Beyond human: AI, superhumans, and the quest for limitless performance & longevity

Summary

This discussion featured presentations on using artificial intelligence to combat aging and develop life-extending technologies, along with a demonstration of advanced prosthetic technology. David Sinclair from Harvard opened by presenting aging as a treatable medical condition rather than an inevitable process, arguing that aging is the root cause of most diseases including Alzheimer’s, diabetes, and cancer. He explained his “information theory of aging,” proposing that bodies age due to software problems in cellular information systems rather than simple wear and tear. Sinclair described his lab’s breakthrough work using three specific genes (Oct4, Sox2, Kaler4) to reverse aging in animal models, successfully restoring vision in blind mice and treating various age-related diseases. He emphasized how AI has dramatically accelerated this research, reducing what would have taken 160 years and billions of dollars to just six months and $50,000.

Alex Zhavoronkov from In Silico Medicine followed, discussing his company’s AI-powered drug discovery platform that has generated over 22 developmental candidates in just a few years. He presented a successful case study of a drug for idiopathic pulmonary fibrosis that progressed from AI design to completed Phase 2A clinical trials, demonstrating improved lung function in patients. Zhavoronkov highlighted how AI can accelerate early-stage drug discovery while noting that later clinical phases still require traditional timelines due to regulatory requirements.

The session concluded with Tilly Lockey demonstrating advanced bionic prosthetics, including detachable hands controlled by wireless muscle sensors. Her presentation showcased how technology can transform disability into augmented capability, emphasizing the importance of user-centered design and accessibility in developing assistive technologies.

Keypoints

## Major Discussion Points:

– **Aging as a treatable medical condition**: David Sinclair presents the revolutionary concept that aging should be classified as a disease rather than a natural process, arguing it’s the root cause of most illnesses including Alzheimer’s, diabetes, and cancer, and that treating aging could cure these diseases by allowing the body to heal itself.

– **Information theory of aging and epigenetic reprogramming**: Sinclair explains that aging is primarily caused by loss of epigenetic information (like corrupted software) rather than physical wear, and demonstrates how specific genes (Oct4, Sox2, Klf4) can reverse aging by resetting cellular age, successfully restoring vision in blind mice and treating various age-related diseases.

– **AI-accelerated drug discovery for longevity**: Alex Zhavoronkov showcases how artificial intelligence can dramatically reduce drug development time and costs, demonstrating his company’s success in developing aging-related therapeutics, including a drug that completed Phase 2A trials for idiopathic pulmonary fibrosis in just 18 months using AI-generated molecules.

– **Technology as human augmentation and empowerment**: Tilly Lockey demonstrates advanced bionic prosthetics that go beyond replacing lost function to providing superhuman capabilities, emphasizing how technology can transform perceived disabilities into superpowers and advocating for accessible, customizable prosthetics that enhance human potential.

– **Collaborative innovation for social good**: The discussion emphasizes the importance of interdisciplinary collaboration between AI researchers, medical scientists, and end-users to develop technologies that address fundamental human challenges, with speakers advocating for redirecting AI talent toward longevity research as one of the most impactful applications.

## Overall Purpose:

The discussion aims to showcase how AI and advanced technologies are being applied to solve fundamental human challenges, particularly aging and disability, while inspiring the audience to consider redirecting their talents toward these high-impact areas that could dramatically improve quality of life for humanity.

## Overall Tone:

The tone is consistently optimistic and inspiring throughout, with speakers expressing genuine excitement about breakthrough discoveries and future possibilities. There’s an evangelical quality to the presentations, particularly from Sinclair and Zhavoronkov who passionately advocate for prioritizing longevity research. The atmosphere becomes more personal and interactive during Lockey’s segment, but maintains the same hopeful, forward-looking perspective. All speakers share a sense of urgency about the potential impact of their work while remaining grounded in scientific evidence and practical applications.

Speakers

– **LJ Rich**: Conference host/moderator – appears to be moderating the AI for Good conference sessions

– **Tilly Lockey**: Bionic prosthetics user and advocate – Co-designer and developer of bionic arms, lost hands to meningitis septicaemia at 15 months old, works on accessibility and design of prosthetic technology

– **Alex Zhavoronkov**: Founder and CEO of In Silico Medicine – Longevity expert, AI-powered drug discovery specialist, focuses on aging research and pharmaceutical development using artificial intelligence

**Additional speakers:**

– **Alex Zhavoronkov** (mentioned at beginning): Described as “good friend” coming up later, appears to be the same person as the main Alex Zhavoronkov speaker

– **David Sinclair**: Harvard professor and longevity researcher – Expert in aging research, works on reversing aging processes, developed gene therapy approaches for age reversal, mentioned as having a lab at Harvard

– **Chris, James, Michaela and Harry**: Panel participants for upcoming session on “understanding emergent intelligence in work, agentic, robotic and creative” – specific roles/expertise not detailed in transcript

# Comprehensive Report: AI for Good Conference Discussion on Longevity Research and Human Augmentation

## Executive Summary

This discussion from the AI for Good conference featured presentations on using artificial intelligence to combat aging and develop life-extending technologies, alongside demonstrations of advanced prosthetic technology. The session, moderated by LJ Rich, brought together leading researchers and advocates to explore how AI can address fundamental human challenges, particularly aging and disability. The overarching theme centered on redefining traditional medical paradigms and showcasing technology as a force for human enhancement.

## Key Speakers and Their Contributions

### David Sinclair – Harvard Professor and Longevity Researcher

David Sinclair opened the discussion with a presentation that fundamentally challenged conventional thinking about aging. His central thesis positioned aging not as an inevitable natural process, but as a treatable medical condition that represents the root cause of most diseases. Sinclair argued that “if you’re not really worried about aging, maybe you’re too young, maybe you’re in denial, but what we now know is that aging, there’s a universal process that drives it… aging is a medical condition and that it is increasingly preventable and even treatable.”

Sinclair explained how aging qualifies as a disease by referencing the Merck Manual’s definition: “for it to be a disease it has to happen to fewer than 50% of us. If it happens to the majority of us, we call it aging.” This distinction, he argued, is arbitrary and prevents proper medical treatment of aging.

He introduced his “information theory of aging,” which reconceptualizes biological aging in computational terms. “Our bodies, when it comes to aging, are more like computers. We have information that we get from our parents, and that that information gets lost over time… it’s a software problem, not a hardware problem.” This analogy proved crucial in bridging biological research with AI applications.

Sinclair presented evidence from his laboratory’s work on cellular reprogramming using three specific Yamanaka factors (Oct4, Sox2, Klf4). His team has demonstrated the ability to reverse aging by approximately 75% in cells and tissues without causing cancer, successfully restoring vision in blind mice and treating various age-related diseases including glaucoma, Alzheimer’s, and motor neuron diseases.

He emphasized how AI has dramatically accelerated this research, reducing what would have traditionally taken 160 years and billions of dollars to just six months and $50,000. Sinclair announced that Life Biosciences would begin their first human clinical trial of gene therapy for vision restoration in January, marking a significant milestone in translating laboratory discoveries to human applications.

However, Sinclair acknowledged significant challenges regarding accessibility and cost. He expressed concern that gene therapies currently cost hundreds of thousands of dollars, potentially limiting access to wealthy individuals and countries. His team is working to develop chemical alternatives that could reduce costs dramatically, making these treatments globally accessible.

### Alex Zhavoronkov – Founder and CEO of In Silico Medicine

Alex Zhavoronkov followed with a presentation focused on AI-powered drug discovery and its applications in longevity research. He provided crucial context for the urgency of aging research, noting that “68 million people ballpark die each year most of them died due to aging. That is more than any war ever fought in this world and we see a lot of protests… I think that extending productive longevity, extending the qualities for everybody on the planet, is probably the most altruistic and the most impactful thing to do.”

Zhavoronkov revealed his personal commitment to this cause: “I myself made a longevity pledge, so I’m not married. I don’t have a family… and I donate most of what I make right now to longevity causes.”

He demonstrated how his company’s AI platform has nominated more than 22 developmental candidates in just a few years, dramatically accelerating the traditional drug discovery process from 2.5-4 years to their typical 13 months (with the longest being 18 months) for reaching developmental candidates. He presented a detailed case study of their success with a drug for idiopathic pulmonary fibrosis that progressed from AI design to completed Phase 2A clinical trials, showing improved lung function in patients.

However, Zhavoronkov provided important perspective on AI’s limitations in drug development. He explained that “if we were to reach artificial superintelligence today, it will take several years to discover a drug, to discover and develop a drug, to put it on the market because you still need to do a lot of testing… whatever you have a developmental candidate represents what you had in AI 18 months ago.” This insight revealed crucial limitations that temper AI hype, showing that even perfect AI cannot eliminate the time lag of biological validation.

### Tilly Lockey – Bionic Prosthetics User and Advocate

Tilly Lockey concluded the session with a powerful live demonstration of advanced bionic prosthetics, including detachable hands controlled by wireless muscle sensors. Having lost her hands to meningitis septicemia at 15 months old, Lockey brought a unique perspective as both a user and co-designer of prosthetic technology.

Lockey’s presentation showcased how technology can transform disability into augmented capability. She demonstrated various prosthetic models including Hero Arms, Hero Pro, Hero Rugged, and Hero Flex, showing modular designs that allow for specialized attachments for different activities, including drumming and piano playing. Her wireless prosthetic technology makes advanced prosthetics accessible to more types of amputees and enables innovative features like detachable hands.

Crucially, Lockey challenged traditional medical paradigms around prosthetics. She criticized the conventional approach of providing cosmetic prosthetics without consulting users about their needs and preferences. “After being given a cosmetic glove and not asked any questions, not asked, what do you want? I think I have some Bionicons that I like covering glitter… you don’t need to cover it up. You can actually accentuate it in a really beautiful light.”

Lockey advocated for prosthetics to be seen as beautiful augmentation rather than hidden medical devices, representing a fundamental shift from concealment to expression. She emphasized that “having four limbs is a human right. It’s no point in tech being there if people don’t have access to it,” elevating the discussion beyond individual health to questions of justice and accessibility.

## Major Areas of Agreement

### Technology as Human Enhancement

All speakers demonstrated strong consensus around technology serving as a force for human enhancement rather than merely addressing deficiencies. Sinclair’s age reversal research aims to restore youthful function, Zhavoronkov’s AI-driven drug discovery targets the root causes of disease, and Lockey’s prosthetics provide capabilities that can exceed normal human abilities.

### Accessibility and Universal Access

Both Sinclair and Lockey particularly emphasized that advanced medical and assistive technologies should be accessible to everyone, not just wealthy individuals or countries. Sinclair expressed concern about gene therapy costs limiting access, while Lockey advocated for prosthetics as a human right. This shared commitment to accessibility represents a significant ethical stance in technology development.

### User-Centered Design and Collaboration

The speakers advocated for involving end users in the development process. Sinclair emphasized making treatments globally accessible, Zhavoronkov highlighted serving real medical needs, and Lockey stressed the crucial role of co-design with prosthetic users.

## Unresolved Challenges and Future Directions

### Timeline and Accessibility Concerns

Several significant challenges remain unresolved. The timeline for when aging reversal therapies will be widely available to the general population remains uncertain. Cost and accessibility challenges persist, particularly for advanced gene therapies in developing countries.

### Safety and Regulatory Considerations

Safety concerns about reversing aging too much, which could cause cancer or death, require continued research. Regulatory approval processes still require traditional timelines despite AI acceleration, creating a bottleneck between AI-driven discovery and clinical implementation.

### Technical Limitations

Lockey acknowledged technical limitations in her prosthetic systems, including uncertainty about the range limitations of wireless control systems. The scalability challenges for making advanced prosthetic technology accessible as a “human right” require continued attention to manufacturing costs and distribution systems.

## Implications for AI Development and Social Policy

The discussion revealed important implications for AI development priorities. The speakers collectively argued for redirecting AI talent towards longevity research as one of the most impactful applications, given that aging causes more deaths than any war in history.

The emphasis on accessibility suggests that AI development should prioritize solutions that can be democratically distributed rather than creating technologies that exacerbate existing inequalities. The focus on user-centered design implies that AI systems should be developed in close collaboration with end users.

## Broader Cultural and Philosophical Implications

The discussion challenged fundamental assumptions about human nature, aging, and disability. By reframing aging as a treatable medical condition rather than an inevitable process, the speakers suggested a profound shift in how society approaches mortality and human limitation. Similarly, by presenting prosthetics as augmentation rather than replacement, they challenged traditional notions of disability and normalcy.

## Conclusion

This discussion represented a convergence of cutting-edge AI research, biomedical innovation, and assistive technology development around a shared vision of human enhancement and social good. The speakers demonstrated how AI can accelerate solutions to fundamental human challenges while emphasizing the importance of accessibility, user-centered design, and collaborative innovation.

The session successfully challenged traditional medical paradigms while maintaining scientific rigor and practical focus. The speakers’ shared optimism about technology’s potential for positive impact, combined with their realistic assessment of current limitations and challenges, provided a balanced perspective on the future of AI applications in human enhancement.

The discussion’s emphasis on longevity research and human augmentation as high-impact applications for AI talent suggests a potential reorientation of research priorities towards addressing fundamental human challenges. The consistent focus on accessibility and human rights provides an ethical framework for ensuring that these technological advances benefit humanity broadly rather than exacerbating existing inequalities.

Alex Zhavoronkov: Welcome, everybody. Thank you for coming to this session. It promises to be very interesting. We have my good friend Alex coming up later. We’re going to have a chat. What I want to talk to you today about is a new concept. It’s relatively new. It may be new to many of you. We’ve been working on this for 30 years. Only recently, it’s become mainstream. Not everyone’s heard of it. What I’m going to talk to you today about is using AI and regular drug development to tackle what I believe, and now many doctors believe, is the major cause of illness on the planet. Of course, I’m talking about the aging process. If you’re not really worried about aging, maybe you’re too young, maybe you’re in denial, but what we now know is that aging, there’s a universal process that drives it. Everything from a simple yeast organism, microorganism, to a mouse, monkey, human, liver cell, brain cell, skin cell. This process is universal. I’m going to tell you about what I believe to be this process and some evidence for that. A very exciting discovery over the last few years in my lab and in some of my colleagues’ lab about resetting the age of organs, and in animals at least so far, resetting the age of the body. Why would we want to do that? Why is this a big deal? Well, consider that Alzheimer’s disease, diabetes, cancer, these are not just diseases that you treat, they’re actually symptoms of this process that we now believe we understand called aging. What we’re finding now in my field of longevity is that when you reverse the age of an animal that has a disease, whether it’s Alzheimer’s or motor neuron disease or even cancer and frailty, the body heals itself and can cure illnesses that it has never been able to cure before, not even with modern medicines. Think about that. We stand on the precipice of having medicines, some of which Alex Zakharov will tell you about today, and I’ll tell you about some of our drugs that we’re working on, that is planned to reverse the age of the body to get us back to at least our organs to being very young again and healing themselves. There is a reason why we don’t have diabetes when we’re 5 or 10 or 15 years old. Why don’t we have Alzheimer’s in our 20s and 30s typically? The reason is because our bodies can deal with the problems, but as we get older we don’t fight against these problems and we just cannot heal the way we used to. Now that we’re able to in many ways reverse aspects of aging in these animal models, and hopefully very soon we’re going to have some medicines on the market, and you can see some of the human studies coming out very soon. Alex has done some, we’re doing some, we’re already seeing promising results. When this happens, we will be able to treat diseases in a new way. It’s a revolution in medicine that we haven’t seen since the invention of antibiotics, in my opinion. Alright, let’s go to the first slide. So if there’s only one thing that you remember today, if nothing else, it’s that aging is a medical condition and that it is increasingly preventable and even treatable. So instead of thinking of diseases and aging as separate things, which is the way medicine has been practiced for the last few hundred years, increasingly in science and in medicine we think of aging as the root cause of most disease, disability and death on the planet, and that we’ve only been treating the symptoms of aging, which we call diseases, without tackling the root cause of the problem. So what I want to tell you about today, what if aging is a disease? We can call it a disease, it’s only a definition. I want you to think about what is the definition of a disease. I’ll give you one definition that’s on the shelf in my office at Harvard. A disease is a decline in function leading to disability, illness or death. Sounds like aging, doesn’t it? But why don’t we include aging in that definition of disease? The reason, according to the Merck Manual of Geriatrics that is on my shelf, is that for it to be a disease it has to happen to fewer than 50% of us. If it happens to the majority of us, we call it aging. And that puts it, unfortunately, into a bucket that we’ve neglected for hundreds of years. We’ve said, oh, aging is natural, something we have to accept, it’s a disease we need to focus on. But that time is over. We are now working on aging at Harvard, in Alex’s labs and labs around the world, and it’s one of the hottest areas for investment besides AI. And the unification of AI and longevity research is extremely exciting. It’s leading to the ability, in the case of my lab and Alex’s company, to find drugs that can slow and even reverse aspects of aging in animals and in humans, work that would have normally taken us hundreds of years, we can do in a matter of months. So what we’ve learned over the last 30 years of research, since I was in my 20s, and now I’m 56, is that a major cause of aging is not just wearing out, we’re not just pieces of meat that break down, that’s the old view of aging. What we think now is that our bodies, when it comes to aging, are more like computers. We have information that we get from our parents, and that that information gets lost over time. And that’s a major, if not the major cause of aging and disease on the planet, in ourselves, in our pets, in animals, and even in plants. It’s a remarkable hypothesis, if true, this information theory of aging. And when we came up with it in my lab, it was not because it was a dream. We were just making up the theory based on the evidence that we were seeing time and time again. The evidence is screaming at us that our bodies age because of a software problem, not a hardware problem. So what do I mean by that? Well, first of all, let me start with what the evidence, some of the evidence that we are computers more than pieces of meat. There is a study of hundreds of twins, identical twins, two of which I show here. And in this Danish study, they showed that these identical twins, they age at different rates, even if they have the same DNA, the same genetic code, but they age differently. Why? Well, because they’re in different parts of the womb. And then when they are in the world, they’re eating differently, smoking differently and behaving differently in ways that affects the rate of aging. And later, we promise you we’re going to talk about ways to slow down the rate of aging and stay alive and healthy until these medicines become available for all of us. But I think you can see that these people are different looking, right? The one for me, the one on the right is younger looking. And it turns out how young you look is a pretty good predictor of how old you are inside, notwithstanding Botox and other things, that doesn’t count. You cannot get plastic surgery and hope to get younger. So what do I mean about information? Well, the main one that you might be thinking of information in the body is the genetic code, the genome, which is represented here by that blue double helix on the screen. But there’s another part of information in the body that’s just as important. And without it, we would never have become a baby in the first place. And it’s called the epigenome. And this epigenome controls the genome. It is the structures that wrap the DNA up into bundles, tight bundles to turn genes off or opens up the DNA and makes a loop to make a gene turn on. And we have roughly 20 or so thousand genes in every cell. And that pattern of bundles and loops and bundles and loops determines whether a cell becomes a liver cell or a brain cell or a skin cell. And what we’re finding over time is that this pattern of bundles and loops across those 20,000 genes changes in predictable ways that can lead to disease and aging. Oh, I want to point out one thing on this slide is that we can map those changes in many different ways. We can look at which genes are on and off. It’s called transcriptomics. And we can feed that into AI and develop clocks that tell us how old a mouse really is, not based on months, but based on biology. And we can do that for humans. We can take a cheek swab or a blood test and look at using AI to look either at the genes that are turned on and off or at chemicals that are added and subtracted on the DNA itself, which on this image is represented by the yellow little tag, which are called methyls. So if anyone has talked about with you a DNA methylation test, they’re talking about these little tags across the genome, millions of them, that predictably change with aging. And if you smoke, if you’re obese, if you don’t exercise, if you do all the wrong things, the change of those chemicals will happen quicker than if you live healthy. And we can actually use these tags to estimate your biological age. So it’s a new term. We’ve had chronological age, which is how often we’ve gone around the sun. But what’s more important for your future health, and in fact, how long you’re going to live, is your biological age. And as I said, you can measure these chemicals. And this is called a Horvath clock, named after our good friend and colleague, Stephen Horvath, who discovered in 2013 that these chemical tags on the DNA represent age and health. And there are many of these. There are even kits you can now have. You can have kits to measure your biological age and see how well you’re doing and when you might die. And when you might die. The good news is you can change that. And we can talk about how we do that in a minute. But what we know is that if you have an older biological age, you will likely develop diseases earlier and die younger. So how do we test this? One experiment that took us 13 years, we published it in 2023 in the journal Cell, was to mess with the epigenome, to scratch the CD. At Harvard, I have to explain to the students what a CD is. And if you don’t know, these were really great information storage devices in the 1990s. You could fit about 20 songs on them. They were awesome. But the problem with them was they got scratched. And you couldn’t read the music anymore. And I think that’s a good analogy for aging. Or if you don’t know what a CD is, think of a corruption in the software as a computer or a phone gets older. Now, what we’ve been looking for is what happens to a mouse if you scratch the CD, if you mess up those bundles and loops. And we were able to do that in technical ways I won’t get into. It’s not important. But we messed up the epigenome. We made those chemical tags change. And if we’re wrong, the mouse should be fine. It might just read older, but it won’t get older, right? The clock could just be a clock. But if the information theory of aging is correct, if the clock advances, aging will also advance. And we got that result. When we advanced the clock, the biological clock, the mouse literally got older and got diseases and died younger. As you can see in this example of two mice that were born at the same time, on the same day, from the same mother, but one has had its aging accelerated. And what we like to say in my lab is if you can give something, you can take it away. So now I’m going to tell you briefly about how we reverse that process using what we had dreamed of and hoped that would be there is a backup copy of the software. We hoped that there was going to be a way to reinstall… We want to install the software in this animal so that it can be young again and fight disease and heal. So how would we do that? Well, it took us about, now going on, 10 years to achieve that. And now, in retrospect, it sounds very easy, and in fact it is, now that we know how to do it. But at the time, it was very difficult. Why? Because if you reverse the age of a mouse too much, it will die. In fact, in the field, people were trying to do this and publishing amazing papers, but the subtext was, oh, unfortunately the mouse died after two days. So that’s not going to be a therapy anytime soon. So we needed to figure out how do you reverse aging back to a point where it doesn’t cause cancer, doesn’t kill the animal, but makes them 50 or 75% younger. And stop. Don’t go anymore. You don’t want to become a five-year-old anymore or zero. Zero is bad. So what we did was we took a leaf out of the book of a very famous scientist. Some of you may have heard of him. Professor Shinya Yamanaka, who won the Nobel Prize for discovering a set of genes that can turn an adult skin cell, or really any adult cell, into a stem cell to make what we call an induced pluripotent stem cell. And that was definitely worthy of a Nobel Prize. Many of us use this technology now to grow stem cells. We don’t need embryos anymore. We can take your skin and turn it into stem cells and then make little brains in the lab and skin in the lab. This is what we do. But that’s not going to work for aging, as I mentioned. We needed to find a different way. And what we discovered and published in 2020 was that if we use some of Yamanaka’s genes, not all of them, but some of them, they turn out to be very safe. And there were three particular magical genes, when put together, that take aging back about 75% and then stop in human cells and in mouse cells, in liver cells, in brain cells. It was a remarkable discovery by my student, Wang Cheng Lu, who’s now at MIT and doing very well on his own. So these three genes are called Oct4, Sox2, Kaler4. You don’t need to remember that. But if I say OSK, I’m referring to these genes. What do they do? They’re called transcription factors, and they turn on other genes and set in motion a program that now we know reverses aging. Are they designed to reverse aging? Maybe. Maybe. We think they’re involved in the regeneration of a limb when you cut it off, a salamander or a tail from a fish, but they’re also very important for making a baby out of an egg. And we’re now using that beautiful process to regenerate tissue. So how do we introduce these three genes and turn them on in a tissue? Well, we use a virus. That’s one way. Standard domesticated virus called an AAV, as shown here. And we chose to tackle blindness. Actually when I say we, I mean Wang Chenglu decided. I protested. I thought curing blindness was about the dumbest thing I’d ever heard. Because why don’t we choose something simple like making the liver healthy? That would be easy. But no, he wanted to cure blindness. And so I let him do that. And what I’ve learned over the years, if my student really wants to do something, I’m not going to prevent them because they’re often smarter than I am. And in this case, I’m glad I did because he showed for the first time, as I will show you in this figure, which was published in Nature in 2020, he was right. We could reverse the age of an optic nerve. In the lower panel is the reversal of aging. These nerves, which are glowing orange, are much younger and they heal in ways that had never been seen before. And he sent me these pictures by text in 2017. And he said, David, do you see what I see? And I said, I think so. And he said, well, what do you see? And I wrote back after thinking for a few seconds, I think I see the future. And it’s turned out to be right that many labs are now using this method to regenerate tissue, to regrow tissues and to make animals live longer. And even monkeys see better and cure blindness. And humans are next. In January, we hope we’ll be the first patient to be treated with this gene therapy to reverse aging in the eye. So what we’re looking at on the screen up here behind me is the optic nerve, which has been damaged in a mouse. It doesn’t grow. In fact, a lot of the nerves are dead because you don’t see them. But what we’re also seeing is the reprogrammed ones that are young are growing back. This is the equivalent of growing a spinal cord if you’ve damaged it. Optic nerves don’t grow back in old mice or even middle-aged mice, only in very baby or very young mice. And so that was damage. But what about other things? What about diseases? So we tested glaucoma, which many of you know about. It’s one of the world’s leading causes of blindness. You can induce glaucoma in mice, and we cured that with the help of Professor Bruce Cassander at Harvard. But then I said to Professor Cassander, Bruce, let’s see if we can cure blindness in old mice. And he looked at me strangely because he’s still thinking, why would we want to cure blindness in old mice? And I said, let’s just try it. And this was the result. He sent me these videos. This is a mouse. Ignore the poop. The poop is irrelevant. The mouse is just a little bit scared. But this is a blind mouse that’s old, cannot see. We know because it’s not moving its head. A normal mouse that can see will move its head in response to those lines. But this mouse cannot see. But a mouse that’s the same age treated with this technology just for six weeks regained its vision, and this was the first time we could show that even loss of vision during old age is reversible. And hopefully you can see that mouse is now turning its head. And we’ve now done this many times. For many different diseases, we’ve published that multiple sclerosis is treatable in animals. What else? We’ve done Alzheimer’s. We’ve reversed aging in the brain. And mice can learn again and remember things again, and even memories come back. And we’ve treated motor neuron diseases and healed the kidney and muscle. And other labs have even put this same set of genes into old mice, and they’ve lived longer. Okay, so that’s great. But now imagine this works, okay? So now we can cure glaucoma, cure macular degeneration, which is also looking promising. But what about other diseases? Can we do other diseases? Well, we’re working on whole body rejuvenation now at a company called Life Biosciences, which was started out of my lab in 2018. And they’re going to be the group that does the first clinical trial in January. They’re based in Massachusetts, which is near my lab. And the team is amazing. And they’re also going to be treating a condition called Nyon, which is like a stroke in your eye. You wake up blind, and there’s nothing you can do about it. Doctors tell you, I’m sorry about that. But we hope to be able to tell patients, we’ll get your vision back. It’ll take a few days, but you’ll see again. Why am I hopeful? Well, my mouse eyes are pretty different than a human eye. There are membranes that are different. I can understand skepticism about the mouse. But now, we’ve shown that it also works in primates. You can restore visual function in a close cousin of ours. And that’s why I think we have a greater than 80% chance of curing blindness in people next year. And this is the timeline of Life Biosciences, just to show you. The drug is going to be called ER100. And it’s going into these two diseases soon. Some of you might be thinking, well, David, that’s very expensive. These gene therapies cannot be used for everybody. It’s again, the elite get the medicine, rich countries, rich people. I agree. We do not want only rich countries to have this technology. So we’ve been working. have been working since 2020 on reducing the cost of these medicines from a few hundred thousand to a few thousand and maybe one day a few hundred dollars, maybe a few dollars eventually. How? By making a pill that does the same thing. So in 2023 we published in a peer-reviewed journal, in 2023 we showed that chemically we could reverse aging in human cells. That was the first time anybody had shown that. But a human cell is not a human. So we’re working towards a couple of goals. And this is where AI comes in. We don’t want to have a cocktail of chemicals. In 2023 the cocktail had six molecules. That’s too much for medicine. We need to come down to two or hopefully one chemical we can use as a pill. But also we need it to be very safe, we need to be very effective and hopefully very cheap. So this is where AI comes in. So I asked AI, one of the chat bots, if we do what we’ve just done by hand, how long would it take us? Remember we’re trying to get from those six molecules down to one chemical that does all of those things. It told us that just the reagents alone would be over a billion dollars. The total cost would be 27 billion. And it would take us at least 160 years. I said if we didn’t have robots, how long would it take? It said 6,000 years. So now with AI, this is literally how long it took us to find these molecules. It was $5,000, mostly electricity and I guess some salaries. $50,000 total. And then it took us less than six months to get to this point. So just to wrap up, I want to tell you about how amazing AI is for this field. This is some examples of the kind of molecules that we’re now testing in mice. We have positive results of rejuvenating old mice. They are healthier, they’re fitter, they can see better, their skin is better. And we hope to test our first patients with chemicals next year. We’ve developed our own system of filtering so that we don’t have to put them into animals or humans to find out that they’re toxic. You can now do this the way Alex will tell you. We filter these before they ever hit an animal. And we’re going through the various organs as a lab. These are my wonderful lab members who are ticking off the various tissues and organs and showing so far in every case that we can treat and in many cases cure an incurable disease, at least in animals. And so this might be the future of medicine. A very simple pill that you can take that resets your age. You take it and every 10 years your doctor gives you a new pill to take for a couple of months. That’s what we’re aiming for. I can’t promise we’re going to get there in the next few years, but I can tell you that I can see how this future is going to happen very likely within our generation and certainly within our children’s generations. And so time’s up. I’m going to finish here. I hope that I’ve inspired you and made you think a little bit about the potential arc of your life, that it may be longer than you thought and that we need to stay alive until these technologies get here. Thank you very much. Thank you.

LJ Rich: Thank you so much. I wish we had time to chat more. But sadly, we’re going to have to move on to our next guest, but David Sinclair, everybody amazing And if you weren’t inspired enough then have I got news for you. Yes, you’ve you’re here You don’t need the whole seat. You just need the edge. Yes. Let’s go on to our next one We’re going to welcome another longevity expert with an incredible set of skills and more patents than I’ve had cups of coffee this morning To hear about his amazing work in drug discovery and beyond It’s a real pleasure to invite to the stage the founder and CEO of in silico medicine Alex Zhavoronkov, thank you very much

Alex Zhavoronkov: Hi everybody, it’s a great privilege for me to speak to you today. Thank you And it’s gonna be very difficult to follow David because he is the king of aging or aging research so to speak and Nobody presents better than David so And does a really valuable job on work for all of you to stay younger, hopefully so whatever you’re gonna hear now, it’s gonna be downhill, but But We are the AI for good conference and what I’ve noticed is that at this conference There are very few health care sessions. And I think that it’s very important to emphasize that AI for health and AI for life AI for longevity is Likely the most important cause you can focus on you know, why? Because we can talk about AI killing us at some point in time all we want or harming us But one thing that will kill you 100% of the time and 100% Certainty is aging and age-related diseases. So in the long term, you’re all dead Actually, some of you may be in the short term. So if you think if you if you are thinking about Kind of you know, 20 50 year horizons, you might want to refocus your priorities and think about how do we? Go after aging. I Represent the commercial company. So don’t buy sell or take any drugs based on what I say today And since we’re at the AI for good conference Do you think there is a consensus for? What it is to be good and I have given that a lot of thought in the past and I wrote several articles I used to be a contributor for Forbes comm for five years so you can go back and see some of those musings So imagine that this life is a video game So what are the winning rules what like if somebody is up there and they’re accounting your goodness What would they look at Would they look at your net worth at your money at those points that we are accumulating? Would there look at how happy you are? How much happiness did you bring to other people? Would they look at your relationships connections? I think that they would look at your impact on science and technology and human development and Also on the number of quality adjusted life years that you’ve created for the others maybe for yourself So it’s also a very egoistic thing to do going after aging but 68 million people Ballpark die each year most of them died due to aging That is more than any war ever fought in this world and we see a lot of protests Alex Zhavoronkov, David Sinclair, Alex Zhavoronkov, Alex Zhavoronkov, Alex Zhavoronkov, Alex Zhavoronkov, the quality of life goes down as well. So I think that extending productive longevity, extending the qualities for everybody on the planet, is probably the most altruistic and the most impactful thing to do. I think I want to encourage all of you to think about that deeper and join us on the longevity quest, especially if you’re a good AI scientist, and you are selecting whether to go into advertising and sales to sell people something they don’t need, or to try to go into something that make people live longer. I myself made a longevity pledge, so I’m not married. I don’t have a family, have, of course, a great partner now, also very dedicated to longevity, and I donate most of what I make right now to longevity causes, and I donate quite a bit to a conference called Aging Research and Drug Discovery in Copenhagen, happening at the end of August, a five-day event. I highly recommend going there. So David presents there almost every year, and he is a keynote speaker there. So I think that there is nothing more important than aging research, and I don’t care whether you’re from another planet, what color you are, what gender you are. If you are really productive, and if you are very focused on aging research, you can call me and maybe you can join the company. So to maximize quality, we started in Silica in 2014 at the NVIDIA GTC conference. I actually presented in 2014, showing that, you know, can NVIDIA solve aging? At that time, people didn’t understand what we were doing, and then we had to create a company that is very carefully crafted to be with The second thing that we have realized, exists and it’s real, is the disconnect between AI and drug discovery. So currently, and I’m going to make a very bad, bold claim that many of you probably will criticize, think if we were to reach artificial superintelligence today, it will take several years to discover a drug, to discover and develop a drug, to put it on the market because you still need to do a lot of testing. So it usually takes us months to develop a new model and put it on the market. So we sell software, but it will take us several years to be able to see if it works on humans or not. And I’ll switch the side here because I really want you to see the slides. I’m going to go very quickly. I have 70 slides and 12 more minutes to go. So kind of the rule of thumb, if you are looking at kind of AI powered drug discovery, is that whatever you have a developmental candidate, and I’m going to explain to you what it is, is going to be, so it basically represents what you had in AI 18 months ago. If you have a drug in phase one, human clinical trials, it probably represents something that you had two and a half years ago. So it actually takes a long time to get an AI powered drug discovery drug to clinical trials. And in order for me to be able, I’m an AI guy, in order for me to be able to actually move pretty quickly and with very high level of quality of the molecules so that we can later sell them to pharmaceutical companies for a lot of money that we can reinvest in research, I partnered with Dr. Ren Feng who is one of the best drug discovery, drug hunters in the world. And together we managed to create a software suite. I don’t like to use the word AI, it’s software because you can actually get it either via commercially available route or we also open source quite a bit of software. And then we use the software to generate novel molecules for a broad spectrum of diseases, mostly going after age related diseases and dual purpose targets that are implicated in aging and disease at the same time. And we try to take them all the way as far as possible into human clinical trials to see if the generation conditions that were used to design the drug in the original kind of design phase are confirmed experimentally all the way to human clinical data. And now we have several cases where we have reached human clinical trials, actually 10, and one has completed phase 2A. I’m going to talk about that later. Also whatever I say, we try to be maximally transparent so we publish quite a bit of research papers and sometimes patents and sometimes… David Sinclair, Alex Zhavoronkov, Alex Zhavoronkov, Alex Zhavoronkov, David Sinclair. Drinking this conference is not because I don’t like to drink, it’s because I have to work at night. So look at some of the papers. And this is one of the very important slides. So if you’re not familiar with drug discovery and development, this is how it looks like. So first you start with identification of protein targets. So basically trying to model the disease, understanding why disease is happening. So for Alzheimer’s, for example, we still don’t have good targets. So that time may take decades and cost you billions of dollars. And then once you identify a target, you take it either via chemical route or biologic route. So either you do a small molecule. So David actually talked a lot about how he managed to discover small molecules for reprogramming. That’s like crazy feat. Really, really cool. Superb work. So follow life biosciences. We are also taking a small molecule approach. But you can also do antibodies and maybe fast forward a little bit. Because then you have to inject instead of taking it orally. But here you can see at the very bottom the amount of money you need to spend at every stage. And the second line is number of years. So it actually is a very time-intensive process. So if you are getting into this field and if you really want to do an AI-powered, AI-generated drug yourself, prepare for 10 years with a company. So I’m with a company for 10 years now, for 11 years. And I’m planning to spend hopefully another 11 years, maybe more. And one very important milestone where AI can really help you accelerate, right, so to go from 0 to 100 in seconds, is the early stage drug discovery. So that’s where you can design the bullet. Once you reach this point called the preclinical candidate or developmental candidate, that’s it. You are moving with the speed of traffic, this very regulated area. Yes, you can cut some time and costs with AI-based submissions and regulatory reviews. But the actual time savings comes at the very beginning. And later, super-regulated territory moving with the speed of traffic. So what can a generative AI do for you today? I’m going to show you some case studies, because otherwise it’s just blah, blah. A very important slide. So now we actually do have benchmarks. We know how long does it take you to go from 0 to 100. developmental candidate. Usually at that time, you identified the target, generated the molecule, performed a huge number of experiments in cells, performed experiments in mice and other animals, you have to do those. We try to do less of them with AI. But then you need to go into humans. You cannot do it without animals. So we also do 28-day non-GLP toxicity studies in two species before we nominate developmental candidate. So that is a very important milestone, as I’ve explained. So far, since 2020, we nominated more than 22 developmental candidates. So we’re nominating a few in a few weeks, we hope to announce. 10 reached human clinical stage. That’s a big milestone. One completed phase 2A. And the typical time for us to reach a developmental candidate now, depending on the complexity of the target, is about 13 months. Longest time, and we published a big paper on that, was 18 months for a very novel target that doesn’t have a lot of evidence that it’s working in disease. Our capacity was nine drugs in 2022. By the way, the FDA usually approves about 50 drugs a year. So ballpark. And in my opinion, about seven of them are innovative. Everything else is just copy, paste, or combine. So nine preclinical candidates at the early stage is actually a very considerable number, even for a big pharmaceutical company. And this is kind of a visualization of that. So traditional approach usually takes you about two and a half to four years, depending on the complexity of the target. And we managed, my record was nine months for a reasonably novel target. And the drug that is currently in phase 2 complete, phase 2A complete, took us 18 months. And now we are trying to improve this process to accelerate it, the drug discovery process at the very beginning. And we are doing it with robotics. By the way, if anybody tells you a story that, oh, AI powered robotics and robotics powered AI machine learnable data is going to transform drug discovery, no. Incremental advances, but those advances are very valuable. So here is actually a video of my lab. It’s not animation. It’s real footage. I wanted to make sure that it looks like a spaceship. And people feel that they work in the future. So please visit. It’s in Suzhou in China, moving to Shanghai now. So here is a workflow. You take a tissue, cell, or organoid, throw it in the lab. The lab picks it up, grinds it, microplates it, does quality control, passes it to another room. I have a bunch of autonomous guided vehicles with micrometer precision. Part of the sample goes into the incubator, part into the imaging station. Depending on how much sample you’ve got, you can do several types of imaging, usually three or four. And then you also do three types of what we call omics. So we do transcriptomics, methylation, and whole genome sequencing from the sample. And then all of this data comes into AI layer. And that’s where magic happens. So AI automatically identifies protein targets of interest and creates an automated validation plan. So then it basically takes two compounds from libraries, or it does CRISPR, or it does RNAi, looks at ways to validate its hypothesis. And if the hypothesis is correct, it gets rewarded. If it’s not correct, it gets punished. And also, if we identify some promising targets, we pursue them later in the human lab. We’re constantly improving this lab. So we’re trying to constantly improve the capabilities. So now we are going to the next level of automation and getting this kind of automated companions. I saw a few funny ones here at the conference. But one of the reasons to do this in China is that you can actually do things very quickly and with very high quality. And right from the factory. You can get a platform for a non human level automation Teach it how to pipette how to do agent changes many equipment types are not exactly Designed for human Automation, so you need to have human fingers. So it’s actually easier to do it with a platform like that rather than Trying to over optimize Automate with robotic arms so Are there any good experimental case studies of novel drugs discovered using AI? So my first venture into experimental validation of my generative system The one that we came up with in 2018 called generative tensorial reinforcement learning Way before, you know chat GPT announced their chat GPT So that paper made us famous together with Alana’s put a good sick out of that time He was at Harvard then he went to University of Toronto. We still collaborate and we’re very very good friends That paper went viral. So got very high altmetric score. If you know what it is, it’s a measure of attention And in that paper, we got a protein target from a partner who was testing us Using generative tensorial reinforcement learning in 21 days synthesized six molecules four of them worked one reached the mouse And got pharmacokinetics into them in my mice and we also got it in 46 days So that was in my opinion as one of the records it was criticized by more traditional Medicinal chemists as it always does and we got another nation by technology paper explaining why it should work And what was the point of the study and last year another group out of the University of Cambridge by Tom Blundell Take a picture of this if you want to get into details really powerful paper So they actually started tracking all of the experimental validation Exercises around the world from generative systems. And if you look at reinforcement learning are quite a bit of those experimental Publications were coming from my group. So we kind of proved that Generative is not just blah blah. It really can generate good molecules and Afterward we started looking for ways to Really impact the industry to do a big demo novel target novel molecule all the way to human clinical trials can we do that and That’s where I for the first time tried to go after aging in a big way So we tried to identify protein targets that are implicated on aging and disease at the same time So there are many proteins that actually move in both directions, right? They change with age and also in a disease So we published a paper how we can identify those dual-purpose therapeutic targets Identified some that are implicated on many hallmarks of aging. So David kind of very briefly mentioned Kind of the hallmarks that may be affected by reprogramming most of them can be affected by reprogramming but with small molecules We really need to choose the protein targets that are implicated maybe as many in as many as possible So we identified one published a nature by technology paper, which explained how we did this from zero To small molecule chemistry all the way to human clinical trials So if you are to look at one paper, look at that one and in this paper, we showed that we Cracked biology using AI correct chemistry using AI Identified the proper indication for this target protein Protein target small molecule pair and took it all the way to human clinical trials So we did a phase zero trial in Australia and healthy volunteers. Then we did two phase ones in New Zealand and on China that was during COVID So it was pretty hardcore and then we started to phase two ways in idiopathic pulmonary fibrosis It’s an age-related lung disease kind of the Alzheimer’s of the lung Which hits the elderly and usually they expire in a very short period of time due to the loss of forced valve capacity so scarring and less lung so we started phase 2 a trials in two places and Also while we were doing it Using that, we demonstrated and used our robotics lab to show that this compound is a xenomorphic compound. So it actually reverses the senescent signature, the aging signature in multiple cell lines or cell types. And published a paper on that pretty recently. I’m going to skip the details. And one of those phase IIa trials has completed late last year. And we just published a paper a month ago in Nature Medicine. It’s a high-profile journal in our field, very difficult to publish, has to go through multiple peer-reviewed cycles. And there, we did a placebo-controlled double-blind study with four arms. We have 30 milligrams QD once a day, 30 milligrams BYD twice a day, and 60 milligrams once a day QD. And demonstrated that compared to placebo, our 60 milligrams actually shows very promising force vial capacity change. So instead of just slowing down the decline, we increase the force vial capacity. And that is very rare for everything that has been tried in idiopathic pulmonary fibrosis. It’s one of the top results. And if we remove one of the outliers on the placebo, it becomes very, very clear how it works. So look at this paper and check out the results. But in addition, what we did, we collected proteomic data from patients at every stage, like at several stages of the clinical trial. We drew their blood, took whatever was possible to extract on the proteomic side, so all the proteins using a system called O-Link, analyzed it, and showed that we actually confirmed the antifibrotic signature with a drug. So that’s also pretty rare. So it took us about five years to get there. But we’ve demonstrated that you can actually generate a reasonably safe for this indication and promising efficacy drug out of generative AI with a novel target. And we also measured the aging clocks. So we used aging clocks that David was also talking about at the very beginning when we were trying to select the target. So it kind of gave us indirect evidence that this is an age-associated target. And we also used the proteomics data that we’ve collected from the patients now to analyze the potential effects on what we call biological proteomics age using several aging clocks around the world. I know I’m almost done. And we also explained how we got there. up until the proteomics and our kind of paper is below. Now we’re submitting our most important paper, our magnum opus, so if you work for one of the top journals please be lenient, where we show the effects of this drug on proteomics aging clocks. Now I’m almost done, one minute, 30 seconds. We have another person that we need to do, we have to keep to time.

LJ Rich: So now environmental sustainability, very important. He’s still going, we love you though Alex, thank you so much. And come to my conference. Yes, come to his conference. Wow, thank you so much, I’m so sorry to cut you off. Sorry. No, no, no, it’s fine, I understand. We just have so many amazing speakers, it’s really difficult. Alex, ladies and gentlemen, who would have believed it? You wouldn’t think it, but he’s over 250 years old and it’s just amazing. Thank you so much to Alex and also David before that. And now it’s time for me to invite one person who is just an all-round awesome human being. It’s such a pleasure to be on stage with her again. I’m not even going to do a long introduction because we’re short on time, but what we’re not short on is awesomeness. Everybody, please join me in welcoming Tilly Lockie. There we go, it’s lovely to see you again. We’ve been on stage together before. You too, we’re best friends at this point. Well, yes, I mean there is something really fascinating when you meet somebody who’s creative and who loves technology, which we do, and of course you are wearing the new model of these. Can you tell us a little bit about that? I know, it’s like sometimes tech takes ages to

Tilly Lockey: advance, but literally like from the last time I saw you to now, we’ve got a complete new model, which is so exciting. So I helped to develop and co-design Bionic Arms, and last time I saw you, we were wearing the Hero Arms, which were like tried and tested for like 10 years. And then just like last month, we launched not one, not two, but three new products. So this is the Hero Pro, there’s the Hero Rugged, which is like ultra strong, super durable, crazy farmer guys use it, and then there’s the Hero Flex. which is the activity arm, which you saw me playing drums with earlier. Yes, I mean, this is just a fantastic idea.

LJ Rich: It’s like a Swiss army knife, but for your hands. Yeah, exactly. And there’s something quite special about this particular model, which is that you can separate the hand and use it independently, which we’re going to do, right? Yeah. So you need a bit of help, so I’m going to literally… Come here. Which way do you want me to twist? That way? Twist and pull. Twist and pull. It’s like opening a bottle. There we go. I’ve got it. You’ve literally given me a hand on stage. Given you a hand. And look, the first time we did this, I was completely and totally surprised. Now I’m just chilled holding your hand. And I can still move it. And it’s still moving. Isn’t that amazing? Yes. You can put it on the table. I will. Oh, you can move it around? Is this like the Addams Family? Oh, my goodness. That’s hilarious. Okay, what’s the range on this? How far away can you do this?

Tilly Lockey: I actually don’t know, because I was a little bit delusional. I was thinking it doesn’t matter, but there is a limit. I’ve been told by the CEO, but I haven’t tested that yet. Maybe we’ll have to send it over there.

LJ Rich: Well, we’ve got a few minutes. We could just slowly walk it all the way over to the door. When it stops working, we can just get somebody to bring it back. But I’m just going to grab this for you, because I think one of the most exciting things about this is obviously all the possibilities that this opens for you in terms of your creative style and all the things that you want to do. Shall I give you your hand back? Yeah, you can give it back. Or you can keep it. I don’t mind. I mean, I would quite happily keep it as a piano player, of course. It would be quite amazing. We could do literally a duet where you could maybe control more of these at once. I mean, at the moment, how are you controlling these? So, these bionic arms, they’re controlled by EMGs.

Tilly Lockey: So, there’s nothing invasive at all. We’ve just got these sensors here, which we call milepods, because they’re totally new with the company as well. Because they’re wireless, and like you saw, the hand can move when it’s not even attached. Because of that nature, that wasn’t just for fun to detach a hand. It also has these benefits, because before now, these prosthetics have only been available for below-elbow amputees. But now, because of the wireless nature, it means above-elbow amputees can wear them, because these milepods can just clip in and out. They can go anywhere. So, you can operate them using a muscle on your back or on your neck. Like, wherever you have strong muscles, you can operate a hand. So, it makes it more accessible to, like, all these other disabilities. , we are going to talk a little bit about the technology that is out there and how it is being used to operate these. You find when we were talking on stage before that these movements and gestures seem to happen as if they are a part of your body because obviously we talk and we gesticulate as well. It is quite amazing that you can have this extra layer of expression. So I think it is probably now is the time we need to talk about the back story, how it is so driven. It is like, you know what, I want these to do more. So tell us the story. Tell us how you started. Well, it has been a journey to say the least. I lost my hands to meningitis septicaemia when I was 15 months old. Honestly, that was an accomplishment to even be alive. They really did not think that was going to happen. So the fact that I was able to live and lost my hands at the wrist even, I could have lost my legs or anything. So I was actually really quite lucky, which you probably would not think in the crowd. Losing your hands is probably a nightmare, but obviously I get to wear all this cool tech and it has definitely been important to me to get them the best they can possibly be. And even wearing these now, these are like the top of the game. There is still so much more that we can do and a way to go. But yeah, my first prosthetic I ever got was literally not technological at all. It was like a tube to put your arm in, a couple of hooks.

LJ Rich: because this is the next model up. What’s been the thing that frustrated you about earlier models? What were the driving forces, and can I also quickly add that for a company to ask an end user, we’re listening, what do you need, to have this sort of powerful collaboration has worked out so well, not just for the company, not just for you, but for everybody that’s using this. It’s such a great model, and I know that people will be taking that and running with it. So, yes, before I got all excited about that, tell us more. Sorry, what was the question again? I was asking how, what you were frustrated with before.

Tilly Lockey: Yeah, so, to be honest with you, with the hero arm that I was wearing, I thought it was the best thing ever, and it was the best thing ever at the time. It’s only when I’ve upgraded to this one, and I wear those ones now, I’m like, damn, we’ve come such a far long way, because it takes a lot of practice to get used to. I’m like, oh, but I’m sentimental about my old hero arms, but now I wear them, and I’m like, no, these ones are so much better, so much dexterous, more dexterous, and yeah, so there was a couple of things you’re frustrated with. For example, my earlier prosthetics, it was how slow they were, painfully slow. Like, I think I was in McDonald’s just trying to eat some chicken nuggets, and it just took so long just to do all that. So these are actually the fastest and the strongest and everything on the market actually now, so they’ve broken records.

LJ Rich: Do you think you would ever wear something that doesn’t look sort of human, if that makes sense? So right now, we’re on a model where there are four fingers and a thumb. that you can pick things up. I’m sort of saying this with the knowledge that you have a set that allows you to do something quite musical. Yes. But what else, I tell you what? Why don’t you tell our amazing audience

Tilly Lockey: the things that you can do with your other attachments? So, yeah, there are other Hero Flex, and the Hero Flex is basically the socket and then unlimited attachments, like, you know, like anything that you can 3D print and pop into this little wrist socket, you can wear, you know. And I actually know a guy who 3D prints his own. He made, like, a little Lego man hand, which is pointless. It doesn’t do anything, but it’s pretty fun, you know. But, yeah, there’s all these attachments. And me, personally, I’ve got one that allows me to play the drums. So you can, like, slot the drumsticks in and look like a crazy, like, centipede when I’m, like, wearing them. But it allowed me to play the drums. And I have another prosthetic, which isn’t technological at all, a literal, like, DIY project that allows me to play the piano, which is made out of a toilet roll tube and a golden rake. So it just shows, like, what you can do. A golden rake? A golden rake. Is that so you can play more than one note at once?

LJ Rich: Yeah, it’s triads, triad chords. I feel a collaboration coming on, because we’ve talked about this the last time as well. But just the idea, and, yes, you need to look up the video of Tilly playing the drums, because it’s really fun. And I think it also illustrates that just because, you know, you can augment yourself with so many different ways by thinking literally outside the box. So I guess let’s talk a little bit about what’s next for you. What’s the future hold? Well, my absolute goal is to have the prosthetics

Tilly Lockey: match the ones that we see in the sci-fi films at the end of the day. So that’s always going to be my ultimate goal. Be brutally honest with the feedback and, like, give as much input as I can to ultimately make the prosthetic better for anyone who might use it, like, all over the world. To have, you know, an upgrade or a design thing that I implemented in the hand be somebody’s favourite product or part of it in another country, and, like, it helps them on the day-to-day. That’s what it’s all about. So I want to keep just being honest and giving feedback and testing them so that one day we can get them just as good as human hands and, if not better, which scares a couple of people. But that’s my vision. And also to get them as accessible as possible, make sure everybody has access to them. I always say having four limbs is a human right. It’s no point in tech being there if people don’t have access to it. So doing all we can in that regard as well.

LJ Rich: Yeah, and in terms of the way that these are made has it changed over the years? Obviously, from wearing a harness and some rubber bands, this is quite far away. And I noticed you’ve got lights in. I don’t know if you had lights in the previous ones?

Tilly Lockey: Yeah, well, they’re not meant to be glowing. It’s because they’re actually dying right now. You’ve literally used them so much. And it’s a USB charger, everybody.

LJ Rich: So if anyone’s got a charger… No, you’ve got one, haven’t you? That would be great. But I love the idea that, you know, the human body can be charged up just like our smartphones, just like my iPad. I think if you’ve got a USB-C slot in your phone, you can probably charge your phone from it as well, which is pretty cool. So it’s like hand or arm, which one is more important? I can just imagine the next time we meet. I mean phone or arm. Yes. You just have like, well, actually, LJ, I now have put four extra batteries in this and I’m just sort of walking around with a super heavy battery, but you could charge the phones of everybody in the room. Yeah, exactly. I mean, this opens up this idea of augmentation as opposed to this being thought of as a disability. This is a superpower and we’re talking about superhumans. So I think this is a great way to show that just because you need some augmentation, it doesn’t mean that you are treated differently. In fact, you will have the ability to do whatever you like. So what’s next for Tilly Lockie? Well, you’re right. Like there’s a lot of like fear mongering, I think when it comes

Tilly Lockey: to tech and that’s when I like to like come in and show people that there’s all these amazing, we’re here like AI for good, you know, we’re hearing about all the amazing advancements that is really changing this world for the better. So I wanted to even be here today. And for me, yeah, I want to try and get them the best they can possibly be. You know, I’m obsessed with like the design. So we’re going to get all the new colors, like the hero arm has all these different colors. They’re under development. And then hopefully you’ll be working with some designers to really bring to light the expression because there’s all these mental benefits as well as the physical with these bionic arms. And that’s what I like to talk about. I have one more question before we finish.

LJ Rich: This is amazing, isn’t it, everyone? Yes. An audience, I did say before Tilly came on, I said this audience at AI for good is absolutely astounding because it’s filled with people who not only understand and are interested in technology, but they’re interested in innovation and doing it for good. So I’m so glad that we got to connect you all together. And of course, you’re going to be around and you have a LinkedIn as well as an Instagram. Yes. So get ready. You might have to hide. But in terms of what we’re doing for the future, what I ask all of the people that I’m lucky enough to be on stage with is I ask, what do you want help with? Because, as I say, this audience is amazing. There are people in here who change the world on a daily basis. And now, if you have anything that you’re struggling with or that you would love to do next or that you would like help with, please tell me. What is it?

Tilly Lockey: Well, I think at the forefront, accessibility is so important. And we have a charity called Open Bionics Foundation, and investors are always very welcome into the company. Anything that can see it grow more is amazing. And for my own personal ideals and goals within prosthetics and design, co-design is so important. I’m on a mission to have prosthetics seen in a beautiful light. I think that’s been my own personal journey. After being given a cosmetic glove and not asked any questions, not asked, what do you want? I think I have some Bionicons that I like covering glitter. And that’s sort of like a statement for me now. Coming up, I’m just being like, you don’t need to cover it up. You can actually accentuate it in a really beautiful light. So that’s what I try to do. I want to see them on the runways and in all the fashion magazines. That’s my goal. That’s wonderful. Everybody. Well, thank you so much. Tilly, Lachie, ladies and gentlemen and everyone, what an amazing guest.

LJ Rich: Thank you. Thank you so much to Tilly. And yes, her story continues. And audience, thank you so much for being a part of that. It’s really fun having so many innovative speakers. And we have even more for you. So many more, in fact, that we’re sort of bringing some extra chairs. Let me just get here so you can see me and I can see you as well. And yes, music is something that has always kind of connected us together. So many people in technology and musicians. Can I see a quick show of hands? Audience, how many of you play a musical instrument? Can I just see? It’s a surprising amount. everybody thank you very much because and this is really strange I did a conference a few weeks ago for the travel technology industry and I asked at the beginning if I was doing a show with my piano and I asked at the beginning how many of you are musicians and maybe three people put their hands up so there is a really interesting crossover between sort of AI and technology and creativity and we know you love music and on Friday evening there’ll be something awesome for you as well in fact talking of that we do have a fantastic panel coming up understanding emergent intelligence in work, agentic, robotic and the keyword creative so let me please invite to the stage very quickly Chris, James, Michaela and Harry and you will enjoy our next panel thank you

Agreement points

Technology as a Force for Human Enhancement and Social Good

Speakers

– Alex Zhavoronkov
– Tilly Lockey
– LJ Rich

Arguments

Technology for Social Good and Impact

Prosthetic Technology and Human Augmentation

AI for Good Community and Collective Problem-Solving

Summary

All speakers agree that advanced technology, particularly AI and biomedical innovations, should be developed and applied to improve human lives and solve significant societal challenges. They emphasize moving beyond fear-mongering about technology to showcase its positive applications.

Topics

Development | Human rights | Sociocultural

User-Centered Design and Collaborative Innovation

Speakers

– Alex Zhavoronkov
– Tilly Lockey
– LJ Rich

Arguments

AI-Powered Drug Discovery and Development

Accessibility and Design Philosophy in Assistive Technology

Collaborative Innovation and User-Centered Design

Summary

All speakers advocate for involving end users in the development process and creating collaborative partnerships that benefit not just companies and individual users, but entire communities. They emphasize the importance of listening to user needs and feedback.

Topics

Development | Human rights

Accessibility and Universal Access to Advanced Technology

Speakers

– Alex Zhavoronkov
– Tilly Lockey

Arguments

Technology for Social Good and Impact

Accessibility and Design Philosophy in Assistive Technology

Summary

Both speakers emphasize that advanced medical and assistive technologies should be accessible to everyone, not just wealthy individuals or countries. They advocate for reducing costs and increasing availability of life-changing technologies.

Topics

Human rights | Development

Similar viewpoints

Both speakers view their technologies as enabling human augmentation and enhancement rather than merely treating deficiencies. Zhavoronkov’s age reversal research aims to restore youthful function, while Lockey’s prosthetics provide capabilities that can exceed normal human abilities.

Speakers

– Alex Zhavoronkov
– Tilly Lockey

Arguments

Cellular Reprogramming and Age Reversal

Prosthetic Technology and Human Augmentation

Topics

Development | Human rights

Both speakers recognize the intersection between technology and creativity, with Zhavoronkov noting the connection between AI and innovation, and Rich observing the strong correlation between technology professionals and musical creativity.

Speakers

– Alex Zhavoronkov
– LJ Rich

Arguments

Technology for Social Good and Impact

Technology and Creativity Intersection

Topics

Sociocultural | Development

Unexpected consensus

Redefining Traditional Medical and Disability Paradigms

Speakers

– Alex Zhavoronkov
– Tilly Lockey

Arguments

Aging as a Medical Condition and Root Cause of Disease

Accessibility and Design Philosophy in Assistive Technology

Explanation

Both speakers challenge traditional medical paradigms – Zhavoronkov argues aging should be classified as a treatable disease rather than natural process, while Lockey advocates for prosthetics to be seen as beautiful augmentation rather than hidden medical devices. This represents a fundamental shift from acceptance to active intervention and enhancement.

Topics

Human rights | Development | Sociocultural

AI and Robotics as Creative Enhancement Tools

Speakers

– Alex Zhavoronkov
– Tilly Lockey
– LJ Rich

Arguments

AI-Powered Drug Discovery and Development

Prosthetic Technology and Human Augmentation

Technology and Creativity Intersection

Explanation

Unexpectedly, all speakers demonstrate how advanced AI and robotics enhance rather than replace human creativity – from drug discovery to musical performance to prosthetic design. This counters common fears about AI replacing human creativity.

Topics

Sociocultural | Development

Overall assessment

Summary

The speakers demonstrate strong consensus around technology as a force for human enhancement, the importance of user-centered design, and the need for universal accessibility to advanced technologies. They share a vision of AI and biomedical innovations as tools for augmenting human capabilities rather than merely treating deficiencies.

Consensus level

High level of consensus with significant implications for reframing how society approaches aging, disability, and human enhancement. Their collective vision suggests a future where technology enables human flourishing rather than merely addressing problems, requiring shifts in medical paradigms, accessibility policies, and public perception of advanced technologies.

Different viewpoints

Unexpected differences

Overall assessment

Summary

The discussion featured minimal direct disagreement as speakers presented complementary rather than conflicting viewpoints on technology for good

Disagreement level

Very low disagreement level. The speakers presented different but complementary approaches to using technology for human benefit – pharmaceutical/biological intervention, prosthetic augmentation, and collaborative innovation processes. The lack of direct disagreement suggests a shared vision among the speakers about technology’s potential for positive impact, with differences mainly in methodology and focus areas rather than fundamental philosophical disagreements. This alignment may reflect the conference’s ‘AI for Good’ theme, which naturally attracts speakers with similar values regarding technology’s beneficial applications.

Partial agreements

Similar viewpoints

Both speakers view their technologies as enabling human augmentation and enhancement rather than merely treating deficiencies. Zhavoronkov’s age reversal research aims to restore youthful function, while Lockey’s prosthetics provide capabilities that can exceed normal human abilities.

Speakers

– Alex Zhavoronkov
– Tilly Lockey

Arguments

Cellular Reprogramming and Age Reversal

Prosthetic Technology and Human Augmentation

Topics

Development | Human rights

Both speakers recognize the intersection between technology and creativity, with Zhavoronkov noting the connection between AI and innovation, and Rich observing the strong correlation between technology professionals and musical creativity.

Speakers

– Alex Zhavoronkov
– LJ Rich

Arguments

Technology for Social Good and Impact

Technology and Creativity Intersection

Topics

Sociocultural | Development

Key takeaways

Aging should be redefined as a treatable medical condition rather than an inevitable natural process, representing the root cause of most diseases

The information theory of aging suggests cellular aging is primarily a ‘software problem’ (epigenome corruption) rather than hardware breakdown

AI can dramatically accelerate drug discovery from traditional 2.5-4 years to 9-18 months for developmental candidates, though clinical trials still require years

Cellular reprogramming using three Yamanaka factors (OSK) can safely reverse aging by ~75% in cells and tissues without causing cancer

Modern bionic prosthetics represent human augmentation rather than just replacement, offering capabilities that can exceed natural human function

Co-design with end users is essential for creating effective assistive technology that meets real-world needs

Longevity research represents one of the most important applications of AI for social good, given that aging causes more deaths than any war in history

Resolutions and action items

Life Biosciences to begin first human clinical trial of gene therapy for vision restoration in January 2024

Continue development of chemical alternatives to gene therapy to reduce costs from hundreds of thousands to potentially hundreds of dollars

Pursue whole-body rejuvenation research through Life Biosciences company

Submit research paper on proteomics aging clock effects (described as ‘magnum opus’)

Continue improving automated drug discovery lab capabilities with robotics integration

Develop new prosthetic colors and work with designers to enhance aesthetic expression

Promote accessibility through Open Bionics Foundation and seek investors for company growth

Unresolved issues

Timeline uncertainty for when aging reversal therapies will be widely available to the general population

Cost and accessibility challenges for advanced gene therapies, particularly in developing countries

Safety concerns about reversing aging too much, which could cause cancer or death

Regulatory approval processes that still require traditional timelines despite AI acceleration

Limited understanding of optimal dosing and timing for cellular reprogramming treatments

Range limitations and technical specifications for wireless prosthetic control systems

Scalability challenges for making advanced prosthetic technology accessible as a ‘human right’

Suggested compromises

Developing both expensive gene therapy approaches and cheaper chemical alternatives to serve different economic segments

Using AI filtering systems to reduce animal testing while still meeting regulatory requirements

Creating modular prosthetic designs that can serve multiple disability types through wireless technology

Balancing aesthetic beauty with functional capability in prosthetic design

Focusing on dual-purpose therapeutic targets that address both aging and specific diseases simultaneously

If you’re not really worried about aging, maybe you’re too young, maybe you’re in denial, but what we now know is that aging, there’s a universal process that drives it… aging is a medical condition and that it is increasingly preventable and even treatable.

Speaker

David Sinclair

Reason

This fundamentally reframes aging from an inevitable natural process to a treatable medical condition, challenging centuries of medical thinking. It’s provocative because it suggests we’ve been approaching disease treatment backwards – treating symptoms rather than the root cause.

Impact

This comment established the entire conceptual framework for the discussion, shifting the conversation from traditional disease treatment to aging as the primary target. It set up the revolutionary premise that underpinned all subsequent technical discussions about AI-driven drug discovery.

Our bodies, when it comes to aging, are more like computers. We have information that we get from our parents, and that that information gets lost over time… it’s a software problem, not a hardware problem.

Speaker

David Sinclair

Reason

This analogy is profound because it completely reconceptualizes biological aging in computational terms, making it seem solvable rather than inevitable. The software/hardware distinction suggests aging can be ‘debugged’ or ‘reinstalled’ rather than just managed.

Impact

This metaphor became the bridge between biological research and AI applications, making the connection to computational solutions feel natural and logical. It prepared the audience for Alex Zhavoronkov’s AI-focused presentation by establishing aging as an information problem.

68 million people ballpark die each year most of them died due to aging. That is more than any war ever fought in this world and we see a lot of protests… I think that extending productive longevity, extending the qualities for everybody on the planet, is probably the most altruistic and the most impactful thing to do.

Speaker

Alex Zhavoronkov

Reason

This comment reframes the scale and urgency of aging research by putting it in stark comparative terms. The war analogy makes aging research feel like a moral imperative rather than just scientific curiosity, challenging how we prioritize global problems.

Impact

This shifted the discussion from technical feasibility to moral urgency, elevating longevity research to a humanitarian cause. It provided ethical justification for the massive resources being devoted to this field and connected individual health concerns to global impact.

If we were to reach artificial superintelligence today, it will take several years to discover a drug, to discover and develop a drug, to put it on the market because you still need to do a lot of testing… whatever you have a developmental candidate represents what you had in AI 18 months ago.

Speaker

Alex Zhavoronkov

Reason

This insight reveals a crucial limitation that tempers AI hype – even perfect AI can’t eliminate the time lag of biological validation. It’s a sobering reality check about the practical constraints of translating AI advances into medical treatments.

Impact

This comment grounded the discussion in practical realities, managing expectations about AI’s immediate impact while explaining why current clinical trials represent older AI capabilities. It added necessary nuance to the otherwise optimistic narrative about AI-driven drug discovery.

I always say having four limbs is a human right. It’s no point in tech being there if people don’t have access to it.

Speaker

Tilly Lockey

Reason

This statement reframes prosthetics from medical devices to human rights, challenging how we think about technological access and equity. It’s particularly powerful coming from someone who directly benefits from and helps develop this technology.

Impact

This comment elevated the entire technology discussion beyond innovation for its own sake to questions of justice and accessibility. It provided a moral framework that connected individual technological advancement to broader social responsibility, influencing how the audience might think about AI development priorities.

After being given a cosmetic glove and not asked any questions, not asked, what do you want? I think I have some Bionicons that I like covering glitter… you don’t need to cover it up. You can actually accentuate it in a really beautiful light.

Speaker

Tilly Lockey

Reason

This challenges the medical model’s assumption that prosthetics should hide disability rather than celebrate capability. It’s a profound shift from concealment to expression, from shame to pride, fundamentally changing how we think about human augmentation.

Impact

This comment transformed the discussion from purely functional considerations to questions of identity, self-expression, and the social meaning of technology. It suggested that the future of human-AI collaboration might be about enhancement and celebration rather than just restoration.

Overall assessment

These key comments collectively transformed what could have been a technical presentation into a philosophical and ethical exploration of human enhancement and mortality. Sinclair’s reframing of aging as a solvable information problem established the conceptual foundation, while Zhavoronkov’s moral urgency arguments provided ethical justification for the massive resources being devoted to this research. His practical insights about AI limitations added necessary realism to balance the optimism. Lockey’s contributions elevated the entire discussion beyond individual health to questions of human rights, accessibility, and the social meaning of technological augmentation. Together, these comments created a narrative arc that moved from scientific possibility through moral imperative to practical implementation and social justice, making the discussion far more compelling and comprehensive than a simple technology demonstration.

How to reduce the cost of gene therapies from hundreds of thousands to thousands or hundreds of dollars to make them accessible globally

Speaker

David Sinclair

Explanation

Sinclair acknowledged that gene therapies are expensive and expressed concern about only rich countries and people having access to this technology, emphasizing the need to democratize these treatments

How to reduce cocktail of six molecules down to one or two chemicals for aging reversal pills

Speaker

David Sinclair

Explanation

Sinclair mentioned they need to simplify their chemical cocktail from six molecules to ideally one chemical that can be used as a pill, which is where AI becomes crucial for optimization

Testing the range limitations of the wireless bionic hand control system

Speaker

Tilly Lockey

Explanation

When asked about the range of her detachable bionic hand, Lockey admitted she didn’t know the exact limit and hadn’t tested it yet, suggesting this needs further investigation

How to make prosthetics more accessible and ensure everyone has access to them

Speaker

Tilly Lockey

Explanation

Lockey emphasized that having four limbs is a human right and expressed concern about advanced technology not being accessible to everyone who needs it

Further research on dual-purpose therapeutic targets that are implicated in both aging and disease

Speaker

Alex Zhavoronkov

Explanation

Zhavoronkov mentioned they identified proteins that change with age and also in disease, suggesting this is an important area for continued research to maximize therapeutic impact

Analysis of proteomics aging clocks effects from their clinical trial drug

Speaker

Alex Zhavoronkov

Explanation

Zhavoronkov mentioned they are submitting their ‘magnum opus’ paper showing the effects of their drug on proteomics aging clocks, indicating this is an ongoing area of research

How to achieve prosthetics that match those seen in sci-fi films

Speaker

Tilly Lockey

Explanation

Lockey stated this as her ultimate goal, indicating significant research and development is still needed to reach human-level or superhuman prosthetic capabilities

How to integrate prosthetics into fashion and design to change public perception

Speaker

Tilly Lockey

Explanation

Lockey expressed wanting to see prosthetics on runways and in fashion magazines, suggesting research into the aesthetic and social aspects of prosthetic design is needed