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

Summary

---

Impact Theory: "AI Just Compressed 160 Years of Aging Research — Here's What They Found"

Host: Tom Bilyeu
Guest: Dr. David Sinclair
Release Date: April 16, 2026

---

Episode Overview

In this thought-provoking exchange, Tom Bilyeu and renowned biologist Dr. David Sinclair delve deep into the fusion of artificial intelligence and longevity science. Their conversation covers major breakthroughs in reversing aging, the mechanics of biological "information theory," and the astonishing ways in which AI has compressed centuries of scientific experimentation into a few short years. Dr. Sinclair shares firsthand results from his Harvard lab and offers candid, sometimes awe-inspiring perspectives on where technology, biology, and the future of human health are headed.

---

Key Themes & Discussion Points

1. AI’s Transformative Role in Longevity Research

• AI as a Force Multiplier: Dr. Sinclair explains how the integration of AI has allowed his lab to screen virtually billions of chemical compounds for potential anti-aging effects—an endeavor that would have taken multiple human lifetimes prior to modern computation ([01:16]).
  • "We’ve now screened probably about 8 billion virtual chemicals for one that will reverse aging... 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 160 years for my team to have finished that experiment." – Dr. Sinclair ([01:16])
• Acceleration & Democratization: AI’s ability to simulate and virtually test compounds skips costly and time-intensive laboratory work, making high-impact technologies potentially available as affordable pills or topical treatments ([01:16]–[02:24]).
• From Genes to Pills: With AI, Sinclair's lab moves from expensive gene therapies to the possibility of single-molecule interventions for age reversal ([04:01]–[04:51]).

2. Understanding the Biology: Information Theory of Aging

• Aging as Information Loss: Sinclair’s core hypothesis is that aging is driven by the degradation of cellular information, specifically epigenetic changes that misdirect gene expression ([08:35]).
  • "Aging is a disease caused by information degradation over time. But cells store the original healthy information in a kind of backup." – Tom Bilyeu, summarizing Sinclair’s view ([08:12]-[08:35])
  • "That's the theory that my lab works on, that we came up with. It's not universally agreed upon... but it has to be there because we use it every day to achieve what we do in the lab." – Dr. Sinclair ([08:35])
• Methylation & Cellular Memory: The movement and misplacement of methyl groups on DNA disrupts proper gene expression—akin to scratches on a CD preventing a clear reading of the original data ([17:59]–[21:16]).
  • "The problem with aging is the cell forgets which genes to read... it's a complete disaster. We get gray hair, we get wrinkled skin, we get disease, we die." – Dr. Sinclair ([21:13])
• Resetting vs. Rebuilding: The approach is to 'reinstall' healthy cellular instructions, not rebuild cells from scratch ([16:06]–[17:20]).

3. Experimental Evidence & the ‘Backup Disk’ Analogy

• Mouse & Monkey Models: Demonstrated age reversal and disease mitigation—sometimes with results even more dramatic than anticipated ([12:24]–[13:41]).
  • "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." – Dr. Sinclair ([12:19])
• High-Impact Examples: Reversing blindness in monkeys and planning human trials ([13:15]–[14:04]).
• Testing the Theory: Using precisely-timed DNA breaks to prove that distraction of sirtuins (cellular caretakers) rapidly ages mice, with methylation patterns indistinguishable from naturally aged animals ([25:11]–[27:47]).

4. AI’s Creative Power in Scientific Discovery

• Beyond Pattern Recognition: AI in Sinclair's lab (in collaboration with Stanford) discovered a novel way to predict biological age from data—not just validating, but exceeding current field knowledge ([41:12]–[43:04]).
  • "It came back and said, ‘Hey, did you guys ever think of this before?’ and came up with a completely new way... not only that, it proofed the data, it did the statistics, it wrote the paper up for us...” – Dr. Sinclair ([41:12])
• AI as a True Collaborator: The model’s creativity surprised Sinclair, challenging the assumption that only human intuition drives major breakthroughs ([43:11]).

5. The Limits and Possibilities of Simulation

• Can We Simulate a Cell?: While billions of virtual chemistry experiments are a reality, simulating an entire cell from the ground up remains out of reach due to biological complexity ([29:54]–[33:41]).
  • "Trying to model a cell is going to take a lot more work. It's not impossible because you will make assumptions but there's no way in my lifetime that I believe that a cell can be modeled from the ground up, looking at every molecule, really." – Dr. Sinclair ([30:35])

6. Human Implications & Path to Translation

• State of Testing: Direct interventions have reversed age not only in isolated human cells but also in animal models, with diseases like MS, kidney, liver disease, ALS, and even aspects of Alzheimer's showing robust improvement ([51:33]).
  • "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." – Dr. Sinclair ([48:12])
• Personalized Organoids: Sinclair describes the ability to grow ‘mini brains’ and other organoids from human cells, testing aging and age reversal in complex 3D tissue ([53:24]–[54:33]).
• Ethical Ambiguity: The creation of miniature brains with measurable brainwaves raises profound questions about consciousness and laboratory ethics ([55:03]–[55:13]).

---

Notable Quotes & Memorable Moments

• The Chills of Scientific Progress:
  • "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.” – Tom Bilyeu ([06:43])
• On Science’s Relentless Experimentation:
  • "With the scientific method, which is fail, fail, fail, and then something works.” – Dr. Sinclair ([10:45])
• On AI Surpassing Human Creativity:
  • _"The arrogance was I’ve got all this knowledge and experience and gut feeling. But here I'm seeing the beginnings of creativity. That can be super creativity in the future." – Dr. Sinclair ([43:11])
• Personal Reactions:
  • _"That makes me want to punch you. I'm inspired." – Tom Bilyeu (on Sinclair’s youthful age at 56, given the research) ([53:01])
• On Complexity and the Unknown:
  • _"One of the problems with science is we don’t know what we don’t know usually, and that's the tough part." – Dr. Sinclair ([35:01])
• On Cloning and Age Reset:
  • _"All of us, we were once the age of our parents. So I was...I was 30 years old twice in my life: when I was conceived and when I hit 30." – Dr. Sinclair ([37:28])
• On Humility & Progress:
  • _"There’s obviously something I don’t understand. I’ll figure it out. This is the whole point of the scientific method.” – Tom Bilyeu ([10:05])

---

Timestamps for Critical Segments

• [01:16]— AI compressing 160 years of research into months
• [08:35]— Introduction of the "information theory" of aging
• [12:24]— Proof of concept: Aging and de-aging mice & monkeys
• [17:59]— DNA methylation explained in aging
• [25:07]— Experimental validation: Mice artificially aged via targeted DNA breaks
• [29:54]— Barriers to full cellular simulation
• [41:12]— AI achieves creative, novel discoveries in the lab
• [51:33]— List of diseases showing reversal, including MS, ALS, Alzheimer's (in mice)
• [53:24]— Growing and de-aging miniature human brains/organoids
• [55:03]— Brain organoid consciousness and accompanying ethical questions

---

Conclusion

The discussion captures a pivotal moment in biomedical science, with artificial intelligence enabling discoveries once unimaginable. Dr. Sinclair’s work suggests not only that aging can be dramatically slowed or reversed, but also that AI will increasingly drive creative advances—sometimes faster than experts can publish them. The capacity to test billions of compounds, reset biological age in cell and animal models, and apply these lessons to multiple diseases heralds a future in which radical healthspan extension is more than speculation. However, as Tom and David note, many mysteries and ethical questions remain—part two awaits.

---

Transcript

[A]

When you manage procurement for multiple facilities, every order matters. But when it's for a hospital system, they matter even more. Grainger gets it and knows there's no time for managing multiple suppliers and no room for shipping delays. That's why Grainger offers millions of products in fast, dependable delivery, so you can keep your facility stocked, safe, and running smoothly. Call 1-800-GRAINGER Click grainger.com or just stop by Grainger for the ones who get it done. If you work in university maintenance, Grainger considers you an MVP because your playbook ensures your arena is always ready for tip off. And Grainger is your trusted partner, offering the products you need all in one place, from H Vac and plumbing supplies to lighting and more. And all delivered with plenty of time left on the clock. So your team always gets the win. Call 1-800-GRAINGER visit grainger.com or just stop by Grainger for the ones who get it done.

[B]

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?

[A]

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 8 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. 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 160 years for my team to have finished that experiment. And the cost would have been in the many billions of dollars.

[B]

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

[A]

Yeah, well, a big one was we. We need to thank Demis Hasabis 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 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.

[B]

Is this based on shape or.

[A]

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.

[B]

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?

[A]

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's partly AI, it's partly brute force, 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.

[B]

And the hit is this shape matches this shape and allows the chemical reaction to transpire.

[A]

Exactly. In this case, what we're doing is. Is virtually impossible. 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. And I'm asking my team 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? Because 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.

[B]

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

[A]

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

[B]

What do you test it on?

[A]

Cells. And that's also where AI comes in. We've got an AI system we developed. It's machine learning 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 92 year old going back to look more like the 20 year old cells from a 20 year old? And we've been working for about three and a half, four years on that.

[B]

Did you guys have to train your own model or.

[A]

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.

[B]

And it was just like, this cell young, this cell old, this cell young, this old. And then it gets to the point where it's like you don't have. It just looks at it, it knows the patterns and it says, ah, that's an old cell, young cell, whatever. Yeah, fascinating. Okay, how much? So when people talk about 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?

[A]

We looked at millions. But these days we can actually train very quickly. The models have gotten better at learning.

[B]

Who's doing the underlying models?

[A]

My lab at Harvard.

[B]

You guys are starting from scratch.

[A]

Yeah.

[B]

Damn.

[A]

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.

[B]

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' 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. Um, 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 the fullness of time. 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 direction 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. So ah, 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. So far so good.

[A]

Yes, 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, 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.

[B]

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, 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.

[A]

That's correct.

[B]

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

[A]

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

[B]

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.

[A]

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 the heck it's working because we're getting results that are nearly unbelievable. And we have to figure out how is that possible. The nuances we'll argue probably for the next 50 years, but it doesn't change the fact that we're doing it.

[B]

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.

[A]

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

[B]

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?

[A]

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

[B]

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

[A]

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? In 2020, on the COVID of Nature magazine, which is as good as you can get as a scientist. We published 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 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.

[B]

Okay. In mice.

[A]

In mice. And we've done it in monkeys. We've cured as effectively.

[B]

Or is that more complicated?

[A]

It's actually not that. Not more complicated in a monkey, if you want to get into the details. 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.

[B]

We're hitting pause for a moment, but there's plenty more ahead, so don't go anywhere. Thanks for sticking around. Let's get right back into the action. Okay. Uh, so membrane was a concern, but just didn't end up in practice, being a problem.

[A]

Right. Um, 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%.

[B]

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, their genes. Genes.

[A]

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

[B]

Okay.

[A]

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

[B]

Sure.

[A]

The three genes have a name. OCT4SOX2K4OSK, we call them, and they were, we can talk about how they were found, but essentially those three genes, now we mimic those with three chemicals and hopefully, as we discussed, get down to one pill that we can take.

[B]

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. But we don't know exactly how we're then able to get the cell to be 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. 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, right 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. Maybe it's a little bit of a skin cell or a brain cell or whatever. And so it starts getting confused.

[A]

Yeah.

[B]

How do we do so far?

[A]

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

[B]

You can and have, but that's very generous. 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 its self. 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.

[A]

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

[B]

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

[A]

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

[B]

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

[A]

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. Restoring.

[B]

Interesting. The way I'm thinking of it, which is probably messy because I've not thought about it like you have. 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. We're going to have to explain methylation. Do you want to give people a quick primer on exactly what is breaking down? Like what methylation is, the tight winding, all that. And then we can get back to why I say building.

[A]

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

[B]

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

[A]

Not. Not just an eye cell, 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 the software. Then it makes new proteins, makes new lipids, makes new. I didn't come.

[B]

There were cells that didn't divide. So there are cells. Oh, yeah.

[A]

Your heart, your brain, mostly. That's the same cells you had when you were a teenager.

[B]

That is wild. Okay, didn't know that. Thank you.

[A]

Yeah.

[B]

Okay, so sorry. Explain to people methylation just real fast. Yep.

[A]

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 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.

[B]

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

[A]

Mostly. Mostly. Not all genes make proteins. Some of them make other things like rna. But yes, mostly it's. We're talking about those proteins that become enzymes that do the work of living and repairing.

[B]

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, by the way, I had to look all this stuff up. Enzymes, like do a thing. They like move around.

[A]

They're machines, for sure. They're really super interesting machines. They all. There's tens of thousands of different ones. Yeah. And they do cool stuff, 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 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. But what comes next Is dependent on those three steps in the rung of that protein 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. 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, 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. 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 that

[B]

basically say, stop reading or only read this.

[A]

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.

[B]

Okay. And the methylation has to be redone Every time a cell divides or takes damage.

[A]

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. 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 disease, we die.

[B]

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

[A]

We do. And the. The history is 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 do yeast cells that make things like beer vegemite, which I really like. Tom, I'm going to 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 Guarenti. And we worked together, he and I and a team, and we put out a paper, he and I, just on my first big paper in my 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 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. 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 have is broken chromosomes. They hate broken chromosomes. Yeast cells and our cells, they can die if they don't repair a broken chromosome, or in our case yet, 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 an. Have two roles. One is to control these methyl patterns and genes, but they also go repair

[B]

DNA and they'll prioritize repairing the DNA.

[A]

They do, Otherwise there's no cell.

[B]

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.

[A]

Yes. And remember how I said there was a test of the theory which was if we cause aging, do we get

[B]

an old mouse so you just go up their DNA basically to cause the sirtuins to run over, to have to fix it?

[A]

Well, I wouldn't use the word f because we were very precise. We were surgical about it. We found an enzyme that is in a slime mold found in the forests around here. And it turns out there's an enzyme that cuts DNA rarely in mouse and human cells. A dozen or so sites, maybe a couple of dozen, but not thousands, 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 turn that stem cell into a mouse, which is standard procedure for students these days in my lab. And that mouse. Now, we could turn on this cutting enzyme from the slime mold and surgically create a few of these broken chromosomes. Not a lot, not enough to kill the mouse, but just enough to distract the sirtuins 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 experiment's not going to work. Nothing happened. But then I thought, 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, and this was the year 2012, and I got a photo on my old iPhone and it was a photo of a sick mouse. And the text was, should we kill the mouse? Because it's looking really sick. And I said, tell me, which is this mouse? They said, oh, that's the one we treated with the slime mold cutting enzyme. And I said, that's not a sick mouse. That's an old mouse right there. So that was the first evidence that by distracting the sirtuins to broken chromosomes, it leads to an acceleration of aging. And we published that. It took another decade to publish. And this was this big paper I mentioned was in the. In the journal Cell, which again, is pretty hard to get into. And this was the one that said that the change in information, loss of information, was the cause of aging in mammals.

[B]

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

[A]

Nothing, literally nothing.

[B]

You wouldn't be able to tell.

[A]

Right? That was the cool part. Except that they were 50% older. And we can measure, and we did, we measured the methyls across the chromosomes and we can use those changes as a clock. And in fact 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.

[B]

Yeah. Okay. Wild. So that sets us up well for understanding the thing that we're chasing now. 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?

[A]

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 7 billion or 8 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 8 billion, which we thought two years ago, mind blowing. 8 billion. Normally a pharmaceutical company might screen a couple of million. Right? That's physically we can now do an infinite number of molecules. We believe that we're going to cover all possible chemicals, which is orders of magnitude bigger than what we've been doing. And that's only happened in the last year that we could do that. But what's speeding it up is the ability to turn physical world into synthetic virtual. And instead of it taking a year to figure something out, it can be milliseconds in some cases.

[B]

And what's the most complex thing we can simulate? Is it the cell? Can we simulate a liver like no.

[A]

Biology is so much more complicated than most engineers understand. We still probably only know about 3% of biology. So trying to model it is pretty hard. And then the complexity I mentioned that it took all of DeepMind and those guys until recently to figure out how to just model a protein of a thousand amino acids. Even 20amino acids was a challenge. Trying to model a cell is going to take a lot more work. It's not impossible because you will make assumptions but there's no way in my lifetime that I believe that a cell can be modeled from the ground up, looking at every molecule, really.

[B]

In your lifetime.

[A]

Right. And I'm an optimistic guy.

[B]

I was gonna say, you don't plan to die too early. That's wild. So you're saying we're, what, more than 50 years away? That's crazy.

[A]

To model a cell.

[B]

Yeah.

[A]

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 around in that cell. But to say just if you're right, well, 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.

[B]

Yes. Taking a short break, but there's more impact theory after. Stay tuned. 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, 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 20, 30. That's four years for anybody keeping score. He says we'll hit AGI. The question becomes, is artificial general intelligence, given that at that point it will be able to improve itself? Is artificial superintelligence four years in 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.

[A]

I agree, but you just said that

[B]

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 is like 2:25 IQ or something. But I've seen his tweets. I don't buy it. So two 50, 300, 405, a thousand IQ. Like what does that start looking like?

[A]

Well, it's not I.Q. that's the problem. The, 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.

[B]

Yes. So you don't believe, you think that advancements in computation are going to stall out?

[A]

Even if they don't, it probably would take more molecules than exist in the universe without quantum computing to calculate what happens in a cell within one second. It's that complex. It's mind boggling how complex a cell is. So we can model one molecule hitting another and we can even model, I can imagine modeling 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 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 methyls, and we know that proteins get folded. 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 there.

[B]

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?

[A]

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. And we, we know that dages 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. Not too old.

[B]

How dare you?

[A]

It's going to be older than a baby. And I could inject that into an. Into 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 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.

[B]

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

[A]

No, he genetically modified a baby to be resistant to hiv. But his goal is to. And he's doing this offshore from us, of course. But there. There is the technology to do that.

[B]

But let's just say we haven't cloned humans.

[A]

That's the punchline. 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 wild. But. Okay, 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.

[B]

What?

[A]

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

[B]

So wait, at what point are you saying this is like, we know this, that embryos are.

[A]

People have measured it. Yeah.

[B]

Wild. Okay.

[A]

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

[B]

So at some point post conception. Exactly.

[A]

Day seven to day nine, we know that.

[B]

Whoa.

[A]

And the baby goes back to being age zero again.

[B]

Whoa.

[A]

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

[B]

That is wild. Okay. Didn't know that. New info.

[A]

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

[B]

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?

[A]

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

[B]

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

[A]

We have a pretty good idea in my lab, but we haven't told the world yet.

[B]

Okay.

[A]

I do text my student often and walk into the lab and say, have you figured out, have you proven it yet? But we're doing those.

[B]

If someone needs to hack your phone.

[A]

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. 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, 7 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 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, 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 way do this as scientists is we look for necessity and sufficiency.

[B]

Necessity from an evolutionary standpoint.

[A]

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 trigger that event, is that sufficient in itself to make the change? And when you get necessary insufficiency satisfied, you're really onto something. And it was similar with that mouse, right? We knew that it was necessary 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.

[B]

Okay. This brings me back to something I asked you earlier, but I want to push on it a little bit, so. 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 as you're approaching it. Has AI made any novel discoveries, novel insights that aren't already in the literature or. Or is it just doing a really good job of recognizing patterns in like what we already know about a cell?

[A]

We in my lab collaborated with another group out of Stanford who developed an agentix system with multiple agents, about a dozen agents that did different things. And we fed it our data. Interestingly, it's the data that map 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 what happened was the agents went to work and about a month later, and there was 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, 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 you know, 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, 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. Damn 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 biological.

[B]

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

[A]

Or 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 think most people who are not like us at the forefront of technology, like discussions with My father, who's 86, I can never be creative. That's just human arrogance. They definitely are already creative. And it'll only get better.

[B]

Yeah, 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 you guys have created in your lab a 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? Is it that you don't think we require the understanding or the sophistication in order to profoundly de. Age the. 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 and advantageous for age reversal?

[A]

Yes, absolutely. We're doing it already. There's no question about that. Why? We don't need to know every molecule.

[B]

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.

[A]

Oh, yeah.

[B]

Than the simulation.

[A]

Yeah, yeah. My students, I mean, they're in their 20s, 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 1904, 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. And then it was like three weeks later, Wright brothers.

[B]

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

[A]

Yeah, well, we don't need to know. And we still don't know how every molecule in an airplane functions. Yeah, fair. We don't need to model that. 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, what's the wind, 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.

[B]

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 cell doesn't seem like it's relevant, to be honest. So now we've got. We're showing great signs that we can improve eyes. I want to feel as good as I did when I was 25. I want to look as good as I did when I was 25. 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 all hell breaks loose, or getting into some sort of decoherence where it's like 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. So how far can we take this?

[A]

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 H0, 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.

[B]

It's incredible.

[A]

And that's how you make a clone, by the way. But that doesn't work 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.

[B]

I love the way you always whisper cancer.

[A]

Yes, well, I. I will say cancer's been in my family, so it's not.

[B]

I just had skin cancer, so I feel anyone's pain.

[A]

Yeah, well, the. 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 observer.

[B]

I need to follow up on that in a minute, but keep going.

[A]

I'm going to 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 75%, 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 really hard. But it's an enclosed system and safer to deliver to humans than giving it iv.

[B]

Interesting.

[A]

But it worked in the eye and that.

[B]

Because you can keep it localized.

[A]

Yes, got it. Exactly.

[B]

Okay.

[A]

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, one Cheng Lu, 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 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 mice and monkeys. Mice, then monkeys. And then we. We've moved on since then. So the. The mice were in. We were doing that in 2018. 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 old mice get their ability to learn, and they even get, we think, get some of their memory back from their childhood.

[B]

Okay. Huh?

[A]

Yeah. Yeah. And that's in an Alzheimer's context and in all, just old age. Mouse.

[B]

A lot of people just sat up.

[A]

Yeah.

[B]

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

[A]

Yes. Now, Alzheimer's is a big disease, 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 your brain can fight the disease. Even if you have the genes for Alzheimer's. ApoE4 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.

[B]

Yo.

[A]

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

[B]

How many diseases have you tackled so far?

[A]

Let's let me try to list them. And just in my lab, and 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.

[B]

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 disease that you all just mentioned. All in mice or all in mice and monkeys?

[A]

In monkeys, we've only done the eye. Okay, okay. 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

[B]

could grow my old skin back to that.

[A]

You're almost middle aged skin.

[B]

I hope I'm only middle aged.

[A]

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

[B]

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

[A]

Yeah, you're very younger. Yeah, I'm 56.

[B]

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

[A]

Blame my parents.

[B]

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

[A]

Kind of you, Tom.

[B]

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

[A]

Correct.

[B]

So we build the cells up. So it's. So we have, in theory, tested on human cells. We just grew them ourselves.

[A]

Yes.

[B]

Okay.

[A]

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 redifferentiate them into organs or tissues. And we do that in my lab. We grow miniature brains.

[B]

Get the out of here.

[A]

And they.

[B]

You have miniature brain? Human brains, yeah.

[A]

And you give them Alzheimer's. We make them old.

[B]

What?

[A]

And then we de age those with our chemicals.

[B]

Do they look like brains?

[A]

Yeah, of course. Yeah. What they look like if you cut them through. They've got all the str structures.

[B]

Hold on.

[A]

Brain.

[B]

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

[A]

They're blobs, but with the same structures.

[B]

Do they look like a brain?

[A]

They look.

[B]

I need to know if they look like they look. That is on the Sistine Chapel. It's got the shape and all that. Like. Is that what we're talking about?

[A]

The Cysteine Chapel? Yeah.

[B]

You know that God is sitting inside of a thing That's a brain that's been vivisected.

[A]

Surely I don't know that.

[B]

What?

[A]

Okay.

[B]

Anyway, so the brain has a super recognizable shape. You're saying it's in that shape?

[A]

This is why it's a little pink.

[B]

Coming to your lab. This is crazy.

[A]

Let's have some 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.

[B]

This is incredible.

[A]

And you can measure the brain waves.

[B]

Can I ask, what are the ethics on this? This.

[A]

It's. If you're. Are they conscious? I don't know. But they have brain waves. We don't. We don't know what they're thinking, though.

[B]

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

[A]

Day or night, Verbo Care is here 247 to help make every part of your stay seamless. If anything comes up or you simply need a little guidance, support is ready whenever you reach out. From the moment you book to the moment you head home. We're here to help things run smoothly, because a great trip starts with the right support. And hey, a good playlist doesn't hurt either. Jake.

[B]

I'm Jake Stauch, co founder and CEO of Serval. We built Servl to automate the IT work that slows companies down. Onboarding password resets, access to applications. My laptop stopped working.

[A]

While employees wait for help, their real work is put on hold.

[B]

IT desperately wants to automate this work, and that's why they need Serval.

[A]

You just tell Serval what you want

[B]

to automate in plain English and it's built. No drag and drop workflows, no expensive consultants.

[A]

Employees get unblocked and IT teams go

[B]

from drowning in tickets to building what actually matters. With Cerval, IT becomes the AI engine

[A]

powering the entire company.

[B]

This is a new way to run it.

[A]

We guarantee you'll automate 50% of all tickets and we'll prove it to you in a free four week pilot.

[B]

Go to cervel.com tickets that's S-E-R-V-A L.com tickets it.