Longevity as a product idea
I got excited about life extension back in 2012. Like anyone over 30-35 years old, I experienced the fear of death. Until then, you do not fully realize that you are mortal. And then you start noticing examples from the outside: Paul Allen (Bill Gates 'partner) died at 65, Steve Jobs at 56, Bill Gates' mother at 64, in fact, the list is long.
I began to dive into the topic and found out that, according to scientists, the maximum age of a person in his body is 120 years. I started to try new technologies on myself. Having tried the technology associated with lightening blood with a laser, I did not get sick at all for several years. And if he fell ill, then at 18-20 hours.
And I realized that I didn't want my son to die at 80-90 years old. This was one of the motivations for the Longevity InTime project. As one of our experts, Vadim Gladyshev, director of Harvard Medical School, says, there are two motivations for scientists to join the project. The first is basic research that can be used for laboratory testing, and then move on to clinical trials in humans, and the second is personal motivation. I think these two motivations can be cited not only in relation to scientists.
I put together a team of scientists who at different times have steered the project in the right direction. Academician Andrey Lisitsa and respected longevity scientist Alexey Moskalev were among the first to join. He formulated for us the main biomarkers that influence life expectancy. In the process, we were joined by Professor of Tokyo Medical University Hospital Haik Arakelyan, leading expert in the field of artificial intelligence Roman Dushkin, head of the FMBA laboratory Mikhail Klyuchnikov, Professor Alexei Molodchenkov and American physicist, specialist in the field of machine learning Alexander Skarlat.
The result is a product that will comprehensively investigate the top 20 deadly diseases for 400 biomarkers and provide personalized recommendations based on machine learning algorithms.
There are billions of scenarios for the development of diseases, for each scenario there is a request to search for data confirmed by clinical trials. No human resource can formulate a request in real time and receive relevant answers, that is, search for information in unstructured sources and mark it up according to the required criteria. Every day, one hundred thousand articles on different topics are uploaded to medical databases like Pubmed and Mimic, and these are new requests. Therefore, we use neural networks that learn in the process of performing such complex tasks.
Let's say we want to calculate for a specific person with a bunch of individual physical parameters, what effect the level of glucose or cholesterol has on obesity and the likelihood of heart attack, stroke, diabetes and some types of oncology. To do this, we remotely measure the client's glucose and cholesterol levels. These are personalized parameters that are influenced by many factors. While there are no recommendations based on personal tracking, you have to work with what you have. Start from general research and build a future trajectory on them. If there is such a study, artificial intelligence will find this information, compare it with your parameters and say that, most likely, you have such and such a trajectory of life.
To take biomarker readings, we are developing a portable container for remote collection of five types of biomaterials. In it, they will be sent for further research to the mass spectrometer of the laboratory. In comparison, Australian government-funded Atomo Diagnostic is now making a blood collection container. We will take five types of biomaterials.
In our roadmap, we set a year to track 20 major diseases that can shorten life by 20-30 years. Probably, we will need a little more time to do so many calculations and check them with scientists.
Fitness trackers as a way to enter the mass market
The cost price of such a product is high, so we assume that its target audience is people with high incomes. Therefore, we decided to create a โworkaroundโ - mobile applications that everyone can afford.
In April, we launched AntiObesity and BMIObesity early obesity apps on the App Store and Google Play. During synchronization with fitness bracelets, the application receives data for analysis (heart rate, blood pressure, etc.), and AI-algorithms form a forecast of the likelihood of diseases along with personal recommendations to prevent or minimize the consequences of their development. This is a beta version for now, but we are preparing to release an updated version in early August.
We decided to test whether it would be possible to monetize the app through in-app purchases. So far, the hypothesis is this: one generated report on the risks of fatal diseases and recommendations for reducing these risks will cost 99 cents. Free for the first two weeks or if the risks and recommendations do not change. This report will be the equivalent of a visit to a doctor or an additional opinion, given that, on average, 25-30% of all deaths worldwide are due to medical errors. In Russia, a doctor's appointment costs from 1000-1500 rubles, but we will sell understandable results and recommendations 15 times cheaper. We will check the market reaction.
Also, in connection with the situation with the coronavirus, we launched a test of the main product - the AntiCoronaVirus application. This application is more for the public good, we did not try to make money on it and did not promote it in any way. We want to improve it first.
Development with zero investment and investment
I slept through the time when money was handed out to all startups in a row. And given the situation with the coronavirus, many investors "went into hibernation" and are waiting for the markets to normalize.
We had a great idea, some groundwork, but there was no money. I wanted to put together a good team as quickly as possible. Therefore, we began to estimate the cost of work per hour for each team member (or partner). Once a month we sign an act according to which we convert the amount of work performed into the company's share. Now I have given out to my partners about 0.2% of the company's shares. For these two tenths of a percent, specialists have made a product that would cost tens of millions of rubles. And I didn't spend them, but I have already tested the command and hypotheses. When an investor enters the project, he will buy an already tested case.
The motivation of my partner-employee is as follows: today, from the current value of the company, he sold an hour of his work conditionally for $ 50, and if the company costs 10 times more, then the cost of his work will proportionally increase. And if we paid in money, then no matter how much the company cost, the cost of an hour would be the same. Perhaps there would have been a bonus, but in any case, the partner would not have received 10 times more for his work. Of course, the partner of such a motivation system has a risk that the company will not rise in price, but there is also a risk in a company that pays salaries. When an employee works for a salary, he is less motivated to make the company worth 10 times more. In short, the main motivator for all parties to the transaction is the rapid increase in the company's capitalization.
Lack of money works wonders. So far we have not found large investors, but we have succeeded in something else. Google allocated us $ 100 thousand in loans for server capacity, this will be enough for a couple of years. Filed a patent application in the US for $ 70 - the cost of the fee. The application was prepared by the artificial intelligence algorithm of AI Legal company from Kazakhstan, reviewed by a patent lawyer from India. Domains were bought for $ 0.88 apiece. The company was registered for $ 300 - directly by ourselves.
It works now, but we would like to speed up. For example, find partners who will help create a company worth $ 100 billion within 2-3 years. The amount is more an indicator of the usefulness of society than an end in itself.
International team
I put together a team from 11 countries: Russia, Kazakhstan, Azerbaijan, Belarus, Ukraine, India, Nigeria, Indonesia and the USA. There are consultants from the USA, Finland and the UK. I found Russian-speaking guys thanks to Habr Career.
We did not have a task to assemble a multinational team. Everything turned out by itself. When we were negotiating with some investment funds in the UK and the USA, they said that they do not cooperate with companies in which most of the people from Russian-speaking countries work. And the Russians also work there. By and large, they will not be able to verify the real physical location of the employee. Nevertheless, we honestly write that 30% of our team works from Russia, 70% outside Russia.
Now we have 50 contracts with specialists, of which 30 people are constantly working, 20 for various reasons have paused the project so far. But we parted well with everyone, I plan to return some employees when the financial opportunity arises.
The whole team is working part-time. This does not affect the quality of work, but sometimes deadlines have to be shifted. We are in touch all the time, once a week we have a general conference call. We discuss all current tasks in Slack, use the Trello and Jira task tracker, store the knowledge base in Gitlab.
Previously, in chat rooms, we tried to communicate in English. But why, if everyone understands Russian? With the arrival of employees from other countries, we switched to English in chats where there are foreign colleagues. But where there are none, we still speak Russian. We are testing different options for interaction.
So far, we do not receive profits and injections from investors. There are only enthusiasts in our team, and we are looking for any help that would help with the development of the project.