Navid Alipour is co-founder and managing partner at Analytics Ventures, a global venture studio providing end to end infrastructure to ideate, plan, form, launch, fund and validate brand new ventures in artificial intelligence.
Navid has co-founded and invested in multiple AI based companies including CureMetrix and CureMatch. He looks to identify top tier scientists, academics or corporate partners, and then to work with them to turn their R&D into viable businesses.
Navid serves on the board of Tech San Diego, helping develop San Diego's now thriving eco-system. He also serves on the boards of CureMetrix, AV Lab, Kazuhm, CureMatch & AlphaTrAI. He has been a panelist, judge, speaker or moderator at forums such as UCSD, USD, SDSU, CyberHive, HeraHub, MIT Forum, RSNA, U.S. News STEM Conference, and CONNECT.
Navid holds a BA from UC San Diego, and a JD/MBA from the University of San Diego.
Navid, thank you so much for coming on the show or this podcast or event. I'm not really sure what this thing is. I think it's a show, but you have you being on here certainly up levels it no matter what it is.
Well, I don't know about that, Clint, but it's a pleasure to be here with you and your listeners.
Yeah. Hey, you're doing some really, really interesting things, which is why I wanted to reach out to you, particularly around artificial intelligence, around cancer and heart disease detection. Maybe we start there. How did you figure this out?
Yeah, no, it's a great question and I'll start out by sharing that I joke that I'm the black sheep of the family in that everyone's a doctor except for me. So my father's a retired cardiologist. My brothers are doctors, on my wife's side, her dad, her stepdad, her sisters. So I have a long business background, but healthcare's always been in my blood.
And we have a small venture fund here in San Diego and before the current fund, we had met the scientists, literally rocket scientists at UC, San Diego that had done work for NASA and Los Alamos in the past, helping detect anomalies in space weather, which I knew nothing about. But these guys were what did this for NASA. And so when they came to us, they didn't have a business plan or business model, they just said, "Hey, we're these AI machine learning experts."
And this is back in 2013, maybe late 2012, even when we first met them, when it wasn't in the news as much as it is currently. And they said, "Anywhere you could apply it to make a prediction or recommendation or forecast or to detect anomalies that don't belong in images or data sets. There's an opportunity to make revenue on a business sensor, have efficiencies and save money."
And we said, "Well, we're in San Diego, it's a healthcare town. What can we do in healthcare?" And in the interest of time, we said, "Can you detect a breast cancer in a mammogram better than existing computer assisted detection technologies?" And they very confidently said, "Yeah, we can do that." And so that was the genesis of starting the first company CureMetrix, where now fast-forward, we got the first of its kind FDA cleared product where we're 99% accurate in detecting breast cancer. And what we are, knock on wood, close to getting cleared by the FDA, we're just software. We're digital health, but it's a diagnostic. So you have to go through the FDA for this. We found we can also detect heart disease from the same mammogram, so it's a two for one.
No way. Wow.
And that's not to take away from breast cancer, but heart disease is called the silent killer amongst women. And that 65% of them die on that first heart attack, completely asymptomatic. Where us men tend to have chest pain or shortness of breath and we go to our doctor and they tell you to eat better and exercise and get on a statin and hopefully no surgical procedure is needed. But just getting on that statin reduces the risk of a cardiac event by 50 to 60%.
Now we get those symptoms, but if women don't, then you hear about them dying at 54 or 58 or 46, and imagine if that woman at the age of 40 or 42 on her first mammogram, not a triathlete, eats well, maybe not great all the time, but in roughly good health, doesn't know of any heart disease in the family. But now in her mammogram, we detect and score the calcification in the arteries and that leads to her to go to a cardiologist, do an EKG, do a blood test, a stress test, and get on a statin.
And now you have a 42-year-old woman that, but for that, would not see a cardiologist till a potential heart attack 10 years later, if not earlier, assuming she survives that. So that early detection is huge and calcification builds up in all our bodies, men and women. We just have the ability to detect it from this existing procedure called a mammogram, which is not comfortable. Women don't love it, needless to say.
But if you're going in for no extra radiation, no extra discomfort, it's a two for one, get your breast cancer screening and also get your breast arterial calcification score. And so that's what we're really excited about in delivering better care for women's health and their families. It impacts the whole family if you can impact, if you can detect cancer or heart disease early in a loved one.
Yeah. Where do you think we're at in the AI for healthcare stage? Do you think we're at the beginning or has this been something that's been working on for a long time and now AI's in the conversation because of ChatGPT? I'm just wondering where you kind of think we're at there.
So that's a really good question. So to peel back the layers on the onion, as they say, on that, before getting to healthcare. Artificial intelligence has been around for decades and it has these fits and starts and it's in the news and then it disappears. I think the reason it's sustainable now and it's not going to disappear comes down to the fact that we have more data to process. Whether it's Fitbits we wear or iWatches or any IOT device, and I'm not just talking healthcare, but a Tesla is an IOT device that's connected to the internet. And as the engineers say, anything that's connected to the internet is a node. That node generates data, well that data is useless unless you can process it. So we have more data, we have higher compute capacity to process that.
This GPU capacity that was initially these chips built for video games that are now processing data in other capacities. And third, we have the cloud, whether it's Amazon's Cloud or Microsoft's or Oracles or others, but obviously Amazon, Microsoft being the two big ones. So you could process data for much cheaper without having your own data center. So we use AWS and I could ramp up and process 10 million mammograms tomorrow and ramp it back down and I don't have to have my own hardware. And so that's a significant cost saving because you need to train the algorithms and you need this data.
And so that's where without AWS, we would not be able to do what we do without significantly more investment in the hardware. So that's where now more data, higher compute capacity, lower costs to process it. And so you have more companies innovating and developing AI for different capabilities.
Now within healthcare, healthcare is its own animal and there's regulatory hurdles. We're not just helping you deliver pizza faster or walk your dog app or a dating app. And as it should be because it impacts us at healthcare level, it's regulated and that's a good thing. So it takes longer.
Now, the law is always going to be behind technology in any industry, including healthcare. I mean in cryptocurrencies, the law's always behind technology, but I think they're doing a really good job catching up at the FDA and in the regulatory bodies as there's these new technologies coming to market. And so within digital health and augmentative intelligence, as some call it there, there's more and more products coming to market.
And in fact, it was just, there's a publication recently of all the FDA cleared products, AI products, and we were on that list of course. And the first bite of the apple or the biggest bites of the apple are in the imaging diagnostic space because that data is there and it's digitized. And then there's other applications within healthcare that might take a little longer, but there's a lot of work being done around AI for drug discovery, for getting clinical trials to market faster. And we could touch on all that as well in some capacities.
But I think historians will look back on this decade and which we're in the earlier part of still, and we're at a huge cornerstone historically, and we look back in 10 years. Modern medicine I think is going to advance more in the next 10 years than the last 50 years combined.
Yeah, isn't that crazy? That's so wonderful. That sounds so exciting. What a time to be alive.
It is indeed. And at the end of the day, our body, our DNA is software. It's code, right? And so if we can tap that, all the information is there to say, Clint, based on your molecular makeup, you need these probiotics or you are prone to this disease or ailment. So let's be proactive in your care or to detect an anomaly in the image.
Years earlier, I just saw a piece on CNN, an esteemed institution back east that was saying how their own personal patients at this clinic were detecting breast cancer up to four years earlier is what they were talking about. Well, we're detecting cancer in some cases six years earlier. So we're getting images from an institution and they don't tell us what they found. And we not only detect the cancer, but in some cases we've detected it up to six years earlier. So imagine just the odds of survival being significantly higher as well as the cost to the healthcare system, which all of our healthcare insurance keeps going up and up and up every year.
We're not getting any healthier, right? That's why we're paying more. But if you can help deliver better care and reduce wasteful spend and take care of an ailment or treatment earlier, it costs much less and the odds of surviving are much higher.
Where are we at with precision medicine? And you may have to define this for me because what I think about is these really complicated surgeries and AI doing those instead of a human doing those. But where are we with that whole thing?
So precision medicine, to bring true precision medicine, has to be based on each single one of us. Each as they say is an N of one. So if God forbid, everyone in one building had lung cancer, no two cancers are molecularly ever the same. So our co- founder at CureMatch, so we have CureMetrix on the diagnostic side and CureMatch is the company specific for not just women's health and breast cancer and heart disease, but for any man, woman or child that has any cancer. What we do on the CureMatch side of the business is we take what's called the next gen sequencing panel.
It's lab work. I'd say it's like the 23andMe of that patient's specific cancer, and it's the molecular makeup of the cancer. And Dr. Kurzrock, amazing oncologist, if you look her up, K-U-R-Z-R-O-C-K, how you spell her name for your listeners, being Canadian by birth, she always said cancer's like a snowflake. No two snowflakes ever look the same. So why should this treatment be the same for this patient, this patient, this patient just because the cancer was found in the same organ of the body, the lung, the liver, right? Because no two are the same.
So what we're bringing is true precision medicine, to CureMatch because we take that lab work, and again, we're digital health. We don't have a lab, we don't want to have a lab. There's other great labs that take that cancer biopsy, the blood biopsy, and they'll sequence it and they'll produce literally a 30, 31 page PDF that says this is the molecular makeup of this person's cancer and all the variants and the complexities of it. Some are more complex, some are less. And the more complex they are, the more combinations there are.
And so for a three drug combination for example, there's four and a half million combinations that's beyond human cognition. I don't care how smart anyone is, you can't process, the human mind cannot process four and a half million combinations. So that's where we come in.
We're not replacing the doctor, we're empowering the doctor and we're another arrow in their quiver in this fight against cancer to deliver better care and select the optimal combinations earlier, that'll help prolong the life of a patient and save their life in many cases. And so that's where, that is true precision medicine, based on individual people. And so there's a lot being done in that space and it's an exciting time in that regard.
And why you can say precision medicine is the iWatch. I like to talk about this because it's personal for me. Someone very close detected a high resting heartbeat from their watch. And because of that, they went to their primary care doctor that sent them a cardiologist and they did EKGs and stress tests and led them to be on heart disease medication that has potentially added decades of life for them. So I think that is precision medicine to a degree because of that individual person's heartbeat and high resting heartbeat when they were just sitting down reading a book or having dinner, but for wearing that watch, they didn't feel any different. They wouldn't have known that their heartbeat was higher than it should be just sitting down.
Yeah, that's super incredible. You said something really interesting there. Well, you said a couple things. One, we have all these devices that are tracking us and you wouldn't think of maybe a Tesla being that obviously the Apple Watch us, the Fitbit, those types of things. But even my bed tracks me, I have that Eight Sleep bed. Literally everything is tracking us at this point and showing like, "Hey, you didn't get a good night's sleep. Here's what you need to change." Those types of things and I think that advancement may be underrated here in the AI discussion, just how much data is being collected on us.
You are so spot on there, Clint. And sleep is so important by the way. And I'm someone that I feel good with six and a half hours sleep, but I'm trying to, and I track my sleep, I'm trying to get seven hours, I'm trying to get a little bit more. And that it impacts your health. And if you don't track it, you don't know. And so it becomes, there's the term gamification. It's almost gamification to be tracking and watching these daily, weekly, monthly trends, whether you're taking more steps or you're burning more calories or you're getting more sleep and a lower heartbeat at night.
And this is all our bodies. It's not overnight one decision eating that pepperoni pizza is not going to, one slice isn't going to kill you, but it adds up. Going on that one run or going to the gym that one time isn't going to make you healthy forever, but it's a culmination of things. And so to be able to track it, I think it's going to help prolong lives and save lives.
And it's not just about, and we're hearing this more and more, it's not just about living longer chronologically, but having your chronological age and your biological age. So you could be 59 years old and doing a genomic test, it could say like, you're a 49-year-old. And so I don't want to live to a hundred and be in nursing home. I want to live as long as possible to maybe 98, 99 years, not the last 10 years being low quality. So I do think we're at that point with, as people are calling it wellness science, longevity science, and there's more money being purported by of course billionaires on down that all want to live forever. So I won't go down the rabbit hole of living forever.
But I do think that the person that's going to live to be 150 years old is alive right now. And I do think that we can all live longer. And now we do have a blip in the statistics here within these last couple of COVID years as the average lifespan in the US has come down a bit. But why? Because people weren't taking care of themselves. They weren't going in for that mammogram because guess what? Mammography wasn't considered an essential service in 2020. So millions of women didn't get it in 2020 and delayed it until 2021. And those that had cancer, they were farther along because of that.
Yeah the side effects of that, the whole pandemic are like, I don't think we fully understand.
Just how kind of all the various effects that we weren't even thinking of at the time that are now. And that's a great example. People had to put that off for an entire year. You put that off for an entire year and that could be the difference in everything, which is really crazy. The second thing you said a little bit earlier is you said the AI's not going to replace the doctor. It's going to enhance their experience.
And this is, as you look at all these news articles about AI, AI kind of being like the buzzy topic over the past few months, the concern and the worry of many people's like, "Hey, this is going to replace a lot of jobs." What is your take on that?
So I love that question and I've, needless to say, had it over the years, I'll take it back to when the automobile was invented. You had so many people that said, what's this going to do to all the people taking care of the horses and the buggies and the horseshoes and they're going to lose their jobs? And guess what they did.
But look at all the jobs that were created because of the automobile, at the factories, at the gas stations, at the oil companies, at the mechanics, you name it. Anything that goes with the automobile, it's created tens of millions if not hundreds of millions of jobs that were not anticipated. And if you go to the turn of the century and the 2000s, I mean there was no Facebook or Twitter or Snapchat or Google to the stage. Maybe they had started right around then, but all these tech companies, LinkedIn, didn't exist. And look at all the jobs they've created, Zoom. Well, we're not on Zoom at this moment, but jobs get created and they're higher paying jobs in many cases.
So yes, AI will replace some jobs. Maybe we won't need as many taxi drivers or truck drivers, or maybe we don't need as many lawyers. Not to get a lawyer joke in there, but the ones that are there will be higher paying jobs and there'll be jobs created to stick with automobile example, you're going to need someone with a PhD in AI ethics to train the algorithms and the Ford Motor Company or GM or Toyota what to do in 10,001 different scenarios.
Because if you are driving in your car and your daughter's next to you and there's a truck that's about to hit you and it's most likely going to kill both of you, and there's a car in front of you, there's pedestrians to the left of you, as a human—again, not to get into the ethics of it—but as a human, you're going to want to live. You're crank to the left and you might hit some pedestrians and there might be lawsuits from that and you have insurance and it’s horrible to give you that analogy. But as Ford Motor Company, if you're training the algorithm for that driverless car, what do they do? I mean, this is an ethical question.
Yeah, that's way interesting.
Do they make an automobile that's driverless and you and your daughter or son or a relative or loved one in front are sitting in it? Do they make that veer into the pedestrians? Because if they do now it's Ford Motor Company that's going to get sued and they have deeper pockets than an individual. And so that's just one example of all the jobs that are going to be created that we can't even imagine.
And the lawyers are never going to go away by the way, needless to say that they're going to be right in there drafting these laws. And the law's always behind technology, by the way, whether it's in healthcare or in driverless cars or cryptocurrency. So the law's always going to be catching up to technology because first the technology comes out and then there's a problem that needs a solution and guardrails on the freeway, so to speak.
Those are super interesting, complex questions that are going to be—how do you answer those? That's wild.
I mean, we live in truly, I think his historic interesting times. Not that any time probably isn't interesting that people are living in, but I do think to get philosophical with it, in the history of human evolution is that it's looked back upon this century and this 10 years here, the 2020 to 2030, it's going to be historic and some good and some bad.
At the end of the day, the automobile is not good or evil. So many people have lost their lives because of an automobile accident. And you could say pollution and smog and this and so many bad things. But there's the good that is immeasurable, right? And AI is, it's just a technology. It's not good or evil. Certainly, there will be bad actors that will use it for bad purposes. And so our job as a society and the country, and I do think there's a national security component of this, is to be able to mitigate and stop the bad actors. While there are incredible good uses of the technology to improve all our lives.
You also have an AI fund. So what do you look for in a company or startup when you're investing in them?
Yeah, no, that's a good question. And that's really how we first started investing in software companies and then companies applying some AI applications. And being based here in San Diego, UC, San Diego in our backyard is really one of the epicenters, one of the birthplaces of artificial intelligence hearkening back to the beginning of the university. And so we're around it, it's in our ecosystem. And so we just meet really brilliant, smart people that come from all over the world to come here and get their PhDs and their masters in artificial intelligence and the data sciences. And so we started investing in them and meeting the people.
And I always say that, you know, you don't have to be a data scientist. You don't have to be technical to understand or benefit from artificial intelligence in your career because you also have to just, there's a strategic value. So if you're a real estate agent or an accountant or an interior designer or an architect, you don't need to be an AI expert. But to stay ahead of the competition or to keep pace, you need to know and become educated on how AI can be used in your industry to again, help make a detection, a prediction, a forecast, detect anomalies that don't belong in data sets, that are going to bring efficiencies and are going to increase revenues.
And so that's what we look for at a startup. We look at whether it's in healthcare or it's a sales enablement product or advertising or applying to the financial markets or industrial automation and the IOT devices that need maintenance and to detect something before it breaks down. We look, do they have domain expertise in this field? And then is there enough data to train the algorithms? How do you get that data? And then do they have the technical expertise, the AI expertise, the data science expertise to then take advantage and unlock value from that data? And it doesn't matter if they don't because if they're the domain expert. We could then bring the AI component and the expertise from our team and our bench and those that we know.
And that's how we ended up starting CureMatch and CureMetrix. Dr. Kurzrock is an amazing lady and as we're having this conversation on an International Women's Day, which I think is today and hats off to all women and with her as our partner here, truly one of their top oncologists in the world and her passion is seeing patients and doing research. She would've never created CureMatch on her own. And so when we met her and we saw the amazing work she was doing for cancer patients that were being transferred to Morris Cancer Center when she was here in San Diego that were on their deathbeds in many cases, that ended up in some cases if not being cured, living much, much longer.
We said, "Look, let's build something here because you're one person, you could only see so many patients in a day in your lifetime. Let's build something that could be of benefit to doctors and patients and pharma companies at a global level to impact more lives." And so we married her domain expertise, we brought the AI expertise, we brought the clinical expertise from our chief science officer and the clinical team she's built there and that's where the company came from. So whether we're starting a company like that and putting in the first money or we're looking at an existing company that has already put some of those pieces together and we look to see can we be helpful or are we just money to help fuel them to go faster?
Why did you choose to be based in San Diego?
There's a saying, happy wife, happy life.
Yeah. All right. That's what I thought. Well, it's the most beautiful city in the world, too. It's just so beautiful.
Look, I love it here. It's apples and oranges. I love the mountains and getting up to Montana and Utah and Idaho and it's great to visit Florida and the East Coast. There's beautiful parts all over, but I came to San Diego to go to college and then I met my wife in grad school and she was from here. And so we ended up settling down here. And the rest is kind of history.
It's something I've been thinking about as it pertains to AI is whether a lot of people are now saying, "Hey, this is going to be as big if not bigger than the internet." And as we move forward here as a society, what comes out of that? What does that mean? What type of opportunities are created inside of this? And I wonder, when we were in the nineties, the big companies could be so easily disrupted, right? Because they were slow and bulky and they didn't believe maybe that the internet or whatever it is. And it seems like we're not in that position today. Seems like the big companies are very aware of AI. So how do you think of that?
So that's a loaded question. There's so much to get into there. I mean, some examples from the past, I mean, Microsoft wouldn't be here if IBM didn't brush them off. They're the operating system too, I think at IBM, and I don't know the full history, but Microsoft may not be here if IBM took advantage of it. Or Xerox, everyone knows that Steve Jobs at Apple saw that first Macintosh, the idea of it at Xerox and one of their labs was Xerox said, "No, we're, that's not what we do." And so they went by the wayside—Blockbuster and Netflix, right?
It was back in 2007 or 8, the famous earnings call with the CEO of Blockbuster. And I think one of the analysts asked them, what do you think of competitors? What is it Redbox or am I saying or Netflix? And he scoffed. I remember listening to the guy and he literally scoffed and said, "Oh, they're not competition. People like to come into the Blockbuster store for an experience and they like to come in with their boyfriend and girlfriend or spouse and pick something out and get a bag of popcorn."
It was, and needless to say, Netflix got the last laugh and they even toyed with him I think. And Netflix at the time spent the last few pennies they had to fly to Blockbuster headquarters and the Blockbuster folks, there just to needle them. And so the rest is history.
I think in the present day you have these huge companies, Google, Amazon, you could say Facebook and others, and there is a consolidation happening that they end up buying and acquiring smaller companies or even public companies that are smaller and they roll them up and there's even a concern that they're hiring PhD students before they finish their PhDs.
And so the problem then becomes, well, who's going to be the PhD that's going to teach future students? So this is a challenge. They throw so much money at someone and they're like, forget the PhD. I'm going to go work at Google. And so I think the beauty of capitalism is that the pendulum swings.
So right now you see in the news and are we in a recession or we going to be in one? How bad will it be? And all this talk. You're seeing, the tech companies, they're doing layoffs and those people that are getting laid off, some of them might go work in another big company, but some of them might say, you know what? I have that idea to go start a company and I'm going to go do that. Or my friend has started a company, I'm going to go join that private company.
And so that kind of the burning of the forest creates then the nutrients for the new forest to rise. And so I think there's always going to be that innovation and that pendulum swinging. And there will be new companies, there will be, and the big Amazon does an amazing job at innovating and they operate almost like a startup. They have something called no team within Amazon. No team is bigger than a two pizza team, I think they call it. So you could feed the whole team with two boxes of pizza. And they really run Amazon as a startup in that way, I think to maintain that competitive advantage and not become a big bureaucracy and to keep that edge, but that people then leave Amazon and they can go somewhere else and they could bring that same type of culture and notion.
So yeah, I think it's a challenge, but the pendulum always swings and we're going to have amazing companies built in a downturn. Sometimes they say the best companies are built in the downturns and the next Google, Facebook, Apple, Amazons are being built right now.
The one that I think is particularly interesting, for your industry and medical tech and AI medicine, all that type of stuff is Apple. It seems like Apple has with the watch and all their various Apple Fitness, Apple Health, all these various things, it seems like they're going to be a major player in this.
They are. And I mean look, healthcare is 20% of the US economy. And so you have all the tech companies looking to get a piece of that pie from Apple to Amazon.
COVID, for better or for worse, sped up digital health by three if not five years. Because now people see that telehealth works, you don't need to go to your doctor. You could see them from your laptop just like you and I are doing right now for a lot of procedures. And so now there's efficiencies that come with that and people might be prone to get a diagnosis for let's say sleep apnea because they could do it at home instead of going into a center and they're going to end up living longer because of that.
And so there's all these offshoots, but yeah, absolutely Apple, Amazon, they're all spending a ton of money hiring doctors, bringing in insurance industry experts. You have companies like CVS, your traditional pharma retailer that a couple of years ago bought Aetna, so now they're an insurance company.
And so we're going to go toward this value-based care system where you don't get paid, a doctor doesn't get paid a fee for service, but it's delivering value and helping someone improve an ailment or symptom or have a positive result. And that's going to need a lot of technology. And so all these companies are looking to crack their chip, and get in, you know the healthcare system is very regulated and we have incredible infrastructure in good and bad ways, but it's happening.
I mean, Oracle bought Cerner and is getting into healthcare that way and they want to of course bring them their Oracle Cloud to compete with Amazon's Cloud and Microsoft's. But I think that the delicate balance we'll have to strike with ourselves as individuals is our privacy. How much information do you want Apple to have on you? And then what if Apple owns an insurance company? Will they be able to use that to charge you more or less for your health insurance, to say, you know, slept six and a half hours instead of eight hours, we're going to charge you more for your insurance. And so there's going to be some interesting stuff coming.
Yeah, we're going to get into a lot of weird ethical questions for sure. Even Amazon getting into the prescription drug business, Amazon knows everything there is to know about me and if I start getting my prescription drugs from them, now they know all my health stuff too. Like ailments, things like that. That's kind of crazy.
Well it is. And so this whole direct to consumer approach is happening and the healthcare system or they'll go kicking and screaming to try to avoid that, right? But the efficiencies will come from as much direct to consumer as possible and Amazon is absolutely trying to be that, right? And all of us who are Amazon Prime members, what else can be offered to us? And Clint, we know which probiotics you ordered last month and we know you didn't sleep enough and we have an insurance company and we're going to charge you more if you don't take that medicine tomorrow.
I mean it gets into some interesting dynamics, but it's, I think at the end of the day we have to balance the privacy factors and I'd say we're in the middle between China and Europe. Europe with GDPR privacy is an even bigger deal and China, what's privacy? And they'll use the patient's information there. And by the way, they'll have a competitive advantage in some ways because they don't have to worry about HIPAA and things like that. They'll just take your information and train their algorithms to detect things and come up with technologies that'll benefit people in China and what, guess what, they might develop something that benefits us too.
But it's an ethical question and so we're kind of in the middle, I would say, between China and Europe in regards to where we fall on privacy and it's a delicate balance to strike.
Yeah, for sure. Finally, I'll let you go because I want to be respectful of your time, but what are you most excited about at the end of this decade? We get to 2030, what do you think is something that's going to be, we're looking at or think now I can't even believe that happened?
So I was asked recently something along the lines of, what's a headline you think we'll see in 2030? And I said, I think it'd be pretty amazing to say the average lifespan is a hundred years and the first person has lived to 150. And that sounds crazy, but maybe it's not 2030, but it's maybe 2035. And I think that we're all going to live much longer and higher quality lives and it's a matter of what we are going to do with that extra time, right? And yeah, are we going to use it efficiently or?
I think it's an amazing time and I'm personally very excited about it and I think for us, what gets us up every day is to say, "Okay, if we can do things even one day faster, we're going to impact someone's life." And whether it's to detect breast cancer and heart disease earlier in women, or it's to recommend the best combination of drugs for a lung cancer patient with our CureMatch technology, or it's to help one of the pharma companies get a clinical trial to market faster so that not only can people benefit faster, but at the end of the day you have to follow the money. The pharma company wants to make money and if you help them make money faster, they get a drug to market faster and FDA cleared faster, then there are going to be people that will benefit from that cancer drug that getting earlier approval would not.
Yeah, that's fascinating. That's crazy. Navid, thank you so much for coming on. We got to have you on again. I can talk to you forever on this stuff. This is very cool.
Thank you Clint. No, it's been a pleasure and I have a lot of fun talking about this, so I appreciate the time.
Yeah, congrats on everything you're doing we'll catch you down the road.
Thanks so much. Take care.