way, way bigger than amd at $4.2, palantir at $7 and tesla at $13

[00:00:00]Hello everyone and welcome back to the world's best investing podcast. As many of you know, I'm an early AMD and Palanteer investor. I went long AMD at $4.2 per share in 2016 and I went long Paluntu at $7 per share in 2022 and we're skipping Tesla, which I went long in 2016 at $13 per share. Those were all great picks and they have changed my life. They've enabled me to do investing full-time. However, what I'm working on now is going to be orders of magnitude bigger than that. I am working towards a single pick in the biology space that I believe is going to eclipse all of these picks in the past combined because this is all humans will care about in roughly 5 10 years time. The only thing the average human is going to care about is sticking around for a long and good time with loved ones. And biology is now changing in a way that enables us to do that. the businesses we've seen, the giants we've seen emerge from the internet like Amazon and stuff like that. Now, we're going to see the same with biology because we now see the emergence of horizontal biology platforms. Bottom line is I prepared an in-depth presentation just for you guys with the entire map of the biology space. This is a value chain that goes from atoms all the way from physics to chemistry to the emerging biology to the final clinical outcomes. An AI or a combination of AIS is going to be connecting atoms to clinical outcomes.

[00:01:23]How patients actually do over time. And the AI is going to do something which I call extended lifespan per token. Every single token that a model prints is going to be expanding the lifespan of billions of people worldwide. And this is going to be among the biggest industries in the world. It's going to make many of the industries that today are hot look outdated and even small. So let's get deep into today's presentation. And this is the full map.

[00:01:48]We're going to do roughly between eight and nine tickers, I believe. So, we're going to look at eight nine companies.

[00:01:53]And this is going to do a full sweep of the work that I've been doing for roughly 2 or 3 years now. You're going to get it all in this video. So, let's get deep into it. Okay. So, the bottom line of the thesis is life extension per token. This is really what the service is going to be about. You're going to have an AI model running in the background with all of your biology information and it's going to be telling you what you should do to enhance your lifespan. It's going to be a little bit black mirror but I think that if you can take this with a grain of salt and also focus on enjoying life I think it's going to bring about tremendous innovations that are going to be great for everyone. So the value chain which is it primarily ends in biology that's what this is concerned with goes from atoms to clinical outcomes. So a bunch of things are happening now which enable this to happen. So in the past biotech was a very boom and bust kind of environment and you had to kind of get lucky both as an entrepreneur and investor to get one thing right. What's changing now is that our ability to read biology which is this curve here the sort of green mint color interpret biology which is the darker green curve and write biology which is the red curve orange depending on how you perceive colors. These three things are going up exponentially. our ability to read both the proteome, epigenome and genome is going up a lot and the resolution is rising exponentially. This is important because as we've discussed and we will review this now, the human body is just a giant Lego puzzle. You have proteins with specific shapes binding onto proteins with other shapes with the inverse shape. It's just like Lego.

[00:03:21]Okay, that's the proteome. Beneath that, we have the epiggenome, which is the layer that decides what genes cells read and therefore what proteins get printed into the proteome. And then finally the genome which is where the source code is contained. All of that is getting to the point where we're going to be able to read the entire data set real time at every single second of the day basically. So that's going to be a very exhaustive data set on on your biology.

[00:03:46]Now our ability to interpret that data was basically zero just 3 years ago. Now with the AI revolution we can increasingly dump all of this data into an AI model and the model will make sense of it for us. This is not something a human mind can do but AI is very good at that. So the read function and then the interpret function basically paves the way to with maximally precise accuracy introduce write methods. Now we can actually write back into biology. So bottom line is if your AI says hey this bit of your biology is degrading you're now increasingly able to write back into your biology and stop that or improve whatever physiological process and so forth. And then simultaneously the cost of these three things is collapsing really fast. So we've seen the cost of peptides in the US drop 80% over the past year specifically. That's that's um that's specifically the case with GLP1s.

[00:04:40]So this thing is massively deflationary and just our ability to read, interpret and write biological code is rising very fast. What this means is we're going to see horizontal platforms. Um as I was saying this changes biotech fundamentally. Biotech was a trial and error process. just a few years ago. Now it's increasingly datadriven and it's going to come down to ontologies as we will see now. Ontologies, by the way, for those of you that are new to the channel, just digital twins, exhaustive digital copies of reality that AIs can use to learn and predict stuff. So basically the end game here is have an AI that does the read and interpret function and can predict what it is that you need to write into into your biology to extend your lifespan. That's why the whole essence of the presentation is extended lifespan per token.

[00:05:27]So this is a new scaling law you have.

[00:05:29]You're going to have your biomarker network. There's going to be one platform that holds the vast majority of your biomarkers. That platform is going to be constantly sending those biomarkers to an AI model. And then the AI model is simply going to return predictive insights. This is thanks to this key value pair. You're going to have your biomarkers at any point in time. And then the AI is going to be able to see what intervention leads to what biomarker delta. So if you have high cholesterol did did the last intervention that you did decrease cholesterol or increase it. Was already your cholesterol at optimal levels. So did you actually want to go up or down?

[00:06:04]The bottom line is you're going to have your biomarkers. You're going to do stuff and then the AI is going to learn from the delta which is going to broadly translate to a clinical delta. So this is the key value pair that AI is going to use to understand what any person on the on the network at any point in time should do to optimize clinical outcomes and then by extension health span that's what we care about. So previously human life was about escape velocity 1.0. So you would live a number of years and then after that you basically die because your biology degrades over time.

[00:06:36]You're not escaping death. I'm not saying we're going to escape death, but for sure what we're entering now is life extension per token. And I think we're looking at some form of longevity escape velocity whereby in the next 5 to 10 years if you do this properly and you're sort of forward looking and you innovate a little bit obviously uh always with the help of doctors and stuff which I am not. So none of this is medical advice and shouldn't be interpreted as such.

[00:07:03]This is just for educational purposes.

[00:07:05]Bottom line is at some point in in the next 5 to 10 years we enter a an inflection point in human history in which you every year you live you gain the ability to live additional years and that's life extension per token and it ends in something really big right so if you think about consumption dollars in relative terms people pay a lot of money for a lot of things today and that's fair enough those are useful things like for example streaming music streaming mo movies education traveling and stuff but in this sort of world where whatever incremental dollar you allocate towards health means more time here on earth and with your loved ones and stuff. My view is this is going to absorb a lot of uh consumption dollars. Consumers are going to flock in this direction like we haven't seen in history. So let's just review the the value chain on a first principles basis. We've discussed in past videos how cells produce amino acids which then form chains short short chains known as peptides. So they come together into peptides. Peptides are just chains of amino acids. Then peptides because of electromagnetic attractions and I have a bunch of videos on that that you can see fold up into proteins. That's why alpha fold was so big because it enables us to predict what chain of amino acids therefore what peptide folds into what protein. In the body the three-dimensional shape of a molecule determines its function. This is true for proteins. And so proteoforms are proteins with a slightly different shape. the cell has ways to change the shape of an end protein slightly so that it goes on to perform a slightly different shape in the body. Clinical outcomes whether someone is healthy, someone is not healthy, all happen along this causal chain. And so if you're healthy, it means this chain is working.

[00:08:50]If you're not healthy, it means this chain is not working. Now the body is just atoms at the essence. Amino acids are essentially collections of atoms.

[00:08:58]Illness, as I was saying, is essentially protomic dysfunction. And so an AI that gets a view this chain longitudinally over time. So it's able to see how it evolves over time and at the deepest level all the way from atoms to the chemical stuff with amino acids then the physics you know how these things are interacting in the body per electromagnetic attractions and then how it evolves into biology. An AI that sees that it's going to learn how to play chess with a protein. So it's going to map it. It's going to learn how it evolves over time and then it's going to outplay the bad guys. The bad guys like pathogens and so forth. And but as I said, this is not medical advice, okay?

[00:09:37]This is just first principles analysis.

[00:09:39]They all play at the proteomic level to some degree to a large degree. So if you have an AI that's able to outplay them and predictively, preventatively tells you is able to predict what you should do before a bad guy even has a chance to win, well that's going to be extremely valuable.

[00:09:56]So beneath the surface, we actually have more data to read. I've discussed this as well in other videos, but peptides are basically a shortcut. Peptides are something cells print. When they read genetic code, they print these peptides, and then the peptides either go out into the body and perform functions by themselves or become proteins by folding. Whichever way you look at it, that's just a shortcut. What's happening underneath that is you have the epiggenome which tells cells what part of the genome they should be reading and then the genome itself which is the genetic source code if none of this works if the cell machinery isn't actually working then peptides the epiggenome or genome won't really be useful because the cell itself can't work and that's where where we introduce cell therapy which we've discussed at length already in all of my videos that just consists in introducing new cells which go and do what healthy cells should be doing anyway. it. The bottom line is this is just a stack. It's more data to read. So if you can read the proteome, you can read the epigenome and the genome, that stack tells you what the body is actually doing and then you can contrast whether it's actually what it should be doing or not. And then you can decide what to write back into the biology. So the value chain splits into two buckets which is read and write. In the read function we have nilus which reads the proteome. Then we have Tempers which is which gets an oncology molecular read. So they're very good at cancer tests and so those tests then give them a lot of molecular data about different cancers and then how that correlates to outcomes. Then we have Caris Life Sciences. The ticker is dollar C AI. It should be plotted here on the presentation but it's not. And it does the same thing except it gets deeper genetic level than Tempest does.

[00:11:40]And there's there's a trade-off there.

[00:11:42]And you can see it in the in the deep dive that I posted today, but these two companies do essentially the same thing.

[00:11:47]Then Recussion Pharmaceuticals ticker RXRX does all the wet lab complexity. So looks at all the molecules and drugs very closely and that's a very hard thing to do and then produces data that trains AI models. They have a physics AI model, then they have a chemistry AI model. And these two models understand how physics and chemistry emerge into biology. So bottom line is they can fine-tune drugs to lower toxicity and enhance uh efficacy of the drugs and that's because they have that bottom layer of the AI value chain that I was talking about. They go into the atoms and then they see how atoms translate into biology. So if you want to think about it this way, this would be sort of the lowest level. They come in at the atom level. Then you have nillas that comes in at the proteomic level which is relatively complimentary but it sits slightly higher up in the value chain.

[00:12:37]Then you have Tempest and Caris life sciences who do oncology data. So that would be a higher level of abstraction.

[00:12:43]And then finally we have HIMS which is a D2C distribution layer. So although the market sees them as a GB1 company, this is actually a D2C healthcare infrastructure. They own the point of contact with the patients and thanks to HIMS labs, they can get in an increasing volume of biomarkers, more and better biomarkers for everyone. So it's just a different way of getting molecular data at scale. It's they're essentially going after the same thing as Tempest and Caris and we'll discuss that now but Tempest, Caris and HIMS are simply seeking to build a healthcare ontology which is essentially the data layer that becomes predictive and acts out the the um the scaling we were looking at which is extended lifespan health span per token. Then in the right function we have a bunch of companies like immunity bio in which I'm an investor they reboot the immune system. Then we have Mick Therapeutics here on the screen and what they do is leverage a third branch of the immune system that has been abandoned by imunotherapy and has extraordinary properties which make it highly complimentary to immunity bio.

[00:13:47]Then we have nerve genen and I posted a deep dive on this company this week. It promises to be something like OSPIC for the nervous system and become a platform for nervous system repair which is going to be foundational because the nervous system I would say is as important as the immune system itself and these two companies I think are a pretty exhaustive bet on that and then we have parabulus which is a company that's working on a technology which is called helicons. So traditionally it's uh a lot of targets in the body have been undruggable. We talked about the protein being a Lego puzzle. Proteins with a shape binding onto proteins with an inverse shape. The thing is if the shape of a protein isn't squiggly enough, if it doesn't have deep pockets and deep things coming out, protuberances, it's hard to bind onto those structures. So a lot of the processes in the body, including a lot of the things that shouldn't be happening, are protein to protein interactions with flat surfaces.

[00:14:42]So uh it's hard to bind onto them. And then a lot of the times those interactions are happening inside the cell. So the problem that this company solves is twofold which is one the ability to bind onto flat surfaces and the ability to do that inside cells. So having permeated cell membranes the biomarker layer is interesting and I think that a lot of the value is going to acrue here because if you don't have the predictive capabilities if you don't have the ontology which is the AI model telling each person what they should be doing to extend health span per token the scaling that we were talking about the right function is not very valuable if at the limit you have no idea what someone should do someone should reasoning at the limit if you have no idea what someone should be doing to optimize their health at all then the right function has no value right so just a quick recap nilus biotechnology here's the name of the tickets by the way which I know you guys like they get proteomic data single molecule proteome at scale that's important because as I said every single illness at the very least leaves a proteomic signature and you can on a first principles basis stop that illness if you interrupt or if you revert the proteomic dysfunction then recussion pharmaceuticals RX RX X is a generative AI drug machine which maps atoms to biology. So that's complimentary to a proteomic read and that's going to be very important. Then we have Tempest and Caris which is molecular oncological or oncologic I'm not sure molecular cancer data to understand all kinds of tumor types and what you should do to enhance patient outcomes over time. And then we have HIMS which is D2C for last. What I call LS is longevity as a service and it connects biomarkers to outcomes and that's just a slightly different approach to this company here. The thing is you can put these companies into two broad buckets which is who owns distribution and who does not. And this is important for reasons that we will see now. Essentially if you own distribution you have pricing power and you own the relationship with the customer. And then if you if you don't own distribution then you have to roll up into into a distribution network and that might is likely to erode your margins and and pricing power and stuff like that. But it doesn't have to be an absolute terms because a lot of these vertical solutions with no distribution are actually quite hard to replicate. So we will see cases of strong prices pricing power there too. Nilus biotechnology and recursion pharmaceuticals do not own distribution.

[00:17:07]These are powerful reads. These are very hard to replicate but they sell into other people's rails. Then we have 10% carriers who own distribution but they run on incentives underlying under underlying rails which is basically the traditional hospital system. Nothing wrong with that. I've been a customer.

[00:17:25]They've actually saved my life many times. So I'm very grateful for that.

[00:17:28]But the incentives are not optimally geared towards this new health care industry which is longevity as a service. and it's a function of optimizing your health rather than making money when you are sick. So the incentives are wrong there. There are some avenues down which these companies may evolve differently and I cover these in their respective deep dives. But then we have him who does own distribution and does have in my opinion the right incentives. They own the direct customer relationship and they are aligned with keeping you well. They make more money by optimizing your health over time.

[00:18:01]That is actually what the business is evolving onto now.

[00:18:05]So now we get into depth with the various companies. We have Nautilus biotechnology. Again the place in the in the chain is the proteome read layer single molecules. So they increasingly understand what's happening in your protein. They have a technology called iterative mapping which maps up to 10 billion intact proteins per run. So if as they roll this out, this is going to end up in a place in which they know exactly what's happening in your proteium at all times. And then if we look at read write functions, excuse me, which we will do next, such as peptides and epigenome tuning, genetic editing.

[00:18:37]This uh an exhaustive proteomic read tells you straight away what it is that you need to completely sap an illness and just destroy it. The platform itself is called the Voyager platform and scientifically in my opinion this is very hard to replicate. The first assay is life for tow proteo which is very important in neurodeenerative conditions. So like Alzheimer's and stuff like that. Uh these diseases seem to be at least at the end stage of proteomic dysfunction the result of proteins misfolding and taking on shapes which they shouldn't be taking on. And then the price as I was saying this is this is a sort of early stage company in in my opinion they're looking at an inflection point in the next year year and a half two years in which this is increasingly a protoform level map at scale. They go from not only doing degenerative neurodeenerative conditions to pretty much any condition that we think is incurable. I think these guys end up with a deep proteic protein.

[00:19:34]Protetoforms again are just slight modifications of proteins because shape determines function and so a slight change in the shape is going to change the function in the end slightly and that's how this function emerges. Then we have 10% caris which again the place in the chain is just getting a molecular read on cancers and linking that to clinical outcomes. They do this by having really good tests which um hospitals and physician physicians like and that enables them to get a lot of data which they then put into a platform and correlate to outcomes. So the platforms are increasingly intelligent and are able to tell physicians what any patient should do to optimize outcomes and and that's incredibly powerful and you can see examples of that in the deep dives. Caris seems to get a deeper read.

[00:20:19]They read the full exo while tempest AI reads only a select gene panel. So that's important because as science discovers more biomarkers which are relevant in cancer and oncology having a more exhaustive data set enables you to remine your proprietary data. So you don't need to go and scan every single patient again. You just have an exhaustive data set and I do believe that might give them an advantage over the long term. Caris is already producing cash as I highlighted in today's present um deep dive. However, Tempest operates at more scale and as we know in the digital economy, scale is incredibly important. But I I do believe there's a trade-off between scale and depth and we are going to learn something about proprietary data uh in this particular business case. These are molecular reads paired with outcomes and it's very hard for anyone else to do that because it's hard to integrate into hospitals. So I'm quite bullish on these companies too. By the way, I only own a position in hints at immunity bio. Just so you guys know, I I say this all the time, but I just want to make it clear that the vast majority of these are companies that I'm studying today. Then we have recussion pharmaceuticals, which maps physics and chemistry to biological emergence. The human body is, as I said, in essence just atoms and because of physics, they aggregate into molecules and then molecules give way to to biology fundamentally carbon based. And so the complexity here is the wet lab which is going through all the molecules and uh sequencing them understanding the structures understanding being able to predict how the physics are going to behave how the chemistry is going to behave and so get getting this very exhaustive read and training a very capable AI that maps physics to chemistry to biology they're able to fine-tune drugs and so the highlight from the deep dive is that they're able to take drugs which have been abandoned by industry just because they were too toxic and they weren't really working because of the toxicity. Lower the toxicity and actually make it work. So that sort of suggests that these guys have a very good read on physics and chemistry and how it maps onto a biology. Then we have him um it should it shouldn't be him and HIMS it should be him and hers labs. So apology for the typo. This is the two D2C delivery layer that owns the customer. So they own the relationship. They have HIMS labs which they are compounding over time adding more and better biomarkers and it's built for less. The fundamental distinction between HIMS and other companies uh that exist in the healthcare space is the incentives. And the reason they've been criticized so much over the past few years is that they truly do think about the customer which is something you don't see in uh for example Tempest AI and Caris life sciences earning school transcripts.

[00:22:58]They do mention stuff like predictive um diagnostic yield. So what percentage of times they are getting diagnostics right which is important for patients but they never talk about how do they think about radically improving patient outcomes and I can guarantee there's a lot that could be done there beyond the diagnostics actually on top of the diagnostics they don't really talk about it this is not to say I don't like the companies I do think the management teams are great but qualitatively if you look close enough I think there's a big distinction to be made in terms of incentives now my view is that the read layer is going to roll up into the ontology. Again, the ontology is just the AI model built with lots of proprietary data that no one else has access to in terms of scale and quality that is predictive in terms of biology and is therefore able to extend health span per per token per dollar by extension. So, the people that don't the companies that don't own distribution by default are just going to be rolled up into these infrastructures. They're going to have to do distribution via these ontologies like Kims, Whoop, Aura, whichever one you you think is going to win and they're going to become a part of the ontology.

[00:24:06]Their pricing power is going to be a function of how difficult they are to replicate. In my view, these companies that I mentioned like Nillos, biotechnology, recursion, and all of these are actually quite hard to replicate. So I do believe they will see an an an appealing element of pricing power. But I do think that most of the value and the power is going to acrue to the ontology because that's the channel that becomes the engine of predictive insight and it tells customers what to do to avoid getting sick and aging at all. Without the ontology basically the right layer is relatively useless. It doesn't actually move us forward much relative to where we are today. Although you know it it is a big jump and and you will know that if you've tried peptides which I don't suggest you should of course none of this is medical advice but the bottom line is that most of the value in my opinion becomes the ontology because that is how read functions get delivered and priced and that is also how write functions get delivered and priced and actually the dynamics behind the ontology are a lot simpler to analyze in these cases. If you want to get read and if you want sorry if you want to get deep into the read and write functions you need to get good at biology. If you want to understand the ontology all you need to understand is what I call the Costco algorithm which is an an incredible focus on delivering more value per dollar spent to end customers such that that generates massive network effects which then basically crowd out every single other player. On the other hand, if you want to get good at reading and writing biology, you need to really understand that. The main takeaway for you if you don't want to do that is that these things are really taking off. And so the biology ontology is coming. Now we have the right layer. And there's four foundational interventions that I've found that I've done deep dives on. And I don't think this is an exhaustive list. I think we're going to see a massive explosion here. Every single day that I sit down to work, I'm learning something new about biology. And so this space is just tremendously complex and rich. But I do think there's an opportunity to find massive players here. So as I said, I'm an investor in immunity by IBRX because I do think that rebooting the immune system is going to be something foundational especially over the coming five years per things that I'm seeing a view of the world that I have. But the bottom line is we have four companies here which I've studied.

[00:26:14]The last one I'm actually doing a deep dive on now at present. So this is going to be a highle overview but I think you're going to find it useful. So we have immunity bio ticker IBRX. They reboot the immune system. MC therapeutics ticker I N KT actually not M I N K it's ticker I N KT and they do immunity too but they leverage an abandoned third branch of the immune system which has extraordinary properties and sort of complete some of the loopholes that I see here in immunity bio this kind of imunotherapy like K and ENK uh car so these are just ways of engineering tea cells and natural killer cells then we have nerf genen which which is about nervous system regeneration as I And then we have parabilis which repairs errors in protein networks. So as I said illness often perpetuates itself at the proteomic level. These are just errors in terms of how proteins are relating to each other. And with parabolas technology you can actually hypothetically on a first principles basis fix that. This one by the way as I said I suspect could evolve into something like oenic for the immune system. So going deep into immunity bio what they've built is an IL is 15 super aagonist interlucan 15 is a receptor present in macrofasages and other immune cells and when you bind onto that with a shape that's just the inverse shape of the receptor in question you're telling the cell to activate proliferate so reproduce and activate others and what this seems to do is reboot the immune system in an orchestrated durable manner that's not a that's not an actual medical claim okay I'm just going by the latest research this is not approved by the FDA and stuff at least yet. So this is obviously just for educational purposes. This is in my view a platform not a drug because what is 15 seems to do on a first principles basis is tell these cells so it it basically increases T and natural killer cell count and once you do that you can build specific solutions on on top of those increased cell counts. So once T and ENK cells go up both in terms of their total count and their cytotoxicity, their ability to kill the bad guys, you can build specific solutions on top of that. So you can engineer those tea cells to kill specific bad guys. You can do it with natural killer cells too. And then you can also leverage other pathways which teach the tea cells and the natural killer cells what they should be killing specifically. So this is actually a platform. If you want to draw an analogy here, this looks to me something like Nvidia in the biotech space in this new emerging wave because as I said, this is going to be about longevity as a service. And so it's very important to rejuvenate and weaponize the immune system over time. This is really a foundational aspect in the human body.

[00:28:51]If you don't have a good immune system, well, you know, most likely you're in for a short ride. And that's something that I've had to fix myself. This is why I know that Mick Therapeutics is interesting. The thing with um immunity bio is T- cells specifically they work along what is known as the peptide MHC complex major hystocompatibility complex. Peptides as I said are just chains of amino acids strung together and so te- cells can rec recognize what peptides should be in the human body and which ones should not. So they can recognize peptides of the bad guys and so then they go and kill the bad guys.

[00:29:25]The thing is sometimes things like tumors and stuff will play around with peptides and they will hide the ones that they know tea cells recognize. So there's another avenue which is very interesting and this is the one that I said imunotherapy has left behind and that's the lipid complex. Essentially lipids are foundational structures within human uh molecules, cells and so forth also for the bad guys. So bacteria, viruses and stuff and lipids do not evolve as quickly as peptides.

[00:29:52]They're actually quite hard to evolve.

[00:29:54]And as I said, they are foundational. So there's a very clear self and non-self signal when looking at peptides. Um, INKTs, which is invariant natural killer tea cells, which are really a combination of natural killer and T- cells. So it's it's a merge between the adaptive and the innate immune branch, can tell very easily what's a native lipid and which one is not. So by definition, it's low toxicity, at least per the clinical evidence that's coming out now. And it's native to stroma, too.

[00:30:22]Stroma is basically dense tissue and as you may know a lot of the tumors we struggle with are in dense tissue like the pancreas and liver and stuff. So these cells are native to that tissue and they can swim there and then persist and they are low toxicity which means the allergenic components. The ability to just take these cells off the shelf and then introduce them into a new human is actually a lot higher than Ki or KN.

[00:30:45]So T- cells are natural killer cells and then by definition it would seem to me that the unity economics are going to be a lot better. So, and so that makes it interesting by itself, the ability to leverage a part of the immune system which is independent to the peptide MHC complex. It's low toxicity, native to dense tissue, and then it has the ability to kill. So, it's cytotoxic.

[00:31:07]It's a word you're going to see around a lot, which is why I'm using it, which I I believe it will help you understand that. And then it's also able to regenerate, at the very least promote an environment that encourages regeneration. So it it seems to be anti-inflammatory too. So it it would seem per the early preclinical evidence that not only can this technology be used to kill bad guys including the fortresses around the dense tumors but also it can be used to address conditions in which the immune system is sort of weakened and pro-inflammatory which is something that's increasingly uh common. Then Nerf Gen is working on a peptide called MPG 291 if you must know and it's a peptide that can take the break off nerve repair. So it has a two-part design. It has a cell penetrating sequence which has actually been derived from HIV which is very good at getting into cells plus a sequence that releases the break on axonal repair. If you've taken my biology course there's there's a mod it's not actually a course, right? I mean I have the investing part and then I have the biology part applied to investing. But we have a lesson within the biology module which is about liantan receptors.

[00:32:13]How nothing really happens in the body by chance. It happens because a a molecule of a given shape has binded onto a receptor on a cell. So here what happens is that once there's an injury in the nervous system, there's there's a there's a lian to receptor interaction which tells axons to not repair. And so this peptide gets in there and takes out that interaction between a lian and a receptor. I have all the details in the deep dive and it tells the axon to repair and it seems to work preclinical evidence again not medical advice all of that stuff. My view is that this could potentially become a system level platform because the nervous system is at least as foundational as the immune system and this is this is sort of a vertical manifestation of my foundational peptide thesis. We've seen Ozenic GP1s more broadly bring in more revenue than open AAI and Anthropic combined in the year 2025 by a factor of five or six and that's just one peptide.

[00:33:09]So my view is that we're going to see trillion dollar peptides emerge per foundational category in the body. One of them is obviously uh metabolism which is important for GLP you know GLP1s.

[00:33:20]Then I think the immune system is going to be a big one which is why I'm investing in immunity bio and then a third one a tentative third one I think is going to be the nervous system but there's going to be more of them because I mean everything in the body is essential pretty much. So we're going to see a bunch of these trillion dollar uh giants emerge. Then we have parabolis medicines which has developed a technology called helicons. These are stapled helical peptides that reach flat intracellular targets. Peptides are strings of amino acids as we've discussed. So electromagnetically they're pretty susceptible to changing shape as they interact with other things in inside the human body. Right? So it's very hard to deliver them inside a cell.

[00:34:00]So going through a cell membrane with an intact structure such that they perform a specific job. That simply was just not possible previously. And this company has developed a technology which seems to work which is peptides in a helical form. And so it maintains these peptides known as helicons maintain their helical form can go through the cell membrane and therefore bind onto specific structures inside the cell. This drugs the undruggable because roughly 80% of disease driving proteins offer no pocket for small molecules and sit inside the cell. Right? So if you can get in there and bind onto surfaces which are kind of flat and you can do that because your peptide is going to maintain that sort of inverse flat structure that wraps around the flat surface on a protein inside the cell. You open up this massive field it's literally 80% of disease driving proteins. You can obviously see where this is going now with Nilus biotechnology. If you have an absolute read on the um protomic dysfunction for Alzheimer's or whatever it is and you have the ability to get into the cell and absolutely redirect that basically correct the error in a protein network then that's the end of a lot of things that today terrifies and so the helix is structurally an advancement which I think is very interesting and there's going to be a bit of a flywheel here in my opinion as they crack one protein that they're able to bind on to I do believe this has a some this has some chance of evolving into a platform that can just remediate any protein error, any any network protein error inside the cell. So which I think is very interesting, right? So in terms of capturing this, the most obvious price for me is the Costco for biomarkers. Everything gets delivered through that infrastructure. So again, as is the case with tech, you know, if you want to be a a super semiconductor investor, for example, you can go and look for specific endpoint solutions. So you could have done that. Well, you know, any company that's gone up like a billion fold because they developed this vertical piece of technology that maybe someone else pays a lot of money for that works. But in my opinion, that's hard to do. What what's obviously happening in my opinion is that the read and write functions are taking off vertically and the cost is collapsing to zero. And so most of the value is going to acrue to the ontology, which is, you know, why you have people with him going back and forth saying, "Well, it's a GP1 company. the margins uh quarter overquarter growth getting stuck in short-term financials completely missing the big picture. That's why I have a $2,000 per share intrinsic value target by the year 2030 just because what I'm seeing coming is just extraordinary.

[00:36:25]It's it's an inflection point in human history in which uh again we have the scaling law emerge which is basically extended health span per token and it's going to be you're not going to care about anything else as a consumer pretty much. So it's a massive space. I think it's going to take off vertically in the coming 5 years. now is June 2026. So by the year 2030 31 this is mainstream. I mean it's already mainstream with peptides but this is top of mind for everyone in the economy as is today the case with LLMs. Most of that value acrru to the predictive layer because without it the whole thing just doesn't work.

[00:36:58]You don't really care about reading or writing a fraction as much as you would care if you do have the predictive capabilities. and then read and write do percent upside cases with a lot of upside but just all of that most of that gets rolled up into uh the ontology I have a concept called ontology velocity as many of you know which is you know when everyone this winter was absolutely hysterical about AI killing companies like Dolingo and stuff like that turns out Dolingo's daily active usage just go up vertically and free cash flow per share is going up vertically and of course the gap will close soon but the bottom line is if you have proprietary data access to data at scale and quality that no one else has access to. You can train an AI that at the beginning marginally outpaces the generic model and then as that delivers more value per token to end customers that draws towards you more and better network effects which produce more proprietary data which then enable you to recursively fine-tune the model such that ultimately in terms of token per dollar um in this case extended health span per token per dollar the proprietary model ends up radically outpacing the generic model which doesn't have all of this context.

[00:38:07]So a model that doesn't have access to uh Nillus recussion all the biomarkers from the HIMS D2C infrastructure may be rolled up with the oncology ontology from Tempus and caris life sciences. A model that doesn't have access to that is going to be marked by a model that does have access to that. So this is how value creation works in the modern economy. Now this is going to be a race to zero in many regards because customers want max per dollar, right? No one wants to pay for anything and what they want is maximal health and lifespan extension per dollar spent ideally for $0. We've seen that happen in the internet. At the beginning you had to pay for stuff and now you don't have to pay for stuff. Maybe you just have to look at ads and stuff like that. So this is going to be highly deflationary.

[00:38:50]Historically the healthcare industry has been maximally inflationary. So this is going to be a big reversal of the trends. I think we're going to see margins compress. You already see that with peptides with prices coming down of GOP 1's 80% in 2025. So that's definitely a race to zero. And of course, where is the value occurring to?

[00:39:07]Well, the point of contact with the customer. So in my opinion, pricing power survives most intact at the top at the most predictive ontology which defends its pricing because customers do not care about second best. If you're plugged into an intelligence that keeps you alive, you're going to be wanting to pay for the one that keeps you alive. N plus one versus just N. And that's going to have massive pricing power especially understanding the concept of ontology velocity which is you know a marginal advantage at any point in time does compound into an exponential advantage.

[00:39:37]Now um I used AI to make this presentation right so all the concepts are mine and stuff like that and AI added this slide at the end which I thought was pretty funny. I think it's a post I made on X some time ago and I said until death or defeat is psychological and just coming out of the experiences that I've had uh with healthcare over the past few years now being way above uh baseline in terms of cognitive emotional physical performance all across the board. I do believe this very much to be true and this has been an inspiration continues to be an inspiration in me looking at these companies and understanding the emerging revolution again underpinned by that new scaling law which you know perhaps I should publish a white paper on that or what but the bottom line is that indeed until death or defeat is psychological.

[00:40:20]So if you have the right information, if you have a deep first principles understanding about biology, I think that uh the sky's is the limit right now in terms of probably your own biology, which I cannot advise on because I'm not a licensed physician or anything, but certainly in the case of investments. So what I see coming now is just a massive massive revolution uh way bigger than anything we've seen to date. Like I'm happy I made those investments in in Palunteer and AMD and Tesla and the new ones that I've made I think are going to go very well, but this one this one I think is going to be huge. So, I continue studying this. And I hope you guys enjoyed today's presentation.

[00:40:54]If you enjoyed today's presentation, could you please like and subscribe and share this with one friend whom you think will enjoy it. These deep dives are for free and so the only way this grows is with your help. If you want a first principles basis framework to understand biology deeply like I do, then you may consider taking my course.

[00:41:10]I have the mental framework that I use to find companies early like Tesla, Palanteer and AMD. And then I have a first principles basis biology framework which helps me understand my own biology and then biology in general to make what I think is going to be investments that are going to eclipse my past performance. So thank you guys very much for joining me as always. Take care and see you next