#113 Why AI Could Add Decades to Your Lifespan | Dr. Derya Unutmaz

0:00Welcome back to the podcast. Today, I'm joined by Dr. Durya Unatmaz, an immunologist and aging researcher at the Jackson Laboratory. His work spans immune biology, chronic disease, cancer, aging, and increasingly artificial intelligence. Especially relevant to today's conversation, Durya is also a scientific collaborator with OpenAI. He has co-authored research examining how AI could accelerate scientific discovery. He's received early access to OpenAI's models and used the technology to help solve a three-year-old

0:31immunology mystery in his own laboratory. More recently, he joined OpenAI's Builders Unscripted podcast to discuss building tools for biology with codex and a future in which AI may help scientists simulate experiments before actually performing them in the lab. We began the podcast with one of Durya's most boldest predictions, that the next 10 to 15 years could represent a uniquely important window for longevity. He believes we may be approaching what is known as longevity escape velocity. This is a hypothetical point at which advances

1:05in medicine extend remaining life expectancy faster than time passes. It is a highly uncertain forecast and we examine the scientific reasoning behind it. From there, we explore how AI is already beginning to change the way research is conducted, analyzing high-dimensional biological data generating hypotheses and helping scientists determine which experiments are most likely to produce meaningful answers. Durya also makes a provocative argument. If AI becomes sufficiently

1:35reliable at reducing diagnostic errors and detecting patterns that physicians miss, there may eventually come a point when choosing not to use AI becomes medically irresponsible. We also discuss why cancer remains so difficult to cure and how AI could help personalize treatment by integrating a patient's genetics, immune function, medical history, tumor biology, and so many other layers of biological data. We then move into the biology of aging. Why aging may reflect a progressive loss of resilience and

2:07communication across cells and tissues. What unusual animals can teach us about cancer resistance and DNA repair. What partial cellular reprogramming might make possible and why the brain may be uniquely difficult to rejuvenate. Toward the end of the conversation we make the discussion more practical. Durya explains how someone could begin constructing what he calls a simple mini digital twin by organizing longitudinal health information, establishing personal baselines, and giving AI enough context to help

2:38evaluate what changed before and after particular interventions. Durya brings an especially valuable perspective to this discussion because he approaches AI as a biologist first. He understands both the extraordinary potential of these tools and the complexity of the systems they are being asked to interpret. That complexity is one of the reasons this subject is so compelling to me. Aging is not the product of a single pathway or an isolated defect. It emerges from an immense network of cells and tissues

3:08communicating, repairing damage, adapting to stress, and gradually losing the ability to maintain those functions. We can now generate enormous amounts of biological data, but collecting data is not the same as understanding it. AI may not replace biological intuition or clinical judgment, but it could expand what scientists and physicians are able to see. It may help identify patterns across genetics, immune function, metabolism, protein, medical history, behavior, and even environmental exposures that would otherwise be

3:39impossible for any individual human to integrate all together. You don't have to agree with Durya's timelines to find this conversation valuable. What matters is the direction of the science and the questions it forces us to confront. Can medicine move beyond population averages and toward a deeper understanding of individual biology? Can we detect disease earlier, design more informative experiments, and reduce avoidable medical errors? And can AI help us navigate biological complexity without deplacing the validation,

4:14the judgment, and the human responsibility that rigorous science requires? Durya approaches these questions with the curiosity of a working scientist and an optimism that is both refreshing and infectious. He gives an enormous and often intimidating subject a very human dimension, and I'm excited for you to hear his perspective. But before we begin, I want to mention two ways you can stay connected and support the show. First, if you're not already subscribed to my weekly newsletter, I encourage you to sign up right now. Stop what you're doing. Go sign up. Each week, I highlight new research that I find especially

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6:34conversation with Dr. Durya Unutmaz. I'm so excited to be sitting here with Dr. Durya Unutmaz, who is one of the handful of scientists that has had access to collaborate with OpenAI, one of the world's leader in artificial intelligence. He's also an aging researcher. He's an immunologist, really just a match made in heaven to sit down and talk about the role of AI in aging research and in medicine. So I'm

7:07super excited to have you here today. I'm very excited to be here. Thank you. As we both know, aging is a very, very complex process, many factors involved. It's heterogeneous. It's so complex. And it just seems like so almost impossible to solve. And yet I've heard you say something that's very interesting. I've heard you say, if you could try not to die within the next 10 to 15 years,

7:37you might want to try to do that because you could live an extra 50 years. Can you explain and unpack why you think that? What makes you believe that? Thank you. So first of all, I'm very excited to be here. I'm a big follower of your podcast. I think it's maybe the best aging or longevity podcast. So this is a great pleasure. Yeah, so I've said that quite a few times in the last year or two, actually. And it may not even take 10-15 years,

8:10might be even closer. The reason is that the technology, especially because of AI, is expanding exponentially. So our minds think in a linear term. So we think that the next 10 years is going to be as much advanced as the last 10 years or the last 15 years. But that's not what's going to happen. The next 10 years, you can think of it as more advanced than the last century. So imagine that you were living in early 1900s. And somebody told you that, you know, we're going

8:42to have vaccines and you will never get smallpox or you won't die of tuberculosis. You know, people would laugh at you. That's not possible. So that's the speed that we're talking about. But there's something even more important. Because of this acceleration, the advances of treating diseases is also going to accelerate dramatically. So we will get to a point what's called the longevity escape velocity. This was coined by Aubrey de Grey, who's, as you know, is a great aging researcher.

9:20So the point is that we will come to a point in the next, I would say, probably 8 to 10 years, where every year you live is going to add more than a year to your life. So let's just say, you know, 10 years ago, 10 years later, you get a cancer that's normally is not curable, and you only have one or two years to live. But that during that one year, there is going to be a new treatment that will cure that cancer. So automatically, it's going to add several years,

9:54or maybe 10-15 years to your life. Or we're already starting to see that with the GLP-1 drugs, receptor agonists, which are adding about 5 to 10 years to a lifespan of people who are obese or have chronic conditions, will have sort of the muscle generators, which I think will have tremendous impact on the aging population, because as you know, that's a huge problem. So all of these things will add up. And the technology in AI is going to keep accelerating.

10:28So 10 years later, what will happen in a year will be like what happens in 20 years of advance. And then we'll get to a point, probably 15, maximum 20 years, where we will be able to completely reverse the aging process. So if you're 80 years old, nine years old, you will get back to age 30, 40, whatever. So that's going to add up 50 years or 100 years to your lifespan. And then you can keep doing that and extend it almost indefinitely. So I think this is probably the

11:02most critical time in human history. So try not to die for the next 10 years. And we're going to talk about all these things. I want to talk about curing disease. I want to talk about reversing aging, age reversal. All of that is on my agenda to talk about with you today. But you mentioned something. You mentioned that right now, the, you know, artificial intelligence as a general term, you know, is accelerating at was an exponential rate. I've heard you talk about this Moore's law, and how the, you know, the software itself is accelerating, right at this

11:40exponential rate. Maybe you could explain a little bit about like, what, what does that mean? And then how, how do you think that'll translate into biology? Because, you know, humans, we're not software. And there are things that, at least in my opinion, you know, you have to still test safety, right? I mean, so like, if you're, you know, accelerating the computational speed, and therefore you can test a lot of things that are what are what's called in silico for people listening,

12:12we're talking about testing things like just modeling them, and maybe you can explain this a little bit better. But then at a certain point, you still have to test about, you know, safety. And you definitely that there's there are things that I think need to still be done in human trials. So I'd love to hear how you think that's going to happen. I think that's the most critical question, because people always bring that up. Okay, well, you know, if you generate drugs, within hours, you still have to test them on humans for five years, maybe sometimes longer, how are you going to deal with that? But let me,

12:45let me first start with how AI is accelerating biology now. So we can think of it in terms of phases, and because now and five years later, it's going to be very, very different. So right now, especially in the last year or two, since, you know, LLMs came out, you know, their intelligence had been accelerating. Initially, it was fairly smaller productivity gains. For

13:16example, you know, when GPT-4 was was out, I would ask it to sort of scan the literature and tell me what's the latest on this topic or that topic. And that saved me, you know, hours, sometimes days. But then as the models advanced, especially the after 01 model, the reasoning models started to come out, and now we have the GPT-5 Pro model, 5.5 Pro model, what happened was that now they were able to think and plan. So you could start to ask very sophisticated questions. For example, here is a huge

13:52biological data set, a million data points or 10 million data points, go over this, not only just analyze it and group them, but what what is the insight from that data? Human mind is not able to do that. And in fact, we had such data sets, which took us months to analyze, like, you know, a PhD student work on it, using deep learning, we still couldn't really truly understand what that data meant. We know these genes are up, this metabolites are changing, this is happening. How do you

14:26bring all that together? And so now AI models are able to do that. So you I've tested, for example, latest GPT-5 Pro model, you can upload millions of data sets that we accumulate over years, maybe. And then in matter of minutes, you get not only the complete analysis, recently, I had a 40 page report from GPT-5 Pro, which was an analysis of this, what's called the RNA sequencing, lots of millions of data points. But it

14:59also provided incredible insight, like what is the, what does this data mean? What should be the next questions to ask? So that automatically contracts months, sometimes years of analytic work into matter of minutes or hours. So so that that is already accelerating, forcing the drug design parts. I think every pharmaceutical company is going to eventually use AI generated an AI generation for developing new drugs, things that took years of screening of

15:34small molecules now take, you know, hours or days. So so tremendous acceleration there. And then I think, again, more recently, because the models have advanced so much, that you can also ask things like, okay, so this is great. This is the hypothesis. In fact, AI can even generate the hypothesis for you. Well, what sort of experiment I should do to address that? I mean, people have to realize that what we do in in biology is experiments, but we don't really know what's the

16:07best experiment to do. I mean, that's kind of my job, but I have some intuition, we should do this to address that question. But is that the ideal experiment is does that have all the controls, everything? So AI models are not able to tell you, sort of simulating, if out of this 100 potential experiments, you can do this two are the best ones, because this is going to give you the best output. And I've been testing that. So so that is another acceleration. Now, you don't have to try 100 things for a year, you can

16:40just try two things for a few weeks and get get the output. So that's what's possible now already tremendously accelerating the R&D part. But then the the second part, which I think is more important part, is how do we apply that to clinical trials and regulations. So it still takes years to try everything on humans. And I think the solution to that will be what I call the digital twin. And this this term is around for for several

17:10years. So the idea is that if we have lots of lots of biological data, and when I say lots, it's, it's a lot petabyte bytes of data. If if AI comes to a point where we're going to need much more compute than we have today, today is to able to compute all that, and really kind of simulate a whole biological organism, a whole human being, but not just your phenotype, but but also your metabolism, your immune system, your gut microbiome, your genetics, and all kinds of data sets are put together. And so it knows your biology in a temporal way, in a totally

17:51functional way, then you can ask the question, Okay, so if I give this drug to this person, what kind of effect it will have, if they have this disruption, is it going to have a side effect? Or is it going to be effective? So literally, we can cut down clinical trial time, from years to two matter of months, or even weeks. So you can actually do the trials in a very small subset of patients, because you can choose the patients, you can say, Okay, AI told me that these, these, these people, this drug is going to be effective 100% to them. And so so you

18:27just tested on those people. And in fact, that will go into the personalization, there's going to be 1000s of drugs for for different people. So that that will cause tremendous acceleration, we're not there yet. But I'm betting on that, that within the five, five to 10 years, we will get there. So the iteration process the on humans is going to be all digital as well. And then maybe the manufacturing will be a little bit still will take time, but but we can even

18:58improve that part too. So at some point, we will come to a point where treatment on demand. So you go to an AI model, analyzes your genome, your biology, orders the this small molecule of the drug or treatment just for you to the manufacturing facility. And next week, you get your drug and you get treated. That's the world I'm imagining. So I want to get back to this concept of digital twin. Again, when we talk about personalized medicine,

19:31but if I understand correctly, so, you know, if we are if we have this digital twin, which is all the genetic data, metabolomic, proteomic, biomarker, just everything, right, all this data, and more that we're not talking about. And now we have AI, which can then, you know, do all these scenarios and figure out like how this drug is going to affect or how this treatment is going to affect this person. You're saying that the clinical trial that may have taken, you know, a few years can be condensed down. And perhaps we can look at after doing the in silico experiments, you can look at

20:06some biomarkers and know, like, is this going to affect their fertility? Like, you don't want to give someone a treatment that's going to make them infertile or, you know, so you think that's going to be AI is going to be able to identify how to know if it's going to affect like fertility or cognition or life expectancy or, you know, just just from the whole composition of the person and doing, I don't know, all these tests? Yeah. So, I mean, the path there requires several steps of validation.

20:41And that I think we will get to a point where when we have super intelligence that we'll be able to trust super intelligence, you know, almost 100% that we don't need to validate it even with biomarkers or whatnot. But to get to that point, it's sort of like the self-driving cars, right? So,

21:02to get to a self-driving level, I mean, it has to be 99.999% safety, you have to sort of validate it.

21:12You know, what happens if somebody is crossing the street, right? So, that scenario has to happen, and then you record it. And sometimes you won't do the right thing. Maybe, you know, it won't stop. That's why we still have to like look at this, you know, be ready to take control. But if it does stop, and it stops and saves lives again and again and again. And right now, you know, self-driving cars are probably about 10 times safer, they will be maybe 100 times safer. So, you get to a point that

21:43you trust the AI rather than the driver, right? So, you say, okay, so I trust, I want the AI to decide for me to drive. So, I think we'll get to that point for biology too. It will take a little bit longer because of the extreme complexity. And then we'll have to have a very clever benchmarking and validation ways. There, the biomarker is going to be really important. Because, again, you know,

22:14if you're developing an aging drug that you claim will let people to live to 150, well, you can't wait, even if somebody 100 years old takes it, you still have to wait 150 years, 50 more years to validate that. So, that's not going to work out. So, we have to be able to predict that. But actually, probably aging is the easiest in some ways to predict because we have so many biomarkers or

22:45functional outputs we can measure. We know how they are in an old person and in a young person. So, if your VO max suddenly gets, you know, like a 20-year-old, wow, that's amazing. If your muscles are as good as a 30-year-old, if your skin looks like a 20-year-old, that's what my mom is waiting for, you know, that's proof. And you'll immediately see that, I mean, immediately, weeks or whatnot. So, I think, again, it will take time. That's the part that's going to take time, the sort of

23:22trusting AI to tell you, yes, if you take this drug, you will be treated or you will reverse aging. We still have about a decade. That's why I'm saying like, you know, otherwise, it would take, it would happen even earlier. You mentioned superintelligence, artificial superintelligence, ASI. Maybe you could talk a little bit about, just for people to have understanding right now, the difference between artificial intelligence, artificial generalized intelligence, AGI, and then the superintelligence,

23:56because you said, once we get to the superintelligence, we're going to trust it, right? Yeah. So, I mean, I don't know. Do we know what those differences are? Can you explain a little bit? Yeah, of course, you know, this changes on a daily basis, what the definition are, depending on whose definition. But, you know, I've been thinking about AGI, ASI for decades. I mean, it's not something that I started to think about it recently. And so, the way I originally defined AGI,

24:26it's artificial general intelligence. So, what that means is that, first of all, it's artificial, right? So, it's not human intelligence. It's artificial intelligence. And then it's general. What that means is that, if AI learns one set of rules, or one set of knowledge, that it can generalize that to something else. And that's how our brains are intelligent. Because you can be an amazing chess player. In fact, you know, AI beat the chess champion, Kasparov in 1997, I think, like decades ago.

25:04But that was not general intelligence. It was super good. Or AlphaGo beat, you know, the world champion in Go, which is much more difficult game. To be general, AlphaGo, you know, learning how to play Go, or chess should be able to, I don't know, solve a problem in aging, right? So, it should be able to transfer that information. I think the amazing thing about LLMs, what we call large language models,

25:35is that they acquire this ability. Which, honestly, I didn't think this would happen so easily. I was expecting AGI to happen maybe a decade ago. So, in my opinion, we have already achieved what I call Level 1 AGI, artificial unit. Because if I ask GPT-5 Pro model, you know, something that it hasn't trained on, like an experiment that I have done. Or if I say, okay, think of the experiment as a video

26:07game. Design another experiment for me, like you are playing a video game. So, that's transferring completely different area to a biological system, and is able to do that in an amazing way. But we still need to go through several levels. I think the next level is going to be memory. So, they don't have persistent memory right now. They have some memory. They know about you. They know about what they've learned in the internet. But they need to be able to manage the context, you know,

26:40because there's a continuum. Life is a continuum. And then the other one is going to be the self-learning, right? So, maybe that's Level 3. It doesn't matter. And that's coming soon. You know, AI companies are saying, like, we think that the real-time learning is coming maybe by next year. And then the third level, what I call the physical intelligence. So, people, again, confuse this greatly. Because the true human level intelligence is physical intelligence. It's not cognitive

27:13intelligence. So, for millions of years, we evolved to survive in a physical world. We didn't have language up to, I don't know, 10,000 years ago. Like, we didn't know how to write.

27:25This cognitive part has developed in the last, you know, maybe 10, 20,000 years. Before that, in fact, animals have very good physical intelligence. We're imprinted and born with that intelligence. So, an enamel or child knows, already have a world modeled. They know that, you know, if I drop this, it's going to fall and doesn't have to test it a million times. And that's, of course, what we need for robots, for embodiment. And you can see that, you know,

27:56that's taking a long time. You know, it's more difficult to train a robot to behave like a child than have GPT-5 solved the most difficult math problem. So, and we'll get there. I think people are working on these moral models and physical intelligence, whether we need another algorithm or not. So, that will be the final level of the AGI level. Once we have all those levels, then, and once the AI is able to self-learn, then that's the super intelligence. Because at that point,

28:30it can train itself, you know, maybe thousands, maybe millions fold faster than we're able to do.

28:38And there's a limit to human intelligence, right? So, even the smartest person in the world can only do so much. And super intelligence, what I would define is that you will have the intelligence of combined totality of humanity at some point. Like, if I bring a million top scientists in the world, of course, they can solve, you know, like a Manhattan project. They brought all these brilliant minds. It wasn't one person's. They were able to solve very hard problems. Super

29:09intelligence will get to that level. We'll be able to do what thousands of scientists can do in a year, we'll be able to do in a day. So, I would probably trust that. Wow. That's pretty exciting. I mean, and it also kind of brings in this concept of when you talk to people about AI, and not everyone has the understanding of it as you, for sure. You hear that there's a pessimistic versus optimistic view, right? And oftentimes, if I talk to people,

29:44I hear a lot of pessimism. I hear perhaps they don't understand their fear of the unknown of what AI is capable of. I mean, the super intelligence that you're talking about, I feel if you explain that to some people, it would scare them even more. You know, perhaps they are worried about the cultural ramifications, economic ramifications, but also just this Terminator situation where, okay, well, they're super smart. They're going to want to then take over the world, and they don't need us anymore, right? But you have such an optimistic view. I mean,

30:14we're talking about solving, aging, living to be 150 or more. Why do you have such an optimistic view? Are you worried at all about the other pessimistic sort of viewpoints? Absolutely not. And I'll tell you why I'm so super optimistic about it. When people make those statements like AI is an existential threat for us, you know, it's going to destroy humanity, I make the counterpoint, there's only one existential threat to humanity, and that's humanity.

30:47So if you look at history, human beings killed more humans than everything put together, caused more suffering than anything that humans have been exposed to. You know, even animals, I don't think they, maybe infectious diseases at some point might have caused a lot of suffering, but the real danger is the human intelligence. So let's do a tot experiment. Let's imagine that

31:21we live in a parallel universe, and in that universe, the world have decided that anyone above the IQ of, let's say 100 is a danger to the society. Because if you get very intelligent, you can come up with ideas that could be very dangerous, right? And that's true, actually, that's how it happened. And then if you if you have an IQ of 105, you get imprisoned immediately. So you're not allowed to to participate in society, or you get killed or whatever, that the the society has decided

31:54intelligence is dangerous. So we're going to stop it. What kind of a world we would live in, we would not have anything that we have right now, we will live in, probably just as farmers, you know, basic physical intelligence we have, and try to survive, you know, in a world where the average lifespan was 30 years old, or something like that. So that's, that's how we should view AI. And and the other point is that about this sort of AI is going to take over and is going to replace us.

32:27I see it exactly the opposite, because AI is is is an incredible enabler, it gives you superpowers. Even now, I feel like I have superpowers. You know, I've never been this busy in my life, you know, I actually sleep less, which is not a good thing, by the way, I don't recommend it. But because I can do so much, it's so empowering. You know, my mom was 86 years old, you know, she told me that chat GPT changed her life, she, and she's energized, she doesn't worry as much about her health. And

33:04it's just been an incredible impact. And this is going to accelerate. And at some point, we will get, we will sort of merge with the with the AI, in a way that we will have direct interaction with AI through neural link type of brain interfaces. So we'll have the sort of the intelligence of AI in our own brain,

33:30not only directly, but also indirectly by sort of engineering our biological system. So why shouldn't everybody have an intelligence of Einstein or even higher, right? So the difference being an Einstein and a normal person with a normal IQ is probably few gene mass single point mutations. So if we can engineer that, if AI can teach us how to do that, then we are, we're also going much, much higher. So as long as we, we keep the agency, I think that's the only thing that we have to really protect,

34:07that we are the decider, or we see AI as a collaborator, as sort of another species that will live together, and we empower each other. In a way, it's our child, right? So it's been created by us. I see the chance of a worse world extraordinarily, of course, it's never zero. But you know, the moment you're born, you're gonna die, right? So so you're destined to die. And now AI is giving us this

34:40opportunity to save, literally save billions of lives. I'm not talking about saving lives as like extending their life for five years or 10 years, you're talking about 1000s of years. So that's true saving lives. That's the potential. And the risk is, again, I think that the key risk is is humans, humans, you miss using AI, that's what we have to sort of maybe train or aligned AI, you know, don't, don't look at the bad humans,

35:10you know, you can you can judge the better, the better world for us. So, of course, I might be wrong, but I'm pretty sure I'm gonna be right. I agree with the statement of we have to watch out for the humans, for sure, like, because you're right, like they can, and have in the past been the biggest threat to humanity. So I want to, there was a couple of things that you mentioned when when you were talking about, you know, ASI and this

35:43ability to self learn, and you're even talking about some of some of the ways that you use, you know, GPT-5 Pro and helping with designing experiments and interpreting results. And that was a question that I had as a biologist. And as you mentioned, you know, we do experiments, we're testing hypotheses, and then we have all this data and these results. And we have to know what result is meaningful, and what anomaly is meaningful, because oftentimes, the anomaly, which you might ignore, is what you absolutely is the breakthrough, right?

36:17And that is a sort of intuition, this biological intuition. And so you, do you think, first of all, do you think we're that, that, you know, the models we have now can already are capable of that sort of biological intuition? And if not, like, how far off is that? Yeah. Yeah, that's, that's a great question. In fact, you know, I see that intuition maybe sort of the the last mile or the top 10%, or 10% of the of the solution, because 90%

36:53AI models are able to come up with, because it's knowledge based also in humans is, you know, for for a medical doctor, for a scientist, for whoever, 90% or 95% is based on what's known, how you process that knowledge, but there's that extra five 10% totally dependent on your intuition. Like you, you, if you're a doctor, you see a patient coming through the door, you know, that guy's having a heart attack, you haven't checked anything yet, somehow, you know, you don't

37:25know how, you know, the same thing in the lab, like, in fact, I would, I would bet with my students and postdoc, I would say, Okay, I bet you if you do this experiment, you're going to get this result. And I've never lost a bet. And they stopped betting against me, even though it might look counterintuitive, oh, no, that's never gonna work. Somehow, I know. How do I know? Because, you know, I've been working in the lab for 30 plus years, and, and you, you acquire certain things that are not in the literature,

37:58or, you know, you can't really read a textbook and learn it, you only do it by by practicing it. So the the models up to, I would say, 5.5, until recently, were were great at that 90% level. So especially after GPT-5 Pro came out. So you know, I would ask it to, for example, I would give it an experiment that we have already done. It's a very complex experiment took two weeks, I already know the result, because we're done the experiment. But I wanted to see how the model would predict the

38:33outcome of the experiment. And they would do, you know, not just GPT-5, but several other models as well. They will come up with 90%, 80 to 90% correctly. That's, that's pretty good. They would say, Okay, this is what's going to happen after two days after one week after two weeks. But that extra level of intuition that I, I have, I would have predicted was still somewhat lacking. I think GPT-5.5 crossed that threshold. So I repeated that with with the 5.5 Pro model, because I always say Pro,

39:11it's very different than the thinking, of course, very, very different than the instant model, because Pro is reasoning, much, much longer, it's thinking. So in some cases, I pushed it to think for two hours. So two hours in AI thinking is like years of thinking for a for a human being. So that model really crossed that threshold. And that example I gave you, it was almost 100%. I mean, I would say 98% correct, what I would have predicted, like, I would not have bet against 5.5 Pro myself.

39:48So that to me is, is actually really mind boggling, because I couldn't understand these models are being trained with all of the information, we can't compete with that, right? So it's, they can put these patterns together. But how is it that the model has now almost the experience that I have that I spent 30 years acquiring that experience, that intuition, that is now getting to that level? That is, that is a mysterious, but I live through it now.

40:25What sort of you said you pushed GPT 5.5 Pro to think for two hours. I mean, what sort of prompt are we talking about? Or is it just the data set to and prompt? I mean, So those are usually data sets. I might have broken a record because I even asked the friends at OpenAI, I don't think they pushed it that far. So this was actually the two hour one was

40:52huge data sets, millions of data points. And then I also said, okay, don't just analyze it, write a huge report, you know, 30, 40 page, whatever length, and then, you know, come up with a lot of insights about this data, what questions to ask, and what would do we learn the mechanism, it was an immunological data set, sequence and genes and proteins and all that. And so that one,

41:23I think 112 minutes, I remember that. And it came up with this 40 page report, which I was just unbelievable. This is unbelievable. You know, the analysis part, the previous models were able to do as well, you know, you know, they say, Okay, well, there are these type of genes and this type of protein. So it means this and that, you know, it derives from that information. But to come up with an insight, what that could mean,

41:54or what would be the next question to ask? That's, that's a very, very high level of reasoning. And so, yeah, it was, it was worthwhile two hours, for sure. I mean, that's very exciting to hear you say that, because that was kind of my, I wanted to know, I wanted to know, is that something that is already possible? And it seems like it is. And so, it also leads to the next question, which is, you know, all these scientists now really need to start understanding how to use AI in the right way. Right? I mean,

42:26this is like to help them. I mean, that's going to happen, right? That's basically, you know, we all, we all use Google now. Remember when Google was like new? Right. So, I mean, it's eventually going to happen. But it's very exciting to think about how AI is going to change research and medicine. And that's something, you know, you mentioned, and I talked, I said, I want to get back to this digital twin idea, because I've heard you talk about it. And it's very exciting to me. You know, we've heard for decades now that personalized medicine is coming, we're going to have personalized medicine. And yet, still, we just don't have it. It's just not there.

43:03And I've heard you, I've even heard you say something sort of interesting, which perhaps, I'm not saying the direct quote, but that it kind of should be medical malpractice in a way for a physician today, right now to not be using AI. So, can you talk a little bit about why you said that, what it means for a physician to use AI responsibly, also how patients can self-advocate for themselves, because that's also another area? Yeah. In fact, I said, after a one model came out,

43:41I think that was sort of the first reasoning model. And I was testing a lot of, I mean, I have a medical degree, but I don't see patients. But you know, I have a lot of friends, and I have some knowledge of how medicine works. So, I've been testing lots of medical questions, and some of them are hard, some of them are, you know, sort of real time data. And, you know, before 01, it was great in sort of reaching to the literature, you know, like, the physician might lack certain, certain knowledge,

44:15so it knows what was published recently, and things like that. But it was not at the reasoning level. So, 01 model was able to reason. And the reasoning is extremely important in medicine, because, you know, even if you have all the information, you still have to sort of consider that person's context and, you know, what would be more likely to treat that person. And we don't always know the answer as well, or how to diagnose it. And so, I think 01 was able to get to that point. And at that

44:53point, I said, right now, it's unethical for physicians not to use AI anymore. I didn't say malpractice yet, but it truly unethical in the sense that, you know, you can use it, you can still do your judgment, obviously, but it will prevent you missing some sort of an obvious mistake, or, you know, sometimes not obvious mistakes, or diagnose things that require multiple

45:24clinical specialities coming together, and you don't have that capability, you live in a village or something. But now, I think, I feel that it is truly going to be considered malpractice, my opinion, it's not legally so, but eventually it will be, because the current models, the advanced models are able to diagnose and write a treatment protocol better than, or as good as a specialist

45:56in that field. It's not just a, you know, family physician, let's say, you know, you have a very complex cancer, you know, you know, the mutations and what's not, and you go to a specialist, like an oncologist who is very, very specialized on that. I believe that the current models are at that level. So, and of course, not every specialist is the top specialist, right? So, if that was the

46:27case, we wouldn't have millions of misdiagnosis and mistreatment in the US alone every year, I think they said something like 12 million misdiagnosis. I think 700,000 people suffer from it, die from it, from misdiagnosis. Some of them is totally innocent, you know, any doctor could have missed it. But now AI wouldn't miss that. So, even a specialist might make a mistake or misdiagnose,

47:03or mistreat, because they lack certain things that the model does not. So, I mean, imagine that, you know, you refuse to use MRI machine or CT machine, because you say, well, you know, that's too much technology, I'm just gonna, you know, just do an x-ray, because that's enough for me, and you miss a tumor. The AI models are able to detect certain tumors, like breast cancer, years before a radiologist is able to see that. So, if you miss that, I mean,

47:38that person is gonna die, if you don't know. So, to me, that becomes a malpractice because the technology is at that level now. It wasn't, it wouldn't be malpractice, you know, missing a breast cancer, you know, five years ago, because nobody could. We didn't have that technology, but now we have that technology. So, you should definitely use it. And this is going to save a lot of lives. I mean, if you could just reduce the misdiagnosis and, again, bring every doctor to super doctor

48:15level, I think that would be a really good thing. So, what you're saying is based on, you know, what current data that doctors have available to them, whether it's an MRI, whether it's an ultrasound, whether it's blood biomarkers, this sort of data is what is given to, you know, a model like GPT 5.5 Pro, for example. And with that data, they're able to better diagnose, better to predict,

48:50to see things. Like, you mentioned cancer. Is that better than a radiologist, Ken? Is that some, is that like based on, you know, what kind of data is imported? Yeah, these are studies. I think Google did a recent study. In fact, a science paper came out recently, which was done with O1 preview model, which is a very old model. I mean, the current models are probably 10 times or maybe more. Was that like the first pro almost? Yeah, it was the first sort of the reasoning model that I

49:21early tested in 2024, September, it came out. And they found that O1 model did better than average doctor in diagnosing, like significantly better. They didn't miss. And so imagine the current models, how good they are. But I think it's not just sort of diagnosing a disease because that's actually a small part of the job of a doctor. It's really, there's a continuum. Most diseases, you know, okay,

49:56if you have a flu or some bacterial infection, you know what to do, you give it, and then you see an output. But a lot of disease, even in that condition, that may not be true because, you know, you might have a mutant virus or bacteria. So you might have to change the treatment, or might have a little bit of a side effect. So there's a lot of continuum there. So I think AI can be involved in all of that process. So if you can continuously feed the data, okay, well,

50:26the patients, we gave this treatment, it's doing well, the blood pressure is down. But, you know, has this symptom, that's, and so what should we do, change the dose of the drug, or add this, or remove that drug and give another antibiotic, like there's a constant process there. And that, that, that's not always that constant. Because, you know, people, people don't go to doctor every day, right? So you get a prescription, you see something works, and they go back. And so what if

50:57there's something that's continuously monitoring you post-treatment? For cancer, it's very important, because cancer is a very dynamic disease. There's the cancer, which is constantly trying to survive, and mutate, and counteract against the immune system. So you give it, so, you know, you give a drug, chemotherapy works, and then the cancer comes back again, right? So why is that? Because mutations are accumulating. So can we catch that earlier? Can we change those decisions? Can we

51:32make sure that we give more, multiple drugs, or different drugs? So before the cancer has the opportunity to come back, we prevent that possibility. So all of these decisions can be made together with, with AI. And I, I think it's going to have tremendous, tremendous impact on healthcare. I do want to get back to the cancer equation in a minute. But before that, I just think that, you know, physicians, not all physicians know how to use AI. They don't know which models to use. Do they

52:05use GPT 5.5 Pro, or Claude, or, you know, how do they sort of responsibly use it, which you kind of talked about a little bit, but without, you know, outsourcing their clinical judgment? Do you have any opinions on like the different models to use? And I do know you have a collaboration with OpenAI. You've been one of the first scientists really testing these models in a biological sort of arena. But I do kind of, I do think that people and physicians that are listening want to know what, how, what,

52:39what models do they use? We definitely are talking about, if we're talking about OpenAI, it's not, it's, it's got to be the pro, right? It's got to be the reasoning model. But yeah, I mean, what about Claude? What about Gemini? Yeah. So I think people have this sort of a misunderstanding of, they think of AI as okay, we have AI, we have internet, so let's just use the internet, we have AI, let's use the, but this is advancing so rapidly. The AI model that we use,

53:12one month ago, is not the same AI model we use now. So it's just doubling in intelligence every few months. You know, I gave the example of one preview, some people got stuck at the GPT 440 model. So, oh, yeah, I used it and it hallucinated a lot, even, you know, or one wasn't so good, you know, it was making mistakes. That's like an ancient history. That's why I haven't even asked about hallucinations. Yeah, so it's, I mean, the advantage that I have is that, you know, because I'm all in AI,

53:47I'm continuously testing. And so I can see the evolution of these models, and they get, you know, 90% better, 95% better, 97% better, like it just continuously updates itself. And then eventually, right now, with 5.5 model, I don't see any hallucinations whatsoever. I mean, there might be 0.1%, but it's extremely rare. And so your trust level goes up, again, it's similar to like

54:19self driving cars, right? So we had self driving cars for almost a decade, maybe. And they just keep on getting better and better, because their AI models are getting updated. So my advice would be, doctors should see this, not something optional, like they have to update their knowledge, medical knowledge, periodically, in fact, they have to have tests to do that to be certified, or they have to update on new drugs that are coming out, right? So you can't just rely on some drug that came

54:53out five years ago, 10 years ago, you need to know what what was approved last month, and you need to update your your knowledge. In a similar way, even more so, they have to constantly update their AI knowledge. So AI has to be part of their their their practice. And of course, my recommendations always use the latest top model you can use. Right now, it's GPT 5.5. In fact, I would always use

55:23for complex problems, the pro model, because that thinks in minutes, but at least if you're using it on a daily basis, in a rapid fashion, always use the thinking model, the thinking model is different than the instant model, instant models is also getting better. But it needs to reason, it needs to think. And especially if you're putting in lots of patient data, and analyzing that you definitely need the pro model. And then there are there are these companies like open evidence. And you know,

55:54I think most doctors are starting to use that open evidence, basically, I think, applies the latest model, somehow it updated. So the doctors don't have to worry about it. And I think there's going to be more companies like that will provide that service. So the doctor doesn't have to worry, should I use 5.5 Opus 447, the whatever the sort of the the harness model is going to pick the best one for for medicine and apply it there. And of course, hospitals should should implement AI,

56:28just like you know, big tech companies are, you know, there's enterprise level of AI, that can be more secure, you know, protect the patient data. So it should be like, you know, in front of the patient in hospital, you see these monitors, like the heartbeat and all that stuff should be an AI monitor, like constantly monitoring the data, and then giving information to the nurses to the doctors, okay, this is this last situation. And now with AI agents, you can do that, like I do it for my,

56:59my daily life, like for my email, automatically, my agents go and check my email, and they tell me what's important. So I don't have to go through hundreds of emails. So you know, this, this is waiting for you, you have a podcast with with Rhonda today, so you better be prepared for that. So yeah, it needs to be fully integrated, almost like a co physician, like you have the AI doctors working together with real doctors, right? Have you I've noticed, like some of the the companies

57:32that I've corresponded with, or interacted with, it seems like they use clod a lot. I mean, I don't know if you've experimented with that. But I'm kind of curious why, why that, why that, you know, certain model, versus like, I bet, yeah, in all fairness, I've never used it, I use, you know, I've been using GPT and the pro. And so every like you said, you know, every time the hallucinations are like ancient history for me, like, I remember, that was a big thing. Yeah, but it's going so fast and better now.

58:04But like, what, what, what, what's the difference between, you know, for example, clod and GPT 5.5 pro? For certain things, there is no more difference, because the intelligence has, has peaked for, you know, for doing regular diagnosis, not very, very complex cases. Cloud is great. Cloud is also very, very good, like the Opus 4.7 model, the recent model for analyzing data sets. So it's,

58:35you know, you can take also millions of data, analyze it and do a great job. My preference is, you know, GPT 5, right now is 5.5 pro, because it, what I mentioned, it has this extra insight. I mean, for me, I need that extra level of insight. That's predictive. And I don't wish in that intuition and really kind of a deep understanding. But if I'm, if I'm going to

59:08diagnose and treat a subtype of a lung cancer, I'm pretty sure, you know, Gemini 3.1 pro or cloud 4.7, they all do a pretty good job. I think there is some reason people prefer cloud is that it's, maybe it's more pleasant to interact with, you know, kind of more human-like. I think GPT models are starting to get there, but still, there's something about cloud that people enjoy,

59:39you know, interacting with it. It's, it's really a matter of taste. I think it's, it used to be more, more personable. And so it doesn't really matter. I mean, I think they're, they're, they're really, they're all super top levels, unless you're doing like a research or a very, very complex problem. You know, for example, we did a test with, with a colleague of mine on, on skin disease with GPT-5 pro model, you know, it was able to diagnose a skin disease that

1:00:12my friends couldn't really diagnose just based on a photo and, and a symptom. The other models couldn't do that. They could do 90% of the cases as well. But there's that one extra case or two extra case that is really difficult. This could go anywhere. The pro, the GPT pro model was able to cross that, that threshold. So those kinds of cases, you really need that very high level, like, you know,

1:00:42you don't, you don't go to a professor at Harvard for, for any, any reason, right? So it has to be very specialized disease that other doctors couldn't diagnose or something like that. So that's how, that's how I view it. We just, there was just news yesterday, um, from open AI of, uh, GPT Rosalind, which I know you can't talk about much, but from what was publicly available, it seems as though it's going to be used in drug discovery. Um, I I'm wondering what you think in terms like the future

1:01:16of aging research, biology, medicine, are we going to be using these more specialized types of AI models? Or do you think more of a generalist like GPT 5.5 pro and, and the, you know, the subsequent ones that come out after it are going to be the key to unlocking, you know, medicine breakthroughs and biology breakthroughs? Um, my preference would always be the generalized models because again, you know, going back to AGI, AGI. Uh, so if, if a model is, has, um, it, you know, of course there,

1:01:54there are some utilities of models that are only trained on, I don't know, like the EKGs or, um, RNA sequencing or something like that. And they, they'll be very, very good at that. Like the, the, the best chess player AI model or, um, the, the best go go player AI models, but they will miss that, uh, connection because again, I view medicine as a kind of a holistic, uh, um, art in a way. Uh, if you are just trying to analyze one set of data, the, the specialized models could be, could be very,

1:02:28very, very useful. In fact, you know, I gave the example EKGs, um, most generalized models were not terribly graded. Um, for some reason, you know, the, the EKG images were, were not, they were not very good at diagnosing what, what, what it was showing. Um, and, and, you know, specialized models were very good because they were trained with, you know, millions more EKG data sets than the generalized model was. Um, but, uh, but I think, you know, if, if he can train the generalized model

1:03:02or fine tune it or over train it, uh, I don't know how to say it, um, uh, then they will be better than specialized models all the time. Um, because not only they have, they know all about EKGs, but they know all about radiology. They know all about RNA. They know all about proteins. So they can take that information and, and, excuse me, analyze the EKG, the, the electrocardiogram, your, your, your heart beats in the context of all the other biology. So that will, that's very enriching, uh,

1:03:38knowledge. Um, but, um, I think, you know, specialize in the sense that you can take these big models and you can sort of, I don't know, harness them or fine tune them because there's a lot of data sets that's not public. So these, these models, they don't have access to that. You might have some, um, data, you know, locked in certain, uh, because of regulatory reasons, whatever. So you can take, take a big model. In fact, you don't need, you may not even need the, the closed model. You can even

1:04:11take some of the open source models, which are, which are not getting very good. If you, uh, you can train them on that. Uh, and they also have the generalized knowledge and combined with that, they'll probably do better. So I want to, I want to talk about there's treating disease, there's curing disease, and then there's reversing aging. So let's, let's start with curing disease, treating diseases, curing diseases, because, you know, obviously we do die of age-related diseases, cardiovascular disease being

1:04:43the number one killer in, in most developed countries. We have cancer. That's a really big one. And, and with cancer, it's just such an awful disease to have. And anyone that's listening that has either had cancer or knows someone that has had it, you know, knows this is, this is true. But also I think, you know, cancer, a lot of people think about it as one disease, non-scientists, non, you know, physicians, they kind of think about cancer as just this one disease, right? As you and I both know, it is definitely not one disease. It's hundreds of diseases.

1:05:21I'm curious on, first of all, you know, we still don't have a cure for cancer. I mean, we've, we've made a lot of progress, right? And different cancers and can be treated better than others. But can you talk a little bit about why it's been so hard to find a treatment for cancer? Yeah, I think the, the, the, the important thing to clarify is that cancer is not one disease. It's probably a hundred disease, a hundred different diseases that have probably hundreds of sub,

1:05:52sub, sub diseases or sub, sub types, if you like. And in fact, a certain cancers are a hundred percent curable or 95% curable. You know, like child, some of the child leukemias, which were completely fatal, you know, a couple of decades ago are now, you know, 90% or, or close to a hundred percent curable. If you catch, uh, uh, certain cancers early enough, again, a hundred percent, uh, uh, cure rates almost. Uh, so, so, uh, because it's, it's a very, very, uh, different set of, uh, uh, diseases, um, the,

1:06:29the cancer of pancreas is very different than cancer of, uh, lung cancer or breast cancer, or there are some cancers that are so slow. Like if you get, um, certain types of cancers, if you're age 80, doctors don't even bother to treat it because by the time that will, unless we cure aging first, uh, because by the time you die of aging, you know, that, that cancer is not going to kill you. Aging is going to kill you first, or, you know, there's certain prostate cancers, uh, at a certain age.

1:07:01So, uh, that, that's why we have to really understand that this is a very complex biology, but more importantly, why cancer is such a challenge is that the, the cancer cells are part of us, right? So, uh, if you're infected with the bacteria or a virus, you know, it can kill you, right? They're extremely dangerous, but we are able to recognize them as an enemy, as a threat, your immune system, and we can fight back, you know, uh, not always successfully, but most of the

1:07:34time very successfully. And we can also target them very specifically. Like we have an antibiotic that will only act on the bacteria. It's not going to touch your normal cells because it's only, uh, a foreign or organism, but cancer is not like that. So if I try to stop cancer with something, I'm also trying, I'm also stopping some other cells that are normal, right? That's why people lose their hair. Their immune system is greatly weakened because the immune system has to divide your, um, hair has to, hair cells have to divide. So you, you block them because they, the cancer cell is also

1:08:10dividing and, and your side effects of chemotherapy sometimes worse than having the cancer, like hundreds of thousands of people die because of that. So the revolution in cancer was, uh, recently because of what we call immunotherapy. The question was, why, uh, can we make the immune system to recognize cancer as foreign threats? Like they're kind of like terrorists, right? So a terrorist, you will not know if that's an enemy or not. They look like you, you know, they just come in and then

1:08:43they, they create, uh, so the immune system is seeing it that way that it thinks that the breast cancer cell is not so different than a normal breast cell, you know, like epithelial cell, whatever. And so it doesn't know what to do. If it could teach the immune system, or if it could remove some of the brakes that it has regulation and let it recognize and attack the cancer cells, then that could have a tremendous effect. That was the hypothesis and it actually worked. So cancer

1:09:14immunotherapy, I think, uh, is, is more powerful now than, than chemotherapy and radiotherapy put together. I mean, they're still have a, have a role. Um, and of course, the other thing is that how can we make the treatments very specific, right? So if I give a chemotherapy, that's not specific. It's like trying to hit the patient on the head and hope that the cancer will die before the patient dies. But if I know this single mutation that's happening on, you know, whatever, uh, EGF receptor,

1:09:45uh, in certain cancers, I can develop a small molecule, which will only act if there's that mutation on the EGF receptor or whatever. And so it's not going to touch anywhere else. It's only going to target the, the, in fact, people call them smart drugs and they're, they're extremely effective, right? So, uh, if you have that particular mutation, you're 1% of the lung cancer patients, you get treated with that drug, you get almost a hundred percent cure rate. Um, but again, you know, uh, we can make this even much better. So for example, immune system can be engineered something

1:10:23that we work on, uh, in the lab, uh, to recognize like literally engineer, we take the cells out, we train them, we put genes into them and say, okay, so if this gene binds to a cell, assume that that's a threat and kill that, that, and so it's called CAR T therapy and they will go and seek out whatever, uh, the, the cancer cells that have that marker and kill them. The advantage of that is that cancer doesn't have much way to escape that. It can try to suppress the immune system, but other than

1:10:56that, even if it mutates, you know, the, the immune system will still recognize it and find that few cells that are hiding somewhere and, and destroy it. And, and that's showing incredible results. So the mRNA vaccines, which I think is going to be revolutionary is, uh, is on that basis, right? So, um, and that really personalized the cancer. So I have a breast cancer, but my breast cancer has certain type of mutations that other patients don't have. So even if the immune system can recognize X patient,

1:11:31it won't recognize mine because the cancer has different mutations. If I take those mutations and synthesize what's called the RNA and then give it back as a vaccine and train my immune system and tell the immune system, look, if you see these mutations in these genes, that's an enemy, go destroy that. That's mRNA vaccine. And that becomes extraordinarily powerful because now you're directing your immune system to, to an internal threat just in you. And let's say the, the, the cancer

1:12:05required different mutations. You can create another, uh, mRNA vaccine and then train the immune system to that as well. Um, so, uh, you know, I think that's those, those are the, the, the, the difficult parts, but, but we see the light at the end of the tunnel. Okay. Uh, cancer, cancer is going to be a hundred percent curable, uh, probably less than a decade. How is AI going to make that happen? Yeah. So in fact, it's already making that happen. You probably heard of this story from Australia. This

1:12:38computer scientist, um, had chat GPT and, and some other AI models to develop an mRNA vaccine for his dog. His dog had, uh, uh, I think a melanoma and he, um, he got it sequenced. He took the sequence and gave it, gave it to, to an AI model and the AI model designed the precise mRNA molecule that needs, that the dog needs, dog's immune system needs to be trained, got it synthesized. And I think

1:13:09it was able to apply it in three months. Um, probably could have been shorter if there wasn't regulations. And, and, and the tumor started to, to regress and the dog is, was, was, was alive when it was supposed to die. So, I mean, that, that, that's a very, uh, uh, obvious and simple version, but because there are hundreds of difference of cancer types, you can imagine that we'll have maybe a hundred different treatments for just the type of a lung cancer. Someone will be mRNA, someone will be small

1:13:45molecule targeting that. Uh, so to be able to develop those on demand or very, very rapidly, we're going to need AI. So the AI is going to model every possible mutation and we'll screen millions and millions of compounds. And so we'll, we'll, we'll get to a point where we'll have hundreds of new drugs coming out every month, maybe, you know, uh, uh, and you will, you know, this, this thousand drugs is for breast cancer patients, but you know, if you have this and this, this mutations, and if it's stage four, then you take this combination. If it's that,

1:14:21yeah, you, you, you take this protocol. Um, and, um, that, that's, that's how AI is going to, of course, you know, if you get to digital twin, that, that will accelerate. Right. And that's, that's the next question is, you know, so let's, let's say we have the true personalized medicine and personalized cancer treatment, but you also need to know about side effects. Like, am I going to take this mRNA vaccine and my immune system's going to go crazy and start to inflame my heart and give me myocarditis or something? Right. So how do you also see this,

1:14:52the digital twin, which now has, you know, genomic information, all your proteins, metabolites, and everything in real time data, then it can also simulate, well, what's going to happen if we give this specific mRNA vaccine, cancer vaccine, or this small molecule to this person? Absolutely. I mean, you know, so, so you mentioned myocarditis, which by the way happened during a COVID pandemic, and that's why there was a lot of anti-vaccine sentiment, but people didn't appreciate

1:15:25that, you know, COVID viruses self-caused myocarditis. Yes, the vaccinated people, young people at one in 5,000 to one in 10,000 rate got myocarditis. It wasn't, it was mostly fatal, but the question should be asked, like, why is it that one out of 10,000 got myocarditis and the other ones didn't? Or in fact, we can reverse that question. You know, we get, we vaccinated everybody, but if you were a young person, your, your chance of dying from COVID was, let's say, one in 1,000,

1:15:58one in 10,000. So 999 people got, didn't have to be vaccinated. But to save that one person, we have to give that vaccine. Or I'll give another more general, you know, we give statins to anyone who has high cholesterol. So I think like one out of five, one out of 10 people truly benefit from that. High cholesterol doesn't automatically, doesn't mean you're going to get atherosclerosis, you need to have inflammation, this and that. But because we don't have the data, we cannot predict

1:16:29that. It's not personalized. Millions of people take statins and to save few thousand people. Yes, that's, that's, that's a good thing because you don't know. So AI will be able to do that. So we'll, we'll tell you, okay, not only we'll create the drug just for you, but also we'll say, okay, you don't have to take this, this medicine. You should take this. Or maybe you don't even need any, any treatment at all. Like you have an infectious disease or whatever, or maybe certain cancers,

1:17:03this will be enough. Like we give extra chemotherapy plus immunotherapy plus radiotherapy. Why are we doing that? Because we're not sure if one is going to be enough or not. And, and so that will dramatically reduce the, the, the side effect issue. You might still have some side effect, of course, but it's manageable. It will be manageable side effect. It's not going to kill you, for example. What about using AI to predict cancer a decade or years before it forms based on

1:17:38your proteins and metabolites and your biomarkers and maybe perhaps your genetics too, right? Like how do you see that we're talking about personalized cancer treatment, but what about being able to prevent cancer before it happens, you know, years before it happens? Yeah. Again, great question, because I think this is, this is so important that people don't think about very much. We say health care. No, we don't have health care. We have sick care, right? So we, we never take care of healthy

1:18:13people. Like you don't go to a doctor to say, oh, how healthy am, or just, just go to a doctor and say, can you check my immune system? You know, is it, is it healthy? Am I going to, am I going to get sick? Am I going to have cancer? They won't be able to answer that question. Only if you get sick, they will treat what the problem is. And so the preventative medicine is going to be so absolutely critical. I think not all, but most diseases can be prevented. Some are just bad luck. You know,

1:18:47it happens no matter what you do. If, even if you live the perfect life, you might still get certainty, but, but a lot of them were because of your genes and so on. A lot of them can be prevented. And I think AI is going to be amazing in that because it's already able to do that. There was a study from a UK biobank. UK has this amazing biobank with 500,000 people, lots of data sets, incredible data sets. And so, and this was actually done, I think more than a year ago with models that were a year or two years old, they took a lot of that data and they were able

1:19:24to predict about thousand diseases before they happen. Of course, this was kind of retroactive. So they knew what, what people were going to get based on their data that was collected years before, but they, I was telling you, okay, this patient is going to have this disease that, but not patient, normal, healthy people, they're going to get this and that. So that, to me, that was, that was amazing. And that's going to get better and better because there are, there are, there are always signs, like cancer doesn't just develop in days. It takes years. If we probably, most of us might have some

1:20:02cancer cells, you know, most of it controlled by immune system and so on. And it, you know, slowly grows, it has to have another mutation, another mutation, but, but there's probably some signs of that somewhere, you know, whether it's in your metabolism or this, you know, and AI, even if it's a hundred percent, we'll be able to say, okay, look, I think that, you know, if this is, if this is the lifestyle that you continue, your chances of getting this disease is now is 85%

1:20:32or whatever. Like I wear a glucose monitor. Um, I'm not diabetic, you know, uh, but I want to see every minute or every five minutes, what my sugar levels are in a continuum. Or if I eat something, you know, is it spiking? Is it coming down? Because I want to prevent insulin resistance. That's one of the worst things that can happen to you. If I, uh, if I don't do that, I won't know until I get diabetes. My, my insulin, if, if, if, if my sugar is constantly, uh, spiking and then,

1:21:06you know, insulin is just working too hard and harder that could continue for years, by the way, um, uh, that at some point it's going to break, right? Um, for some people, it might continue 50 years. Nothing happens. Some that might be five years, but that data set probably has that predictive value that plus my, my age, my genes, but whatever. So, uh, yeah, that, uh, I think, um, everyone's going to have their own, um, AI. I don't know how, what to call it, uh,

1:21:38health coach or something, uh, but, but it will, it will continuously analyze the data. Um, and, and hopefully we'll, it will be much easier to collect data because that, that's another issue. You know, we don't collect data. Like we know nothing about, uh, you know, there, there are more than thousand metabolites in our bloodstream. So we look at maybe, you know, 10 of them, 20 of them only if we get sick, not even for a checkup. So we have to have a continuous, um, like a glucose monitor. I want to see what my, you know, uh, proteins are changing. Hormones are changing,

1:22:13you know, in a reasonably continuous manner. Such a good point. And I'm so glad you brought up the UK biobank study. I remember, I think the model was like called Milton or something. And it was, it's a AstraZeneca owned, like developed it or something. And, and I remember looking at this study because like you mentioned the biobank data is huge data set and it's spanning many decades. And so I think they looked at, you know, like over 200 plasma proteins, you're talking about 10, we're talking about 200, right. And, and all the other data, right. And they were able to predict, and I think cancer

1:22:48and neurodegenerative disease were at the top of like 10 years before, and they were able to look at the people. So the AI predicted it based on, based on all this biometric data. And then they looked and said, Oh, yep. Those people actually did end up getting cancer and Alzheimer's disease. And it was very accurate. And, and to me, the exciting thing here is that you can intervene before it happens. You can make lifestyle changes, you can make dietary changes. I mean, these things matter, they do matter. And, and that is exciting, because then you don't even have to get to the drug

1:23:24part, which, you know, maybe you will. But if you can make these changes, if you know, hey, I'm on this trajectory to get cancer, I have all this inflammation, I have all these things happening, if I don't make a change now, then in 10 years, I might have a cancer. It's very motivating, you know, for someone. So it's very exciting, as well. And then having AI in there is just going to make it even even better. And then I want to get in, I want to get into age reversal. And before we get to that, you know, you, you've really been a pioneer in this, the field of AI being involved

1:24:03in biology. You know, you were talking to me about your, your blog, I don't know, was it 30 years? Biosingularity, yeah, 25 years ago, 2025 years ago. Yeah, so you have this blog, biosingularity predicting, can you can you talk a little bit about it? Yeah, sure. So, in fact, I got interested in AI early 90s. After I graduated medical school, you know, I was very interested in computers. When I was a teenager, the first computers had come out at the time. And you know, I was trying to code and, you know, just, I mean, I loved it. It was it

1:24:37was just so wonderful. But you know, I went to medicine, because I figured biology is much more complex. So I should first try to figure that out. But then immediately, I realized, and I'm sure you did too, you're a scientist as well. The biology is so incredibly complex. I said, well, I mean, you know, we don't have any, any chance of figuring this out, you know, because there's going to be so many, so many data sets. So that's when I first got interested in, in AI. Of course, at the time,

1:25:07you know, AI was, was very primitive. But fast forward, you know, one of the, one of the books that influenced me was from Ray Kurzweil. I'm sure a lot of people follow technology know him. He wrote this book, singularities is near. So, so he called a point of singularity where the computation or technology is advances exponentially, so much that you cannot even predict what will happen next day.

1:25:39I mean, because it's sort of like a self-training AI models. And he had these figures where he would plot the advances of AI, you know, say, you know, by 2029, it will be at the human brain level and, you know, will reach AGI. And, you know, it was just unbelievable. And most people thought that he was just talking crap or, you know, science fiction, you know, they didn't believe it. How could that happen? And so on. But, you know, I got, I got very excited. In fact, I have a signed copy from Ray for the book. And so being inspired from that, I started this

1:26:14block called biosingularity. So I said, okay, so, so computation is going exponential, but biology is sort of a computation as well. I mean, it's, it's based on information. And so, but it's just much more complex. So it should also expand exponentially. And if you, if you plot that curve, that, that means that by, you know, based on my calculations, 25 years ago, in fact, I wrote it on the, on the about page of the blog, by, by year 2035 or so, we should be able to treat all diseases.

1:26:49And by 2045 or so, that we should be able to completely reverse aging. In fact, by 2050s, we will get to a point where I call human 2.0, because at that point, we have a complete understanding of biology. Then we can truly engineer it, we can create new biological organisms, we can, you know, change our biology, our genome, reprogram it, and rewrite our immune system. Yeah, exactly. In many possible ways, because it's kind of a messed up if you if you think about it,

1:27:22like, you know, biology, we think is a miracle, but it's a, it's a bad kind of a legacy engineering, right? It's not, it's not a bad engineering, it's a legacy, because biological system finds something, it can't get rid of it, can't start from clean slate. So it builds on top of it. So you get regulation over regulation over regulation. And then of course, you know, with like immune system that I study, you know, you get lots of autoimmune diseases, immune system kills a lot of people, you know, even during like pandemics and things like that, or it doesn't recognize the cancer cell

1:27:53and things like that. So why, you know, we should be able to design like immune system 2.0, like clean slate, really greatly engineered immune system. Well, and I said, you know, by 204550, we'll get to that point. And actually, you know, again, at the time, it sounded really crazy to people. But now I feel like I was, I was too conservative, we'll probably get there. But but the key point is that I wrote specifically in the about, we will do this because of artificial intelligence, you know, I was just

1:28:26taking the plot that Ray plotted, you know, I said, Okay, by 2029, AI is going to be at that point, it will be good enough to apply to the biology. And that will allow us to solve diseases and then the aging. The fact that, you know, the timing was was pretty good. Again, even even a bit conservative. I feel great about it. That's why, you know, I'm all in on AI, like, wow, it's happening. It's really happening.

1:28:58So aging is very complex. And, you know, as you know, it's not one process. We've got these 12 hallmarks of biology, we now have 12, genomic instability, mitochondrial dysfunction, you know, cellular senescence, on and on, we've got there's 12 of them. And we know, organs are aging at different rates, they're, they reach their peak at different rates, and they age at different rates, and everything is interacting in a very complex way. What do you see as the bottleneck

1:29:32for understanding the aging process and also reversing it?

1:29:40I mean, more so than the bottleneck. This is the way we have to think of aging.

1:29:48Biology actually is programmed to prevent aging, right? So it's not like, it's not like a car, in a way, because once you make a car, you have to constantly bring it to a repair shop, or you have to repaint it. Biology does that internally. If it didn't, we would age immediately. Like there's a disease called progeria, these children get aged, by the age of seven, eight,

1:30:18they become like an 89 year old, because of single point mutation in one of their genes, because they lose their ability to repair, whether it's the DNA repair, whether it's getting rid of the old cells, or cleaning up the tissues, and then regenerating like stem cells, creating new cells. So this program continues for sometimes decades, otherwise, we wouldn't survive. For some animals, for some organisms, it's only a couple of years. For us, it's about, you know, maybe 50, 100 years.

1:30:50For some veils, it's hundreds of years. So, so, you know, same biology, it's just that one of them decided that, you know, I can keep a veil, or, you know, whatever, some animals, you know, older, longer, because they don't, they're not getting hunted, or they can reproduce later, and so on. So what happens in the biological system is that somehow, this program breaks down, and you start to lose what's called the resilience, right? So when you are age 30 or 40, you're,

1:31:24you're resilient, you can tolerate much more damage than someone who's 70 years old, 80 years old, because your, your, your systems are, you know, even if you get wounded, or if you get sick, you can recover easier. But that, that sort of, that resilience is lost. And that the reason why it's lost that there is a sort of an information loss, because the biological system has a certain information that it knows when certain genes should be turned on, when things should be regenerated,

1:32:02when it needs to be like your skin, you know, why you get wrinkles, because your cells stop making collagen, and then all kinds of crap accumulates under your skin. And then, you know, the guy, the guys who are like the macrophages, or whatever, was supposed to clean there, they don't do their job. There's some sort of a breakdown in information or communication, or, you know, intracellular communication is one of the hallmarks of aging. And then, of course, why that happens is, is that 12 hallmarks is, is the reason, many reasons, you know, for example, the bacteria in your gut

1:32:37is, is, is a reason. So, so these bacteria produce all kinds of metabolites that help your immune system to constantly regenerate, keep it in optimal shape. If that changes, then, you know, your metabolism is changing, your glucose levels, your mitochondrial mutations, and so on and so forth. So all of these things accumulate, you know, epigenetic changes in DNA mutations, and somehow, the, the biology forgets, oh, well, what am I supposed to do? Like, how, how am I dealing with

1:33:08that? Also, because, because when a damage happens, it's harder to fix a damage than prevent it, right? So if, if you're continuously taking care of your car or your house, the likelihood of, you know, breaking down is much less than if you wait until like, okay, nothing works. Yes, you can reverse it, it's going to take a lot more effort. And so I think what will happen is that for a younger

1:33:40individuals in the next decade or so, they're, for them, it's not just, it's not going to be reversal, it's going to be prevention of the aging process, it's going to be maintaining that process, the resilience decades more. So we will come to a point where if you're 20, 30, whatever years old, you won't age anymore, because it's going to be constant reversal. But people who have already aged,

1:34:11you know, let's say you're 80 years old, 90 years old, then we're going to have to reverse that process. That's, that's a more difficult, we'll be able to do it, definitely we'll be able to do it. But it will require a lots of engineering approaches, because you need to fix most of those hallmarks. If you're younger, you prevent those hallmarks from happening, you maintain the information much, much longer. Both of those will happen. We just need to figure out what that information is

1:34:48being lost. And we put it back. Do you think so let's first talk about preventing the aging if you're a younger person, because it's easier to do, always prevent. If, if, if you have a person, you know, is 20 or 30 years old, do you think that the approach would be finding, first of all, do we even know all the repair processes that are we have discovered, we have what we know, right? Yeah, but again, we have a lot to discover, we have, we probably have a lot to discover. And so

1:35:21like, do you think there's going to be a discovery where we figure out like, you know, we know things like autophagy, stem cell depletion, you know, all these stress response genes, like antioxidant, like all these things, DNA repair, mitochondrial, the way mitochondrial repair itself, right? Are we going to be enhancing or like tuning these up so that they keep working at their prime continually? Or do you think we're going to have, again, this like information where we why, why are those things going down? Are we going to just then go to the information of it, epigenetics, perhaps? And is it going to be

1:35:58more targeted towards those genes? Or are we going to have more of this, you know, we'll get into this cellular reprogramming and partial reprogramming. But I'm curious, like how you see AI coming into that process? Like, I guess, we don't know, that's the part of the problem. But then we have to figure out how to give these, you know, treatments to people, right? That's another part of the equation. So I mean, I think, you know, the ones that you mentioned about sort of the lifestyle changes, and they, of course, help a lot, but they only slow down the aging process. There's, I don't think

1:36:33there's anything that reverses that process, there might be some sort of local reversal for a temporary period of time, maybe, but it's still kind of trying to, you know, hope that things won't go bad a little bit longer. Like, for example, some people can live to 200, others only to 60, right? So there's something good about those who live to and in fact, there are super centenarians who can make it to 110 years old, very, very few people. But I think it's mostly genetics. I mean,

1:37:06their lifestyle might have helped a little bit. Something about their biology is able to maintain that information much, much longer that program. So we have to get to the core, what what are the things that are disrupting that information loss? And yeah, it's, of course, you have to focus on the on the genome, because that's, that's sort of the blueprint. It's not just that it's sort of what affects you afterwards, you know, that your your microbiome, your metabolites, you know, how those

1:37:39things are changing, whether they're accelerating or reversing, you know, like, it has to be kind of an engineering approach as well. Like, you know, the skin aging is is a very different problem than immune aging, than the brain aging, right? So your skin cells are constantly renewing. So all you have to do is to have sort of the programmed stem cells to go in there, clean the environment, senescent cells and get it get it regenerated and produce collagen whatnot. But the brain is not like that, right? So you

1:38:11don't you don't want to regenerate your your neurons, you will lose your identity. So they have to be dealt in a different different way. Some of it will be, I think, for the younger population. It seems like, you know, redesigning certain biology would be sounds radical, but it would be more foolproof. Right? So what if we could change the genome through genetic engineering, like, we add certain genes, or we change certain genes such that the DNA damage is checked, you know, much, much longer, you know, because there are in fact,

1:38:49there's certain animals who have better DNA damage proteins, they kind of evolved to do that. Like elephants rarely get cancer, right? Because they have this gene called p53, you have multiple copies of that p53 is kind of like the guardian of the genome, you know, prevents the genome from getting too much mutations and prevents cancer. So somehow elephants have three, I don't know how many copies, but they get very rarely cancer. Naked mole rats, you probably know that very well. You know, they're

1:39:22they're like rats, they live underground, but normal rats live a couple of years, and these guys live 30, 40 years. So it turns out they have some mutation in some immune gene called C gas that's also involved in immune optimization and DNA repair, just like, you know, one or two genes make a huge difference. So can we engineer humans to block that degradation of information? For those who have

1:39:53already had the damage, then we're gonna have to think about repairing that, reversing it, and then maintaining it. That's, that's going to be a bit more challenging. But we'll get to that too. What do you think about so the gene, going to gene therapy, there's obviously gene editing, gene therapy. And, and right now, we only know what we know, right? Again, like, with these longevity genes we know about. But do you think that that AI is going to be able to help us analyze the human

1:40:27genome? And I don't know what other data sets it will need, but we'll give it everything and help us figure out, well, actually, there's interaction of these genes together. And when they're, you know, like all these combinations, is that something that you think is going to happen? We'll actually figure out there's a lot more to this equation than we originally knew. Yeah, that that's the critical problem, because we know what all the genes are in the genome, like we have, we have decoded completely. And we pretty much know their functions, most of them.

1:40:59Even if you don't know every single gene involved in aging, we know a lot of them. The problem is that different gene, first of all, can create different proteins, you know, there's all the splicing that happens and so on. But but even without that, in a different context, so if you hide, the same protein can kill a cell or cause a survival, like an immune system, we have these receptors called TNF receptors, or whatever, they can, they can have a survival signal, or a death signal, suicide

1:41:32signal, depending on the context of the of the cell. So that is very, very critical that how, as you pointed out, how these genes and proteins, in a network fashion, in a sort of a topological network, you know, what do they do? Like, if I interfere, like these, probably we'll talk about that these things called Yamanaka factors, where you can, you can generate a stem cell from a normal cell, right? So like, complete regeneration. But but the the problem is that they can also cause

1:42:08cancer, because they only need to be active in certain time, if they're active all the time, they can cause teratomas and things like that. So that that part is so complex that we absolutely going to need AI to simulate that for us. If I have this gene, in the context of all the other things at certain age, with these epigenetic programs, plus all the metabolites, and so on, because those are constantly signaling the cell and, you know, doing, letting the proteins do something and so on,

1:42:44what would happen if I interfere with that particular gene? Or how can I improve that? If you have a, because you have to consider the other genome to like your gene therapy might be very different than somebody else's, because you might have some great genes that are synergistic with that other person might have not so great genes, if even if you try to improve it, that would actually work worse, or it wouldn't, it wouldn't help. So it's just a matter of complexity, there's so

1:43:15much information that the AI has to not only put that together, but have sort of almost a temporal simulation of the model. Like, that's a very important point. Because right now, the models are kind of static, they they have a good understanding, but they don't know what would happen if a cell comes next to a tumor, just two minutes earlier, the the cell next to it, what that context affects, there's a behavioral issue. It's the same problem with the robotics, right? So

1:43:51kind of the physical intelligence or the biological intelligence, once those models are evolved with a lot of data, I think we'll, we will be able to simulate this and AI will be able to decide, this is the gene therapy you should get. So you need a new copy of immune system, but let me design it for you. It's so exciting, because not only are we talking about, you know, extending our lifespan and curing disease, but we're talking about like, getting rid of side effects, in a way. I mean,

1:44:23you know, people all respond to different foods and treatments and everything differently, right? Like, some people have a terrible response to perhaps maybe a vaccine, and others don't. And so it's really exciting to think about that. Which I, by the way, call human 2.0, and maybe we'll get to human 3.0, which will happen at this biosingularity moment. What that means is that, you know, we kind of re-engineer ourselves. I always think about, like most scientists or most doctors think, like,

1:44:58what's wrong with this person or patient? I always think the opposite. There are certain people, I'm saying, what's right about them? Like, this person has smoked for 50 years, never got a lung cancer, or, you know, had a terrible diet, or whatever. This one lived to be 110 for, you know, whatever reason. And so what is good about those people? Why can't we take what's good about all of those people and then re-engineer those that are not so lucky to be born with what's so good? And then,

1:45:32you know, even make it better. So that's the human 2.0. Right. I mean, that's exciting to me as well, right? I mean, we do know, like you said, we can live, humans are capable right now of living to be, is the whole, I think the oldest was like 121, maybe? 123, that's a French woman, Clement. I mean, the fact that right now, in 2026, we know that humans can at least live to be 123 is exciting. At least 115. I mean, 115, 116, that's considered sort of the current limit. But,

1:46:05you know, only 300 people in the world are 110 and older. Why is that? Why not the rest of the 8 billion? Right. Yeah. It's fascinating. And I'm so excited for, you know, having this super computing power to help us figure that out. What did you think when, you know, the Yamanaka factors were discovered by Shinya Yamanaka and all of a sudden you could take this old cell and completely revert it to, you know, essentially induced, you know, pluripotent stem cell? Do you remember, like,

1:46:38is that, was that something, did aging come into your mind at that point where you're thinking, well, that's the youngest almost you could get? I mean. Yeah. Of course. In fact, at the time I was, part of some aging groups, I think, like, an hour after the paper was published, I was, you know, typing there, you know, like, this is, this is it. This is amazing. So, I should say that there were two moments for me that, that I thought that aging was, was going to be reversible or curable,

1:47:12however you call it, kind of like the chat GPT moment of biology. The first moment was the, the sheep that's called Dolly, you probably know, it was the first cloned ship, yeah, a sheep. It was 1996, seven or something like that. I can't remember exactly, but it was in 90s. And so, basically, the scientist took a cell from, you know, from one sheep and then recreate exact copy

1:47:46of that sheep, you know, by, by cloning it. It was, it was at the embryo level, but it was sort of like exact copy of it. So, that means that there was enough information that you could just, like, recreate the same person again and again and again, right? And then the second, of course, the, the Yamanaka factors in 2016, I think. And that was the moment that, that we knew that we could completely erase the, sort of the age of the cell on the cellular level, and then bring it

1:48:24back to a pluripotent stem cell level, and then use that to recreate the whole biological organism. So, so it means that we have unlimited supply of regenerative capacity. Like, it's, there is, there's, there's no limit to it. In fact, we already know that, like, so our, our DNA just keeps, for billions of years, it keeps going on. And the fact that you could do that in the lab, and you could, you could generate it, was, was, was amazing. But of course, the, the problem was,

1:48:56okay, so then how do you apply that? And in fact, I think there was just a recent study that started in Japan using the Yamanaka factors in, in clinical trials, because, you know, it was not a very controlled system. Like, you didn't know if those cells would develop tumors, you know, in mice, they, they did some of them tumor tumors, you know, whether you can control them, or importantly, I think there's got to be a trial started by David Sinclair soon. Can we do like partial reprogramming?

1:49:31Because most of the time, you don't want the pluripotent cell, all right, you just want your skin cells to go early enough to their sort of more stem like level. Like I work in immune system. And for us, I can divide like, immune cells into naive memory and effector and differentiated. So the naive cells are kind of the young guys, they have huge potential to expand and, and make memory and, and affect the population. And the other ones constantly die and get older. Can we actually

1:50:04revert the cells towards the naive? And I actually spent a long time trying to do that. Um, so maybe this partial programming will, will, will enable that and, and that's, that will be revolutionary. Because then you can, if you can also deliver those, then you can make most of your old skin cells turn into a younger version. I think the trial is going to be for I with David Sinclair. Um, yeah, so, uh, but again, it's, it's, it's, um, the, these, these things showed us that,

1:50:39uh, we can reverse aging, right? But when people say, oh, that's impossible, like this is this, you can't, you can't reverse aging, like, you know, this entropy, whatever. Um, but we, we, we do it in the lab all the time. Uh, why not do it in a, in a total organism level? So with this partial cellular reprogramming, as, um, as you mentioned, you know, you're, you're basically taking an old cell and putting these four different proteins, I think they can do it with fewer now, but putting them on for a shorter period of time on the cell and that it's

1:51:12changing the, the epigenetic program. And in a way that it's still, the cell still keeps its identity, it doesn't become a stem cell, but it seems to be more youthful. Um, I know there's been some work and I haven't followed all this literature since I, the first, you know, some of the first studies that came out, but I think it was like Juan Carlos, um, he, he's now, I think at Altos labs, but he at the time was at the Salk Institute and, um, he had done this in, in mice. I think they were even maybe perhaps progeria mice or some sort of accelerated aging model. And there was some

1:51:47reversal of, you know, certain organs seem to be rejuvenated in a sense. And, um, the, the life expectancy was extended in those animals. But what's interesting is that not all of the 12 hallmarks of aging go away. Yeah. Right. And so you would hope that you would reverse aging totally, but there's genomic, you know, somatic mutations are still there. I think telomere don't get mitochondria. So do you think, first of all, I don't, I I'd love to understand why that is.

1:52:20So what is it if you're, if you're essentially, you know, wiping out the epigenetic current epigenetic