Arnon Chait: 0:00

So what we did simply is ask the right questions about the proteins, namely, where did you come from, and that information existed, the shape of the proteins and how they connect to other proteins, which is really the hallmark of life and the hallmark of disease processes, whether you have it or you don't have it. So finally, I'm getting to the bottom. The bottom line is

Harsh Thakkar: 0:21

what's up everybody, this is harsh from qualtivate.com. And you're listening to the life sciences 360 podcast. On this show, I chat with industry experts and thought leaders to learn about their stories, ideas and insights, and how their role helps bring new therapies to patients. Thanks for joining us. Let's dive in. All right, welcome to another episode of Life Sciences 360. I am super excited to introduce this guest that we have here. He has tons of experience. In his past role as a scientist. He's doing a lot of interesting things in the healthcare innovation space. He's an entrepreneur, scientist, educator, has tons of experience in innovating, and commercializing, you know, science. So please welcome Dr. Arnon Chait. He is the CEO of Cleveland diagnostics. Welcome to the show Arnon.

Arnon Chait: 1:11

It's my pleasure. Thank you.

Harsh Thakkar: 1:13

Yeah, so I want to start off by asking you like, like I said, at the top, like, you know, you have, I'm really excited. I know, we're gonna dive into the whole cancer diagnostics, and the advancements, because that's what you're up to right now. But I want to ask you about your past role as a scientist at NASA, I think you worked there for 35 years, which sounds crazy, because I'm trying to think when I was born when I was preparing, and I'm like, wow, that's a lot of experience. So talk to us about that time at NASA.

Arnon Chait: 1:44

Yes, so thanks. And I have to say that when you when you sort of described I kind of looked at myself and said is this me? And it's mostly because I don't think there's ever worked a day in my life until I became a CEO, I guess, because work is, is has a different connotation. What I did is simply explored whatever I wanted to. And the other side kind of thought that this was useful. And NASA is was the best organization I've ever worked for, I have to say except in the diagnostics, maybe. But it's, it allows me simply to explore whatever I wanted. I never had a boss actually. But in particular, NASA is has this kind of unusual way that they treat scientists, at certain level, at least, but I never worked on biotech, because that's part of the division that I had. And I have to say that the last 20 years at NASA was on a part time basis. Otherwise, I couldn't have started companies or do things of that nature? And my background really is not at all I don't have actually I'm I have to confess I don't have any formal education in either biology or healthcare or anything like that everything. And that's actually I think it's a great advantage to some extent, because you can think out of the box. People say that a lot. But it really is I'm more of an engineer or physicist, and I learned from everybody around.

Harsh Thakkar: 3:04

Yeah, so I have to ask you that. So when you transition from NASA to Cleveland diagnostics, did people actually tell you that, hey, you don't have this educational background? Why are you going in this domain?

Arnon Chait: 3:17

By that time we did already myself and my co founder, who's also an unusual person, we did another company that is today still running. It's a revenue based company that works on small molecule drug discovery, and which has anybody from Pfizer, down to the smallest biotech's customers? So people kind of understood already that maybe I asked a lot of questions, and I learned from the answers. And you really don't necessarily have to have a certain level, any formal education to understand the crux, because I am, whenever I walk into the lab, the first thing that they tell me is keep your hands off. So and I recognize my limitation, let's call it this way. But I could think and I think that most people who can think can be can actually move from field to field rather easily.

Harsh Thakkar: 4:07

So then, then, you know, you you were working in NASA, you had, you know, obviously, I guess had a very long career span there. What was the driver for starting Cleveland diagnostics?

Arnon Chait: 4:22

Wow, that's that's a great question. You bring me back into your right into a long, long time. So I was doing a sabbatical at Tufts University at the time, I had the chair professorship there that they wanted me to take as a full time and I kind of I did back and forth every two weeks back and forth from Boston. And I just looked at their house. Truly, that's all it's all related to that. I looked at the housing price prices at the time in Lexington, and I said, you know, I could never leave here. I don't care how much they pay me. And and at that time, I read I always read books outside my field. So I read an interesting book that was written by my co founder Boris who came from Russia, or soon as they opened the doors in the 90s. And he wrote this interesting book that is sort of outside every everybody's field. That is really the basis of what we do I have to say, and so we got off got together. I found him by by calling his the editor, where do you send the checks? The royalty checks to it? That's how you find people at the time. And so that's that's how we kind of started the first company. And then another one.

Harsh Thakkar: 5:26

Yeah, Tufts tufts is a great school. My wife went to Tufts, and she graduated in 2014. Or, 2015. Yeah.

Arnon Chait: 5:33

So after?

Harsh Thakkar: 5:35

Yeah, so I went to her graduation. And I met, I met her in Boston when I was working from 2012 to 16. So I can relate when you said, Oh, I can never live here, because more than 50% of my earnings are going to my rent and and other

Arnon Chait: 5:52

Yeah, yeah. And you can do and companies you could expenses. do everywhere. And it's actually the backwater of the backwaters of Cleveland, which has very, very strong health care. Everybody knows about Cleveland Clinic and the university, hospital and other, it allows you to do as much work if not more than on the coasts, without all the attention, let's call it the noise that comes in, in the starting, say, a company in Cambridge, or, you know, in Palo Alto, or whatever, you know, and nobody knows about you can keep cells company in Cleveland forever, if you want to. Yeah, but that's that's kind of another whole discussion.

Harsh Thakkar: 6:26

Yep. Yep. Yeah, there's and then, you know, there's obviously lots of markets in us, like you said, Cleveland is one, Minnesota is a market, whereas there's a lot of startups, Houston is is another silent one for biotech and pharma. I mean, people know, Jersey, Boston, San Francisco, but there are a lot of other markets, you know, where there's smaller companies where people don't know much about them.

Arnon Chait: 6:50

Yeah. And, and you can, as a foreign from an investor perspective, and what we have done, for example, if you run diagnostics, just the final round that we just did of$75 million from from really one of the top VCs growth stage novo, which is basically as belongs to Novo Nordisk foundation. You know, they before that we sort of raised very little money for where we are, it's and people were always investors always amazed. And I simply said, Yes, but look at it cost me, I don't know, $1 a square foot the rent here relative to I don't know how much in Palo Alto or whatever.

Harsh Thakkar: 7:29

Yeah, yeah. So it's very interesting what you're doing at Cleveland diagnostics. I was doing some research. And from what I can tell, you have a proprietary technology. The is that the SIA, is that correct?

Arnon Chait: 7:42

Yes. Today, we call it SIA solvent interaction analysis, which is too complicated, and somebody is going to change it soon.

Harsh Thakkar: 7:48

So how can you explain that to a child? Because it sounds very complicated?

Arnon Chait: 7:53

No, it's actually quite simple. And actually, it gets to the basic, the basic essence of how science develop science develops, typically, by using tools that we have. So a long time ago in in diagnostics, when ideally you would want to work with blood or something like that, because it's easily accessible. And it has all the information really about what's happening in your body. So the if you look really at the components, what's in the blood, you can look at proteins and proteins, really where you you're looking right now at proteins. And the same for me, we both really are a definer manifestation of life. So there was a technology that was invented in in the 70s, late 70s. That allowed us to ask how much of a given protein what's the concentration in blood for any protein that we want for $2 or so. So that became the mainstay of all modern clinical medicine today. But more recently, of course, once we started sequencing DNA, then came another technology we use, we, we call that DNA sequencing with other there were simpler versions of it that allows us to interrogate specific genes and RNA and other components of earlier. So the question is, and I'm finally getting to the question that you asked, so you have but then everybody now starts using this new tool. But really, fundamentally, we have to step back and ask Where where is the information about the disease that you're studying resides? So you can look at very upstream piece of information which is DNA and RNA, and some could be very specific to cancer of course, because the instructions are coming from mutated form of DNA or from methylation, which is epigenetics and such, or, or you can ask the question is and that everybody's doing all their work there. Why because we have a machine that does that. But people forgot the fact that actually the information that is most related close to the manifestation of cancer resides in proteins. But again, what people, People actually when they tried to use these Eliza machines to find how much to answer how much of a specific biomarker that is a protein there, they found that it's not specific to cancer because you could get more of a protein in your blood because you have inflammation or whatever. So what we did simply is, is ask the right questions about the proteins, namely, where did you come from, and that information existed, the shape of the proteins and how they connect to other proteins, Protein Protein interaction, which is really the the hallmark of life and the hallmark of disease processes, whether you have it or you don't have it. So finally, I'm getting to the bottom the bottom is, what we develop really is a simple way to use existing machines, like these are lasers that everybody else has, by adding one more simple step that allows us finally to say, You came from a cancer cell or not. So we convert information for how much to what type or have a protein you have, and that makes could be made specific to cancer.

Harsh Thakkar: 11:04

So So then, when you talked about this, you know, identifying whether that came from a cancer cell or not. So what are the different types of cancers where your technology has been proven to give the results?

Arnon Chait: 11:20

that we have, we're benefiting from actually, as people say, you know, stepping on, on, on developments that have on the shoulders of other people who who really develop this entire concept that and have documented over 1000s of papers that about the changes to the structure of proteins in relation to the disease process, and these are very well documented. So suppose so the quick answer for what you and I could work on any cancer, because all of them result in changes, because because of the word cancer means you have certain changes in the function of proteins, more than almost anything else. And the technology is agnostic to any cancer. What we have done today is went after the simplest of all, four large cancer, which is we looked at one particular biomarker that is used today in prostate cancer, PSA, which got a lot of rap, but it's still the best screening tool for that exist in the field. But we instead of asking how much PSA Do you have, which is not specific, we ask the right question is, could you tell us Do you have more of the PSA that comes from cancer cell or not? And that one we did using a simple index that comes out of what we do.

Harsh Thakkar: 12:36

Right, right. Okay. Okay. Yeah, I mean, I'm not from from the limited knowledge I have about this space by working with clients who are cancer therapeutic companies and also having, you know, friends or family members who have gone through some, you know, phases in their life with any treatments. I know that cancer screening is, you know, can be really tricky. Because if you're over screening, or overtreating that can be costly, but if you're under treating, there could be some risks. So how do you strike that balance with you know, what the the diagnostic advancements that you're developing?

Arnon Chait: 13:13

You would just hit right there on what we think is is the key issue today in cancer diagnostics. So we have some people, especially with sequencing tools are going upstream like Grail, for example, is the most celebrated example. Yeah. And looking for telltale sign. We're very upstream for many, many, many proteins. So they're doing screening, and but other techniques also do screening, for example, PSA, which is asking how much of a PSA is for prostate cancer. Mammograms are for breast cancer, a low dose cities for lung cancer, etc. Those are all screening what we think is the most important piece if you look really at the diagnostics, workup, namely what's happened to you when you flunked screening, we need when you flunk screening, typically, there is nothing between death and a biopsy, we have to do something. Namely, we go in with a fine needle biopsy, sometimes under CT, sometimes if it's simpler, you can do it. You can do it straight out. But the key thing is this is an invasive process. And after most of the screening tools that we have, typically we end up with negative biopsies, which means that you didn't need to do that. And what's happened for example, if you are let's forget about prostate cancer and move to breast cancer about I don't know 70-80% A large number of these positive mammograms end up as negative ignoring even the this this and anxiety that you are the are placing on a patient, you know that you tell him Oh, you're positive screening. But think of a lot of young women with dense breast tissues that are representing maybe 50-60% Almost of all women, young women, I have to go and they image very poorly. So they have to come every year and what is the management of those patients? So what we did altogether is realized that the right answer in the workup of of cancer is not to so reinvent the wheel upstream in the screening, but then simply provide a simple tool for people who are already flunking the screening tools. So our solution actually is very specific for changes that due to cancer, so it answers specifically, do you have a benign process, you don't need a biopsy, for example, that's what we do with our, with our ISO PSA for prostate cancer. Or we can say we think that you're still high risk, and you should really go to a biopsy that by itself serves a huge burden in the healthcare system. And we think that this is much more productive place to be today.

Harsh Thakkar: 15:47

Hmm. Interesting. And when you when you talked about, you know, finding the right time or the right moment, when you say, Okay, you should go and do the diagnosis or no, you don't need to, right. So how is that, like in case of prostate cancer or any other cancers? What is that right time when your technology should be used to make that yes or no decision?

Arnon Chait: 16:15

Okay, let's move to prostate cancer. That is true, because what we're doing today is saying, for every man should really be screened after a certain age 50, whatever it is, to get a baseline and also to monitor whether PSA is going up, if going up, it could be maybe your prostate is going up is larger, so you have more PSA. It could be that you wrote bicycle to the phlebotomist. Believe it or not, it would dump a lot of PSA into your circulation, for obvious reasons, or you have inflammation, whatever. But immediately after that the solution is today to do a either an MRI or a complex, very complicated biopsy, that is MRI fusion, it's called MRI fusion biopsy, or more traditionally, to stick you with 12 needles simultaneously to sample the geography of the prostate in that so you miss a lot of cancers in this. But you also have a lot of false positives. So the false positive and those that that PSA has results basically in a lot of negative biopsies could be up to 70-80% Sometimes. So what we're saying is all right, you have a high PSA, you run automatically our test immediately after that, and we'll tell you that if you're below a cutoff level, the physicians very simply can say to the patient, in the clinical study, 95% of the people say, or whatever, you know, that had this number below six, which is our cutoff, were proven to be benign. So I'm still gonna monitor you because you have high PSA. And we may decide to do something next year. But we don't need to do today. And what's happened to people who routinely like, like people who flagged mammograms as dense breast tissue, what's happened to those people? Who are you know, whoever, once you have high PSA, you stay with high PSA. So what do I do next year? Do I want to give you another biopsy? The story today is that once you get a couple of those biopsies, you're not going to show up at for the for the third one, you're only going to show up when you have metastatic disease. So that is the biggest solution. There, I think the value of the solution that we provide today

Harsh Thakkar: 18:23

that's and and I'm you know, as you mentioned that using this in different studies and having the patients and the physicians respond to how your your cancer diagnostic technology has been used for prostate cancer or other cancers. I'm curious to ask you when when you were starting the first time to test this on patients or working with physicians, what kind of feedback did you get? And then how did that help you evolve or fine tune the technology? Over the past few years?

Arnon Chait: 18:57

He was again touching something that is really dear for my heart actually. Because typically when you start something new people ask you whether you are you are kind of using the most modern tools that exist today. And at the time, even at that time, even sequencing was just beginning. And regardless people were doing PCR is people are doing basically looking upstream wherever DNA and RNA changes, isolation, whatever. And the index is really interesting because I looked at it as more of a physicist and I've simply said, why should they go farther from the disease? Where I can I can actually be very close to it by simply looking at the proteins that are involved in the disease process. But everybody is saying, What are you doing? You're doing you again, looking at PSA PSA already proven to be wrong. So PSA is just a biomarker, you know, so but why would you do that? And but then simply by asking a different question about the same protein. You know, we got the right answer in the sense as that we can actually tell you about prostate cancer. So yeah, so it's kind of a it's an interesting thing you'll say something old like PSA. And then immediately people get connotation that it must not be accurate. But if you ask the right question, it's good. And surprisingly and just to close this the question, surprisingly, of the name, we actually call our product today, ISO PSA, which is short for isoform isoform these proteins was, was different, different structure. So it's a combination of these two word ISO PSA. And actually, people say, Oh, so you work it on PSA, they actually liked PSA, they just know that it's not specific. So all we say is that we're looking at the same old PSA that you always use. We just made it sing.

Harsh Thakkar: 20:43

And when you when you're working on this ISO PSA that you mentioned, so we talked a lot about, you know, detection and diagnosis and your technology. But if you step outside of the cancer world in other parts of the healthcare domain, what have you learned from, you know, having this working as building this technology and working your role as Cleveland diagnostics? What are you seeing in other areas where of the healthcare where something like this needs to happen?

Arnon Chait: 21:19

Kind of you're asking me maybe to step back

Harsh Thakkar: 21:19

yeah, it's very interesting what you said about, from what we did to what really is the lesson to learn. And it is that I think that people fall in love with tools that they have. And actually, this is, again, without without saying anything bad about other fields, as they say, but at least in you know, getting down to the simple level, right, what I've physics, and in Applied Maths, which is what I did, elegant and simple answers always get extra credit. And in biology, on the observed, you know, being in the technology side of things, other hand, the if you look really at the history of biology, it's got more it started off as observation perhaps, but it's because we have better and better tools, we ask more and more specific questions. And it's sort of like think of the whole thing is, as a forest, we are now able to look at the leafs of every tree. And then before that we could look just at the trees may be but with physics, actually, the whole approach is, hey, can you look at the entire forest, and in one shot, find something that tells you whether it's a sick forest or not, without actually so it's the reductionist approach in science, I think, that I don't prescribe to subscribe to it doesn't mean that we don't need to understand the origin of diseases. And they're very important for finding therapeutics, for example, and other things. But for example, if you are in diagnostics, and you need to find a low cost, simple in the lab, a non invasive diagnostics because I work with a lot of clients on software piece, you I think that you're asking automatically for something simple, that is easy to explain to the physician, and in 10 minutes, they can explain it to their patients, and it should work. So that cannot be looking at 100,000 methylation pieces and putting it into a black box. Like for example, gray, I love gray. And they actually did a lot of positive things for our fields. But for where we are, I think we provide simple one number that is excellent, implementation projects. And even in that field, if you're developing a software or technology platform, the simplest features are the most hardest to build, right? Because you have to sort of, you know, consider the entire user persona like take, for example, the iPhone, right? It's, it's a simple technology, there are no buttons, there's just one button, but that one button is so powerful that it can do you know, 50 different things, if you know how to develop it, right. So that's maybe an example. So it's interesting that you say when people say, Okay, it's simple, that should be simple. But no, when you try to chase and come to a very simple answer to a problem, usually there's a lot of hard work that goes behind the scenes that never gets noticed. What are your thoughts on that?

Arnon Chait: 24:16

Yeah, you're Yeah, you're absolutely right. So we are saying that we are saying something big. We're looking at the structure of proteins. I mean, there are people, there are dozens of people, perhaps I don't know, maybe I'm exaggerating, they got Nobel Prize for solving the structure of proteins. You know, this is a big part of biology actually. Because it's connected to function and etc, etc. But what we did in a sense is we said, you know, cancer is heterogeneous disease, and I cannot say that, that I know the structure, I cannot tell you where all the atoms are, but that's irrelevant for the question that the clinician wants. They want to know should you give this person a biopsy or not? That is a different question. So the fact that I'm looking at structure is a sidebar for them. And therefore, my solution has to be something that works for a population. First of all, if you're only working for somebody, you're doing precision medicine, and you are trying to customize maybe a drug a targeted drugs for a mutation in EGFR or whatever you we don't, we're simply saying somebody walks out of the street. And I want to know, if they have in their high risk for prostate cancer, should they get a biopsy? That's a different question. So going back to your question, it's, it's, it's, it's quite interesting, because I think that the, the solution has to fit the final application. And it's almost kind of dictate that you what you're not going to do rather than what you will do. If and if they have 10 minutes with the patient again? And how do they need to explain to them, you know, what are you doing at that point, if you're providing, for example, an AI based tool that is a black box, you know, that you cannot explain, we simply say, the gods of the diagnostics told us that you need to get a biopsy. That's not good.

Harsh Thakkar: 26:07

Yeah, yeah. Yeah, it's very, yeah. You mentioned about AI. And that's kind of what's happening, what's happening there is, you know, I hear that term so much that it's, you know, everybody's building an AI platform or using AI. But when you ask them, you know, very few people are actually able to articulate and explain how they're using AI, or what is the percentage accuracy? What is their data set? How was it trained? Like, you could go on and on right to ask those questions and poke sort of the right answer from them.

Arnon Chait: 26:41

Yeah, and we look, for example, results, or PSA, we looked at one proteins, and again, going back to how different fields look at the same problems. So if you have one parameter solution, or one parameter biomarker, you can validate it clinically, extremely simply, there are no miracles. If you have a solution that is built out of 100,000 individual pieces, you can never validate it, I don't think that none of the tools could be validated. I wrote originally, I was introduced to neural nets, I think in early 90s. And I actually wrote a code at NASA to optimize the composition of off a an alloy that is used as a first stage of a turbine for a military aircraft or whatever, which you want to make him as high temperature as possible. So I took a neural nets actually, and then wrapped it around and optimizer conjugate gradient optimizer. Without getting too technical, I kind of at that point, realize how hard it is to validate those and you need and how the data set that you need to validate grows almost exponentially with the number of features that are in the model. So you know, the approach that we have is, we have a super simple, it doesn't solve every problem for every person, but it improves a lot of the upstream screening that is not specific. So even if you make a small difference, actually downstream from screening, you can actually make a huge difference on cancer, because then you're allowing those tools, the screening tools to be used confidently, because they don't cause a needed biopsies downstream and you can catch more people. And you can tell people, it's not a big deal come in and get some blood test. And if it positive don't fall off, you know, don't don't just faint, because we have another simple blood test that everybody can understand. And it doesn't cost an arm and a leg to the healthcare system. And that's the way that you could actually make a big progress in chancer.

Harsh Thakkar: 28:41

Yeah, I have to ask you this, with the amount of experience you have and the background you have, I can imagine there are a lot of people that message you or email you or want to pick your brain that are trying to do similar projects or, you know, trying to make the overall healthcare industry better in the future. Have you heard of any interesting stuff that people are working on?

Arnon Chait: 29:05

I routinely I mean, what you said is exactly right. And I think that it's it's actually my job partly or for anybody who knows something to, to just to give their opinion, you know, and it's really the other side is can you can ask me and you can ask five other people. And it's but yes, I do. I mean, especially the VCs typically call you with endless number of potential, should I invest in this? Or should I invest in that? And I usually are kind of more refrained from the business side, but on the science side, I can I can definitely say some common common sense things to me. And, and most of them most of again, most of the ideas that people have are, as you said, it I think is a simply maybe it's the flavor of the month, you know, and so that's okay, but show me how your flavor is unique then, but I would like to go and simply say, Could you show me the person that invented the next ice cream. Because that is far more interesting. It has an open field of opportunities.

Harsh Thakkar: 30:07

Yeah, I can tell you just from the software side that the flavor of the past 12 months has been AI and machine learning because yeah, yeah, Right? What what what advice do you have for

Arnon Chait: 30:18

so MLMs have huge potential no question about it, I cannot see instantly how they can do a huge damage for for diagnostics that are as in compared with where we want, let's call it this way. Yes, they can screen people to take screened says to take actually screening test by looking at the virtual data, or, you know, the machine data that already exists in the healthcare system. And I think that everyone may or I know has a thing, and several other large systems use their epic data for to say, and that's, that is super legit, for for for this, but eventually it comes down to Alright, look at the biology and tell me what do I need to do next? That is a clinical question. And I would I would kind of stop before saying AI would solve that one to. somebody who's 10-15 years behind you? Or let's put it What advice do you have for your younger self? Yeah, I honestly all I can say what I learned. And I'm not saying that, you know, this the smartest advice thought that was ever given. But I think that you should always just be I give this advice, actually to a lot of our younger staff who come to me and say, Oh, we're belong in our career. And, you know, especially the Z gens, you know, somehow they're very, very interested in their career for some reason, and their progression versus their peers and all of that, sit, sit, forget it. I mean, just, I never had a five year plan ever. You know, I don't know what I'll what I'll do a year or two from now. It's just keep yourself interested, learn new things. And after a while you're, and most importantly, every 10 years or so switch fields, if you can, don't hyper specialize, because depending on your brain, I guess some people definitely work better when they're hyper specialized, and we need them. But for me, at least it was was a lot of fun to simply kind of talk with different people about if I could talk to you today about nuclear physics and how Lunar Atmosphere is, is modified by solar wind plasma, and how metal is work. And, and even some biology, you know, so that because I learned that from people who really understood these fields, and it's a lot of fun, then your life kind of is full, and then eventually, they'll pay you more even believe it or not, because it just comes with the territory, but don't ever have a career.

Harsh Thakkar: 32:41

Right, right. It actually reminds me I don't know if you've watched this another podcast episode of Joe Rogan, where he was interviewing Naval Ravikant. So Naval is a Silicon Valley, entrepreneur investor. And they were talking similar like, Okay, why? Why is it interesting if a person has three or four different things that they're doing? And I don't know exactly how Naval explained, but he said something like, Well, if you see a person riding a cycle, it's interesting. If you see a bear at a circus, it's interesting. But if you see a bear riding a cycle, that's, you know, even more interesting. So what he was getting at is, you know, when you combine things that are not supposed to, like, take for your example, 35 years at NASA, starting a cancer diagnostic venture, going deep, like, you know, out from an outsider, when somebody sees that they're like, wait, what, why did you do this, right? So that that draws people to your, to what you're doing. And I think that's a really good advice is, you, you have one life or whatever you're doing, and you might want to put 70% effort to your main, whatever thing is, and then but also leave 20 or 30%, to tinker with five other things, because you get a lot of experience, and it makes your story more interesting.

Arnon Chait: 34:03

And actually, I kind of envy what you do you know, you have you have let's call it a yes, you have something that brings you paycheck every every month, and everybody needs to feed their family, and that's your primary responsibility and all that. But at the same time you talk with all kinds of interesting people, myself included, but let's it it kind of gives you ability you to your mind to flourish. And I will bet you that you're a heck of a lot more productive in your other life, just because of these conversations.

Harsh Thakkar: 34:33

Yeah, and the intention was never, you know, a lot of people asked me like, how did I start and how did the podcast start? And the intention was very simple. When I started it was like, Hey, I'm already having these conversations with people. They're not related to my job, like you're not my prospect or a client that would bring me business or whatever, but I really love what you're doing and I want to know more. And yes, when I go back and I shut my laptop and I solve a problem, it gives me different take like, oh, he mentioned this, should that I use here? Should I use that advice here like it obviously, you know, conversations give you fresh perspectives. Otherwise you just keep doing the same old, you know, whatever your role is so, yeah, I mean, I don't regret starting it. I love having these conversations and like you said, you know, it's something different that I get to talk about or hear about from people.

Arnon Chait: 35:24

Yeah. And again, going back to what we said, even before we started recording, I think that your style of true interest in not only not only what you do, but because there are many, many that covers specific fields and all of that, but it's the entire package of how does somebody start something? Why do they do it? How, you know, how did the Yin and the Yang connect, in this? This is this is so it's so powerful, and it's kind of after a while, you say, Wow, I mean, I, you know, I can You can see the connectivity. There was a famous programme on BBC a long time ago. That's called I think, connections. It's was before when you're when you were younger?

Harsh Thakkar: 36:05

Yeah, I don't know.

Arnon Chait: 36:06

I remember. Yes. And it was actually it was a TV, I think this guy actually connects how everything that was ever invented, how it connects in this less transparent way to something else. And really, that's this is, so if you want to be productive, just be curious and interested. I'm not saying anything new now. Yes. You know, that's how that's how I did my life at least.

Harsh Thakkar: 36:29

Yeah, that's, that's, I think we can end the podcast right there if we want to, but before we end it any where can people connect with you learn about what you're doing? Or, you know, talk to you.

Arnon Chait: 36:40

So I say to everybody in the company, whatever you want, just walk in. So please, if you have any good ideas, or you just want to talk or whatever, I you know, I've not I mean, I busy but just like you are, you know, you have your day job, as I said, But you find time to, to do this small stuff because it's so interesting to other people. So yeah, I mean, they could contact me on my email. That's I'm always so it's my first dot last arnon.chait And then the I think we are Cleveland dx. So it says a double D.

Harsh Thakkar: 37:13

Okay. Great. Thank you so much. I really appreciate your time. It was a pleasure talking to you and learning about your journey. And I wish you tons of success with Cleveland diagnostics.

Arnon Chait: 37:23

And thank you very much. I mean, it's it's a great opportunity. And I'm sure we're bumping into each other I can see already a couple of places. So yes,

Harsh Thakkar: 37:32

saying yes, thank you. Thank you so much for listening. I hope you enjoyed today's episode, check out the show notes in the description for a full episode summary with all the important links. Share this with a friend on social media and leave us a review on Apple podcast, Spotify, or wherever you listen to your favorite podcast.