AI Just Compressed 160 Years of Aging Research — Here's What They Found | Dr. David Sinclair | Impact Theory w. Tom Bilyeu & David Sinclair

You don’t understand aging. You’ve been lied to about what’s possible for the human body. You think the march toward old age and disease is inevitable, and that genetics are your destiny. The reality is, we’re living through the greatest turning point in the history of human health—and most people have absolutely no idea just how fast it’s all happening. What if I told you that not only can we slow the aging process, but that it’s starting to look like we can actually reverse it? Thanks to the exponential power of AI and the most cutting-edge biotechnology, what used to take centuries and cost billions is now possible in months, for a fraction of the price. Today on Impact Theory, we are joined by a true pioneer who’s redefining what it means to get old—someone at the intersection of AI, biology, and longevity: researcher, Harvard professor, and world-renowned expert on aging, Dr. Sinclair. Strap in, because this episode is packed with groundbreaking science, jaw-dropping experiments, and a vision for the future that will challenge everything you think you know about what it means to stay young. Let’s dive in. What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:join me here at ZERO TO FOUNDER https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show: https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show SCALING a business:see if you qualify here. https://tombilyeu.com/call: https://tombilyeu.com/call Get my battle-tested strategies and insights delivered weekly to your inbox:sign up here. https://tombilyeu.com/: https://tombilyeu.com/ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,Tom Bilyeu’s Mindset Playbook https://open.spotify.com/show/47VE90Cittmo6TGGFqg2xf —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram:https://www.instagram.com/tombilyeu/ Tik Tok:https://www.tiktok.com/@tombilyeu?lang=en Twitter:https://twitter.com/tombilyeu YouTube:https://www.youtube.com/@TomBilyeu Blinkist: Start your free trial at https://blinkist.com/impactQuince: Free shipping and 365-day returns at https://quince.com/impactpodIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactAT&T Business: Switch to AT&T Business at business.att.com http://business.att.comNetsuite: Right now, get our free business guide, Demystifying AI, at https://NetSuite.com/Theory https://netsuite.com/TheoryKetone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderSumm: code TOMVIP20 for 20% off your first year at https://summ.com?via=tombilyeu&coupon=TOMVIP20 https://summ.com?via=tombilyeu&coupon=TOMVIP20Shopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactCozy Earth: code IMPACT for 20% off https://cozyearth.comQuo: Try for free PLUS get 20% off your first 6 months at https://quo.com/impact Learn more about your ad choices. Visit megaphone.fm/adchoices https://megaphone.fm/adchoices See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Tom Bilyeu?

  Let's talk about the moment your side hustle becomes a real business. Now, I see way too many founders stay stuck running their business like it's a hobby. And then they wonder why nothing ever feels real. It doesn't feel real because it's not. I've helped thousands of entrepreneurs build real businesses through Impact Theory University. And the first step in the path to building a legit business is registering it. And Taylor Brands will help you do just that in a few clicks. An LLC, a limited liability company, is an official business structure. It's inexpensive to form and very simple to maintain. It's how you open a business bank account. It's how customers know they're dealing with a real company. And Taylor Brands doesn't stop at the filing.

• 0:45Tom Bilyeu?

  Their dashboard tracks what comes next based on what your business actually needs. So EIN number, registered agent, licenses and permits, bookkeeping, business banking. I tell every founder the same thing. Just make it official. Click the link in the show notes to check out Taylor Brands and get started on your business today. This is a paid advertisement. You are at the intersection of my absolute fascination with health, which is where it's intersecting right now with AI. I've heard you say that AI is making things possible in human longevity that previously weren't. So what specifically has AI put on the table that wasn't possible before?

• 1:24Speaker 01

  Well, so the big thing is the speed that we can do things. We currently have technology that can reverse aging in animals. And we'll find out this year if it works in people. But it's an expensive technology. It uses genes and we have to introduce genes into the body or the eye in this case. That's potentially hundreds of thousands of dollars to do that. So what we wanted to do in my lab was democratize this technology. So how do you do that? Well, AI is helping. We've now screened probably about eight billion virtual chemicals for one that will reverse aging. So that instead of introducing genes, which is expensive, we could take a pill or rub it on our hair or our skin.

• 2:04Speaker 01

  And I asked one of the AI sites, how long do you think this would have taken in a normal world, pre-AI? And it estimated it would have taken about one hundred and sixty years for my team to have finished that experiment, and the cost would have been in the many billions of dollars.

• 2:23Tom Bilyeu?

  Why is that? Is AI just, is it crunching numbers, pattern recognition? What is it that makes AI able to shorten the timeline?

• 2:32Speaker 01

  Yeah, well, the big one was, we need to thank Demis Hassabis for his, and his team, of course, for elucidating the structure of all of the proteins in the body. We didn't have that until, what was it, about four or five years ago? And now that we have those structures, those proteins, we can virtually dock billions of molecules into each of one of the, each of those proteins, and find ones that modulate those proteins.

• 2:58Tom Bilyeu?

  Is this based on shape?

• 2:59Speaker 01

  Yeah, mostly, and charge. So we know the behavior of atoms and small molecules, and now we know proteins, and proteins are vibrating, so it's a little complicated.

• 3:08Tom Bilyeu?

  You say we know. So one of the things I want to know about AI is AI getting to the point where it understands the fundamental rules that govern biology, or is it just learning all of the patterns in the literature?

• 3:25Speaker 01

  Oh, it's, it's more than in the literature. It's understanding the patterns in biology and how to extrapolate from atoms to molecules to proteins. That's a big jump. We couldn't do that more than five years ago. And now AI is really using that. I mean, it, it's partly AI, it's partly brute force, you know, just mathematics. But on top of that, you can add now intelligent agents that can take the results from those screens, as we call them, and we typically get hundreds of thousands of hits, as they're called.

• 4:02Tom Bilyeu?

  And the hit is this shape matches this shape?

• 4:04Speaker 01

  Yeah.

• 4:05Tom Bilyeu?

  And allows the chemical reaction to transpire?

• 4:08Speaker 01

  Exactly. And we, in this case, what we're doing is Is virtually impossible. Uh, at least five years ago, it was impossible. And that is that we're trying to find one chemical that does the work of three that we currently have. So when we reverse aging in a mouse, we give it a cocktail down its throat of three chemicals. And in drug development, finding one better chemical can cost hundreds of millions of dollars and years of work. And I'm asking my team and and our collaborators, find one molecule that does what those three do and even better. And then, once you've found those hundreds of thousands, which ones are most likely to work?

• 4:49Speaker 01

  Cause in in the lab, it's not that easy and it's very expensive to order and synthesize thousands and especially hundreds of thousands of molecules.

• 4:58Tom Bilyeu?

  Order meaning online. Like, I need to get some of these molecules in the lab so I can get the mouse to eat it.

• 5:05Speaker 01

  Well, we don't test it on a mouse initially. That would be prohibitively expensive.

• 5:10Tom Bilyeu?

  What do you test it on?

• 5:11Speaker 01

  Uh, cells. And that's also where AI comes in. We've got an AI system we developed. It's machine learning, um, with a layer of AI that can look at cells from humans that we grow in the lab. And we paint them so they've got colors and so we can see different shapes and things that are happening inside the cells live. Or we kill them and stain them. And then the visual, we use visualization to say, is that cell from a ninety two year old? Going back to look more like the twenty year old cells, from a twenty year old. And we've been working for about three and a half, four years on that.

• 5:47Tom Bilyeu?

  Did you guys have to train your own model?

• 5:49Speaker 01

  Yes, we absolutely did. So we got the cells from these people who are young and old. Yeah. And we trained the model and it took a long time to get that right.

• 5:57Tom Bilyeu?

  And it was just like this cell, young, this cell, old, this cell, young, this cell, old. And then it gets to the point where it's like, you don't have to tell it anything. It just looks at it. It knows the patterns and it says, ah, that's an old cell, young cell, whatever.

• 6:06Speaker 01

  Yeah.

• 6:08Tom Bilyeu?

  Fascinating. Okay. How much so when people talk about, um, large language models, they're talking about billions of parameters. How many cells did you guys have to feed it in order to get it to be able to discern a cell?

• 6:21Speaker 01

  We looked at millions, um, but these days we can actually train very quickly. The the models have gotten better at learning.

• 6:30Tom Bilyeu?

  Who's doing the underlying models?

• 6:33Speaker 01

  My lab at Harvard.

• 6:34Tom Bilyeu?

  You guys are starting from scratch.

• 6:36Speaker 01

  Yeah.

• 6:36Tom Bilyeu?

  Damn.

• 6:37Speaker 01

  That's why it took a few years, but we're there. We're there now. So my team used to be just biologists who wrangled a yeast cell and now a mouse. But I've had to build up my team with real bioinformatics and AI expertise.

• 6:51Tom Bilyeu?

  Okay, you just gave me the chills. So here's the thing I'm trying to get people to understand that we're living through the weirdest moment in human history ever, and that when we think about the direction of travel, and I'll press you on timelines, but I know they're going to be wrong. Don't worry about that. I just want people to understand that things are moving faster than they realize. I did not realize that individual labs were able to train this from the ground up, which is utterly fascinating. But, okay, so I'm going to lay out, so I've done a bunch of research on you, obviously. I'm going to lay out what I think are the presuppositions that will make this interview make sense to people. I want them to understand the perceived direction of travel as of today. Of course, it's going to be wrong in fullness of time.

• 7:36Tom Bilyeu?

  I understand that. I'm sure you understand that. But it gives people a direction of travel. So I'll give you what I think you're saying. If any of this is wrong, let's correct it now so that as we go through the interview, people can understand what you're actually trying to achieve. Okay, so this is me trying to channel what I think you're saying in a very simple, direct way. So biology abides by a set of rules, which means that AI is going to be able to understand it. So like the laws of physics, we may not know what they are, but there are rules. Those rules are ultimately going to be knowable. So same thing with biology. AI is ridiculously good at identifying the patterns inherent to these rules, giving AI predictive powers over biology.

• 8:21Tom Bilyeu?

  So if you fold a protein this way, then it will do this. Okay? Next is aging is a disease caused by information degradation over time, but cells store the original healthy information in a kind of backup.

• 8:41Tom Bilyeu?

  So far, so good.

• 8:42Speaker 01

  Yes, with a with a little footnote, which is that's the theory that my lab works on that we came up with. It's not universally agreed upon that there that there is a backup copy, but it has to be there because we use it every day to achieve what we do in the lab.

• 9:01Tom Bilyeu?

  Okay, so that one may be a little bit controversial, but this is the point of science is we're going to find out. Okay, uh, so next, any disease tied to a breakdown in information integrity theoretically can be effectively treated if you can get the cell to start reading this somewhat controversial healthy backup data again.

• 9:23Speaker 01

  That's correct.

• 9:23Tom Bilyeu?

  Okay, therefore, AI is going to dramatically accelerate the effective treatment of many, if not all, information-related diseases, including aging.

• 9:35Speaker 01

  I would say that's as close to a factor as we could get right now.

• 9:39Tom Bilyeu?

  This is the the presuppositions that you're operating on. So presumably in time, some of this will end up being true, some of this will need to be modified, whatever, but these are the things that give you the courage and momentum to go down this path.

• 9:53Speaker 01

  Yes, and even if some of it isn't 100% true, we're still going to achieve the goal because we can do it. We're just trying to figure out how how the heck it's working because we're getting results that are nearly unbelievable and we have to figure out how is that possible? So the nuances we'll argue probably for the next 50 years, but it doesn't change the fact that we're doing it.

• 10:13Tom Bilyeu?

  Yeah, that makes sense to me. I have a I'm haunted by, and I can't remember if it was Max Planck who said it, but whoever said the quote that science advances, not one insight at a time, but one funeral at a time, that winds me up. It drives me absolutely crazy that people cannot go, well, there's obviously something I don't understand. I'll figure it out. This is the whole point of the scientific method. In business, I literally teach this. I call it the physics of progress. The physics of progress is make your best guess. You're going to be wrong to some degree. Run the experiments, figure out in what way you are wrong, adapt, get a little bit better, and then you just run that cycle over and over and over. So I presume that's what you're doing as you march down this path. Yes?

• 10:53Speaker 01

  Yes, with the scientific method, which is. Fail, fail, fail, and then something works.

• 10:59Tom Bilyeu?

  Okay, so we know what we believe and what we're pursuing. It's the things that I just ran through. What would be the falsification? How would we know if we're like, oh, we just proved that this doesn't work?

• 11:14Speaker 01

  Well, we've been doing those experiments, trying to disprove our theory. That's what we do as scientists.

• 11:18Tom Bilyeu?

  What's the, what would be the kill shot, though?

• 11:21Speaker 01

  There are two main tests. One is if we degrade the information in the cell in an animal, for instance, or take a mouse. And the mouse gets sick and dies, but it doesn't get old. Then that's not aging, that's just sick. But we did that experiment and the mouse got old. And we published in 2023 that information loss is a cause of aging in mammals, in us. Then the next test is, can you reverse that process? Can you bring back the information like my hypothesis says we should be able to do? Even if we don't know where it's stored. And in 2020, on the cover of Nature Magazine, which is as good as you can get as a scientist, we published

• 12:04Speaker 01

  that we could do that, that we could get back that information somehow using three genes that embryos usually use to reset their own age from their parents' age. And so those, if they had failed, if we couldn't reset age, if we couldn't age a mouse forwards, then, okay, we'll move on, we'll do something else. But the fact is now, Tom, that in my lab, we can drive aging in either direction at will, using the technologies that we've developed, and it's only going to get better.

• 12:31Tom Bilyeu?

  Okay, in mice?

• 12:33Speaker 01

  In mice, and we've done it in monkeys. We've cured.

• 12:36Tom Bilyeu?

  As effectively, or is that more complicated?

• 12:40Speaker 01

  It's actually not that, not more complicated in a monkey. If you want to get into the details, it's, there's a, there's a membrane in the monkey that the mouse doesn't have that might have inhibited the genes getting into the back of the eye, the retina. But it worked fine and the monkeys, they got their electrical signals back in their optic nerve. And we believe that that is good enough signal to go into humans to treat blindness, a couple of diseases in humans. And if all goes well, we'll get ready to start the trial.

• 13:07Tom Bilyeu?

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• 13:51Tom Bilyeu?

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• 14:37Tom Bilyeu?

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• 15:34Tom Bilyeu?

  Thanks for sticking around. Let's get right back into the action. Okay, so membrane was a concern, but just didn't end up in practice being a problem.

• 15:44Speaker 01

  Right, but the fact that it worked in monkeys where a lot of things in mice don't translate into primates was a big deal. And so that raised my confidence level from 50% to about 80, 90%.

• 15:55Tom Bilyeu?

  Okay, but the big question mark right now is you're reaching into a black box that we don't quite understand why it's working because you're, you know, the effect is that using those three chemicals?

• 16:08Speaker 01

  The genes.

• 16:09Tom Bilyeu?

  Genes?

• 16:09Speaker 01

  Well, we, we've got three genes and three chemicals.

• 16:13Tom Bilyeu?

  Okay.

• 16:14Speaker 01

  I don't know why it's three, but that's just what turned out.

• 16:17Tom Bilyeu?

  Sure.

• 16:18Speaker 01

  The three genes have a name, OCT4, SOX2, KAL4, OSK, we call them. And they were, we could talk about how they were found, but essentially those three genes, and then we mimic those with three chemicals. And hopefully, as we discussed, get down to one pill that we can take.

• 16:33Tom Bilyeu?

  Okay, we'll get to the pill in a second, but so we administer the genes and the chemicals, which we don't have to get too specific. I think people will not be able to track, but we've got those things. We get them into the system somehow, some way, whether it's injecting pill later, whatever, whatever. But we get them into the system where they need to be.

• 16:53Speaker 01

  Yep.

• 16:54Tom Bilyeu?

  But we don't know exactly how we're then able to get the cell to de-age. I don't know if we're comfortable with that word, but we know that it happens. And so your best guess is that there is somewhere in there, the storage of the youthful, healthy cell, like a backup, and that is somehow getting the cell to reread off of that versus the, whoo, I might be jumping ahead here, but the methylated cell, that's sort of gotten the scratches on the CD of the DNA, if you will, over time, which is causing it to, again, I worry about getting too far ahead, but I'm going to say it anyway. The cells begin to de-differentiate. So an eye cell stops being just an eye cell.

• 17:39Tom Bilyeu?

  Maybe it's a little bit of a skin cell or a brain cell or whatever. And so it starts getting confused.

• 17:45Tom Bilyeu?

  How'd we do so far?

• 17:46Speaker 01

  Brilliantly. Yeah, I couldn't have said it better myself.

• 17:49Tom Bilyeu?

  You can and have, but that's very generous. Uh, okay, but we now at least hopefully people following along at home, understand what we're trying to do when we say that we want to take this. We want to take a view of aging, that it is an information integrity problem, that there is something akin to a backup copy of what the cell should look like. But aging is basically, oh, we stopped checking the original work and we just start going off of like the repaired house, if you will. And it's like, well, the repair starts to get weird unto itself. And so now it's, we're not replicating properly. Okay, you guys have a mechanism to somehow, some way get it to build as if it were a young cell again.

• 18:35Speaker 01

  Well, build isn't the right word. It's to read the right genes at the right time.

• 18:39Tom Bilyeu?

  Why do you say build isn't the right word?

• 18:42Speaker 01

  Well, when you reinstall software, I guess you could call that a build, but I don't mean it's not physically building.

• 18:47Tom Bilyeu?

  The cell is being built, which is why I think of it as building.

• 18:51Speaker 01

  Oh, well, you're, you're putting together two theories. My theory is that the cell and aging is information. And the old theory is that the cells just break down, wear out. And so you're thinking rebuilding that repair, right?

• 19:05Tom Bilyeu?

  Interesting. The way I'm thinking of it, which is probably messy, because I've not thought about it like you have, uh, goes something like this. I, I'm not playing a song. I'm building a house. And so the cell is physical. So when the DNA gets, uh, we're going to have to explain methylation. Um, do you want to give people a quick primer on exactly what is breaking down? Like what methylation is, the tight winding, all that. Uh, and then we can get back to why I say building.

• 19:36Speaker 01

  Sure. And I, I think where you're going is that when we restore the information, the cell can now rebuild itself.

• 19:43Tom Bilyeu?

  The cell, when it builds, the next one will build it better and it will be a truly differentiated cell.

• 19:49Speaker 01

  And not, not just the next one, not just the next one itself. We, we restore the health and the youth of nerve cells in the eye and elsewhere that don't divide. The cell itself gets healthy, doesn't need to even grow. It can just reinstall.

• 20:06Speaker 01

  The software, then it makes new proteins, makes new lipids, makes new.

• 20:11Tom Bilyeu?

  I didn't know there were cells that didn't divide. So there are cells that don't divide, full stop.

• 20:15Speaker 01

  Oh, yeah, your heart, your brain. Mostly that's the same cells you had when you were a teenager.

• 20:20Tom Bilyeu?

  Is wild. Okay, didn't know that. Thank you.

• 20:23Speaker 01

  Yeah.

• 20:24Tom Bilyeu?

  Okay, so sorry, explain to people methylation just real fast.

• 20:27Speaker 01

  Yep. All right. So that we've got six feet of DNA in our cells. Every cell gets wrapped up. And there are 20,000 genes, but not every gene gets turned on in every cell. The cell wouldn't function. So we need a certain set, let's say 10,000 nerves nerve genes, and then a different set for skins, skin genes. So it's like a piano. You've got lots of keys. Every piano has the same set of keys, but how you play them makes the difference to the music. The cell works the same way. Same genes, play them differently. You get different cell types, you get a different.

• 20:59Tom Bilyeu?

  What is the play, just to not be analogy? Is it the creation of proteins?

• 21:05Speaker 01

  Mostly, mostly not all genes make proteins. Some of them make other things like RNA.

• 21:11Speaker 01

  But yes, mostly it's we're talking about those proteins that become enzymes that do the work of living and repairing.

• 21:17Tom Bilyeu?

  Okay, so we are even in a cell that isn't dividing. So I was wrong about necessarily what is being built. But the thing that we're fixing is the building mechanism itself. So we're building enzymes, which, but I had to look all this stuff up. Enzymes like do a thing. They like move around.

• 21:37Speaker 01

  They're machines for sure. They're really super interesting machines. They all there's tens of thousands of different ones here and they do cool stuff.

• 21:43Tom Bilyeu?

  So wild.

• 21:43Speaker 01

  Including reading the genes themselves. So the DNA is a is a string of chemicals, right? And the cell has to open up the DNA strand. It's double stranded. It's a helix, like a think of it like a a spiral staircase. It looks like that. But to read it, you have to open up the stairs, each stair splits in half. And the cell can read that half of the of the stair. And a gene is about a thousand or more of those steps. It'll read that and it and the. A set of three of those steps determines which amino acid goes next in the protein. There's what's called a start set, start codon. And it always starts with the amino methionine. So every protein has a methionine, almost every protein has a methionine.

• 22:27Speaker 01

  But what comes next is dependent on those three steps in the rung of that staircase. Or in this case, the letters on the DNA, A, T, C, or G. Now, that's a gene, right? A thousand of those steps in that chemical. And there are 20,000 sets of those that are all encoding proteins at the amino acid level.

• 22:49Speaker 01

  That's your blueprint. But the problem with aging is the cell forgets which genes to read. It actually turns off some genes and for the most part turns on other genes. And now, instead of having skin genes turned on, it's some skin genes and some nerve cell genes and kidney genes start to look more like liver genes that are coming on. And that inability to read the right music of the cell, I think, is the major reason why we get sick and get old. And that's reversible. These methyls are little chemicals that get attached to the rungs of the staircase, the steps. And if you have lots of them on the steps, the cell doesn't read the gene anymore, shuts it down and you don't make protein.

• 23:33Speaker 01

  So that's why a nerve cell isn't turning on skin cells genes because they've got a lot of these methyl chemicals on the ladder, on the steps.

• 23:42Tom Bilyeu?

  They're basically say, stop reading or only read this?

• 23:45Speaker 01

  Yeah, don't read this one. Go and read the one you need to go read. But those methyls, including more complicated structures, larger structures, are what determines when we're developing in an embryo, go over here and make some skin, but go over here in the skull and make some more brain cells.

• 24:01Tom Bilyeu?

  Okay, and the methylation has to be redone every time a cell divides or takes damage?

• 24:07Speaker 01

  Yes, every time a cell divides, you should be getting an identical methylation pattern between the daughter and the parent cell. And there are copying mechanisms, we understand how that works. It changes during development, of course, because we're starting from one cell that has to become hundreds of different types. The problem is, as we get older, through things that we believe are largely due to cell damage and stress in the cell, those chemicals on the ladder, on the stairs, they get misplaced, they get taken off where they shouldn't be and put on where they shouldn't be.

• 24:41Tom Bilyeu?

  Hmm.

• 24:41Speaker 01

  And now the cell doesn't know what to do. It's reading the wrong music and it's a complete disaster. We get gray hair, we get wrinkled skin, we get diseased, we die.

• 24:49Tom Bilyeu?

  Do you have a hypothesis as to what is making the methylation be imperfectly replicated?

• 24:57Speaker 01

  We do. And the history's not well known. When I came to the United States, you probably noticed, I don't have a strong American accent, not yet. I went to MIT and the goal was to figure out why the yeast cells that make things like beer, Vegemite, which I really like, Tom, I'm gonna get you some. The yeast cells, they're microscopic, but they have chromosomes just like we do. And so I figured if we can't figure it out for yeast cells, we'll never figure it out for humans. So for four years, I worked under the tutelage of a professor who deserves a lot of credit, Lenny Guaranty, and we worked together, he and I, in a team, and we put out a paper, he and I, just on my first big paper in my

• 25:41Speaker 01

  life, The Cause of Aging in Yeast Cells. And that was the blueprint, excuse the pun, for the rest of my career, including the information theory of aging, which we're talking about now. And in that, I said that what's largely changing these patterns of gene expression, what we just called the process of aging, is that proteins that should be turning genes on and off, we call them Regulators, protein regulators of genes, they get distracted by doing other things. And there's one set of enzymes and proteins that I've worked on my whole career that came

• 26:21Speaker 01

  from these studies called sirtuins, based on the gene called Sirtu in yeast. Sirtuins, they actually go to the DNA and they tell the cell, shut this gene off. They see the methyls, they see those chemicals, and those chemicals are really simple, by the way, they're just a hydro, carbon with three hydrogens. These methyls are recognized by the sirtuins, and these proteins go in and help shut off the gene and stop the cell from accidentally reading it. They bundle it up, they actually bundle up the DNA like a little package. Versus a big loop of open DNA that gets read. So it's a bundling sirtuin. And there are a few breakthroughs. One was that the sirtuins get distracted, and one of the major distractions that they

• 27:06Speaker 01

  have is broken chromosomes. They hate broken chromosomes. Ye cells and our cells, they can die if they don't repair a broken chromosome. Or in our case, you can get cancer. So a cell panics when there's even one break on a chromosome. And it often happens when cells are dividing and they're trying to separate the chromosomes. They get caught, tangled up, break, now you're screwed if you don't fix it. So the sirtuins have two roles. One is to control these methyl patterns and genes, but they also go repair DNA.

• 27:36Tom Bilyeu?

  And they'll prioritize repairing the DNA.

• 27:38Speaker 01

  They do. Otherwise, there's no cell.

• 27:40Tom Bilyeu?

  Okay, and so if you're doing something that breaks your DNA frequently, you're going to age fast, presumably because it's going to be so distracted repairing that, that it's not doing its job with the methylation.

• 27:51Speaker 01

  Yes, and remember how I said there was a test of the theory, which was if we cause aging, do we get an old mouse?

• 27:58Tom Bilyeu?

  So you just go fuck up their DNA, basically, to cause the sirtuins to run over to have to fix it?

• 28:02Speaker 01

  Well, I wouldn't use the word F. Because we were very precise. We were surgical about it. We we found an enzyme that is in a slime mold found in the forests around here. And it turns out there's there's an enzyme that cuts DNA rarely in mouse and human cells.

• 28:22Tom Bilyeu?

  Hmm.

• 28:22Speaker 01

  Um, yeah, a dozen or so sites, maybe a couple of dozen, but not thousands. And because you'd kill the mouse. So we surgically inserted that slime mold DNA into the mouse itself at the stem cell stage. And then we turned that stem cell into a mouse, which is standard procedure for students these days in my lab.

• 28:43Tom Bilyeu?

  Damn.

• 28:43Speaker 01

  And that mouse, now we could turn on this cutting enzyme from the slime mold and surgically create a few of these broken chromosomes. Uh, not a lot, not enough to kill the mouse, but it just enough to distract the sirtuans from their normal job, make the cells panic and see what would happen. And we did that for three weeks in these young mice. And nothing happened. It's like, oh goodness, the the experiment's not going to work. Nothing happened. But then I thought, you know, when you get an X ray, you get a lot of broken DNA, but you don't feel it. So let's just wait to see what happens. And I went to Australia, uh, and this was the year two thousand twelve. And I got a photo on my old iPhone and it was a photo of a sick mouse. And the text was,

• 29:27Speaker 01

  should we kill the mouse because it's looking really sick? And I said, tell me which is this mouse? And they said, oh, that's the one we treated with the slime mold cutting enzyme.

• 29:35Tom Bilyeu?

  Hmm.

• 29:36Speaker 01

  And I said, that that's not a sick mouse. That's an old mouse right there. So that was the first evidence that by distracting the sirtuans to broken chromosomes, it leads to an acceleration of aging. We published that. It took another decade to publish. And this was this big paper I mentioned was in the um, in the journal Cell, which again is pretty hard to get into.

• 29:56Tom Bilyeu?

  Hmm.

• 29:56Speaker 01

  And this was the one that said, uh, that the change in information, loss of information was the cause of uh, aging in mammals.

• 30:05Tom Bilyeu?

  What's the difference in phenotypical expression between something that's naturally old and something that ages due to the breaking of the DNA?

• 30:14Speaker 01

  Nothing.

• 30:15Tom Bilyeu?

  Literally nothing you wouldn't be able to tell.

• 30:17Speaker 01

  Right. That was the cool part, except that they were fifty percent older and we can measure. And we did, we measured the methols across the chromosomes. And we can use those changes as a clock. Uh, and in fact, you, you probably know, you can get a DNA methylation clock done these days commercially. So we did, did it on the mice and the mice were 50% older. Those methyl changes were the same as an old mouse, just happening 50% faster.

• 30:41Tom Bilyeu?

  Yeah. Okay, wild. So that sets us up well for understanding the thing that we're chasing. Now.

• 30:54Tom Bilyeu?

  One thing I've heard you say about AI is just the timeline speed up that we get here. So what is the, where are you injecting AI into this process? Is it simulating biology and simulating cells and just like running thousands of experiments in the amount of time that we can normally run one? Or where's the real advantage?

• 31:15Speaker 01

  Uh, it's simulating experiments. So normally we would, in the case of finding chemicals, have to get, make the chemicals, which can take weeks for each one. Imagine trying to do seven billion or eight billion. So we don't have to make them anymore. We can actually, we're now at the point, and this is, this is new news. I think everyone would be interested in hearing. We started doing eight billion, which we thought two years ago, mind blowing, eight billion. Normally a pharmaceutical company might screen a couple of million, right? That's physically.

• 31:47Speaker 01

  We can now do an infinite number of molecules. We believe that we're going to cover all possible chemicals.

• 31:53Tom Bilyeu?

  Whoa.

• 31:54Speaker 01

  Which is an order of magnitude bigger than what we've been doing. And that's, that's only happened in the last year that we could do that. Um, but what's, what's speeding it up is the ability to

• 32:05Speaker 01

  turn physical world into synthetic virtual. And instead of it taking a year to figure something out, like it can be milliseconds in some cases.

• 32:15Tom Bilyeu?

  And what's the most complex thing we can simulate? Is it the cell? Can we simulate a liver like?

• 32:22Speaker 01

  No, biology is so much more complicated than most engineers understand. Um, we still probably only know about three percent of biology. So trying to trying to model it is pretty hard. And then the complexity, I mentioned that it took all of DeepMind and, uh, and those guys until recently to figure out how to just model a protein of a thousand amino acids. Even twenty amino acids was a challenge. Trying to model a cell is gonna take a lot more work.

• 32:50Tom Bilyeu?

  Hmm.

• 32:50Speaker 01

  It's not impossible because you will make assumptions, but there's, there's no way in my lifetime that I, I believe that a cell can be modeled from the ground up, looking at every molecule.

• 33:02Tom Bilyeu?

  Really? In your lifetime?

• 33:05Speaker 01

  Right. And I'm an optimistic guy.

• 33:07Tom Bilyeu?

  I was gonna say, you don't plan to die too early.

• 33:09Speaker 01

  No.

• 33:09Tom Bilyeu?

  That's wild. So you're saying we're what, more than fifty years away? That's crazy.

• 33:12Speaker 01

  To model a cell?

• 33:13Tom Bilyeu?

  Yeah.

• 33:14Speaker 01

  With every molecule? No. I mean, you can make assumptions. You can say, okay, generally, these proteins are around in this cell and these proteins are in that cell. But to say, to, if, if you're right, well, if, if they can do that, what they should be able to do one day is to take an egg and the genome and what we call the epigenome, those methyl marks, and predict what the human looks like.

• 33:37Tom Bilyeu?

  Yes. Taking a short break, but there's more Impact Theory after. Stay tuned. You're probably paying two salespeople to do one salesperson's job. That's where today's sponsor, Pipe Drive, comes in. An intelligent AI-powered sales CRM loved by growing sales teams. Pipe Drive just launched meeting intelligence built right into the CRM. Before every call, it pulls deal history and past conversations into one brief. During the call, it records and takes notes. After it, it drafts your CRM updates to review and approve. So there's less admin and more selling. Switch to a CRM built by salespeople for salespeople

• 34:21Tom Bilyeu?

  and join the over one hundred thousand companies already using Pipe Drive. My link gets you an exclusive thirty days free instead of the usual fourteen day trial. There's no credit card payment needed. Just head to pipedrive.com/impact to get started. Again, that's pipedrive.com/impact and you can be up and running in minutes. Thanks for staying tuned. Now, let's get back to it. I suppose this is where I confess my base assumptions. My base assumption is that our current quest for intelligence shows no signs of asymptoting. So it's going to keep getting better. And so as we come up with more efficient algorithms,

• 35:06Tom Bilyeu?

  as we're able to make bigger and bigger data centers, that AI will get smarter and smarter. If you can believe that in very narrow ways, we've had AI achieve something like 147 IQ. And that's, I mean, what are we, you know, 50 years into like real AI development? So it's like in the next 50 years, given where we're at? No way. Like you will, this is going to be ignorant and wrong, but it will give people certainly how I view this. 10 years, I can't fathom a universe in which 10 years we don't hit artificial general intelligence. Ray Kurzweil has been right with his predictions, I think 87% of the time. Says 2030, that's four years for anybody keeping score. He says we'll hit AGI.

• 35:50Tom Bilyeu?

  The question becomes, is artificial general intelligence, given that at that point it will be able to improve itself? Is artificial superintelligence four years and one day? Like, or does it take longer? But I don't, I cannot fathom a universe in which we don't hit artificial superintelligence in 50 years.

• 36:10Speaker 01

  I agree.

• 36:11Tom Bilyeu?

  But you just said that we won't be able to map a cell. And so what I'm saying is you'll get into an upward spiral of intelligence where we can't imagine doing it now because being locked into, in my case, very low IQ, in your case, better. But like, as you start pushing this into the, I mean, what does, the smartest guy ever clocked, I think it's like 225 IQ or something, but I've seen his tweets, I don't buy it. So 250, 300, 400, 500, 1,000 IQ. Like, what does that start looking like?

• 36:43Speaker 01

  Well, it's not IQ that's the problem. The problem is the compute. Now, maybe with quantum computing, we're going to get there. But using traditional computers to model the interactions in a cell even for a millisecond would be more calculations than have ever been performed in a computer to this point.

• 37:01Tom Bilyeu?

  Yes. So you don't believe, you think that

• 37:07Tom Bilyeu?

  advancements in computation are going to stall out?

• 37:11Speaker 01

  Even if they don't.

• 37:14Speaker 01

  It probably would take more molecules than exist in the universe to, without quantum computing, to calculate what happens in a cell within one second.

• 37:23Tom Bilyeu?

  Hmm.

• 37:23Speaker 01

  It's that complex. It's it's it's mind boggling how complex a cell is.

• 37:29Speaker 01

  And so we can model one molecule hitting another and we can even model, I can imagine modeling

• 37:36Speaker 01

  a million molecules. But molecule, modeling an entire cell with the quantum effects that happen, a lot of it's unknowable without going in and disturbing it as well. So that's another issue. But what what I'm not saying is that we can't model a cell. I think that we will be able to model a cell. And I know very smart people who are doing that or trying to do that right now. It's just that we don't know enough about biology yet to make an absolute model from the ground up. There's a lot of assumptions. We know the fundamentals. We know that there's DNA and these metals. And we know that proteins get folded.

• 38:12Speaker 01

  But I can tell you from work in my lab, there's a whole area of biology that we've been missing that will hopefully allow us to figure out where this backup copy is. And without knowing that stuff.

• 38:24Tom Bilyeu?

  Describe the black box into which you're appearing right now. What do you mean? There's a whole area of biology that we don't know.

• 38:32Speaker 01

  Well, one of the problems with science is we don't know what we don't know usually. And that's the tough part. But one of the big questions in biology right now is, how does a cell de-age? I I do like that word.

• 38:47Speaker 01

  And we we know that it deages, not just from work in my lab and others like mine. But we know that you can take a skin cell, I could take your skin cell. I could take the DNA out of your cell. It's going to be old.

• 38:59Tom Bilyeu?

  How dare you?

• 39:01Speaker 01

  It's going to be older than a baby. And I could inject that into an into a an egg. And theoretically, actually, it's not even theoretical. I could turn that into sperm and turn it into an egg. Now I could fertilize you and make a clone out of you. This has been done with with simpler organisms, but we can do it with humans. Eventually, I think that's how IVF one day may be done. If people want to have children, if they don't produce eggs, we can.

• 39:31Tom Bilyeu?

  Chinese doctor that went to jail, I forget his name, but follow him on Twitter. Shout out. He cloned humans, right?

• 39:38Speaker 01

  No, he genetically modified a baby.

• 39:41Tom Bilyeu?

  Got you, got you.

• 39:42Speaker 01

  To be resistant to HIV. But his goal is, and he's doing this offshore from US, of course, but there is the technology to do that, but let's just say.

• 39:52Tom Bilyeu?

  But we haven't cloned humans. That's the punchline.

• 39:54Speaker 01

  No, no, but we've cloned monkeys. Okay, so let's just stick to monkeys. So it's not controversial, although even that's controversial. How about we talk about Barbara Streisand's dogs? She's cloned those or Tom Brady, who recently got his.

• 40:07Tom Bilyeu?

  Wild, but okay.

• 40:08Speaker 01

  All right, so we can do that. That was done with old DNA. The DNA gets reset. The information can be reset in that cell. The other thing we know is that if you have parents who are 30 years old and they have a baby for the first week of life, that embryo is 30 years old.

• 40:26Tom Bilyeu?

  What?

• 40:27Speaker 01

  Yeah, we're not always young when we're alive. Babies would be born old if there wasn't a reset switch.

• 40:34Tom Bilyeu?

  Uh huh. So. So wait, at what point are you saying this is like, we know this? The embryos are.

• 40:40Speaker 01

  People have measured it, yeah.

• 40:41Tom Bilyeu?

  Wild, okay.

• 40:43Speaker 01

  It's a fairly new discovery, which is why it's shocking, but it will be known one day.

• 40:48Tom Bilyeu?

  So at some point, post conception.

• 40:53Speaker 01

  Exactly, day seven to day nine, we know that.

• 40:55Tom Bilyeu?

  Whoa.

• 40:55Speaker 01

  And the baby goes back to being age zero again.

• 40:58Tom Bilyeu?

  Whoa.

• 40:59Speaker 01

  All of us, we were once the age of our parents. So I was, my parents were about 30, I was 30 years old twice in my life. When I was conceived and when I hit 30.

• 41:09Tom Bilyeu?

  That is wild. Okay, didn't know that in UFO.

• 41:12Speaker 01

  And then the same, the same mechanisms, we believe, same mechanisms are what we're using to reset the age of human tissue and monkeys and hopefully.

• 41:20Tom Bilyeu?

  Okay, so we got onto this because there's certain things we don't know. You're saying we don't know why those reset?

• 41:25Speaker 01

  We don't know how it resets. How does the cell know what it was 30 years ago?

• 41:31Tom Bilyeu?

  And does this feel like it falls into a category of the unknowable? Or just we don't know yet?

• 41:37Speaker 01

  Um, we have a pretty good idea in my lab, but we haven't told the world yet. Okay. I do text my student often and walk into the lab and say, have you figured it out? Have you proven it yet? But we are doing experiments. Somebody needs to hack your phone. Uh, no, they shouldn't, but you probably could with your team out there. But yeah, so, so Chris Petty deserves great credit. His PhD is on finding what we call the observer, which is what Claude Shannon in at MIT in the 1940s called the backup copy.

• 42:10Speaker 01

  So what we imagine and are testing is whether there are structures that get laid down when we're very young, during our youth, that can be accessed later in time in a 60 year old, seven year old to reset the age. Not, we don't want to go back to zero. That was the breakthrough we had in 2020. You don't want to go back to zero. I mean, first of all, who wants to do high school again? But you'd probably get cancer. So we don't do that. So we figured out a way using those three genes, OSK, to go back 75, 80% and stop. How does that happen? We think there are little messages with new biology, structures, chemicals that are new to biology, that AI may not have figured out or will not figure out easily. Maybe they would.

• 42:54Speaker 01

  But that's where we're at now. If we, probably in the next few months, we'll know if we're right. And I've been saying that for about a year now, but we're close. We, one, one way we do this as scientists is we look for necessity and sufficiency. Necessity from an evolutionary standpoint? No, from a genetic standpoint. What we do as scientists, geneticists, is if we change something, let's say if we knock out a gene, is that gene necessary for the reversal of aging? Is that process necessary? And then if we force it to turn on and now we, we trigger that event, is that sufficient in itself to make the change?

• 43:37Speaker 01

  And when you get necessary and sufficiency satisfied, you're really onto something. And it was similar with that mouse, right? We knew that it was necessary

• 43:49Speaker 01

  that these methylation changes could rewind aging. But also we knew that was sufficient to change those methyl chemicals to cause aging. And that's why I'm so certain that the information theory of aging is correct.

• 44:03Tom Bilyeu?

  Okay, uh, this brings me back to something I asked you earlier, but I want to push on it a little bit. So when you interface with AI, I've always said, if AI can come to understand the fundamental laws of physics, it will be able to make novel breakthroughs and all bets are off. Technological singularity, the world is unknowable to us.

• 44:26Tom Bilyeu?

  As you're approaching it, has AI made any novel discoveries? Novel insights that aren't already in the literature? Or is it just doing a really good job of recognizing patterns in like what we already know about a cell?

• 44:43Speaker 01

  We, in my lab, collaborated with another group out of Stanford, who developed an agentic system with multiple agents, about a dozen agents that did different things. And we fed it our data. Interestingly, it's the data that mapped those chemicals on the DNA that change with time. And we did mouse, we've looked at tissues from mouse at very young age, middle age, about our age and then even older equivalent. And uh, the, the, what happened was the, the agents went to work and, and about a month later, and there were some iterations, right? It probably didn't take a month, but we got the data back from this group we were collaborating with at Stanford,

• 45:28Speaker 01

  out of Stanford. And what was incredible was it didn't just come up with validating what the field, the smartest people in my field had done over the last 10 years. Which, which by the way, would have been, oh yes, you can make a clock out of DNA methylation using these parameters and this, you know, Markov modeling and, and uh, you know, all, all sorts of, of what was already known in the literature, which is what I was expecting. What happened was it came back and said, hey, did you guys ever think of this before? And came up with a completely new way of looking at the data and making a new model to predict biological age out of the data we gave it. Not only that, it, it proofed the data, it did the statistics,

• 46:12Speaker 01

  it wrote the paper up for us and presented us with the finished product. Which sucked because we want to be co-authors too. So we changed a few words, but now I'm a co-author with an AI system.

• 46:24Tom Bilyeu?

  Damn.

• 46:25Speaker 01

  That we have, it's up online, but it's not published yet, but you can, you can find it on bio archive. If, you know, if you do Sinclair, Agentic, uh, biological.

• 46:35Tom Bilyeu?

  What was that moment like? Was it like, holy shit, this is just had a novel insight and how fast are we going to improve or?

• 46:42Speaker 01

  It, it was a holy shit because I thought that my job was not at stake. The arrogance was I've got all this knowledge and experience and gut feeling and I'm really creative. But here I'm seeing the beginnings of creativity that can be super creativity in the future. And I, I think most people who are not like us at the forefront of technology, like discussions with my father who's 86, AI can never be creative. That's just human arrogance. They definitely are already creative and it'll only get better.

• 47:18Tom Bilyeu?

  Yeah, that's, uh, that's encouraging in terms of its capabilities. If it's actually able to understand the fundamental rule set and then say, hey, what about this? Okay. So we, you guys have created in your lab, a, um, model that is able to actually gain real creative insights based on what it understands about biology and all the cells that it's seen and all of that. So where is this pointing? If you don't have conviction that we'll be able to simulate a cell in your lifetime,

• 47:57Tom Bilyeu?

  is it that you don't think we require the understanding or the sophistication in order to profoundly de-age the body? Because the body is the very complex system that you don't think that we can fully understand. But yet you seem optimistic that we'll be able to influence these incredibly complicated processes in a way that is both knowable. So we can do it repeatedly, um, and advantageous.

• 48:29Speaker 01

  For age reversal?

• 48:30Tom Bilyeu?

  Yes.

• 48:31Speaker 01

  Absolutely. We're doing it already. There's no question about that.

• 48:34Tom Bilyeu?

  Why?

• 48:34Speaker 01

  We don't need to know every molecule.

• 48:36Tom Bilyeu?

  Okay. So it's just as long as we understand how to manipulate the output, nothing else matters. And knowing why it works is the hard part. Manipulating it to give us a predictable outcome is hard for sure, but far easier

• 48:50Speaker 01

  Oh, yeah.

• 48:50Tom Bilyeu?

  than the simulation.

• 48:51Speaker 01

  Yeah, yeah, my students, I mean, they're in their twenties, they have regular tools, they can do it. You, I could set you up in the lab, you could do it. It's not that difficult now that we have this hypothesis that appears to be true. In the same way, I often refer to the Wright brothers because this is our Wright brothers moment for humanity when it comes to aging. And there are skeptics, like in nineteen oh four, people were saying, in fact, the New York Times published that it would take a million years or more to figure out how to fly. Uh, and then it was like three weeks later, Wright brothers.

• 49:20Tom Bilyeu?

  That's how I feel when you say that a cell won't be replicated, but you know, it's all right.

• 49:23Speaker 01

  Yeah. Uh, well, the, we don't need to know, and we still don't know how every molecule in an aeroplane functions.

• 49:30Tom Bilyeu?

  Yeah, fair.

• 49:31Speaker 01

  We don't need to model that. We, we can make assumptions, we can make generalizations about wind flow and wing structure and metals. Same with the cell. But you still need to know what's the metal?

• 49:42Tom Bilyeu?

  Hmm.

• 49:43Speaker 01

  What's the wind? How, how does air work? And there's still a few missing pieces when it comes to biology to be able to simulate an entire cell. But when it comes to aging, I think the big breakthrough was understanding that aging is information and that it can be reset.

• 49:56Tom Bilyeu?

  So how far are you going to be able to push that, though? So I'll give up, doesn't matter, we're not going to be able to simulate the cell, it doesn't seem like it's relevant, to be honest. So now we've got, uh, we're showing great signs that we can improve eyes.

• 50:12Tom Bilyeu?

  I want to feel as good as I did when I was twenty five. I want to look as good as I did when I was twenty five. Like, how realistic is it that we can go in and influence multiple systems in the body in coordination with each other and not end up with whatever catastrophe looks like. So I imagine the catastrophic fail here, which you've already mentioned, is cancer, that would be one catastrophic fail state where you go back too far and it just all hell breaks loose, or um, getting into some sort of decoherence where it's like, uh, we are telling the cell to move in a direction and we can't stop it or whatever. And so it just basically becomes a pluripotent mess.

• 50:55Tom Bilyeu?

  Um, so how far can we take this?

• 50:59Speaker 01

  Yeah. Well, pluripotent is a good word. We'll just explain it that that's a cell that can become any other cell type, so age zero. And that won the Nobel Prize. Shinya Yamanaka deserved the Nobel Prize for figuring out that using his Yamanaka genes, of which we use a subset, can take cells back to being pluripotent. It's incredible. And that's how you make a clone, by the way. But that doesn't work for for human health, because as you said, if your cells lose their identity and become age zero, you're not going to live very long. And even if you do it in a few cells, you could get cancer. So that was the tough part. I love the way you always whisper cancer. Well, I will say, cancer's been in my family, so it's not. I just had skin cancer, so I feel anyone's pain.

• 51:42Speaker 01

  Yeah. Well, the good news is, we find that when we reprogram cancer cells, the majority of them slow down or die. So de-aging cancer doesn't make it worse. It actually makes it die and shrink. So that's good. I'm not so, I'm not worried actually about cancer anymore. The the observer is. We're gonna need to follow up on that in a minute, but keep going. The the, I'm gonna call it the observer, the backup copy, because that's what we call it in the lab. The observer has a way of stopping the reversal at about seventy five percent. And it works in every tissue that we've tested it in. We started with the eye and human skin cells. Really, not because the eye is easy, it's because it's

• 52:22Speaker 01

  really hard. But it's an enclosed system and safer to deliver to humans than giving it IV. Interesting. But it worked in the eye. Because you can keep it localized? Yes. Got it. Exactly. Okay. So putting genes into the human body is is, you know, done, but it's the FDA here in the United States is much more comfortable treating the eye because it's done every week in in patients. So we started with the eye, and it was a hail mary experiment. My student, Wan Chenglu, who at one point was ready to give up because it was so difficult and so challenging and things weren't working. But he went for the eye, he chose the eye. I

• 53:05Speaker 01

  let him go for the eye, even though I thought it was crazy to try curing blindness. But he did it, and the results were clear, that we could reverse the age of the eye, the back of the eye in particular, which is the problem for all. And mice and monkeys. Mice, then monkeys. And then we we've moved on since then. So the mice were in, we were doing that in two thousand eighteen, right? It was even pre-COVID, we had some of these results. So now we're, you know, how many years, seven years or so later, we've done a lot. We've done whole mouse brain. We can reverse the age of the brain. And the results are that my old mice get their ability to learn and they even get, we think, get some of their memory back from their childhood.

• 53:45Tom Bilyeu?

  Okay. Uh huh.

• 53:48Speaker 01

  Yeah.

• 53:49Tom Bilyeu?

  Yeah.

• 53:49Speaker 01

  And that's in an Alzheimer's context and an old, just old age mouse.

• 53:54Tom Bilyeu?

  Okay, a lot of people just sat up.

• 53:56Speaker 01

  Yeah.

• 53:57Tom Bilyeu?

  Uh, okay. So obviously we haven't done human trials yet, but it does this give you a level of optimism that will be able to positively impact things like Alzheimer's?

• 54:09Speaker 01

  Yes. Now, Alzheimer's is a big disease.

• 54:12Tom Bilyeu?

  Yeah.

• 54:12Speaker 01

  And it would be a little bit outrageous for me to say it's going to be easy to cure Alzheimer's. That's crazy. But do I see a path to doing that? And why would I think that my approach is more effective than the tens of thousands of scientists that have come before me, who are probably kind of pissed with me if I say this kind of stuff? The reason is that we haven't addressed Alzheimer's from an aging point of view. Most of Alzheimer's is aging. You don't get Alzheimer's typically when you're 12 because the brain can fight the disease, even if you have the genes for Alzheimer's, APOE four allele. You don't get it till you're 60, 70, 80. Why? Because the brain has to get old first. And what we've discovered is if you de-age the brain, the disease goes away.

• 54:54Tom Bilyeu?

  Yo.

• 54:56Speaker 01

  And that we find true for every disease we've tackled so far.

• 55:01Tom Bilyeu?

  How many diseases have you tackled so far?

• 55:03Speaker 01

  Let's, let me try to list them. And just, just in my lab, I'm not the only one working on this. We've done multiple sclerosis, all right? So MS, we've, we've published that that, that is ameliorated by de-aging the nerves. We've done kidney, liver disease. A big one is ALS, motor neuron disease. Nothing you can do for those patients, really. That looks like it's working in my lab. And skin, de-aging. We're now working on hair and hearing.

• 55:39Tom Bilyeu?

  Damn, man. Okay, so all of that is incredibly encouraging. You said some positive words about your reduction in fear around cancer, but you haven't tackled cancer yet. The diseases that you all just mentioned, all in mice or all in mice and monkeys?

• 55:59Speaker 01

  In monkeys, we've only done the eye.

• 56:01Tom Bilyeu?

  Okay.

• 56:02Speaker 01

  So in mostly it's growing human tissue from scratch. Well, the two ways we do it, we either grow flat cell layers of human skin. So if I took a biopsy back to my lab from your skin, I could grow a flat layer.

• 56:14Tom Bilyeu?

  My old skin, back to that?

• 56:17Speaker 01

  Your almost middle aged skin.

• 56:20Tom Bilyeu?

  I hope I'm only middle aged.

• 56:22Speaker 01

  Yeah, you're a bit younger than me, I think.

• 56:24Tom Bilyeu?

  I don't think so. I'm 49.

• 56:28Speaker 01

  Yeah, you're a bit younger. Yeah, I'm 56.

• 56:31Tom Bilyeu?

  That makes me want to punch you. I'm inspired.

• 56:34Speaker 01

  Blame my parents.

• 56:35Tom Bilyeu?

  It is. Well, I want to blame your protocols. I'm secretly hoping that this is all something I can learn to do myself.

• 56:41Speaker 01

  That's kind of you, Tom.

• 56:42Tom Bilyeu?

  So a lot of the testing that we're doing in labs is basically on just cells.

• 56:46Speaker 01

  Correct.

• 56:46Tom Bilyeu?

  So we build the cells up. So it's so we have, in theory, tested on human cells?

• 56:51Speaker 01

  Yes.

• 56:52Tom Bilyeu?

  We just grew them ourselves?

• 56:53Speaker 01

  Yes.

• 56:54Tom Bilyeu?

  Okay.

• 56:54Speaker 01

  That's our standard. And human cancers, we grow in the lab, lung, colon cancer, melanoma, we grow those. But we go one step better, somewhere towards a monkey, even better in some cases, we could, we do, and we could take your cells, instead of making them flat, we could re-differentiate them into organs or tissues. And we do that in my lab. We grow miniature brains.

• 57:23Tom Bilyeu?

  Get the fuck out of here. And you have miniature brain, human brains?

• 57:27Speaker 01

  Yeah.

• 57:27Tom Bilyeu?

  In your lab.

• 57:28Speaker 01

  And we give them Alzheimer's, we make them old.

• 57:29Tom Bilyeu?

  What?

• 57:30Speaker 01

  And then we de-age those with our chemicals or our genes.

• 57:34Tom Bilyeu?

  Do they look like brains?

• 57:36Speaker 01

  Yeah, of course. Yeah.

• 57:36Tom Bilyeu?

  What?

• 57:37Speaker 01

  They look like if you cut them through, they've got all the structures of a human brain.

• 57:40Tom Bilyeu?

  If I look at it, am I, do I feel like I'm looking at a G I Joe brain that actually has like all the structure of a human brain? There's no way. They're like little blobs, right?

• 57:49Speaker 01

  They're blobs, but with the same structures.

• 57:52Tom Bilyeu?

  Do they look like a brain?

• 57:55Speaker 01

  They look like.

• 57:55Tom Bilyeu?

  I need to know if they look like.

• 57:57Speaker 01

  They look like.

• 57:57Tom Bilyeu?

  The fucking thing that is on the Sistine Chapel, it's got the shape and all that. Like, is that what we're talking about?

• 58:04Speaker 01

  The Sistine Chapel?

• 58:05Tom Bilyeu?

  Yeah, you know that God is sitting inside of a thing that's a brain that's been vivisected.

• 58:10Speaker 01

  Surely.

• 58:11Tom Bilyeu?

  I don't know that. What? Okay, anyway, so the brain has a super recognizable shape. You're saying it's in that shape. This is wild.

• 58:18Speaker 01

  It's a little pink blob of tissue.

• 58:20Tom Bilyeu?

  I'm coming to your lab later today. This is crazy.

• 58:23Speaker 01

  Let's have someone from my lab take a photo, send it over. And sometimes they grow little black dots which are eyes that grow. You get two little dots.

• 58:30Tom Bilyeu?

  This is incredible.

• 58:32Speaker 01

  And you can measure the brainwaves.

• 58:33Tom Bilyeu?

  Can I ask, what are the ethics on this?

• 58:36Speaker 01

  It's fine.

• 58:37Tom Bilyeu?

  Are they conscious?

• 58:39Speaker 01

  I don't know, but they have brainwaves. We don't know what they're thinking of.

• 58:43Tom Bilyeu?

  This is insane.

• 58:47Tom Bilyeu?

  That's it for part one. Make sure you are subscribed so you do not miss part two coming up soon.

• 58:56Tom Bilyeu?

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• 1:00:02Speaker 02

  That sound? That's pro squash. Some of the fastest rallies in sport played inches from the crowd. The National Squash League is back in full swing for season four. The sport is heading to the 2028 Olympics for the first time, and you can see it up close right now in a city near you. Watch every match for free on YouTube. Search National Squash League and hit subscribe or follow live updates on Instagram at nsl.sport.