In this episode, Daphna sits down with Elio, co-founder of LongevAI, a platform using artificial intelligence to help longevity clinics analyse biomarker data, streamline documentation, and build personalised client plans.

With a background in computer science and a lifelong passion for health optimisation, Elio offers a grounded, honest perspective on what AI in longevity medicine can do today, and where the limits still lie.

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What We Cover

• How LongevAI was founded and what problem it solves for longevity clinics

• What it means to automate clinical documentation without removing the clinician from the process

• How AI interprets biomarker data, and why speed and accuracy both play a role

• The hallucination problem: what it is, why it happens, and how it is being managed

• Data privacy, GDPR compliance, and anonymisation in clinical AI tools

• The importance of human oversight, why the clinician must always approve before anything reaches the client

• How AI and clinicians can learn from each other in a feedback loop

• Wearable integration and the role of continuous vs snapshot data

• Where AI in longevity is heading in two to five years, including gene therapy modelling and whole-cell simulation

• Why younger people are beginning to engage with longevity, and what still holds them back

About the Guest

Elio is the co-founder of LongevAI, a software platform built for longevity clinics. He holds a double bachelor's degree in Computer Science and Information Science, and has been focused on health optimisation and AI since his teens. He co-founded LongevAI in December 2024 alongside Cosmina Druica, whom he met through a longevity meetup community in the Netherlands.

🔗 longevai.com

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Beyond Longevity is hosted by Daphna Stern · beyond-longevity.co.uk

In this episode:

00:00 Welcome and Guest Intro

01:52 Elio Background and Origins

04:01 What Lev AI Does

05:06 Automating Clinic Workflows

07:52 Speed vs Accuracy

11:45 Oversight and Patient Trust

12:57 Privacy and GDPR Security

13:56 How the Model Improves

15:06 Limits Data and Hallucinations

21:37 Training and Integrations

23:45 Personal Biomarker Walkthrough

28:34 Explaining LLMs to non-tech people

33:38 Future of AI and Longevity

35:32 Young People and Longevity

39:56 Rapid Fire Questions

43:45 Wrap Up and Key Takeaways

Foreign.

Welcome to Beyond Longevity, the podcast that explores not just how we age, but how we can build a longer, healthier future for ourselves.

My guest today on Beyond Longevity is Elio Verhoff, co founder of Longevity, a company building AI tools for longevity clinics to help clinicians analyze biomarker data, streamline documentation, and create personalized client plans more efficiently.

Elio comes from a computer science background and from a young age he was deeply focused on health, energy, performance and the question of how we can use better tools and better thinking to improve long term well being.

That eventually led him into the longevity field, where he began exploring how AI could be applied in a way that is actually useful in clinical practice.

In this conversation, we discuss what AI in longevity medicine can and cannot do today, where it can help clinicians, where the risks still are, and why human oversight remains essential.

We also discuss where Elio believes the future of AI and longevity may be heading over the next few years.

Hi Elio, welcome to Beyond Longevity.

It's really nice to have you on.

You are co founder of Longevity, an app, a technology that helps clinicians in their daily life.

Why don't you introduce yourself a little bit to our listeners and tell us a bit more about why you founded longevais and what it does?

Sure.

Thank you for having me.

I am a longevity enthusiast and I'm also very much into AI, learning as much about it as possible and applying it to my daily life and in the business as well.

I'm Elio Verhoef from the Netherlands.

I'm half Austrian and I've studied the double Bachelor of Computer Science and Information Science.

And ever since the larger AI models have been rolled out, I started to get into that, like GPT3, et cetera.

At the same time, for the entirety of my life I've basically been focusing on optimizing almost everything I can see.

Optimizing the computer, but also optimizing myself as a person in terms of personal development, in terms of optimizing my energy, health especially, and wellbeing.

So from the start I've avoided sugars and all these kind of things, and later on I got into longevity, even though I didn't really know the term yet, like finding ways to optimize how I behave, how I feel, my health in general.

And because I was into this and at the same time because I didn't see a lot of other people similar to me or around me who had the same passion, I started looking online for communities that I could join that were similar to this.

Then I found a Community in the Netherlands that organizes monthly meetups in Longevity.

And that is where I met Cosmina.

Every month I've been joining these meetups.

And that's because we knew each other, we saw how well we could work together.

She had extensive consultancy experience and I had AI experience, and we were both dedicated to longevity.

So then in December, she asked me, well, do you want to start a company together, focus on longevity by leveraging AI?

And I obviously said, yes.

Fantastic.

Sounds really insightful, your lifestyle and at such a young age.

So you're ahead of the curve, I think.

Do tell us a bit more about Longevity AI.

What exactly is it?

What does it do?

Who is it for?

Longevity AI started initially as a longevity a company to help longevity companies by leveraging AI.

So it was a consultancy company to apply and build custom software for longevity clinics, where that custom software leverages AI as effectively as possible to help them automate operations, be more effective in what they do.

And we also, yeah, started working with researchers this way.

And this is still partly our business model, but now we're shifting more into software as a service because we've seen that longevity clinics specifically had quite a few of the similar issues across all of them.

And we can build a platform that can cover most of them in a very configurable, customizable way.

And that's what we did.

So now this is a more scalable way to help, way more longevity clinics help their people improve their health.

And that's what we started doing while still working together with researchers and other companies on the site.

You mentioned that you have custom software to automate the work.

What exactly does that mean in this specific case?

It's a software for the clinicians of these longevity clinics, and they do have quite a few manual operations with regards to the data of their clients.

And we'll be interpreting the blood work, creating client reports, creating notes during conversations with clients, and quite a few other things like creating an action plan for the clients.

All this documentation, note taking and creating of shareable documents for the client in a branded clinic style can take many hours, actually for every single client if you don't automate it in some way, especially if you want to go a bit more comprehensively.

So if you have these conversations, which can sometimes take three or more hours, and then you have to have somebody in that conversation to take notes during the call and then process it afterwards, that Just is a lot of overhead for these clinicians who would rather spend time on their clients.

So we can think about how to utilize AI in a smart way in each of these steps without taking over the job of the clinician.

So the clinician remaining in control while AI guiding them on the steps where automation can be present.

And that is mostly in creating initial versions of the documents that the clinician would otherwise make himself.

And also of course, to just record the conversation, like right now we're being recorded, we can create a transcript of that and we can use the transcript to convert it into a meeting notes summary or a client summary for longevity clinicians that we can then share with them.

And the same goes for biomarker interpretation.

Instead of having to read the lab reports, you can let AI read it, extract the biomarkers, link them to the reference ranges that your clinic uses, all automatically, and then review those instead of having to do that from the lab report itself.

There's many different steps.

Also, the action plan can be initially generated by using the methodology of the clinic.

So first, if you just once capture the methodology your clinic uses for generating actions for any kind of client in any kind of situation, after that, AI can do that to some degree.

It can create an initial version of an action plan for you to review and edit.

And then once it's approved by you as a doctor, the client will also be able to see.

That sounds amazing and it sounds like a real help for the clinicians.

So just to briefly sum it up, what you've said, and correct me if I misunderstood, but on one hand, it's saving the clinician's time with the bureaucracy of things, getting the actual notes down facts.

But it also helps the clinician interpret the biomarker data for interpreting biomarkers.

Why would a clinic use an AI tool?

Is it because of speed or because of accuracy?

Or both?

Mostly for speed, but also for accuracy in some regard.

Because in the end, with these kind of things, always the clinician should remain in control and be able to see what is being shown to the client in the end, and perhaps also why that would be shown to the client.

So when interpreting biomarkers, usually the clinicians have some kind of intuition and knowledge and framework on doing so.

However, everybody has a limited memory and everybody has a limited capacity.

AI models are trained on trillions of documents across the Internet to have a broad knowledge on all kinds of topics.

And if you can prompt an AI model well, then you'll be able to extract to some degree what other clinicians might have extracted from those biomarkers.

And if you also connect it to your own methodology, what the AI model can do is it can generate for each of the health domains that you might adopt as a clinic, a description of what is the case for this client, what has been found for the client, and that description, because it uses both the general knowledge of the AI model and your clinician's knowledge, is usually initially to some degree, not what you would have done yourself as a clinician.

And in some cases it can be better or more comprehensive, more accurate than what you would have come up with as a clinician.

And then by combining that initial great input from the AI and your own knowledge and expertise as a clinician, you come to create more appropriate description of what is going on for any client.

And this way you also improve as a clinician, because you'll see what the AI comes up with with regards to some health domain, and then you can verify that against literature, against your own knowledge to check if there is anything that you don't agree with.

And if there's something that's new to you, you can learn from that.

And it's the other way around as well.

After such a description is generated by the AI model, as a clinician, you can give feedback to the AI model and the AI model will take that feedback into account in all future generations of the description of such a health domain to improve its own output over time as well.

It says symbiosis.

It's a co working of the human and AI to speed up the process and improve accuracy.

How accurate is the AI at the moment?

That's very difficult to say.

I don't know.

I know that AI still makes mistakes.

The AI models I've seen right now are incredible and I would trust them personally more than I would trust a longevity clinician.

However, if it doesn't have the access to latest research, and I would, if I wouldn't have the knowledge I have with regards to longevity itself, then I would be more skeptical because sometimes it does come up with over utilized conclusions or opinions in the longevity space that are incorrect.

And this is because it has been the main way of reasoning, for example with Alzheimer's and Tauplex, etc.

There's many different partly correct interpretations of these subjects.

The AI model is trained on all of this old data and therefore it can conclude old incorrect things.

So I'm not sure how accurate it is.

But I do know that for every answer I get out of the AI model, I will of course validate it against my own knowledge.

But if it's something that I don't know a lot about, I will definitely also use AI to perform deep research on the web with regards to the latest research on that topic to get a better understanding of that topic so that I can improve my decision making for myself.

So if you use it correctly, I see it can be a great benefit to the clinicians and to the patients and everyone involved.

But aren't you afraid that it can sort of lead to the clinicians not double checking what AI tells them?

Because you previously said the clinician should be in control.

So how can I as a patient be sure that the clinician I'm seeing knowing he's using AI, be made comfortable and be reassured that the clinician is not just using straight up the AI interpretation of my results, but is actually applying his own knowledge to it.

When you are going to any kind of clinician, there is a chance that even though they don't tell you they are using AI to interpret your data and the chance is increasing because AI is becoming more prevalent and more used.

So it's always the responsibility of the clinician to do this and there's not really anything we can do too much about that to always 100% enforce that.

However, like in the system we built, there's always the need for the clinician to first approve before any information is shown to the client.

So it always needs to be approved.

It's always the responsibility of the clinician.

How does Longev AI deal with privacy and security issues because those are quite prevalent and relevant in today's world?

Well, we prevent them.

We try to prevent them as best as possible, making sure that only the clinician and the administrator of the clinic have access to the data of the clients.

And also the solution is GDPR proof.

So all data is hosted in Europe, but 100% guarantee it depends on the clinician not sharing any data.

And for the AI models we are ensuring that all the data sent to the AI models is first anonymized to not share any of the clinics information with an AI model, even though the AI models already are saying that they don't train on that data.

So the companies behind the AI models say like hey, we don't train on the data you send.

The models are hosted in Europe.

But just to be sure, we also anonymize the data before sending it.

So for the non tech people, in sort of layman's terms, how does the AI learn if it doesn't use the data from the patients, that it's being

Fed the AI learns from the feedback from the doctor.

So if there's a description of a certain health domain, like for example cardiovascular health, the AI will have initially generated a description of what my current cardiovascular health is based on my ldl, hdl, apob, lp, little a, all these biomarkers.

Biomarkers.

And well, the clinician can come back to that description of the health domain and say, well, actually this is a bit too technical, so could you make the description a bit less technical?

And then for all future generations, the AI model will be able to adjust its descriptions accordingly to be less technical.

However, it can be content related as well.

Like for example, if the doctor shares well, this is a good analysis, but you didn't take the LP values into account correctly, you have to do this, this and this, then we can save that feedback together with the description, which doesn't contain the name of the client, to in the future ensure that the AI model will not miss out on that interpretation of lp.

Again, what do you see as the limits of AI, as in data gap bias or lack of longitudinal data, which is data that's collected from the same group of people repeatedly over a period of time, rather than snapshot data.

You know, me going to the doctor once every three years, having a blood test done once, and that's it.

One major limitation is the amount of data that an AI model can process.

At any given point it's expanding, but it is still not that large.

And to be able to give an accurate analysis of any given person, you do need either a very large amount of data or a selection of the most critical data.

And that sub selection can be relatively difficult to get.

You need expertise in the longevity fields for that and you need to spend some time to create that.

And a sub selection could be some general information like age, weight, length, gender, and the core biomarkers over the past five years, as well as how I'm feeling over the past five years and a general description of my life, like the most important things that can still be provided in a relatively short amount of text.

If you're able to provide that well, then the AI model can perform really well.

However, with lots of this longevity related data, it can become a lot quite quickly and some of it can be somewhat relevant to a certain degree.

So the difficulty here is before applying any analysis, before sending the data to an AI model to first select the most relevant data and exclude all the potentially irrelevant data.

Because the more data you send to an AI model, the worse its output.

This is because just like a human it kind of has to pay attention to all kinds of different pieces of information, different words, and the more that becomes, the lower quality the outputs become.

And at some point it cannot even handle more than a certain amount of data and it will just not be able to produce any output.

I have a return.

What exactly do you mean by the data cap?

What I meant was the AI, in order to produce its model, it needs to have comparative values or consecutive values, or continuous values.

If you just feed it one off data in the abstract, is that a limitation of AI, that it doesn't have the continuity?

Well, that's not necessarily a limitation of AI itself.

It's more about the software built around the platforms.

Because this is about providing context, right?

Providing context about you as a person, what you have been through so far in your life.

Yes.

It's up to you to provide that information to the AI model.

Or we need to have a software that keeps track of this.

So this is also where ChatGPT or Cloth's memory comes in, where across conversations, it will look for relevant pieces of information and store those to bring them up in the future again to have some context about you.

How do you prevent AI making up its own mind as to what goes into the gap?

So, you know, we all know when we use ChatGPT, and sometimes the AI doesn't really know what to answer, it just creates a fake answer that sounds very real but is absolutely not.

How do you prevent that from happening?

Or can you prevent that from happening?

Up until now, there is not, as far as I'm aware, a 100% foolproof way to prevent this from happening.

There might be some models out there that are not necessarily as smart, but very secure in this area, where they will only respond to information that is indeed correct or indeed provided.

However, how it Basically works is LLMs are prediction machines.

They predict the next word in a given text.

So if an LLM is trained on always seeing the words Singapore after beautiful country, then it will produce that word in almost all cases, unless there is a significant difference in other input tokens.

So even if you explicitly tell it not to generate the word Singapore in that sentence, it will still do it, because that's what it has been rewarded to do in its training procedure.

Models have become a lot more, a lot better at this prevention of hallucination over time.

There's also things you can do to prevent it yourself.

For example, explicitly instructing it to not provide any information that's not explicitly present in the output or deductible from the input and to reduce the creativity of the model, there's a parameter that you can set for these models when you use it in code, and it is called the temperature of the model.

If the temperature is high, it will be more creative.

It will allow for more variations of words as the prediction.

If it's lower, it will be more consistent and robust.

So lowering the temperature will prevent that more from happening.

If the instruction is also to not include those random, do always use the inputs.

But surely clinicians are not tech people in most cases and they surely don't have the time to train the system.

So what security do you build in for that not to happen?

Because that's quite a scary thought as a patient and I guess also as a responsible clinician, that you just have AI creating facts rather than relying on real facts.

So the system is already including all the instructions under the hood to prevent this from happening as best as possible.

By indeed instructing the AI model to only use the data that is provided about the specific clients, as well as the expertise of the clinic that is provided explicitly to only use that for its output and nothing else.

That will help a lot as well as reducing the creativity of the model will also help a lot to provide better outputs.

And of course just always using the latest models that are the best in overall knowledge of the longevity field, as well as the lowest rates of hallucination.

But in the end, there's always the need for this clinician to review the output before editing or updating it.

Do you train the clinicians that use your model?

Yes, we'll just give instructions on how to use the software.

We don't have that yet, but we will also generate a full documentation system on how to use it with short videos so that it's easy to use.

But we've built it in such a way that.

Yeah, to make it as intuitive as possible and as similar as possible to other tools out there, other platforms, EPDs to.

Yeah.

Make it easy to use for the clinicians.

Why do you think building AI system custom solution is very important for clinicians, especially clinicians sort of in the longevity field?

Well, because it doesn't exist yet.

To build something that takes into account the entire client journey over a long period of time with all their data from perhaps wearables as well, but also from their biomarkers, perhaps from their DNA tests.

And then automating every step along the way as best as possible, while taking into account the feedback from the clinician.

It's worth to have that in one platform and that one platform did not exist yet, so we decided to build it.

So that's why this custom platform is important to have.

You've mentioned wearables.

How important do you think is the integration of existing systems and how important is compliance overall?

How important is compliance?

I think it's perhaps the most important because if you're not compliant, you will get fines.

So as a clinic clinic, you need to be compliant.

And wearable integration, I'm not too sure.

I think you can get very far as a clinic with just blood biomarkers, perhaps also of course, consults to figure out what's going on and to track those over time.

But wearables can add an additional layer of information, especially with regards to how well some patients sleep or clients sleep, because that's not always easy to guess for themselves and to share that way with the clinic.

For your personal health, it's worse to have a wearable like a whoop to see how well you sleep, how you respond to certain stimuli or foods or supplements to improve your health yourself as well.

So let me ask you a personal question.

Now, I'm sure you've put your own data into Longevos and and if you don't mind, do tell us about the results and there were any surprises.

So I have indeed put my blood biomarkers into the system.

This was initially mostly to test the entire technical side of things to see are the biomarkers extracted correctly, which needs to work for any kind of lab report, right?

And indeed also what's descriptions does it generate for me and what advice does it give for me specifically?

The tricky part here is that my blood work is already quite good, so there was not a lot to improve upon, honestly.

So I didn't get a lot of interesting feedback from the system because, well, my biomarkers are already quite good and I already went over it manually with AI before.

But I can go into how I go about that.

But what I do is I first of course get my PDF lab report and this is something important, I think to come back to the context aspect.

AI models work best if you give them the relevant information and the relevant information only.

If you give more information than necessary, it will perform worse in general.

So what I did is not directly upload my PDF lab report to the AI model for analysis.

Initially I uploaded the lab reports to an AI model to just extract the biomarkers from it.

Of course, I check if that's correct.

I extracted the biomarkers and the reference ranges for me specifically, and of course the units and then I used that text, excluding the logo of the company, excluding all the images and text about when the lab report took place and what the name of the lab is and all the extra details like the reference number, it was all removed and instead I could just use this text as input for the AI model for my analysis.

Then I will have text about myself ready to give some context.

And this is about who I am, what I do, what kind of exercise I do, what kind of diet I follow, what kind of supplements I take, perhaps medical history as well.

And I use that combined with this list of biomarkers to say, well, this is important as well to provide a role for the AI model.

Please act as my longevity doctor Here is my lab report and here's some information about myself.

So this would be I'm a 23 year old male, I run a lot, I go to the gym, I try to sleep as best as possible and generally get good sleep.

My resting heart rate is this my HRV, is this my VO2 max, is this just as context?

And I'm generally feeling good.

Here are my biomarkers including the reference ranges from the lab used.

What do you see?

What do you notice here?

So what catches your attention and what advice would you give me as my longevity doctor and then also why would you give me this advice?

Then I'll get an output, I'll look into it, I'll see if all of this makes sense to me.

And for the parts that I don't fully get or that I perhaps even doubt, I will ask questions back to the AI model to clarify or for specific things that are more in depth, I will go into a separate AI chat and go specifically into that direction.

For example, it might give me some feedback that well, I need to do something about my liver biomarkers, but I know that my liver is.

And then I can look into a separate AI chat into the intervention that it suggests for that by using a deep research functionality.

And in that case the AI model can perform deep research into the latest literature by searching the web, searching PubMed, using a connection to PubMed to directly look into papers and provide me answers based on that.

And this is also important, I guess for people at home to look into these integrations with sources like PubMed.

For example, in cloths AI you can just go to the Connections tab and search for PubMed and turn it on and then Claude can on its own look for these sources in PubMed with regards to your questions.

That's very interesting.

And then does it tell you what you should do or shouldn't be doing or does it merely interpret your genome or biomarkers?

If you ask for advice, it will provide advice.

If you ask for merely interpretation and things to look out for, it will provide that.

My example I asked for both.

So I will also get some concrete advice and it will be relatively high level initially.

But if I provide some more context or some things that I would like to prioritize in terms of type of interventions, then it can go into that as well.

Going back to investors that are not in the AI field or the medical field as such they just want to invest in the longevity markets what do non tech decision makers most often struggle to grasp when it comes to AI in healthcare?

That's a difficult question.

I wouldn't necessarily know too much what they would struggle to grasp, but I do think there might be some outdated views or perhaps incorrect views from the past as well.

Because some examples I've seen are that people who are not up to date with AI can have one bad experience with AI models in general and then overgeneralized.

That's to mean that also they are models today behave in the same way.

For example, this can be hallucination, right?

They've seen perhaps links to sources being generated that do not exist and this can be very troublesome nowadays with the latest models that always never happens anymore.

And for the rest I think that many people underestimate what AI is capable of, especially models like cloth Opus 4.6 or AIs like Gemini 3.

These models are just very capable and they do indeed still have biases.

They do indeed still they are indeed still capable of hallucination.

However, if you prompt it in such a way that you say here's my data and for everything that you're not sure of, please search for extra confirmation either in PubMed or the web and based on that give me like the interventions or suggestions to improve my health.

For every intervention or interpretation the AI model makes of your health data to ask, please explain your reasoning behind why you interpret it in this way and why you suggest this intervention for me specifically.

And of course if it doesn't do this adequately initially, you can always ask a follow up question to do that anyways.

And this is also what we do in Longchamp os.

So in the platform for all the suggested interventions or actions the doctor can see with by hovering over an eye circle what the reasoning was for the AI model to select that intervention for the specific client.

And this is very important to have because then you can understand and yeah, what is going on?

Why it was selected and how to improve your library of actions as a clinic to adjust your selection for specific clients in the future.

You mentioned two AI models that already exist, you know, Genesis and Aion.

But can you just explain a little bit more to our non AI specialists who are listening to the podcast, what exactly they do, what they do.

Let's start with cloth, because I like cloth a lot myself.

It is a large language model which is able to predict text in the sense that if I say hello, I am, the next word will probably be a, because usually a null would come next.

And the AI model can do that.

And it can do that continuously over a large sequence of text.

It is also trained on this role play of it being the AI and you being the human.

So when it receives a message from the human, it will predict the next token as the AI model trying to generate a response.

If we would not add this layer of training, then it would just continue with whatever you were saying as if it were you.

So that's basically what it tries to do.

It tries to predict the next words in a response to you.

And the next word can be any word in English or any other language.

Usually that's just the basis of it.

And it will use its internal knowledge of being trained on basically the entire Internet as effectively as possible to create a good answer for you that has been rated as good during the training phase by other humans and sometimes also some other computers.

And what it can also do these days is tool calling.

And tool calling basically means that it can use external resources to inform itself and create a better output and to perform actions in the real world, like sending an email, booking a call, or creating a slide deck or running a piece of code.

It can do all of these, but it can also do a tool call to just search the web or get information from PubMed by searching for any term like diabetes.

So in that way, AI models now can first use its tool calling mechanism to get information from the Web or PubMed or other sources and then perhaps execute some actions and then let you know, hey, this is what I searched for, this is what I did.

Here's my answer to your question.

Gemini does a similar thing.

Yes, it's very similar in most aspects.

However, the connections to external sources, like perhaps applications like Monday or Asana or others, is still lagging behind Claude.

Claude is capable of integrating with almost any system relatively easily from their website directly.

Let me, you know, go back to sort of the field of longevity and AI.

Where do you currently see its limitations?

And in two Years.

In five years, where do you see AI and longevity heading?

Good questions.

I think we went into a lot of limitations already, like the bias from the training data, the occasional hallucination, the not having the full context of you as a person at all times if you don't explicitly provide it.

And also the limitation of not being able to process huge amounts of data at once, since it will only be able to process a certain amount of tokens or parts of words at one moment of time.

So, yes, this is currently a limitation.

It has been a limitation for quite a while and there are techniques to go about this effectively.

What I see AI being for the coming years, it's difficult to predict, but it has been getting better and better and there are very many different ways in which you can creatively solve many of these problems I just talked about.

And it's just a matter of time for those to be implemented.

So I do see the possibility of AI becoming way, way smarter and kind of super intelligence being achieved, because in some domains this is already very close to that.

I would trust AI more in many aspects than many of the people I know.

And of course there are still some things where people, yeah, have the upper hand.

And that would still be, in my opinion, emotion related cases or judgment or interpretation of more complex cases that have a lot of nuance.

However, for the technical cases, AI is already exceptional.

You're pretty young, so with extended help or without, you still have quite some years ahead of you.

Where do you personally see the longevity field going and where do you see the AI connection taking it?

I haven't yet the personal experience to be able to come up with my own grounded conclusion, but I do see other researchers who have their interpretations.

And what I see also looking at it logically is that gene therapies might be a very good opportunity for AI models to look for new gene therapies and look for the implications of those in the human body.

And I also see the possibility of modeling a full human cell, including all its organelles and other aspects over time to then be able to model organs and the human body more effectively, to then look for what kind of interventions, if combined and structurally applied over time to such a model of a human, would lead to which outcomes.

This is still incredibly complex because you would also kind of need to simulate the brain and human decision making.

But if possible, it would at least, even if imperfect, leads to a lot of breakthroughs in the longevity field, a lot of molecular interventions being generated, and also perhaps gene therapies being generated that can bring Us closer to living forever, which is one of my goals.

I would like to live forever, and I think that AI could help us in that.

I'm not sure if I'll reach it in my lifetime, but I'll try my best to do so.

And yeah, this phenomenon of technology becoming more capable and finding ways to lower biological age more quickly than humans naturally advance in their biological age over time is called longevity escape velocity.

And I think we can achieve it.

Well, look, I don't think I'll see it in my lifetime, but you're young enough to stand a very good chance that you will.

But it's very interesting to have someone as young as you take such an interest in the developing field of longevity and what can be done.

And I find it very encouraging because I've been speaking to a lot of people in their 50s, 60s, 70s about longevity.

And they all feel that the hardest thing is to motivate the younger generation to look after themselves and take an interest in longevity.

But it certainly seems you have in your private life as well as in your business life.

Do you see that around you too, from people in your age group, that it's a concern or an issue of interest?

I do, and I feel like there are many factors at play here.

I also see quite a few young people who do turn to the longevity movement and who are interested in improving their health.

But in terms of human nature, many people start addressing a problem once they see it.

And if you're young, you don't see the problems yet.

So I can understand that point of view.

And what I also notice is that it's perhaps to some degree a lack of self awareness, perhaps where people are not necessarily aware or perhaps even they don't have the information about what the implications are down the road if they continue in the same way as they have been living, which is perhaps not as healthy as they could be living.

Yeah, there's not yet that many examples around of people consciously focusing on their health and spending a lot of effort on that and showing what the benefits are.

Not in the future, but also in the moment.

And I feel like this is coming up a bit more.

And if this is coming up a lot more, then surely people, even younger ages, will start to replicate that.

Another factor is probably all the different things these days that are attracting attention of humans.

In terms of social media mostly, I see, or entertainment services can grab a lot of attention from people.

And if you grab the attention of people, well, they don't have the attention anymore or the time and energy to look into longevity or spend time looking for ways to improve themselves.

Thank you so much, Elio.

It was super enlightening to have you on the podcast today.

I usually ask five rapid fire questions to all my guests in the end.

So if you're okay with it, here we go.

The first question might not be so applicable to you, but let's try it.

What's the single best advice you would give your younger self?

It would probably be to be aware that whatever I say or do, it doesn't actually matter as much as I think and that I shouldn't care at all about what people think of me and just have fun.

I have to say it's amazing that at 23 you've already come up with that.

Normally takes another 23 years at least for people to realize that.

So good for you.

The next question Name one habit everyone should adopt for a longer, healthier life.

Meditation.

It allows you to be more self aware, to think better, think clearer, and to make better decisions for your future self.

And that way you get to spend more time looking into the right practices for longevity learning and improving your health across many, many years and to keep improving it over those years.

If you weren't in longevity science, longevity tech, what career would you have chosen?

AI Like AI development going into achieving super intelligence.

Basically why I want to live forever is because I enjoy life incredibly much and I also enjoy learning and all the various experiences I can have throughout my lifetime and I feel like those are so much more diverse than what I've been exposed to in the past 23 years that I want to keep experiencing more things, but also especially learning more things.

I want to learn more.

Well, practically all languages if possible, initially the most spoken ones, Spanish, Chinese, Hebrew, Arabic, Japanese.

I already speak German, Dutch, English and some French.

But yeah, like just learning languages, learning about cultures, learning about AI and learning about how to improve my thinking, all these things.

It's incredibly interesting and fascinating to me and I would like to see whatever is possible on this earth to.

Yeah, achieve that.

Amazing.

It sounds great.

It's a good reason to live forever.

Exactly what microdose habit 5 minute routines

yield outsized longevity benefits Performing coherence Breathing for five minutes before going to sleep.

A one time action could be to place some books below your pillow to elevate your head, to improve the clearance of waste products in your brain during sleep and in the morning to go outside for five minutes and get as much sunlight as early as possible.

What's the craziest longevity myth you've encountered and Is there any truth to it?

I don't know that many too crazy ones.

But I mean, eggs, just eating eggs is being bad for your health because they contain cholesterol.

That's just an old myth that has been debunked.

But still, some people do believe it.

Dietary cholesterol is taken up for around 11% from food, and cholesterol from foods does not imply increased LDL cholesterol in blood biomarkers.

Correct.

Like you said, it's been proven for quite a while that that's a fact.

Well, Elia, thank you so much for coming on Beyond Longevity.

It was really insightful and amazing to believe that you're only 23 years old with such a vision, such a creativity, and such a thirst for living forever.

Yeah.

So thank you so much.

Thank you for having me.

It was a very fun podcast to be in and I had a great time.

Great.

Thank you so much.

Thank you.

Adios.

That was Elio Verhoff, co founder of Longevity I. Elio and I not only talked about the technology itself, but the way Elio thinks about AI as a tool to support clinicians rather than replace them.

There is real potential here when it comes to saving time, improving workflow, and helping doctors make better use of complex health data.

But there are also clear limits and a real need for caution, oversight and responsibility.

We also touched on something broad, the fact that a younger generation is starting to engage seriously with longevity, not just as an abstract idea, but as something worth building on.

And whether or not we ever get close to the kind of future Elio emerges, it is clear that AI is going to play an increasingly important role in how longevity medicine develops.

Thank you very much for listening to Beyond Longevity.

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