Chapter 1

1. Exponential Technologies

The best way to understand exponentials is through a story. Apparently, chess originated in India several hundred years ago. When its creator showed off the game to the king, he was asked to name any reward in appreciation. To which, the creator asked for a grain of rice for the first square of the board and double that thereafter. One grain for the first, two for the second, four for the third, and so on. Doubling with every square.

The king laughed, “Are you sure you want such a small reward?”

The creator responded, “Yes, just a few grains of rice are enough.”

Surely the king did not understand the power of exponentials. Exponential growth is very deceptive in the beginning. We think it’s small, but it’s really not. As you reach half the chessboard—the 32nd box—you reach 2,147,483,648 grains of rice. When we double that for the 33rd box, we reach 4.3 billion grains. When we finally reach the end of all 64 boxes on the chessboard or 264 − 1, we arrive at 18.5 trillion grains of rice. That’s more than the global production of rice today.

Obviously, the king didn’t see this coming. Both he and the kingdom went bankrupt in trying to fulfill the promise. That’s how exponentials work. They are slow to start but multiply in impact as you go along.

The Shift to Digital

Before we go further, it’s important to understand what makes something exponential. To grasp that, we must be clear about the shift to digital.

Over a decade ago, many of us still used landline phones, watched cable TV, rented DVDs, called for taxis, took photos using cameras, and drove over to Borders to browse and buy books. During the past 10 years, we watched each of these fields permanently getting altered. Today, we stream movies on Netflix, call Uber to get somewhere, and we keep buying new phones to take better photos. We can’t think of what not to use Amazon for. It’s true—our life has turned digital. Most of us are so hooked to a device that to disconnect digitally is considered being mindful now.

Whether we realize it or not, we are living in exponential times. We are part of a grander, digital game.

How does a field become digitized? By finding a way to convert itself into zeros and ones, the basic language of computers. When music became digitized, we could more easily transmit a song as zeros and ones through the internet. When photography became digitized, we could convert pixels into zeros and ones. When books became digitized, we could access it via Amazon Kindle, a digital device.

Peter Diamandis, the founder and chairman of XPRIZE, says that when a field becomes digitized, it becomes information enabled. It’s easier to access, share, and distribute information or data. Once you can represent something in zeros and ones, then it enters an irreversible phase of exponential growth. When a field goes digital, it has the potential to steadily double in performance. (Recollect the story of chess we began the chapter with.) Industries and companies failing to recognize that shift are inevitably disrupted.

With $16 billion in annual revenues, Kodak’s leadership position was unquestionable in 1996. Maybe that’s why its executives didn’t see (or didn’t want to see) the wave of digital photography. When photography became digital, it moved from a physical setup of bulky cameras with film rolls to a more virtual environment—a smartphone in your pocket. Moving to digital, photography became cheaper and cheaper to the point where it became virtually free. You could distribute your photos infinitely. Companies like Instagram built apps that made average people take cool photographs. Meanwhile, Kodak went bankrupt in less than 20 years from its peak in mid-1990s.

Ironically, a Kodak engineer invented the digital camera back in 1975, but his bosses shut him up. Kodak’s bread and butter was camera film. But going digital meant making no money on its cash cow. Don’t you see that it was actually their mindset that led to the fall?

Other industries have experienced their own “Kodak moments” by ignoring the shift to digital, whereas new entrants capitalized on the move. iTunes disrupted music. Amazon disrupted retailing and books. Airbnb gave us an alternative to hotels. Uber changed our commute. Amazon Web Services helped us rent servers. (We will delve deeper into the topic of disruption in part 4 of the book.)

Unlike Kodak, today’s leading companies are transforming themselves faster than ever, riding one exponential wave to the next.

Self-Driving Cars

Back in 2012, I sat in a very different-looking Lexus—it was one of Google’s early self-driving cars. Wires ran from a laptop to the steering wheel and into the mechanics of the vehicle. A gray-looking device called the LIDAR scanner sat on top of the car. It rotated all the time and scanned the entire environment. The LIDAR served as the eyes of the self-driving car. From what I gathered, it cost $200,000 to convert the Lexus into a self-driving unit. The LIDAR itself cost $75,000. The automotive industry dismissed Google’s effort as a research project. Well, it was one.

Photo credit: Binod Bawri

The Google Self Driving Car Project started in 2009. A decade later, Google’s self-driving car, called Waymo, is a Chrysler Pacifica minivan. On their website, the company says that they are “building the World’s Most Experienced Driver.” They can make that claim because when one Waymo car drives a mile, the entire Waymo system learns. Through 2018 and 2019, the company started to serve the Phoenix area with a self-driving, ride-hailing service called Waymo One.

Recent announcements say a production-ready LIDAR scanner will cost as little as $500. Meanwhile, Elon Musk thinks visual recognition via cameras is better than LIDAR scanners. Tesla’s upcoming robotaxis don’t even plan to use LIDAR. Think about it: LIDAR that resulted in disrupting cars in the last decade are already somewhat obsolete. (We’ll revisit this point later in chapter 11.)

We can clearly see that exponential technologies are disrupting cars and phones. So why wouldn’t these technologies find their way into healthcare and gastroenterology?

Stool DNA Tests and Self-Driving Cars

What do stool tests have to do with self-driving cars? We’ll soon find out. But let’s first go back to the discussion we had earlier on the shift to digital. Screening for cancer through colonoscopy, while a gold standard, reminds me of a field that’s at the crossroads of disruption—much like music or photography was before iTunes or smartphones.

If you consider the argument for a moment, a colonoscopy is largely limited to one doctor and one patient at a time. When a doctor performs a colonoscopy, he or she can’t scale beyond the procedure. With each procedure, the learning happens within the capacity of that one doctor. To put that into context, only a very small percentage of doctors become endoscopists. And an even smaller percentage achieve mastery in endoscopy. Statistically speaking, we are talking of a very few highly qualified individuals who can reliably screen for cancer using sophisticated methods.

Ask yourself what could make the field of cancer screening go digital? Something that can accelerate cancer screening and give control to the nonexperts. I’m sure you might have arrived at DNA testing or artificial intelligence. We’ll explore them both in this chapter.

Now ask yourself these five questions:

1. Can stool DNA testing be ultimately represented in zeros and ones, the language of computers?
2. iTunes allows musicians to reach millions of people. With DNA testing, is it technically possible to screen millions of people for colon cancer?
3. The more Google’s self-driving car drives, the more it learns as a system. Could it be technically feasible to reduce screening error rates with more screening data?
4. Smartphones made the average user into a photographer. Could a DNA testing kit (backed by a sophisticated lab) simplify screening to a point where the patient has more control?
5. Amazon made it easier for people to buy books. Could DNA testing make cancer screening dramatically more convenient?