A (11:40)
Interesting thing is it's easier to solve a big problem than to solve a small problem. It's really counterintuitive, really. And here's why. If you have something that is so audacious, the best and the brightest in the world want to work on the toughest problems. The people who are successful, they want to create legacy. And what they want to work on is something, if they can, are successful, changes how humanity is going to live in the future, right? That means it allows you to get the best and brightest to focus on your problems. Number two, when you have the team and you have a great moonshot, good, audacious idea, everybody who wants to invest, they say, look, these guys have an unbelievably great idea. And look at the kind of people they have assembled. You get the investment. So it is really easier to solve a big problem than to solve a small problem. Imagine if you say, I'm going to develop another iPhone app that will help you find a roommate, People say, good luck, have fun with it. And when you come back and tell someone, hey, we are going to actually make humanity a multi planetary society. We are going to make illness optional. What if we can actually solve the problem that no one ever have to develop a cancer or have depression or ever have Alzheimer's, People say, sign me up, tell me how you're going to do that. And suddenly you now have the best and the brightest in the world who want to work on the problem that you set out to do. Let me take this framework and let me apply it to the company that I started because that actually will ground it so you can see how to apply it, right? So seven years ago, I found myself in a really tough predicament. My dad was diagnosed with stage four pancreatic cancer. And there is nothing I could have done at this point. He was given three months to live and unfortunately, that's all he got. And it occurred to me that there was nothing. There was no symptom, there is nothing we could have done. Why in this age, we can't find a way to detect early stage cancers. Why is it that we had to wait until he was at stage four and there was nothing that we could have done? I started to think back and looked at his life. He had high blood pressure, he had diabetes, he had all these chronic diseases. And we just accepted that's how it is. He's getting older. Obviously he's going to have high blood pressure, of course he's going to have heart disease, of course he's going to have diabetes, of course he's going to gain weight. This is all we accepted and we say, beta sacrifice. Why does it have to be this way? Because humans have not changed in the last hundred years as a species yet. Younger and younger people are getting more and more chronic diseases. There has to be something. We actually have changed. So this is how I started. I said, what if? And every moonshot idea, every project I do, I always start, what if. What if we can actually understand what changes in the human body at the onset of these chronic diseases, whether it is cancer or diabetes or heart disease or Alzheimer's. If we can do that, if we understand what is changing in the human body, then we will be able to potentially prevent the disease from happening, diagnose them early, and, God forbid, outright reverse them. If we can do that, then I ask myself if I could actually be successful in solving this problem. Would it help a billion people live a better life? And the answer was 8 billion. Every one of us is going to suffer from it. So he said, good. So why this is check mark? Because we know that this is a big problem that we could attack. And then I asked myself, why now? And we said, look, to solve this problem, there are three things that have to happen. We have to be able to digitize the human body. We have to be able to take massive amount of data that's coming from this digitization and to be able to process the data. And number three, the AI has to be powerful enough to be able to make sense of all this data. So I said, okay, let's understand what Is it that's going on in the industry that will allow us to do so? The cost of sequencing is still $1,000 to understand just the one simple sample. And if you were to sequence, it's $1,000. And we said that's too high. We can't solve this problem. But it used to be $100,000, $10,000. It is thousand dollars. In the next three to five years, it will come down to $100 because it cost is plummeting. Guess what? Seven years later, Ilana, it is now down to almost five dollars. Right? So think about it for a second. When we were 10 times optimistic, it turns out we were 20 times pessimistic because the costs were plummeting even faster than we imagined. And I will come back and explain. When technologies are on exponential curve, our human mind cannot ever fathom how fast they move, because human mind is designed to think linearly, whereas these technologies are growing exponentially. And for example, if you are to ask and say, Elana, you have two options. I'll give you a hundred million dollars right now, or I can give you $1, but for the next 30 days, I will double every time. So I'll give you $1 today and the $2 tomorrow. And I'm going to give you four and then the eight. And you do the math and say eight becomes $16, becomes $32, $64 and then 64, 1, 28, $256, $512, $1,000. And I'm already at eight days. And I'm thinking in 30 days, never is going to be a million dollars, let alone hundred million dollars. Give it to me now without realizing that 30 doubling later, it is a billion dollars. And that is the part of the thing that human mind just never because it looks at the early things and it forgets the part. So that was first part was cost of sequencing was coming down. And then we looked at and saying to process, we will never have access to supercomputer, but we can use the cloud computing. And we looked at the cost and they said cost was at that time was about $47. And we say, wow, that's a lot of cost to process a single person's data. But it has come down. Cost of storage is coming down. The cost of CPUs are becoming more and faster and cheaper. This should come down to about $10 in the next three to five years. And today we spend about $1.50 on that. And AI, by the way, everyone realized that AI was going to be more and more powerful and we will have the AI that we needed for doing that. So we realized the time to start was then. Then came the biggest part, why me? And again, I am not a scientist, I am not a doctor. And here is a problem that I'm trying to solve in healthcare. Know nothing about it. And most people will be so scared about not knowing anything. People say you're not a doctor, you're not a scientist, how are you going to solve it? And I think as I was telling you, that non experts are better at solving the problem than experts are. And here's why. Every expert takes the foundation to be granted because that's what makes them an expert, the foundation of the industry. They take it for granted because the foundational knowledge is what makes them an expert. Non expert are the only one that can challenge the foundation of everything that experts have taken it for granted. So here I am never done healthcare company and my first thing was pay. They said everyone in the industry is asking, they want to know about your DNA. And they were tens of companies doing DNA testing and everyone thought DNA is unique to every individual. If they could somehow understand the DNA which they understood software for body, if they knew your DNA they could find out what is going on. And my first reaction as in non expert was does your DNA change when you gain £100? So if you do my DNA test today and I gain £100, has my DNA changed? And the answer is no. Now if I become diabetic, has my DNA changed? No. If I have a heart disease, my DNA changes? No. I have depression, anxiety, does my DNA change? The answer is no. And my point was in fact even after you die, your DNA doesn't change. So if you were to look at DNA of a dinosaur, it's the same DNA. So if DNA can't even tell you you're dead or alive, how will it ever tell you you are becoming healthier or sicker? And that was my first thing was as a non expert ICA set. DNA is not the place to look for what changes. And then back to my Khan Academy, what happens to the DNA? And we say what DNA makes rna and it's the gene expression or your RNA that's always changing, but not your DNA. So your genes don't change, but your gene expression is always changing. So what does someone like me say, let's go measure gene expression. We don't know how to do that. But in my framework you never focus on how. You simply say okay, so we're going to go measure gene expression. Does that solve the problem. And then as I start to dig in, it turns out 99% of all the genes that actually are expressed in our body don't come from our mom and dad. They actually come from these microbiome that are in our gut, in our mouth, and all over us. 100 trillion these microbes. So I said, wow. So then I start doing the research. So you go to Google Research or Google Scholar and you say diabetes and microbiome, obesity and microbiome, Heart disease and microbiome, Parkinson's and microbiome. It turns out every disease has microbiome that is connected to it. My first reaction was, if everyone believes the microbiome is so important and everyone believes the microbiome causes all these diseases to happen, 10 companies probably they're doing microbiome testing, then why is this problem not getting solved? And then you go back to your first principle. What question are they asking? And it turns out to date, every microbiome company is asking the same wrong question. So every microbiome company is trying to find out what organisms are in Alana's gut, what organisms are in Naveen's gut, what organisms are in the gut of people who have Parkinson's or Alzheimer's or diabetes or obesity. And you know, my first reaction was organisms are like tiny human beings. Just like human beings, they make behave completely differently based on the environment. So same organism will produce something good in good environment and will produce something bad in the bad environment. Just like a human being, you take a human being, put them in a good environment, good behavior, put them in the bad environment, the bad behavior. So what if we focus not on what organisms are there, but we focus on what they are producing, what they are expressing and how it is interacting with the human gene expression. If we can do that, we can solve this problem. That's all. What my. Why this? Why now? Why me? So we know now we are going to measure the human gene expression and we are going to focus on what microbes are expressing and producing and we are going to look at the interaction of that and that's how we're going to solve the problem. Now comes the next part. How are you going to do this? And that's really interesting because I'm just going through the process of how I went through. So this is literally took me, you know, 30, 45 days of just doing the research and say, all right, so now we know what needs to be done, we just don't know how. So I'm thinking this got to be the really easy thing to do. If I'm thinking about it, somebody's probably done it, right? My first reaction was, you're going to think it's really crazy. I thought, you know, NASA JPL is sending these rovers to Mars looking for sign of life. They have to have figured out how you go out to figure out what these organisms are doing. So I'm going to go to the NASA jpl, I'm going to talk to the scientists and I'm going to say, hey, guys, don't you already know how to do this? And can I license the technology? And they look at me say, no, not really. We don't care about what organisms are doing. We're just trying to figure out any sign of life there. And I'm thinking, bunch of morons. If I need to go to NASA Houston, the headquarters, that's where they got all the good stuff. I went there, I'm touching the moon rocks, I'm talking to all the scientists, but it's still no solution. And then I'm starting thinking, wait a sec, I need to start looking at some of the universities. So I went to Stanford, I went to mit, and I'm now going to Duke, and I'm looking at all the universities. There's got to be someone who solved this problem. Still, I'm out of luck. So now I'm at Lawrence Berkeley, Lawrence Livermore, all these national labs trying to find out what to do. And they're saying, well, we are looking at multiple ways, but we don't have a solution yet. Then I was at Los Alamos National Lab, and as you recall that Los Alamos National Lab is famous for what, Lana, what is it famous for?