1 00:00:00,120 --> 00:00:02,520 Brian, thanks for speaking at the university conference. 2 00:00:02,600 --> 00:00:05,040 Balji, it's my pleasure. I love being here. 3 00:00:05,120 --> 00:00:07,920 Awesome. All right so last year you came 4 00:00:07,920 --> 00:00:11,200 by at the opening of network school on the very first day. 5 00:00:11,320 --> 00:00:13,080 Thank you for coming to the ribbon cutting and grand 6 00:00:13,080 --> 00:00:16,320 opening. This year we've moved very far. 7 00:00:16,320 --> 00:00:20,600 We've built a lot of stuff. We've had, you know, more than 8 00:00:20,600 --> 00:00:24,280 1000 people have come and really on our way towards building 9 00:00:24,520 --> 00:00:26,160 network school and startup societies. 10 00:00:27,000 --> 00:00:31,440 And you've also come a long way. Blueprint has has come very far. 11 00:00:31,720 --> 00:00:34,520 It's become a global movement. Don't die in Blueprint. 12 00:00:34,760 --> 00:00:37,800 We actually have Blueprint bars, as you know, at NS and all the 13 00:00:37,800 --> 00:00:41,440 blueprint kind of stuff. What is the what's the summary 14 00:00:41,440 --> 00:00:44,760 of the last year few years? Give give the give Brian 15 00:00:44,760 --> 00:00:45,840 O'Brien. And then I want to talk about 16 00:00:45,840 --> 00:00:48,680 the longevity network scene. It was initially an intrigue. 17 00:00:48,760 --> 00:00:51,000 When this went viral a couple years ago, it was kind of like 18 00:00:51,000 --> 00:00:53,600 what is happening. There was the tsunami of hate 19 00:00:53,720 --> 00:00:58,040 and then it went into this quasi curiosity mode of what is this? 20 00:00:58,040 --> 00:00:59,920 Is this real? Is this sketch? 21 00:01:00,360 --> 00:01:02,320 And then there's been this gradual acceptance, like 22 00:01:02,320 --> 00:01:04,920 actually it's legitimate and it's interesting. 23 00:01:05,400 --> 00:01:07,840 The hate has tapered off substantially and now it's 24 00:01:07,840 --> 00:01:10,640 really gone into a movement. I'd say then the Netflix 25 00:01:10,640 --> 00:01:15,400 documentary was out in January that had a a deep global impact. 26 00:01:15,520 --> 00:01:21,160 And so now over the past, I'd say maybe 5 to 6 months, I'm now 27 00:01:21,680 --> 00:01:24,240 with some of the most powerful and influential people in the 28 00:01:24,240 --> 00:01:28,560 entire world. It's just been this, this really 29 00:01:28,560 --> 00:01:33,120 remarkable trajectory towards credibility where I mean, 30 00:01:33,120 --> 00:01:36,880 yesterday I was with, well, I wouldn't say, I won't say who I 31 00:01:36,880 --> 00:01:38,640 was with, but like some of those powerful people in the world 32 00:01:39,120 --> 00:01:41,240 working on health and Wellness protocols and whatnot. 33 00:01:41,240 --> 00:01:45,160 So it's just been interesting to go from what is this to now like 34 00:01:45,160 --> 00:01:48,240 deeply engaged in in the world order. 35 00:01:48,320 --> 00:01:50,320 So that's been, it's a fun, been a fun trajectory, very 36 00:01:50,320 --> 00:01:51,720 unexpected. That's amazing. 37 00:01:51,720 --> 00:01:55,080 So you know what I would say, and I will propose this to you 38 00:01:55,080 --> 00:01:57,360 and maybe maybe you'd agree, maybe you won't, but let's 39 00:01:57,360 --> 00:02:00,160 discuss. I actually think the Don't Die 40 00:02:00,160 --> 00:02:02,200 network state is actually the key to both. 41 00:02:03,200 --> 00:02:07,720 And the reason I say that is I think the primary barrier to 42 00:02:07,800 --> 00:02:12,320 longevity and more generally transformative biomedicine is 43 00:02:12,320 --> 00:02:15,560 the American regulatory state and more generally the American 44 00:02:15,560 --> 00:02:18,960 healthcare apparatus and how it only treats people when sick. 45 00:02:19,280 --> 00:02:22,880 And it acknowledges that death is or thinks death is notable. 46 00:02:23,120 --> 00:02:28,800 And also we get almost no news about the incredible 47 00:02:28,800 --> 00:02:33,000 developments that have happened in academic biomedicine in terms 48 00:02:33,000 --> 00:02:36,760 of reversing aging in mice or even in humans. 49 00:02:36,920 --> 00:02:39,320 And there's some amazing phenotypes like, you know, I can 50 00:02:39,320 --> 00:02:42,800 put this one on screen which shows two mice of the same age, 51 00:02:42,800 --> 00:02:46,560 and the first one is balding and older and the second one is 52 00:02:46,560 --> 00:02:49,160 clearly younger. Or here is like a side effect I 53 00:02:49,160 --> 00:02:52,520 can put on screen of a couple of men who had a cancer drug and it 54 00:02:52,520 --> 00:02:56,920 caused as a good side effect their hair to reverse color and 55 00:02:56,920 --> 00:02:59,960 to go from Gray to less Gray. And you've probably seen 56 00:02:59,960 --> 00:03:02,240 actually, are there other examples you've seen that are 57 00:03:02,240 --> 00:03:05,280 like that, that have these amazing visual undeniable 58 00:03:05,280 --> 00:03:06,960 effects within humans or animals? 59 00:03:07,160 --> 00:03:09,920 Yeah, I mean, many, there was a study out in June from a bunch 60 00:03:09,920 --> 00:03:13,160 of Chinese researchers who put over the over expression of FOXO 61 00:03:13,160 --> 00:03:16,640 3 and they packaged it up in a messing common stem cell that 62 00:03:17,400 --> 00:03:20,880 people are doing interesting experiments that have measurable 63 00:03:20,880 --> 00:03:22,320 impacts. You could read them out or you 64 00:03:22,320 --> 00:03:24,480 can visually see them. And I agree with your framework 65 00:03:24,480 --> 00:03:27,640 that when you seeing is believing that a lot of people, 66 00:03:28,000 --> 00:03:29,400 I mean apology. I think your point is correct 67 00:03:29,400 --> 00:03:33,840 that when I started doing this years ago, I tried to achieve 68 00:03:33,840 --> 00:03:35,920 the best biomarkers of anywhere in the world. 69 00:03:35,920 --> 00:03:38,080 Like that was like a way to say there's a healthiest person in 70 00:03:38,080 --> 00:03:40,640 the world, there's a richest, fastest, can there be the 71 00:03:40,640 --> 00:03:43,960 healthiest? And what I learned is nobody 72 00:03:43,960 --> 00:03:47,880 cares about biomarkers. They just wanted to look at skin 73 00:03:47,880 --> 00:03:50,040 and face. And, you know, and so I had been 74 00:03:50,040 --> 00:03:52,840 on this deeply caloric restriction diet, and I'd lost 75 00:03:52,840 --> 00:03:54,840 so much fat in my face. I hadn't really paid attention 76 00:03:54,840 --> 00:03:57,760 to aesthetics. And people could not see the 77 00:03:57,760 --> 00:04:01,560 project when my face was gaunt. And so it's remarkable that I 78 00:04:01,560 --> 00:04:05,240 agreed that people understand longevity basically through the 79 00:04:05,240 --> 00:04:07,400 face. And So what they see, they can 80 00:04:07,400 --> 00:04:10,160 believe. Or hair, But that really is, I 81 00:04:10,160 --> 00:04:12,760 think, the entry point for most people in understanding what is 82 00:04:12,760 --> 00:04:14,280 aging. Do therapies work? 83 00:04:14,280 --> 00:04:16,200 If you can't see it there, they don't believe it. 84 00:04:16,560 --> 00:04:19,200 That's right. And I think there is some sort 85 00:04:19,200 --> 00:04:23,160 of meta logic to that because for many people, longevity is 86 00:04:23,160 --> 00:04:27,160 about aesthetics and you know, people wanting to look younger 87 00:04:27,640 --> 00:04:31,400 and you know, if they had the trade off between looking 88 00:04:31,400 --> 00:04:34,160 younger and actually in a sense biologically being younger, some 89 00:04:34,160 --> 00:04:36,480 fraction, but actually even take the first over the second or 90 00:04:36,520 --> 00:04:39,920 what have you, right? But I think it's almost like the 91 00:04:39,960 --> 00:04:42,040 entry point. It's a little bit like, you 92 00:04:42,040 --> 00:04:44,520 know, with with cryptocurrency, people seeing was believing 93 00:04:44,520 --> 00:04:48,000 because it generated transformative wealth and then 94 00:04:48,000 --> 00:04:49,760 they paid attention to the theory underneath. 95 00:04:50,240 --> 00:04:54,280 And so if with longevity we can generate transformative health, 96 00:04:54,720 --> 00:04:56,520 then we'll get people to pay attention to the theory 97 00:04:56,520 --> 00:04:58,760 underneath. So what are the top three or 98 00:04:58,760 --> 00:05:00,880 five? You mentioned the fox O 99 00:05:01,120 --> 00:05:03,400 overexpression, Fox O3 overexpression, right? 100 00:05:03,640 --> 00:05:05,800 Was it Fox O or Fox P? Fox O3. 101 00:05:06,400 --> 00:05:09,960 OK, give me like 3-4 five other. What are what are your top ones 102 00:05:09,960 --> 00:05:12,840 that have like a visual phenotype that you think is 103 00:05:12,840 --> 00:05:16,120 really impressive? Yeah, I mean, sadly, I would say 104 00:05:16,120 --> 00:05:19,760 there's probably, honestly, there's skin therapies, I'd say 105 00:05:19,760 --> 00:05:23,640 hyperbaric oxygen therapy and then just the cumulative effects 106 00:05:23,640 --> 00:05:26,080 of good sleep, exercise and nutrition. 107 00:05:26,600 --> 00:05:29,000 Those are the ones that aesthetically work the best. 108 00:05:29,000 --> 00:05:30,880 Of course you can. Go ahead, go ahead. 109 00:05:30,920 --> 00:05:33,120 That's on humans. So what about on mice? 110 00:05:33,200 --> 00:05:35,840 What about on animals, where we can do a lot more in terms of 111 00:05:35,840 --> 00:05:39,040 genetic manipulation? Yeah, I mean on mice, I, I guess 112 00:05:39,040 --> 00:05:42,240 I've seen enough of these studies where it seems like you 113 00:05:42,240 --> 00:05:46,200 can generate really compelling effects on mice in many 114 00:05:46,200 --> 00:05:48,120 different ways. And this is seen with caloric 115 00:05:48,120 --> 00:05:50,520 restriction, it's seen with rapamycin, it's seen with 116 00:05:50,880 --> 00:05:54,560 metformin. So I in some regards that the 117 00:05:55,000 --> 00:05:59,560 the bar is lower on mice visual outcomes because there's plenty, 118 00:05:59,560 --> 00:06:02,200 there's a, there's a lot of them with humans, I think it's much 119 00:06:02,200 --> 00:06:03,520 harder. Interesting. 120 00:06:03,520 --> 00:06:07,680 So why do you think that is? Do you think that the effect is 121 00:06:07,680 --> 00:06:10,600 there in humans but it's not as visible? 122 00:06:10,920 --> 00:06:14,080 Or do you think it doesn't translate from muscle musculus 123 00:06:14,080 --> 00:06:16,320 to Homo sapiens? Yeah, it's a good question. 124 00:06:16,320 --> 00:06:20,240 It reminds me of anyone, anyone who's selling the skin cream 125 00:06:20,760 --> 00:06:24,840 basically shows the same, you know, 4812 week transformation 126 00:06:24,840 --> 00:06:27,720 of a light wrinkle line to a, you know, a smooth wrinkle line 127 00:06:27,720 --> 00:06:30,080 or something like that. It's kind of the same with, with 128 00:06:30,200 --> 00:06:32,680 my studies. If you, if you see enough of 129 00:06:32,680 --> 00:06:35,240 these, you kind of like have a general pattern of like roughly 130 00:06:35,240 --> 00:06:37,880 what you show in the mice, whether it's a, a biopsy or 131 00:06:37,880 --> 00:06:39,520 whether it's hair growth or something like that. 132 00:06:39,840 --> 00:06:43,120 So I mean, I guess I I do, there's credibility to it, but I 133 00:06:43,120 --> 00:06:45,600 also take with a grid of a grain of assault of, you know, there's 134 00:06:45,600 --> 00:06:47,680 translation issues from from mice to humans. 135 00:06:48,080 --> 00:06:51,080 But yeah, really, I mean, I think mice, mice are compelling. 136 00:06:51,320 --> 00:06:55,440 Humans ultimately respond most strongly to other human faces. 137 00:06:55,640 --> 00:06:56,320 Interesting. Yeah. 138 00:06:56,320 --> 00:06:59,880 So I mean some of the large effect size things that have 139 00:06:59,880 --> 00:07:02,640 been done in mice, obviously there's the GFP mouse where you 140 00:07:02,640 --> 00:07:05,440 can make a mouse glow in the dark by taking green fluorescent 141 00:07:05,440 --> 00:07:08,080 protein. You can, you have like a 142 00:07:08,080 --> 00:07:11,000 myostatinol mouse where you can make them like incredibly 143 00:07:11,000 --> 00:07:13,320 muscular and there's various treatments that can do that. 144 00:07:14,880 --> 00:07:19,680 And there are all kinds of knockout mice and gently altered 145 00:07:19,680 --> 00:07:22,280 mice that do, you know, crazy things. 146 00:07:22,800 --> 00:07:28,400 And the, you know, my view is it should be possible. 147 00:07:28,560 --> 00:07:30,480 There's also like the Doogie mouse, I don't know if you know 148 00:07:30,480 --> 00:07:33,000 this, by Joe Shen more than 20 years ago, where they made the 149 00:07:33,000 --> 00:07:35,800 mice smarter and they seem to be able to run mazes faster, right? 150 00:07:36,440 --> 00:07:39,560 So my view is that we are finally, after more than 80 151 00:07:39,560 --> 00:07:45,560 years exiting the FDA era where the focus was on minimizing side 152 00:07:45,560 --> 00:07:49,560 effect size and finally re entering the era of large effect 153 00:07:49,560 --> 00:07:52,120 sized drugs that may actually have more side effects but also 154 00:07:52,120 --> 00:07:55,880 have larger effects like Ozempic, which you don't need 155 00:07:55,880 --> 00:07:57,840 to. I mean, of course they need to 156 00:07:57,840 --> 00:08:01,080 do studies to determine whether Ozempic work, but people don't 157 00:08:01,080 --> 00:08:04,080 need to see whether Ozempic works like they don't need to 158 00:08:04,080 --> 00:08:06,600 like squint and be like, you know, did it work or not? 159 00:08:06,600 --> 00:08:11,760 It's a it's just such a visible effect that it's like caffeine 160 00:08:11,760 --> 00:08:14,200 does it, does it wake you up? You can take it and it's like 161 00:08:14,400 --> 00:08:16,480 end of 1. You know, it works, right? 162 00:08:16,880 --> 00:08:20,720 So all the GLP ones, that's the new era. 163 00:08:20,720 --> 00:08:23,160 We're back to the era of the wonder drugs potentially. 164 00:08:23,440 --> 00:08:25,680 So why don't we have Ozempic for cognition? 165 00:08:25,680 --> 00:08:28,960 Why don't we have Ozempic for longevity, Ozempic for muscle 166 00:08:28,960 --> 00:08:31,520 mass? We should, we will, we could 167 00:08:31,560 --> 00:08:36,240 maybe right, if we take all of these things that we think work 168 00:08:36,520 --> 00:08:44,159 in humans or like mice or human adjacent mammals or perhaps like 169 00:08:44,159 --> 00:08:47,160 for example Alzheimer's drugs have a neuroprotective effect, 170 00:08:47,840 --> 00:08:50,200 but they also have a neuro enhancing effect, many of them, 171 00:08:50,320 --> 00:08:54,040 right, like drug repurposing. And so my thought is A, we have 172 00:08:54,040 --> 00:08:56,760 all of these interventions on this side which have these 173 00:08:56,760 --> 00:08:59,520 amazing visual phenotypes, and then B, we have all these 174 00:08:59,520 --> 00:09:02,880 jurisdictions on this side. And So what I want to do is 175 00:09:02,880 --> 00:09:07,560 offer $1,000,000 longevity prize for the people who take these 176 00:09:07,560 --> 00:09:10,840 visual interventions and carry it all the way through to 177 00:09:10,840 --> 00:09:13,880 actually have them legal in these jurisdictions. 178 00:09:13,880 --> 00:09:16,760 And when I say legal and functional, they should have a 179 00:09:16,760 --> 00:09:19,480 paper at least on archive, the documents, everything, all data, 180 00:09:19,480 --> 00:09:24,440 open source, open state. And they should have a law there 181 00:09:24,640 --> 00:09:26,640 that allows them to do it. Ideally, there's some government 182 00:09:26,640 --> 00:09:29,600 official who's even welcoming it in in the area and there's 190 183 00:09:29,600 --> 00:09:32,280 countries and a lot of them are doing interesting things in 184 00:09:32,280 --> 00:09:34,240 crypto and now they can do interesting things in bio. 185 00:09:34,240 --> 00:09:36,680 Let me know your thoughts. I, I agree with you entirely. 186 00:09:36,720 --> 00:09:39,480 The, the thing that we get people most excited is when they 187 00:09:39,480 --> 00:09:43,400 see visual effects by far. And when you do a comparison and 188 00:09:43,400 --> 00:09:46,760 contrast to when we talk about machine intelligence, we talk 189 00:09:46,760 --> 00:09:49,680 about orders of magnitude of scaling of efficiency. 190 00:09:50,000 --> 00:09:54,560 When you talk about human effects, you're saying 10%, 191 00:09:54,560 --> 00:09:58,480 fifty percent, 20%. It's such a disparity between 192 00:09:58,680 --> 00:10:01,160 the caabilities we have in improving our machines and 193 00:10:01,160 --> 00:10:05,000 intelligence and what we can do with human lifespan, healthspan, 194 00:10:05,000 --> 00:10:07,560 ability. And so the question is, and I 195 00:10:07,560 --> 00:10:10,320 think what you're getting at the heart of it is what is the 196 00:10:10,320 --> 00:10:13,640 system to create larger effect sizes? 197 00:10:13,920 --> 00:10:17,280 And so we see, for example, like what's with the GLP ones that is 198 00:10:17,280 --> 00:10:21,280 potentially the first mainstream longevity drug where now it's, 199 00:10:21,280 --> 00:10:23,520 it's well, it's characterized well enough where you're seeing 200 00:10:23,840 --> 00:10:25,640 cognition benefits, you're seeing, I mean, you're seeing 201 00:10:25,640 --> 00:10:28,240 all sorts of benefits out, I mean, in addition to weight 202 00:10:28,240 --> 00:10:30,480 loss. And you know, most people, I 203 00:10:30,480 --> 00:10:33,720 mean, I, I am micro dosing that now myself, even though I have 204 00:10:33,720 --> 00:10:37,720 no need to lose weight, I can basically try to achieve a dose 205 00:10:37,720 --> 00:10:39,280 where I can achieve some of the health benefits. 206 00:10:39,640 --> 00:10:42,440 But that that has very compelling, it's a very 207 00:10:42,440 --> 00:10:43,920 compelling offering. You know, it's a very simple 208 00:10:43,920 --> 00:10:45,920 drug. And so I think you're right is 209 00:10:45,920 --> 00:10:49,560 if we can train people's attention to jurisdictions where 210 00:10:49,560 --> 00:10:52,880 you can actually move this thing forward, because it's very, very 211 00:10:52,880 --> 00:10:54,520 hard to innovate in the United States. 212 00:10:55,360 --> 00:10:57,400 It's burdensome, it's complicated. 213 00:10:57,920 --> 00:11:00,040 And and of course it's not without, it's without reason, 214 00:11:00,040 --> 00:11:01,800 right? Like they, they are trying to 215 00:11:01,800 --> 00:11:05,320 make things safe, but just over time, the bureaucracy owns the 216 00:11:05,320 --> 00:11:07,320 situation. It just becomes a paralysis 217 00:11:07,320 --> 00:11:08,800 state. So it's the same situation we 218 00:11:08,800 --> 00:11:12,320 have with China, where China is racing forward on energy and AI 219 00:11:12,320 --> 00:11:14,720 and a bunch of other things, and the US is having a difficult 220 00:11:14,720 --> 00:11:17,280 time because we have this large regulatory state that slows 221 00:11:17,280 --> 00:11:18,840 things down. So I think it's on point. 222 00:11:19,080 --> 00:11:22,080 Awesome. So you know, I might, I want to 223 00:11:22,240 --> 00:11:24,080 discuss the whether they make people safe or not. 224 00:11:24,080 --> 00:11:28,760 But let's say we do $1,000,000 longevity prize will and let's 225 00:11:28,760 --> 00:11:32,840 say we get to work. Will you come with me and talk 226 00:11:32,840 --> 00:11:36,160 to the the people there and, and maybe try it out if it's real? 227 00:11:36,720 --> 00:11:39,200 Yes, absolutely. I mean, we are looking for safe 228 00:11:39,680 --> 00:11:44,520 efficacious therapies that have large effect sizes and that's 229 00:11:44,520 --> 00:11:46,600 just it's really limiting right now in the field. 230 00:11:47,240 --> 00:11:49,760 Amazing. And The thing is Type 1 meaning 231 00:11:49,760 --> 00:11:52,560 just testing safety is actually very inexpensive. 232 00:11:52,840 --> 00:11:56,160 It's Type 2, which is testing quote efficacy that's much more 233 00:11:56,160 --> 00:11:59,280 expensive. And just determining whether 234 00:11:59,280 --> 00:12:03,080 something is safe, you could have a totally different drug 235 00:12:03,080 --> 00:12:05,400 regime that just tested for safety. 236 00:12:05,840 --> 00:12:07,520 And there's a lot of things you can do by the way, with like 237 00:12:07,520 --> 00:12:10,320 potentially patient derived organoids where you've got like 238 00:12:10,320 --> 00:12:12,920 a proxy for the human that you're testing the drug on 239 00:12:12,920 --> 00:12:14,720 beforehand. You could do much more with 240 00:12:14,720 --> 00:12:17,880 pharmacogenomics, you know, pharm GKB and various kinds of 241 00:12:17,880 --> 00:12:19,520 resources. If everybody had a genome 242 00:12:19,520 --> 00:12:23,400 sequence, you could at least see whether you know, for example, 243 00:12:23,600 --> 00:12:26,920 your warfarin dose is dependent on VKRC one and C by two A9. 244 00:12:27,240 --> 00:12:29,800 And so you could see what your dose was before taking the drug. 245 00:12:30,400 --> 00:12:32,520 You could look at relatives and what their dosage was. 246 00:12:32,520 --> 00:12:34,600 You could look at people who are distant familiar relatives if 247 00:12:34,600 --> 00:12:36,800 you had genomic databases and what their response was. 248 00:12:37,080 --> 00:12:39,600 So much we can do with technology in terms of just 249 00:12:39,600 --> 00:12:42,800 quickly checking whether something is safe and then 250 00:12:44,400 --> 00:12:46,840 willing, willing buyer, willing seller, right? 251 00:12:46,840 --> 00:12:49,440 Like a like a minimal necessary regulation, right? 252 00:12:49,840 --> 00:12:52,760 And you know, this brings me, you know, when you said whether 253 00:12:52,760 --> 00:12:55,240 or not they care about safety, it's a little bit like saying 254 00:12:55,240 --> 00:12:58,880 the TSA cares about safety. Do they really, you know, 255 00:12:58,880 --> 00:13:01,880 because it's, I mean, that's, that's the benign way of looking 256 00:13:01,880 --> 00:13:04,360 at it, right? And the other way of looking at 257 00:13:04,360 --> 00:13:09,760 it, they just care about optics. And so they're not optimizing, 258 00:13:09,760 --> 00:13:12,960 for example, type one versus type 2 errors and the 259 00:13:12,960 --> 00:13:15,400 sensitivity and specificity, the false positive, false negative. 260 00:13:15,680 --> 00:13:18,280 Alex Tabrock and others that you know, marginal revolution have 261 00:13:18,280 --> 00:13:22,320 written about drug lag, which is if you have a drug that was 262 00:13:22,320 --> 00:13:26,840 approved but it was delayed by lit six years by the FDA, then 263 00:13:27,080 --> 00:13:30,440 all of the incremental, you know, morbidity and mortality 264 00:13:30,440 --> 00:13:32,920 over that window is directly attributable to FDA. 265 00:13:33,200 --> 00:13:37,480 Because the bio medicine was the same at the time that that new 266 00:13:37,480 --> 00:13:39,920 drug application that NDA was submitted, right. 267 00:13:40,440 --> 00:13:42,960 It's not the dry medicine change that the approval time was so 268 00:13:42,960 --> 00:13:46,240 long that it slowed it down. It was literally just pure 269 00:13:46,240 --> 00:13:48,360 information in a sense that they were getting over that time 270 00:13:48,360 --> 00:13:51,040 period. And so that is the unseen. 271 00:13:51,040 --> 00:13:52,560 You know, one of the things I thought about is running a 272 00:13:52,560 --> 00:13:56,480 Google ad campaign at some point to find somebody who's who had a 273 00:13:56,760 --> 00:14:01,080 sibling or a, or a relative or, you know, parent that had passed 274 00:14:01,080 --> 00:14:03,520 away of some condition during this period. 275 00:14:03,920 --> 00:14:08,920 And, and say, well, did you know that six years later a drug was 276 00:14:08,920 --> 00:14:12,080 approved that would have saved them and then talk to them. 277 00:14:12,080 --> 00:14:15,280 And that's a way that you could actually make drug lag visible, 278 00:14:15,520 --> 00:14:18,320 you know, on the screen, right. That's like one example of how 279 00:14:18,320 --> 00:14:21,920 quote safety often is actually unsafe because it's so risk 280 00:14:21,920 --> 00:14:24,280 averse that it's reward averse. Let me know your thoughts. 281 00:14:24,560 --> 00:14:26,640 Yeah, I mean, yes. And that statement, if you think 282 00:14:26,640 --> 00:14:30,840 about the, the way we do things, and I actually globally, I was 283 00:14:30,840 --> 00:14:34,360 going to say the US, but people, we allow people to experiment 284 00:14:34,560 --> 00:14:37,040 with fast food and a lot of sugar. 285 00:14:37,040 --> 00:14:41,280 And right, like you, you are, you have the freedom to kill 286 00:14:41,280 --> 00:14:45,600 yourself and to experiment. Does fried food cause me to age 287 00:14:45,600 --> 00:14:48,080 and potentially develop disease and lead to my lifetime? 288 00:14:48,080 --> 00:14:49,400 So you have the freedom to do that. 289 00:14:49,400 --> 00:14:52,280 But if you want to try to experiment, to do something good 290 00:14:52,280 --> 00:14:55,800 for yourself, you can't, it's against the law. 291 00:14:55,840 --> 00:15:00,000 And so it's really backwards in that if you try to step on 292 00:15:00,000 --> 00:15:03,760 anyone's ability to sell anything that causes someone to 293 00:15:03,760 --> 00:15:09,040 die, you know, people are are are upset, but yet we can't get 294 00:15:09,040 --> 00:15:11,960 the system to move so that we can self experiment on things 295 00:15:11,960 --> 00:15:13,880 that could help us. Now, clearly there's going to be 296 00:15:13,880 --> 00:15:15,560 complicated outcomes. It's not the case that people 297 00:15:15,560 --> 00:15:17,520 are going to make good decisions, but that's just 298 00:15:17,520 --> 00:15:19,360 humans. So it really is backwards that 299 00:15:19,360 --> 00:15:22,080 and I think it's really a stifling factor on our ability 300 00:15:22,080 --> 00:15:24,080 to make any progress. Whereas like when you look at 301 00:15:24,080 --> 00:15:27,480 computation, it's just like anything that produces better 302 00:15:27,480 --> 00:15:31,560 compute or intelligence go like very little guardrails. 303 00:15:31,760 --> 00:15:33,920 So it's really a constraining thing for us humans. 304 00:15:34,240 --> 00:15:35,880 That's right. I mean, The thing is, as I said 305 00:15:35,880 --> 00:15:38,200 before, and you probably also observed, you can go bungee 306 00:15:38,200 --> 00:15:42,000 jumping, you can go skydiving. Euthanasia is even legal in many 307 00:15:42,000 --> 00:15:44,280 states, right? Like you could join the 308 00:15:44,280 --> 00:15:46,680 military. There's many different contexts 309 00:15:46,680 --> 00:15:49,880 in which you're allowed to take a very serious risk of dying, 310 00:15:50,400 --> 00:15:52,760 right? And with essentially no upside 311 00:15:52,760 --> 00:15:54,600 when it's bungee up or skydiving, it's just pure 312 00:15:54,600 --> 00:15:56,360 thrills. I mean, fine, right? 313 00:15:57,040 --> 00:16:01,560 So why can't you take a risk of taking a new new drug or device 314 00:16:01,560 --> 00:16:03,720 or something like that that could improve your life, right? 315 00:16:03,720 --> 00:16:06,600 Why is why is human self improvement of all things, the 316 00:16:06,600 --> 00:16:09,120 thing which is stopped, right? And it's always framed as 317 00:16:09,120 --> 00:16:11,840 protecting you from yourself. But what if you had better 318 00:16:11,840 --> 00:16:14,440 information? You know, for, for example, the 319 00:16:14,440 --> 00:16:19,000 FDA doesn't really like it's a circle phase 4, you know, like 320 00:16:19,120 --> 00:16:22,400 post market surveillance. It doesn't include real time 321 00:16:22,400 --> 00:16:25,560 reviews from hundreds of countries or like, you know, 322 00:16:25,560 --> 00:16:29,240 hundreds of jurisdictions. Like, whereas even like Reddit 323 00:16:29,240 --> 00:16:33,200 reviews do the upvotes on Reddit or whatever are drawn from 324 00:16:33,200 --> 00:16:35,480 around the world. So you can, like, Crowdsource 325 00:16:35,480 --> 00:16:37,880 all of this data from around the world because biology remains 326 00:16:37,880 --> 00:16:41,520 the same. And that kind of, you know, 327 00:16:41,520 --> 00:16:43,560 because it's locked at the low estate rather than the level of 328 00:16:43,560 --> 00:16:48,320 network, you know, like, like an Uber rider, their rating, the 329 00:16:48,360 --> 00:16:50,360 sourcing information from New York City and when they take a 330 00:16:50,360 --> 00:16:51,720 ride in London and so and so forth. 331 00:16:52,040 --> 00:16:56,000 But drug responses aren't for people of similar biology around 332 00:16:56,000 --> 00:16:57,440 the world. They're not centrally pulled 333 00:16:57,440 --> 00:16:58,400 together. And they could be. 334 00:16:58,400 --> 00:17:00,880 Yeah. Yeah, I mean that the some of 335 00:17:00,880 --> 00:17:04,280 the risks like the GLP ones for example like blindness, I think 336 00:17:04,280 --> 00:17:08,920 there's like 3000 plus lawsuits against the GLP one manufacturer 337 00:17:08,920 --> 00:17:11,680 right now. But you can, you can take 338 00:17:12,000 --> 00:17:14,960 precautions by getting your genome sequenced and see if you 339 00:17:14,960 --> 00:17:16,839 are at risk for those kinds of conditions. 340 00:17:17,160 --> 00:17:20,240 So like you're saying, you can do really basic inexpensive 341 00:17:20,240 --> 00:17:23,920 tests to see if you are a responder, non responder or a 342 00:17:23,920 --> 00:17:27,240 higher risk for these outcomes. And now that our testing is 343 00:17:27,240 --> 00:17:30,720 getting so cheap, is this a really great path where if, if, 344 00:17:30,760 --> 00:17:34,080 if the, if the regulatory structure says, hey, this is too 345 00:17:34,080 --> 00:17:36,840 complicated and too high risk. I mean, you could even offset 346 00:17:36,840 --> 00:17:38,800 that and say you, you, you can characterize yourself much 347 00:17:38,800 --> 00:17:41,240 better to lower that risk. So, I mean, it really is. 348 00:17:41,240 --> 00:17:44,640 It's very hard to justify the argument that that we should be 349 00:17:44,720 --> 00:17:46,280 as limited as we are. That's right. 350 00:17:46,280 --> 00:17:51,880 And now, so let's say there's three treatments which assume 351 00:17:51,880 --> 00:17:56,840 you had a friendly regulator, OK, somebody who, and crucially 352 00:17:56,840 --> 00:18:01,680 by the way, people would sign something that's similar to like 353 00:18:01,680 --> 00:18:05,720 being a test pilot because no plane crashes, no planes, no 354 00:18:05,720 --> 00:18:08,680 train crashes, no trains, right? If there were people who are 355 00:18:08,680 --> 00:18:10,920 willing to take the risk of a plane crash, you never have 356 00:18:10,920 --> 00:18:13,240 transatlantic flight, you never have flight at all in the 1st 357 00:18:13,240 --> 00:18:14,800 place, right? And there were a lot of plane 358 00:18:14,800 --> 00:18:16,800 crashes early on because people are figuring it out, right? 359 00:18:17,120 --> 00:18:18,560 And we could think of those people as heroes. 360 00:18:19,080 --> 00:18:22,680 So let's assume there was some jurisdiction where it had a 361 00:18:22,680 --> 00:18:27,320 crypto like attitude like you know where if it's willing by or 362 00:18:27,320 --> 00:18:29,640 you're signing all your stuff away, you recognize you're 363 00:18:29,640 --> 00:18:32,320 taking a risk, you're an adult and blah, blah, you're being 364 00:18:32,400 --> 00:18:34,440 appropriately informed, compensated, whatever. 365 00:18:34,440 --> 00:18:39,920 The thing is, what are the three most promising treatments that 366 00:18:39,920 --> 00:18:42,600 we should look at that will have large visual effect size? 367 00:18:42,880 --> 00:18:45,880 I mean, if you look at the evidence on what has worked, 368 00:18:45,920 --> 00:18:50,920 it's been gene therapy, cell therapy, you know, at the very 369 00:18:50,920 --> 00:18:55,720 top and then yeah, lifestyles thing. 370 00:18:55,720 --> 00:18:57,040 So probably gene therapy, cell therapy. 371 00:18:57,560 --> 00:18:59,320 What about? Parabiosis, for example. 372 00:18:59,680 --> 00:19:01,920 I'm not sure it's going to have the effect size that was big 373 00:19:01,920 --> 00:19:04,360 enough. Because I've seen varying 374 00:19:04,360 --> 00:19:05,680 studies on that. You've probably looked at it 375 00:19:05,680 --> 00:19:07,960 more closely. Yeah, I mean, so I did this with 376 00:19:07,960 --> 00:19:09,760 my father. I also did several of the 377 00:19:09,760 --> 00:19:11,920 treatments. Now you can do the pheresis 378 00:19:11,920 --> 00:19:14,360 without a donor and just replacing all of your plasma 379 00:19:14,360 --> 00:19:17,440 with with albumin. People are now generating the 380 00:19:17,480 --> 00:19:19,840 synthetic plasma for the replacement. 381 00:19:19,840 --> 00:19:22,680 So I think it has some promise, but I don't think it's ever 382 00:19:22,680 --> 00:19:25,720 going to have the effect size that a cell therapy or gene 383 00:19:25,720 --> 00:19:28,520 therapy would have. OK, I we'll see what we'll see 384 00:19:28,520 --> 00:19:31,080 the data comes out, but my guess is that gene therapy, self 385 00:19:31,200 --> 00:19:34,320 therapies will will be the biggest ones and also and also 386 00:19:34,320 --> 00:19:37,040 the OS case stuff. OK, All right, so gene therapy, 387 00:19:37,040 --> 00:19:39,920 cell therapy, OS K All right, so let's go in order gene therapy, 388 00:19:39,920 --> 00:19:42,720 which genes? What looks interesting? 389 00:19:43,200 --> 00:19:46,080 I mean, the ones that people been playing with like 390 00:19:46,080 --> 00:19:52,280 Telomerase or Klotho, Fox O3, full of statin, I mean those are 391 00:19:52,520 --> 00:19:55,160 the ones that mostly have been top of charts, but people are 392 00:19:55,160 --> 00:19:57,600 working on several others. I mean, now that it's really a 393 00:19:57,600 --> 00:20:00,520 fascinating field where it is emergent in that we're looking 394 00:20:00,520 --> 00:20:03,800 at much more underappreciated genes that we have been before. 395 00:20:04,560 --> 00:20:08,600 So yeah, this is like probably like endless and it's just the 396 00:20:08,600 --> 00:20:09,640 case. We haven't been able to 397 00:20:09,640 --> 00:20:12,320 characterize these things very well to line them up and say I 398 00:20:12,400 --> 00:20:15,920 mean like for example, when you look at what the new limit, so 399 00:20:16,160 --> 00:20:18,640 Brian Armstrong and Blake Byers built stood up new limit. 400 00:20:19,160 --> 00:20:21,120 When you look at what they've done with the transcription 401 00:20:21,120 --> 00:20:26,520 factors, like basically saying like what, what changes things 402 00:20:26,520 --> 00:20:29,600 inside the body, they turned it into a computational problem. 403 00:20:29,960 --> 00:20:32,240 Whereas before when you're screening like what are the 404 00:20:32,240 --> 00:20:34,760 combinations? It's a very hard problem because 405 00:20:34,760 --> 00:20:36,280 the combinatorial set is so large. 406 00:20:36,760 --> 00:20:40,440 And so now that they they, they took a, a tech approach to a, a 407 00:20:40,440 --> 00:20:42,320 biological problem and they laid. 408 00:20:42,400 --> 00:20:43,800 Transcription factor binding sites. 409 00:20:44,240 --> 00:20:45,760 Exactly. And they'd be doing so much 410 00:20:45,760 --> 00:20:49,240 progress in in making it a computational problem versus 411 00:20:49,240 --> 00:20:51,720 what people are doing before a much slower academic cloud 412 00:20:51,720 --> 00:20:52,480 approach. So I think. 413 00:20:53,280 --> 00:20:55,760 Yeah, I think, I think, I think some combination can work. 414 00:20:55,760 --> 00:20:58,280 The problem is that what you do in silico and to be clear, I 415 00:20:58,280 --> 00:21:00,800 like new limit and it's great, but some compliment of those 416 00:21:00,800 --> 00:21:04,240 because the in silico approach will also have often false 417 00:21:04,240 --> 00:21:07,560 positives and false it depends on how specific that situation 418 00:21:07,560 --> 00:21:09,040 factor binding site kind of thing is. 419 00:21:09,040 --> 00:21:11,800 But I think I think there's definitely promise to to 420 00:21:11,800 --> 00:21:12,760 combining both. Go ahead. 421 00:21:13,520 --> 00:21:15,720 I'm imagining that it's probably just an intuition building 422 00:21:15,720 --> 00:21:18,360 process that even if there is some false positives in your, 423 00:21:18,360 --> 00:21:22,520 you have some degree of accuracy where you can entirely get there 424 00:21:22,520 --> 00:21:24,320 still. It's just this like, how do you 425 00:21:24,360 --> 00:21:28,160 how do you take this insanely large problem, make a somewhat 426 00:21:28,160 --> 00:21:31,040 approachable and have a more sophisticated like iteration 427 00:21:31,040 --> 00:21:33,040 speed? So I think if you look at their, 428 00:21:33,160 --> 00:21:35,360 their progress for the past couple years, that's what I 429 00:21:35,360 --> 00:21:37,440 think is probably the potential for gene therapy. 430 00:21:37,640 --> 00:21:40,160 You know, people are looking at things like can we do the four 431 00:21:40,160 --> 00:21:42,120 hour sleep gene, for example, right? 432 00:21:42,120 --> 00:21:44,520 Which is like very appealing. You get more time in life. 433 00:21:44,920 --> 00:21:47,160 So I think there's a pretty long list of things people start 434 00:21:47,160 --> 00:21:49,240 trying to knock off. So let's let's go through. 435 00:21:49,360 --> 00:21:50,640 So let's just talk about that for a second. 436 00:21:51,320 --> 00:21:54,280 Less sleep, right? Less fat, you know, or less 437 00:21:54,280 --> 00:21:58,400 weight, OK, like Ozempic, but gene therapy, Ozempic, more 438 00:21:58,400 --> 00:22:02,560 muscle, right? Maybe faster healing perhaps for 439 00:22:02,560 --> 00:22:04,120 a lot of things that's, that's helpful. 440 00:22:08,120 --> 00:22:10,800 Perhaps, you know, like reversing graying of hair. 441 00:22:10,800 --> 00:22:13,120 I mean, I, I actually, I actually think it looks a little 442 00:22:13,120 --> 00:22:18,040 distinguished, but that's fine. You know, a lot of the skin 443 00:22:18,040 --> 00:22:20,240 stuff like wrinkles and and so on and so forth. 444 00:22:20,560 --> 00:22:25,440 What are the other upgrades that you think are interesting? 445 00:22:25,560 --> 00:22:27,040 Go ahead. Cognition, Right Intelligence. 446 00:22:27,040 --> 00:22:28,200 Cognition, of course. Right. 447 00:22:28,200 --> 00:22:29,560 Yeah. And and actually that. 448 00:22:29,560 --> 00:22:32,360 Could take several forms because there's like spatial rotation, 449 00:22:32,600 --> 00:22:37,000 this musical ability, right? Also, I think sight hearing, you 450 00:22:37,000 --> 00:22:37,920 know? Yeah, yeah. 451 00:22:37,920 --> 00:22:40,400 I mean, I, I recently discovered, I mean, over the 452 00:22:40,400 --> 00:22:44,040 past couple years, I've got mild to moderate hearing loss from 453 00:22:44,280 --> 00:22:47,240 listening to loud music too loud as a kid and also shooting guns. 454 00:22:47,640 --> 00:22:50,360 And that leads to dementia. A gun guy, I would never have 455 00:22:50,360 --> 00:22:51,960 thought. That honestly, yeah, yeah. 456 00:22:51,960 --> 00:22:54,160 I mean, we grew up with guns. Like it was like an omnipresent 457 00:22:54,160 --> 00:22:57,360 thing in our lives. We just everyone had guns and we 458 00:22:57,360 --> 00:22:58,440 were. We're all shooting them all the 459 00:22:58,440 --> 00:22:59,680 time. But yeah, so we. 460 00:23:00,680 --> 00:23:02,640 Yeah, I basically, I have. I discovered this. 461 00:23:02,640 --> 00:23:04,480 I didn't realize I had any deficiencies until I got the 462 00:23:04,480 --> 00:23:06,280 test. And so now I'm going to get a 463 00:23:06,280 --> 00:23:08,680 hearing aid because there's evidence that it can lead to 464 00:23:08,680 --> 00:23:11,880 dementia. I'm guessing most Americans, 465 00:23:12,200 --> 00:23:14,600 most people in the world probably have hearing loss. 466 00:23:14,600 --> 00:23:16,920 Our modern day world was not built for our ears. 467 00:23:16,920 --> 00:23:20,760 Like concerts, like 120 decibels damage starts happening over 468 00:23:20,760 --> 00:23:23,720 about 90. So we just, we have way too many 469 00:23:23,720 --> 00:23:25,200 loud noises, ambulances, etcetera. 470 00:23:25,200 --> 00:23:28,360 So but yeah, hearing and gene therapy, we've been wanting to 471 00:23:28,360 --> 00:23:30,840 find a gene therapy because I would love to be able to fix my 472 00:23:30,840 --> 00:23:32,680 hearing. There's a few groups who've 473 00:23:32,680 --> 00:23:34,720 tried it, who've been playing around, but there's nothing even 474 00:23:34,720 --> 00:23:36,360 close. So that's what I'm saying. 475 00:23:36,360 --> 00:23:39,240 This is so promising is once you if we actually can figure out 476 00:23:39,240 --> 00:23:42,880 how to efficiently do gene therapies and do them in a state 477 00:23:42,880 --> 00:23:45,800 that allows rapid iteration and we can try these things and we 478 00:23:45,800 --> 00:23:49,360 can pass some safety threshold, then I think it really could be 479 00:23:49,360 --> 00:23:53,600 this like this new era of how we think about ourselves entirely. 480 00:23:53,760 --> 00:23:55,320 That's right. Because I think gene therapy, 481 00:23:55,320 --> 00:23:57,840 what's interesting about it is it blurs the difference between, 482 00:23:58,160 --> 00:24:01,160 I mean, biochemistry and genetics are very closely 483 00:24:01,160 --> 00:24:06,120 related, as you know, you know, because you'll have a compound 484 00:24:06,120 --> 00:24:10,040 and then there's like, like some small molecule and it will 485 00:24:10,560 --> 00:24:13,680 trigger like a protein will bind to it and that protein will then 486 00:24:13,920 --> 00:24:16,840 bind to the genome and unlock mRNA that'll do other things. 487 00:24:16,960 --> 00:24:19,440 So biochemistry and jigs are, are, are tightly related. 488 00:24:19,960 --> 00:24:22,840 And one of the things I've observed is that many of these 489 00:24:22,840 --> 00:24:25,640 things people are fine with solving them at the biochemical 490 00:24:25,640 --> 00:24:28,840 level. Like for example, caffeine does 491 00:24:28,840 --> 00:24:33,560 make you smarter, right? Or, you know, people are OK with 492 00:24:33,840 --> 00:24:36,360 chemistry to dye their hair blonde, for example. 493 00:24:36,680 --> 00:24:38,440 They're like, Oh my God, you can't solve at the genetic 494 00:24:38,440 --> 00:24:39,880 level. Or actually, another example is 495 00:24:39,880 --> 00:24:41,480 they're OK with solving at the surgical level. 496 00:24:41,480 --> 00:24:44,720 So for example, height, it's OK to take HGH, which is a 497 00:24:44,720 --> 00:24:48,680 biochemical solution, and it's OK people do a very painful 498 00:24:48,680 --> 00:24:51,120 thing of limb lengthening, which is a surgical solution. 499 00:24:51,840 --> 00:24:53,880 But the genetic solution, Oh my God, right. 500 00:24:54,440 --> 00:24:57,840 And because they just have this weird, you know, hang ups, they 501 00:24:57,840 --> 00:25:00,040 don't actually understand the distinction or the relationship 502 00:25:00,040 --> 00:25:02,040 between biochemistry and genetics and how close they are. 503 00:25:03,120 --> 00:25:04,600 And you can do that for all kinds of traits. 504 00:25:04,880 --> 00:25:08,600 Surgical and biochemical are OK and genetic is Oh my God, right. 505 00:25:09,080 --> 00:25:11,640 But gene therapy is actually something that's more like 506 00:25:11,680 --> 00:25:14,880 biochemistry because it's an injection or whether it's an 507 00:25:14,880 --> 00:25:17,280 injection or something similar to that depends on the mentality 508 00:25:17,280 --> 00:25:21,040 of it. But it has, it's like it feels 509 00:25:21,040 --> 00:25:23,680 to people like a drug because on somebody who's already living, 510 00:25:24,400 --> 00:25:27,200 but it changes them in an upgraded way, right? 511 00:25:27,240 --> 00:25:30,200 And it's actually been starting to work for people who had 512 00:25:30,200 --> 00:25:32,080 sickle cell and other things. You can put that on the screen, 513 00:25:32,280 --> 00:25:36,120 right? So, OK, so there's gene therapy, 514 00:25:36,200 --> 00:25:39,080 right? And then let's talk about cell 515 00:25:39,080 --> 00:25:40,920 therapy. What should we? 516 00:25:40,920 --> 00:25:44,480 Look at, I mean, there's a, we've been trying to get access 517 00:25:44,480 --> 00:25:47,840 to cell therapies for years and it's never been possible because 518 00:25:47,840 --> 00:25:53,600 they're just too high risk. So we're, there's some attempts 519 00:25:53,600 --> 00:25:56,440 at doing this with mesenchymal stem cell therapies, you know, 520 00:25:56,800 --> 00:26:01,320 stem cell therapies. We have not yet seen a lot of 521 00:26:01,320 --> 00:26:02,920 success with these. We don't see a lot of good 522 00:26:02,920 --> 00:26:05,320 evidence, you know, as as one approach, whether it's from your 523 00:26:05,320 --> 00:26:09,080 body or someone else's body. But there's some of the more 524 00:26:09,080 --> 00:26:13,040 advanced things to, to change more sophisticated by pathways 525 00:26:13,040 --> 00:26:15,360 in the body that just don't have good pathways. 526 00:26:15,360 --> 00:26:18,600 Like it's just a very hard drug to get approved and there hasn't 527 00:26:18,600 --> 00:26:21,440 been good, good ones to like. It's a very hard development 528 00:26:21,440 --> 00:26:22,640 path. You know, an interesting 529 00:26:22,640 --> 00:26:29,120 question that occurs to me is depending on what age these 530 00:26:29,120 --> 00:26:31,200 treatments would be effective. For example, let's say you've 531 00:26:31,200 --> 00:26:37,400 got a car and you, you hit the brakes and you hit them hard 532 00:26:37,400 --> 00:26:39,640 enough and it doesn't crash into the wall, right? 533 00:26:39,640 --> 00:26:43,320 There's a, there's a point of no return by which if you hit the 534 00:26:43,320 --> 00:26:45,080 brakes, it doesn't matter. You're still smashing into the 535 00:26:45,080 --> 00:26:48,880 wall, right? But that's just a thought 536 00:26:48,880 --> 00:26:52,600 experiment. You might actually from the 537 00:26:52,600 --> 00:26:57,280 ranks of older people or people who for a reason are pursuing 538 00:26:57,280 --> 00:27:00,920 euthanasia, especially the people who are pursuing 539 00:27:00,920 --> 00:27:03,160 euthanasia in places like Canada or whatever, right? 540 00:27:04,080 --> 00:27:08,040 You might say to them, why not try one of these treatments? 541 00:27:08,320 --> 00:27:10,920 Because if you're if, you're going to die, right? 542 00:27:11,280 --> 00:27:14,080 Like like can't like can't like hard teeth therapies for cancer. 543 00:27:14,880 --> 00:27:16,000 Exactly. That's right. 544 00:27:16,000 --> 00:27:18,400 Just like, you know, for example, with this a novel 545 00:27:18,400 --> 00:27:21,000 argument that strikes me that similar to the ACT UP argument 546 00:27:21,400 --> 00:27:25,080 that was made in the late 80s, early 90s that finally 547 00:27:25,080 --> 00:27:27,640 liberalized the FDA after many decades. 548 00:27:27,880 --> 00:27:33,040 Basically a lot of people who had HIV said we're going to die, 549 00:27:33,480 --> 00:27:37,600 so why don't you approve AZT and these other things so at least 550 00:27:37,600 --> 00:27:40,120 we've got a fighting shot. You know, Dallas Buyers Club 551 00:27:40,120 --> 00:27:43,120 documented that, right? So it strikes me that a 552 00:27:43,120 --> 00:27:46,760 potential extension of that would be, given that there's 553 00:27:46,760 --> 00:27:49,600 millions and millions of senior citizens out there, right? 554 00:27:49,920 --> 00:27:53,800 Those who were like, you know what, might as well give it a 555 00:27:53,800 --> 00:27:56,320 shot towards the end. Now, the tricky part about that 556 00:27:56,320 --> 00:27:59,880 is in that car analogy, they might be close enough to the 557 00:27:59,880 --> 00:28:02,760 wall that even if the treatment worked, it couldn't reverse it, 558 00:28:04,080 --> 00:28:07,520 but maybe it could, I don't know, it's case by case kind of 559 00:28:07,520 --> 00:28:09,600 thing to figure out, you know, maybe it's a powerful enough 560 00:28:09,600 --> 00:28:11,840 thing that just rewinds it. One of the things I know you 561 00:28:11,840 --> 00:28:15,120 know this, but like that's always struck me and and many 562 00:28:15,120 --> 00:28:18,440 others who have written about this is a relatively older 563 00:28:18,440 --> 00:28:25,040 couple can have a child that is a newborn that, you know, 564 00:28:25,320 --> 00:28:28,600 clearly there's something of regenerative in our in our 565 00:28:28,600 --> 00:28:31,160 bodies that can birth again, right? 566 00:28:31,440 --> 00:28:35,920 Like and so maybe that can be triggered even much later in 567 00:28:35,920 --> 00:28:38,040 life than we think. Yeah, this is like Yamanaka 568 00:28:38,040 --> 00:28:40,720 factors. Take an adult stem cell, you go 569 00:28:40,720 --> 00:28:44,640 back to a pluripotent. So you make a 7 year old liver 570 00:28:45,600 --> 00:28:47,720 young again. And of course, like now, people 571 00:28:47,720 --> 00:28:49,640 are go ahead. Yeah, so, so with your biomarker 572 00:28:49,640 --> 00:28:53,200 thing, you could gauge someone's life expectancy and if they've 573 00:28:53,200 --> 00:28:57,160 got only five years to live, give them what you know. 574 00:28:57,160 --> 00:28:59,640 Obviously they have to opt in, sign up all the stuff they've 575 00:28:59,640 --> 00:29:01,560 only got a few years to live. Try it. 576 00:29:02,160 --> 00:29:04,680 What's the loss exactly? I mean, that's The thing is the 577 00:29:04,760 --> 00:29:07,400 the exciting thing is the technology is here. 578 00:29:07,400 --> 00:29:12,000 Like we actually know we can turn an adult stem adult cell 579 00:29:12,200 --> 00:29:16,160 into a pluripotent cell. Like legit age reversal. 580 00:29:16,520 --> 00:29:19,080 Of course, now there's complications with, you know, 581 00:29:19,080 --> 00:29:20,560 can you avoid it being cancerous? 582 00:29:20,560 --> 00:29:21,600 Cancerous. Yes, of course. 583 00:29:21,640 --> 00:29:23,040 Right. Can you avoid, you know, the 584 00:29:23,040 --> 00:29:26,480 other off target outcomes? So like there's things we need 585 00:29:26,480 --> 00:29:30,560 to sort through, but they the cool thing is in 2025, we now 586 00:29:30,560 --> 00:29:34,400 know we can legitimately reverse age in a stunning way. 587 00:29:34,880 --> 00:29:37,200 And This is why I think your sentiment is correct. 588 00:29:37,200 --> 00:29:39,640 Apology where we're too damn slow. 589 00:29:39,840 --> 00:29:46,160 I like, I, I don't, I don't know why we are not pursuing 590 00:29:46,240 --> 00:29:49,200 longevity like we're pursuing artificial general intelligence. 591 00:29:49,600 --> 00:29:52,600 You know, when I look at 2025 and I try to zoom out to the 592 00:29:52,600 --> 00:29:56,120 perspective of the year 2500, either you're, you're working on 593 00:29:56,120 --> 00:30:00,840 AGI or you're building don't die like everything ultimately build 594 00:30:00,840 --> 00:30:04,600 up to like your existence or your shared existence with the 595 00:30:04,600 --> 00:30:07,080 with AGI. And so I just don't know why it 596 00:30:07,080 --> 00:30:09,960 doesn't have a similar level of fever pitch build around it. 597 00:30:10,360 --> 00:30:13,000 I think, I think a big part of it is the stuff that I think 598 00:30:13,000 --> 00:30:15,520 about a lot, which is the governance part, because you 599 00:30:15,520 --> 00:30:21,080 need risk tolerant jurisdictions since and that's that's the 600 00:30:21,080 --> 00:30:23,040 whole change in your way of thinking. 601 00:30:23,040 --> 00:30:26,080 It's like, you know, as we've talked before, like you know, 602 00:30:26,080 --> 00:30:29,200 the traditional financial system assumes you have some degree of 603 00:30:29,200 --> 00:30:31,920 inflation every year and you lose some of your health. 604 00:30:31,920 --> 00:30:34,360 One 2% inflation, lose some of your health every year. 605 00:30:34,600 --> 00:30:37,160 The social medical system assumes you lose some of your 606 00:30:37,160 --> 00:30:39,080 health every year, So you lose some of your wealth and you lose 607 00:30:39,080 --> 00:30:41,560 some of your health. And so it's a paradigmatic 608 00:30:41,560 --> 00:30:44,680 change. Even if crypto has bar charts 609 00:30:44,680 --> 00:30:47,160 and, and, and graphs that look like the traditional financial 610 00:30:47,160 --> 00:30:49,280 system, it's moral premises are fundamentally different. 611 00:30:49,640 --> 00:30:53,880 Even if you or I will publish graphs that look like 612 00:30:53,880 --> 00:30:57,160 traditional biomedical papers, the moral premises are totally 613 00:30:57,160 --> 00:31:01,760 different because it says maybe we don't have to die. 614 00:31:01,960 --> 00:31:04,400 Right. Yeah, that was that was our 615 00:31:04,400 --> 00:31:06,920 first phone call. Like I think you called me. 616 00:31:06,920 --> 00:31:09,360 Hey, Brian, I think we have the same philosophy. 617 00:31:09,360 --> 00:31:11,840 I reject inflation and you reject death. 618 00:31:12,040 --> 00:31:14,360 Yes, it's been gradual. That's like we we're after the 619 00:31:14,360 --> 00:31:17,360 same concepts. That's right, because actually 620 00:31:17,360 --> 00:31:20,160 it's and The thing is that wealth in a sense is accumulated 621 00:31:20,160 --> 00:31:22,720 life force, right, because you're spending hours of your 622 00:31:22,920 --> 00:31:25,560 conscious, highly productive time on accumulating wealth. 623 00:31:25,560 --> 00:31:27,560 And so it's diluted from you. It's like you're spending 624 00:31:27,680 --> 00:31:29,200 they're taking away your life. You know, there's this movie 625 00:31:29,200 --> 00:31:33,920 called in Time by by Andrew Nichols that actually makes that 626 00:31:33,920 --> 00:31:35,760 metaphor. It's like, you know, sci-fi 627 00:31:35,760 --> 00:31:39,200 drama, but the concept is like, the currency is ours, right? 628 00:31:39,200 --> 00:31:40,440 Like hours of your life were there, right? 629 00:31:40,760 --> 00:31:43,000 Yeah, yeah, on I guess there's a side tangent on this biology. 630 00:31:43,000 --> 00:31:46,400 I just finished his book Passion the Western Mind by Richard 631 00:31:46,400 --> 00:31:50,040 Tarnas. And he he tries to go through 632 00:31:50,040 --> 00:31:55,080 the major epochs of ideology and he starts with Plato and 633 00:31:55,080 --> 00:31:57,680 Aristotle going through the Renaissance or going through 634 00:31:57,680 --> 00:32:00,560 Christianity, medieval times, Renaissance, Enlightenment, 635 00:32:00,560 --> 00:32:03,680 modern day, scientific era. And if you look at it from that 636 00:32:03,680 --> 00:32:06,120 perspective of like a Ray Dalio principles perspective where you 637 00:32:06,120 --> 00:32:08,840 zoom out, you say, hey, there's these like these big economic 638 00:32:08,840 --> 00:32:11,200 cycles, reserve currency that they go through the same stuff 639 00:32:11,200 --> 00:32:13,600 every time. It's we're really due for a 640 00:32:13,640 --> 00:32:16,120 major ideological shift right now. 641 00:32:16,360 --> 00:32:20,160 And I think you're on this, I'm on this, that it's not some 642 00:32:20,160 --> 00:32:23,560 slight tweaks to the, to the continuum slightly. 643 00:32:23,560 --> 00:32:28,000 It is like a wholesale change on scale with other major 644 00:32:28,000 --> 00:32:30,480 ideological shifts. And I think what we're talking 645 00:32:30,480 --> 00:32:34,720 about today is basically a part of that that is like this 646 00:32:34,760 --> 00:32:39,840 unleashed want for health, right? 647 00:32:39,840 --> 00:32:40,960 We don't. This is not about living 648 00:32:40,960 --> 00:32:43,080 forever. It's not about transhumanism. 649 00:32:43,320 --> 00:32:46,720 It's about we all appreciate waking up in the morning feeling 650 00:32:46,720 --> 00:32:48,560 good. We don't like aches and pains. 651 00:32:48,560 --> 00:32:50,320 We don't want to be diseased. We don't want to see our loved 652 00:32:50,320 --> 00:32:52,320 ones die. It's about feeling great and 653 00:32:52,320 --> 00:32:54,840 being great. And that that ideology is not 654 00:32:54,840 --> 00:32:57,120 part of our zeitgeist. Like we just accept this slow 655 00:32:57,120 --> 00:32:59,600 decay, death and decline. And it's like almost something 656 00:32:59,600 --> 00:33:01,920 where you celebrate. It's like I'm living life by 657 00:33:02,240 --> 00:33:03,800 slowly killing myself. Yeah. 658 00:33:03,800 --> 00:33:07,280 Well, human self improvement is, I think the way that I think 659 00:33:07,280 --> 00:33:11,240 about it, where that's a continuum from simply eating 660 00:33:11,240 --> 00:33:15,200 right and doing better to hitting your genetic limits and 661 00:33:15,200 --> 00:33:18,680 then surpassing them, right. And, you know, towards that end, 662 00:33:18,680 --> 00:33:21,760 by the way, I think the influence of, let's call it 663 00:33:21,880 --> 00:33:24,320 Dharmic and cynic thought on the world. 664 00:33:24,320 --> 00:33:29,440 So, you know, for centuries, India and China, like Marco Polo 665 00:33:29,440 --> 00:33:32,480 sought out India, That's Marco Polo sought out China. 666 00:33:33,000 --> 00:33:35,560 And Columbus saw that India, that's actually how the Americas 667 00:33:35,560 --> 00:33:37,720 discovered why Native Americans are called Indians, because 668 00:33:38,000 --> 00:33:40,720 India was a large enough economic power, it wasn't 669 00:33:40,720 --> 00:33:44,120 unified, but as it was worth sailing to and, and for Columbus 670 00:33:44,120 --> 00:33:46,760 to do it. And so for, for the last few 100 671 00:33:46,760 --> 00:33:48,720 years, India and China have basically been through their 672 00:33:48,720 --> 00:33:50,960 equivalent of the dark ages. And now they're coming back. 673 00:33:52,360 --> 00:33:55,800 And those two schools of thought have a different view on the 674 00:33:55,800 --> 00:34:00,760 human body and they're they're they're less in the Abrahamic 675 00:34:00,760 --> 00:34:05,280 school of thought is as kind of weird hang ups in certain ways 676 00:34:05,280 --> 00:34:07,440 that the Chinese and Indian schools of thought are more 677 00:34:07,440 --> 00:34:09,440 generally the Synic and Dharmic schools of thought don't. 678 00:34:10,320 --> 00:34:12,400 And so like, for example, something I've thought a lot 679 00:34:12,400 --> 00:34:16,040 about, I may have mentioned this to you, the concept of genomic 680 00:34:16,040 --> 00:34:18,600 resurrection or reincarnation. Did we talk about that? 681 00:34:19,000 --> 00:34:20,159 We did. Yeah, yeah, yeah. 682 00:34:20,159 --> 00:34:23,400 So but explain it. You could get your DNA sequenced 683 00:34:23,840 --> 00:34:28,400 and stored on on desk and we already can do chromosome 684 00:34:28,400 --> 00:34:33,159 synthesis for eukaryotes like, you know, we could do it for 685 00:34:33,159 --> 00:34:36,320 actually you could synthesize an entire prokaryotic chromosome 686 00:34:36,520 --> 00:34:39,600 from disk and actually have that thing swim around in a test 687 00:34:39,600 --> 00:34:42,719 tube. And as our abilities of 688 00:34:42,719 --> 00:34:45,800 chromosome synthesis get better, you could have imagine just 689 00:34:45,800 --> 00:34:49,760 projecting out to be able to do it for full complex human 690 00:34:49,760 --> 00:34:52,800 chromosomes. And it's just, it's like a 691 00:34:52,800 --> 00:34:54,920 technological direction. It's kind of like if sequencing 692 00:34:54,920 --> 00:34:56,480 got better, synthesis will probably get better. 693 00:34:56,480 --> 00:34:58,160 And it might take, you know, a few decades, but it'll get 694 00:34:58,160 --> 00:35:00,160 there. Which means that if you have a 695 00:35:00,160 --> 00:35:05,240 file, you could, if somebody was well behaved, you could write 696 00:35:05,240 --> 00:35:07,800 their genome to a blockchain or something like that. 697 00:35:07,960 --> 00:35:12,040 And if they had good karma, then the community would reincarnate 698 00:35:12,040 --> 00:35:16,600 them 50 or 100 years hence or whenever chromosome synthesis 699 00:35:16,600 --> 00:35:18,440 becomes feasible because they have the data there. 700 00:35:18,760 --> 00:35:20,960 And you know, people argue the genome isn't everything, but 701 00:35:20,960 --> 00:35:22,880 it's a lot. It'll get you a lot. 702 00:35:22,880 --> 00:35:25,960 And then you could replay all their past life experiences for 703 00:35:25,960 --> 00:35:29,080 them if you recorded it, and they would just be born with all 704 00:35:29,080 --> 00:35:31,400 this amazing knowledge of everything that happened, you 705 00:35:31,400 --> 00:35:35,960 know, And that's something where if someone was going in on one 706 00:35:35,960 --> 00:35:39,160 of these missions where they're trying out this experimental 707 00:35:39,160 --> 00:35:42,000 drug, you could sequence their genome and say, if it doesn't 708 00:35:42,000 --> 00:35:45,480 work out, you'll be reincarnated with the knowledge that you had 709 00:35:45,480 --> 00:35:48,120 in life and so on and so forth. And we'll kind of we'll look 710 00:35:48,120 --> 00:35:50,240 after you in the next in the next life, right? 711 00:35:50,920 --> 00:35:53,680 And so that's like a Dharmic inspired way of thinking about 712 00:35:53,680 --> 00:35:56,920 it. And then the psych inspiration, 713 00:35:56,920 --> 00:35:59,840 among other things, is just extreme pragmatism and just 714 00:35:59,960 --> 00:36:02,160 experimenting with things. This is actually, you know, the, 715 00:36:02,160 --> 00:36:04,680 the the western mindset used to also be like this Banting and 716 00:36:04,680 --> 00:36:05,800 best. Do you know that story? 717 00:36:06,160 --> 00:36:07,840 I don't. So Banting and best this 718 00:36:07,840 --> 00:36:11,000 important story for you. You know like 19 if I get the 719 00:36:11,000 --> 00:36:14,160 dates right. I think 1921 they started 720 00:36:14,160 --> 00:36:16,680 experimentation on insulin supplementation. 721 00:36:16,720 --> 00:36:20,880 Said hypothesis could treat diabetes and they tested it on 722 00:36:20,880 --> 00:36:26,040 1st like dogs then and it worked and then they treated, you know, 723 00:36:26,040 --> 00:36:28,520 they tried self experimentation and they went for patient 724 00:36:28,520 --> 00:36:32,280 volunteers and those patients just like stood up in bed like 725 00:36:32,280 --> 00:36:34,400 this. It was like the canonical bench 726 00:36:34,400 --> 00:36:36,760 to bedside concept like a bubbling beaker. 727 00:36:36,800 --> 00:36:40,560 You know, it's brought there and they iterated on formulation and 728 00:36:40,560 --> 00:36:43,760 so on and I think you know right now it's so hard to go from like 729 00:36:43,760 --> 00:36:46,680 pill to oral to an injection to a patch. 730 00:36:46,960 --> 00:36:49,960 There's a whole process and whereas in some ways it's kind 731 00:36:49,960 --> 00:36:53,840 of like going from web to a mobile client to like a command 732 00:36:53,840 --> 00:36:56,280 line client, it's kind of the same drug but in different 733 00:36:56,280 --> 00:36:58,400 formats or whatever. So they just did all that. 734 00:36:58,400 --> 00:37:01,040 It wasn't case control studies. It wasn't, it was just 735 00:37:01,040 --> 00:37:03,440 iteration, quick iteration and seeing it. 736 00:37:03,440 --> 00:37:07,720 And it was a large. And by 1923 they had Eli Lilly 737 00:37:07,800 --> 00:37:09,920 doing scale production of insulin for the entire North 738 00:37:09,920 --> 00:37:11,680 American continent. They'd won the Nobel Prize. 739 00:37:12,920 --> 00:37:15,680 And that was a time when pharma moved at the speed of software 740 00:37:16,200 --> 00:37:20,200 where you went from my dear to execution and via iteration in 741 00:37:20,200 --> 00:37:22,280 like 2 years. And one of the issues I think a 742 00:37:22,280 --> 00:37:25,200 lot about is there's no case control studies on case control 743 00:37:25,200 --> 00:37:28,200 studies. There's no regulatory science on 744 00:37:28,200 --> 00:37:30,600 the regulator themselves. Why don't we have a jurisdiction 745 00:37:30,600 --> 00:37:34,800 where you can just iterate your way to, you know, to something 746 00:37:35,480 --> 00:37:38,080 we're, we're crucially, by the way, the patients aren't simply 747 00:37:38,080 --> 00:37:40,080 patients are participants in their own health. 748 00:37:40,080 --> 00:37:43,160 They're not like a row in a table like a very 20th century 749 00:37:43,160 --> 00:37:45,000 study like Framingham or something like that. 750 00:37:45,160 --> 00:37:46,960 That's just like a row in a table, right? 751 00:37:47,360 --> 00:37:50,160 Whereas if it's a human being there and they've got a mobile 752 00:37:50,160 --> 00:37:53,560 app and they can send you back what their experience of the 753 00:37:53,560 --> 00:37:55,560 treatment is and they're an active participant in their own 754 00:37:55,560 --> 00:37:56,680 health, it's totally different paradigm. 755 00:37:56,680 --> 00:37:59,000 It's bi directional. And the very simplest version of 756 00:37:59,000 --> 00:38:01,400 that is adaptive clinical trials, which show you can like 757 00:38:01,400 --> 00:38:05,960 converge on an endpoint faster if you know you're not simply 758 00:38:05,960 --> 00:38:10,160 doing in like a like a really dumb open loop, you know, trial 759 00:38:10,160 --> 00:38:12,160 right where you're not. You're basically taking 760 00:38:12,160 --> 00:38:15,520 information from the trial as it's going to to adjust things 761 00:38:15,520 --> 00:38:17,480 so very shortly. But an adaptive clinical trials. 762 00:38:17,560 --> 00:38:18,800 But I think you can do much better than that. 763 00:38:18,800 --> 00:38:21,640 No, no software is designed with case control studies. 764 00:38:21,880 --> 00:38:24,280 SpaceX wasn't designed with case control studies. 765 00:38:24,280 --> 00:38:28,400 You know, I'm saying that there isn't some utility to them, but 766 00:38:28,400 --> 00:38:32,760 they're really for teasing apart small effect sizes rather than 767 00:38:32,760 --> 00:38:36,440 large effect sizes that are so manifest that they just jump out 768 00:38:36,440 --> 00:38:38,280 at you. Yeah, this probably goes back to 769 00:38:38,280 --> 00:38:40,480 the beginning of our conversation where if you have a 770 00:38:40,480 --> 00:38:45,040 large visual effect size that gets everyone involved and if 771 00:38:45,040 --> 00:38:47,440 you can, if you can do so in a jurisdiction to be like, hey, 772 00:38:47,840 --> 00:38:51,680 you can go from idea, you know, in gene therapy, cell therapy or 773 00:38:51,680 --> 00:38:53,760 something else. And I mean, I guess that's why 774 00:38:53,880 --> 00:38:56,520 people are so interested in peptides is peptides are drugs, 775 00:38:56,880 --> 00:38:59,280 right? Peptides give you a drug like 776 00:38:59,280 --> 00:39:01,600 effect sizes. The problem is they're poor, 777 00:39:01,600 --> 00:39:04,080 poorly characterized. There's questions about how 778 00:39:04,080 --> 00:39:06,040 they're manufactured and did the quality of those things. 779 00:39:06,040 --> 00:39:08,120 And then also you have a lot of off target effects. 780 00:39:08,120 --> 00:39:12,000 So I mean, peptides are inherently A risky path when 781 00:39:12,000 --> 00:39:13,880 you're not doing a well characterized path. 782 00:39:13,880 --> 00:39:18,360 But yeah, I mean, this is if we could demonstrate some like one 783 00:39:18,360 --> 00:39:25,240 or two wins of a process from a talented drug developer to a 784 00:39:25,360 --> 00:39:28,520 actual thing that works that has a low safety profile, large 785 00:39:28,520 --> 00:39:30,920 effect. Now, I guess like TBD, whether 786 00:39:30,920 --> 00:39:33,880 we can see that because I mean, biology is complicated. 787 00:39:34,360 --> 00:39:35,400 Maybe that's too big of an asking. 788 00:39:35,400 --> 00:39:38,640 Maybe, you know, the GLP ones, they kind of hit that sweet spot 789 00:39:38,640 --> 00:39:40,680 where it's the immediate visual effect of fat loss. 790 00:39:40,680 --> 00:39:43,360 And it has all these follow on health benefits where even if 791 00:39:43,360 --> 00:39:45,920 you're not obese or if you're not having an an eating problem, 792 00:39:46,320 --> 00:39:48,600 they it. Gives you discipline. 793 00:39:49,040 --> 00:39:50,400 Yeah, exactly. Yeah, yeah. 794 00:39:50,560 --> 00:39:52,800 That reminds me of like in the future, like we talk about like 795 00:39:52,800 --> 00:39:55,240 you and I have imagined what gene therapies we say hair, 796 00:39:55,240 --> 00:39:57,440 skin, you know, muscles, etcetera. 797 00:39:57,880 --> 00:40:01,440 But it might be fun to have a thought experiment of like what 798 00:40:01,440 --> 00:40:04,720 would gene therapy be like in 30 years? 799 00:40:04,720 --> 00:40:09,200 Like what kind of nuanced, expansive concepts would what do 800 00:40:09,200 --> 00:40:11,240 we think about now? We we think about like designer 801 00:40:11,240 --> 00:40:14,120 babies of like we're going to choose height and eye color, but 802 00:40:14,120 --> 00:40:16,040 let's how are you doing some human experience? 803 00:40:16,280 --> 00:40:18,120 Mini circle is interesting because they have the concept of 804 00:40:18,120 --> 00:40:20,760 a plasmid that's reversible. You can turn it on and turn it 805 00:40:20,760 --> 00:40:23,960 off, right? So almost like, you know, I have 806 00:40:23,960 --> 00:40:26,080 some caffeine and I don't, right? 807 00:40:26,480 --> 00:40:29,280 You could or you change your, you know, put on your contact 808 00:40:29,280 --> 00:40:32,560 lenses or not. You know, biology is 809 00:40:32,560 --> 00:40:34,000 complicated. There's all kinds of off target 810 00:40:34,000 --> 00:40:34,880 stuff. It's not true. 811 00:40:34,880 --> 00:40:36,920 You'll get it right. People's biology is different 812 00:40:36,920 --> 00:40:37,720 and old. It's true. 813 00:40:38,200 --> 00:40:43,040 But there's something cool that you're of a very specific target 814 00:40:43,040 --> 00:40:46,240 that can flip on and off a protein that can add or delete a 815 00:40:46,240 --> 00:40:49,000 function, right? And Patrick Friedman, I think, 816 00:40:49,000 --> 00:40:53,720 had that therapy and he showed his, this is a mini circle, you 817 00:40:53,720 --> 00:40:55,040 know, which is doing the plasmid. 818 00:40:55,200 --> 00:40:59,320 So Patrick Friedman and Farb Navy both had the therapy and 819 00:40:59,320 --> 00:41:02,720 Patrick said that he was just crushing it with his VO2 Max. 820 00:41:02,920 --> 00:41:04,880 Farb also said the same thing, right? 821 00:41:05,440 --> 00:41:09,600 And that's something where it's end of one, but they know their 822 00:41:09,600 --> 00:41:11,120 own body better than anybody else. 823 00:41:11,120 --> 00:41:14,080 It's end of one, but it's over. It's it's a lot of variables. 824 00:41:14,080 --> 00:41:15,720 Which end of 1. You know, Mike Snyder's thing on 825 00:41:15,720 --> 00:41:18,800 this from many years ago. The we're talking about Mike 826 00:41:18,800 --> 00:41:23,080 Snyder's prophet Stanford. And he, yeah, the intergrome, 827 00:41:23,200 --> 00:41:25,880 right? He just took every possible 828 00:41:25,880 --> 00:41:29,360 thing from expression to the rest and just ran on himself. 829 00:41:29,640 --> 00:41:33,760 And he's able to, for example, see himself getting sick in the 830 00:41:33,760 --> 00:41:37,320 gene expression data like a few days before he actually got 831 00:41:37,320 --> 00:41:38,400 sick. Right, exactly. 832 00:41:38,920 --> 00:41:42,280 And so we could with better metrics. 833 00:41:43,160 --> 00:41:45,880 That's another piece of this actually, diagnostics are non 834 00:41:45,880 --> 00:41:50,560 invasive, right? So if you had constant like 835 00:41:50,600 --> 00:41:55,680 whole, you know, like like all 30,000 odd or 22,500 whatever, 836 00:41:55,760 --> 00:41:58,760 you know, a human gene expression panel and you track 837 00:41:58,760 --> 00:42:01,160 that and maybe you know, metabolomics and so on. 838 00:42:01,160 --> 00:42:04,360 You did something like the Mike Snyder intergrome while you're 839 00:42:04,360 --> 00:42:07,040 giving people this gene therapy or self, but you just got a much 840 00:42:07,040 --> 00:42:10,440 richer readout, you know, from them right now. 841 00:42:10,440 --> 00:42:12,080 You're not just relying on self report. 842 00:42:12,600 --> 00:42:15,280 You know, it is it's like your biomarker stuff, obviously, but 843 00:42:15,280 --> 00:42:17,720 it's just like, you know, we're getting 10s of thousands of 844 00:42:17,720 --> 00:42:19,880 them. That seems like, you know, 845 00:42:19,880 --> 00:42:22,600 something that is very feasible because costs are coming down of 846 00:42:22,600 --> 00:42:25,200 all that sequencing and so on. That'd be a very good thing to 847 00:42:25,200 --> 00:42:26,560 do. Like an inch groan like thing 848 00:42:26,560 --> 00:42:28,000 that's like a time series inch groan. 849 00:42:28,000 --> 00:42:30,520 First you get whatever data points on them to determine 850 00:42:30,520 --> 00:42:33,040 healthy, then you impose A stimulus, and then you look in 851 00:42:33,040 --> 00:42:35,600 state space and you see is it actually changing anything or 852 00:42:35,600 --> 00:42:38,400 not and what's a change? Yeah, that's exactly what I'm 853 00:42:38,400 --> 00:42:42,920 doing at Blueprint. So we are going to try to do for 854 00:42:42,920 --> 00:42:44,480 five, we guess we've done that for myself. 855 00:42:44,520 --> 00:42:48,680 Like when you build an ASIC, you know, if you ever haven't, if 856 00:42:48,680 --> 00:42:51,920 you have not built an ASIC, it's really cool that if you like get 857 00:42:51,920 --> 00:42:55,240 a picture and zoom in, zoom in, zoom in, zoom in, zoom in, you 858 00:42:55,240 --> 00:42:59,160 find that it's like down to the nano level of like granularity. 859 00:42:59,160 --> 00:43:01,520 It's beautiful. It's amazing we can design these 860 00:43:01,520 --> 00:43:02,880 structures. It's crazy, yes. 861 00:43:03,160 --> 00:43:04,720 It's crazy. And The thing is you can 862 00:43:04,720 --> 00:43:09,160 characterize this at every level, every circuit design you 863 00:43:09,160 --> 00:43:11,240 can characterize. You can say what happens then I 864 00:43:11,240 --> 00:43:15,480 do this and and it's like we can characterize our machines and 865 00:43:15,480 --> 00:43:19,960 our intelligent machines so well from the circuit all the way up 866 00:43:19,960 --> 00:43:22,080 to the application layer. We can characterize the entire 867 00:43:22,080 --> 00:43:24,400 stack, the servers they run on the energy. 868 00:43:24,640 --> 00:43:27,880 Whereas in human biology we can't characterize. 869 00:43:27,880 --> 00:43:30,800 You can get like a really high level of like, what does my 870 00:43:30,800 --> 00:43:34,640 blood draw say, right, Like my cholesterol or my my liver 871 00:43:34,640 --> 00:43:36,800 enzymes. But you can't get down and 872 00:43:36,800 --> 00:43:40,640 characterize these, these these smaller molecular interactions, 873 00:43:40,720 --> 00:43:43,560 which like you're saying, they read out really important data. 874 00:43:43,560 --> 00:43:46,240 Am I getting sick? Am I showing signs of disease? 875 00:43:46,840 --> 00:43:48,400 Am I responsive to this drug or not? 876 00:43:48,400 --> 00:43:51,480 In what ways? It's like if we can pair the 877 00:43:51,480 --> 00:43:55,400 characterization with these jurisdictions with good drug 878 00:43:55,400 --> 00:43:57,200 developers, like that's your path. 879 00:43:57,200 --> 00:43:59,720 I mean, and then we could open up and once people get like a a 880 00:44:00,400 --> 00:44:03,080 few successes, we'll say, huh, like this is a thing. 881 00:44:03,080 --> 00:44:05,600 We can, we can actually do it. I was talking to Jason Kelly the 882 00:44:05,600 --> 00:44:08,080 other day. He's founder of Ginkgo Bioworks. 883 00:44:08,560 --> 00:44:10,040 Yeah, I was, I was first. Yeah, exactly. 884 00:44:10,040 --> 00:44:13,360 I was first money in. And they, they really led the 885 00:44:13,360 --> 00:44:16,520 charge for biotech in industrial, industrial 886 00:44:16,520 --> 00:44:19,360 applications. It was a really great moment. 887 00:44:19,680 --> 00:44:21,960 They've run into some roadblocks and doing it, which like, you 888 00:44:21,960 --> 00:44:24,480 know, breaks my heart because there's nothing I want to see 889 00:44:24,480 --> 00:44:27,640 more than biotech in the industrial world, like really 890 00:44:27,640 --> 00:44:30,760 smart biotech designs. And we are saying today like we 891 00:44:30,840 --> 00:44:33,160 we're talking about doing something together and we both 892 00:44:33,160 --> 00:44:38,600 just like, why can't we build human with the same fever pitch? 893 00:44:38,600 --> 00:44:42,120 We build tech like why, Like why can't we chase this thing? 894 00:44:42,280 --> 00:44:44,800 They're just, it's so frustrating. 895 00:44:44,800 --> 00:44:47,280 That it boils down to risk tolerance. 896 00:44:47,280 --> 00:44:50,960 And the reason is with tech we can have crashes because when 897 00:44:50,960 --> 00:44:54,240 crashes and the computer crashes and crashes all the time, it's 898 00:44:54,240 --> 00:44:58,120 like acceptable to fail, right? With humans, people get really 899 00:44:58,120 --> 00:45:03,400 mad if there's a failure, right? They are, you know, but but they 900 00:45:03,400 --> 00:45:07,680 didn't used to with Bantic and best in that era, people were 901 00:45:08,040 --> 00:45:11,480 more reward seeking and less risk averse, right. 902 00:45:12,040 --> 00:45:16,600 And I think we're kind of coming back to that era gradually where 903 00:45:16,600 --> 00:45:19,560 risk tolerance has radically increased over society over the 904 00:45:19,560 --> 00:45:21,840 last few decades. I think. 905 00:45:23,000 --> 00:45:30,120 And you know, one thing on this is an intermediate between 906 00:45:31,120 --> 00:45:32,600 though, maybe we can just go to the full thing. 907 00:45:32,920 --> 00:45:39,160 I think just the diagnostic zone, right, where you can just 908 00:45:39,160 --> 00:45:41,920 get get genome sequence without a prescription from your doctor. 909 00:45:41,920 --> 00:45:45,600 It's so stupid. The entire FDA, you know, CDRH 910 00:45:45,600 --> 00:45:48,720 thing where it's like requiring a prescription to look at the 911 00:45:48,720 --> 00:45:51,000 mirror. Like why should you need a why? 912 00:45:51,000 --> 00:45:52,880 Why should that be bottleneck through the medical system to 913 00:45:52,880 --> 00:45:56,000 get to get your genome sequence? When when the AI models are 914 00:45:56,000 --> 00:45:58,720 smarter than my doctor like why do I have to go ask them? 915 00:45:59,040 --> 00:46:00,120 Exactly. That's right. 916 00:46:00,120 --> 00:46:04,040 So I think there is definitely room for something where you get 917 00:46:04,040 --> 00:46:07,720 your GM sequenced by, for example, Nucleus or some at home 918 00:46:07,720 --> 00:46:10,720 kind of device, you know, like as Oxford Nanopore or something 919 00:46:10,720 --> 00:46:12,800 like that gets smaller and smaller eventually get there. 920 00:46:13,200 --> 00:46:15,840 Or you just do in the lab and you just get the file back and 921 00:46:15,840 --> 00:46:17,880 you have it interpreted by something like an open source 922 00:46:17,880 --> 00:46:21,200 version of Prometheus. And then but but it's an AI 923 00:46:21,200 --> 00:46:23,560 enabled thing and it's all done locally. 924 00:46:23,640 --> 00:46:25,800 And so I've got some companies that I'm looking at that do some 925 00:46:25,800 --> 00:46:29,000 combination of these things. But you know, I think the second 926 00:46:29,000 --> 00:46:33,520 generation of truly personal genomics could be another big 927 00:46:33,520 --> 00:46:34,960 thing that we do. And maybe that's the 928 00:46:34,960 --> 00:46:38,000 intermediate step, that then people have enough data and 929 00:46:38,000 --> 00:46:42,000 enough time series on themselves that then that opens up. 930 00:46:42,200 --> 00:46:45,080 Basically, the reason that's interesting is now you're not 931 00:46:45,080 --> 00:46:49,720 flying blind every you have a sense of your own dashboard and 932 00:46:49,720 --> 00:46:51,800 your own metrics. Like you're one of the very 933 00:46:51,800 --> 00:46:54,120 first on this. Actually, there's that guy, the 934 00:46:54,120 --> 00:46:56,120 measured man. There's the article in the 935 00:46:56,120 --> 00:46:57,920 Atlantic many years ago on this guy. 936 00:46:57,920 --> 00:46:59,320 He's a prophet. Forget his name. 937 00:47:00,160 --> 00:47:01,320 You should maybe meet him if you haven't. 938 00:47:02,240 --> 00:47:03,440 It's back with the Atlantic was good. 939 00:47:04,400 --> 00:47:12,080 But the so the this guy also is measuring all kinds of time 940 00:47:12,080 --> 00:47:16,120 points on himself and he and he found he was like an early 941 00:47:16,120 --> 00:47:17,640 quantified self kind of person, right. 942 00:47:18,040 --> 00:47:22,760 So if you have that dashboard now you have a sense of kind of 943 00:47:22,760 --> 00:47:25,040 what's the speed bump? What's a real major thing? 944 00:47:25,520 --> 00:47:27,760 And then you're kind of better able to process what an 945 00:47:27,760 --> 00:47:30,000 intervention is doing. Is it messing you up? 946 00:47:30,000 --> 00:47:32,320 It's just moving these genes, that kind of thing like these 947 00:47:32,320 --> 00:47:34,960 expression levels etcetera. Do you actually in your all your 948 00:47:34,960 --> 00:47:37,840 bio maker stuff, have you done gene expression time series on 949 00:47:37,840 --> 00:47:40,760 yourself? By the way, his name is Larry 950 00:47:40,760 --> 00:47:42,000 Smarr. That's him. 951 00:47:42,000 --> 00:47:43,800 Yeah, that's the guy. I've never met him. 952 00:47:43,920 --> 00:47:48,280 When was this done? Do you know him? 953 00:47:48,760 --> 00:47:51,720 I don't know if I just know him from the I think Larry Smarr, 954 00:47:52,040 --> 00:47:55,840 Mike Snyder and you should do if you want, we should all do a 955 00:47:55,840 --> 00:47:57,600 podcast together or something because you know, but. 956 00:47:57,600 --> 00:48:01,280 That's a good idea. Those those two people are very 957 00:48:01,280 --> 00:48:02,480 aligned with our way of thinking. 958 00:48:02,880 --> 00:48:04,840 Yeah, So he's a founder of the National Center for 959 00:48:04,840 --> 00:48:08,400 Supercomputing Applications at NASA, born in 1948. 960 00:48:08,640 --> 00:48:10,200 So he's a. Yeah, so he's 80 or something 961 00:48:10,200 --> 00:48:11,800 now, maybe. I'm not sure what health he's 962 00:48:11,800 --> 00:48:15,520 in, but him and Mike Snyder are both pioneers in this space. 963 00:48:15,520 --> 00:48:18,480 Mike Snyder with the intergrome, Larry Smarr, Right. 964 00:48:18,800 --> 00:48:22,120 And I think it's worth, I bet, by the way, if you put that into 965 00:48:22,120 --> 00:48:24,880 AIAI is very good at expanding suggestion lists. 966 00:48:25,960 --> 00:48:28,440 So if you put both of those there and say who else is like 967 00:48:28,440 --> 00:48:30,600 that, you'll come up, but maybe others will come up too. 968 00:48:30,600 --> 00:48:33,480 Yeah, he found early Crohn's disease in himself before his 969 00:48:33,480 --> 00:48:35,400 clinically diagnosed. I mean, this is like, so I have 970 00:48:35,400 --> 00:48:38,640 kernel on my desk here. This is this is the brain 971 00:48:38,640 --> 00:48:42,600 interface I built. I measure my brain every day 972 00:48:42,600 --> 00:48:43,960 now. I'll show you. 973 00:48:44,320 --> 00:48:45,680 Yeah. So this is like characterization 974 00:48:45,680 --> 00:48:48,560 the brain like you can start. You can see the inside here 975 00:48:48,560 --> 00:48:49,440 maybe. Yeah. 976 00:48:49,440 --> 00:48:52,440 So he said like we did. We we spent seven years building 977 00:48:52,440 --> 00:48:54,560 this technology. It's basically wearable fMRI 978 00:48:54,920 --> 00:48:58,120 using light, using light. And this is the problem is we've 979 00:48:58,120 --> 00:49:00,400 never had the ability to characterize our brains. 980 00:49:00,720 --> 00:49:04,200 Like if you want a brain scan, if you do FM RI or you can MRI, 981 00:49:04,360 --> 00:49:07,800 FM RI is a functional side, MRI is a structural side, but just a 982 00:49:07,800 --> 00:49:10,000 go to facility sitting there in an environment that's 983 00:49:10,000 --> 00:49:12,120 claustrophobic. But now I can basically 984 00:49:12,120 --> 00:49:15,800 characterize my brain every day looking at the functional 985 00:49:15,800 --> 00:49:18,360 networks. You know, I have a I my 986 00:49:18,360 --> 00:49:22,080 intuition, by the way, does that need to be, can you walk around 987 00:49:22,080 --> 00:49:23,960 with that or is it have to be plugged into the wall? 988 00:49:24,320 --> 00:49:26,640 Yeah, yeah, it, it's a stationary. 989 00:49:27,040 --> 00:49:31,200 It's stationary. You know, a fun test would be 990 00:49:31,920 --> 00:49:35,280 what does it look like when someone is just scrolling versus 991 00:49:35,280 --> 00:49:36,800 what does it look like when they're on the treadmill? 992 00:49:37,440 --> 00:49:40,720 Yes, exactly. And like I didn't do this but my 993 00:49:41,400 --> 00:49:44,840 my colleague did. He drank alcohol for science and 994 00:49:44,840 --> 00:49:46,680 he saw an immediate aging of his brain. 995 00:49:46,680 --> 00:49:48,040 I think it was two to three years. 996 00:49:48,040 --> 00:49:51,360 The following day. We it's been cool to just see 997 00:49:51,600 --> 00:49:53,440 basic correlations. What happens when you're sleep 998 00:49:53,440 --> 00:49:54,760 deprived? What happens when you exercise? 999 00:49:54,760 --> 00:49:56,600 What happens you eat? Well, like you can run all these 1000 00:49:56,600 --> 00:49:59,360 experiments now or I couldn't before, but this is like the 1001 00:49:59,360 --> 00:50:01,600 characterization thing. This, the characterization thing 1002 00:50:01,600 --> 00:50:06,200 is what we've been missing is people run to do therapies and 1003 00:50:06,280 --> 00:50:08,960 even when they do therapies, it's poor care, poorly 1004 00:50:08,960 --> 00:50:09,760 characterized. It's. 1005 00:50:09,800 --> 00:50:13,080 Very, let it be characterized. Let alone before, like having 1006 00:50:13,080 --> 00:50:16,000 something like you're saying some time series longitudinal 1007 00:50:16,000 --> 00:50:18,480 data looking at these things, which is I've, you know, I've 1008 00:50:18,480 --> 00:50:21,080 become the most characterized person human in history. 1009 00:50:21,320 --> 00:50:23,760 There's no human that has, it's more characterized than myself. 1010 00:50:24,120 --> 00:50:28,160 And so we have, it's enabled us to find so many insights that we 1011 00:50:28,160 --> 00:50:31,000 just haven't been able to find in the literature or through 1012 00:50:31,000 --> 00:50:32,200 other observations through doctors. 1013 00:50:32,200 --> 00:50:34,600 It's just like it's it's all about measurement. 1014 00:50:35,280 --> 00:50:38,320 And what is what is the cost per day of your characterization by 1015 00:50:38,320 --> 00:50:40,640 the way, like what, what acids are you running? 1016 00:50:41,200 --> 00:50:42,720 Honestly, it's not that expensive. 1017 00:50:42,880 --> 00:50:48,240 These are like, I mean blood draws, saliva, stool, 1018 00:50:48,280 --> 00:50:55,560 mitochondria, fitness tests, imaging, methylation, 1019 00:50:56,560 --> 00:50:58,280 proteomics. You're like, I don't know. 1020 00:50:58,280 --> 00:51:01,240 She's like not honestly, probably I'm guessing without 1021 00:51:01,240 --> 00:51:03,320 thinking about this more robustly, but something like 1022 00:51:05,280 --> 00:51:08,200 50,100 thousand a year. Like not, it's not that much. 1023 00:51:08,320 --> 00:51:14,160 It's like 1000 a day and so if we could bring that down to 10 1024 00:51:14,160 --> 00:51:15,760 bucks a day. Or. 1025 00:51:17,240 --> 00:51:20,240 Even 1000 a month? Yes, exactly right. 1026 00:51:20,360 --> 00:51:21,440 Actually it's less than 1000 a day. 1027 00:51:21,440 --> 00:51:26,360 If you're saying 100, if you're saying 100 KA year, right, then 1028 00:51:26,520 --> 00:51:29,480 it's somewhere between 2:00. It's like 203 hundred a day, 1029 00:51:29,640 --> 00:51:31,600 right? It's already not too bad for 1030 00:51:31,600 --> 00:51:36,280 what it is, right? So it's like, you know, the 1031 00:51:36,280 --> 00:51:40,760 $1000 genome. There's, there's, I mean, I've 1032 00:51:40,760 --> 00:51:43,240 run a clinical lab. There's definitely returns on 1033 00:51:43,240 --> 00:51:45,320 scale with clinical lab for all kinds of stuff with 1034 00:51:45,320 --> 00:51:47,400 instrumentation to reagents to everything. 1035 00:51:48,040 --> 00:51:51,640 So I've thought about a residential clinical lab where 1036 00:51:51,640 --> 00:51:56,400 we just build it into network school and because it's a pain 1037 00:51:56,400 --> 00:51:59,560 to go and you know, you want is a phlebotomist to come to you, 1038 00:52:00,040 --> 00:52:02,000 right? You want mobile phlebotomy, you 1039 00:52:02,000 --> 00:52:06,760 want saliva, you want ideally even in in your apartment or 1040 00:52:06,760 --> 00:52:09,600 something like that. You can just leave your 1041 00:52:09,600 --> 00:52:11,320 toothbrush or something like that. 1042 00:52:11,680 --> 00:52:13,920 And then it can just, you know, you spend in a cup or something. 1043 00:52:13,920 --> 00:52:19,080 And then there's various ways of probably streamlining, if not 1044 00:52:19,080 --> 00:52:22,200 fully automating something, make it easier to get samples. 1045 00:52:22,200 --> 00:52:23,640 I. Have a sensor in my toilet. 1046 00:52:24,480 --> 00:52:27,440 Yeah, exactly that. Kind of automatically, yeah. 1047 00:52:27,960 --> 00:52:31,640 Bill Gates was interested in this 20 years ago and you could, 1048 00:52:31,840 --> 00:52:34,440 you know, like you're in color imagery and so on and so forth. 1049 00:52:34,440 --> 00:52:37,520 There's a lot you can, you can do with that and you just have 1050 00:52:37,520 --> 00:52:40,960 it there and, and might maybe, you know, you just image it and 1051 00:52:40,960 --> 00:52:44,400 so on and so forth. I think the Chinese could do a 1052 00:52:44,400 --> 00:52:48,360 lot here because they they can innovate on hardware and, and 1053 00:52:48,360 --> 00:52:49,880 there's something to be done here because they're building a 1054 00:52:49,880 --> 00:52:51,480 lot of cities. So they could actually install 1055 00:52:51,480 --> 00:52:54,000 these smart toilets. Some kind of Chinese, Japanese 1056 00:52:54,000 --> 00:52:55,480 companies. The Japanese also do a lot of 1057 00:52:55,480 --> 00:52:58,880 toilets. Yeah, I just watched that South 1058 00:52:58,880 --> 00:53:00,920 Park episode where they got the Japanese toilet. 1059 00:53:01,640 --> 00:53:04,360 Anyways, yeah, this is it's also the the characterization thing 1060 00:53:04,360 --> 00:53:06,880 is really cool because, you know, I when I started 1061 00:53:06,880 --> 00:53:09,480 Blueprint, I was trying to solve my own problem. 1062 00:53:09,480 --> 00:53:11,720 Like, how do I actually find nutritious food that's third 1063 00:53:11,720 --> 00:53:15,160 party tested and low on toxins? And then friends and family are 1064 00:53:15,160 --> 00:53:17,000 like, hey, can I have access? I'm like, sure, here we go. 1065 00:53:17,000 --> 00:53:18,600 And it just like accidentally became a business. 1066 00:53:18,880 --> 00:53:21,560 But the problem with that is like, even though I'm trying to 1067 00:53:21,560 --> 00:53:24,400 solve a real problem in life, people have such a negative 1068 00:53:24,400 --> 00:53:27,880 connotation towards therapies, like towards things you actually 1069 00:53:27,880 --> 00:53:29,240 like. It's complicated. 1070 00:53:29,240 --> 00:53:31,800 The characterization is much more friendly, right? 1071 00:53:31,800 --> 00:53:34,320 If you're helping someone measure themselves, there's much 1072 00:53:34,320 --> 00:53:36,480 less blowback. And so I'm moving very hard in 1073 00:53:36,480 --> 00:53:39,240 that direction where I'm going to help people characterize 1074 00:53:39,240 --> 00:53:42,320 themselves. Yeah, just characterize yourself 1075 00:53:42,320 --> 00:53:43,880 like me. Like, let's just like, get this 1076 00:53:43,880 --> 00:53:46,880 based on measurement. It's a much safer path to build 1077 00:53:47,160 --> 00:53:52,800 because then people somehow they don't have suspicions like 1078 00:53:52,800 --> 00:53:56,040 they're doing me now, but like really I'm like, I don't like, I 1079 00:53:56,040 --> 00:53:59,080 don't want to be doing this like like I there's so many like if I 1080 00:53:59,080 --> 00:54:02,320 were to out to make money, there's like 1000 other ways I 1081 00:54:02,320 --> 00:54:05,320 want to do to make money. So anyway, yeah, so. 1082 00:54:05,720 --> 00:54:07,800 The thing is actually, you know, years ago. 1083 00:54:08,160 --> 00:54:10,280 That's why, you know, my first company was in diagnostics, 1084 00:54:10,680 --> 00:54:13,280 because also diagnostics has a lot of advantages. 1085 00:54:13,280 --> 00:54:16,440 First is it's half computer science because after you do the 1086 00:54:16,440 --> 00:54:19,960 assay, then you can analyze everything on the computer and 1087 00:54:19,960 --> 00:54:21,760 you can make all the dashboards and so on. 1088 00:54:21,760 --> 00:54:23,920 So it's like at least half within our bailiwick. 1089 00:54:25,120 --> 00:54:27,240 It can't hurt the person usually. 1090 00:54:27,400 --> 00:54:28,840 So the risk is way, way, way lower. 1091 00:54:28,840 --> 00:54:30,960 So you can move much faster. You know, the worst thing you 1092 00:54:30,960 --> 00:54:33,640 can do is you can give them a wrong result, but you can move 1093 00:54:33,640 --> 00:54:35,120 close to speed of computer science. 1094 00:54:36,200 --> 00:54:39,680 And then most interestingly, it means that the intervention is 1095 00:54:39,680 --> 00:54:43,720 informed. So arguably diagnostics creates 1096 00:54:43,720 --> 00:54:47,960 a room for therapeutics. So perhaps, yeah, we start with 1097 00:54:48,080 --> 00:54:51,560 the quote, you know, the universal longitudinal 1098 00:54:51,560 --> 00:54:56,640 diagnostic network state, first longitudinal longitudinal 1099 00:54:56,640 --> 00:54:58,480 network state, then longevity network state. 1100 00:54:58,480 --> 00:54:59,560 Cool. I like this. 1101 00:54:59,560 --> 00:55:05,040 So basically we get a cohort of people together, we say we are 1102 00:55:05,040 --> 00:55:08,440 going to invest the capital to do high fidelity 1103 00:55:08,440 --> 00:55:10,080 characterization. I love this. 1104 00:55:10,080 --> 00:55:12,160 We don't, we don't know why, right? 1105 00:55:12,160 --> 00:55:15,440 We don't know to what end. My standard integral for 100 or 1106 00:55:15,440 --> 00:55:16,440 1000 people. Yeah. 1107 00:55:16,600 --> 00:55:20,040 Now it, and it may not be useful to be this characterized for 1108 00:55:20,040 --> 00:55:21,560 five years, 10 years, like we don't know. 1109 00:55:21,800 --> 00:55:25,320 But like we want this longitudinal characterization so 1110 00:55:25,320 --> 00:55:30,240 that when we find things to to trial, we have this time series 1111 00:55:30,240 --> 00:55:33,680 longitudinal data that we can, yeah, that gives us a better 1112 00:55:33,680 --> 00:55:36,480 shot identifying what therapies are going to what we'll be 1113 00:55:36,480 --> 00:55:39,640 responsive to, what we won't be and then see the off target 1114 00:55:39,640 --> 00:55:41,440 effects. And so probably a much better 1115 00:55:41,440 --> 00:55:42,880 way. Whereas right now evenly when 1116 00:55:42,880 --> 00:55:46,920 you look at the clinical trials, they are so poorly 1117 00:55:46,920 --> 00:55:49,520 characterized. It's clearly like I read this 1118 00:55:49,520 --> 00:55:51,600 one. Scalar variable on a very 1119 00:55:51,600 --> 00:55:53,800 complicated human. I read these studies. 1120 00:55:53,800 --> 00:55:56,600 I'm just like, this is not reliable data. 1121 00:55:56,600 --> 00:55:58,600 Like when you're telling me it does blank and blank and it has 1122 00:55:58,600 --> 00:56:01,800 these side effects like sure, like nothing close to what I 1123 00:56:01,960 --> 00:56:03,560 would think could be. So even this way. 1124 00:56:03,920 --> 00:56:06,320 That's why like the GLP ones are a good one because like they 1125 00:56:06,320 --> 00:56:09,120 have them good characterization better than than like, you know, 1126 00:56:09,360 --> 00:56:13,760 a, a non characterized peptide. But that said, like we don't 1127 00:56:13,760 --> 00:56:15,720 know a lot, which is why like all this stuff is coming out 1128 00:56:15,720 --> 00:56:17,040 like blindness and stuff like that. 1129 00:56:17,040 --> 00:56:21,280 So yeah, we really, I guess like this conversations land just on 1130 00:56:21,280 --> 00:56:24,080 characterization as a key input. But then we need to go to these 1131 00:56:24,080 --> 00:56:28,880 jurisdictions to figure out with that how we leverage fast track, 1132 00:56:28,960 --> 00:56:33,400 safe, high effect size therapies because we need a like we need a 1133 00:56:33,400 --> 00:56:36,240 moment in for longevity. We need something, yeah. 1134 00:56:37,720 --> 00:56:41,080 What you saying? Like it could be the case that 1135 00:56:41,080 --> 00:56:44,160 it's like, you know, the history book says, you know, from like 1136 00:56:44,160 --> 00:56:49,040 2025 to the 2035, these new therapies came out and I had 1137 00:56:49,040 --> 00:56:52,880 this like started this cubit of effect or there's like a moment 1138 00:56:52,880 --> 00:56:55,680 where something happens, maybe the GLP ones that story of like 1139 00:56:55,960 --> 00:56:57,360 people are familiar with doing these. 1140 00:56:57,480 --> 00:56:59,400 They're important. They are but but but but we 1141 00:56:59,400 --> 00:57:01,720 could have the azempic for aging would be that moment. 1142 00:57:02,320 --> 00:57:05,440 Yeah, like something something pretty, but that the effect size 1143 00:57:05,440 --> 00:57:07,960 is big enough where everybody turns their head and they're 1144 00:57:07,960 --> 00:57:12,120 like, I understand reality differently now because of that. 1145 00:57:12,400 --> 00:57:14,640 Yes, that's right. And I think I do think that's 1146 00:57:14,640 --> 00:57:18,040 possible simply because we're seeing it in mice and so on. 1147 00:57:18,040 --> 00:57:20,520 We just got to figure out what buttons to press within humans. 1148 00:57:20,960 --> 00:57:24,880 But I really like the idea of the longitudinal network state 1149 00:57:25,400 --> 00:57:27,560 and then the longevity network state or the longitudinal 1150 00:57:27,560 --> 00:57:30,000 special economic zone, longitudinal diagnostics. 1151 00:57:30,000 --> 00:57:33,760 And then and we can cost that out and we can do something with 1152 00:57:33,760 --> 00:57:35,280 it as a Desi thing, you know? Desi. 1153 00:57:35,280 --> 00:57:35,920 Yeah. Yeah. 1154 00:57:36,480 --> 00:57:40,320 We could or we could do a nutritional raise, but it's just 1155 00:57:40,320 --> 00:57:46,880 a very, very scoped thing, which is these end people for this 1156 00:57:46,880 --> 00:57:49,600 period of time. And frankly, they could even pay 1157 00:57:49,600 --> 00:57:53,200 for it where it's something where it's not that expensive. 1158 00:57:53,200 --> 00:57:56,280 It's just expensive enough to be within like a, you know, sort of 1159 00:57:56,360 --> 00:57:59,040 pro zoomer budget, like a crypto person, right? 1160 00:57:59,800 --> 00:58:02,320 And and they could maybe live in their school and be part of the 1161 00:58:02,320 --> 00:58:03,680 study and. You know right? 1162 00:58:04,240 --> 00:58:06,960 I like this. So you have the list of all the 1163 00:58:06,960 --> 00:58:08,640 instruments that you do for your measurements. 1164 00:58:10,040 --> 00:58:11,720 Do you have that in a public spreadsheet or do you have that 1165 00:58:11,720 --> 00:58:13,160 internally? I need to put it together. 1166 00:58:13,320 --> 00:58:16,040 Yeah, basically, yeah. What what would like, what would 1167 00:58:16,040 --> 00:58:17,720 a high fidelity characterization look like? 1168 00:58:17,720 --> 00:58:20,720 What, what tests would you need? What machines would you need? 1169 00:58:21,000 --> 00:58:23,400 What labs would you need? Because some of the some of the 1170 00:58:23,400 --> 00:58:26,840 assays we want done are just not available, the off the shelf 1171 00:58:26,840 --> 00:58:28,560 labs. So even if we had some people 1172 00:58:28,560 --> 00:58:32,120 who could spit up some nice so so it could accommodate some of 1173 00:58:32,120 --> 00:58:34,800 the unique things we want to measure, that'd be great. 1174 00:58:35,600 --> 00:58:39,080 Yeah, I think and we we do it in participation with NUS or 1175 00:58:39,080 --> 00:58:42,160 somebody like a, you know, some, some sort of local diversity or 1176 00:58:42,160 --> 00:58:45,600 hospital and I think we could do something here. 1177 00:58:46,360 --> 00:58:48,000 I think it's something very, very cool. 1178 00:58:48,240 --> 00:58:51,680 So if anything, it's like a new model of even it's a new model 1179 00:58:51,680 --> 00:58:56,040 of healthcare really like. The key is, you know, like at 1180 00:58:56,040 --> 00:58:58,640 Stanford, there was this concept called back again when Sanford, 1181 00:58:58,640 --> 00:59:01,240 back when Sanford was good residential education. 1182 00:59:02,400 --> 00:59:06,280 And the idea was that the education didn't stop when you 1183 00:59:06,280 --> 00:59:08,600 were, it wasn't one hour in class. 1184 00:59:08,600 --> 00:59:10,360 You were 23 hours outside of class. 1185 00:59:10,360 --> 00:59:13,640 And so education after the dorm rooms and sort of so it was 1186 00:59:13,640 --> 00:59:15,680 like, you know, immersive in that sense. 1187 00:59:16,040 --> 00:59:19,080 So this is. Residential health, Yeah, Yeah. 1188 00:59:19,080 --> 00:59:21,160 I mean, this is kind of like we, we've, you and I have been 1189 00:59:21,160 --> 00:59:23,680 spinning around this idea, I mean, since our, since we first 1190 00:59:23,680 --> 00:59:28,600 met of like how we bridge the worlds of ideology, health, 1191 00:59:28,840 --> 00:59:33,640 governance, nation state, like what is this moment? 1192 00:59:33,800 --> 00:59:36,480 And I I like this as a path because it does. 1193 00:59:37,000 --> 00:59:40,160 It bridges, it brings together the things people care about. 1194 00:59:40,440 --> 00:59:42,880 Yeah, and there won't be that much resistances because we 1195 00:59:42,880 --> 00:59:45,200 could start with the launch tool measurements. 1196 00:59:45,200 --> 00:59:47,720 No one gets hurt, right? Exactly. 1197 00:59:47,760 --> 00:59:51,240 You have enough data to show what normal looks like, what 1198 00:59:51,240 --> 00:59:54,520 interventions look like, what fitness looks like, what bad 1199 00:59:54,520 --> 00:59:56,160 foods look like. You've got all their 1200 00:59:56,160 --> 00:59:58,200 nutrigenomics. You got their pharmacogenomics. 1201 00:59:58,480 --> 01:00:00,920 It's a whole thing. There's a whole stack of 1202 01:00:00,920 --> 01:00:03,960 actually open source software that you develop in doing this. 1203 01:00:04,320 --> 01:00:07,320 Yeah, I like this a lot. And then we can also, if we, OK, 1204 01:00:07,320 --> 01:00:10,480 this is probably too much, but then we we add some organelles, 1205 01:00:10,600 --> 01:00:12,480 right. So now you start doing try 1206 01:00:12,560 --> 01:00:14,800 therapy trials against the organelles instead of. 1207 01:00:14,800 --> 01:00:18,000 So then you've got organoids. Exactly. 1208 01:00:18,000 --> 01:00:20,640 Yeah, exactly. So now you start doing the the 1209 01:00:20,640 --> 01:00:23,720 experimentation, high throughput experimentation on your 1210 01:00:23,720 --> 01:00:26,960 organelles and then proxies. Exactly. 1211 01:00:26,960 --> 01:00:29,760 Yeah, that'd be bad. I mean, that'd be amazing if we 1212 01:00:29,760 --> 01:00:30,640 actually set this up. All right. 1213 01:00:30,760 --> 01:00:32,920 Yeah, OK, good outcome. All right. 1214 01:00:32,920 --> 01:00:36,200 So we've got a plan for the longevity network state towards 1215 01:00:36,200 --> 01:00:38,040 the longevity network state making progress. 1216 01:00:38,240 --> 01:00:39,720 OK. Well, thank you very much, 1217 01:00:39,720 --> 01:00:41,720 Brian. Thanks for having me, really 1218 01:00:41,720 --> 01:00:43,720 enjoyed this. Awesome talk soon, OK? 1219 01:00:43,840 --> 01:00:43,960 Bye.