So tell me more about what that stage looks like. Are you funding research? Are they already formed companies when you're investing? As I mentioned, we invest very early. So the earlier the better, as a matter of fact, we prefer to be the first institutional money but you're right. is that early that it doesn't even have to be a company, in place where there is no necessity for a business to be in place a business team to have coalesced around the business idea, fueled by that scientific innovation.

Instead, a lot of times we invest in ideas. As you.

said, we, we have. , festivals, PIs And their researchers. Post-doc researchers, come knocking our door and they say, we have this idea. And actually we think that we have a breakthrough against that idea, but we're not quite sure how to take it to market.

 Even in such an early stage, we invest where we would partner with that PI and their team by giving them both financial cow. As well as human capital or basically people who work for Kairos, but we lend to those early stage companies to provide, if you will, their insights into how to take such early ideas and commercialize them.

Basically, those are also scientists, since we invest in scientific innovation, those folks who help the PR. Transition. The idea from the lab to the market are also scientists, but scientists that previously have had the experience of what it takes to transition those ideas from the stage of an idea to a product, something that can be sold commercially.

And can you just define PI just in case people don't know.

Yeah. And I apologize. Yes. This is the term that we using science And academia for principle investigator, those are basically the head researchers.

And how do you meet them and how do they meet.

Yeah. So we like to, um, establish, quote unquote, formal relationships with our partner universities. We bought that. Typically speaking with the top universities in the nation, we started with a focus on Caltech, a fantastic institution here A block away from our office here in Pasadena.

And then we've had tremendous success with PIs, principal investigators coming out of Caltech. And then that success led us to theorize that it would make sense to do the same thing that we did for Caltech, for a number of other institutions, top research institutions in the nation. And we took the model and we expanded it.

little bit over another dozen institutes, say universities across the nation. We partner formally meaning that we establish a connection with the folks Who manage licensing process and taking the idea, the scientific invention and formalizing it into a patent or patents.

those are typically speaking, the offices of technology transfer , and the pre pandemic.

We, we used to do Kairos days as we call them. And we hope to return to those. We would fly. Let's say to one of our partner universities, like Northwestern, like Cornell, and we would spend a whole day with their faculty, their PIs and their teams where they would quote unquote pitch.

 on their scientific invention to see if it makes sense for us to invest either in that incubation form that I mentioned, or through M series seed series, a investment. And we will do that on a regular basis.

I think you told me that you promise you will take a pill.

Yeah. One of the largest challenges that PIs that we're commercially minded at Caltech and a number of other universities is that not that many VCs are willing to take a pitch from them.

Despite the fact that we are talking about mindblowing scientific Inventions Inventions that will definitely change the path of humanity to the better. and despite the fact that it would be a scientific innovation that will say, look I'll cure cancer. People were just not willing. VCs were not willing to take a page So we started our model by promising

They want to page come on over. we Will have coffee together. Come on over here at our office in Pasadena, we'll do a zoom call. We'll do a teams, call whatever have you, And we have a team of scientists. run the diligence organization here.

As you mentioned, many, that's a team of scientists that. Really appreciate science. And we really like to , get into the depth of the science. And then we will listen to the science. We will ask a lot of scientific questions. So our advice is to any of those PIs that want to take us on our. Come over and be ready to talk science.

You don't have to convince us that cancer is a big unmet need that, uh, that there is a big market for cancer therapeutics. As an example, I'm using life sciences examples. Despite the fact that we invest in both life sciences and physical sciences. So you don't have to have just a, new, amazing therapeutic for us to listen.

You can have a brand new. , femtosecond laser. As an example, we have a company that we have invested in from university of Colorado in Boulder, in brand new femtosecond lasers, highest power, highest frequency lasers. Yeah, but I think, you know, traditional VC might say, great, you've got a femtosecond laser. who's going to use it. And, tell me about your, you know, customer segmentation. I think you're willing to hear about the breakthrough, , even if the go to market hasn't been fleshed out.

That is absolutely correct. You're you're upset. Right? Many the PIs, the research is faculty and their team. And amazing on scientific matters, But they haven't been trained to truly discern the commercial application and. Don't worry about that. That's the reason you partner with a VC like Kairos ventures. We will bring that commercial acumen, and identify the appropriate markets to go after and support.

 So, uh, my email is Alex at carers ventures to come. You have a scientific invention, or you want to talk to us by all means.

But you still have mostly, it's a scientific breakthrough, but it really is often coming out of university. Do you care? Like is peer reviewed science? Still the gold standard. Is that something you look for publications?

Absolutely. We do. peer review science. Is definitely gold standard.

We do like to read the science papers and the nature papers and , the high impact publications. we also use subject matter experts in the domain to advise us, to guide us, to educate us about that specific domain.

Because look at the end of the day, despite the fact that we have scientists as our analysts, We don't expect them to know everything, but we do expect them to be able to go and identify. Who the top subject matter expertise in that domain, the key opinion leaders. Sometimes we use the term right KOL these guys, the key opinion leaders, where they could tell us for example, oh my God, this is true.

Truly a breakthrough innovation or they may tell us, oh, we have seen a dozen of those. And let me show you papers about those. , the key challenge that needs to be overcome is X has PEI a solved X. And if they have great, if they haven't, we will do more research

I mean, it's interesting. You referenced nature and science, which I don't know, we've been delivered to my house as far as I can remember

forever

forever since the Dawn of time is what I was going to say. Um, , how is science evolving? Like, do you see more? things coming out of a private enterprise?

Like there's a lot of research going on in the Googles and the Facebooks, Yeah.

look, you're absolutely correct that there is tremendous research, strong research, taking place at places like Google that you mentioned, even at places like Facebook, which has received a lot of hits recently, they do fantastic research. But if there are those.

A small number of companies, the vast majority of corporations, even in the life sciences space, like Merck, like Pfizer, like Moderna those guys, do research, but at much later stage, they like to in effect by research from. PIs that are doing early research, typically speaking at university labs, and I should also add national labs So, yes. So while there is research done at the commercial level, most of the R and D monies are. Not R

And they have a, a line item on their budgets for R and D the vast majority of that lineup. Is on development, and the real research today here in this nation. And I would say in most of the Western world, and also in most of the developed world, across the world, look at China.

Most of the research is done and the early research is done at universities. and I have to tell you many the research that, those folks who have. Those PIs are doing is amazing. As I said earlier, it's going to change the way we know this world to be is going to change it in dramatic ways.

And it has been changing the world in dramatic ways. If you were to look at our standard of living even a short 60, 70 years ago, it was nowhere near to what it is today. And that is due to the scientific inventions coming out of research labs. You, our universities, Alex, do you have thoughts on how the world is going to change? Like medicine has obviously been step changes. We've just saw the, you know, the COVID vaccine, climate might be another area. Do you have certain areas where you think science is going to reshape the world?

Absolutely I do. And thank you for the question. Many you're upset, right? Cancer 50 years ago. If you received a cancer diagnosis, 50 years. Irrespective of the cancer. It was pretty much a death sentence today for probably, I would say the majority of indications, especially if they are quarterly

They are not death sentences. And that is because of the progress that science has made in the pharmaceutical field. And we will see tremendous more progress. We are really in, in pharmaceutical specifically. we have only been scratched. The very, very top of a very, very large iceberg.

, we don't understand well, biologics, we don't understand the proteins well today, artificial intelligence is changing this in a very, very significant fashion. and you will see us changing the way we identify drugs. We find drugs with the help of artificial intelligence, artificial intelligence in general.

It's going to improve the world significantly. There's a lot of colloquialism going on about, bad side of AI. And there are definitely dangerous to AI, but we are that discussion, we are ignoring how AI has been improved. The world and it is going to improve the world significantly in the last few decades in the next 10, 20 years, I'm talking about, I'm not talking about the end of the 21st century.

 but there are a number of other domains that are going to improve significantly. The very recent cop 26 conference, as an example, highlights dangers of our industrialization that we've had in the last 150, 180 years to our environment.

And it is a huge challenge. And it is science. That's going to solve this challenge, the de-carbonization of the environment of the atmosphere as an example, or what do we do with plastics? The challenge of plastics, plastics are amazing materials. There are amazing materials because they are cheap. They are like, They can carry your Coke without any problem, but because they are amazing materials, they are also very challenging.

 They can last in a landfill for 400 years, 200 years, and that's, we need to do something with plastics. We need to do something with the carbon that we are emitting into our atmosphere and the solution. To those challenges are going to come from the scientific labs that I mentioned in universities and national labs.

We already see, we are evaluating dozens and dozens of opportunities, that, uh, , amazingly, so, reducing the carbon footprint of, industries in a wholesale fashion. As an example, cement production produces about. Percent of the total CO2 emission load that we add every year to the atmosphere, if we can change how we make cement so that the carbon footprint goes to the.

We have contributed in a huge way to the decarbonization of the atmosphere, same thing with steel production. there are many innovations to receive from universities that are focused exactly on those challenges. Look, we see transportation lithium-ion batteries is just the first stage in making transportation be carbon free, so to speak, So as you can tell them, terribly excited. You know, when I talk to my kids about the things that we see on an everyday basis, I'm so excited about their future.

You know how we say a lot of times, oh, we are really pessimists about what's going to happen in the future. And we are really pessimists about world that our kids are going to live in. And I can see why people could be, especially with what. see every day in the media in terms of wars hunger and pandemics.

 But what we see here at Kairos at research labs make us optimistic about the future. So when I talked to my. Over the dinner table about the things that I see at universities, it becomes a very exciting conversation. You can see their eyes light up and they say, I want to know more about this. they say, basically I want to become a scientist.

And I really hope that that's what we here at Kairos and our peers that invest in scientific endeavors can actually capitalize. We can capitalize. At transition where kids do not want to become investment bankers, so VC, but they want to become, chemists. they want to become, physicians. They want to become doctors.

They want to become individuals who will change the world.

Okay. I was going to ask where you would advise your children. I think you have high school, age sons, where you would advise them, you know, what field of work. But I realized having my own children that you can't actually advise your children. Where would you go, Alex, if you were going to be entering university, what do you find particularly motivating you?

you know, as I said, I'm an electrical engineer by education. So one would imagine that that would be telling my kids to go to software. in some regards I, I would, but. more precise. I will tell them, become a scientist and then learn how to apply AI in your domain because AI is going to be the defining technology of our generation.

And I suspect over the next few generations to, AI is an enabler. it's like, a, you know, a Flathead screwdriver, like a Phillips screwdriver that once we invented this, all of a sudden we construction changed. And similarly speaking AI, which we have now invented in its early form, admittedly, it is going to be technology that can be used for the betterment of the world.

So applying. In a scientific endeavor is whether I would be, advising my kids to do, you want to become, uh, physician. You want to become a doctor. Great. Look at how AI can help you become a better doctor. You want to become a, chemist. Fantastic. but make sure that you learn how a.

That tool can make you?

a better chemist, a bettering vendor of solutions to multiple challenges that we have. We continue as, as humanity. we are in the business of creating challenges for ourselves and solving them Yeah, you don't have to get a PhD to learn AI. um, look AI in the simplest way, but I can put it Uh, at least the way we have configured it today, the way we have created, AI tools today, right? With, CNNs, convolutional, neural networks and so on and so forth, they are pattern recognition engines.

At the most rudimentary level, they are engines that they can identify patterns. In very complex looking systems. So patterns that we as humans cannot identify because we have a ton of data about something and it looks like noise to us. So AI today is fantastic in pattern recognition. So where would that be?

Very applicable? Well, let's at it in a little bit more involved fashion.

 The human body human biology, and biology in general is nothing more than a very, very complex system. we as humans, we understand human biology. Very little today. We are very experimental in our approach, Victoria. And in our approach, I would say where we say, let's try this and see what happens.

Okay. It didn't work. Let's try that and see what happens. Okay. That didn't work. And the reason we do that is because we don't understand how our human body works, how human biology works.

We don't understand how our cells work, how they self assemble into organs and how then the organs work with other organs to produce goodness For the human, but also to produce disease for the human like cancer. We don't understand that. And that is because they have very, very complex systems.

 So complex systems are defined by a ton of data that now as humans, we just pick a little thread of data and we try to find a therapeutic for cancer. As an example, AI will be able to look at those systems. Holistically look at the data, describing those systems holistically and start identifying patterns that us as humans cannot identify.

AI will be our assistant, our assistant. And we'll say you may want to look at this direction. AI will not miraculously go and find the cancer therapeutic, but is. Point us towards directions that we seek today, experimentally and thus very slowly and very expensively. That is the reason I said earlier. AI is going to change in the next 10 years.

Maybe I'm too aggressive, maybe 20 years. The way we discover drugs, because we are going to S. Doing what we have been doing so far where it says, I've got an idea, let me try it on mice. I'm going to another idea when you try the mice and we try a thousand ideas of mice and we sacrifice millions of animals every year to try our ideas and see what works because we are not sure that things will work.

So we need to try them on other animals, huge ethical repercussions surrounding that, but also huge economical repercussions surrounding that approach. AI will change that and other technologies like, uh, organoids, artificial human organs, where we can replay.

The animal with a microfluidic device where we have lung where we have a pancreas in a little microfluidic device of humans and bio mimetic of humans and try drugs in those organoids and collect data to what end for AI to start observing how things change. By putting a little bit more of this drug or a little different drug and so on and so forth and recognizing patterns that us as humans cannot recognize today.

and things are going to become much, much better, not only for humans, by the way, but for the animals, we sacrifice upwards of 150 million animals a year. What about, I think you're very convincing in the using AI to let's say identify therapeutics. what about the process of getting therapeutics into market? Where are we today in terms of the approvals? And he is not going to be able to keep up with. Our advances in the identification and just the advances in science.

Yeah. so look, there's a lot of discussion about the FDA approval process in therapeutics, the phase one phase two phase three studies that are required for any drug To get approval by the FDA and how slow it is. And people claim that seen efficient look, the reality is the best system in the world today.

There is nothing compared to it in any other nation, all other nations. With no exception. Look at that system. The system that we have today with the FDA process, as the model of how you find new drugs and approved drugs, you take them to market. That doesn't mean that because it's the best that we have, that we cannot improve on it.

And we will look, the FTE federal agency. And as a result, it has its challenges like any federal agency, but it's probably one of the best run agencies that we have. Across all of them, and in the case of the FDA, they like to adjust because they understand that adjusting makes the world better.

And they have proven that in the middle of our pandemic, it is their adjustment that has made of vaccine that didn't exist. I think I'm confident that once AI kicks in and starts producing more and more successful drugs, the FDA will start recognizing the value of AI and start requiring fewer and fewer proofs first in animals. And then in humans, Before they start approving drug for mass use. so you also talked a bit, you referenced that, uh, funding. I think federal funding as a percent of GDP has consistently gone down and I assume that sort of the NIH, NSF funding, can you talk, maybe also just talk about different funding sources, uh, in our portfolio, we come across SPRs and maybe as TTRs a little bit.

Yeah, absolutely. And you're absolutely correct. The major sources of basic research funding in the life sciences, are the NIH, the national institutes of health and in physical science. Uh, the likes of the NSF, the national science foundation DARPA, right from the DOD. Most of the companies that we have supported, there are PIs that personal investigators have previously received some kind of a ground and R O one grant, let's say from one of those agencies , basic research grants. Now the moment you commercialize. your opportunity and you create a small business to commercialize the opportunity that our grants like the SPR grant that's meant to support small business.

The SB in SBAR is small business to support small businesses in their efforts to continue research and to commercialize such research. The same thing stands by the way for STTR small business technology transfer. research grant. So, um, both the SBI RMS, the Dr. Programs are focused on small businesses and especially small businesses that are conducting the research.

So it's great that they exist and we encountered. Every one of our PIs, any one of our companies to appropriately pursue, and those programs. And the reason I'm saying appropriately is because there is a little bit of a catch there. I mean, if you're good at grant writing and you're good at research, you can be getting grants until the cows come home, so to speak and forget.

That you created the small business, not for research purposes, but to take your research and commercialize it. So that's the catch. that you may start getting.

And getting them and getting them and forget that your goal was to take something

cause they have a lot of caveats, right. Or you get a grant to do very

specific things.

oh yeah, yeah. You're absolutely correct. And I should have said that, when you get on SPR is not just a blank check and it says here's a million bucks. Go for it. good luck. Go to Denny's and have as many of those omelettes as you can. they, instead of. Look, you have given me, uh, a specific plan of research with specific goals and you are funded to do exactly that.

And you're going to be giving me reports on a very regular basis. So once you get that money and you started using the. You are going to spend a lot of time on that specific plan So the non-dilutive funding is good. As long as the focus is not taken off the ball.

Yeah, absolutely. So Typically speaking, our average check size is about $5 million that we've been investing in startups, So what happens then? Right? If we give a seed, some of your $5 million.

And as I said earlier, we like to be the first institutional money. If they are successful using that $5 they will. More and more dollars. So we partner with other VCs, all like-minded VCs that we are aware of that would be interested in scientific inventions that will change the world. that like to invest in later stages,

And so you mentioned if the company is successful, they'll they'll need additional money. What have you learned about what tends to make your company successful? If you look back at the wins and fails from Kairos,

Yeah.

So, you know, one would think, if you're invested in the hard sciences, Alex, probably the hardest piece is the science And no, that's not entirely true because look, if you have a PI who has worked for 10, 20 years and that scientific matter science for them is easy. The tough part, failures come from lack of focus.

And typically speaking, lack of focus comes from a poor team.

So, we have noticed that, teams focused on the goal. Are the ones that produce most successes and scientific inventions obviously are a necessity, right? If your product doesn't work, it doesn't work. But, most of the failures come from poor.

As you can tell from my perfect Southern California accent, I grew up here in Pasadena. No, I'm kidding. I'm Greek. As you can tell from my last name, I grew up in Greece in Athens, Greece, I loved it there actually, we spent as a family a year during the pandemic, back in Greece, where the kids went to school there and everything else, and they enjoyed it.

 And, I love what we do here at Kairos, I have to tell you, uh, this position has made me an optimist about the future. . I guarantee you this, we are going to be in a better place in 10, 20 years from now than today.

We have been today is a better place than 20 years ago. It's just that as humans, we actually don't see those improvements because we are in the middle of the improvement. But I'll give you another example here in LA.

Look at where we were 20 years ago or 40 years ago with smog. And when we are today, right? The same thing by the way, was the case in Athens, in Greece, the smog was horrific in Athens, in Greece, and it, these technological improvements coming from scientific invention that have made those places. We don't have smoke here.

I'm looking outside my window and it's a beautiful day here in Southern California. And I can see for miles miles away for the years ago, I wouldn't be able to see two blocks away due to the smog.

what was the science that improved the smog?

Oh, yeah. Perfect examples. Catalytic converters to start with. Right. We have had catalytic converters for years, and we are not thinking about them as amazing things.

And they are amazing things because they take a very, very dirty exhaust from an internal combustion engine and , make that exhaust very, very clean I love it. Well, Alex, this conversation has made me more optimistic.

I'm

really excited for your mission and what you guys are building at Kairos.

We are excited too. Thank you very much for the time many.