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

31:02You 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 TOLT experiment. Let's imagine that we 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 have an IQ of 105, you get imprisoned immediately. So you are not allowed to participate in society or you get killed or whatever.

31:51The society has decided intelligence 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 how we should view AI. And the other point is that about this sort of AI is going to take over and is going to replace us. I see it exactly the opposite because AI is an incredible enabler. It gives you superpowers. Even now, I feel like I have superpowers.

32:40I've never been this busy in my life. 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. My mom was 86 years old. She told me that ChatGPT changed her life. She's energized. She doesn't worry as much about her health. And it's just been an incredible impact. And this is going to accelerate. And at some point, we will sort of merge with the AI in a way that we will have direct interaction with AI through Neuralink type of brain interfaces. is so we'll have the sort of the intelligence of AI in our own brain, not only directly, but also indirectly by sort of engineering our biological system.

33:36So why shouldn't everybody have an intelligence of Einstein or even higher, right? So the difference between an Einstein and a normal person with a normal IQ is probably few gene single point mutations. So if you can engineer that, if AI can teach us how to do that, then we're also going much, much higher. So as long as we keep the agency, I think that's the only thing that we have to really protect, that 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 the moment you're born, you're going to die.

34:34So you're destined to die. And now AI is giving us this opportunity to 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 thousands of years. So that's true saving lives. That's the potential. And the risk is, again, I think the key risk is humans. Humans misusing AI. That's what we have to sort of maybe train or align AI. Don't look at the bad humans. You can judge the better world for us. So, of course, I might be wrong, But I'm pretty sure I'm going to be right.

35:23Dr. Rhonda Patrick:I agree with the statement of we have to watch out for the humans, for sure. Because you're right. They can and have in the past been the biggest threat to humanity. So there was a couple of things that you mentioned when you were talking about ASI and this ability to self-learn. And you're even talking about 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.

36:03Dr. Rhonda Patrick: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? And that is a sort of intuition, this biological intuition. And so do you think, first of all, do you think that 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 a great question. In fact, I see that intuition maybe sort of the last mile or the top 10 % or 10 % of the solution. Because 90 % AI models they are able to come up with.

36:55Because it's knowledge based also in humans. 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 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 know how you know. The same thing in the lab, like in fact, 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.

37:39And I've never lost a bet. And they stopped betting against me, even though it might look counterintuitive. Oh, no, that's never going to work. Somehow I know. How do I know? Because, you know, I've been working in the lab for 30 plus years and you acquire certain things that are not in the literature or, you know, You can't really read a textbook and learn it. You only do it by practicing it. So the models up to, I would say, 5.5 until recently were great at that 90 % level, especially after GPT-5 Pro came out. So I would ask it to, for example, I would give it an experiment that we have already done.

38:24It's a very complex experiment. It 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 outcome of the experiment. And they would do, you know, not just GPT-5, but several other models as well. They would come up with 90%, 80 to 90 % correctly. 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 would have predicted was still somewhat lacking. I think GPT 5.5 crossed that threshold. So I repeated that with the 5.5 PRO model.

39:08Because I always say PRO, it'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 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. so 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 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.

40:19That is mysterious, but I live through it now.

40:25Dr. Rhonda Patrick: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 too and the 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 huge data sets, millions of data points.

40:58And then I also said, okay, don't just analyze it. Write a huge report, 30, 40 page, whatever length. and then come up with a lot of insights about this data, what questions to ask, and what do we learn, the mechanism. It was an immunological data set, sequence and genes and proteins and all that. And so that one, I think 112 minutes, I remember that. And it came up with this 40-page report, which I was just, this is unbelievable. You know, the analysis part, the previous models were able to do as well. You know, they say, okay, well, there are these type of genes and this type of protein, so it means this and that.

41:46You know, it derives from that information. But to come up with an insight, what that could mean or what would be the next question to ask, that's a very, very high level of reasoning. and so yeah it was it was worthwhile two hours for sure.

42:06Dr. Rhonda Patrick: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 this 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 um it's very exciting to think about how ai is going to change research and and medicine and that's something you know you 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.

42:52Dr. Rhonda Patrick: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. And 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.

43:34Yeah. In fact, I said after O1 model came out, I 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 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 sort of real time data. And before 01, it was great in sort of reaching to the literature, you know, like the physician might lack certain knowledge, so it knows what was published recently and things like that. But it was not at the reasoning level.

44:22So one 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 O1 was able to get to that point. And at that point, I said, right now, it's unethical for physicians not to use AI anymore. I didn't say malpractice yet, but 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 clinical specialities coming together, and you don't have that capability, you live in a village or something.

45:29but now I think I feel that it is truly going to be considered malpractice in 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 in that field. It's not just a family physician. Let's say you have a very complex cancer, 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?

46:25So, if that was the case, we wouldn't have millions of misdiagnoses and mistreatments in the U.S. alone every year. I think there's something like 12 million misdiagnoses. I think 700 ,000 people suffer from it, die from it, from misdiagnosis. misdiagnosed. Some of them is totally innocent. Any doctor could have missed it. But now AI wouldn't miss that. So even a specialist might make a mistake or misdiagnose or mistreat because they lack certain things that the model doesn't have. So I mean, imagine that you refuse to use a MRI machine or CT machine because you say, well, you know, that's too much technology.

47:19I'm just going to, you know, just do an X-ray because that's enough for me. And you miss a tumor. The eye 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, that person is going to die if you don't, if you don't know So to me, that becomes a malpractice because the technology is at that level now. 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.

48:07I mean, if he could just reduce the misdiagnosis and, again, bring every doctor to super doctor level, I think that would be a really good thing.

48:18Dr. Rhonda Patrick: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 to see things like you mentioned cancer is that better than a radiologist can is that some is that like based on you know what kind of data is imported 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.

49:12I 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 early tested in 2024, September, it came out. And they found that one 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, if you have a flu or some bacterial infection, you know what to do.

50:00You 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, the patients, we gave this treatment, it's doing well, the blood pressure is down, but, you know, has this symptom, that symptom. So what should we do? Change the dose of the drug or add this or remove that drug and give another antibiotic.

50:41Like there's a constant process there. And that's not always that constant because, you know, people don't go to doctor every day, right? So you get a prescription, you see something works, and then you go back. And so what if there'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 a drug, chemotherapy works, and then the cancer comes back again, right? So why is that? Because mutations are accumulating.

51:27So can we catch that earlier? Can we change those decisions? Can we make 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 AI. And I think it's going to have tremendous, tremendous impact on healthcare.

51:52Dr. Rhonda Patrick: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 use 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 think that people and physicians that are listening want to know what models do they use.

52:40Dr. Rhonda Patrick:We definitely are talking about, if we're talking about OpenAI, it's got to be the pro, right? It's got to be the reasoning model. But, I mean, what about Claude? What about Gemini? Yeah. 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 used one 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.

53:27So, oh, yeah, I used it and it hallucinated a lot. Even, you know, O1 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, I'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.

54:09And 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 self-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 out five years ago, 10 years ago.

54:54You need to know what was approved last month, and you need to update 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 uh their their practice and of course my recommendations always use the latest top model you can use uh right now it's gpt 5.5 uh in fact uh i would always use for 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 model is also getting better, but it needs to reason.

55:42It needs to think. And especially if you're putting in lots of patient data and analyzing that you definitely need to pro model. And then there are these companies like Open Evidence. And you know, I think most doctors are starting to use that Open Evidence, basically, I think, applies the latest model, somehow it updates 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 4.7, the whatever the sort of the the hardness model is going to pick the best one for medicine and apply it there.

56:22And of course, hospitals should implement AI just like big tech companies are. There's enterprise level of AI that can be more secure, protect the patient data. So it should be like 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 daily life, like for my email, automatically my agents go and check my email and they tell me what's important.

57:06So I don't have to go through hundreds of emails. So, you know, this is waiting for you. You have a podcast with Rhonda today, so you better be prepared for that. But 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.

57:29Dr. Rhonda Patrick:Have you, I've noticed like some of the companies that I've corresponded with or interacted with, it seems like they use Claude a lot. I mean, I don't know if you've experimented with that, but I'm kind of curious why that, you know, certain model versus like, 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, but it's going so fast and better now. But like, what's the difference between, you know, for example, Claude and GPT 5.5 Pro?

58:11For certain things, there is no more difference because the intelligence 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, you know, it 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. The intuition. The intuition and really kind of a deep understanding.

59:06But if I'm going to diagnose 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 some reason people prefer cloud is that maybe it's more pleasant to interact with, kind of more human-like. I think GPT models are starting to get there, but still there's something about cloud that people enjoy interacting with it. It's really a matter of taste. Is it more personable? I think it used to be more personable. And so it doesn't really matter. I mean, I think they're really, they're all super top levels, unless you're doing like a research or a very, very complex problem.

1:00:00You know, for example, we did a test with a colleague of mine on skin disease with GPT-5 Pro model. You know, it was able to diagnose a skin disease that my friends couldn't really diagnose just based on a photo 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, that could go anywhere. The GPT-PRO model was able to cross that threshold. So those kind of cases, you really need that very high level. Like, you know, you don't go to a professor at Harvard for any reason, right? So it has to be very specialized disease that other doctors couldn't diagnose or something like that.

1:00:55So that's how I view it.

1:00:58Dr. Rhonda Patrick:We just, there was just news yesterday from OpenAI of GPT-Rosalyn, 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. I'm wondering what you think in terms like the future of 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 the subsequent ones that come out after it are going to be the key to unlocking, you know, medicine breakthroughs and biology breakthroughs. My preference would always be the generalized models because again, you know, going back to AGI, AGI.

1:01:48So if a model is, has, you know, of course there are some utilities of models that are only trained on, you know, like the EKGs or RNA sequencing or something like that. and they they'll be very very good at that like the the best chess player ai model or um 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 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 very useful in fact you know i gave the example EKGs, most generalized models were not terribly graded.

1:02:36For some reason, you know, the EKG images were not, they were not very good at diagnosing what it was showing. 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. But I think, you know, if we can train the generalized model or fine tune it or over train it, I don't know how to say it, then they will be better than specialized models all the time. 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, excuse me, analyze the EKG, the electrocardiogram, your heart beats in the context of all the other biology.

1:03:35So that's very enriching knowledge. But 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 models, they don't have access to that. You might have some data, you know, locked in certain because of regulatory reasons, whatever. So you can take a big model. In fact, you may not even need the closed models. You can even take some of the open-source models, which are not getting very good. But you can train them on that. And they also have the generalized knowledge.

1:04:21And combined with that, they'll probably do better.

1:04:25Dr. Rhonda Patrick:So I want to talk about there's treating disease, there's curing disease, and then there's reversing aging. So let's start with curing disease, treating diseases, curing diseases. Because, you know, obviously we do die of age-related diseases, cardiovascular disease being the number one killer in most developed countries. We have cancer. That's a really big one. 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 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?

1:05:14Dr. Rhonda Patrick:As you and I both know, it is definitely not one disease. It's hundreds of diseases. I'm curious on, first of all, you know, we still don't have a cure for cancer. I mean, we've made a lot of progress, right, in 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 important thing to clarify is that cancer is not one disease. It's probably 100 different diseases that have probably hundreds of sub-sub-diseases or sub-types, if you like. And in fact, certain cancers are 100 % curable or 95 % curable.

1:06:02You know, like some of the childhood leukemias, which were completely fatal, you know, a couple of decades ago, are now, you know, 90 % or close to 100 % curable. If you catch certain cancers early enough, again, 100 % cure rates almost. So because it's a very different set of diseases, the cancer of pancreas is very different than lung cancer or breast cancer. or there are some cancers that are so slow, like if you get 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, because by the time you die of aging, you know, that cancer is not going to kill you.

1:06:56Aging is going to kill you first or, you know, there's certain prostate cancers at a certain age. So 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 cancer cells are part of us, right? So if you're infected with a 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, not always successfully, but most of the 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 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 hair has to, hair cells have to divide.

1:08:06So you block them because the cancer cell is also dividing. 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 recently because of what we call immunotherapy. The question was, 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 they 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 breast cell you know like epithelial cell, whatever.

1:08:55And 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 immunotherapy, I think, is more powerful now than chemotherapy chemotherapy and radiotherapy put together. I mean, they still have a role. 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.

1:09:38But if I know this single mutation that's happening on, you know, whatever, EGF receptor 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, in fact, people call them smart drugs, and they're extremely effective, right? So if you have that particular mutation, you're 1 % of the lung cancer patients, you get treated with that drug, you get almost 100 % cure rate. But again, you know, we can make this even much better. So for example, immune system can be engineered something that we work on in the lab to recognize, like literally engineer, we take the cells out, we train them, we put genes into them, 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 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 that even if it mutates You know the immune system will still recognize it and find that few cells that are hiding somewhere and destroy it And that's showing incredible results.

1:11:08So the mRNA vaccines, which I think is going to be revolutionary, is on that basis, right? So 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, it won't recognize mine because the cancer has different mutations. If I take those mutations and synthesize what's called 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.

1:11:54And that becomes extraordinarily powerful because now you're directing your immune system to an internal threat just in you. And let's say the cancer required different mutations, you can create another mRNA vaccine and then train the immune system to that as well. So, you know, I think those are the difficult parts, but we see the light at the end of the tunnel. Cancer is going to be 100 % curable, probably less than a decade. How is AI going to make that happen? Yeah. So, in fact, it's already making that happen. You've probably heard of this story from Australia. This computer scientist had chat GPT and some other AI models to develop an mRNA vaccine for his dog his dog had 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 it was able to apply it in three months um probably could have been shorter if there was regulations and the tumor started to regress and the dog was alive when it was supposed to die.

1:13:21So, I mean, that's a very 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 treatment treatments for just the type of a lung cancer. Some will be mRNA, some will be small molecule targeting that. 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 will screen millions and millions of compounds. And so we'll get to a point where we'll have hundreds of new drugs coming out every month maybe, you know, and, you know, this thousand drugs is for breast cancer patients.

1:14:14But, you know, if you have this and this mutations, and if it's stage four, then you take this combination. If it's that, you take this protocol. And that's how AI is going to, of course, you know, if you get the digital twin, that will accelerate.

1:14:30Dr. Rhonda Patrick:Right. And that's the next question is, you know, So 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 is going to go crazy and start to inflame my heart and give me myocarditis or something, right? So how do you also see this, the 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?

1:15:11Absolutely. I mean, you know, 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 that, you know, COVID viruses self-caused myocarditis. Yes, the vaccinated people, young people at 1 in 5 ,000 to 1 in 10 ,000 rate got myocarditis. 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 vaccinated everybody. But if you were a young person, your chance of dying from COVID was, let's say, one in 1 ,000 and one in 10 ,000.

1:15:59So 999 people 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 that. It's not personalized. Millions of people take statins to save a few thousand people. yes that's that's a good thing because you don't know um so ai will be able to do that so we'll we'll tell you okay not only um 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, this will be enough.

1:17:04Like 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 so that will dramatically reduce the side effect issue. You might still have some side effect, of course, but it will be manageable side effect. It's not going to kill you, for example.

1:17:27Dr. Rhonda Patrick:What about using AI to predict cancer a decade or years before it forms based on your 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 so important that people don't think about very much. We say health care. Now, we don't have health care. We have sick care, right? So we never take care of healthy people. 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 gonna am i gonna get sick am i gonna have cancer they won't be able to answer that question only if you get sick they will treat what the problem is um and so the preventative medicine is going to be so absolutely critical old.

1:18:38I think not all, but most diseases can be prevented. Some are just bad luck. It happens no matter what you do. Even if you live the perfect life, you might still get certain disease. But a lot of them are 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 to predict about 1000 diseases before they happen.

1:19:30Of course, this was kind of retroactive, so they knew what people were gonna get based on their data that was collected years before. But I was telling you, okay, this patient's gonna have this disease, that, but not patient, normal, healthy people, they're gonna get this and that. So to me, that was amazing. And that's gonna get better and better because there are always signs. Like cancer doesn't just develop in days. It takes years. If we probably, most of us might have some cancer 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 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 100%, 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 or whatever like i wear a glucose monitor um i'm not diabetic you know uh but i 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.

1:20:52That's one of the worst things that can happen to you. If I don't do that, I won't know until I get diabetes. My insulin, if my sugar is constantly spiking and then, you know, insulin is just working too hard and harder, that could continue for years, by the way. At some point, it's going to break, right? 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 i think um everyone's gonna have their own uh ai i don't know what to call it uh health coach or something uh but but it will it will continuously analyze the data um and and hopefully will it will be much easier to collect data because that's another issue.

1:21:50You know, we don't collect data. Like we know nothing about, you know, there are more than 1 ,000 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, like a glucose monitor. I want to see what my, you know, proteins are changing,

1:22:12Dr. Rhonda Patrick:hormones are changing, you 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 an AstraZeneca-owned, like developed it or something. And I remember looking at this study because like you mentioned, the Biobank data is a 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? Oh, yeah. And all the other data. Right. And they were able to predict, and I think cancer and neurodegenerative disease were at the top of like 10 years before.

1:22:53Dr. Rhonda Patrick:And they were able to look at the people. So the AI predicted it 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 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 that is exciting because then you don't even have to get to the drug part, 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.

1:23:35Dr. Rhonda Patrick: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 having AI in there is just going to make it even better. And I want to get into age reversal. And before we get to that, you've really been a pioneer in the field of AI being involved in biology. You were talking to me about your blog. I don't know. Was it 30 years ago? Biosingularity, yeah. 25 years ago. 20, 25 years ago. Yeah. Yeah. So you have this blog, Biosingularity Predicting. 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.

1:24:26I was very interested in computers when I was a teenager. The first computers had come out at the time and I was trying to code and just, I mean, I loved it. It was just so wonderful. But 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, that biology is so incredibly complex. I said, well, I mean, we don't have any chance of figuring this out because there's going to be so many, so many data sets. So that's when I first got interested in AI. Of course, at the time, AI was very primitive.

1:25:10But fast forward, you know, 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, Singularity is Near. So he called a point of singularity where the computation or technology advances exponentially so much that you cannot even predict what will happen next day. I 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.

1:25:57And 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 very excited. In fact, I have a signed copy for the book. And so being inspired from that, I started this blog called Biosingularity. So I said, okay, so computation is going exponential, but biology is sort of a computation as well. I mean, it's based on information, but it's just much more complex. So it should also expand exponentially. And if you plot that curve, that means that by, you know, based on my calculations 25 years ago, in fact, I wrote it on the about page of the blog, 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 change our biology, our genome, reprogram it, rewrite our immune system. Yeah, exactly. In many possible ways, because it's kind of a messed up if you think about it. Like, you know, biology, we think is a miracle, but it's a bad kind of a legacy engineering, right? It's not a bad engineering. It's a legacy because biological system finds something.

1:27:33It can't get rid of it. 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 otimian 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 and things like that. So we should be able to design like immune system 2.0, like clean slate, really greatly engineered immune system. And I said, by 2045, 50, we'll get to that point. And actually, again, at the time it sounded really crazy to people, but now I feel like I was too conservative.

1:28:16We'll probably get there. But the key point is that I wrote specifically about we will do this because of artificial intelligence. You know, I was just taking the plot that Ray plotted. You know, I said, OK, 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 aging. The fact that, you know, the timing was pretty good. But again, even a bit conservative, I feel great about it. That's why I'm all in on AI. Like, wow, it's happening. It's really happening. So aging is very complex.

1:29:02Dr. Rhonda Patrick:And as you know, it's not one process. We've got these 12 hallmarks of biology. We now have 12. Genomic instability, mitochondrial dysfunction, cellular senescence, on and on. We've got, there's 12 of them. And we know organs are aging at different rates. 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 for understanding the aging process and also reversing it? I mean, more so than the bottleneck. this is the way we have to think of aging. Biology actually is programmed to prevent aging, right?

1:29:54So 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, they 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.

1:30:40Otherwise, 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. For some veils, it's hundreds of years. 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 resilient, you can tolerate much more damage than someone who's 70 years old, 80 years old, because your systems are, you know, even if you get wounded or if you get sick, you can recover easier.

1:31:43But that resilience is lost. And the reason why it's lost is 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, when 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 guys who 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.

1:32:26And then, of course, why that happens is that 12 hallmarks is the reason, many reasons. For example, the bacteria in your gut is a reason. 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 biology forgets, well, what am I supposed to do? Like, how am I dealing with that? Also, 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 is 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 uh what will happen is that for a younger individuals in the next decade or so, for them, it's not going to be reversal.

1:33:50It'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 are 20, 30, whatever years old, you won't age anymore because it's going to be constant reversal. But people who have already aged, let's say you're eight years old, nine years old, then we're going to have to reverse that process. That's a more difficult. We'll be able to do it. Definitely we'll be able to do it. But it will require lots of engineering approaches because you need to fix most of those hallmarks. If you're younger, you prevent those hallmarks from happening.

1:34:36You maintain the information much, much longer. Both of those will happen. We just need to figure out what that information is being lost, and we put it back.

1:34:51Dr. Rhonda Patrick: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 you have a person, you know, who's 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? But again, we probably have a lot to discover. And so like, 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 um 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 the epigenetics perhaps um and is it going to be more targeted towards those genes?

1:36:00Dr. Rhonda Patrick: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 there's anything that reverses that process.

1:36:36There 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 couldn't make it to 110 years old. Very, very few people, but I think it's mostly genetics. I mean, their 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.

1:37:17What are the things that are disrupting that information loss? and yeah, of course, you have to focus on the genome because that's sort of the blueprint. It's not just that, it's sort of what affects you afterwards, your microbiome, your metabolites, how those things are changing, whether accelerating or reversing. It has to be kind of an engineering approach as well. The skin aging 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 regenerated and produce collagen and whatnot.

1:38:09But the brain is not like that, right? So you don't want to regenerate your neurons. You will lose your identity. So they have to be dealt in a 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 much, much longer. Because there are, in fact, certain animals who have better DNA damage proteins. They kind of evolved to do that.

1:38:56Like elephants rarely get cancer, right? Because they have this gene called p53. They have multiple copies of that. p53 is kind of like the guardian of the genome. It 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 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.

1:39:39just like one or two genes make a huge difference. So can we engineer humans to block that degradation of information? For those who already had the damage, then we're going to have to think about repairing that, reversing it, and then maintaining it. That's going to be a bit more challenging, but we'll get to that too.

1:40:07Dr. Rhonda Patrick:What do you think about, so the gene, going to gene therapy, there's obviously gene editing, gene therapy, and right now we only know what we know, right? Again, like with these longevity genes we know about, but do you think that AI is going to be able to help us analyze the human genome? 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.

1:40:47Yeah, that's the critical problem because we know what all the genes are in the genome. Like we have it decoded completely And we pretty much know their functions, most of them. Even 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 that splicing that happens and so on. But even without that, in a different context, so the same protein can kill a cell or cause a survival. Like in immune system, we have these receptors called TNF receptors or whatever. They can have a survival signal or a death signal, suicide signal, depending on the context of the cell.

1:41:35So 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, what do they do? like if i interfere like these uh um 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 uh uh but but the the problem is that they can also cause cancer 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 part is so complex that we absolutely going to need AI to simulate that for us.

1:42:24If 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 letting the proteins do something and so on, what would happen if I interfere with that particular gene? Or how can I improve that? Because you have to consider the other genome too. 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. Even if you try to improve it, that would actually work worse or it wouldn't help.

1:43:12So it's just a matter of complexity. There's so much 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 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 cell next to it, what that context affects, there's a behavioral issue. It's the same problem with the robotics, right? So kind of the physical intelligence or the biological intelligence. Once those models are evolved with a lot of data, I think we will be able to simulate this and the AI will be able to decide, this is the gene therapy you should get.

1:44:07So you need a new copy of immune system, but let me design it for you.

1:44:12Dr. Rhonda Patrick: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, you know, people all respond to different foods and treatments and everything differently, right? That's why 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 we kind of re-engineer ourselves.

1:44:52I always think about, like most scientists or most doctors think, like what'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 had a terrible diet, or whatever. This one lived to be 110 for 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 what's so good and then you know even make it better so that's the

1:45:35Dr. Rhonda Patrick:human two-point right i i 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 i didn't 23 this 123 french woman okay i mean the fact that 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, you 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 supercomputing power to help us figure that out.

1:46:21Dr. Rhonda Patrick: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 reverse it to revert it to and you know essentially induced you know pluripotent stem cell do you remember like is 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 uh of course uh in fact at the time i was um part of some aging groups uh uh 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 i should say that there were two um moments for me uh that that i thought that aging was was going to be uh reversible or curable however you call it um kind of like the chat GPT moment of biology the first moment was uh the um the sheep uh that's called dolly uh you probably know it was the first cloned ship yeah a sheep um it was 1996 seven or something like that i can't remember exactly but it was 90s.

1:47:34And so basically, the scientists took a cell from, you know, from one sheep, and then recreate exact copy of that sheep, you know, 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 Yamanaka factors in 2016, I think. And that was the moment that we knew that we could completely erase the sort of the age of the cell on the cellular level and then bring it back to a pluripotent stem cell level.

1:48:27and then use that to recreate the whole biological organism. So it means that we have unlimited supply of regenerative capacity. Like, there's no limit to it. In fact, we already know that. Like, so 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 generate it was amazing. but of course the the problem was okay so then how do you apply that in fact i think there was just a recent study that started in japan using the yamanaka factors uh uh 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 did some of them tumor tumors you know whether um You can control them.

1:49:22Or importantly, I think there's got to be a trial started by David Sinclair soon. Can we do like partial reprogramming? Because most of the time you don't want the pluripotent cell. 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 immune cells into naive memory and effector and differentiate it. So the naive cells are kind of the young guys. They have huge potential to expand and make memory and affect the population. And the other ones constantly die and get older. Can we actually revert the cells towards the naive?

1:50:07And I actually spent a long time trying to do that. So maybe this partial programming will enable that. And 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 Davis Higler. Yeah, so, but again, it's, these things show us that we can reverse aging. But when people say, oh, that's impossible, you can't reverse aging, this entropy, whatever. But we do it in the lab all the time. Why not do it in a total organism level?

1:50:56Dr. Rhonda Patrick:So with this partial cellular reprogramming, as you mentioned, 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 it's changing 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. I know there's been some work, and I haven't followed all this literature since the first, you know, some of the first studies that came out. but I think it was like Juan Carlos Epizusa.

1:51:34Dr. Rhonda Patrick:He's now, I think, at Altos Labs, but he at the time was at the Salk Institute. And he had done this in mice. I think they were even maybe perhaps progeria mice or some sort of accelerated aging model. And there was some reversal of, you know, certain organs seem to be rejuvenated in a sense. And 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. Right. But there's genomic, you know, somatic mutations are still there. I think telomere don't get recent.

1:52:14Dr. Rhonda Patrick:Mitochondria. Mitochondria. So do you think, first of all, I don't, I'd love to understand why that is. So what is it if you're essentially, you know, wiping out the epigenetic, current epigenetic program and reverting it back, why does not everything change? I don't know if you have any ideas, but do you think AI is going to help us understand that? Definitely. I mean, I should also point out that we do need to generate lots of data. So I think, you know, whenever I talk about AI, people say, okay, well, why can't AI do it now? For two reasons. One is that we don't have enough data. So we probably know maybe 10, 20 % of all the biology.

1:53:01We still have lots of data to generate. The second is - We're talking about scientists. Yeah, scientists or automated lab, whatever it is. So, I mean, right now we're able to generate millions of data points in one experiment, you know, but even that's not enough. Like we need to generate billions of data points and so on. So, but of course, to handle that, we also need super intelligence and super compute. So we have to have compute that's thousands of times than what's available. And people say, okay, well, you know, why are they building all these data centers? Isn't this enough? And so on.

1:53:38Well, we're going to need it. If you want to cure all diseases and reverse aging, we're going to need probably we're going to need data centers in the space and a lot more because so much data has to be in real time sort of simulated. And we might get much more efficient doing that as we learned algorithms. So that's one issue. The other is that, as you pointed out, something very important. I mean, this partial reprogramming or total reprogram, they're super exciting, but they don't solve, they don't completely solve the aging problem. They will make your eyes see better for certain periods if you're 80 years old or your skin gets better.

1:54:26but will it work on your heart muscle or on your brain cells, neurons, which is the critical point because if you can have a perfect body, but if your brain is aging, then that's it. So will it modify the sort of the microbiome that has now the environment of an old person? Because if that happens, if your metabolism is an old person's metabolism and microbiome is an old person's metabolism, and your DNA has accumulated a bunch of mutations and mitochondria has a bunch of mutations, you can reverse that a bit, have some regenerative capacity, but they will quickly become old again. Right. Because the environment is not great, right?

1:55:16So like if you live in a bad neighborhood and you created this beautiful house, you know it's but it's very bad neighborhood your house is not going to last very long there so your your neighbors has to be clean as well so i think it's a great thing and that's probably going to add certain uh years to lifespan and the quality of life uh for sure uh but we we have to push that much much further um and then really understand whether it's 12 hallmarks actually i I asked JGPT recently came out with another four or five hallmarks. What were they? I can't remember exactly. One of them was related to immune system.

1:55:55This was recently. But yeah, it was quite interesting. I'm trying to remember. One had to do with metabolism. You know, because we kind of classify hallmarks based on what we can measure and see. and I think AI can see a little bit more than we can. So anyway, this is going to be a serious engineering problem. I would be very surprised if we have like one pill you take and then you suddenly become young again. That seems very unrealistic to me.

1:56:34Dr. Rhonda Patrick:I mean, you know, and then the other question is, in the lab, the way we're delivering these treatments is like an adenovirus, right? And then it's like, well, is that going to cause cancer because the virus is going to go to the right cell? Exactly. I mean, there's definitely a lot of engineering. We have to develop. So one of the things that I like doing with the AI model is to develop some new methods, new technologies. They have a bit too much guardrail, so they don't allow me to go too deep in it. But, you know, because I don't think we have enough tools. Of course, we have CRISPR now, but actually, Doudana's lab just came out with something even better for bacteria, for genome editing.

1:57:17So imagine there's probably all kinds of other tools that we can build that will make this localization, the editing much more perfect, and it has to be programmable. You have to literally create circuits. We can program immune cells in culture. We can give a drug, it will shut down their response, or we can create and or gates and not gates. If they see two molecules, then they respond. If they see one, they don't. You can literally program the biology. So we have to develop these new tools that are better than viruses maybe, generate lots of data sets, and be able to manipulate the organs and so on.

1:57:57It could be that for some organs, when they're too old, it might be just too difficult to repair them. So you might consider just putting a new one. You know, like it might be a point of no return, your kidneys or whatever. Then you'll have these organ factories. 3D printed. 3D printed. And actually, we did a lot of collaboration with a colleague of mine. You know, he can print, you know, small tissues, lungs, and pieces like that. So some of them will be kind of transplanting new organs. Some of them will be re-engineering.

1:58:34Dr. Rhonda Patrick:And then the digital twin, the analysis and simulation will be able to figure out, are you going to reject this? Or would you need to not reject it? That's right. Right. What do you think of the new data that came out using this model called GPT-micro4B? the GPT micro 4b, where I guess there's this model that was used to figure out how to make certain mutations in the four different Yamanaka factors to make them more effective. So they were able to basically 50-fold more, be more effective or efficient at increasing this induced pluripotency. How do you interpret that data? So I don't think that model is any better than what we have right now.

1:59:23Probably current models are much better. I think probably there might have been two differences, and I don't know all the details, but one is that they probably removed the guardrails because there's a lot of biosecurity guardrails in the current models. if you ask the same question to gpt 5.5 it will refuse to do it it'll say oh this is a biohazard like what if you mutate and create a new virus or cancer whatever so that might be one reason and then the other is like if you let these models think longer so like gpt 5.5 pro and and the thinking and the instant model is the same pre-training but pro model can take two hours Thinking can take two minutes.

2:00:09So the longer they can think, the more they can iterate. They can run these scenarios again and again and again. So my speculation is that that model probably ran for a long period of time. Of course, you need a lot of computing, a lot of tokens, not a problem for OpenAI. Then you will probably come up with a solution that even a more intelligent model couldn't come up in a shorter period of time. Because that particular case is really running experimental scenarios. Like, okay, if I do this mutation, what would be the potential outcome? Like, it's running all the simulation. Oh, yeah, okay, so what if I change that mutation to here?

2:00:52And then what if I add another mutation and running the experiment again and again and again? So, you're constantly making the solution better and better and better as you think longer. um so uh and and this will get better so if you have much more compute much more intelligence and you say okay um gpt7 or 6 whatever is go and think for a month you know find the perfect molecule that will bind to this receptor and this will cause that it'll probably figure that out

2:01:25Dr. Rhonda Patrick:what is it it sounds like we're going to need to do a lot of this type of simulation and by were, I mean researchers and scientists, what is it going to take to remove some of those guardrails in that environment for researchers to be able to make these new discoveries? And what sort of, I guess, I mean, how do we protect from a new crazy biohazard or, you know, biosafety issue? Well, I mean, I think like OpenAI is partnering with, you know, trusted people. So you have to be approved by them. So I think then whether it's a company or something like that, it's the same problem with cybersecurity, right?

2:02:05So Anthropic has this new model called MITOS and they decided not to release it because they said it's too dangerous for cybersecurity because this model can just crack into any, can find all these things that others cannot see. So in fact, even the governments thought that that was important that they should. I don't know if they're exaggerating, if it's true or not. So you have to put that guardrail if you release it to the world, because somebody can use that model and then hack into your bank account, or somebody can use it to create a new virus gene or something like that. So I think that will be made individual persons or institution basis that these these, hopefully, these companies will share that because they might decide not to share it.

2:03:02I say, well, okay, why don't we just develop all the drugs internally and not release any of these models? Some might be doing that, for example. I don't think that would be a good thing because what you really need is, as I said, you need a lot of data. You need a lot of scientists putting all that data into the models, but not only the data, but their experience. In a way, in the, let's call it the wild or the world, you're actually training those models. Even if it's super intelligence, it's going to be so hungry for data that you're going to have to collaborate or release it to others. Also, I think this will be important to democratize healthcare.

2:03:52Because one question everybody asks, okay, well, if you find the treatment for aging, this is only going to be available for the super rich. I'm never going to be able to afford it or treatment for cancer. I say the opposite. Actually, thanks to AI, it will be super affordable because if you can create a drug, like a startup, let's say, can now compete with a big pharmaceutical to a company, they can find a drug using AI 100 times cheaper. And if you can do the clinical trial using Digital Twin, that's where all the money goes. Like you could develop a drug for a couple of million dollars rather than a couple of billion dollars.

2:04:30So the cost of drug development or treatment development will be magnitudes lower, and that will give a huge number of people access to that. But of course, AI has to be shared. It's a thing. It's a product of all humanity, and it should be the possession of all humanity. That's how I view it.

2:04:56Dr. Rhonda Patrick:Except for going back to the thing that you mentioned at the beginning of this podcast, which is that humans in the wrong hands, that is the problem. And that is something that needs to be taken very seriously. But the solution to that is also AI. So right now, I mean, I hear that like Mythos basically finds all these loopholes in cybersecurity issues that people couldn't figure out for decades. They didn't even know they existed. So it's just patching all these security bugs. So it will create almost the perfect secure systems. Like it will be unhackable. because METOS is actually preventing us.

2:05:42So to prevent that from happening, you still need AI. You might still have some bad actor trying to develop a virus that will cause a pandemic. To prevent that, you also need AI. So the AI should be able to predict it and already create the vaccine ready. We'll say, well, somebody might make this virus, so let's get ready for it. So AI is the solution to all our problems.

2:06:08Dr. Rhonda Patrick:perspective, you always seem to have a positive outlook. I wanted to ask you another question about, you know, we're talking about these simulations and how we're going to, you know, using AI to essentially run these clinical trials cheaper because we're going to do this, you know, these simulations and have, you know, biomarker data and it'll just be, you know, shorter and cheaper and easier. The question is always, what do you measure, right? What is the biomarker? What's the endpoint, right? And in aging, You can now see, I mean, almost a new study every day coming out looking at these epigenetic aging clocks.

2:06:46Dr. Rhonda Patrick:And that's, you know, so as most people listening to this podcast know, I've had Steve Horvath on a couple of times, and he's sort of the pioneer in these epigenetic aging clocks. and they've now developed over you know the last decade or so and become much more of a biological marker of age like your biological age not just to be able to predict your actual chronological age and so um you'll find now studies are looking at treatments and whether or not it can reverse quote-unquote reverse biological aging or epigenetic aging but it's not clear that that's necessarily, you know, if that's really reversing aging, right?

2:07:29Dr. Rhonda Patrick:So what do you think, from your perspective, what should we be looking at in terms of some of these functional outputs? Yeah, I mean, those epigenetic markers are very useful, but I don't believe that they are terribly useful as predicting true aging. I mean, there's a very significant problem with those markers. Usually, they're done through blood analysis. But in the blood, you have, like, you know, I work with T cells. So you have these cells that we call effector cells that have lots of epigenetic change because they differentiate it, and they continue to accumulate in old age. And then you have these naive cells that have, you know, more pristine kind.

2:08:25So it's a combination. So depending on what that combination is, is going to affect the output of the... So you can actually just look at the proportion of your T cell, differentiated T cells, and you'll probably get the same kind of information. and it doesn't tell you like what's happening in the skin or the brain or the heart you know that doesn't mean that if if the immune cells are getting younger or the young ones are expanding and the old ones are dying that doesn't mean that your skin is getting younger or your liver is getting younger so that it has a very limited use in my opinion but we really don't need that because aging is probably the easiest way to measure.

2:09:12We know exactly what goes wrong in old age, right? So you can't breathe that well. Your heart doesn't work that well. Your muscles don't work well. You can only raise so much because your weakened muscles or your VO max is lower. these are all phenotypic like you don't even have to probably withdraw a blood just measuring the ability of of an elderly person uh can they walk uh better you know 100 meters than they used to like because that's looking at the total biology like you know your cells your metabolism or whatever muscle, to me, that's, or your cognitive abilities. But those can't be simulated.

2:10:06I mean, in - They, eventually they can be. Right now they can't, they can't be simulated. Because as I mentioned, the AI is missing that behavioral physical intelligence in the real world, because that's, that's most things are happening in real life uh but um i think i think they can be simulated but more importantly uh i think eventually you have to whatever the ai comes out with you need to try it on on the humans right so uh my point is that you don't have to uh do anything too fancy or wait decades to see the effect. If I give this treatment to, I don't know, 80-year-old, and they're suddenly able to breathe well, you know, their VMX went up, they're sharper, they can think better, they can remember better.

2:11:05You can look at their immune system, and we can see that the cells are, we know which cells are younger or worse. Or you can look at their skin like oh wow the skin is getting young you see it you don't even have to do anything um so so there are so many features phenotypic features of aging that could be um objectively measures actually and not just subjectively you will see the effect very very quickly like this partial reprogramming trial they're doing it's it's done for glaucoma patients i i guess uh because that happens in old age, right? So your cells are aging. So, I mean, if these people start to see, it works, right?

2:11:47Their cells got regenerated. You don't need to look at their epigenetic. So I think it will be a combination of those measurements. Probably we will come up with, and AI will probably come up with this set of biomarkers. I don't think we know because it's going to be a set of biomarkers. Like, you know, your glucose, your cholesterol might be high when you're 30, and it will be high or low when you're 80. I mean, there's not a very specific marker that will tell you your age, for example, just looking at that. But the combinatorial effect, AI probably will be able to predict your age, looking at all kinds of data sets and say, oh, this guy must be, you know, 52 years old based on this, you know.

2:12:34Dr. Rhonda Patrick:I know we have that model clock base that's looking now at a variety of small molecules that might reverse epigenetic aging. And now there are some data sets showing that if you reverse epigenetic aging, there is some functional correlation with some functional improvements like pre-frailty, things like that, you know, like improve. But at the end of the day, you know, I think it'll be interesting to see if there's going to be companies that come out trying to sell some sort of drug claiming it reverses aging when they're really just looking at one biomarker, which is reversing. As I said, it's mostly the immune aging that they're looking at or sort of maybe getting rid of the terminally differentiated immune cells.

2:13:20Like, for example, in old age, you accumulate these CME-specific T cells. CMV is a virus that you can't really get rid of, so the immune system constantly have to keep it under check. And those immune cells, they kind of become like missionaries. They should retire, but they keep on expanding. And some individuals might have like 20%, 30 % of all their T cells just dedicated to like one peptide of this CMV. and they're not helpful, but they become harmful because those guys are old, they should retire. They don't and they cause inflammation because they're active and they don't give place for the young guys to come in.

2:14:04And they are epigenetically closed because they're differentiated, their telomeres are shorter. So you might be getting rid of some of those cells with certain treatments, which is great. but then you have the indirect effects, right? So if you can control the immune system and inflammation, that's going to have huge effect all over your, that doesn't mean your skin got just regenerated, but it will help clean up. Yeah, yeah, exactly. Also, the other thing I was thinking about is like,

2:14:40Dr. Rhonda Patrick:you know, you're mentioning VO2 max and, you know, muscle strength, muscle mass. We have all these markers that sort of like decrease with age. And yet we don't know necessarily that they cause aging in a way. So the question is like, will AI be able to take all this correlational data? Like we have all this, you know, all these different functional, you know, endpoints that we look at and be able to differentiate it from like personalized, you know, this personalized data set versus like actually like how do you cure aging? Like what do you change that's going to drive, you know, reverse the aging?

2:15:19Dr. Rhonda Patrick:I mean, there's a lot of questions. You mentioned something interesting that had to do with the brain. And that is something that I've been thinking about as well because, you know, we have a lot of repair processes in our body, right? We can repair a lot of DNA damage and, you know, mitochondrial function and, you know, all these things. But in the brain, we could grow new cells, replace the old cells. In the brain, it's not as robust, right? There's some parts of the brain that you can grow new neurons, neurogenesis. There's neuroplasticity. That's a big part of the repair process in a way. But it's not like a big – you're not totally replacing the brain and you don't want to, as you mentioned, because then memories go away and your identity gets very complicated.

2:16:10Dr. Rhonda Patrick:How do you see AI intervening in that? Like everything's great if we can reverse our heart aging and all this, but our brains, that's so important. Now, is it just going to be a delay, age-related disease, neuroinflammation, all that stuff? we can fix that, but like, are we going to be able to really reverse brain aging? You know, I would have to ask AI to figure that out. But, you know, I can think of several scenarios how that might happen. First of all, you know, neurons or the brain overall must have some very good maintenance policy, right? So there are neurons that live for decades, maybe 78 years and not just neurons but there are other cell types that can live for very long they don't divide very much there is some regeneration it's not like zero and that's very important because that means that if you let's just do a total experiment let's just say that you replace 0.01 percent of your neurons every month or every year something like that i don't think that's going to make a huge difference in your brain structure, because what they're doing is that they're probably, you know, there's some neuron somewhere interacting with a bunch of other neurons, synapses, and then it gets replaced.

2:17:32And then new neurons might have a few other synapses other than that, but that's going to replace that network anyway, because they have that capability. So if you do this slowly, I think you won't lose a lot. In fact, we still lose memories, right? So we can't remember everything or we hallucinate all the time. Talk about hallucination. Imagine that this happened to me. No, no, it didn't happen. No, no, I remember that. So that's like brain, maybe part of it is new neurons that just didn't know. So they just made it up, right? So that's one thing. The other thing is that these neurons probably have some internal abilities to regenerate.

2:18:17What I mean by that is that, you know, the cell can maintain itself if it has, you know, sort of a great way to clean up internally, like autophagy is a very important mechanism, as you know, or it has some really special DNA damage correction ability, like stem cells have that, right? So pristine stem cells, they don't get old. You know, even in 100 years old, they're still like a young person. So, and then you have all these other cells, like glia cells and so on, that are there to prevent all the other stuff that happens, the inflammation. You know, glia cells, of course, are part of the immune system in a way, but they are, like, the immune system is not allowed into the brain.

2:19:06very rare cases. It's like a protected area because the immune system causes too much damage. And if you can't replace it quickly, that's a huge problem. But they have their own network of cleaning up and they probably have some sort of like a lymphatic system and so on. So if we can figure that out, or if I can figure that out, we might be able to really maybe not completely regenerate but extend it um quite significantly maybe another 10 10 years 20 years 30 years for whatever and then we might come to a point and this goes into a little bit of a science fiction now you know uh let's say in 50 years time ai might be able to figure out all of the synaptic connections in your brain like every single neural network and the neurotransmitters and everything else.

2:20:02So eventually it might be able to like literally simulate your brain. You're going to the matrix level. So that might allow AI to like say, okay, I'm going to replace all these neurons, but I'm going to make sure that they reconnect all these synapses so that you don't lose your identity. Or alternately, I can keep a copy here and then we can create a new brain and then transfer to that new brain, that exact state that I found. I'm not saying that this is possible right now. That's really science fiction area. But you can imagine that at some point we might get to that level. So I'm not too worried.

2:20:47I think if it can pass this couple of decades and then keep the brain healthy and self-preserving for maybe age 120, 130. And in fact, people actually who live to age 100, they have very sharp minds. Because if you don't have a sharp mind, you don't live very old. So that's like super correlated. So if you can keep it for a couple of more decades, we'll probably find some other solution.

2:21:16Dr. Rhonda Patrick:So if we can keep the neuroinflammation low, if we can increase brain-driven neurotrophic factors, some of these things that we know does play a role in improving neuroplasticity and growing new neurons. So do all the things that we can, at least in some predictable way, help turn up the brain. And we can have chips for the memory part. We could always supplement that. So increase the capacity. And hopefully AI will help us figure out how to deliver these therapies to the brain. Yeah, delivery is always the biggest problem. Right. Well, this has been such a fascinating and exciting conversation, Duria.

2:21:53Dr. Rhonda Patrick:I have a couple of more questions, closing questions for you. And I really kind of was just wanting to know if you had access, let's say there was no guardrails and you had access to all this data in aging biology, the T-cell, all the T-cell repertoire,

2:22:18Dr. Rhonda Patrick:longitudinal cohorts, centenarian data, Like everything, just anything you can imagine. You had it all. And you had this model that was amazing that you could put it in. You're describing heaven for me. Yes, yes. What would be the prompt? What would be the question you would ask it? I mean, there'd be more than one, but what would be the first? Yeah, hoping that the AI won't answer 42 as an answer. the so so so the the first thing i would probably ask is um not not saying that just go figure out aging or whatever because i think there has to be there has to be certain sequence so imagine that you have all this data uh what would be the the most practical um uh quickest way you can develop an intervention to an elderly person, say age 70, 80 years old, that will immediately add five years to their lifespan.

2:23:19So to me, that would be the most critical immediate question to ask because that population doesn't have a lot of time. And so we have to develop these technologies extremely quickly and should have even two years, three years extend so that I can come up with the next prompt after that. So I guess that would be the first prompt I would ask.

2:23:49Dr. Rhonda Patrick:That's great. What, okay, there's another question. So this one is, there's no money. Money's no object, okay? There's no, like you have complete like access. You're describing so many heavens now. I know. I'm just, I'm curious what your answer is. You're going to personally build your own digital twin. Which I plan to. Right now. What test would you prioritize? What data sets would you prioritize? How can a person get them? How often would you take these tests? How would you organize this information into the AI to really get the biggest bang benefit from the information it's going to give you?

2:24:33Dr. Rhonda Patrick:Right. But you said money is not an issue. Money is not an issue. Right. Money is not an issue. So I would divide it into two parts. One part is that we have to, so what I would do is set up a huge lab, you know, partially automated lab, where I would generate enormous amount of data on the cells, on the tissues in the lab, because we have to go by the first principles to understand what's going on, let's say, in an individual T cell. All these thousands of proteins, metabolites, what are they doing? Then that will enable me to create what's called the virtual cells. And then eventually virtual tissues and your half cells are in a special temporal manner are behaving and so on.

2:25:26So that would probably be the most expensive part of it. And I'll need a lot of money. You said no limit, right? Okay. Okay. But the second part would be sort of what we talked earlier, kind of the behavioral data from the humans. And that data is not just, of course, all kinds of plasma levels of proteins metabolize your full microbiome, your full genome sequencing. And all of these things are possible, by the way. I mean, if the cost is not an issue, you can easily, like UK Biobank has done it for 500 ,000 people, you can do it for a million people. And I think if you did it in a million people, that would pretty much cover all the possible humanity.

2:26:14I mean, it's not like everybody's perfectly different. We share a lot of things. And so from the humans collect lots of biological data, but very importantly, behavioral data. I think this is something that's totally missing in a digital twin. Like, you know, we're talking earlier, ability of someone to walk certain distance, ability to, you know, raise some weights. These don't show up in any biomarker sets, but they could be extremely important. Or ability to think, you know, their cognitive level, that could be directly brain aging related. And I mean, lots of things. And, you know, what happens when humans are in certain environments?

2:27:07You know, in certain environments, even if you are having a very sort of healthy lifestyle, that may not help you much. For example, I lived in New York City for a decade. My stress level was so high. And that stress level is so harmful for you because the immune system is constantly thinking there's a threat out there and then it's causing a lot of inflammation. In fact, I think people who live in New York has twice as much heart attack risk or something like that. You know, that your environment, your emotional states and how you interact with other people, all of these things will impact your aging process, your resilience to the life, your optimistic level.

2:27:55By the way, being optimistic is one of the best things you can do for aging. And study after study show that. So being able to absorb bad things that happen to you and then keep going. So resilience. But these are behavioral data that's not available in the biological set. So yeah, I would do that for a million people all over the world, different parts. And then on the lab, every single cell type that I can find, decode those, put them all together to the super intelligence, and voila, we have digital twin.

2:28:32Dr. Rhonda Patrick:Okay, Doria. So let's say someone wants to build their little mini digital twin right now using the models we have access to today. The type of data that we can aggregate at the consumer level today, biometric data that we can put in, how would you build that mini digital twin today? Yeah, great question. I mean, in fact, it is possible to build sort of a mini digital twin that doesn't have to be as sophisticated as I described, because that one is more sort of clinical trials and developing treatments. But, you know, going back to the example of the UK Biobank, you know, they didn't have trillions of data sets.

2:29:16They only used a few hundred data points from each person, and they were able to predict a lot of diseases. So that means that, you know, we can have a lot of predictive power with the data that we're collecting today. You know, another example is this glucose meter that I have. You know, every five minutes it shows my glucose level. And then I take that data and, of course, I put it to chat GPT. And once you add additional data set, that becomes very, very valuable. because let's say that you have your lab values, your cholesterol, your glucose, your everyday, the steps that you took and your sleep and so on.

2:30:01So these are actually very rich data on their own because their accumulation of lots of underbiology that results in that, but also that puts AI into a context, your mini-digital twin. So my suggestion would be to provide the AI as much data as they can and on a daily basis and keep it in the same context, so same window, so the model can remember that. actually there are there are some tricks to do that as well you can keep it as like a database and tell them ai model go check my database and see what my new you know based on my new data how things have changed what suggestion you could give i for example provide all the supplements that i take you know you know the type of food that i eat all of these things will make will make a big difference.

2:31:04So the model start to really personalize, you know, sort of the style, it will know your style and will make suggestions for you, rather than giving blanket statement, like you should work 10 ,000 steps. Well, you know, knows that like the area cannot walk 10 ,000 steps every day, but I think 3000 would be enough for him.

2:31:28Dr. Rhonda Patrick:And what kind of model are we talking about would you be using the GPT 5.5 Pro? And then what about, you know, these agents and codecs? And how does that come into helping analyze that database that you're creating? Yeah, I think, you know, these models are becoming more agentic all the time. I know OpenAI, for example, they integrated agents into their codecs model, decoding model. And soon, I'm sure it will be part of all of chat GPT. You don't, you don't, I don't think you need very sophisticated models for that. What is important is that really maintaining that context. So hopefully, the models will have a larger memory, and they can remember.

2:32:11So ChatGPT can keep certain memories about you, but it's still kind of limited. It's not just ChatGPT, like you can use Gemini, for example, which has a longer context windows, or cloud for that matter. I think most of the models can handle that information. They don't have problem dealing with large data sets. As I mentioned, I can put millions of data sets and they're able to analyze that. What they need is that they need to remember how things were a month ago, because that's before and after. Before and after is extremely valuable. So the model will know. He started taking vitamin D3. Oh, these things changed after that that you may not notice or is glucose looks better because of you know when that's that change happened so it starts to make those links and and that's i think the critical point because you need all of that context in the in the ai model to to give you a sort of a better uh prediction on what to use and what not to use okay you were using that well maybe that was not a great idea.

2:33:23So change it or change the doors or whatnot.

2:33:26Dr. Rhonda Patrick:Yeah, that's interesting. It kind of reminded me of a question that I did want to ask you about, you know, these AI models and future AI advances. When you think about these qualities, so like persistent memory, expanded context handling, it seems like those seem to be more important. Absolutely. I think for me, memory, which brings the context. So the models are now able to think for quite long time and they don't, because previously the models would just, even in the same context window, if you had a million context windows, after a while they would just fall off because they would forget even what they were thinking about.

2:34:11Now they have this ability to constantly go and check on it. So I think in the next few months, this is going to happen. So that will have a tremendous impact. Memory is everything.

2:34:27Dr. Rhonda Patrick:So how long are we talking? Like, let's say, you know, you started a vitamin D supplement six months ago, put that you have the same window and you start in that window, you have that, you know, entry point that the date, and then you keep adding about, you know, you add your data in, it's got all the data. Right now, can it go back that far? Or how far can it go back? If you have that data somewhere in your database, for example, I adapted a technique that Karpathy, who's a famous AI researcher, described. So you can turn, you can create your own wiki, sort of Wikipedia kind of a thing, like personal.

2:35:12you take you know if you have all your data somewhere you can ask ai just pull all that and put it into a wikipedia like you know you can do it daily or weekly depending on the environment whatever and so now you're building your own database health database which ai can help you update it if you have that data it can go years doesn't matter like you can have 10 years of data it will analyze all of that. But it has that memory, it can like... Yeah, so in the same context, if you provide all of that, I mean, it's still limited with, you know, maybe a million tokens or something, but no one's going to have million token data set, even if you calculate 10 years.

2:35:59So that's not a problem. The problem is like, if you want this to be continuous. Like you just give AI, okay, here's the data today that it should be able to remember what was yesterday, what was two months ago. So you don't have to give, you know, all of the, you don't have to keep your own database and give all that again and again, because you have to do that every time, right? So your whole, and that will spend a lot of tokens and stuff like that. So, but, but I think this is, this is going to be, this is going to be sold. How do you not bias?

2:36:35Dr. Rhonda Patrick:How do you lower the ability of yourself to bias what, you know, GPT 5.5 Pro is going to feed you back, right? Like based on what you're asking it. And I mean, I find sometimes I might be able to bias it a little bit. Do you know what I'm talking about? Yeah, sure. I mean, that's why I think we are in sort of the experimental phase. In a way, everyone has to do their own kind of validation as the models are getting better. What I mean by that is that, again, you know, of course, don't try harmful things and then, you know, don't go into risk. But, you know, for daily use, you might be taking vitamin D, and then you stop taking vitamin D.

2:37:25So you're just doing an experiment, like before and after, and then you collect that data before and after. And then AI gives you one solution, says, well, you know, taking this dose of vitamin D I think is important. So then you can start that dose again and then see what happens. If you reach the same level as before, it means that AI made a good prediction. Like you need to see after. you have to have that record before and after so that you you are the judge well what this was a good idea so i'm glad that i listened to judge pt well if it wasn't a good idea it didn't kill you it didn't make you sick so that's that's also fine yeah i guess for someone that's already taking

2:38:09Dr. Rhonda Patrick:a lot of supplements for example they're not going to have that before and after then also you have to know like how long do you wait you know for example to for the washout period and Yeah, the hope is that if you provide that very frequently, in fact, I can mention one thing. For example, the lab values, like you go and measure your cholesterol, glucose, sodium, whatever, they always give you a range, right? So if it's within this range, it's normal. Well, how do you know that? Because you can be at the top of the range. That might be your abnormal. somebody else is normal somebody might be a little bit over the normal and might still be okay or vice versa because we don't know the level on a personalized level so we calculate population base so okay so this range is good for this population so in a way if you have three or four measurements let's say every few months you can develop your own set point normal you know the AI will know your normal for glucose is 90, not 70, not 100, or not 105.

2:39:27Somebody else might be 102. So it knows that based on that measurements. So then it starts to give you advice based on your data set, your set points. Because if yours is 100 and suddenly dropped to 70, maybe that's not a good thing. I'm just giving an example. So that's why that continuous data collection is so important. With Glucose Meter, I collect it every five minutes. The more data, the better.

2:39:59Dr. Rhonda Patrick:Well, Durya, thank you so much for sitting down with me today and talking about this exciting, I mean, frontier that we're exploring, you know, curing disease, extending human life expectancy, obviously health span, reversing aging, perhaps getting to human 2.0 where we're enhancing genetic features as well. Very exciting time to be in. And if we cannot die in the next 10 to 15 years, it may be even more exciting. Yes, absolutely. Because the last thing I will say, this is so unique in human history. Because a decade ago, if you said someone, well, you should be very healthy, do this, do that.

2:40:50They can say, well, it's only going to extend my life maybe two years or three years. I just want to live my life. And I don't care about living a few more years as an old age. And that was perfectly relevant. That's not the case now. Living an extra one year could make you reach that threshold where there's going to be the ability to treat many diseases and reverse your aging and give you another decade, give you another 20 years. And then once you reach that, you get another 10 years. So even every day counts now, in my opinion. so that's why don't die

2:41:34Dr. Rhonda Patrick:well people can find out more about your research and they can follow you I follow you on X maybe you can tell people how to follow you what your user your Twitter follower or sorry your X user handle is and where else they can find you yeah my main account is in X it's at Derya D-E-R-Y-A-T-R under Dash. If they write Dario Nutmaz, I think I'll show up. That's where I do most of my communication. I have a LinkedIn account, but I don't post that often there. I've been planning to start up a sort of a YouTube channel, but I don't think I'll ever do that because I'll never have the time. It's really amazing what you're doing because video takes a lot of effort.

2:42:26so for me the fastest way in fact I even had a Substack account but just couldn't find the time to write long messages so X is the best way

2:42:39Dr. Rhonda Patrick:well I really encourage people to follow you on X you post I mean just every day there's something interesting that you're posting on X and so I highly recommend that people do follow you as many already do so thanks again for the research you're doing and for I'm excited to see what's going to happen in the next couple of months. Looking forward to it. Very optimistic. Thank you. Thank you very much. It was great. I want to thank Dr. Durya Anatmas for joining me today and for giving us such an optimistic, thoughtful, and wide-ranging look at how artificial intelligence may change biology, medicine, and the science of aging.

2:43:20Dr. Rhonda Patrick:If you haven't already, I highly recommend following him on X. His handle is at DuriaTr underscore. That's D-E-R-Y-A-T-R underscore. He regularly posts fascinating papers, experiments with new AI models, insights from immunology and aging research, and his perspective on where medicine and science may be heading. I follow him myself, and there's almost always something interesting to learn from what he shares. I also want to mention that we've put together detailed show notes for this episode. They include the full transcript, timestamps, explanations of the major concepts we discuss, and links to research on AI reasoning, disease prediction, cancer, aging, and partial cellular reprogramming.

2:44:04Dr. Rhonda Patrick:You can find all those resources at foundmyfitness.com forward slash episodes. Just select the episode with Dr. Duryea Anatmaz. And then I also want to take a moment to thank Thank you for supporting the show. Found My Fitness is entirely ad-free. We do not accept sponsorships, interrupt these conversations with advertisements, or shape our content around commercial interest. Our goal is to keep every episode as rigorous, evidence-based, and independent as possible. That independence is made possible by listeners like you. If you value these long-form, deeply researched conversations, one of the most meaningful ways to support our work is by becoming a Found My Fitness premium member.

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2:45:32Dr. Rhonda Patrick:You can support the show and become a premium member at foundmyfitness.com forward slash premium. Again, that's foundmyfitness.com forward slash premium, P-R-E-M-I-U-M. Thank you so much for listening and thank you for supporting Found My Fitness. I'll talk to you soon.