#31 - The Don't Die Network State | Bryan Johnson

25:17But it feels to people like a drug because it's on somebody who's already living, but it changes them in an upgraded way, right? And it's actually been started to work for people who had sickle cell and other things. We can put that on screen. So, okay, so there's gene therapy, right? And then let's talk about cell therapy. What should we look at? I mean, there's a, we've been trying to get access to cell therapies for years, and it's never been possible because they're just too high risk. So there's some attempts at doing this with mesenchymal stem cell therapies, you know stem cell therapies um we have not yet seen a lot of success with these we don't see a lot of good evidence you know as as one approach whether it's from your body or someone else's body um but there's some of the more advanced things to to change more uh sophisticated biopathways in the body that just don't have good pathways like it's a very hard drug to get approved and there hasn't been good ones too like it's a very hard development path you know an interesting question that occurs to me is, depending on what age these treatments would be effective.

26:30For example, let's say you've got a car and you hit the brakes and you hit them hard enough and it doesn't crash into the wall, right? There's a point of no return by which if you hit the brakes, it doesn't matter. You're still smashing into the wall, right? But as just a thought experiment, you might actually, from the ranks of older people or people who for whatever reason are pursuing euthanasia, especially the people who are pursuing euthanasia in places like Canada or whatever, right? You might say to them, why not try one of these treatments? Because if you're going to die, right? Like can't, like can't, like heart T therapies for cancer.

27:14Exactly. That's right. Just like, you know, for example, with this novel argument that strikes me that's similar to the ACT UP argument that was made in the late 80s, early 90s that finally liberalized the FDA after many decades. Basically, a lot of people who had HIV said, we're going to die. So why don't you approve AZT and these other things? So at least we've got a fighting shot. Dallas Buyers Club documented that, right? So it strikes me that a potential extension of that would be, given that there's millions of millions of senior citizens out there, right? Those who were like, you know what, might as well give it a shot towards the end.

27:55Now, the tricky part about that is in that car analogy, they might be close enough to the wall that even if the treatment worked, it couldn't reverse it. But maybe it could. I don't know. It's a case-by-case kind of thing to figure out. Maybe it's a powerful enough thing that just rewinds it. One of the things, I know you know this, but like that's always struck me and many others who have written about this is a relatively older couple can have a child that is a newborn that you know clearly there's something regenerative in our in our bodies that can birth again right like and so maybe that can be triggered even much later in life than we think yeah this is like yamanaka factors take get an adult stem cell, you go back to a pluripotent.

28:41So you make a seven-year-old liver young again. And of course, like now people are... Go ahead. Yeah. So with your biomarker thing, you could gauge someone's life expectancy. And if they've got only five years to live, give them what... Obviously they have to opt in, sign up all this stuff. They've only got a few years to live. Try it. What's lost? Exactly. I mean, that's the thing is the exciting thing is the technology is here. Like we actually know we can turn an adult stem adult cell into a pluripotent cell like legit age reversal of course now there's complications with you know can you avoid it being cancerous cancerous yes of course right can you avoid uh you know the other off-target uh outcomes uh so like there's things we need to sort through but the the cool thing is in 2025 we now know we can legitimately reverse age in a stunning way.

29:34And this is why I think your sentiment is correct. Topology where we're too damn slow, right? Like I don't, I don't know why we are not pursuing longevity. Like we're pursuing artificial general intelligence. You know, when I look at 2025 and I try to zoom out to the perspective of the year 2 ,500, either you're, you're working on AGI or you're building don't die. Like everything ultimately builds up to like your existence or your shared existence with AGI. And so I just don't know why it doesn't have a similar level of fever pitch build around it. I think a big part of it is the stuff that I think about a lot, which is the governance part, because you need risk tolerant jurisdictions since, and that's a whole change in your way of thinking.

30:23It's like, as we've talked before, the traditional financial system assumes you have some degree of inflation every year, and you lose some of your health, one, 2 % inflation, lose some of your health every year. And the traditional medical system assumes you lose some of your health every year. So you lose some of your wealth and you lose some of your health. And so it's a paradigmatic change. Even if crypto has bar charts and graphs that look like the traditional financial system, its moral premises are fundamentally different. Even if you or I will publish graphs that look like traditional biomedical papers, the moral premises are totally different because it says, maybe we don't have to die, right?

31:02Yeah. That was our first phone call. I think you called me, hey, Brian, I think we have the same philosophy. I reject inflation and you reject death. Yes, exactly. I congratulate that. So we're after the same concepts. That's right. Because actually, and the thing is that wealth, in a sense, is a cumulated life force, right? Because you're spending hours of your conscious, highly productive time on accumulating wealth. And so it's diluted from you. It's like you're spending, they're taking away your life. You know, there's this movie called In Time by Andrew Nichols that actually makes that metaphor.

31:34It's like, you know, sci-fi drama, but the concept is like the currency is ours, right? Like hours of your life or whatever, right? Yeah. I guess as a side tangent on this theology, I just finished this book, Passion, the Western Mind by Richard Tarnas. And he tries to go through the major epochs of ideology. And he starts with Plato and Aristotle going through the Renaissance, going through Christianity. medieval times, Renaissance, Enlightenment, modern-day scientific era. And if you look at it from that perspective, like a Ray Dalio principles perspective, where you zoom out, you say, hey, there's these big economic cycles, reserve currency, that they go through the same stuff every time.

32:12We're really due for a major ideological shift right now. And I think you're on this, I'm on this, that it's not some slight tweaks to the continuum. it is like a wholesale change on scale with other major ideological shifts. And I think what we're talking about today is basically a part of that. It is like this unleashed want for health. This is not about living forever. It's not about transhumanism. It's about we all appreciate waking up in the morning feeling good. We don't like aches and pains. We don't want to be diseased. We don't want to see our loved ones die. it's about feeling great and being great.

32:52And that, that ideology is not part of our zeitgeist. Like we just accept this slow decay, death and decline. And it's like almost something where you celebrate. It's like, I'm living life by slowly killing myself. Yeah. Well, human self-improvement is, I think the way that I think about it, where that's a continuum from simply eating right and doing better to hitting your genetic limits and then surpassing them. Right. And, you know, Now, towards that end, by the way, I think the influence of, let's call it, dharmic and sinic thought on the world. So, you know, for centuries, India and China, like Marco Polo sought out China and Columbus sought out India.

33:34That's actually how the Americas discovered, why Native Americans are called Indians, because India was a large enough economic power. It wasn't unified, but it was worth sailing to and for Columbus to do it. And so for the last few hundred years, India and China have basically been through their equivalent of the Dark Ages, and now they're coming back. And those two schools of thought have a different view on the human body, and they're less... The Abrahamic school of thought has kind of weird hang-ups in certain ways that the Chinese and Indian schools of thought or more generally the Sinic and Dharmic schools of thought don't.

34:10And so, like, for example, something I've thought a lot about, I may have mentioned this to you, the concept of genomic resurrection or reincarnation. Did we talk about that? We did, yeah. Yeah. Yeah, but explain it. You could get your DNA sequenced and stored on disk. And we already can do chromosome synthesis for eukaryotes. Like, you know, we could do it for actually, you could synthesize an entire prokaryotic chromosome from disk and actually have that thing swim around in a test tube. and as our abilities of chromosome synthesis get better you could imagine just projecting out to be able to do it for full complex human chromosomes and it's just it's like a technological direction it's kind of like if sequencing got better synthesis will probably get better it might take you know a few decades but it'll get there which means that if you have a file you could if somebody was well behaved you could write their genome to a blockchain or something like that.

35:07And if they had good karma, then the community would reincarnate them 50 or 100 years hence or whenever chromosome synthesis becomes feasible because they have the data there. And, you know, people argue the genome isn't everything, but it's a lot. It'll get you a lot. And then you could replay all their past life experiences for them if you recorded it. And they would just be born with all this amazing knowledge of everything that happened, you know? And that's something where if someone was going in on one of these, missions where they're trying out this experimental drug, you could sequence their genome and say, if it doesn't work out, you'll be reincarnated with the knowledge that you had in life and so on and so forth, and we'll look after you in the next life, right?

35:50And so that's like a dharmic inspired way of thinking about it. And then the psychic inspiration among other things is just extreme pragmatism and just experimenting with things. This is actually, you know, the Western mindset used to also be like this Banting and Best. Do you know that story? I don't. So Banting and Best, this is an important story for, you know, like 19, if I get the dates right, I think 1921, they started experimentation on insulin supplementation, said a hypothesis could treat diabetes. And they tested it on first like dogs, then, and it worked. And then they treated, you know, they tried self-experimentation and they went for patient volunteers.

36:29and those patients just like stood up in bed like this. It was like the canonical bench to bedside concept, like a bubbling beaker, you know, it was brought there. And they iterated on formulation and so on. And I think, you know, right now it's so hard to go from like pill to oral to an injection to a patch. There's a whole process. Whereas in some ways it's kind of like going from web to a mobile client to like a command line client. It's kind of the same drug, but in different formats or whatever. So they just did all of that. It wasn't case control studies. It was just iteration, quick iteration and seeing it.

37:03And it was a large fact. And by 1923, they had Eli Lilly doing scale production of insulin for the entire North American continent. And they'd won the Nobel Prize. And so that was a time when pharma moved at the speed of software, where you went from idea to execution and via iteration in like two years. And one of the issues I think a lot about is there's no case control studies on case control studies. There's no regulatory science on the regulator themselves. Why don't we have a jurisdiction where you can just iterate your way to, you know, to something? Where crucially, by the way, the patients aren't simply patients, they're participants in their own health.

37:40They're not like a row in a table, like a very 20th century study like Framingham or something like that. That's just like a row in a table, right? Whereas if it's a human being there and they've got a mobile app and they can send you back what their experience of the treatment is and they're an act to participate in their own health, that's a totally different paradigm. It's bidirectional. And the very simplest version of that is adaptive clinical trials, which show you can converge on an endpoint faster if you're not simply doing a really dumb open loop trial. where you're basically taking information from the trial as it's going to adjust things very shortly in adaptive clinical trials.

38:17But I think you can do much better than that. No software is designed with case control studies. SpaceX wasn't designed with case control studies. I'm not saying that there isn't some utility to them, but they're really for teasing apart small effect sizes rather than large effect sizes that are so manifest that they just jump out at you. Yeah. This probably goes back to the beginning of our conversation where if you have a large visual effect size, that gets everyone involved. And if you can do so in a jurisdiction to be like, hey, you can go from idea in gene therapy, cell therapy, or something else.

38:52And I mean, I guess that's why people are so interested in peptides is peptides are drugs, right? Peptides give you a drug like effect sizes. The problem is they're poorly characterized. There's questions about how they're manufactured and the quality of those things. And then also you have a lot of off-target effects. So, I mean, peptides are inherently a risky path when you're not doing a well-characterized path. But, yeah, I mean, if we could demonstrate some, like, one or two wins of a process from a talented drug developer to an actual thing that works, that has a low safety profile, large effect.

39:29Now, I guess it's TBD whether we can see that because, I mean, biology is complicated. Maybe that's too big of an ask. Maybe, you know, the GLP ones, they kind of hit that sweet spot where it's the immediate visual effect of fat loss. And it has all these follow-on health benefits where even if you're not obese or if you're not having an eating problem, they, you know, it gives you power, gives you discipline. Yeah, exactly. Yeah. Yeah. That reminds me of like in the future, like we talk about, like you and I have imagined what gene therapies, we say hair, skin, you know, muscles, et cetera. But it might be fun to a thought experiment of like, what would gene therapy be like in 30 years?

40:04Like what kind of nuanced, expansive concepts would, what do we think about? Now we think about like designer babies of like, we're going to choose height and eye color, but like how would you think about the human experience? Mini circle is interesting because they have the concept of a plasma that's reversible. You can turn it on, turn it off, right? So almost like, you know, I have some caffeine and I don't, right? You could, or you change your, you know, put on your contact lenses or not. But, you know, biology is complicated. There's all kinds of off-target stuff. It's not trivial to get it right.

40:35People's biology is different. All that's true. But there's something cool to the idea of a very specific target that can flip on and off a protein that can add or delete a function, right? And Patrick Friedman, I think, had that therapy. And he showed his mini circle, you know, which is doing the plasmid. So Patrick Friedman and Farb Nevy both had the therapy. and patrie said that he was just crushing it with his vo2 max farb also said the same thing right and um that's something where it's n of one but they know their own bobby better than anybody else it's n of one but it's over it's it's a lot of variables which are n1 you know mike snyder's thing on this from many years ago the didn't talk about mike snyder's prophet staford and he yeah no my yeah the integrum right he just took every possible thing from expression to the rest and just ran on himself.

41:29And he's able to, for example, see himself getting sick in the gene expression data, like a few days before he actually got sick, right? Exactly. And so we could, with better metrics, that's another piece of this actually, diagnostics are non-invasive, right? So if you had constant whole, like all 30 ,000 odd or 22 ,500, or whatever, you know, a human gene expression panel, and you track that and maybe, you know, metabolomics and so on. You did something like the Mike Snyder integrome while you're giving people this gene therapy or cell therapy. You just got a much richer readout, you know, from them, right?

42:10Now you're not just relying on self-report. You know, it is, it's like your biomarker stuff, obviously, but it's just like, you know, we're getting tens of thousands of them. Yeah. That seems like, you know, something that is very feasible because costs are coming down of all that sequencing and so on. That'd be a very good thing to do, like an intagram-like thing that's like a time series intagram. First you get whatever data points on them to determine healthy, then you impose a stimulus, and then you look in state space and you see, is it actually changing anything or not? And what's it changing?

42:36Yeah, that's exactly what I'm doing at Blueprint. So we are going to try to do, I guess we've done that for myself. Because when you build an ASIC, if you have not built an ASIC, it's really cool that if you get a picture and zoom in, zoom in, zoom in, zoom in, zoom in, you find that it's like down to the nano level of like granularity. It's beautiful. It's amazing we can design these structures. It's crazy, yes. It's crazy. And the thing is you can characterize this at every level, every circuit design you can characterize. You can say, what happens when I do this? And so like we can characterize our machines and our intelligent machines so well from the circuit all the way up to the application layer.

43:20We can characterize the entire stack, the servers they run on, the energy. Whereas in human biology, we can't characterize. You can get like a really high level of like, what does my blood draw say? Right? Like my cholesterol or my liver enzymes. But you can't get down and characterize these smaller molecular interactions, which like you're saying, they read out really important data. Am I getting sick? Am I showing signs of disease? Am I responsive to this drug or not? And in what ways? And so if we can pair the characterization with these jurisdictions with good drug developers, that's your path.

43:57And then we can open up. And once people get a few successes, we'll say, huh, this is a thing. We can actually do it. I was talking to Jason Kelly the other day. He's founder of Ginkgo Bioworks. Yeah, exactly. I was first money in. And they really led the charge for biotech in industrial applications. It was a really great moment. They've run into some roadblocks and doing it, which breaks my heart because there's nothing I want to see more than biotech in the industrial world, like really smart biotech designs. And we were saying the other day, we're talking about doing something together. And we were both just like, why can't we build human with the same fever pitch we build tech?

44:39Like, why? Like, why can't we chase this thing? It's so frustrating. I think it boils down to risk tolerance. And the reason is with tech, we can have crashes. Because there are crashes and the computer crashes and it crashes all the time. It's like acceptable to fail. Right. With humans, people get really mad if there's a failure. Right. They are, you know, but but they didn't used to. With Banting and Best in that era, people were more reward seeking and less risk averse. Right. And I think we're kind of coming back to that era gradually where risk tolerance has radically increased over society over the last few decades, I think.

45:24And, you know, one thing on this is an intermediate between, though maybe we can just go to the full thing. I think just the diagnostic zone, right, where you can just get genome sequence without a prescription from your doctor. It's so stupid, the entire FDA, you know, CDRH thing where it's like requiring a prescription to look at the mirror. Like, why should you need a, why should that be bottlenecked through the medical system to get your genome sequence? When the AI models are smarter than my doctor, like, why do I have to go ask them? Exactly. That's right. So I think there is definitely room for something where you get your genome sequenced by, for example, Nucleus or some at-home kind of device, you know, like as Oxford Nanopore or something like that gets smaller and smaller, eventually it'll get there.

46:13Or you just do it in a lab and you just get the file back. And you have it interpreted by something like an open source version of Prometheus, but it's an AI-enabled thing. And it's all done locally. And so I've got some companies that I'm looking at that do some combination of these things. But, you know, I think the second generation of truly personal genomics could be another big thing that we do. And maybe that's the intermediate step that then people have enough data and enough time series on themselves that then that opens up. Basically, the reason that's interesting is now you're not flying blind.

46:46Every treatment, you have a sense of your own dashboard and your own metrics. Like you're one of the very first on this. Actually, there's that guy, the measured man. There's an article in The Atlantic many years ago on this guy. He's a prof. I forget his name. You should maybe meet him if you haven't. It was back when The Atlantic was good. But the, so the, this guy also is measuring all kinds of time points on himself. And he found, he was like an early quantified self kind of person, right? So if you have that dashboard, now you have a sense of kind of what's a speed bump, what's a real major thing.

47:25And then you're kind of better able to process what an intervention is doing. Is it messing you up? Is it just moving these genes? That kind of thing, like these expression levels, et cetera. Do you actually, in all your Biomecker stuff, have you done gene expression time series on yourself? By the way, his name is Larry Smarr. That's him. Yeah, that's the guy. I've never met him. When was his son? Do you know him? I don't know him. I just know him from the, I think Larry Smarr, Mike Snyder, and you. Yeah. should do, if you want, we should all do a podcast together or something. That's a good idea.

47:59Those two people are very aligned with our way of thinking. Yeah. So he's a founder of the National Center for Supercomputing Applications at NASA, born in 1948. So he's just - Yeah. So he's 80 or something. I'm not sure what health he's in, but him and Mike Snyder are both pioneers in this space. Mike Snyder with the Intergrom, Larry Smarr, right? And I think it's worth - I bet, by the way, if you put that into AI, AI is very good at expanding suggestion lists. So if you put both of those there and say, who else is like that, you'll come up, but maybe others will come up too. Yeah, he found early Crohn's disease in himself before he was clinically diagnosed.

48:34I mean, this is like, so I have kernel on my desk here. This is the brain interface I built. I measure my brain every day now. Ah, I'll show you. Yeah, so this is like characterization of the brain. You can start, you can see the inside here, maybe. Yeah. So this is like, we spent seven years building this technology. It's basically wearable fMRI using light, using light. And this is the problem is we've never had the ability to characterize our brains. It's like, if you want a brain scan, you do fMRI or you can MRI. FMRI is a functional side. MRI is a structural side, but just like go to facility, sit in an environment that's claustrophobic.

49:10But now I can basically characterize my brain every day, looking at the functional networks. You know, I have a, my intuition, by the way, does that need to be, can you walk around with that? Or is it having to plug into the wall? Yeah, yeah, it's stationary. It's stationary. You know, a fun test would be, what does it look like when someone is just scrolling versus what does it look like when they're on the treadmill? Yes, exactly. And like, I didn't do this, but my colleague did. He drank alcohol for science, and he saw an immediate aging of his brain, I think it was two to three years the following day.

49:50It's been cool to just see basic correlations. What happens when you're sleep deprived? What happens when you exercise? What happens when you eat well? But you can run all these experiments now where I couldn't before. But this is like the characterization thing. The characterization thing is what we've been missing is people run to do therapies, and even when they do therapies, it's poorly characterized. It's very poorly characterized. let alone before, like having some, like you're saying, some time series, longitudinal data, looking at these things, which is I've, you know, I've become the most characterized person, human in history.

50:21There's no human that has this more characterized than myself. And so we have, it's enabled us to find so many insights that we just haven't been able to find in the literature or through other observations through doctors. It's just like, it's all about measurement. And what is the cost per day of your characterization, by the way? Like what, what asses are you running? Honestly, it's not that expensive. These are like, I mean, blood draws, saliva, stool, mitochondria, fitness tests, imaging, methylation, proteomics. You're like, I don't know. She's like, not honestly, probably, I'm guessing without thinking about this more robustly, but something like 50 ,000, 100 ,000 a year.

51:06Like it's not that much. It's like$1 ,000 a day. And so if we could bring that down to$10 a day or even$1 ,000 a month. Yes, exactly. Right. Actually, it's less than$1 ,000 a day. If you're saying$100K a year, right, then it's somewhere between$200,$300 a day, right? It's already not too bad for what it is, right? Right. So, um, it's like, you know, the thousand dollar genome. Yeah. There's, there's obviously, I mean, I've run a clinical lab. There's definitely returns on scale with clinical lab for all kinds of stuff with instrumentation to reagents to everything. So I've thought about a residential clinical lab where we just build it into network school.

51:53Yeah. And cause it's a pain to go in, you know, you want is a phlebotomist to come to you, right? You want mobile phlebotomy you want saliva you want ideally even in your apartment or something like that you can just leave your toothbrush or something like that and then it can just you spend a cup or something and then uh there's various ways of probably streamlining if not fully automating yeah making it easier to get samples i have a sensor in my toilet yeah exactly that kind of Just automatically. Yeah. Bill Gates was interested in this 20 years ago. And you could, you know, like you're in colorimetry and so on and so forth.

52:34There's a lot you can do with that. And you just have it there and maybe, you know, you just image it and so on and so forth. I think the Chinese could do a lot here because they can innovate on hardware. And there's something to be done here because they're building a lot of cities so they could actually install these smart toilets. some kind of Chinese Japanese companies because the Japanese also do a lot of toilets. Yeah, I just watched that South Park episode where they got the Japanese toilet. Anyways, yeah. This is also the characterization thing is really cool because when I started Blueprint, I was trying to solve my own problem.

53:09Like, how do I actually find nutritious food that's third-party tested and low on toxins? And then friends and family were like, hey, can I have access? I'm like, sure, here we go. And it just like accidentally became a business. But the problem with that is like, Even though I'm trying to solve a real problem in life, people have such a negative connotation towards therapies, towards things you actually... It's complicated. And characterization is much more friendly. If you're helping someone measure themselves, there's much less blowback. And so I'm moving very hard in that direction where I'm going to help people characterize themselves.

53:39Not drugs and devices. Yeah, just characterize yourself like me. Let's just get this baseline measurement. it's a much safer path to build because then people somehow they don't have suspicions like they do with me now when i'm like really i'm like i don't need i don't want to be doing this like i there's so many like if i were out to make money there's like a thousand other ways i want to do to make money so anyway yeah so the thing is actually you know years ago that's why you know my first company was in diagnostics because also diagnostics has a lot of advantages first is it's half computer science, because after you do the assay, then you can analyze everything on the computer, right?

54:20And you can make all the dashboards and so on. So it's like at least half within our bailiwick. It can't hurt the person usually. So the risk is way, way, way lower. So you can move much faster. You know, the worst thing you can do is you can give them a wrong result, but you can move close to speed of computer science. And then most interestingly, it means that the intervention is informed. So arguably diagnostics creates a room for therapeutics. So perhaps we start with the universal longitudinal diagnostic network state first. Longitudinal network state, then longevity network state. Okay, cool.

54:58Balja, I like this. So basically, we get a cohort of people together. We say, we are going to invest the capital to do high fidelity characterization. I love this. We don't know why, right? We don't know to what end. Myosand or integrome for 100 or 1 ,000 people. Yeah. Now, and it may not be useful to be this characterized for five years, 10 years. Like we don't know, but like we want this longitudinal characterization so that when we find things to trial, we have this time series, longitudinal data that we can, yeah, that gives us a better shot identifying what therapies are going to, what we will be responsive to, what we won't be, and then see the off-target effects.

55:40And so probably a much better way. Whereas right now, even when you look at the clinical trials, they are so poorly characterized. Absolutely. I read this one scalar variable on a very complicated human. I read these studies. I'm just like, this is not reliable data. Like when you're telling me it does blank and blank and it has these side effects, like sure, but nothing close to what I would think it'd be. So even as like, that's why like the GLP ones are a good one because they have done good characterization, better than a non-characterized peptide. But that said, we don't know a lot, which is why all this stuff is coming out, like blindness and stuff like that.

56:17So yeah, really, I guess this conversation has landed us on characterization as the key input, but then we need to go to these jurisdictions and figure out with that, how we'd leverage fast track, safe, high effect size therapies. Because we need a moment for longevity. We need something. Yeah. Go ahead. Say what you're saying. Yeah. No, just like, it could be the case that it's like, you know, the history book says, you know, from like 2025 to the 2035, these new therapies came out and I had this like starting this cumulative effect. Or there's like a moment where something happens. Maybe the GLP ones are that story of like people got familiar with doing these.

56:57They're important. They are. But we could have the exemplary for aging would be that moment. Yeah. Yeah, like something pretty, but the effect size is big enough where everybody turns their head and they're like, I understand reality differently now because of that. Yes, that's right. And I do think that's possible simply because we're seeing it in mice and so on. We just got to figure out what buttons to press within humans. But I really like the idea of the longitudinal network state and then the longevity network state or the longitudinal special economics zone, longitudinal diagnostics. And we can cost that out.

57:32And we could do something as a DSI thing. You know DSI? Yeah, yeah. Or we could do it at a traditional raise, but it's just a very scoped thing, which is these end people for this period of time. And frankly, they could even pay for it where it's something where it's not that expensive. It's just expensive enough to be within like a, you know, sort of pro-Zoomer budget, like a crypto person, right? Yeah. And they could maybe live at network school and be part of the study. Yeah. I like this. So you have the list of all the instruments that you do for your measurements. Do you have that in a public spreadsheet or do you have that internally?

58:12I need to put it together. Yeah. Basically, what would a high fidelity characterization look like? What tests would you need? What machines would you need? What labs would you need? Because some of the assays we want done are just not available off the shelf labs. So even if we had some people who could spin up some nice, so it could accommodate some of the unique things we want to measure. That'd be great. Yeah, I think, and we do it in participation with NUS or somebody like, you know, some sort of local university or hospital. And I think we could do something here. I think there's something very, very cool.

58:48If anything, it's like a new model of, even it's a new model of healthcare, really. The key is, you know, like at Stanford, there was this concept called, back again, back when savings was good, residential education. And the idea was that the education didn't stop when you were, it wasn't one hour in class, you were 23 hours outside of class. And so education happened to the dorm rooms and so on and so forth. So it was like, you know, immersive in that sense. So this is residential health. Yeah, yeah. I mean, this is kind of like, you and I have been spinning around this idea, I mean, since we first met of like, how we bridge the worlds of ideology health, governance, nation state.

59:32Like, what is this moment? And I like this as a path because it does, it brings together the things people care about. Yeah, and there won't be that much resistances because we could start with the lunch tool measurements. No one gets hurt, right? Exactly. And you have enough data to show what normal looks like, what interventions look like, what fitness looks like, what bad foods look like. You've got all their nutrigenomics. you got their pharmacogenomics. It's a whole thing. There's a whole stack of actually open source software that you develop in doing this. Yeah. I like this a lot. And then we can also, if we, okay, this is probably too much, but then we add some organelles, right?

1:00:10So now you start doing therapy trials against your organelles. So then you've got the patient-rived organoids. Exactly. Yeah. Exactly. So now you start doing the experimentation, high throughput experimentation on your organelles. Yep. And they're your proxies. Exactly. Yeah, that'd be badass. I mean, that'd be amazing if we actually set this up. All right. Yeah, okay. Good outcome. All right. So we've got a plan for the longitudinal network state towards the longevity network state. Make a progress. Okay. Well, thank you very much, Brian. Thanks for having me. I really enjoyed this. Awesome. Talk soon.

1:00:43Okay. Bye.