of insurance, regulations, and in some way doctors kind of— it's the old expression, don't hate the player, hate the game. They kind of need to be part of the system. That just stifles innovation. It also creates a system that's entirely orientated about being a sick care system. All the focus and energy right now is on treating disease. I get sick. Right. And then here's this pill. Feels

like it's reactive, not proactive.

It's extremely reactive. It's not proactive at all. You have to wonder why is it that even though we've had such innovation in diagnostic testing, genetic testing being a great example of this, whole genome testing being a great example of this, whole genome testing just refers to reading all your DNA. We're talking about one of the greatest exponential decreasing costs ever. And yet, less than 0.1% of the United States population have actually gotten a whole genome test. That's crazy to me. And if you think about it, even if you look at just a whole genome test, There are studies that show around 3 million Americans estimated have a genetic marker that dramatically increases the risk for something like colorectal cancer, breast cancer, high cholesterol, okay? 3 million Americans, over 90% don't know. And all these diseases, if you know you have these markers, are entirely actionable. Angelina Jolie in 2013 is probably one of the greatest examples of this, and yet people don't know. The reason why is if you think about an insurance company, okay, when does an insurance company actually cover a genetic test? Medicare, the largest insurer in the United States, they will cover a genetic test after you've been diagnosed with cancer. What the hell? That's crazy. I mean, that is sickening. And if you look at the kind of private health insurance, UnitedHealthcare, we mentioned that, we're talking about they will only cover genetic testing in very specific cases where someone has a family history. Even though we know family history fails to account for the vast majority of hereditary cancer cases. And so, from that perspective too, you're like, wow, what is going on where the insurance companies won't even cover a swab? I mean, don't forget, it's a swab. You swab your cheek, you can save your life.

And I also feel like the coverage should adjust as more data comes out about people's health. For example, there has been a huge increase in colon cancer in people from the ages of 25 to 36. So people should be getting more colonoscopies, getting more tests for those things.

Exactly.

And I feel like insurance hasn't adjusted itself for those things. No,

and it's a really slow bureaucratic organization. And I think part of the promise of the new administration, whether you're right or left, I think you have to agree on it, is that making the government more efficient, making the government more in tune to, quote unquote, its customers, which is the populace, which is the people, that's inherently a good thing, especially in healthcare.

Yeah. Okay, so I want to just— I know we briefly discussed, uh, the, the shooting of the UnitedHealthcare CEO, and I agree with what you're saying. The fact that we are in a place right now as a society that actually celebrates the death— not to say everyone's celebrating, but a large majority of people online are actually celebrating and saying it was warranted. He, uh, had stated in his manifesto That we spend more on healthcare than any country in the world, but rank 42nd in life expectancy. I actually believe it's 46th. What do you think those reasons are and what's the correlation between the pharmaceutical industry as well as the food industry as well as the healthcare industry in the US?

Yeah, I think I have a lot of thoughts on this. One is that like, one thing is there's always like this idea of lifespan and healthspan, right? Like we cite life expectancy numbers too, It's like under what conditions are we even talking about life expectancy as well? Someone can live to 80, but they could be dying. They could be dying for 5 years, 6 years. My grandmother, she recently passed away. She had cancer and she was suffering for years. And so, yes, in a way it's longer life expectancy, but in a way it's a much smaller healthspan, which I think gets to the point of what's wrong. Well, let's actually look about what's killing people in the United States today. It's very clear. If you look at all deaths in 2022, 2023, more than 50%, more than 50% are from chronic conditions, cancers, diabetes, heart disease, for example, more than 50% of deaths in the United States. We're talking about millions of people. If you look at in the mid-1850s, what was the number one cause of death? Do you know, any chance? No, probably no idea.

The plague? I don't know.

In a way, bacterial infection. By far, the largest cause of death in the mid-1800s. So now this is actually an interesting kind of thought experiment. It's like, okay, the number one cause of death today is chronic conditions. They basically kill around 55% of Americans, it's approximate. In 1850, the number one cause of death was bacterial infection, and it also killed actually around 55% of Americans. So in both these cases—

Oh, wow.

Dramatically different, dramatically different, right? What actually got us out of the debacle of bacterial infection? Well, it was antibiotics, it was vaccines. Et cetera. In other words, it was technological innovation that enabled the rapid increase in life expectancy. Because if you look at life expectancy, what you notice is that we're going from around almost 40 years to about 80 years, approximately doubling in 150 years. That's what I believe it is approximately in the United States. And from that angle, that's actually a kind of dramatic success, right? But then you look at the last couple years, life expectancy has actually come down a little bit. So, the trend in the immediate short-term hasn't continued. And so, I like to say history doesn't repeat itself but does rhyme. And the best way to think about how do we solve this problem of life expectancy in the future is in the same way that they looked at what was killing us in 1850 and said, okay, bacterial infections are killing us. Hey, here's all these innovations. We need to say chronic conditions are killing us today. What are the new innovations that we need to utilize? It wasn't a single innovation that brought life expectancy forward. It wasn't just vaccines. It wasn't just antibiotics. It was also something like pasteurization. It was also obviously better Better food, etc. And so, that's the kind of framework, my entire framework in thinking about longevity today. I actually gave a talk about this recently at the Buck Institute. We sponsored the Buck Institute Longevity Summit. So, I think this will be on YouTube. My entire theory today on kind of life expectancy and what is the problem is let's focus on what's actually killing us and then let's focus on how do we actually prevent these conditions. So, let's look into it. Look at cancer, heart disease. Yeah. Diabetes, Alzheimer's, these other chronic conditions. Let's just take a random one, heart disease for example. Heart disease is around 50% genetic, it's 50% non-genetic. What actually drives heart disease? It's basically a complex function between genetics, sure, and also the things you know, how much you exercise, what do you eat, what air do you breathe. In other words, a series of different lifestyle and environmental factors. So, these aren't conditions that there's some sort of acute moment where you get cancer. It's a buildup of our behaviors over time. I'm optimistic that we'll actually be able to solve this problem precisely because probably speaking in the mid-1800s, it seemed very unreasonable that we were able to cure us from these invisible enemies that were attacking us, bacteria or whatever. In the same way now, it probably seems very unlikely we'll be able to stop the proliferation of cells, but I think that both need the same kind of framework of addressing and solving, and it's like, Oh, how do we have better food? How do we have better water? There are all these questions as well. How do we have earlier, more aggressive screening? I think it's going to be a complex function of both kind of diet, environment, and trying to change the lifestyle in America combined with innovative diagnostic tests and kind of a real-time tracking health system. All these variables will come together. That's going to catalyze, I think, a new era of American healthcare.

So, it sounds like a super proactive system. Versus the reactive system that we have now. Yes. And so, I was watching one of your earlier interviews. I think it was for 3 Course Founders. It was a great interview. Oh, yeah.

It's a good one. Yeah.

You did say that insurance companies shouldn't exist and won't exist in the future. I just want to understand what you mean by that.

There's actually a YouTube comment of someone griping on that comment, which was, I thought, very interesting because I thought of all the things I said in that interview, that wouldn't be the one. What do I mean by that? Well, so a couple of things about insurance companies, which is that could one imagine a world where everything is out of pocket? I think so. The reason for this is a lot of things. One is that I think everything is going to get much cheaper. So, one thing the person in the YouTube comment, and God bless whoever you are, love the comment. One thing they were kind of griping on is how expensive everything is. And I agree, everything is really, really expensive. I mean, if you look at healthcare expenditure, I can't remember the exact statistic, but in essence, 1%, whatever, 5%, of the people are responsible for 95% of the expenditure.

Oh, wow.

Yeah. Yeah.

That's— it's crazy.

If you're sick, you cost a lot of money, a lot of money if you have some sort of chronic disease or whatnot. Right. So let's be clear on where is the money that insurance companies actually spend going. It doesn't go to 95% of people. It goes to a fraction of people who are sick, old, and dying, likely old, but not always old.

And are those the people that are responsible for the $220 billion? billion in medical debt as well.

Probably, yes. So, okay, the first thing to say about insurance is to say, all right, all these different kind of claims are going toward a small kind of cohort of the population, which will always be true at a population level, right? Obviously, if you're sick, it's your world, you're sick, it's N of 1, it's kind of like everything to you. But at a population level, actually a very small portion of the people are sick. Most people are actually pretty healthy most of the time. Excuse me. So then I think to myself, okay, well, if that's the case, is there a world where all these different diagnostic testing and treating can get much cheaper? And I think the answer to that question is yes. I mean—

But would the US, would the government, all the lobbyists, the companies, would they allow it to get cheaper?

Well, that's the thing. But that's actually the interesting thing, which is it's not a technological limitation. It's an ideological limitation, which I think is actually both really great and really horrible at the same time. It's really great because once you realize that one of the reasons why healthcare is not so cheap is because it's not actually because of the technology, it's great because that means it can physically speaking, there's no laws of physics that's stopping us from being able to actually literally make healthcare cheaper. And then it's really bad because nothing's worse than politics and trying to get the kind of, again, this political regulatory structure to— Yeah. Try to actually bring healthcare costs down. One of my favorite examples is I know someone building a company that— I'm going to do a really bad job synthesizing it down, but the point will come across regardless. Where what they do is they basically use your iPhone camera to make some sort of eye assessment. So, you hold the camera to your eye and it makes some snapshot assessment. And he was telling me about— I was like, well, who are your competitors?

Assessment of what? Really quick.

I don't even remember. Some assessment that they do. I think to get some sense of how with it the patient is, right? Okay. Something about is the patient knocked out or are they fully there? I don't know. And what I thought was so interesting is I asked him, who's the competitor? When I try to think about things in startups or technology, I always try to think about things very inductively. So, whatever someone gives me a data point, I always like to try to connect it to some broader kind of abstracted point about whatever the system. And I said, okay, so who's the competitor? The competitor is not some other sexy Silicon Valley startup that's raised $30 million. And the comparison was some God knows medical device company you never heard of. And he told me that the way it works is they deliberately designed the device in such a way that they basically have consumables such that each time they use the device, it has like a max— it has a really handicapped maximum where maybe you can do 10 patients. So, let's say a patient comes in, I have this device that I can do like 10 clicks of it, and I have to rebuy the consumable to redo it again. And so, what happens is that this device is in every single hospital room across every single hospital in the country. Okay. You see where I'm going with this? And he's like, it's completely arbitrary what they've done. They've literally intentionally designed the device in such a bad way that it costs the hospitals so much money, so much money just to actually replace the consumables. He's like, you could just be done with the iPhone. Everyone has an iPhone already.

Mm-hmm.

And by the way, it's not like you can take only 10 iPhone pictures. You could take a million. Actually, you don't even need to have 100,000 iPhones. You'd have just one that you could just just do it, whatever, a million times, 10 million times, a billion times. And this was such a great example to me. It was so illuminating because I was like, wow, this is such an obviously correct thing to do. Obviously, you should— I mean, this is a nice camera, right? If you can build some software on top of it to make it work, that seems like something that's technically extremely feasible. It's mass adopted, right? The iPhone's mass adopted, which is why implicitly you see Apple becoming more and more like a healthcare company. That's one other thing. And then the last point is like, well then why is that? Why is the hospital not adopt your tech. Politics, regulation, existing infrastructure. And that's what you find so much in healthcare. It's not that there's not technological solutions that don't exist. It's that there's a regulatory, political, paternalistic infrastructure that inhibits innovation. So, this is my long way of saying that if I inductively apply this concept to every single medical device and every single possible care, also implicitly in my mind, I'm kind of swapping out humans for humanoids as well. I think that's going to inevitably happen. I mean, I see the Tesla bots running around. It's just a matter of snapping your fingers and I'm sure a lot of healthcare will be replaced by that, which isn't a bad thing. I think jobs will just get better and better. There'll be more value created for society. All these things considered, we're not even talking about a world where that 5% of people is going to anywhere match near the expenditure.

Mm-hmm.

So, all this being said, I actually think you can very much see a world where you build a real-time consumer-centric quantified health system. That basically no insurance needed.

So let's say regulation hurdles aside, or sorry, regulatory hurdles aside, do you think AI will lower the cost expenditure for healthcare?

For sure, yes, I do. I think there's 2 kinds of companies I like to think of, and I mentioned this at Three Cores too. It's like one kind of company is like, we're going to build software that kind of is for the existing system, right? There's some really disturbing— I read something recently, I think it was on Twitter probably, about how there's still systems that use fax machines and how it still runs a lot of medicine. And then basically they're like, oh, we have this new AI tool that will whatever, you take a picture and it makes it better. That's great. That's really nice. It's sort of like a good fix for a broken system. I always like to think more kind of less evolutionary and more revolutionary in that I think you need to build a system that completely replaces the existing healthcare infrastructure. I don't think anyone can look at the healthcare system today and say, Man, wow, this really works well. And it's crazy.

I don't think I've ever met a single person that's been like, I love American healthcare.

What's amazing too is even the doctors don't like it. If you go ask a doctor, I mean, my dad hates healthcare. He loves patients. I mean, it's almost like a founder thing. You love serving customers. He loves serving his customers. He loves, he gives people their vision back. There's nothing I think greater than that. I mean, that's one of the, just amazing the fact that he can move his hands around and then boom. It's like magic for patients. No.

And sometimes I feel like with my, um, my previous doctor when I had an autoimmune disease in college, it was like me and the doctor, my family versus insurance over and over and over again. And it's exhausting. And I can only imagine for people who don't even have access to like a primary care facility to begin with and things like that, and they have to pay out of pocket, they're not insured, it only gets more difficult for them.

And if you read, you know, Luigi's Manifesto, he talks exactly like that. His mom got sick, then he got sick, and then they started this insurance dance where they wouldn't cover it. And that's not to say obviously whether what he did or not justified, that's besides the point. The point is that there is a system today that impacts millions and millions of Americans where we all, everyone, everyone in the system knows it's not working. And what are we going to do about it? Are we going to build solutions and technology that basically kind of patters to the status quo? Or are we going to throw everything out the window, because we should be, and say, hey, we're going to rebuild from scratch? I like to think of Tesla as always a great example here. SpaceX is always a great example here, right? When Elon built Tesla, he's not thinking, okay, let me look at— let me build some solution that's going to help the manufacturing to improve the production of a Toyota. No, that's not what he did. He didn't build some AI tool that actually made something in the existing infrastructure much worse. He tried to make cars into basically software. Right. It's like it literally becomes a software company in a way, obviously not literally, but in a way. So, kind of the classic first principles thinking. SpaceX too, reusable rockets. It wasn't like, oh, let me go build a company that's going to help Boeing do their things better. No, I'm going to build a company that's going to replace Boeing. I'm going to build a company that's going to replace NASA. In the same way, my ambition is not to build a company that's going to help the regulatory insurance complex. My job is to build a company that replaces it completely. And I think the greatest means and arena isn't shooting someone. It's in the capital markets. It's in giving people better services for cheaper and actually more effectively helping stave off disease and eventually treating disease. How do you do that? It comes down to data because medicine is really just bad data science. When you look at it from that angle, you realize everything we do in the healthcare system is fundamentally wrong, including the way that doctors are generally educated. To be very clear, I have the utmost respect for doctors. My dad's a physician. He's helped save so many patient lives, or visions, I should say more specifically. There's nothing more commendable than becoming a physician, but that does not mean that the entire medical schooling is anywhere near where it should be today. I mean, it's nowhere near where it should be today. These are curriculums that are written without the idea of the internet, much less AI that exists. And so, doctors are overwhelmed. There's far few doctors. There's also an arbitrary restriction on doctors in this country that doesn't really make sense, right? People talk a lot how there's not a lot of a supply of doctors. And, you know, if you think about it, it's sort of, again, kind of regulatory capture here, right?

But why? What was the purpose of that regulation? Like, why would that exist?

I mean, maybe the American Medical Association doesn't want that to happen. The doctor, like, you'd be quite a quasi-union. I don't know. I don't know why it does. I don't know why there's so limited doctors. I mean, probably if you ask, like, the— because I'm sure, like, the American Medical Association has some, like, policy or opinion on this. And I'm sure if you ask them, they probably want to create loops, right? Because like someone has to give someone an MD, right? It's like a JD, right? And there has to be some government that says, yes, you are a doctor, right? It's an entity that says you are a doctor. And this entity, they probably want less doctors because then they can make more money. It's that simple, right? Which is inherently anti-patient, it's anti-consumer. So, my model is almost like, why do you need to do organic chemistry to become a doctor? You really don't. It really doesn't make a lot of sense. Why are you doing Let's be real, you're not going to do calculus on the job. I mean, are you kidding me? Come on. And like, you know what, oh, it teaches you how to think. There's a lot of ways to learn how to think. You don't need to spend, you know, spending 5 years learning, you know, organic chemistry you're never going to do. I'm sorry, it's true. Like, I think Matt's laughing over there, but, um, but like, it's true. You don't need to do that. You need to call a spade a spade. Like, you need to call a spade a spade. You don't need to do that. Um, so that being said, I'm like, hmm, well then, um, you know, why, why could we— and this, this is kind of a radical idea, and I think a lot of people in the medical community would strongly disagree with this idea, but I'm gonna put there because I think it's interesting. Why can't we produce, I don't know, 100,000 doctors, a million doctors in 2 years? What if we looked at the medical curriculum and we applied Department of Government Efficiency energy to it? I'm serious. And we said, hmm, what is actually valuable here? Which I would argue one of the most valuable things is actually bedside manner. It's bedside manner and then some sophisticated AI system, frankly, because someone needs to talk to the patient, understand enough about the kind of specific specialty And they probably need to be able to access and effectively communicate with an AI system. I mean, you're already seeing AI systems that outperform doctors, not only in the accuracy of the diagnosis, but in empathy, which I always thought was interesting. That's surprising.

Oh, I'm surprised. Yeah. But it's a thing. I remember I went to a hospital and I, again, this was when I was having an autoimmune breakout. I had blisters in my throat. It was scary, whatever. I go there and I'm supposed to get an IV. And I remember, like, I— they thought I was a drug addict because I hadn't slept, I hadn't eaten. They refused to give me medication and I couldn't talk. Oh God. So I was writing on, like, on my phone. I was like, ma'am, I can't talk. And I was just crying. And I really didn't want any needles because I'm scared of needles. But I just remember this woman was like, you need to stop crying. And I was like,

oh my

God. Oh my gosh. I just— first of all, like, I'm in so much pain right now. But second of all, I think bedside manner Healthcare is super important. And I think a lot of doctors/nurses just completely neglect that. And also, I feel like it gets worse for different people. Like, I have heard there's a lot of discrimination against people who, like, enter the healthcare system who are insured, who are covered.

Mm-hmm.

Obviously, they get better treatment, they get treated better, they get spoken to nicer, versus if you have less of a— not as good of an insurance or something, sometimes you get more attitude. And I think if that's something that can be fixed completely with AI, that would be amazing.

Yeah. And any diagnosis is really just a series of kind of information inputs that then someone makes some sort of diagnosis, right? It's really, if you go to a doctor, if you think about it, they're really running kind of pretty crude, mostly qualitative models in their head. So, you go to a doctor, they ask you a question, they get information, they ask you another question, they get information, right? And they do ask you a series of questions. There's almost different paths in their mind, right? It's almost like a video game of if this, then that, then this, right? If this, okay, ask another question. Okay, okay, another question. Okay, another question. Oh, based off my kind of decision tree, which they all have in their mind, they've done it, trust me, 10,000 times in asking these series of questions, you should do this thing, right? And then this thing is some drug, maybe it's some follow-up testing, whatever. In other words, what I'm trying to say is that that entire process is completely AI-able. So—

But okay, so it sounds like automated. Like what you said with the doctor in the pocket, does that mean replacing doctors or nurses entirely? I

think, um,

are we gonna have a humanoid doctor?

I think a humanoid doctor would be pretty cool. We should actually put like a lab coat on a doctor and I think she should have a walk around. Maybe buy one of those like Tesla humanoid robots and I put like a jacket on a doctor. on it, like a, you know, like one of those, like, you know, doctor jackets on, and I have a walk-around nucleus. I'd be like, hey, here's the new doctor of the future. That'd be funny. Um, I do think there's a world where doctors are completely supplanted. I, I mean, I— or actually, I should say differently. What do you think about that? I think doctors will ultimately be— the entire job will be redefined. I don't think it's like doctors. It's gonna be something entirely different. I mean, there's gonna be just AI ingesting a lot of data from people. It's going to It's going to be a real-time system that continually tracks you. It's going to be a molecular profiling system that tracks every kind of interaction in your body, your DNA, your microbiome, your epigenetics.

I feel like it's going to know more, like these, let's say hypothetical humanoid robot will know more than a doctor would ever know because it's just so much information.

Exactly. That's exactly correct. And then the AI system, this kind of almost omniscient in silico doctor, is going to ingest all your different diagnostic information and then it's going to basically be able to flag molecular aberrations prior to any phenotypic signal. Let me give you an example of this, a very small example. I sleep on an Eight Sleep mattress, great mattress, shout out to Matteo. He's one of our investors as well. And I woke up one day and my heart rate shot up 50% the night before. And I thought to myself, huh, that's crazy, probably a bug in the system that my heart rate's not at 50%. I was like, that's crazy. There's no way it's true. And then around 24 hours later, I was feverishly sick. And I was like, huh, in some weird way that actually my heart rate jumping up way before actually predicted that I was going to get sick. And then maybe I probably would've actually not maybe, I would've definitely operated the day differently. Ideally, the AI system would go, hey, your heart rate's super up. You should probably go down to the One Medical down your street and get a COVID test ASAP, because this is probably COVID, or whatever the given thing is at the time of whatever it suspects based off it knows where I've been, it knows who I've interacted with. I've interacted with, it knows all this information about me. And then, I actually realized that Michael Schneider, brilliant, brilliant scientist who's done a lot of work in precision medicine and thinking about integrating in different diagnostic tests, he actually has shown in studies that heart rates will jump up prior to actually getting a cold or whatnot. And that's such an obvious thing to me that should be integrated. Obviously, it'd be great before you get sick, before you get sick, because when you get sick, it's like you get sick really hard.

Yeah.

So, if you can prepare yourself, That's the entire ethos of preventative medicine, either preventing the onset of or the development of a fever or cold, etc. So, this is a great example where today, using today's existing software on a device that I don't necessarily think today it's intended for medical purposes, already providing medical insight. And I'm like, wow. Now imagine that again, inductively at scale between Oura, Levels, Eight Sleep, Apple Watch, and then that's on the personal hardware device side. Right. side, on the lab testing side, blood tests, epigenetics, genetics, microbiome, combining all those datasets together that is real-time tracking you and your body, you can identify disease far before the healthcare system can today. Moreover, if you do get disease, you're going to be able to go far and find more targeted actual treatments. I mean, we still live in a healthcare system where drugs actually affect people dramatically differently and it's not even genetically informed. There's an entire class of genetic analysis called pharmacogenomics, basically talks about DNA effects, metabolism of drugs. And yet it's not mass integrated. People still take drugs that are not as effective for them. And so, all these things, I constantly think about how there's already science today of this happening. And I think we're going to go through an internet-like revolution in healthcare.

Okay. Wow.

Sorry, I talk fast.

We got lots of information, lots of things coming. So, in the perfect world, the future of healthcare, give me a snapshot in 100 years. of what the US healthcare system will be like? First of all, are people going to live forever?

I think we can live forever. I think we can live forever. I think the way we think about living forever— so I think about this a lot of ways. One is that I like to think that our base case in the industry, if you're in the health industry, if you're in the longevity industry, our base case as an industry is is we should try to double life expectancy in the next 150 years because that's what we've done. So, there's a lot of people outside the longevity spaces that will go, this is a delusional idea. This is crazy. No way you're going to live forever. A friend of ours, Bryan Johnson, trying to live forever. He's doing amazing work. And people will look at that and say, this is crazy. It's never going to work.

How much has he reduced his age by?

A lot. I mean, it depends on the organ, but a lot. I mean, he measures the different organs and then kind of determines. What's his actual kind of biological age right now? I don't remember it offhand, but it's dramatic. It's really dramatic.

It's crazy.

It is crazy. Ryan is an amazing, amazing person. And so, I think when I think about longevity and you ask like, do you live forever? I think that the kind of worst case, the worst case is we do what we've already done, which is we double life expectancy in the next 150 years. And maybe that sounds sci-fi to some people. I actually think that's like kind of the bare minimum because that's what we've already done. I mean, we've done that before. We know for a fact that it's not science fiction to say we can double human life expectancy. We know for a fact that's not just like fiction. So, why can't we do it again? So, when you ask me if I can live forever, I like to think of it as what are the things that are killing us today in the same way that in the mid-1850 example I gave, it was whatever antibiotics that helped solve that problem. Today, I think what's going to be the kind of silver bullet, so to speak, or the mix of different bullets that are going to solve the problem of chronic disease. I don't think it will be a silver bullet, to be clear. I think it'll be a complex interaction of different innovation. Yeah. And I, on that point, think that if we do this kind of thought experiment enough, I mean, do I think we could live forever? Possibly. I mean, eventually it's going to probably need some sort of kind of molecular restarting of cells, but I mean, still cells are kind of rebirthing themselves all the time. So, I think yes, basically my answer is yes. And I think to bring it down to another level is I think we can double life expectancy in the next 150 years.

I just had a question that popped up in my head. I want to talk about the— there's been a lot of study around the animals that live very, very long. Is it the horseshoe crab that lives like 250 years or something?

There's a horse— there's jellyfish live really long. There's some sharks that live really long.

Yeah, it's the basking shark. It can live over 150 years old. Yeah, yeah, exactly. It's like a fossil, a moving fossil. Yeah. What is the correlation between the animals living long? I heard it's their heartbeat.

I think maybe something like that. It's like a— is it a slower heartbeat? I don't know.

Yeah, they have a super slow heartbeat.

I think that's what it is, yeah. I think we should definitely study these organisms more, and they're definitely probably understudied. One of the things that you realize very quickly when you're in some sort of scientific frontier is that we know a lot less things than people would think, right? You often think like, I imagine like, you know, I don't know about kind of the jellyfish longevity specifically, but I imagine that there's some jellyfish longevity expert There's some people in the world that know that. There's some probably jellyfish journal that if I really got into jellyfish, I could find out this journal. If you go there, read about all the jellyfish, and that I probably, if I read all the papers and kind of digested all the papers, I would realize, wow, people really don't know or understand jellyfish longevity. They really don't. They really know it lives long, but they actually don't understand what the hell's going on. Mm-hmm. And you realize that across many, many kind of different scientific disciplines where what you would imagine is known is actually dramatically less known. And I think that's always kind of interesting going up to the intellectual frontier in that way. So I'm very curious. And I mean, I almost want to, after this podcast, go and look up jellyfish longevity and see if I can actually find a paper on it, which I definitely can.

If you do, send it to me. I will. Okay. But sorry, going back to what we were talking about previously, which is the vision of what it looks like, the world of healthcare looks like in 100 years.

Oh yeah.

Give me the snapshot.

Yeah. I think any physical labor is humanoid For sure. I think for sure. Anything that requires— so I'm gonna tell you my ideal vision. I don't necessarily say—

Okay, you know what? You pretend you are the president of this country.

Wonderful.

Kian country. Nucleus is the capital. It's a wonderful place.

Matt's like, oh God. Oh my God. He's like, this is not good.

Tell me what the perfect healthcare system looks like.

So, I like to imagine a healthcare system that is actually at home and discreet. It's bespoke. Also has good taste, but maybe that's for some people besides the point. Healthcare, they don't have good taste. I mean, we got to have better, more beautiful products. I mean, we got to serve people better.

Yeah, I know. It's very unattractive.

Anyways, I think that we need to be tracking basically every single biomarker in the entire human body, effectively, if not every day, nearly every day.

Biomarker?

What is that? Yeah, so like LDL. If you know LDL, like if you take a blood test, it's like a biomarker, just like some marker in your body that indicates something about your health. Okay, got it. Like that, right? Whether it's your genetics, epigenetics, microbiome, it could be a lot of things. And so, when I think about an at-home health system, I think about a system where there are very miniaturized devices, different devices that, I mean, I really hate to say this, but the whole kind of drop of blood idea isn't the worst idea. That's one of many examples of kind of health tracking where you can get a little prick on your blood, they can tell you all about your different metabolites. So today, if I want to get a blood test, for example, I have to go to Quest. And I go to Quest, first off, it's slow as hell, software's old, you feel like you're in kind of It's like doomsday in there. Like, it does not— not a good vibe.

It's also, it gives you so much anxiety because it's like your test results are back. Yes. It's like, am I positive? Am I negative? What's going on?

Exactly. A lot of anxiety. Super stressful. Patients don't really know what's going on.

50 million different accounts.

Oh my God. 100 different accounts.

Lost passwords. Smart software.

Nightmare. You go there, you show up, you sit down. They don't really know if you've eaten or not in the last 12 hours. They don't really care. You sit down, they shove a needle in your arm. They collect whatever, 6 vials of blood, 7 vials of blood, maybe even more sometimes depending on what kind of test you're doing. And then the results, whatever, take 2 weeks. It's just a bad system. If you actually look at full-body MRIs, another good example of this, where you have to go, it's extremely expensive, it takes your entire day, there's some negative effects of it as well, yada, yada, yada. Now again, apply this inductively across everything where it's like, okay, there's physical infrastructure that produces information. Some of such physical infrastructure has already been And that has been kind of miniaturized and is now wearable. You can think of that Apple Watch as a very early sign of this. You can think of if you could somehow do a blood test at home, not by injecting, but by just simply putting your finger down on a device that then tracks it. You can think of a full-body MRI. Imagine I woke up in my bed. I don't know, this is going to sound crazy and delusional, but that's why we're here. Imagine I woke up in my bed, it's like, oh, we did the body scan. One of my AC becomes a full-body MRI machine. Hey, don't worry, we didn't detect cancer. And it's cancer-free. And there's like no radiation effects. I mean, that'd be amazing. That would be sick. They save more lives than anything else we could possibly conceive. I can imagine also a world where all this data is fed into a single application, a single omniscient health AI, à la the doctor in your pocket. And that's why we really view ourselves as the application layer for healthcare. Because if you notice what I'm describing in this future, there's many different diagnostic devices at home. They're all kind of tracking and measuring different parts of you. But who's going to actually make sense of that data and bring all the pieces together? Yeah. Because healthcare is inherently multimodal, meaning there's many different variables and many different factors. And whether it's a blood test, a genetic test, epigenetic tests, microbiome tests, urine analyses, full-body MRI. Really to give the best proper diagnosis and treatment, you need to integrate all of that data. And the analogy I like to use here is similar to Instagram. It sounds very counterintuitive. What the hell does that have to do with Instagram? The reason for that is it's sort of like the digitization of photos. What do I mean? Well, photos back in the day was in a darkroom. Take a picture, you had to go develop in the darkroom. Very slow, very low throughput. Eventually Kodak came out, got a little bit better, then the iPhone eventually came out and you can see with the rise of the iPhone, an exponential proliferation photo data, right? Obviously if everyone has a camera in their pocket, yeah, there's gonna be a lot more photos. How many pictures? Yeah, I have no clue. Probably much less than other people.

82,000. Wow.

82,000.

Yeah, I got all that storage. I got like the— I maxed out on the Apple storage.

Mm-hmm. How's that? That's like—

these are photos from since I was like—

that's like

12 years old.

That feels like 250 pictures a day or something if I did my math in my head very quickly.

I definitely don't take that many. I do take photos probably on average. On average. Of like random stuff.

Maybe a lot of screenshots. A lot of screenshots. I don't know. 82,000. Wait, actually, 82,000 over what time? Not in a year.

No, no, no, no, no, no, no. This is since I was 14, 13. Oh, okay. Okay.

For some reason I was like, that's impossible. I was like, that would be a little crazy. Anyways, so it's like photos. So where everyone has an iPhone in their pocket and they start taking pictures of things, then what happens is that there's an exponential proliferation in photos, in data, which is photos. And it wasn't the Photos app on the iPhone, right? The iPhone has the Photos app. It wasn't the Photos app that made sense of everyone's photos data, it was Instagram. In other words, a hardware device, namely your iPhone, your smartphone, was responsible for the exponential proliferation in data, namely in this case photo data. And then photo data was actually taken by another company, Instagram, and the data was actually made useful. You could share it, you could like it, you could edit it, et cetera. I really think the same analogy holds for how healthcare, where there's going to be different devices, à la the iPhone, that produce data. And it's going to be a separate company, not the hardware companies, right? Because Apple ultimately is incentivized to sell more hardware, right? That's why they never built Instagram, right? I mean, they have an App Store for that reason. It's for other developers to come in and build software. In the same way that all these different hardware companies, their job is to sell hardware, there's going to be another company, an application layer company that's going to basically integrate in all this data. And then in doing so, we can have that future that I outlined to you, where as all those diagnostic tests get better and better and better, miniaturized and ideally are at home, all that data will combine then into kind of an omniscient AI that's always got your back. It always knows what's going on with you. It knows about every part of your body and will flag far before any onset of disease before it happens. And then in doing that, I think the upper bound of how long we can live is really, really actually indefinite. I really think there's an unbelievable amount that we could survive.

So, I'm very excited. Who says we need our bodies or our organs to survive? What if we just need our brains?

Maybe. I mean, I think there's always this question. If you, you know, I do a lot of meditation. It's like, well, you know, when someone dies, you see their physical body dead. But, you know, unfortunately, you know, if you look at, you know, when I— people in my family have died recently. And, you know, when you see the dead body, it's almost like the body's there, but, you know, they're not. But then it makes you think in just like this kind of causal experiment. Well, it must not be the body. The body isn't the thing, the soul is. And people take this all sorts of ways, but the soul to me is probably a real— it's not probably. To me, the soul is a real thing and the soul is not physical. So there's different planes. There's the intellectual plane, kind of thinking. If you get really, really deep in thinking, you enter a kind of a different universe. It's hard to explain, but it's a different world. If you are thinking right now, we're in the physical plane. I can touch things. If you're dreaming, you're in another world, literally another plane. If you're astral projecting, you're in another world, another plane. Now we're getting into it. But anyways, there's many planes. So the soul is real.

Okay. Yeah, we'll have to dive into that another time. Okay. But circling back on a little bit more regarding the genetic testing. So how do you feel about designer babies?

It's funny that actually on Twitter today people were talking about this and I was trying to send out some corrections to people on how this actually works. Um, I think that— yeah.

And do you think it will be— is, is it possible today to make the, like, Build-A-Baby basically instead of Build-A-Bear?

Build-A-Baby. Um, I, I think that a lot of the, like, genetics is actually much less advanced than people think in that respect. And I was saying this, which is like, people want it to be such that you can use a gene editing device to basically, you know, correct a letter in your DNA, and then all of a sudden, you know, you don't need sleep. All of a sudden, you know, I don't know, you don't— won't get any disease or something. Um, and, you know, it's maybe to some extent true, but reality is with a lot of conditions, they're polygenic. What does that mean? The old model of genetics was that a single gene drove a disease. So, if you think about the word mutation, geneticists don't really use that word anymore, but if you think about the word mutation, there's a mutation in someone's gene, and then because of that, it causes a disease, whatever, cancer. But now the new model of genetic analysis and the kind of architecture and the way that genetics helps shape diseases and traits is that they're polygenic. What this means is they're not driven by one gene, but many genes. And so that means for a lot of conditions, you would need to basically engineer many, many, many parts of someone's genome to actually get the desired effect. You wouldn't just edit a small part. Now, I'm sure very, very kind of smart person's going to say, oh, well, there actually are— there's this variant that actually is— has someone to be a short It is true that despite polygenicity, which is a universal fact, which is like any trait you can think of from height to breast cancer is caused by many genes, it's not caused by one gene or many variants. But if you think about something like short sleep, it was older age recently. Short sleep is interesting because there are known markers that actually can have it such that someone only needs 4 hours of sleep, for example. And in that case, the way that the phenotypes exhibit in humans is one where there are many, many variants. Variants are just DNA differences across many, many genes that basically confer the phenotype as I just described, this kind of idea of polygenicity. But it is true that there are markers that are really, really bad or have really, really large effect on a phenotype. An equivalent of breast cancer, like BRCA, for example. Maybe this is getting too complicated. My point is that

it is speaking a whole other language. I'm here for it though.

No, I know, I know.

I'm just like absorbing what I can.

Okay, it is possible for it— what people want to be true about genetic engineering isn't true, but there's some parts of it.

Won't it be true one day? It's gonna be possible one day, right?

True that it will be true True. If we can do multiplex engineering, so if we can actually in parallel engineer up to a million sites in your DNA, yes, it will be true. But that's kind of my point, which is it's actually much more complicated than people think. That said, and there is a that said, there are markers that are rare that are high penetrant, which means they have a large effect on a phenotype, right? So, for example, if I look at the short sleep example, right, there's a marker that makes someone that they only need to have short sleep and it's just one mark in your DNA. just that marker, then it is true you could engineer that specifically. So in that way, yes, it is true, but in the way that these are polygenic, it's not what people think in terms of engineering. Does that make sense?

More or less. So in the future, are there any patterns today that you've seen will heavily impact people in the future? Like, for example, is there anything that you've tested with genetics, um, that we can use right now with data that you have to predict future outcomes of people's health?

Uh, yeah, any disease. So like, um—

Like, what are the patterns that you've seen, if any?

Uh, you mean like when, when predicting disease, or what do you mean?

Yeah, uh, any patterns of disease that you've seen.

Like in people when they— if like people who are sick or people in their genetics or—

Yeah, in their genetics.

An estimated probably around 5% of customers have a genetic marker that dramatically increases the risk for a condition that they learned through us. Around 80% of customers have a marker for a rare disease that And because they don't have 2 copies, they don't have it, but can dramatically impact their children. For example, I'm a cystic fibrosis carrier. So, I actually didn't even know that until I did Nucleus. I'm a carrier for a variant that if my future son or daughter gets another bad copy, they would have a disease. That's the importance of kind of what's called preconception screening or doing genetic testing in the family planning context. Something that Nucleus is actually going to launch next year. We're actually going to launch the most expansive panel for family planning. Nothing's more important than having healthy children. Mm-hmm. That's another, I think, really interesting thing. Another one is that every person is actually at very high risk for at least one common disease, but basically no one knows it. I'm actually at really high risk for— this is always a funny thing. I'm actually really high risk for schizophrenia. Really?

You don't think? No, I'm kidding.

Matt's like, oh, God. Matt's like, I knew it. Matt's like, this is the 4th time I have to hear him. Yeah, I'm actually really high risk. He's like, here we go again. He's like, oh, wow. He's like, oh, God.

We've been knowing this.

We've been knowing this. Knowing this, exactly. It's just crazy.

But no, any signs or what?

So what's interesting is you can be really high genetic risk for schizophrenia. I'm actually 99% on 99.3 specifically. And what's interesting about schizophrenia is that you can be a really high genetic risk for it, but still have a really low overall chance. That's because schizophrenia is a pretty rare condition, relatively rare condition relative to other common diseases. So said differently, even though I'm high genetic risk, I'm not likely to get schizophrenia. I think I have a 1.5% chance.

But it could be triggered, right? Well, that's the thing.

Well, this is exactly where I was going, which is like, you know, people, you know, one of the most actionable things I learned from my nucleus results was like, I've personally never done any drugs, but I'm like, my God, am I never really doing any drugs? Because I am in 99th percentile for my genetic risk for schizophrenia. I am not going to do drugs. I'm not going to do psychedelics, right? There's no kind of trips like that for me. Pun intended. So that is something that I— get that in the podcast. I'm so

sorry.

You have to get that in the podcast. That is fire. No trips like that. I mean, wow.

It's gasoline. Continue.

And so when you think about what's interesting about something like schizophrenia, and actually really any phenotype, is that— let's think about height. It's a much simpler example. If someone's very tall, it's just a trait. Someone's very tall, great. If someone's very, very, very tall, it's actually a disease. It's called Marfan syndrome. People know that intuitively on the other end as well. If someone's short, they're short, whatever. If they're extremely, extremely short, it's dwarfism. It's another disease. In other words, the extreme end of any phenotype, which just means any kind of trait like height, is a disease. But before then, it's usually some exceptional characteristic. And so schizophrenia is a good example of this where the extreme end of schizophrenia. As you get schizophrenia, it's really bad, but before then, you actually have some kind of extreme cognitive function or ability. And on that point, I've actually noticed a lot of founders who do nucleus have really high schizophrenia risk. Do you know that, Chrissy? Yeah. And so I think about that a lot, and I think it's one of the most underappreciated—

You said founders that do nucleus?

Yeah, founders, founders, people who found— people who start companies who do Nucleus. And, you know, I talk about the results if they're comfortable, obviously, sharing them with me. And, you know, a lot of people are high schizophrenia, uh, a lot of people are, are very high. And that's a great genetic model.

Is it just founders or people in general are high chance of schizophrenia?

Founders. Like, no, no, like, like founders, people who start companies, if they do Nucleus, they're in the 90s. Like, they're very high risk genetically for schizophrenia, right? A typical person's average risk, you know, inherently, definitionally. Yeah, but founders are really high genetic risk. So—

So none of them should be doing

Well, I mean, yeah, I think there's actually 2 extremes I've found. It's either really, really low or really high. Obviously, there's exceptions to this. Most people definitely share average, but I think that's— yeah, I mean, there's something to that. I mean, 99th percentile is a bit extreme. You can be 70th percentile, 80th percentile, which means you have a genetic score that's higher than 80% of people, which again doesn't mean you have an 80% chance. It doesn't mean I have a 99% chance. What it means is just it's higher than 99% of people in my population, which is just like whatever, Middle Eastern. So anyways, I think that that is one of the most interesting and kind of underappreciated parts about genetics today, which is it says something very fundamental. I have other examples too, but—

What are the others?

I'm so curious.

I really want to know.

There's just other examples. There are certain people I know who, for example, they're very high genetic risk for OCD. OCD.

Okay.

Oh my God, you know, you don't— you wouldn't even need their genetics to know. It's like, it's like amazing. It's like, wow, that was genetic. Like, of course, like, you know, some of my friends are like really kind of OCD.

I didn't even know OCD was genetic.

Yeah, yeah, yeah, it is actually. It's pretty amazing, right? And I'm like, wow, what the hell? Like, this person's so high. I'm like, oh, you know, it kind of, you know, you kind of do this, like, it kind of checks out, like, it kind of makes sense, you know? Uh, that's always really interesting. Also, you can be— yeah, yeah,

yeah, I, I feel like The more of these tests that you do, I feel like you're going to be able to figure out so much data. I feel like I saw the screenshot you posted on Twitter of your Eight Sleep. You're in the lowest 20%?

Top 20% latest sleeper, which is crazy, by the way, because I sleep at like 12:00 to 1:00 AM and I'm like, everyone must sleep at 12:00 to 1:00 AM. But then it's like, you're in the top 20%. It's crazy to me. I would think, I don't know why I thought that. Maybe I'm just in such a biased group of people. I am in a very biased group of people that I just inductively got it wrong.

Yeah, it's that founder's mindset. You guys go to bed late.

I guess. Yeah, it's not good. I need to go to sleep. I want to follow more of the Bryan Johnson model, which is like instead of— I like go to sleep around 12, I wake up from like 6 to 7, depends. It's not good. Like, I want, I want more sleep. That's like one of my 2025 goals is get more sleep. I think it's like, it's like, oh, grind, grind. Yeah, it's like I've been grinding for 5 years. I've been on this grind, you know. I, I, I, I, I've, I've worked, you know, 12-hour days for like every day for like 5 years. And 12 hours, by the way, is like 8 to 8, right? There's a lot of time on.

996.

996, yeah, exactly. Let's go. Everyone's like 996. I'm always a little bit skeptical if someone's like, oh, we work really, really, really hard. And they keep saying that, like, is that actually true? Because what I've noticed the most about kind of hard work, and this is a totally different topic, but it's like, it sort of like organically compounds in a way. Like starts compounding in a way that it's almost like, Let's say I could theoretically go from 80 hours and double it to working 160 hours. Nothing would actually happen because there's only so much you can do. I don't think actually then output would double or something. I don't think so. So, you're not as in CEO. There's so much leverage with the decisions you make that if you make a wrong decision, I mean, that's really bad. It's kind of like Elon's thing where he talks about how you shouldn't kind of automate things that shouldn't exist. Great example of this where it's like— Oh, that's a good take. You have to get the decisions right. If you get the wrong decision and then you start working 80 hours a week in that wrong decision, kind of moot point. Who cares? You're making something automated that shouldn't exist.

Yeah. Be productive and efficient with your time. Well, okay. How can people get in touch with you and get their own tests going on? How can we figure out if we're going to be schizophrenic or not?

Yeah, exactly. You can DM me. Twitter, @KianSadeghi5, mindnucleus.com. Um, yeah, DM me any questions you have. I'm always happy to talk about genetics. I love people DM me all sorts of interesting questions, so feel free to DM me anything.

Awesome. Well, it was really great to have you.

Thank you so much. Thank you.