Shaun Newman Podcast - #973 - Ewelina Kurtys

Episode Date: December 23, 2025

Ewelina Kurtys is a Polish neuroscientist with a PhD in Neuroscience from the University of Groningen, specializing in neuroprotection, neurodegeneration, and molecular neuroscience. She is a key figure at FinalSpark, the Swiss biocomputing startup pioneering "living computers" made from lab-grown human neurons (neurospheres) interfaced with electrodes for energy-efficient AI processing. At FinalSpark, she serves as a Scientist and Strategic Advisor, contributing to research, business development, and the Neuroplatform for remote biocomputing experiments. Tickets to Cornerstone Forum 26’: https://www.showpass.com/cornerstone26/Tickets to the Mashspiel:https://www.showpass.com/mashspiel/Silver Gold Bull Links:Website: https://silvergoldbull.ca/Email: [e-mail] Grahame: [phone]Bow Valley Credit UnionBitcoin: www.bowvalleycu.com/en/personal/investing-wealth/bitcoin-gatewayEmail: [e-mail] Prophet River Links:Website: store.prophetriver.com/Email: [e-mail] the code “SNP” on all ordersGet your voice heard: Text Shaun 587-217-8500

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Now, let's get on to that tale of the tape. Today's guest is a Polish neuroscientist, researcher, and entrepreneur specializing in neuroscience, biotechnology, and biocomputing. I'm talking about Evelyn Curtis, so buckle up, here we go. Welcome to the Sean Newman podcast today. I'm joined by Evelyn Curtis. Ma'am, thanks for hopping on. Thank you so much.

Very happy to be here. Now, it's the first time on the podcast. I'm going to guarantee that nobody's ever heard of you. So before we get into Final Spark and biocomputing and all these things that are probably going to make my head melt, could you tell us just a little bit about yourself? So hello, I'm Evelyn Curtis. I'm a neuroscientist by background. And I work with Final Spark.

As a strategic advisor, this is a startup from Switzerland. We are trying to build computers from living neurons. So that's something I will talk about today. And what is your background? Before you get to Final Spark, you mentioned a whole bunch of things there. Can you just break it down for me on, you know, what, how on earth do you get to Final Spark? What were you doing before that?

So I come from Poland. I studied biotechnology and pharmacy, so I was always into medical topics and nature. And later I did PhD in neuroscience because that was the most interesting part for me. I did PhD in the Netherlands and after I came to England and I started in London as a researcher as a postdoc and after I moved to industry because I always wanted to see what is outside, academic world and i discovered the world of startups and i started to work with small companies i discovered that you can actually have your own company so i set up my own activity in the uk and i work now as a consultant always with the small companies and final spark i met at

one of the conferences in london which was a i submit at that time it was really interesting it was time before chat GPT because I think now the discussion about AI is a bit boring because all can be solved with chat GPT but before you had a lot of different things you could do and it was not so much about text more about images but I become really fascinated with AI I started to study this and I kind of like change my route from science to more to engineering but on the commercial side yes and I started to And I started to work also with the finals part because I really like what they do. And this is also a combination of neuroscience and engineering.

So these two fields which are interesting for me. So this is the background in a nutshell. Well, break down computer computing for me. I'm trying to wrap my head around it, but I am a simple man, Evelyn. And I go, maybe you can break it down for me in a way that maybe I can start to understand. Sure, I'm also very simple person, so I can say this, I think, in a simple way. So we try to build computers where in the, as a processor, will be living neurons. The same neurons as we have in our heads.

And this is a normal thing that you can culture different types of cells in the lab. But usually you do this for biomedical research to discover how to treat disease, for example, or how brain works. So we use the techniques which are widely, used, popular, nothing new about this. However, the purpose is different. Because we don't try to study brain. We try to build a computer.

So we have living neurons in the lab, which we put on the electrodes. That's also nothing new because many people do this, since 100 years at least, that they put some tissue on the electrodes, but usually to study how the tissue behaves. But what we try to do is we try to program these neurons. So we try to send them electrical signals, and we also measure the response. the response from neurons because they produce spikes so these are activity of neurons which can be

measured on the electrodes as electrical spike and you know these things are done also on the brain you can have brain stimulation or you can have you can somehow measure the activity of the brain although it's a bit more difficult so so yes so we try to use this all these tools which are widely available for building computer And the idea is that the processor will be just leaving neurons. So no logic gates, no silicon as today, but leaving neurons. Because they are one million times more energy efficient than digital processor.

So that's the reason. We want to solve the problem that AI is using increasing amount of energy. So one day, it can become very expensive. So not accessible to everyone. So we have to make sure that we find a ways how to make AI cheap. And using living neurons would make it one million times cheaper. Okay.

My very limited knowledge on this is, please correct me when I am wrong. You have a computer. It has a digital processor that does basically the thinking. I might be oversimplifying that. And you're going to take, instead of having a digital chip, you're having a brain with neurons that would act in place of that, correct? Yes.

So am I envisioning a lab where you have a computer and somehow it's hooked up to a brain? No, no, no, no, absolutely no. Because actually people often misunderstand our work, so it's good that you mention this. Because we just use building blocks. So it's like you take bricks and you can build house or you can build a bridge or something else.

It doesn't have to be the same structure. So it's very important to say we don't try to build brains. Actually, we want to build something much bigger. Today our prototype is very small. It's just 10,000 neurons, very, very small, half millimeter diameter diameter. However, in the future, we can make huge structure. Say the size again, how small is it? Half millimeter diameter.

Like we're talking like half a millimeter. Yes. for a scientist it's not so uh it's very common to work on stuff which are so small we have microscopes yes and so now it's very small because there are a lot of technical issues if you want to make a huge structure of neurons but in the future we would like that this computer will be very very big so for example hundred meters long but it's not going to be brain brain is very specific, special. It has a lot of fine structures which are created by evolution, you know, million years of evolution. And we do not want to reproduce these structures. We just

want to use the same building blocks, which are neurons, to build something totally different. And why we do this? Because we know that neurons for sure can process information. So we know for sure it works. We just have to figure out how to make it work in the lab. in your half a millimeter size that you're working on how many neurons are in that 10,000 oh man I'm just like I can't even envision what I'm what I'm what I'm staring right I'm thinking of I don't know what you know I get a computer chip is is tiny and it can do mass amounts but you're talking about something in my brain that is absolutely tiny

has a crazy amount of neurons and is going to be way more efficient and way faster than a than a typical digital processor even as tiny as they are am i right in thinking that no it's not about speed actually it's about energy efficiency so what is special about neurons that they are very efficient when they process complex information so that's the advantage however they are not fast so for example things like memory or speed are much better in digital computers there is no doubt. But of course, it costs a lot of energy. So you can consider biological neurons for something what can be slow and complex. Like, for example, generative AI. Yeah. So one of the things, one of the common knocks or complaints against AI is every time

there's an AI search, how much energy is used to generate that. When you're talking about energy efficiency, I think you said it's a million times more efficient. Was that the number I heard? Yes. So you're looking at the global demand on AI generation. When AI gets generated and people are interacting with all the time, the global demand on energy is almost probably astronomical as it continues to climb and more people adopt AI. And you look at how much adoption of AI is coming and has already happened. You're looking at this going, we can find a way to make it way more energy efficient.

Mm-hmm. How, I don't know how to ask this properly. How would the interaction happen for AI to use a bioprocessor? Like, like, you talk about it being 100 meters long or forgive me the number you used eventually, it being very big. How would it interact with that? So living neurons would be a processor. So today you have silicon processors,

you know, which are processing information, logicate, yes, no, zero ones. And of course, when you program, you use high-level programming language, like for example, Python or something else. And you can do AI such a way. You write a code and this code is compiled. You don't have to, today it's all done for you, So it's actually automatic, compiled automatically. But actually, at the end, things in the logic gates are changing.

What you write in the code will affect the hardware. So the same will be with living neurons. We just have different hardware. So that means you will be able to program this. And we don't know yet how. For sure, we assume it will be sending some electrical signals to neurons. And probably interface will also be. digital because today we interface with neurons in the digital way.

So you write code in Python and all is translated into signals, into electrical signals, which are sent to neurons. So we imagine the computer, biocomputer the same way. You will be programming biocomputer. So you will be sending some signals and the neurons will be processing this. Today it's done by silicon and logic gates. Tomorrow it will be done by living neurons and spikes. So it's not that neurons are going to interact with AI.

We are going to build AI which will run on living neurons as a hardware. So it will be a new type of hardware. Yes, I'm trying to grab my brain around this. There's a ton of talk. And once again, this is, I'm like, I'm completely out of my depth. When this got suggested to me, I'm like, that sounds fascinating. I don't know how to wrap my brain around this. But once upon a time, there was lots of things I couldn't wrap my brain around.

And then you open up conversations and you start talking about it anymore and you start to see it. Like there's a ton of talk with building giant data centers, processing centers to basically help with AI. And lots of talk of building that here in Alberta, right? I think I think that's what it is. And I'm probably going to get a ton of chatter on the phone line and everything else of people and giving me more ideas on what it actually is. Is this going to replace that? Like in your mind in the future, instead of having these large data centers, is it going to be this neuron bioprocessor? That's what's going to replace that?

Well, we believe that neurons will not replace completely silicon because as I mentioned, there are still some tasks. which silicon does better. When you have to be quick, or when you have huge memory to store, it's better to use silicon. It will be better also in the future. But we believe that neurons will be complementary. And for sure, they can take over a lot of tasks

from Generative AI, which is actually consuming huge amount of energy. So yeah, definitely we don't think that... And also, you know, the interface with neurons is, digital and we think it will be also in the future. So no, we don't want to really eliminate digital computers. I think it wouldn't be realistic. But we want to be something complementary.

And I believe that the same will be with quantum computing, that it will be complementary to what we have today with silicon. And generally, I think there is such a trend in industry and also research, which I see that there there are more and more variety of chips. So today people build chips which are optimized for some specific tasks. So in the mass market, we have mainly CPU and GPU,

but you can also do chips which are optimized for something else, for some other tasks. And I believe that when the amount of the volume of AI will increase, we will have more and more chips, which will be optimized for some specific things so that you can do them more efficiently. So generally, I believe the field future will be a lot of variety of hardware.

But hopefully for the programmers, there will be some high level language so that you can, you know, for the end user will be no difference. It's just maybe the price will be different of using this, but hopefully there will be no difference for the end users. So when you talk silicon, you go, there's certain things that won't change because the speed of it and the storage capacity. of it are at very high levels. I don't know if that's the right terminology, but they're just very efficient in those realms. At least it will not be replaced, sorry, maybe I should

mention, at least it will not be replaced by living computers. Maybe there will be some other technologies, but the speed and the high storage will not be replaced by living computers, but generative AI maybe in some way can be replaced. And when you come over to the, biocomputing how they kind of coexist is that there's a great growing energy demand with AI and this is something that can help with that yes exactly that's correct so basically living computers can take over some tasks which are very energy consuming for silicon but not everything you you When you say takeover tasks, maybe you've already said this.

Yes. But what tasks can neurons living, like what task can they do a million times better? No, not million times better, with a million times less energy. Less energy, yes. So that's important. Just some difference. So yes, so as mentioned, we believe in generative AI. For example, but you know, these are still speculations

because we don't have yet a biocomputer. We are working on prototype. It's currently very basic, basic stage. But why do we have these assumptions? We can have assumptions on what biocomputer will do based on how living brain is working because brain is also built from neurons. So based on that, we can know which task are better.

And we can assume that the generative AI can be good task for biocomputers. Because complex problems solving with minimum energy is what brain is able to do. And it's because neurons are efficient in this kind of task. So yes, we believe a lot in generative AI. But of course, this is still a speculation. You know, at this stage, we are just,

we manage to store one bit of information in neurons. So just to give you an idea, that is very early stage. trying to understand you put a prompt into I think you mentioned chat GPT so you put a prompt into chat GPT yes in order for it to generate an answer it needs a ton of energy to produce said answer yes is that just the hardware thinking and searching and that's where the energy is consumed right there in trying to find the answer so it can populate it back in? No, no. I think it doesn't work that way. It's just that you know the way how hardware is processing information with zero and once is not very effective. So I would say this is much lower

level on the hardware. It's not about the words or searching for information, but it's more about encoding. And we know for sure that brain is encoding information in much more efficient way. It's just different because brain is not encoding zero and once like computer, but brain is encoding information in spikes in time and the location. So it matters when and where in your brain the neuron is active. This is information. So this is just more efficient from the energy perspective than zero and once in logic gates. It's just the fundamental. So if you go, like in your in your thought process, tomorrow you'd love to have it, but you know, you're in early stages, I go, time frame. Like when you're when you're looking at this realistically, is this like five years out? Is it 10 years out?

Is it shorter than that? This is a good question and very important. So we assume we can build a computer in 10 years, which is very long timeline, but it's very difficult. and that's why it takes so much time. And we are also talking to investors. That's important to mention because currently the project is self-funded. And of course it's going slow. So we assume the 10 years timeline is if we find investors because otherwise it can take much longer.

And we currently search for the time. 50 million investments, 50 million Swiss francs, which is very challenging because it's a very long-term project. Of course, it's also high risk. But yes, assuming we will secure investment, we think in 10 years we can solve this problem, which is actually really, really difficult. Okay, let's play out the scenario then. In 10 years, you have a bio-computer. how in your mind like what world does that create in 10 years like how does this will the end consumer notice anything different or will it be somewhere up the line where somebody's going to really notice a huge giant change yes it's going to be huge change

for the price of AI so we want that end user doesn't feel the difference so maybe you will be able to run chat gpt or you know the same models as in silicon but with much less energy and you know this problem is still not really felt so much because a lot of for example generative AI companies they often they lower the price they for example open AI lost 19 billion dollars already so the price of AI currently is very low because they work for adoption of AI so So they want to, of course, it's normal thing that users have to get used to this. They have to learn how to use it. But you can expect in the future it's going to be much more expensive

because the price, the real price, the cost of AI is much higher. So with time, this problem is going to be more and more painful for the end user. So in the next 10 years, you mentioned 19 billion in losses. They're not going to, you can't survive doing that. So eventually what's going to happen is the price of using AI is going to increase and increase and increase and become more and more expensive, which will mean adoption of it will slow because some people just won't pay for it. And this reducing the energy use, making it more efficient, will actually be able to make that price come back down again so that more users will adopt it. Yes, I don't think it's a matter of adoption. more than matter of accessibility.

Accessibility, sorry, yes. Because I think adoption is going very quick and nothing will change that. It's just that in the future, not everyone will be able to afford that. So there will be more inequalities. That's the real problem, which I think is really coming. And you can hear a lot in the news, you know,

that maybe one day we will not have enough chips for running silicon chips, for running the AI, because they are very complex and they need some rare material so it's not so easy. So we can expect that some solutions have to be found to make it more efficient and maybe to change the hardware so that we can make more of this because living neurons you can multiply practically with no limits. You can make huge amounts in the lab and you don't need any special materials. You need of course highly skilled stuff but this kind of people you can find everywhere once it works it will not be so difficult to do this when you say you can

multiply neurons at basically no stop once again I'm I guess I just go how do you do that well this is just biological process as it happens in your body neurons are just growing you know actually neurons are not dividing when they are major but because these are very special cells, but you can still produce huge amount because you can produce them from stem cells. And today we have methods for which there was actually Nobel Prize in medicine around 15 years ago that you can get stem cells from the skin. So that means that you can take human skin, you can produce stem cells.

And stem cells means that they can become any type of cell you want from the human body. that means you can produce neurons also. So what you do, you can produce these neurons from stem cells and that's why you can do this practically unlimited because, you know, skin is also not such a rare material. So it makes all the process very easy. How does this, I don't even know if it's differ,

but you know, like Elon Musk over here, I've heard a bunch on NeuroLink actually putting, forgive me once again for oversimplifying this, because that's exactly what I'm about to do, putting essentially a chip in the brain, correct? Like interfacing a computer with the human brain. When you hear that idea, are you like, that's a terrible idea or you think that's an interesting idea as well? No, I like, generally, I'm optimistic about the future, so I am excited about new technologies.

Of course, it's not easy to do this. also like it's not easy to make by a computer so these things are very very hard and very slow to develop but actually many people ask about neurolink when they talk with us and this is good point also so what we do is totally different but somehow connected but it's different because they want to make a chip inside the brain so that your brain is more potent or maybe can restore some function of the people who have disabilities for example if you have have some brain damage. You can maybe one day, you can put a chip in the brain

and restore the damage, restore the function. So that's a totally different story, because what we try to do, we don't want to interact with the human brain, but we want to build computer from living neurons, but this outside, this is outside device. This is nothing to do with human body. However, our work can be used in the future

to support such a project like NeuroLink. Because when we learn how to program neurons in vitroids, for the sake of the computer, then maybe this knowledge can also be used by companies like Neuralink to interact with human brain, but it's not our objective. Correct. Your objective is looking at the future and going, there is going to be a mass amount of energy consumption, which will eventually come to the end user, making it more expensive, which means that there will be a barrier of entry for the everyday folk that they will not

be able to afford it. And by doing this technology, it is going to make it energy efficient, lowering the barrier of entry so that people can use AI. Yes, but actually we have to notice that, you know, this process is already ongoing. So it's not that only we are working on how to increase the AI efficiency. This is a very important topic about AI sustainability. And people are doing all the time, are working on the efficiencies. It's just that you can work on them on many different levels.

you can improve the software so it's more efficient you can also try to improve the hardware digital hardware as i mentioned before you can make some chips which are optimized for some specific task or we can think about new type of chips which don't have to be living neurons but what we do is kind of extreme so we want to provide new hardware which is living and that will be ultra-efficient so that's a maximum what you can achieve in terms of efficiency because all these other things are kind of in between. So if you improve software, you can save energy, but not so much as when you change the hardware.

But when you change the hardware, you save much more energy, but not as much as when you would move to living neurons. So these are just a stepwise approach. And I think this problem is clear for many, actually anyone who is using a lot of AI, and people are already working all the time on how to improve the efficiency. In this specific application with Final Spark, biocomputing, it's very much geared towards

AI and solving an energy dilemma. Yes. Will it have other applications? Like, will there be other ways it can be used for energy efficiency? Hard to say for me, to be honest. As I said, it's still speculation, you know? So I don't know. Maybe yes.

I think it's possible that there will be some other tasks, not only generative AI, but it's hard to, hard to say for now. Maybe robotics, but I don't really know a lot about robotics, so I cannot say too much about this. But I think once we learn how to program living neurons, we can use them in many different devices. Is there anything else that I haven't asked about? I'm just, you know, it's I'm stretching my brain as far as it can. go to keep up with you. I'm like, I'm going to have to sit and stew on this for a bit. Is there other things you want people to know about biocomputing in general that you think should be brought up?

Well, they should definitely I recommend to check our website, finalspark.com, because it's nice resource for the, if anyone wants to know more or get in touch with us. Also, what is special about us is that our laboratory is available remotely. And we actually decided to use this opportunity to give, to offer free access to universities. So we have universities from all over the world who are working with us and they get some, they can also do experiments in our lab, but they don't have to go this physically. They can just connect remotely. And later what is even surprising and it's nice surprise for us is that even we started to get commercial clients.

So there are companies or people who come to us and they are, rent access to our lab on the monthly basis, so you can buy subscription. So of course it's not for computation, it's for fundamental research on how neurons process information. However, you can do this. It's only to our knowledge, the only place in the world when you can do this kind of experiments on organoids, on round structures of neurons. If an everyday person wanted to come see the lab, like actually just see what you're dealing with. Is that possible or is that off-limits? No, absolutely not because there are a lot of challenges, you know, when you work with

living neurons, you have to be very careful to keep them sterile. So actually, to enter our lab, it's better if you're a scientist, that's easier. Sometimes we let the journalists in or some guests, but it's very rare and we try not to do this because it is dangerous for ourselves, you know, that they can get contaminated. So we don't do this, but on our website final spark.com you can see the section live and there you can see the live view from our lab and i think that's much more informative for just someone who is not a scientist i think they can get much more from this live view so you can see the signals from the neurons and the live view from the camera so you can see exactly how it works the electrodes

where the neurons are placed because when neurons you cannot really see with the naked eye but you can see the setup yeah so I think it's much more useful and everyone can do this anywhere in yeah well I'm actually I'm actually on final spark right now and I didn't I didn't see the live tab and so I'm just I clicked on it and I'm like what on earth am I looking at Emily what am I looking at when I'm when I'm staring at the live view what is that so this is the view from our lab and you can see the signals from the neurons or the noise because sometimes you just only see the noise. It's a characteristic of any biological signal

that there is always a lot of noise. And you see also the camera view. You see the electrodes. You have four spots. Each of has eight electrodes. And on each of the spot, you have living neurons, which you cannot see with the naked eye. Well, I would suggest anyone listening

to go to FinalSpark.com because you can actually search out a bunch of, what we've talked about on the website. You can actually see the live view. I'm looking at it. I'm like, I need, I need a,

I need Evalina sitting beside me to just be pointing out. That's what's going on here. And this is what's happening there. Because I'm looking at it and I'm like, all right. I don't know if I fully understand. But regardless, it is a tool.

It's sitting right there if anyone wants to go take a look. Evelyn, I appreciate you hopping on and doing this. Thank you so much. Talking a little bit about this. I hope. Well, I hope everybody else's brain is going, what? What is this? Because this is new to me, and I assume it's new to a lot of people as it's in the development stage. But yeah, I appreciate you giving me some time this morning and answering some of my questions on it. And well, I think more

and more people are going to be interested in this as it continues to gain steam, you know, as, as you know over the next decade thank you so much