In the Age of AI

How AI and Emerging Technologies Are Disrupting Industries, Lives, and the Future of Work

sam mielke

New Degree Press

Copyright © 2021 Sam Mielke

All rights reserved.

In the Age of AI

How AI and Emerging Technologies Are Disrupting Industries, Lives, and the Future of Work

ISBN

978-1-63730-434-1 Paperback

978-1-63730-525-6 Kindle Ebook

978-1-63730-526-3 Ebook

To my family and friends,who have always supported me,thank you.

“AI is likely to be either the best or worst thing to happen to humanity”

—Stephen Hawking

Introduction

How much did you drive your vehicle in the midst of the COVID-19 pandemic? Many people realized that actually owning a car might not be the best investment. In a conversation I had with former Uber CPO Jeff Holden, he stated that owning a car will be “economically illogical” as “ridesharing autonomous cars will be four to five times cheaper.” Additionally, he stated, “Aerial ridesharing will also be a better alternative in getting around.”

Autonomous transportation, specifically ridesharing, is coming soon, as hundreds of companies are currently working to achieve an industry goal of having “self-driving cars on the road for commercial use within a year and also have fully operational aerial ridesharing (flying cars) by 2024.” Before I could interject, he continued by saying with full confidence that “by 2030, you’ll need a unique permit to drive a human-operated vehicle,” illustrating how much he believes self-driving cars will take over. As he gave me the scoop, I was in awe, but then I thought about all the drivers that this development would displace. “What would the magnitude of job loss be as the result of this drastic change in how we get around?” I asked.

He replied, stating, “Self-driving vehicle projects take several years to gather data from the real world, but as more data is collected, we will achieve fully autonomous driving, thus ‘no longer needing drivers’—including truck drivers, which is the most common job for males in the US. I see widespread loss of jobs in this industry within the next three to five years, probably resulting in three to five million jobs lost.” He also made the point that there needs to be a massive retooling of workers’ skills or else things are going to get ugly really fast.

Drivers aren’t the only Americans at risk of losing their jobs. According to Mi Zhang, a former Ivy League professor who is now part of Amazon’s business development group, “It’s very scary” that almost 90 percent of Amazon’s investments, if successful, would lead to massive job loss in every industry. He mentioned Amazon Go stores, which are a chain of grocery stores that allow shoppers to check in to the store using just a smartphone app and walk out with what they need, no checking out, no cashiers, no waiting in lines. These stores work by using the same technologies used in autonomous cars such as sensor fusion, computer vision, and deep learning. The store’s sensors can detect when products are taken or returned to the shelves, and it automatically keeps track of them in your “virtual cart” so when you leave the store, you are automatically charged. This new way of shopping is already impacting employment, as Amazon cut thousands of grocery store jobs, and it has the technology to replace all the stockers, warehouse workers, and cleaners—all while being faster and cheaper.

Furthermore, Amazon’s programmable Kiva robot transports items and shelves packages around its warehouse, minimizing the need for workers to constantly walk the factory floor and do intense labor. And lastly, Amazon’s autonomous delivery business is looking to automate its delivery either through drones or autonomous vehicles like Amazon Scout, which is currently being tested in a few states (Bukszpan, 2019).

So how does this impact jobs? Currently, even large companies like Amazon do not have the capability to operate a 100 percent robot-driven factory, and they likely won’t for another eight to ten years, according to Scott Anderson, Amazon’s director of robotics fulfillment.

This information was all extremely shocking because if anyone were to know the future of technology, it would probably be the people innovating the most ambitious projects. Right now, most of the narratives surrounding technology and the future of work are controlled by the media, which is why I’ve made it a point to talk to many high-level tech executives who have seen firsthand the disruption that emerging technologies such as AI, blockchain, AR/VR, computer vision, and 5G networks are having on every major industry.

Artificial intelligence is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings” (Copeland, 2020). AI is still in its early years, but one pattern already present is that today’s technology is advancing far faster than ever before—even faster than the internet. In fact, the latest research from Dell estimates that “85 percent of the jobs that will exist in 2030 aren’t even created yet!” (Tencer, 2017). This prompts the question, How can we educate a workforce to prepare for this level of uncertainty?

Many technologists and economists hold the view that just as cars displaced carriage drivers only to create more jobs, the same will happen today with the AI revolution. About half of the tech experts who responded to a Pew Research survey (52 percent) expect that to happen. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. However, they have faith that human ingenuity will create new jobs, industries, and ways to make a living just as it has been doing since the dawn of the Industrial Revolution. People also have a tendency to bash AI, thinking it is scary and that heavy regulation is needed to stop technological development (Smith and Anderson, 2014).

However, the argument that this technological revolution is similar to previous ones doesn’t fully take into account the rate at which technology is progressing, the pitfalls of the current education system, and the true capabilities of AI. Moreover, AI will someday be able to not only program itself but do almost everything as good as or even better than humans. I believe AI will displace hundreds of millions of jobs, causing more jobs to be displaced than created because of our inability to educate the workforce and prepare them for the jobs AI cannot accomplish. Surely, AI and other emerging technologies will create many new jobs we can’t foresee now, but it will not compensate enough for the amount of jobs lost, especially low-skilled jobs. Many who claim AI will result in positive job growth are assuming that truck drivers will reskill into coders or that manufacturing workers will pick up data analytics.

Next, AI shouldn’t be avoided; we are all responsible for learning the necessary skills to remain valuable in the age of automation. This adjustment means reimagining education, investing in AI at the federal level, and doing everything we can do to advance AI development in a safe way. I also hold the viewpoint that AI will make our lives easier, longer, and more “human.”

In this book, I’ll analyze and explain the impact that emerging technologies have on various existing industries and how that will affect the future of work. By taking an industry-to-industry technological analysis, this book will show the greater trend of this technological advancement: job displacement. Ultimately, this book is all about understanding how technology is changing the world for the better and the worse while also recognizing that jobs are going to be displaced. The future of work is going to change dramatically, but you can prepare for it by gaining a deeper understanding of these emerging technologies and the impact they will have. There are many ways you can capitalize on the impending AI revolution by upskilling your technological literacy, and I’ll be sharing just how to do that along the way.

The world is at a turning point. The next few years will make or break the way technology evolves. We aren’t being proactive in preparing for the age of automation, and if we wait much longer, it will be too late. A McKinsey study has found that 15–30 percent of the global workforce will likely be displaced by automation in the period between 2016 and 2030 (Manyika et al., 2017). Other studies have estimated 50 percent of all jobs will be displaced, yet most people only hold a cursory understanding of AI and current technology at best. I hope this book will help you become more informed about both the present and future state of technology in several major industries.

Personally, I’ve gone through the technological learning phase, progressing from knowing very little to becoming technically well versed across many industries. This journey began when I taught myself how to code from free resources such as e-books, YouTube, and projects. Once I gained the necessary programming skills, I progressed into learning about big data and data analytics, and I applied my skills to awesome projects. I’ve gone from a novice to extremely technologically skilled all from resources widely available to all, no college classes or expensive reskilling boot camps needed. The past few years, I’ve interviewed hundreds of CEOs, innovators, politicians, researchers, and everyday people who are navigating this massive technological change.

I’ve found that most books about AI are too complicated and technical for the average person, so this book will be a useful and informative read for people of all experience levels. Reading this book will prepare you for what is to come. This information isn’t meant to scare you or claim that a bunch of Terminator robots will be roaming the streets in a few years. Rather, it is meant to give you the cold, hard truth about what is to come.

We are going to look at interesting things such as flying cars, personalized medicine (living to two hundred years old?), and VR/AR in education along with the future of food, robots, and much more. I have no doubt that the next couple of decades will be inundated with amazing technical, medical, and societal breakthroughs along with world-changing revelations. As the following chapters articulate, several major industries in the world are about to be completely reconceptualized. More opportunities for leaders, innovators, entrepreneurs, and curious-minded people than ever before in history will exist. From flying cars to robots to curing diseases, the world is changing incredibly fast. Welcome to the new technological revolution!

Part 1

AI Overview

Chapter 1

AI and the History of Jobs

It’s 6:30 a.m. and your AI-powered Eight Sleep smart bed, sleep app, alarm clock, and smart watch have decided that now is the perfect time for you to get out of bed. You grudgingly get up from a long night of rest to grab your daily coffee, which is set to brew as soon as you get up. You sit down at the kitchen table with a tablet that shows you personalized daily news and social media feeds. Then, your AI personal assistant reminds you of your schedule for the day. All this is happening while you eat a well-balanced diet that fits your health and nutritional needs based on data your swallowable digestive system sensor—which continuously analyzes the health of your internal bacteria—shows. After you finish eating, it is time to head off to work. You stand up, hug the kids good-bye, and head out. Today, you have an important surgery to perform in downtown Chicago. Your AI assistant, which has your schedule down, has an autonomous Waymo car ready for you. Your self-driving car takes off. All of this happens in the span of twenty minutes.

At the hospital where you perform soft tissue surgery twice a week, you wheel in a patient. The second the patient gets into the surgery room and sees three surgeons, they say, “Sorry, but I want the surgical robot.” Before the appointment, you gave the patient the option to choose between a human-operated surgery or a robot-dominant surgery. More often than not, patients choose the robot because they know that the STAR—Soft Tissue Autonomous Robot—can outperform human surgeons on the task of stitching up soft tissue. The robot does the surgery five to ten times faster than a human with greater efficiency and precision. This ability is key, since you know that over 30 percent of soft tissue surgeries end in complication, with a portion of those instances ending up in death (Brown, 2021).

At the hospital, you work directly alongside the surgical robot, which allows you to monitor the health and status of the patient while the robot performs the surgery. Using robots results in optimal efficiency and increased safety for the patient. It also allows you to focus on other tasks that the robot can’t execute.

On the ride home from work, you decide to pick up dinner and groceries. Using Flytrex’s drone delivery technology, you will have your favorite fast-food meal delivered to a safe zone, which will then be delivered by an Uber carrier, cutting the average delivery time from twenty-one minutes to seven minutes. Rather than having your groceries delivered, you prefer to head into the store. You walk into the Amazon Go grocery store, which uses technology like computer vision, deep learning, and sensor fusion to detect when items are taken off or put back on the shelves and keeps a tab of them in your mobile app. The store is fully automated, so there’s no need to be checked out by a cashier or use self-checkout, saving you time. Later, you arrive at your parents’ home to celebrate your dad’s seventy-forth birthday. It is a special one, as you almost lost your dad to a degenerative disease, but through personalized medicine and the help of AI, doctors were able to predict, detect, and treat the disease before it had a chance to worsen and spread throughout the body.

While this hypothetical reality may seem like something out of a sci-fi movie, the technologies mentioned are not far from becoming a reality. In fact, almost all the aforementioned technology is already out there, they are just in the process of testing and small-scale adoption. While some may view this reality as harmful or unnecessary, holding fears the technology may be misused. Others believe these innovations will help solve a lot of the problems society struggles with today by making people’s lives easier and freeing them up to do more meaningful work.

What Is Artificial Intelligence?

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence (Beal, 2021). AI has many subsets, most notably, machine learning, which is an application of AI that allows machines to learn and become “smarter” from experience without having to be manually programmed. This ability is what powers Amazon’s product recommendations, Netflix’s movie suggestions, and Facebook’s automatic photo tagging.

AI has two main forms. First, artificial narrow intelligence (ANI), also known as weak AI, is the most common form and refers to a computer’s ability to perform a single task extremely well, such as image recognition, playing chess, or acting as a virtual assistant like Siri, for example. On the flip side, general AI, artificial general intelligence (AGI), strong AI, and superintelligence are a form of AI equal to or greater than human intelligence. The name is derived from the idea that this AI can be applied to all problems, unlike weak AI. Strong AI is like the Terminator. This type of AI does not currently exist, but experts such as Ray Kurzweil and Elon Musk believe we could see it come to fruition within ten to twenty-five years (Goertzel, 2015).

In the early 1900s, pop culture enlightened the world with the idea of artificially intelligent robots, starting with Maria from the movie followed by the “heartless” Tin Man from The Wizard of Oz. In 1950, Alan Turing, a British mathematician whom the Turing Test is named after, dedicated his career to researching the mathematical possibility of artificial intelligence. Turing argued that humans use acquirable information and logic to solve problems and make decisions, so he pondered whether robots could do this as well. This idea was the foundation of his famous paper, “Computing Machinery and Intelligence,” where he explained how to construct AI machines and then measure their intelligence levels (the Turing Test). A few years later, inspired by Alan Turing’s ideas, thousands of mathematicians, scientists, and philosophers began working with the concept of AI and its possibilities.

Innovation and the adoption of AI was immensely slowed because computing power in computers needed to accelerate first. Computers during Turing’s time could be programmed what to do but failed to store what they did. In addition, like every technological innovation at the beginning, it was immensely pricey. In 1950, on a yearly basis, the cost of using a computer cost about $2.4 million, so it was limited to educational institutions and a couple of giant tech companies (Garner, 2021). From 1957 to 1974, the rate of AI development went parabolic in its growth. As computers started to store more and more information, they also became faster, cheaper, and more available to the common person.

Fast forward a couple of decades—computing power is larger, and computers are faster and more affordable, which leads to a breakthrough in many of artificial intelligence’s accomplishments. In 1997, IBM’s Deep Blue, a chess-playing computer program, defeated world chess champion Gary Kasparov (History.com Editors, 2021). The chess match signified the first time a computer had beaten a world chess champion, and this defeat marked a huge leap forward in measuring artificial intelligence decision-making. In this same year, speech recognition software began operating on

Fast forward to today, and computing power and storage—which had been holding us back previously—are no longer roadblocks. Moore’s Law is the observation that the capability and speed of computers doubles each year. Moore’s Law finally caught up to a capability that allows AI to flourish. We are now at a point where AI is booming; every day, new technologies are being developed, and within a couple of decades, these intelligent machines could quite possibly surpass human intelligence. But before then, disruption will happen to jobs, and life in the age of AI a few years from now will be far different from what it is today.

The History of Job Displacement

People feel a sense of mass hysteria when it comes to technology taking our jobs. With every new technological advancement in the past five hundred years, someone has been faced with the idea of their livelihood changing irreversibly.

In the sixteenth century, essentially all jobs were manual labor, but then one day, George Washington’s personal servant, William Lee, had an idea to mechanize the production of stockings by creating the first knitting machine. The machine precisely imitated the knitters’ hand movements, but it was around twelve times quicker. The stocking frame could easily produce massive quantities of stockings. So, Lee went to get a patent from Queen Elizabeth I, but she rejected it because she was concerned about the welfare of stock knitters who would be out of a job (History of Information, 2021).

In the nineteenth century, we saw the textile riots. The Industrial Revolution was gaining momentum, and people were transitioning from rural communities to more compact cities. They typically held jobs in factories where machines were ramping up the speed of output for common items that had been handcrafted by artisan workers. Also, farmworkers were up against mechanization. Population growth was booming, and more food was needed, so manufacturers adopted machines to do almost everything, including sowing seeds and harvesting crops. Like most tech advances that displace labor, the working class had a negative reaction. The “Luddites” was a movement that fought back against the growing use of automation. They rioted at textile factories, destroyed the machines, and even burned down the homes of the business owners (Victorian Era, 2021).

More recently, toward the end of the twentieth century, the car manufacturing boom prominently featured the use of robots in vehicle manufacturing. The robots were mainly used for repetitive, low-skill tasks that sped up output and subsequently lowered costs. This advancement also included assembly-line tasks like spray-painting, part assembly, and welding, which saw a transition from human jobs to robot jobs (with human supervision). Today, you can’t look around a manufacturing floor without seeing some sort of robot.

Throughout every period of technical advancement and change, the total number of jobs has always increased. We have seen technology like computers, steam power, and electricity progress every year for 250 years, and the United States has always maintained an unemployment rate between 5 to 10 percent with some rare spikes and drops (Lebergott, 1957). So based on history, some economists believe the upcoming fourth Industrial Revolution will create more jobs than it displaces.

However, the impending AI revolution is something completely different, nothing like we have ever seen. We are heading for an era in which computer scientists will program us out of work, leading to net job declines unless governments step in to regulate technological advancements (which they shouldn’t do). With that said, we are certainly going to create new jobs that aren’t even invented yet, but it’s going to be incredibly difficult to retrain an entire working class of people. So, while I don’t go as far as Elon Musk in saying that “AI will make jobs kind of pointless,” we will see a massive displacement like never before (Gilchrist, 2020).

Chapter 2

AI Today

“AI is probably the most important thing humanity has ever worked on. More profound than electricity or fire.”

—Sundar Pichai,

CEO

of Alphabet and Google (Petroff, 2018).

Several different technologies and industries are integrated with AI, and in almost all cases, getting AI to make on-the-fly decisions and act like humans are some of the biggest hurdles in AI development. In the early 1990s, Microsoft’s co-founder Bill Gates made a bold prediction that computers would one day see, hear, communicate, and understand human beings, and he was right (Huddelston, 2020).

Seeing

Just like how humans have sight, so do computers. Computer vision—programming a computer to process and identify visuals—has evolved tremendously over the years. In 1959, the first digital image scanner was invented by transforming images into lines of numbers (Demush, 2019). Fast forward to 2009, and Google started using computer vision to test cars on roads where AI could recognize various traffic signs with 99.46 percent precision, which is better than humans (Islam and Raj, 2019). A couple of years later in 2011, the US government used facial recognition to confirm the identity of Osama bin Laden after he was killed by SEAL Team Six (FaceFirst, 2021). Today, AI systems are also exemplary at surveillance and image recognition.

Using AI surveillance in the right way can be a very positive technological asset for society in keeping us safe. Companies like Athena Security are using AI-driven surveillance technology to easily detect handguns and assault rifles instantaneously to protect employees and students from active shooters (Athena Security, 2021). Although most schools and offices have surveillance operators, it has been found that CCTV operators cannot recognize objects in the video feed after twenty to forty minutes of active monitoring. It has been proven that video monitoring operators miss up to 45 percent of screen activity after twelve minutes of continuous video monitoring, which increases up to a 95 percent miss rate after twenty-two minutes of continuous monitoring (Dadashi, 2008). Athena Security’s AI can detect about nine hundred different types of firearms, recognizing threats with extreme accuracy in under three seconds. If the AI recognizes harm, it can call 911 in real time and send the video along as well. This is a case where AI makes a positive difference in the world.

AI cameras monitoring citizens on the streets, albeit controversially, can be a huge asset in high-crime areas. Current AI technologies can pick you out of a large crowd, get a transcript of what you’re saying based on lip movement, and—by analyzing micro-expressions and other biomarkers—actually know how you’re feeling. Using large data sets, AI can improve administrative operations, law enforcement, and national security. For example, in China, they have the “Sharp Eyes” program, where Chinese law enforcement matches social media activity, video images, online purchases, travel records, and personal identity into a “police cloud.”

This technology and cloud of data are tools for authorities to use to keep track of criminals, potential lawbreakers, and average citizens. With tens of millions of video cameras active in China—they have a lot of power in monitoring their citizens to keep them safe—China is, without a doubt, the leader in emphasizing AI facial recognition for surveillance purposes. I’m not advocating AI monitoring of people on the streets, only pointing out that we have the technology to make society safer. These are and will continue to be used in military operations as well.

Hearing

Artificial intelligence is in your home, and it can listen! Whether it is Amazon’s Echo, Google Home, or Apple’s HomePod, these virtual assistant technologies have given us an always on-demand tool, answering any question we have based on simple commands. The future of business and “administrative” work will heavily involve the use of digital virtual assistants like Alexa. In a recent 2020 interview, tech billionaire Mark Cuban said, “There’s no future that doesn’t have ambient computing or voice activation” (Cutsinger, 2019).

I concur with Mark; the reason is because companies are heavily data-driven, and humans are not capable of storing and delivering large amounts of data at the same speed and accuracy of digital assistants. For an example, say you are the CFO of a mid-sized real estate firm, and you have questions on the market outlook for the upcoming year. Today—and even more so in five years from now—every digital assistant will have so much data that they could answer any question someone has. The CFO can say, “Hey Alexa, what are the home mortgage rates in the Houston area in 2021?” and Alexa will be able to immediately give an answer based on the thousands of data points it has. Or maybe the CFO wants to know specifically what Grant Cardone thinks of the current market for residential real estate, based on tons of interviews, YouTube clips, articles, etc. Alexa will respond.

Speaking, Reading, and Writing

Another use for voice technology I see happening soon is digital assistants in hospitals and doctors’ offices. On Christmas Day of 2020, I had a very bad case of pneumonia (which could have been COVID-19?), and the doctor brought me into the room and proceeded to ask question after question, taking the time to type out on his keyboard every word I said. As I was sitting there, nauseous and short of breath, it took my doctor several minutes to record all my information. In fact, a 2020 study found that today, physicians “spend nearly half their time typing in data, and nurses are interrupted from patient interaction every six minutes” (Hartford Healthcare, 2021). As digital assistants become more advanced, they should be in every doctor’s office so that doctors don’t need to spend so long typing every answer. Rather, a digital assistant can take in what the patient is saying and then log the information automatically, allowing the doctor to spend more time focusing on the patient.

Using voice-enabled personal assistants worked well in the Los Angeles Cedars-Sinai Medical Center, where it had Amazon Echoes in more than one hundred patient rooms. “The smart speakers use a voice assistant platform for health care, Aiva, and are intended to help patients communicate with their caregivers. After a patient tells Alexa what they need, Aiva routes it to the right person’s mobile phone. For example, if someone needs medicine, their request goes to a registered nurse. If a response takes too long, Aiva reroutes the request “up the chain of command” (Cimino, Stefanacci and Pakizegee, 2020).

Using the power of AI digital assistants frees up clinicians to deliver quicker, more in-depth care, which leads to better health outcomes and care. Other use cases include voice assistants answering questions about medications, symptoms, and other FAQs. Also, patients can ask for refills for medications from pharmacies and schedule doctor visits through the assistant. They are flexible and include a mode for communicating with nurses, hospital staff, and caregivers using Alexa.

AI digital assistants are still at the beginning of their potential capabilities, but it is without a doubt a space people should keep their eyes on in the health care realm.

Now, the present state of AI assistants is already able to deal with some decently complex commands. In May of 2018, Google wowed the crowd when it demoed its artificial intelligence-powered calling service/personal assistant Google Duplex, booking a hair salon appointment through a phone call perfectly (Callaham, 2021). This was amazing, but what was even more amazing was the fact that the salon’s receptionist had zero clue she was talking to a machine. The AI voice can understand the voice of the human on the other end of the call while also “responding back with correct answers to inquiries and questions. Google Duplex’s voice even put in words like ‘um’ and pause breaks to make it sound more like a real human” (Callaham, 2021). Google Duplex-like assistants are something we will see play a major role in the future, whether it is booking an appointment, making restaurant reservations, or talking to customer service. It will be done through AI assistants where you can’t even tell you are talking to AI.

Next, AI can read at a level that is getting more impressive every day. Google’s Talk to Books is a machine learning-powered tool that scans every single sentence in more than 120,000 books in half a second, then generates a list of quotes/answers from them. One interesting part of Talk to Books is that you can formulate any question using the natural way a human speaks conversationally, and the tool gives an answer back to your question that best matches what a human would want. This is yet another example of AI trying to become more “human,” as this is the toughest problem for AI today. AI systems are excellent at doing repetitive quantitative work, but the real barrier is reasoning, judgement, and understanding uncommon events—all things humans are able to do with ease.

Writing and creating content is also an area in which AI is increasingly improving its performance. AI is writing books and articles in magazines and creating art, music, movies, and much more. An AI tool called GPT-3 from OpenAI is a language model that uses machine learning to produce humanlike text. GPT-3 can make an app, code a website, or even write a full-on essay from just a single prompt. When I heard this, I thought of all the time I could save in college by having a computer write my essays for me! To no surprise, a college student from UC Berkeley named Liam Porr used GPT-3 to produce an AI-written blog post that ended up as the #1 blog post on Hacker Porr intentionally demonstrated that GPT-3 can create content that fools people into thinking it is human, and it worked, which is pretty awesome (Lyons, 2020).

The movie industry is also beginning to experience an AI revolution. AI is being used in Hollywood to screen movie scripts and stories to determine if they will be popular or not, using algorithms to decide if they should be released. AI is demonstrating that it can write a script and edit scenes. Within seven to ten years, there’s no reason that the movie and/or streaming industry won’t be implementing algorithms to source stories, use CGI, and edit films. Musician and singer Taryn Southern was the first to use AI to make music. Her album, I AM AI, was composed entirely by AI, and after the album’s success, she stated, “I imagine in twenty years, coding songs will be commonplace” (Werner, 2019).

Intelligence

Lastly, AI is smart. I think the best way to explain its intelligence is through games. The 22,500-year-old strategy board game Go is played on a 19 x 19 board. Basically, if you believe chess is convoluted, think again. Go requires far more moves than chess, which has a game tree complexity of 10^40—this basically means if everyone on Earth grabbed a partner and played a game of chess, it would be trillions upon trillions of years before they play every different variation of chess. Compared with Go, which has a game tree complexity of 10^360, you can try and picture how much more complicated it is. Go has more steps and requires far more intuition and precise strategy (Metz, 2016).

The first AI chess victory was IBM’s Deep Blue, which won a game against world chess champion Gary Kasparov, and while that was impressive, Go is another story. In March of 2016, Google’s DeepMind team sent out AlphaGo to see if it could beat the second-best Go player in the world, Lee Sedol. Not only did AlphaGo do the seemingly impossible task of beating Sedol, it beat him four times, only losing once (Metz, 2016).

AlphaGo’s victory was stunning to the AI community; they thought this would take more than five years of training and improvements. Now, they were eager to test AlphaGo against more of the top players in the world. Before the next test, Google changed it up a bit. During the 2016 game, AlphaGo was updated and educated using machine learning—basically, it was trained using thousands of past games and therefore was taught which move is the best one given a certain configuration. However, for the next round of matches, Google introduced AlphaGo Zero, which was given zero data but rather relied on “reinforcement learning”—essentially, it learned by playing Go games against itself. Within a few days, AlphaGo Zero (which had no data) defeated the previous version of AlphaGo 100 to 0. Fast forward a couple more weeks, and AlphaGo Zero was able to become the best Go player in the world by beating sixty of the world’s best players (Metz, 2016).

Now, some people may say, “Who cares? Since it is a board game, in what ways can this technology actually help us in society?” Well, Google’s machines challenged real humans in image recognition, and the machines won using reinforcement learning technology and real-time image recognition. Using this technology, the FDA has now allowed AI technology to be used in emergency rooms where the AI is better than doctors at predicting heart attacks, deaths from respiratory issues, and overall sudden death (Gorey, 2019). AI is also being used to spot cancer and other diseases. Private companies like Twitter and Facebook are also using AI to be alerted of potential suicidal users; the US military utilizes AI technology to identify early signs of depression and PTSD in its soldiers (Novet, 2018).

Artificial intelligence has also descended on retail, e-commerce shopping, finance, health care, law, insurance, entertainment, and surprisingly enough, politics. A couple of years ago, an AI robot candidate ran for mayor in a Japanese city. It lost, but not by much (Leadership 4iR, 2018). There is so much to cover and explore in this crazy and ever-changing world we live in. There are many directions AI can take: Will it wipe out half of our population’s jobs? Will AI rather be a tool that we utilize and thus make our lives easier? Will AI cure many of the world’s diseases? What are the ethics surrounding AI going to be like? There are a lot of unknowns, but all we know for sure is that the world will be changed. In the coming chapters, I argue that the future of many industries will be radically changed by AI, some for the better, some for the worse, but one constant is that millions of white-collar and blue-collar jobs will be automated away in the next couple of decades.

Part 2

Tech Disruption

Chapter 3

The Future of the Truck Driving Industry

You know that arm pump we give truckers, hoping they’ll blast their horns? Well, don’t be surprised when you hear a lot less honking in the near future. With the way we’re headed, truck drivers will soon be displaced by autonomous driving technology.

The idea of self-driving cars isn’t new. Most everyone has heard of Tesla and the self-driving cars it’s already developed. But autonomous trucking will cause an actual disruption in our way of life much sooner than Tesla’s self-driving private cars will. AI-powered self-driving trucking is not an “if”; it’s a “when.” And the “when” is coming faster than you think.

Trucking jobs are the most common jobs in twenty-nine out of fifty states in the US, and millions of people work for the trucking industry in non-driving positions (Bui, 2015).

What happens when autonomous trucks roll down the highway instead?

I recently posed this question to dozens of truck drivers over Zoom interviews meant to gauge their stances on the future of their careers. The most common response I got was this:

“I don’t know.”

Even getting on Zoom was a struggle for a bulk of these drivers, and one older driver laughingly said, “Sam, our struggles to navigate this Zoom tell you all you need to know. We’re drivers, not the Geek Squad!”

And he’s right; for most truck drivers, navigating technology and understanding how autonomous vehicles work aren’t in their comfort zones.

There are a lot of “I don’t knows” for truck drivers today. Experts have argued when and to what extent autonomous trucks will change the trucking industry, but one thing is certain: there will be significant disruption to trucker jobs. During my meetings with the truckers, one couple in particular was split on their concerns about autonomous trucks. Meet independent truckers Sean and Andrea Peterson from rural Pennsylvania.

Sean Peterson started laughing when I asked him about the prospect of self-driving trucks taking his job one day. “You’ve still got to have a driver in it because I don’t see the technology working in the big cities. I don’t see it changing fuel or backing into a dock. I don’t see it driving in wind or rain. I don’t see it doing, you know, main things . . . I ain’t really worried about self-driving trucks. They said we would have flying cars by now.”

But when his wife, Andrea, heard that TuSimple, Embark, Daimler, Aurora, and other companies are already deploying trucks delivering freight on Interstate 10, she looked terrified, paused, and quietly said: “Oh . . . really?”

TuSimple executive Chuck Price predicts that there will be self-driving trucks with no human backup drivers on US roadways by the end of 2021 and that TuSimple trucks can already drive themselves under almost any condition (Abt, 2019). The trucks can drive “day, night. And in the rain. And in the rain at night” safely. Andrea was stunned when she heard me relay what executives at the aforementioned companies stated. On the other hand, Sean still expressed his view on autonomous vehicles replacing human drivers.

It is widely known that truck driving can be a demanding job. Truckers have long hours, relatively low pay, and lots of time away from home. So to get a better understanding of truckers, I asked Sean why he does it.

“Believe it or not, I’m a trucker because I love it. It’s in my blood,” Sean said. “I’m a fourth-generation driver. Every day, I am proud of the job I do. All of the food you eat, all of the clothes you wear were most likely driven across the country by a trucker, and it makes me proud to know I am having an impact.”

For background, Andrea Peterson is a former trucker and the mother of a three-year-old girl, Sophie. Her husband, Sean, operates his own truck, where he works long hours, often sleeping in his truck to save money and making it home just a couple of times a month. Sean, who is the family’s only financial provider, made $140,000 a year as an independent trucker, nearly $100,000 more than the average trucker, which sounds amazing until you factor in the costs. Sean paid $90,000 in fuel, $200 a month in maintenance, $14,000 in a motor blowout, and at the end of the year, they made about $34,000 before taxes.

Times are tough for the Petersons, to say the least.

In the past couple of years, Andrea said she and her family “were able to keep their heads above water just fine without any government help,” but if anything out of the ordinary were to derail their monthly budget, they couldn’t handle it. She reminisced about one particularly painful time about a week after their family had to arrange for the funeral service of her mother. Their daughter needed to receive some immunizations, and they just couldn’t pay for it.

“We’re extremely proud of the job we have,” she said. “We feel blessed, but there are times like when the truck breaks down or the pump in the well goes, and we have to take out a loan for the repairs, and that’s when we fall behind—like so many other blue-collar families do.”

The Petersons’ hardships represent an increasing number of blue-collar Pennsylvanians who are working but struggling to make enough to become financially stable. According to a new study by United Way, “Thirty seven percent of Pennsylvania residents could not afford basic needs such as housing, child care, food, transportation, health care, and technology. Up 14 percent from 2017” (Hoopes, 2019). This goes to show that the Petersons’ situation is extremely commonplace in Pennsylvania. Truck drivers are the most common job in a majority of states, and if their already struggling livelihood is on the line, then we have a big problem at hand.

Self-driving trucks will be mainstream far quicker than cars because driving on highways is easier and safer than navigating through urban areas. The talk about autonomous trucks displacing human drivers is interesting because on one hand, there are incredible economic savings from automation, roads would be safer, and trips would become more efficient. On the other hand, there are grave consequences of potentially displacing millions of one of the most common jobs in America, especially considering that these workers typically don’t reskill and have little to fall back on.

The reality is that there will be dramatic disruption to the industry, and there are many reasons why that will happen—whether truckers believe it or not. Three big incentives are driving companies toward self-driving trucks.

The first factor is cost. The trucking industry is an industry of $800 billion per year, and labor’s a third of that cost. Savings from automating labor are estimated by Morgan Stanley to be an astonishing $168 billion per year; this includes the lower cost of labor ($70 billion), better equipment utilization ($27 billion), fewer accidents ($36 billion), and fuel efficiency ($35 billion). That much in savings in itself is enough motivation for companies to replace truckers like Sean and Andrea with artificial intelligence (Viscelli, 2018).

Second, transitioning to autonomous trucks would not only save billions but it would also save lives, something self-driving cars eventually will do as well. Large-truck crashes accounted for the deaths of 4,862 people in 2018, including over 115,000 injuries—the highest ever, according to the National Highway Traffic Safety Administration (NHTSA). In essentially all of these crashes, it was partly due to human driver error. The big culprit for the deaths is driver fatigue due to long driving hours; truckers typically spend over 250 nights per year away from home, often sleeping in their trucks after driving eleven hours per day. It is both physically and mentally demanding. It is also important to remember that self-driving vehicles eliminate texting-related accidents, distraction, and drunk drivers.

The current state of autonomous truck safety varies depending on the company, but right now, AI-powered trucks are scanning mirrors constantly and looking one thousand meters out. They process everything that our brains could never do and react fifteen times faster than we can because these trucks have over ten cameras, five radars, and two lidars that allow for complete vision all around the truck (International Trucks, 2021). In order to be safe, self-driving trucks need to be equipped with multiple technologically advanced features, such as motion sensors, GPS navigation, computer vision, lidar, and other camera systems. The key ingredient in making autonomous trucks safe is having them react quickly to unpredictable events, whether that is a biker not paying attention, a swerving car, or an accident because if the trucks aren’t safe, then they will never be allowed on the road.

The third reason that will make self-driving trucks more commonplace is the efficiency they provide, which includes better equipment utilization as well as time and fuel efficiency. TuSimple says its driverless truck can get coast-to-coast in two days, not the typical four, and it only stops to refuel—which is in the works of becoming a fully-automated process as well. The twenty-six-year-old CEO of Embark points out it is seeing increased fuel efficiency in its trucks because of trucks staying perfectly aligned in their lanes and, unlike humans, being programmed never to break the speed limit. Also, human drivers are only allowed to drive eleven hours at most each day per federal law, and a driverless truck obviously wouldn’t have that limitation, ultimately leading to greater operational efficiency.

So, given all those incentives, just how close are we to becoming fully autonomous?

The Current State of the Trucking Industry

In the world of autonomous technologies, we classify automation by levels from zero to six. Level 0: no driving automation, 1: driver assistance, 2: partial driving automation, 3: conditional automation (driver is required but not required to monitor the environment around them), 4: high automation (vehicle is able to perform all driving functions autonomously under most driving conditions), and 5: full automation (vehicle is able to perform all driving functions autonomously under all driving conditions).

Levels 1–3 are already a reality in both cars like Tesla’s and trucks like TuSimple’s, but these levels are not advanced enough to displace driving jobs. Looking ahead, TuSimple plans to achieve Level 4 autonomy by 2024, meaning its fleets of trucks could operate without human drivers under certain conditions (predictable routes, highways, no rain, etc.). Level 5 autonomy for trucks is often estimated to happen in the early 2030s. In the past year or so, time lines have been altered by COVID-19, as the entire industry was unable to accrue data from test drives, as far less people were on the road and working in person, so it is left to be seen whether these goals will in fact be reached.

Autonomous Refueling and Greater Fuel Efficiency

As touched on previously, removing human drivers results in greater operational efficiencies and lower costs. At the end of the day, reducing costs is what matters most to autonomous trucking owners, and improved fuel efficiency from autonomous cars is imperative to doing just that. In the new trucking industry era, there are three big ways to reduce costs.

The first is by using autonomous technology, which eliminates the need to pay human drivers. The second is optimizing costs savings by decreasing the amount of time trucks are idling and eliminating inefficient human driving tendencies. And finally, the third is electric trucks: cheap batteries have allowed for electric semi-trucks to be half the cost of diesel. In addition, they are autonomous, which results in labor cost savings.

Unlike human truck drivers today who are restricted by stern laws overseeing shift lengths behind the wheel, self-driving trucks operated by companies like TuSimple, Embark, Diamler, Einride, Plus AI, Waymo, Volvo, Ike, Aurora, and Tesla will soon be trucking across highways 24-7, 365 days a year, only stopping to change loads, refuel (electric or gas), and possibly be repaired. Based on current trucking economics and the efficiencies that electric trucks bring, the national trucking research organization estimates that autonomous trucks will result in 30 to 40 percent lower operating costs than their human-driven counterparts (D’Orazio et al., 2020). TuSimple’s driverless AI was 10 percent more fuel efficient than human drivers, as there are many ways to save fuel. Simply slowing down by ten miles per hour can result in up to 20 percent fuel savings (Baker, 2019). And no, that doesn’t mean the trucks will complete their routes slower because there is still no need to stop for rest.

So how can an autonomous truck with no human on board refuel?

This is a common question posed by critics of the technology, and it is not too difficult of a problem. In the early days of full autonomy while we’re still using gas-powered trucks, there will likely be a return to gas attendants who will refuel trucks as they arrive, certify the payment, and perform any quick inspections that may be needed. I’d guess this would occur at certain gas stations every six or so truck stops. Then, the next phase will be to transition to robotic pumps (whether for electricity or fuel). Today, several companies, like Tesla, are working to perfect these refueling robots, which have a tentacle- or snake-like charger that can autonomously connect to a charge port. However, the likelihood of something with the complexity and cost of that technology appearing at every truck stop is not feasible in the next decade. Eventually, however, that may very well be the future—autonomous electric trucks with autonomous robotic fueling stations operating at a fraction of the cost it takes today.

Manufacturing Industry Comparison

In order to fully understand how AI and autonomous trucks are going to displace millions of workers, let’s look back in time. Since the year 2000, more than 6.5 million manufacturing workers have lost their jobs. Of that 6.5 million, 83 percent of the jobs lost—or around 5.4 million jobs—were due to automation. The reason for a loss in manufacturing jobs is obvious in that automation is cheaper and more efficient than human operators. Every year as technologies continue to develop, the costs of robots and automation drop. By 2025, the cost of automation is expected to drop a startling 65 percent (BLS, 2018). Because automation is far less expensive than having employees, we will continue to see manufacturing and trucker jobs become fully automated in the next twenty to twenty-five years, barring government intervention.

The lesson here is what happened next. Most people would think these newly unemployed workers moved onto a different job in the industry or reskilled into a new career. However, what actually happened was that a large number of these workers never recovered. One Bureau of Labor Statistics study from 2014 discovered that 39 percent of displaced manufacturing workers between 2009 and 2012 were still out of work or had jumped out of the labor market within three years of losing their careers (Hernandez, 2018). Essentially, what happened was that very few workers went back to school, and relatively few seemed to help themselves through the use of government programs aimed at helping displaced workers.

The new wave of manufacturing jobs today requires far greater technical skill and education because of the increased complexity of working with automated manufacturing. For the 39 percent of displaced manufacturing workers who didn’t find a new job, what was next for them? Well, the answer for many was that they came to be impoverished and had to rely on disability benefits. Since 2000, workers on disability has grown exponentially by almost four million, with most of the increase in states where most of the manufacturing workers were. In fact, in Michigan, a majority of the 310,000 residents who were displaced out of the workforce between 2003 and 2013 went on disability (BLS, 2016). The reality is that most blue-collar jobs are the easiest to automate through AI and robotics. Unfortunately, it also true that blue-collar workers often have lower levels of education, less skill, and aren’t in a position to reskill into a brand-new career.

After analyzing how automation affected the manufacturing industry and how displaced workers had a hard time finding new jobs, I realized the same thing was likely to happen in the driving industry starting as soon as late 2021. Here’s why: the average age of a trucker is forty nine, with 94 percent of drivers being male and two-thirds of all drivers being white (Edmondson, 2018).

For drivers today, the trucking industry has been a good, decent-paying job for those with just a high school education. According to the American Trucking Association, there are 3.5 million professional truck drivers in the United States, and all together, the industry has more than 8.7 million workers. Of those 3.5 million drivers, the median annual wage is around $45,000 for heavy trailer drivers, $35,000 for light-truck/delivery workers, and $28,449 for driver/sales workers (Solon, 2016). Although these salaries may seem low to some, trucking has been the most common job for men in America for a reason: it has been a relatively stable career and gives you an honest day of work.

Estimates on the number of truckers being displaced is varied, but most news outlets, like the LA say that between 1.7 and 2.5 million American truckers could see their jobs displaced within the next eight years; in the next couple of years, 300,000 truckers could see their jobs disappear (Kitroeff, 2016). That estimate is similar to a joint study done by the United States and the European Union, which predicted a 50 to 70 percent cut in truck driving jobs due to self-driving technology by 2030.

What Will the Future of the Trucking Industry Look Like?

Displacement of millions of truckers will happen, but slowly. The first and current phase of autonomous trucking is driverless trucks with humans there as a backup. The technology will be an aid to truckers, allowing them to truck far longer than the current eleven continuous hours allowed. After enough time spent on the road, the software will accrue enough data and the technology will become advanced enough to handle tough weather conditions and urban areas. As the operational efficiencies and technologies advance, human wages will decrease as their roles become less and less prevalent.

The second phase of trucking prevalent in many companies consists of a fleet of fuel- and electric-run trucks without a human operator besides the lead truck, which will have a human at the helm. Having a fleet of trucks together with one human driver in the lead will serve as a good backup in case of issues, lower wind resistance, and help save on the costs of fuel. Also, the last fifteen or so miles will potentially have human drivers complete delivery.

Then eventually, as the industry becomes fully autonomous, truck drivers will provide essentially very minimal value as the operational efficiencies, lower costs, and increased safety all outweigh the consequences of displacement. Those with post-secondary education or an alternative skill set will leave to other industries that will also likely face the prospect of automation, but for many of these truck drivers, just like the manufacturing displacement, their options are bleak. I believe by 2024, companies like TuSimple will achieve Level 4 autonomy, meaning the trucks will be able to operate without a human present but will be limited in the conditions they drive in (urban/bad weather). Then by 2030, we will likely see fully autonomous trucks and refueling systems, which will result in over two million lost jobs.

So Now What?

During the Industrial Revolution, which people cite as the precursor to what we are going through now with AI, there were mass riots that killed dozens of people, caused billions of dollars’ worth of damage, and forever changed how we look at labor. Labor Day is a holiday because of those riots protesting the economic change, and high school was universally implemented in 1911 partly due to the emotional charge from the riots. With artificial intelligence, we will see the “fourth Industrial Revolution” that will displace “40 percent of jobs within the next ten or so years” at a rate quadruple that of the last revolution, according to AI expert Kai Fu Lee, who I interviewed for this book.

What will happen when the hundreds of thousands of struggling independent American truckers who paid for their own trucks face unemployment and become resentful? There will be a massive uprising and pressure for regulation over millions of people losing their jobs, not only in the trucking industry but everywhere. At the end of the day, people will need to reskill and look for safer jobs, but unfortunately most of the jobs that are considered “automation proof” require high-level skill and are hard for the average American to handle. It is unrealistic to expect truckers to turn into coders—it’s not going to happen. For truckers, there is fear because people generally don’t like change, and change that you don’t understand is even more terrifying.

Current truckers should work with their employers or go through programs to reskill into the technical side of the trucking industry. This doesn’t mean turning into an engineer or coder, but rather a possible fleet analyst or supply chain operator. Since most truckers are over the age of fifty, a majority will be able to retire as truckers, but for those that can’t, it is imperative to not doubt what is coming and begin to pivot their careers.

Chapter 4

Self-Driving Cars

“Autonomous driving is a big technological challenge, probably the biggest of our generation. Younger generations won’t even think about owning cars”

—Tekedra Mawakana, Co-

CEO

, Waymo (Rapier, 2019)

In 1908, Ford replaced the horse and buggy as it mass produced its first car, the Model T. Over the next decade, a rapid shift from carriages to automobiles took place, as they became the dominant mode of transportation in city streets. Yet, by 1910, the vehicle was already proven to be more durable, reliable, and low cost; it was almost universally seen as something that was “here to stay.”

However, people didn’t always think so. Just years before the Model T, the US Congress had a meeting to discuss the future of urban transportation and came to the conclusion that horses were here to stay.

“The dangers are obvious. Stores of gasoline in the hands of people interested primarily in profit would constitute a fire and explosive hazard of the first rank. Horseless carriages propelled by gasoline engines might attain speeds of fourteen or even twenty miles per hour. The menace to our people of vehicles of this type hurtling through our streets and along our roads and poisoning the atmosphere would call for prompt legislative action even if the military and economic implications were not so overwhelming. . . . The cost of producing gasoline is far beyond the financial capacity of private industry. . . . In addition, the development of this new power may displace the use of horses, which would wreck our agriculture” (Adams, 2016).

Another beauty of a quote was when the Times of London newspaper believed “In fifty years every street in London will be buried under 9’ of manure.” This became known as the “Great Horse Manure Crisis of 1894” (Johnson, 2021).

I am highlighting these quotes to illustrate how every innovation always has its fair share of doubters and critics. With autonomous vehicles and autonomous technology in general, an exorbitant number of people claim it’s “sixty years out” or “very dangerous” or “can’t be trusted.” Almost all the arguments that were hurled at automobiles in the nineteenth century are coming back again with autonomous technology, and I see the outcome being the same.

Decades after Henry Ford’s Model T took over the city streets, supporting transportation staples were introduced: stoplights, stop signs, parking lots, garages, highways, car washes, crosswalks, and, of course, gas stations in every city. Once these were added into the driving experience, widespread adoption took place; the horse and buggy were gone. We’re now in an exciting time of transportation, as aerial mobility and autonomous vehicles are threatening to revolutionize the very system that was used for so long.

In 2004, the Defense Advanced Research Competition Association (DARPA) held its first Grand Challenge to prop up research and development into autonomous technology. DARPA granted engineers and scientists from the top institutions a $1 million prize if they could get an autonomous vehicle to navigate a 142-mile course through the Mojave Desert. There were fifteen entrants into the challenge, and all fifteen failed to complete the course. In fact, the best autonomous vehicle was only able to go eight miles before catching on fire (Davies, 2017). It was a low point for the self-driving industry with many doubting its capability, except companies like Google.

In 2009, Google began to work on a self-driving car project that eventually became Waymo. Today, Waymo is considered the leader in autonomous cars, as it was the first to operate a driverless vehicle on public roads. As of early 2020, Waymo’s cars had already driven twenty million miles on public roads in twenty-five cities mainly through its taxi service in Phoenix and San Francisco (Reuters Staff, 2020). Waymo’s cars use high-resolution cameras and lidar (unlike Tesla), which is, in essence, a technology that estimates the distance of another object by measuring the time for reflected light and sound to return. These are the eyes of the car, enabling it to identify vehicles around it, pedestrians, random obstacles, and where it’s moving. So far, Waymo’s ridesharing fleet has been a safety success, and it will only improve as more miles are driven. With all machine learning-enabled technology, more miles driven equals more data, which is what allows the technology to learn and iterate based on past experiences. Ultimately, more information collection will result in more advanced algorithms, which can enable safer cars—a big selling point to those skeptical of the self-driving industry.

Furthermore, Waymo and other autonomous vehicle companies are going to make on-the-ground transportation a lot cheaper. Statista estimates that at annual costs of $6,300, ride-hailing could become cheaper than car ownership by 2027 thanks to self-driving technology like Waymo’s and eventually it will become ten times cheaper than new vehicles (Wong, 2021). Self-driving cars will also significantly reduce the sheer volume of cars on the road, “80 percent of which have people driving alone in them. And also a household’s cost of transportation, which is 18 percent of its income—around $9,000 a year—for an asset that it uses only 5 percent of the time,” said Robin Chase, founder and CEO of BuzzCar, a peer-to-peer car-sharing service, and co-founder and former CEO of Zipcar (Peng, 2020).

The disruption will go far beyond consumer car prices, as legacy automakers, suppliers, insurers, and even the government are likely to lose billions of dollars. Think about the parking fees, taxes, police ticket revenues, tolls, and even flights—one may choose to drive from Boston to Pittsburgh when one can leave late at night, sleep overnight, and arrive in the morning. Next, car insurance with the much safer roads will create quite a disruption—likely leading pay-per-mile insurance models like Metromile to dominate. Now what will happen to auto body shops when there are far fewer crashes, car repairments, and less car ownership in general?

And consumers will save more than just on costs; they’ll also save time and convenience. Although it’s frustrating, most Americans today have somewhat accepted total daily commute times of an hour, but with a robot chauffeur driving them around, it may be something much more tolerable. Your car could transform into anything imaginable—a traveling couch to watch a movie, a boardroom to get work done, or a bedroom to take a nap. You could also be more flexible in where you live now that commutes wouldn’t be as bad. It is quite amazing how a single technology can be so disruptive; transportation is about to be economically cheaper, quicker, and more enjoyable!

Self-Driving Cars Becoming a Reality

First, perfecting self-driving technology is “one of the hardest technical problems that exists—that has maybe ever existed,” according to Elon Musk . . . a man trying to send humans to Mars and put chips in our brains (Genovese, 2021). The reality is that many predictions five years ago were far too greedy on the time line for the technology to work. And it all comes down to data—artificial intelligence is imperative to the success of autonomous cars. Now, the past couple of decades have been revolutionary for AI: ambient voice, computer vision, speech recognition, and image identification have all been integrated into everyday life. AI was once unable to defeat humans in games like chess—a feat that is now a walk in the park.

All this rapid and exciting progress drove experts to make very bullish predictions about what is to come because they saw all the advances in surrounding industries. But with autonomous transportation, the problem is much more complex. Billions of dollars and resources and even having hundreds of companies working on its development cannot solve the problem of adapting to real-world scenarios. This is largely due to limits in data.

The amount of data needed to accurately train self-driving technology is staggering; we feed it constant streams of video of real driving and then use algorithms to help the computer learn good driving habits. Machine learning does better with a copious amount of data, but the issue is it’s very costly. Though the probability of certain incidents that take place on the roads may be very low—say a man in a gorilla suit for Halloween crossing the street or an object flying out of the back of a pickup—these rare events are what throw off machine learning. To compensate for this, automakers have attempted to drive as many miles as they can on public roads and have used simulations to train cars via synthetic data. As Waymo and Tesla do more tests in the streets, progress will be made.

In 2016, Mark Fields, then CEO of Ford, said Ford intended to have robotaxis on the road in 2021 (Ford Media Center, 2016). Elon Musk has said several times that we’d have robotaxis and full self-driving capabilities in 2020 (Kolodny, 2019). And they aren’t the only ones, as almost every company has missed on their time line for autonomous cars. However, I have zero doubt that the genius people behind companies like Tesla, Nio, Waymo, Baidu, and Ford will eventually get us all Level 5 autonomous cars that can drive in bad weather and rush hour traffic and anticipate pedestrians much better than humans.

Self-Driving Cars Are Safer than Human Drivers

It is quite stunning how bad the safety reputation of self-driving cars is. Every time a Tesla or another autonomous car crashes, CNBC, the New York and other publications rush to the keyboard to write articles to bash on the technology. Then, readers get the one-sided impression that autonomous cars are dangerous. What they fail to hear is that most of the crashes reported were ones in which the self-driving technology was not enabled (NTSB, 2021).

For example, in May 2021, two people driving a Tesla in a neighborhood crashed into a tree, and both passed away. This led to numerous negative articles, a drop in Tesla’s stock price, and damaged public perception. What’s wrong about the narrative around the incident is that the owner of the crashed Tesla didn’t even purchase full self-driving features, therefore it is impossible for the autopilot feature to have been the culprit (Brown, 2021). In addition, even if he had had the autopilot feature, it wouldn’t have been enabled since the road did not have marked lane lines (something Tesla requires for autopilot right now). Tesla has access to the data of every car, and after going back to check exactly what happened, they concluded it was human error.

So, what’s the point of all my complaining about the narrative of autonomous cars being dangerous?

Well, car crashes are a major cause of death worldwide, and little has been done to successfully slow the rate. However, now that we have a technology that is so far proven to save lives, the media jumps on it like they do with many disruptive innovations. It’s causing people to have a negative view on the technology, and a Gallup poll suggests that most Americans don’t want to ride in a car that drives itself; only 9 percent said they would get an autonomous car right away, while 38 percent would opt to wait awhile, and, astonishingly, half of Americans say they would never use one (Brenan, 2018)! I believe once the cars are out there and have been further proven to be safe, people will adopt them—remember, many protested the Model T in favor of travel by horse!

Here are the facts: Every year, “approximately 1.3 million people’s lives are cut short as a result of a road traffic crash. Between twenty and fifty million more people suffer non-fatal injuries, with many incurring a disability as a result of their injury” (WHO, 2021). Road traffic deaths and injuries result in enormous emotional and economic losses to individuals, families, and countries. On an injury level, the cost of treatment for recovering from a car crash is costly. Injuries also result in lost workforce productivity for those who pass away or get severely hurt or even disabled. Road traffic accidents cost most countries “3 percent of their gross domestic product.” Furthermore, for those between the ages of five and twenty-nine, getting into a car crash is the likeliest cause of death (WHO, 2021).

So, 1.3 million deaths, crashes causing 3 percent of GDP to be lost, and 50 million injuries—from the millions of miles driven today, it is clear that autonomous cars are already much safer than human drivers. Going back to Tesla, in 2021, when Tesla drivers had autopilot engaged, there was one accident for every 4.19 million miles driven. In comparison, there is one accident every 978,000 miles with human drivers in the United States (Alonzo, 2021). This equates to autonomous cars being nearly ten times safer than human drivers, and this is just with Level 3 autonomy. As more advances in self-driving technology occur, autonomous vehicles will become even more safe for riders.

Personally, I am not a fan of driving because I can’t control what the other drivers on the road are doing. There could be a drunk driver, a person staring at their phone, or people simply driving dangerously. Autonomous cars won’t get drunk or high, and they won’t speed or drive like maniacs. The more self-driving cars on the road, the safer I would feel, and a University of Texas report agrees: “If only 10 percent of the cars on US roads were autonomous, almost $30 billion of savings could be realized via less wasted time and fuel, as well as fewer injuries and deaths. At 90 percent, the benefit rises to almost $120 billion a year” (Fagnant and Kockelman, 2015). Humans make mistakes; it is part of life, but those mistakes should not be at the expense of 1.3 million lives. We need to stop promoting fear, uncertainty, and doubt (FUD) around self-driving cars and instead give them a chance. The data so far has concluded it’s safer, more efficient, and less expensive to travel with this technology, so I am excited for the societal disruption that is about to occur. People don’t trust what they aren’t familiar with; once they take that first ride, taking a driverless trip will slowly become natural.

From Diesel to Electric

Self-driving cars will have a positive impact on the environment, as almost all of them will be electrically powered. Existing combustion engines inject toxic pollutants into the Earth’s atmosphere. Trucks and cars are polluting machines, and they are responsible for 20 percent of the dangerous emissions in the United States. Electric cars have an impressive sustainability stat line. They are 95 percent efficient and release no emissions, whereas traditional gas-powered cars are at just 30 percent efficiency. Their road emissions cause 53,000 deaths per year (University of Cambridge, 2020).

Nobody disputes the fact that electric cars are better for the environment, which is why sales of electric vehicles are expected to hit sixty two million cars per year by 2025 and why companies like Uber, Lyft, Honda, Volvo, Ford in Europe, and others have committed to 100 percent of their car sales being electric (Wood Mackenzie, 2021; Preston, 2021). In terms of the car market, this would mean that over half of all cars will eventually be electric. This shift to EVs will bring about drastic changes to the infrastructure in cities. Charging stations, which are benefiting from advances in renewable energy, will replace gas stations. For Teslas today, a single charge can last you 250 to 400 miles depending on the model, costing around $70 a month in charging costs. The miles per charge is more than enough since the average person drives a little less than 250 miles per week (FHWA.gov, 2018). So theoretically, you could charge it while you sleep on a Sunday and have a charged car all week. Amazing, isn’t it?

2030

The year is 2030, it’s a cold, rainy day in Minnesota. You are a frequent user of the Tesla robotaxi service, where a Tesla is programmed to pick you up at your house and drive you to work every day. You hop in the back seat and speak your destination’s address to the Tesla’s interface, allowing it to drive itself while you catch up on emails, watch Netflix, or nap. As you catch up on the news on the way to work, you see a headline saying that electric self-driving cars have reduced road traffic deaths by 80 percent and that air pollution has cratered. Everyone and their friend have electric cars, with most having autonomous capabilities. With this improved road safety, car insurance costs are dirt cheap, and 20 percent of your paycheck no longer goes to your car. Travel is made fun, whether you’re being driven around by your personal robo-chauffer or maybe even your flying car.

Chapter 5

Flying Cars (eVTOLs)

If you haven’t noticed lately, we are living in the future. Avant-garde technology that was only conceivable in science fiction is now so monotonous that we all but take it for granted. But there is still one thing that is strikingly missing from our futuristic present . . .

Where are the flying cars?

In the words of Peter Thiel, venture capitalist and co-founder of PayPal, “We wanted flying cars, instead we got 140 characters” (Weisfeld, 2013).

One thing that both utopian and dystopian visions of our future seem to agree on is that by now, we’d be flying around our cities. The idea that our skies would be filled with flying cars has been present ever since the May 1923 issue of Science and Invention, which featured a two-wheeled flying car called the “Helicar.” It was assumed to be the solution to New York City’s congested streets. In addition, flying cars were famously depicted in the 1960s television cartoon, The At the crack of dawn, George Jetson would get into his car, fly it to work, and shrink it to the size of a briefcase, enabling him to carry it into the office. Since 1977, every Star Wars film has prominently displayed a future of flying cars in action, and while that may seem unrealistic, so did battle droids, holographic displays, hoverbikes, bionic arms, and laser canons at one point. Yet, these once unrealized technologies now exist today.

The time has come. The flying cars we were promised are here, and they are about to revolutionize society.

Located on a basin in Southern California, the city of Los Angeles is enclosed by broad mountain ranges, forests, valleys, wonderful beaches along the Pacific Ocean, and a nearby desert. It is a vibrant, diverse, and congested city of four million occupants. It is a wonderful city with a problem of population density. A local I spoke to said, “A twenty-five-minute drive in LA can bring a change in temperature of fifteen degrees!”

Crossing an intersection is likened to crossing a border between countries, and that’s not an exaggeration; the average Angeleno spends 119 hours a year in traffic versus the US average of 54 hours. Looking at it on a monetary basis, the median US household sits at $59,000, so a household with only one car spends 10–15 percent of its paycheck (and thus its career) for the “freedom” to be stuck in rush hour traffic. LA and other dense cities have a problem, and it’s only going to get more complex as 68 percent of the world’s population is expected to live in urban areas by 2050 (UN, 2018). As more and more people flock to cities, we will see denser populations, increased traffic congestion, and greater pressure on urban transportation and mobility.

Five hours north of Los Angeles is the headquarters of the leader in eVTOLs (Electric Vertical Takeoff and Landing), Joby Aviation. Joby Aviation has a mission to redefine human networks of transportation by bringing its four-passenger eVTOL aircraft into every city around the world, acting as an aerial ridesharing service for trips between 5–150 miles. Within the next few years, Joby expects to get people places faster and more safely for the same cost as driving and, someday, much cheaper than driving. In a nutshell, Joby Aviation is essentially Tesla meets Uber meets the air.

Although Joby is the current industry leader, it hasn’t always been that way. The project began in 2009 in “The Barn,” a small workshop in the mountains of Santa Cruz. CEO JoeBen Bevirt along with seven engineers worked around the clock to see if their dream of flying cars could actually become a reality. Fast forward to 2017: they successfully flew a prototype for the first time, and two years later, production had begun. During the COVID-19 pandemic—which disrupted some of the in-person test flights—Joby was still able to partner with Toyota for production and became the first eVTOL to achieve US Air Force Approval to fly around military personnel and partnered with Uber to acquire its flying car division, Uber Elevate. Heck of a year given the circumstances. Then in 2021, NASA took a massive bet on Joby’s flying cars, as they collaborated on several electric flight projects.

Today, after more than one thousand test flights and fresh off taking the company public (JOBY), the team is working toward certifying their aircraft by 2023 and then starting their air taxi service in cities like Los Angeles, San Francisco, Miami, and New York in 2024.

In order to better understand how Joby is going to accomplish its promising goals, I interviewed Joby’s CEO, JoeBen Bevirt. I started out by asking him why eVTOLs will be a better way to get around than autonomous cars, something I couldn’t be more bullish on. “Time is the most valuable asset we have, and we, for some reason, have conceded congested travel as just normal daily activity,” said Bevirt.

A silver lining for him with COVID-19 and the work-from-home phase was that people finally started to realize how much more productive and enjoyable it was not to have two hours of commuting each day. Joby is going be extremely transformative for society, perhaps not unlike the transition from horseback to cars. Uber redefined car ownership, and eVTOLs are taking it a step further by providing a faster, safer, cheaper, and more environmentally friendly way of getting around. When asked about Los Angeles, Bevirt gave a realistic scenario in that a Joby aircraft traveling forty five miles from Los Angeles Airport to Newport Beach in its eVTOL would take a mere fifteen minutes with zero operating emissions versus an hour-and-twenty-minute trip in a car.

Ultimately, Bevirt says Joby’s overarching mission is to “save a billion people an hour a day,” something that critics in the media often laugh at. Just like with autonomous cars, the media often promotes FUD— fear, uncertainty, and doubt—around emerging technologies like autonomous cars and aircrafts, even though the data shows it is much safer than the current modes of transportation. Joby and the hundreds of other eVTOL startups are looking to prove them wrong this decade.

It’s not just Joby. By mid-2021, over $2 billion had been invested in at least twenty different flying car companies, and there were over two hundred flying car startups in total with at least a working prototype (Bellamy, 2021). They come in all shapes and sizes, from motorbikes stacked atop billowing fans to wingless and fanless quadcopter drones scaled up to human size to miniature space-pod airplanes. Although there are many different styles of eVTOLs, I want to make it clear that these are not just designs on paper; dozens of companies have completed thousands of human and autonomous test flights. Thus, for the first time in history, we’re past the point of hypothesizing about the prospect of flying cars happening.

The flying cars are here.

But surely only the mega-rich will be able to ride in flying taxis, right?

Well, actually . . .

No, in fact it will eventually become so cost-efficient that “ultimately, we want to make it economically irrational to own and use a car,” Jeff Holder, CEO of Uber Elevate stated (Stuckey, 2018). I’ve gone through hundreds of studies and looked at all the data points and have come to the conclusion that flying car economies of scale, along with achievable technological advancements, will make flying taxis cheaper than driving a car in the mid to late 2030s. In my autonomous cars chapter, I note how ridesharing autonomous cars will be over five times cheaper than owning a car (thus, for some, it will be economically irrational to own one). For flying cars, the numbers are similar. Uber’s Jeff Holden not only agrees with that, but he took it a step further: “My guess is in the next ten years, you will need a special permit to operate a human-driven car,” pointing to how autonomous technology is both safer and more cost-efficient.

But how will flying cars be cheaper than owning and operating a regular car?

Currently, the marginal cost of car ownership—that is, not the initial amount paid for the car, but all the added costs that go with a car (gas, repairs, insurance, parking, registration fees, depreciation, etc.)—is on average between 49 cents and 65 cents per passenger mile. For comparison, a gas-powered helicopter, which has many more constraints than just cost, covers a mile for anywhere between $9 to $50 or even more. For its 2024 launch, according to Archer Aviation, it is projecting to reduce that per-mile price to around $3 per passenger mile then progressively hammer down the price to a little over $1, cheaper than every Uber ride (Kotler and Diamandis, 2020). But Archer, Joby, and the other eVTOLs have a long-term price-per-mile target of 22 cents to 44 cents, which would without a doubt uproot everyone’s transportation habits, as it would be far cheaper than the cost of driving.

To illustrate the amazing cost savings with eVTOLs, look at the example with Archer Aviation. The 18.2-mile trip from Manhattan to JFK Airport is a very popular route and one on which an eVTOL could be used. Note: these numbers are for initial launches, and over time, prices will only get cheaper due to the simple nature of electric propulsion. 3-D printing, automated manufacturing, and scale will make them significantly cheaper to build and exponentially cheaper to operate. And the value you get per mile is a lot more than just cost savings. As we touched on earlier, eVTOLs will save lots of time, especially when used for transport in busy cities.

*Chart showing how on a cost-per-passenger basis, taking an eVTOL will be cheaper and faster than current modes of transportation.

It Will Take More Than Just Flying Cars

Successfully building a flying car that works won’t change the future of transportation at on its own. In order for flying cars to be a normal part of everyday life, the logistics of them need to work as well. You are probably wondering, “From where do they takeoff? What do I do once we land at the destination? Will I always be close to an eVTOL just like Ubers are? How will the sky be regulated to ensure safety?”

First, a special air traffic management system similar to the one today for airplanes will need to be in place for air taxis to scale. The end goal for eVTOLs is for hundreds of them to be zipping around the city skylines at one time, safely transporting people via an air taxi ridesharing service and personal flying cars. The planning is already underway—Joby has partnered with NASA and the FAA to develop an air traffic management system to organize its autonomous air taxi fleet. NASA is using AI-run test animations of hundreds, even thousands, of autonomous eVTOLs flying across the city of San Francisco at one time. With eVTOLs, there is going to be much more independence, no more control towers. Air traffic control will be digital and will use AI and machine learning to monitor air traffic rather than the traditional control towers.

Next, eVTOL companies have begun to team up with real estate developers, architects, designers, and ridesharing companies to design a chain of “mega skyports” or “Vertiports”—essentially a platform on a rooftop. These will be the place where passengers load and unload and where vehicles take off and land. To qualify as eVTOL-ready, a skyport must be able to recharge the eVTOLs in five to fifteen minutes, handle over one thousand takeoffs and landings per hour (four thousand passengers), and occupy no more than three acres of land—which is small enough to sit atop old parking garages or the roofs of skyscrapers.

We have the space for the new wave of transportation—America has almost half a million parking spaces. In a recent survey, MIT professor of urban planning Eran Ben-Joseph reported that in many major US cities, “parking lots cover more than a third of the land area,” while the nation as a whole has set aside an area larger than Delaware and Rhode Island combined for our vehicles (Kimmelman, 2012). But if car-as-service replaces car-as-thing-you-have-to-park, then we’re going to be looking at a huge commercial real estate boom as all those lots get repurposed. Then again, a lot of them could become skyports. Whatever the case, transportation ten years from today is going to look radically different, and it will result in more free time to do what we love, rather than sitting in traffic.

Lastly, air travel today is much safer than road travel, and eVTOLs will be no different, even when autonomously operated. Every day, there are 45,000 planes in the sky, and they have an amazing safety track record. A lot of people are afraid of flying (likely because they can’t control much), yet they comfortably drive on the roads each day, despite the odds of dying in a motor vehicle accident being 1 in 107 for a lifetime (NSC Injury Facts, 2021). For air and space transport (including air taxis and private flights), the odds are 1 in 9,446 for a lifetime, according to the National Safety Council (NSC Injury Facts, 2021). Put the skyports, FAA-approved flying cars, and quality air control systems all together, and by 2024 or so, you’ll be able to order up an aerial rideshare as easily as you do an Uber today. And by 2030, urban aviation could be a major mode of getting from A to B.

Flying Car Technology

In order for a flying car to be sufficient for public transportation, let’s take a look at the technological requirements it will need to conquer: noise (it can’t be loud), safety, price, sustainability, and a long battery life. In essence, it must be far more advanced than a helicopter, as those have been around forever, yet they are not a common mode of transportation. Let’s take a look at a flying car’s closest comparable mode of transportation: a helicopter.

In 1939, the world’s first helicopter, the VS-300, was built, and since then, thousands of models have succeeded it, though none have solved the issues eVTOLs must overcome. Helicopters are loud (105 DB), expensive ($1.8 million), unsustainable, and not as safe as they need to be: 0.72 deaths per 100,000 flight hours (USHST, 2018). After one hundred years, these issues are still present, so how are they now going to be solved by the likes of Archer, Joby, Wisk, Lilium, and others?

Helicopters are loud and relatively dangerous because they have one colossal rotor that creates immense turbulence as the blades move through the air. This is called the “blade-vortex interaction,” or as most know it, the cause of the “thwup-thwup-thwup” sound helicopters are known for. The transmission and engine are noise culprits as well, but the amount of noise they produce is small relative to the noise caused by the big rotor. The danger lies in if the single rotor fails. The nice part of an eVTOL is instead of having a single overhead rotor like a helicopter, both Joby and Archer’s eVTOLs have six rotors, while some eVTOLs have as many as eighteen. These six plus fans or wings combine to generate the same amount of power to fly while also producing much less noise. But wait, there’s more!

The move to six rotors is also the driving factor for safety because even if two or three rotors stop working simultaneously, it can still land easily. It does all of this without sacrificing speed; the six-wing design enables the eVTOL to go 150 mph, or about the same speed as a helicopter. Gas-powered, single rotor helicopters are no more. The future of aviation is electric.

A common question skeptics pose is, “Won’t these flying cars be too loud?” To many people’s surprise, these eVTOLs will be quite quiet. In fact, Joby Aviation says the expected noise signature during a 100-meter hover above the ground will be 65 dBA (much less than a hairdryer), which is sufficient for operating transportation in and around urban environments (Joby, 2021). The best comparison I can give for the sound is that it’s like an air conditioner or background noise in a home or office; due to its electric nature and the construction of the wings, it flies over you almost undetected.

Many experts believe the only way these eVTOLs will work is through distributed electric propulsion (DEP). DEP is utilizing the six electric-powered wings, but instead of a human pilot controlling the rotors in milliseconds, it is computer-controlled (“Fly by wire”). Automating the piloting through DEP is the only way because it is just not plausible for humans to control six to eighteen wings. Electromagnetic motors like the ones used in eVTOLs have seen growing demand in recent years for military drones, as they are “stealthy silent,” can hold heavy weights, and are light relative to existing drones.

Designing the DEP technology/six motors has only been possible due to a couple of key things: first, 3-D printing and additive manufacturing have revolutionized the way we design, prototype, and manufacture safe motors and rotors at large. Second, machine learning algorithms and synthetic data allowed engineers to run hundreds of thousands of complex test flight simulations and then iterate through designs accordingly. As an added bonus, these electric engines are 90–98 percent efficient, whereas gas-powered ones are about 28 percent efficient (Nice and Strickland, 2021).

However, the design is just part of the flying car process. What about the computer-controlled DEP system? Making this a reality also requires four technologies to come together.

greater AI capabilities will need to be able to produce the necessary computational processing power to handle massive amounts of data, analyze it quickly, and then adjust numerous electric motors and air scenarios based on that data instantaneously.

in order to swap out a human’s eyes and ears, we will need advances in sensors that are able to handle gigabits of data at once. GPS, radar, lidar, computer vision, highly advanced visual imaging, and other acetometers are vital for DEP. You’ll recognize a lot of these technologies, as they are already present in autonomous cars and trucks.

there needs to be enough battery life and energy to lift the eVTOL vertically without a runway. The energy required to lift an eVTOL is between roughly 400 kilowatt hours per kilogram (Kuhn, 2020). Thanks to rapid growth in the electric vehicle and solar power industries, there has been increased demand for lithium-ion batteries, which are now being used by eVTOLs as well. The newfound expansion in lithium-ion power has assured engineers that they have plenty of power to not only lift the eVTOL but also fly at an increased distance. Batteries for flying cars are imperative to success since almost all eVTOL use cases will be air taxis, they will be in constant use and will need to recharge quickly in between trips.

material science will need to build metal and carbon fiber composites that are both durable and light enough for safe flights. Then, 3-D printers must become faster at taking the aforementioned materials and constructing them into usable parts so that the entire manufacturing process of building an aircraft is more efficient. This might sound like we need a lot of things to go right for this to work, but you can do this type of analysis with every emerging technology. For example, T-shirts were unable to be made until a materials boom turned plant fibers into cotton fabrics and machine and toolmaking advances turned mammal bones into sewing needles. As time goes on and technologies converge, every challenge eVTOLs face will be conquered by the ingenuity of US engineers and scientists.

Time line

Between now and 2023, the goal for companies like Archer, Wisk, Joby, Velicopter, etc. is to get FAA approval. By 2024, it is expected that dozens of companies will begin to roll out their eVTOLs in cities with traffic problems, like Los Angeles and New York. Initially, they will mainly fly predictable routes such as a convention center to the airport. In comparison, by 2026, they expect to fly eVTOL routes like New York to Boston. As far as the process, to start, it will likely be a person walking to take off, and then when they land, there will be a car at the other end. The next phase would be landing within walking distance to your final destination as more skyports are built.

As far as whether it will be human operated or autonomous, it will be fully autonomous eventually, which will result in cheaper costs; most companies will have a human pilot until they gain the public’s trust and prove the safety of eVTOLs. Safety-wise, it is important to note autonomous aircrafts will be easier to implement than autonomous cars because cars are on the ground with people riding bikes, walking on the side of the street, many other cars just feet away, streetlights, etc.; it’s complex. With air mobility, there is nothing in the way, nothing else in the sky besides other aircrafts, which will all be connected on the same air mobility system, so they are all on the same page.

Let’s look at the prospective time line:

2021–2023

Outpouring of government support for flying taxi initiatives and big funding rounds with Joby, Blade, Archer, Ehang, Lilium etc. becoming public companies.

2024–2025

Launch of commercial flights in two to five main locations (LA, SF, NY, FL, CHI). Costs are similar to that of an UberX or Uber Black.

2030

Global rollout, fully autonomous aircrafts, and on-demand services in all major cities. Flying cars will now be a part of everyday normal life. Our skylines will begin to look the way sci-fi films depicted them. Also, street-legal flying cars will become a popular mode of transportation (amazing!).

2045

By this time, eVTOLs and personal air vehicles (PAV) similar to the street-legal flying cars that transition from road to air will be a normal mode of transportation. You will be able to not only call an air taxi from the city but also from your own roof. This is likely in the form of small landing pads at your home (even a driveway). But at the same time, air mobility skyports will be so ubiquitous that anyone living in a congested area will be able to walk to their local skyport to travel.

Flying cars, for me, are one of the most exciting things society is working on. There is something magical about moving from a 2-D world to a 3-D one. My whole life, there hasn’t been much change to travel, and quite frankly, it’s boring. Taking a flying car will make transportation not only quicker but actually enjoyable—imagine overlooking the skyscrapers in New York on the way to work. Maybe it’s the Star Wars fan in me, but the idea of flying anywhere I want at speeds of over 100 mph is awesome. Two companies I’ll be rooting for are Joby and Archer; they truly are the pioneers of this field, but more than that, they are run by fantastic CEOs who care deeply about bettering society. I was excited both of them went public on the stock market this year, and you better believe I loaded up.

Chapter 6

Finance and Cryptocurrency

From smart watches to commission-free stock trading apps to digital personal assistants to cashless payments and beyond, everything becoming digital and automated is revolutionizing what we do—and how we get things done—in numerous ways. In business, robots are working in factories with humans to build cars, autonomously deliver goods, perform surgery, and make electricity. Blockchain, a digital ledger, is now tracking supplies of precocious metals and overhauling real estate ownership. Blockchain is beginning to make its presence felt in supply chain management, voting, insurance, health care, copyright protection, and much more. But why are we talking about blockchain as part of finance? Because blockchain is upending every procedure, middleman, and system. It’s a good thing as finance will become more democratized, more accessible, and cheaper.

To the delight of many consumers, the financial services industry is undergoing major changes, shifting the power from the powerful Wall Street banks to the people via technology. Banking is becoming more real time based on customer behavior from the influx of big data, blockchain adoption, and gamification of finance.

With most of our money sitting in banks, we are being penalized at a hefty cost, but thankfully, alternatives are on the rise. On average, we pay $360 a year in banking fees. This results in larger banks making over $30 billion in overdraft charges alone (Nova, 2020). So, the banks make all this money, but what do they do with it? Banks invest it to make a buck wherever they can. This often results in banks using the money to fund projects that don’t line up with customers’ values. HSBC, RBS, and Barclays, for example, took a ton of heat and likely lost business when they disclosed billions of dollars in investments in controversial fracking, deep-shore mining, and fossil fuel companies 2021). So, while the bank is making bank, not only is your hard-earned money not working for you (fees, charges, low interest), it might actually be working against you.

The Federal Reserve estimated that fifty five million, or 25 percent of US households, are unbanked or underbanked adults, and globally, two billion people are unbanked (Economic Inclusion, 2017). It is a huge problem that is working to be solved as finance becomes democratized through technology. Sadly, a lot of people around the world don’t live within proximity to banks or have bank accounts. For example, in developing countries like Senegal, only 8 percent of the population has a bank account. In Uganda, it’s 11 percent (Finclusion, 2021). People in these countries are essentially locked out of the financial system with no capability to easily send money, save, or buy things without paper or coins, let alone receive loans. But in the era of mobile phones, accessing essential functions of the financial system has become possible without having to open a typical bank account.

The fact is that banking has a significant effect on the world and the quality of people’s lives. US research suggests that the fast spread of mobile money systems like M-Pesa led to significant economic growth in Kenya. In Uganda, “43 percent of people have a mobile money account. In Kenya, it’s 72 percent” (Finclusion, 2021). M-Pesa is a mobile money service that enables Kenyans to deposit money into an account and utilize all the traditional banking services using PIN-secured SMS text messages, no app, bank branch, or ATM required. Instead, it relies on a primordial technology: people. M-Pesa agents sell cell phone airtime in local markets, exchanging minutes for money and money for minutes. Money receivers load the airtime to their SIM card and then to their cell phone, which then turns the minutes into money. They can now send that money or use it by sending text messages.

As you can see by the numbers mentioned, M-Pesa has succeeded in making a huge difference in the lives of poor families by democratizing finance for the underserved. Recent research discovered that “if a family in Kenya happened to live closer to a mobile money agent close to the system’s launch in 2007, they ended up much less likely to be living in extreme poverty (under $1.25 a day) and less likely to be living in poverty (under $2 a day)” (Matheson, 2016). Struggling families in Kenya routinely go without essential needs if their income drops; but now with M-Pesa, those same families have comfort knowing they can prepare for income drops by having the ability to save money and receive money from family and friends.

Almost all of Kenya now uses M-Pesa, and according to a MIT case study, with nothing more than access to basic banking services, M-Pesa lifted 2 percent of Kenya’s population—over two hundred thousand people—out of extreme poverty. M-Pesa has now expanded its democratized financial services to over fifty million people spanning ten countries (Matheson, 2016).

Traditionally, cutting-edge ideas originating in Silicon Valley move from West Coast introduction to East Coast adoption to gradual European integration and finally, eventually, into the rest of the world. But this process has been turned around with developing world innovation becoming developed world disruption. And more disruption is coming. For far too long, banks have been the central depot that we must trust every time we want to transfer, withdraw, or lend money. Banks are the intermediary in all those processes, profiting off every transaction—but not for long. Blockchain is banks’ biggest threat, and I believe they may eventually get left behind.

Blockchain

“If you don’t understand blockchain, it’s going to smack you down and make you bleed. It reminds me of the early days of the internet.”

—Mark Cuban

Blockchain is a decentralized digital ledger that documents, duplicates, and distributes transactions of goods such as cryptocurrency across the entire network of computer systems on the blockchain. It is a system of recording information so that it is virtually impossible to change, hack, or “own” the system. Blockchain is decentralized so that no single individual or group has control; instead, all users collectively hold the power. Decentralized blockchains are immutable, which in essence means that the data entered cannot be reversed. For example, Bitcoin transactions are permanently recorded and viewable to anybody who chooses.

The features listed above all lead to trust being built into the blockchain, making the intermediaries of the system (the banks) irrelevant. As an example, think about the process of buying Tesla stock. To buy Tesla shares, you have individual buyers, sellers, and bank accounts connected to the brokerage account, the banks themselves, the stock exchange (NYSE, NASDAQ, etc.), clearinghouses that facilitate the payments, brokers, etc. All in all, nearly a dozen different intermediaries are involved in the process. In traditional financial transactions, a trusted third party is essential: if Steve writes Jim a check, the bank (a third party) must process the check and make sure they have the cash to cover the check, all while taking a couple of business days.

Blockchain and cryptocurrencies remove all those intermediaries besides the buyer and seller and lets the blockchain technology do the rest. Therefore, the power of blockchain has alarmed big banks, and all of them are rushing into blockchain, trying to hold onto some share of the future. The problem for them is that they are light-years behind the thousands of innovative cryptocurrencies and blockchain companies developing and using blockchain to disrupt the banks.

Supplementary to that, one of the reasons why many people are unbanked is because they are in need of an official identity. According to recent data published by the World Bank, 13 percent of the global population does not have a documented identity (Hamid, 2019). This figure comprises people like immigrants, and blockchain is the perfect solution to this problem, one where every person has a digital ID that is accessible anywhere with internet access. So what? Well, with this ID, we control our data—providing for complete transparency for ourselves when voting, paying, or managing asset ownership. Most importantly, the unidentified can get an ID to find employment, receive government benefits, and much more.

In addition, think if you lived in an authoritarian or suppressive country like China where they are implementing restrictions on currency and the ability to live freely. You would probably want to convert your wealth into bitcoin rather than keeping it with a government that has total control. Say you flee the country, rather than having to hold dollars in your wallet or carry gold bars around, all you would need would be a password stored in your brain, allowing you to access your wealth anywhere you have internet access. This is an amazing use case for bitcoin; it’s the forgotten use cases like this where blockchain technology derives its value.

The same blockchain process for identifying people can also work with authenticating assets. One prominent example of integrating blockchain to physical assets is land registry, or the process of certifying ownership of land. It is a greater problem than you’d think—for example, when earthquakes destroyed much of Haiti, it left 1.5 million people homeless. The catastrophe also demolished sixty years’ worth of government records, including land registrations 2020). Since the earthquakes, Haiti has made significant strides in the recovery of housing, infrastructure, and economy, but many still remain without any legal documents authenticating asset ownership like land registry. The government has nothing as well. It’s not just Haiti; 90 percent of rural land in Africa is not formally documented, and for land to go through the process, it takes a whopping fifty-nine days (Economist, 2020).

The way records are kept in Africa and other parts of the world is ripe for corruption, along with being a terribly inefficient system for authenticating physical assets, as evidenced by the fifty-nine-day process time. There is zero reason why every single square foot of land around the world isn’t written into the blockchain ledger where ownership is fully transparent. When all physical assets are recorded on the blockchain, it will allow us to trade them, borrow against them, sell them, and fractionalize some of their parts.

Transactions like these are facilitated with smart contracts, which are automatically executed between two parties once contractual conditions are met, all happening without an intermediary. For example, a stock trade between a buyer and a seller can be facilitated through a smart contract where the middleman’s work is automated, leading to a more efficient and cost-effective trading process. This is the future—banks be wary.

Digital Wallets

By the year 2030, I believe nobody will be walking around with a wallet for the purpose of holding money or credit cards. There is no need for it since digital wallets are enabling everyone to have a little bank branch in their pocket as they enable a broad spectrum of services such as insurance, wealth management, loans, instant payment, e-commerce paying, real-time rewards, and access to crypto-assets. In the United States, the leaders in digital wallets are Venmo, Cash App, SoFi, and Chime. In China, they are WeChat and Alipay. Digital wallets are the fastest growing part of finance; there were close to zero digital wallets in 2014, 50 million users in 2018, near 150 million in 2021, and by 2030, it is expected to hit over 250 million. It is estimated the digital wallet market could hit $4.6 trillion by 2025 (Burton, 2021)!

Digital wallets are yet another example of why traditional banking systems will fail while companies like Venmo, Cash App, and other fin-techs will flourish. Numbers-wise, it’s not pretty for the banks; their costs are rising to all-time highs while the utility of their services is decreasing. For example, the cost of customer acquisition (when financial institutions get a new checking account customer) is roughly $1,000, while digital wallet companies only need to spend $20 to acquire a customer (Lydon, 2021). This is due in part to on-trend marketing strategies, low-cost structures, and peer-to-peer payment ecosystems (your friend needs to download Venmo as well for him to receive your payment). Even worse for banks, US digital wallets are surpassing the number of deposit account holders at banks: “Square’s Cash App and PayPal’s Venmo each amassed roughly sixty million active users organically in the last seven and ten years, respectively, a milestone that took JP Morgan more than thirty years and five acquisitions to reach” (Lydon, 2021).

Banks are also losing on the lending market and are unlikely to recover to pre-pandemic levels, as the pandemic was the catalyst to moving things digital. Further, it is estimated that bank interest income on credit cards fell more than 10 percent, or roughly $16 billion, in 2020, and it is likely to drop more than 25 percent further, from $130 billion in 2019 to $95 billion by 2025 with digital wallets picking up market share (Lydon, 2021). Digital wallets are mainly peer-to-peer now, but the next step is for every small business, restaurant, and seller to accept digital wallet payments. They are convenient, data-safe, fast, simple, and require no human contact.

Bitcoin

What’s all the craze about?

Bitcoin appeared in 2008 when an online paper authored by a still-anonymous person (or persons) calling themselves Satoshi Nakamoto proposed a digital peer-to-peer payment system that allows cash to be exchanged without the need for a financial institution. The following year, the first Bitcoin software was made public, but because the coins had only been mined but not traded, there was no way to assign them monetary value. In 2010, Laszlo Hanyecz solved that problem, buying two pizzas—costing $25—with ten thousand bitcoins. At the time, based on the cost of those pizzas, the coins were worth $.0025 each. By 2019, they were just shy of $15,000.

Even after seeing its price cut in half in June of 2021, Bitcoin is still the best-performing asset of the past decade. And it’s not even close, as Bitcoin’s returns are about 1,000 percent higher than the next-closest asset class. Since 2011, Bitcoin’s annualized return is 230 percent, meanwhile, Gold’s is a paltry 1.5 percent and actually had negative returns in five of the past eleven years (Young, 2021). Why am I throwing these numbers at you? (1) It shows how incredible Bitcoin has been as an investment, and (2) it should definitively stop all of the gold vs. Bitcoin chatter. Bitcoin is often called the “digital gold,” and I think it is a fair comparison in terms of both being stores of value, though they are still wildly different. First, Bitcoin actually has valuable underlying technology, whereas gold is essentially useless. Second, although more volatile, Bitcoin has consistently performed astronomically better and has grown in value, while gold continues to trade flat. Bitcoin and gold are both seen as the antidote to inflation.

Around the beginning of the COVID-19 pandemic, the stock and crypto markets tanked, leading a wave of new retail investors to swoop in and ride the recovery back up. As everyday people and now institutional investors like big banks have piled into cryptocurrencies, Bitcoin has garnered a lot of attention as a viable currency for the digital era.

The increased attention has also brought on a lot of critics like Warren Buffet who claim Bitcoin is “rat poison,” or worthless. However, Bitcoin, the trillion-dollar leader in digital assets, is anything but. We touched on the value of blockchain technology, which is obviously its main driver, but it goes even beyond that. Bitcoin is, in essence, the separation between money and state. One of the most important events in US history was the separation of church and state, where we no longer turned to the state to determine what religion everyone was going to practice. Well, crypto is introducing a similar idea in that the government cannot control the money supply. The key to having a money supply where the government is not involved (decentralization) is trust. And if you think of any currency in general, it is all about trust. When someone gives you a $100 bill, you trust that it is not counterfeit and that the currency is recognized as legal tender.

So, for Bitcoin to be a reality long term, everyone has to believe in it. And that is exactly what is happening; more and more people are seeing the value of blockchain technology, which is why Bitcoin is dubbed “the bubble that never ends.” That is in part why Bitcoin always has this cultish feel to it from the people who “pump it” because it is driven on the theory that Bitcoin won’t survive long term unless there is inherent trust in it as a viable digital asset. A lot of prominent Bitcoiners are like the people who claim they met Jesus and now think that everyone has to be a Christian.

The common denominator among the critics of Bitcoin is the understanding of blockchain technology. Old vets on Wall Street like Warren Buffet and Charlie Munger, albeit great investors, are not technologists. It is a simple blind spot for them, and the thing they don’t understand about Bitcoin is that it is the first digital asset to ever be created that you can’t create infinite copies of. Think of a song, or a photo or a video; you have always been able to make a digital copy of them. So what about digital money that can be copied? Well, Bitcoin can’t be, and that is the genius behind it. Bitcoin, as of late, finally got a country—El Salvador—to make it legal tender in June of 2021, something I expect will become more common among countries in a few years. Although I think there are a ton of great uses for Bitcoin and that it will stick around, there are some big threats to its survival.

Likely, the biggest threat to Bitcoin is governments banning it. If Bitcoin is the separation between money and state, the state will likely do whatever it takes to hold onto financial power. For example, China and India have essentially banned Bitcoin as “all banks and other financial institutions like payment processors are prohibited from transacting or dealing in Bitcoin” (Bajpai, 2021). This is likely because China is creating its own digital currency, and anything that is a threat to its authoritarian powers gets shut down. Other countries like Russia, Vietnam, and Bolivia have all made its use as a payment method illegal. Other threats to Bitcoin are that it becomes worthless if someone is able to double spend or hack it. The amount of computing power needed for this to happen is nowhere near possible today, so I wouldn’t count on it anytime soon.

Lastly, crime: I’ve heard a lot of politicians and talking heads say Bitcoin is an accomplice to crime, but I don’t think that’s true. With Bitcoin, every transaction is written into the ledger, so I think you would have to be pretty dumb to try and commit a crime using this currency because there is a permanent record of that transaction in the ledger, and there are companies now that disclose all that information.

The New Age of Investing

In the past three years or so, because of an influx of AI and market-disrupting phenomena causing extreme volatility—like COVID-19—a new age of investing has taken place. The financial markets have long been dominated by the suits on Wall Street.

If there was ever a financial silver lining from COVID-19, it’s that it created a once-in-a-lifetime, maybe once-in-a-generation type of opportunity for everyday people to jump into the capital markets. The surge of the virus and draconian shutdowns tanked the market, causing stocks to become very cheap. Combine that with commission-free trading apps across all the major platforms along with everyone sitting at home, and these changes allowed people to swap sports betting and slot machines for the stock market.

Chat groups like WallStreetBets and people like Dave Portnoy led a rebellion against the hedge funds and old-timers who said they’d lose it all! Most aren’t losing it all, but rather, they are bettering themselves toward financial freedom. 2020 was the black swan event that finally upended the stock market and how people think about growing wealth. For far too long, everyday people have been shunned away, scared of the complexities of investing. But with all the online resources like Twitter, Discord, YouTube, and free investing platforms, it’s never been easier to compete with—and even beat—money managers.

A rise in robo-advisors like Betterment and Wealthfront is democratizing investing for the masses and bringing AI to investing. All from an app, you can answer a few questions about your risk, reward, and goals, and instantly an algorithm works its magic. Based on your goals, it builds a portfolio much like a human financial advisor would, just without the insane fees. AI is disrupting stock trading as well. In fact, “on a typical trading day, computers account for 60 percent of market trades, according to Art Hogan, chief market strategist for B. Riley FBR. When the markets are extremely volatile, they can make up 90 percent of trades” (Isidore, 2018). As someone who trades stocks, I’d estimate that it’s even higher than 90 percent, as hedge funds and even big traders have AI algorithms that automatically buy or sell stocks on certain news events or levels on a chart.

All in all, I think the new entrants into the market are here to stay. In a few years, financial advisors who invest on behalf of clients should and likely will be displaced by AI or even displaced by people simply not needing them. When you break down what the average wealth advisor is doing, they are essentially buying the S&P 500 with hedges, and then they take a 1 to 4 percent fee. There is no need to have a financial advisor anymore since anyone can download a free investing app, open an account in minutes, and buy index funds. However, I’d suggest people at least try and learn how to pick individual stocks rather than concede the fact you cannot beat the market and therefore you must diversify and buy ETFs or index funds.

Warren Buffet has two really good quotes I love: (1) “Diversification is protection against ignorance, it makes very little sense for those who know what they’re doing.” It’s true; if you truly have no clue, then an index fund may be for you, but numbers-wise, you would likely make much more taking the time to learn and invest in great companies. I’ll take Amazon, Tesla, and Square over an advisor hoping to make me 8 percent a year any day. (2) “Diversification may preserve wealth, but concentration builds wealth” (Town, 2021). If you analyze how many extremely wealthy people got to where they are today, it is because they made concentrated bets and succeeded, not because they spread themselves thin. AI and blockchain are disrupting the world of finance, and in this case, it’s a good disruption!

Chapter 7

The Future of Education

Up to 85 percent of jobs that current college students will have in 2030 haven’t been invented yet (IFTF, 2017). Anywhere between four hundred and eight hundred million workers will likely be displaced by then too (Manyika et al., 2017).

Looking back at my life going through the education system, I had two goals: first, to learn about entrepreneurship, and second, to learn about the stock market. Those were two things that, growing up, I always found appealing, but school never even touched on it. So, what exactly did school teach me? Every year, we have an art class, history class, music class, and other liberal arts classes that taught things I was never interested in or found useful. Today, we throw thirty kids in a room with A and B goals and we teach them all C. Schools have essentially no consideration for each individual’s abilities or dreams.

What is the current education system doing to prepare students for the age of automation and life in general? The answer is not much, but there are many emerging technologies and ways that we can prepare for the rapidly changing future of work. In this chapter, I will look at the current problems, some innovative solutions for these problems, and what the future of education can and should look like.

From a high-level view, education in America has four main issues: quantity, outdated quality, curriculum, and cost.

Quantity-wise, there is an enormous shortage of skilled teachers, largely due to declining teacher wages and the high cost of obtaining the required credentials. The US Department of Education estimates that by 2022, the United States will need 1.6 million new teachers. In addition, globally, the shortage is more profound. Within the next decade, UNESCO predicts the shortage of teachers will hit an astonishing height of 69 million (NYSUT, 2019). This will result in nearly 270 million students worldwide without an education. These numbers are concerning considering how after all, teachers are the ones doing the actual teaching. People simply aren’t going into the profession as much, and enrollment in teaching programs and majors are down more than a third in the past decade (Partelow, 2019). Many workers’ wages have been stagnating for a while, but in the case of teachers, it does seem they are relatively underpaid. Considering that there is a dire need to find quality teachers, you’d expect that to lead to higher wages, but it just hasn’t been the case.

Besides paying them more, if we want to fix the teacher shortage, we need to make it easier for outsiders with experience to come in and teach. For example, say a biologist from Pfizer with fifteen years of experience wants to switch careers and teach high school biology; she can’t do that because she may not have a master’s degree or teaching license.

Next is the quality of education. The United States’s two-hundred-year-old educational system is anything but modern; it is serving the needs of a different era. The modern education system was initially created as an assembly-line system that taught future factory workers how to be orderly, docile, and solemn. In the industrial age, factory owners needed their assembly workers to be orderly, “do as I say” type workers that clock in and clock out, doing exactly what they are told. School is excellent at producing workers like that, as sitting in a classroom listening to a teacher lecture is similar to a factory worker following the directions of their boss.

Sociologist and education specialist Dr. John Holm agrees, “If you look at early images of the factory and early images of the school room, there’s not a lot of difference. The children are in rows, they’re facing front, and they’re looking unhappy” (Puckett, 2017). Not much has changed. Think even about the structure of school: at a certain time, the bell rings, and you move to where your schedule has you, and again the bell rings, and you move to a different classroom, where you continue doing exactly as you are told. Everyone does the same things, the same way, all to receive a certification that is not guaranteed to be valuable.

Also, school is centered around standardized tests and a one-size-fits-all curriculum where teachers are often teaching to the test or teaching with standardized testing benchmarks in mind rather than allowing for students to learn information naturally. Schools are good at making us memorize trivia to pass fake tests; when was the last you used long division, cursive, the periodic table of elements, or any history fact we learned? It would be much better if we got rid of art history, mandatory music classes, redundant history classes, and instead taught personal finance, computer programming, and more STEM. In school, we never learn how to file taxes, choose the right health care plan, or make strong financial decisions, but we sure know how to play “Hot Cross Buns” on the recorder!

In addition, the quality of education is hampered when we teach everyone the same way, as it’s both an industrial holdover and a teaching nightmare because of human nature. We are all uniquely wired, so we need a personalized learning experience to strike that learning chord. Additional evidence that the quality of school needs fixing is that more than ever before, students of all grades find school boring and pointless; that is why half of all dropouts cite boredom as the primary reason. In fact, American Progress reported on a US Department of Education study that “over 1.2 million students or 7,000 high school students drop out every day, that is an astonishing 1 student every 26 seconds” (Jimenez et al., 2018). Later in the chapter, we will explore solutions to the boredom, but in essence, it comes down to the fact that students are not only uninterested in the curriculum but also don’t enjoy sitting in rows listening to a teacher read off a PowerPoint for hours each day. Speaking of that—I am confused how, as a society, we dictated that the ideal way to prepare students for the future is to cluster them into a room where they are organized by age, into grades, and required to learn the same material at the same time and pace?

Another cause as to why the quality of education is insufficient is that traditional education systems are designed to maintain equilibrium. Going back to the assembly line example—the line was trained for everyone to essentially do the same job at the same speed.

To illustrate this point, look at the recent decision of the California school system to eliminate advanced math. In June of 2021, California passed a school framework that prohibits students from taking advanced math until their sophomore year of high school; instead, everyone will take the same level of math at the same time. This is a mindless attack on the top 20 percent of students who are more mathematically proficient. Again, this is a proposal based on the idea of equity, the notion that everyone has to learn at the same level for as long as possible. The California school system is worried that “too many students are sorted into different math tracks based on their natural abilities, which leads some to take calculus by their senior year of high school while others don’t make it past basic algebra” (Soave, 2021).

The school department is wrong to think the solution to varying math proficiencies is to level everyone’s ability to learn. In addition, the University of California school system has now banned the SAT and ACT. It looks like they are trying to get rid of how we measure how bad we are at educating students so that we don’t have to think about it. They just want to give everyone a gold star and a pat on the back in the name of equity, which is the “catchall for bad ideas,” as David Sacks, the co-founder and former COO of PayPal said on the All-In Podcast 2021).

I’ve seen a pattern where whenever a proposal or statement includes equity, it usually results in a terrible outcome for the masses. Equality means we all get the opportunity to do something. Equity is when everyone gets the same outcome—leveling all people. The idea that no one can be too far ahead of anyone (limiting advanced math) hurts our ability as a society to innovate and succeed! Why are we slowing kids down?

Ever since California released the plan, there has been an immense amount of backlash from people and organizations such as the California Association for the Gifted, which said it “hinders opportunities for students.” Many parents cited fears over their child’s unique abilities being shortchanged without pathways for students identified as gifted. “I’m concerned about the proposals for the elimination of advanced math classes in the middle school years,” said Wendy Marcus, a parent of three students in Moorpark Unified in Ventura County. “Putting advanced students with average and below-average students in the same class does not work. A lot of times, they just toss those kids aside and give them extra work” (Sydney Johnson, 2021).

California eliminating advanced math is the equivalent to eliminating welfare for the bottom 5 percent. Our job as a society is to “find the broadest set of solutions for the most number of people and solve for both extremes,” according to California billionaire Chamath Palihapitiya 2021). On the lower end as well, some students need more structural support due to learning disabilities, dyslexia, speech barriers, etc., which we work to cater to, but now, if a kid is gifted and has the potential to learn so much more, we take away their opportunity to do so.

In 2018, the Organization for Economic Co-operation and Development (OECD) reported that in math, America ranked thirty-seventh in the world among developed nations. You know the scary thing about that? There are only thirty seven developed nations in the world. The United States was dead last, despite the fact that the United States is the fifth-highest spender per student among the thirty seven developed nations (Deift, 2021; OECD, 2021). So, the problems in our education system are not because of a lack of spending. If we want to move up from last place, we need to make radical changes to how we educate students and stop making the argument we just need more money or resources.

Today’s College Education Isn’t the Answer

Analyzing college in the age of automation first prompts the question, What is the purpose of going to college? In the 1600s, colonial colleges were founded by the Puritans with the goal to develop Christian gentlemen who would be responsible leaders in the new world they lived in. However, now—and for the past hundred years—the purpose of college has been to prepare people for jobs. But what is the point of college when the jobs they are preparing us for don’t exist?

Well, currently, the reality of college is that what and how they teach are outdated and don’t adequately prepare students for the age of AI. Not only that, but college is also outrageously expensive. However, luckily, there are better alternatives that we’ll discuss later on. First, the content being taught in college is extremely outdated and out of touch with today’s needs. Right now, we have tenured professors preaching the same lectures that they have been preaching for twenty years now, often heavily based on theories, formulas, and other nonsense that will never be used in the average student’s career.

Colleges need to be more interactive, personalized, and practical. For example, during my first two years of college, I attended a private liberal arts university where I was required to take courses like theology (three of them), art history, history, philosophy or political science, and other classes that are mostly irrelevant to me as a finance and computer science student. I understand that being well rounded is good, but we should be well rounded in the right things. Rather than three theology or art history classes, we should be taking relevant skill-based classes like computer science, intro to AI, public speaking, personal finance, etc., because those are all imperative to our modern society.

Given that 85 percent of the jobs that will be held in 2030 haven’t been created yet, this means that rather than bottlenecking students down a narrow path to one specialization, colleges should put an emphasis on learning how to learn. That essentially means colleges’ top priorities need to be creating an environment focused on creativity, critical thinking, and problem solving rather than trying to instill knowledge into people through lectures and multiple-choice tests. The valuable assets of the future are creative thinking and problem solving because computers are great at storing, analyzing, and executing repetitive tasks, but they fall short on the human side of life—critical thinking, creativity, and empathy.

Next, a college degree is no longer a golden ticket to a good job, and it surely isn’t worth the cost. One recent study from a University of Pennsylvania Wharton professor found the actual return on the costs of attending college. Careful analysis suggests that the payoff from many college programs—as much as one in four—is actually negative (Capelli, 2015). Incredibly, the schools seem to add nothing to the market value of the students. The study found that essentially, the only colleges with a positive net value are the schools that admit fewer than 20 percent of their applicants, and even in these schools, it’s not guaranteed a degree is worth it because the students that are intelligent enough to get into those schools would most likely succeed regardless of the degree.

Also, college is being oversold as the answer to a great career, as far too many people are taking on massive debt for college only to not graduate. Four-year graduation rates for students attending public colleges and universities are 33.3 percent and 53.8 percent, respectively. Graduation rates are low and decreasing, which is a jarring problem, but even worse is that a lot of students need a couple of extra years to finish their undergrad, which just piles on more debt. One common alternative to a four-year school is getting a two-year associates degree, but that isn’t much better, as the three-year graduation rate is only 29.1 percent (O’Shaughnessy, 2021). So essentially, millions of high school graduates head to community college or college, accrue thousands in debt, and then fail to graduate. These facts show that enrolling in college but not graduating is the worst possible path you can take because you paid tens of thousands of dollars with nothing to show for it.

Obviously, not all occupations currently have an alternative, so it is still important that doctors, scientists, or highly technical occupations like that have higher education levels. However, a large portion of college grads end up working jobs that don’t require a college degree. The unemployment rate for recent college graduates exceeds that of the general population, and about “41 percent of recent college graduates—and 33.8 percent of all college graduates—are underemployed in that they are working in jobs that don’t require a college degree, according to new data from the Federal Reserve Bank of New York” (NAICU, 2020). We need to stop pushing college degrees as the be-all and end-all because it is simply not true that they are as valuable anymore in preparing people for the future. As the value of degrees continues to decrease and the cost of college continues to increase, it will become more apparent to students that college is not the only route to take, and it’s not even the best route for many.

So why is college so expensive? It is quite alarming that the price of college tuition has increased 440 percent in the last twenty-five years, many times the rate of inflation. For high school graduates, it is frightening knowing they could enter a situation where going to college could cost six figures in high-interest student loans, knowing every year the income for recent college grads is decreasing. Back to the “why” behind the cost, the reason it is expensive is not because of professors making more money or from colleges offering more and not even from new buildings. It’s because of colleges’ administrative bloat and ballooning bureaucracies present at all levels of universities.

According to the Department of Education, administrative positions skyrocketed by 60 percent between 1993 and 2009, which was ten times the rate of tenured faculty positions. More recently, universities’ executive, administrative, and managerial offices grew 15 percent during the recession, even after budget cuts and tuition increases (Henchinger, 2012). In essence, administrators are pumping up their salaries year over year while tenured faculty wages stagnate, and the students pay the price.

Oftentimes, these administrators do not do much of anything except chip in to the university bureaucracy. Members of the university administration include the assistant to the assistants of the associate University Dean of Biological Sciences. Universities are using their increased revenues not on more funding for research, scholarships, or improving education but rather on administrative positions. From their point of view, it makes sense; as they get more resources to spend, they want to grow the institution by hiring more people. Unfortunately, taking this approach increases costs and hurts the millions of students drowning in debt.

Also, universities’ handling of their huge endowments (an investment portfolio that manages donations, funds received, etc.) is misguided, with only a fraction of the endowment going to financial aid and a significant portion going to hedge fund managers. Law professor Victor Fleischer did a study exposing the way Harvard, Stanford, Princeton, the University of Texas, and Yale handle their endowments. For Yale in particular, he found that it pays private equity firms $480 million a year to handle its endowment while at the same time only spending $170 million on financial aid for its students, all while hiking up tuition (Fleischner, 2015). Essentially, as the endowments grow each year, the people that benefit the most are not the students or faculty but the hedge funds that manage the money!

Fleischer questions, Who is the endowment there to serve? It is supposed to be for research, advancing education, and science—not for making hedge funds richer. Because of this, Malcom Gladwell once tweeted: “I was going to donate money to Yale. But maybe it makes more sense to mail a check directly to the hedge fund of my choice,” and that is sadly true (Gladwell, 2015). I argue that if colleges are essentially running a hedge fund, they should not be tax-exempt, as it is a horrible misuse of taxpayer money. For an example, if a college has a billion-dollar endowment, it must pay its entire endowment income gains on its students’ education or it will lose tax-exemption status. This would help align incentives and act as a stimulus in lowering the cost of education for students.

Online Education

Unlike college, massive open online courses (MOOCs), distance learning, and digital technology are disrupting the traditional way we learn. I hold the view that the future of education will be a hybrid between high-tech, interactive, in-person classrooms and personalized online courses; the days of sitting in lectures are going to be over. Online courses will also be the top way we reskill the millions of workers who need retraining, but at the same time, distance learning is not sufficient enough to be the only way we educate students.

First, there are many different types of online learning. There is web-facilitated learning (about 30 percent of the course content is based on the internet), blended/hybrid learning (online course content between 30 to 79 percent), and full online learning (80 percent or more online content). Two-thirds of colleges today offer blended or fully online courses, but a large portion of them is still built off of retrograde techniques such as simply prerecording lectures along with facilitating static discussions. Also, the college version of online courses is often criticized for emphasizing memorization and finding a single answer instead of focusing on problem solving and critical thinking. There is a big difference between the way colleges are teaching digitally versus how online course companies like Coursera, Udemy, edX, Skillshare, Khan Academy, etc. teach, because the latter are designed to accommodate students’ short attention spans by giving content in small, five- to twelve-minute chunks along with projects and personalized questions and answers.

Many of those who are bullish on education tech, like myself, believe that we can conveniently and cheaply educate the workforce using the latest digital education technology. On the surface, online education has a lot going for it, and essentially everyone can read, as 99 percent of the United States is literate. Also, most American households (85 percent) have access to internet at home, and 81 percent have smartphones (Joseph Johnson, 2021). With ever-growing access to the internet, online education will be leveraged to retrain the millions that will be displaced by artificial intelligence. A big reason for this is that online education can adjust and evolve the curriculum at a fast rate by using real-time information. Conventional education will not be able to keep up, as it is already struggling to do so. For example, if a recent marketing grad is told by her employer that she needs to know how to do SEO for the job, would it make sense for her to pay a couple grand at a college to learn that or take an online course? Well first, most colleges today don’t even teach that, but regardless, it would make sense to take an online course to learn SEO.

One growing trend in education is companies partnering with education platforms such as Coursera to curate and tailor corporate curriculums so that workers are job ready from the get-go. That is a contrast from traditional education, where employers are far removed from the curriculum-building process. Essentially, the whole idea is that we need hyper-personalization in education. The skills and knowledge we learn need to be relevant to specific industries or jobs that we want because nowadays, people not only have to compete with fellow workers but they also have to compete with robots doing their jobs.

Unfortunately, during COVID-19, we saw the downsides of distance learning in full force. The pandemic created a forced adoption of online education, as millions of K–12 and college students transitioned to a different mode of learning, and for the large part, it flopped. From elementary schools all the way to college, schools were left unprepared, which caused the online learning experience to be less effective than an online course you would find on a platform like Udemy. One of those failures in preparation was making sure every student had access to the internet to learn online. As a country, we cannot have a mass pivot to online learning until nearly every household has the ability to learn online.

For example, the Los Angeles Times addressed this problem by reporting on the situation of Andrew Diaz, a tenth grader who failed his classes largely due to his family’s financial struggles. Before distance learning was the norm, he was a model student, earning As and high Bs in all his classes. Like most families, his mother was busy with work, and Andrew got no supervision from teachers. Andrew gradually began to lose motivation to attend classes, which is to be expected, as it is hard for students to be motivated even in in-person classes (Esquivel et al., 2020).

By spring, Andrew’s school-related depression had taken its toll. “I feel like when I move on to eleventh grade, I’m gonna be behind, and everybody’s gonna, like, be smarter than me, and I’m afraid,” he said (Esquivel et al., 2020).

Andrew’s story is a perfect example of what happens when someone does not have access to a stable internet connection, a phone, or a computer; virtual schooling has the possibility of destroying their educational career. This problem is far more prevalent for lower-income students, as they do not hold the same luxuries. So although online education certainly has its benefits, it also causes severe harm to the less privileged.

Furthermore, another consequence of online learning is that cheating becomes infinitely easier and more common. Having the ability to Google answers allows students to slack in their learning and do things they normally wouldn’t during in-person classes. Websites like Chegg allow anyone to easily pass classes without even listening, which is a big problem because in the age of AI, the most valuable skills will be critical thinking and problem solving. If students are simply looking up answers instead of thinking through them, they are cheating themselves, but you have to blame the system for that.

Next, online classes exclude an essential part of learning—engagement between teachers and their students. Although online platforms are doing a good job with giving students the ability to have personalized questions answered, it will never be the same as talking through issues with the teacher through a built-up relationship. It is far too common for people to think that content is education and that education is content. Online courses where a student is expected to read a textbook and just listen to lectures is not education, in the same way that just putting students in a classroom with a textbook isn’t education. The point is a large part of education is the interaction and engagement that happens in the classroom—something very hard to replicate online. Max Ventilla, the founder and CEO of AltSchool, has said, “The worst use of software in [education] is in replacement of humans, that’s craziness . . . It’s about the relationship that kids have with their peers, with adults. That’s what creates the motivation that creates the learning” (Batelle, 2018).

The future of education is not solely dependent on online courses, but online courses will only continue to increase their disruption of higher education. Online course platforms will cause many colleges to fold, and the only schools that will survive will be those that utilize tech-enabled classrooms with a blended or hybrid format. Online education is currently helping millions upskill or change careers, and that will be even more prevalent in the age of AI when millions of displaced workers need to reskill. In fact, “More than 120 million workers globally will need retraining in the next three years due to artificial intelligence’s impact on jobs, according to an IBM survey” (Kelly, 2020).

Alternative Schools

In the midst of COVID-19, I got the chance to speak with Vishen Lakhiani, CEO of Mindvalley. Vishen, through Mindvalley, is on a quest to make college and traditional education obsolete by building a platform to bring learning to everyone. Vishen’s education experience is extremely similar to a majority of people aspiring to enter the workforce. Vishen realized early on that “your degrees don’t count for much of anything, except for maybe your first job. We have to accept the reality that degrees don’t and shouldn’t matter.” And for Vishen, although he graduated from a very good business school at the University of Michigan, it didn’t matter. He sent his résumé out to hundreds of employers and heard nothing, so he had to settle for working at a company that hired people to pick up a phone and cold-call lawyers and sell them on technology. It was an awful experience, and like the plight of many recent entrants into the workforce, it begs the question, “What am I going to do with my life? Is this it?”

Vishen’s job was awful because whenever he would call lawyers in the middle of the day, he would hear the phrase “F**** off, kids” more times than you could ever imagine. And so, here he was, a twenty-six-year-old graduate being told to f*** off twelve to fifteen times a day. He sank into a depression over the course of the job, but eventually he had his aha moment. After work one day, he started googling and found an online class on meditation, which led to him googling and learning about various other topics that interested him. This became the foundation of Mindvalley, a school where you explore your interests in a way that prepares you for the future of work, where human creativity and critical thinking are paramount.

Mindvalley is one of many new age schools that are replacing traditional education, as it can get you better training, better jobs, and a better network for under $500 a year. Vishen truly believes that five years from now, no one in the world will need a college degree.

Synthesis is another alternative school that gives us a glimpse into the future of education. The idea for the school originated from none other than Elon Musk, who simply wanted a new learning experience for kids on the SpaceX campus. Elon met a man named Josh Dahn who took on the task. The idea behind the school is that there are no grade levels or age separation. Elon believes that age separation fails because kids have various abilities and interests (Phan, 2021).

Synthesis prioritizes problem solving through gamification. This makes sense because kids like games and are more likely to remember and subsequently permanently learn if they are motivated to listen. And although the kids who were allowed into Synthesis were likely smart before this, once they were in the program, some eight- to fourteen-year-old kids in Synthesis went head-to-head with college graduate students in the complex games of the program. The young kids won. It wasn’t even close. In Synthesis, they work through “complex case studies, real-life experiments, simulations, and game-based challenges that were simple to understand but wildly complex and unique by design” (Phan, 2021). Schools like Synthesis are a microcosm of what is to come. Eventually, the school system as we know it will need to adapt to promote real problem solving and innovation. The days of sitting in your desk listening to a teacher lecture for hours on end should be over—learn from doing!

Income Sharing Agreements (ISAs)

Though it may seem improbable, 53 percent of college graduates are either jobless or working a job that doesn’t require a degree.

Students are loading up on debt, paying upfront for a four-year education that has about a coin toss’s odds of securing you a job that would be more valuable than if you skipped college altogether. Sure, colleges have career centers, classes, and fairs, but those things are all separated from the actual process of preparing you for the job. New schools are solving this problem. Programs like the Lambda School and coding boot camps are doing an amazing job fostering a more balanced approach between job placement and education itself. This is because they’re incentivized to do so. These programs use a business model called an ISA (Income Sharing Agreement), which means that students receive education funding in exchange for a percentage of their job salary for a set number of years, but if they don’t land a job, they don’t pay.

The Lambda School and its business model is the future. It will drastically change the business model of higher education. The Lambda school is a six-month, 100 percent online program, similar to the common programming boot camp programs out there today. These are live, online classes in which students have access to one-on-one help from their instructors. Furthermore, students can message their instructors and teachers’ assistants on Slack with any questions that they have, adding a personalized touch to learning.

The big difference from traditional boot camps is that Lambda makes a promise to its students that after learning for X number of weeks, they will be able to get a job. Instead of charging students upfront to take Lambda School classes, its students pay a whopping $0 upfront. How is that feasible? Unlike traditional education, which forces students to take out thousands in loans, the Lambda School has an innovative payment model where Lambda graduates pay 17 percent of their income to Lambda School for the first two years after they graduate only if the student makes greater than $50,000. In addition, tuition is capped at $30,000, which is far less than the average tuition a college graduate pays (Lambda, 2021).

This ISA model works because it aligns the incentives of Lambda School with its students, whereas in universities, the incentives are not aligned. Most people pursue higher education for the purpose of increasing their chances of getting a well-paying job someday, but the problem is that once you graduate, universities hold zero responsibility for that happening. By aligning its incentives with its students, Lambda ensures that it will provide the best learning experience because if students can’t find a job after they graduate or get low-paying jobs, then the school doesn’t make money. There is proof this model works too; 83 percent of the school’s students are hired within six months of graduating, and their median starting salary is $70,000, more than double the median personal income in the United States (Lambda, 2021).

On the school side, having the courses taught fully online means that Lambda School doesn’t incur the typical costs that traditional universities have to pay, such as maintaining expensive buildings and paying for administrative positions. Rather, it can use the savings to invest in job resources, better professors, and charge less. As AI displaces more and more jobs, it will be imperative that schools make getting a job the top priority, and the best way to ensure that happens is by utilizing an ISA model. Lambda essentially provides a risk-free education, and for traditional universities, it begs the question, If you aren’t willing to share that risk, what does that say about the quality of your educational institution? In a world where there will be high job uncertainty, if a school promises that an education will get people a job and it is wrong, why should it still get paid?

Critics of ISAs like the hysterical New York Times and Vox say that income sharing agreements are “indentured servitude,” which is very untrue. To me, indentured servitude is having $250,000 in student debt that you have to pay down and can never get rid of in bankruptcy for the rest of your life.

The Way Forward

Going forward, education needs to be centered around the skills that are not easily learned by AI. Machines today are able to compute faster, work 24-7, and expand their capabilities by acquiring more data, so competing with AI in routine tasks is a bad idea. However, “human skills” like creativity, problem solving, collaboration, communication, leadership, and empathy are becoming more needed in society as these skills are still out of reach for most AIs in the short term. Therefore, it is critical that education in the 2020s puts an emphasis on helping students develop these soft skills, which will require a radical change from how we currently conceptualize the education system.

Looking ahead, online course platforms and new schools with unique business models like the Lambda School will undercut traditional higher education. Someday, every trade that is valuable will get some ISA-type school, and then those skills will slowly get peeled out of a university education. Colleges will self-destruct enrollment-wise but likely stay open and act as museums preserving their history. Not only that, but the US government is continuously cutting funding for colleges and universities, causing them to now rely heavily on enrollment to continue to operate. Coinciding with that is rising student loan interest rates and students taking out loans that they cannot afford given their lower income after graduating.

In a recent interview, Mark Cuban argues that in the near future, a philosophy degree will be worth significantly more than a computer science degree because eventually with AI, “we’ll start automating automation.” If Mark is right that technical jobs, like programming, will be automated, then what do we do? The idea of computer science jobs being displaced is a little far in the future, so STEM is still the place to be. Whether Mark is right or wrong, AI will likely speed up the trend of workers being “free agents” who are constantly looking to find the best opportunity for themselves, even if that means jumping from job to job. We are already seeing the free agent trend happen today with the rise of the gig economy. Gig workers are people like Uber drivers, Upwork freelancers, Shipt shoppers, etc., and this accounts for a big chunk of the US workforce, as 36 percent of US workers participate in the gig economy through either their primary or secondary jobs (Schwartz, 2021). The days of climbing up the corporate ladder at one company your whole career are over.

Getting Ahead in an Age of Automation

As a society, both the institutions and platforms that educate and we as individuals have a responsibility to adapt to the technological times we are in. When the internet was first introduced, the naysayers who brushed off its capability were left behind while the people who recognized its power were immensely rewarded. AI is analogous to the internet but even more complex and impactful than its predecessor has ever been.

On the institutional side, they are not adapting their curriculum to match the different kind of skills that we need to continue to have value in an AI-dominated environment. For example, there are immense shortages of data scientists, engineers, software developers, and many more tech jobs. The skills needed for these jobs are in short in supply; until the education system starts making these skills more accessible to learn, it will inhibit technological development and leave many jobless. Colleges shouldn’t be pushing or even offering majors where the career outlook is grim. One interesting idea for preparing our workforce was from Apple CEO Tim Cook. He, along with many tech entrepreneurs, believes every kid in the world should learn to code. He also said, “Learning how to code is more important than learning any second language.” Tim Cook is right; tech skills like coding are far more important than most skills students are required to learn today, and that’s a big problem (Sonnrad, 2017).

Also, if we want to best prepare the future workforce to hold value in society, they need to possess the skills to do jobs that AI cannot. Most of the skills that are still huge barriers for AI, as studies have shown, involve creative problem solving, leadership, people management, and overall higher social intelligence. These more “human” skills will become far more valuable as AI develops further, yet no emphasis is being placed on them in traditional education today. So higher education must drastically change how and what they teach.

One way to do that is by adopting a relatively new academic principle called humanics, or human literacy. This was first proposed by Joseph Aoun as a solution to an outdated education system. Aoun agrees that education needs to change dramatically if workers are to adapt to this new environment. His solution, which he calls humanics, has three basic pillars. Technical ability: understanding how machines function and how to interact with them. Data discipline: navigating and analyzing lots of information generated by these machines. Human discipline: what we humans can do that machines, for the foreseeable future, cannot emulate. In educational terms, this discipline means less emphasis on listening to professors lecture in the classroom and more emphasis on experiential learning (McDonald, 2019).

A humanics-type approach helps build human strengths like critical thinking, creativity, problem solving, cultural awareness, and entrepreneurship to differ ourselves from artificial intelligence while also embracing technological literacy, which allows students to have at least a cursory understanding of how AI works. Lastly, data analytics is one of the most important skills Joseph Aoun says students need because there will be lots of data that needs independent analysis and interpretation in places that AI cannot easily assess. So, by using a humanics approach to education, we can best prepare people for their future jobs because it leverages both technological and human skills.

Next, another reason why AI-centered education must be a focal point of education is because millions of business owners, teachers, CEOs, and everyday people have no understanding of how to utilize AI. What is going to happen is many small- and medium-sized businesses that have operated their businesses in the same way for many years will soon face the reality that they cannot compete unless they utilize the power of automation the way many big corporations do today. These small businesses are going to rely on young, tech-literate individuals who can act as “business translators” to help transition their businesses in the age of AI.

These business translators will be huge job creators because there will need to be someone who can not only integrate the necessary technological changes but also explain the tech verbiage, ethics, and automation effects in a way that makes sense for the non-tech-literate individual. It is unrealistic for us as a society to expect a fifty-eight-year-old business owner to learn the tech side of automating their business; this is where the business translators come in by using their “humanics” skill set.

On an individual level, there is an abundance of ways in which we try to “robot-proof” our careers. First, being a lifelong learner is more than a teacher cliché; it is a must. Continuing to learn is important since technology is rapidly changing every day and it doesn’t stop for us to catch up. Starting right now, every worker should be upskilling by reading AI books, taking online courses, or doing anything that boosts their technological literacy. A way that is becoming more common is employers are partnering with online course platforms to have employees learn new skills in exchange for certificates. Online courses that help workers retool can really help mitigate the job disruption, but it is up to the individuals to take initiative.

Chapter 8

Health Fixing a Broken System

A little over twenty-nine years ago, Martine Rothblatt’s eight-year-old daughter Jenesis, who was on oxygen, was given a life expectancy of only three to five more years (Stevenson, 2015). Jenesis was diagnosed with pulmonary arterial hypertension (PAH). This deadly disease, which infects about two thousand Americans at a time, is a result of too much pressure in the blood vessels leading from the heart to the lungs, which leads them to cave in and not provide enough oxygen.

Jenesis’s lips would often turn blue even after just mild exertion; she couldn’t even walk up two stairs and would often faint, causing her to spend a lot of her early years in intensive care units. Jenesis was dying, and with no existing solution, Martine decided to tackle this disease herself. The doctors told her that there was no medicine approved for it and that her journey to fight her daughter’s disease in such a short time span is virtually impossible. When she wasn’t taking care of her daughter, her time was spent in medical libraries reading journals and biology textbooks, searching for clues that may help save her daughter. Martine was truly starting this journey from square one; the last biology course she had taken was in tenth grade.

Attempting to cure a disease that has no cure or effective treatment in less time than the time frame before death given by the doctor is beyond unbelievable. Yet, Martine was able to do it, and she saved her daughter.

Today, Martine Rothblatt is a top-paid female CEO in America, and in 2013, she was the highest paid female CEO (Stevenson, 2015). However, even before she started her mission to attempt to cure the disease, Martine Rothblatt had already accomplished some pretty amazing things, so this was just another challenge for her.

How did Martine get to where she is today? It is quite the journey. Martine was born as Martin to a Jewish family in Chicago before moving to California. When Martine arrived at college, it was tough to learn at such a slow pace, so she dropped out to backpack around Europe and Africa. It was on that trip in the country of Seychelles at a NASA space station where she came up with the idea to connect the world through satellite communications (Lieberman and Zhang, 2015).

Martine went back to UCLA and graduated as a double major in business and law while continuing to pursue her passion for space. This led her to start multiple satellite communication-based companies including Sirius XM, the world’s first worldwide satellite radio network. In fact, her college thesis became the first private satellite phone company. In the midst of running Sirius XM, Martine got married, had a child, got divorced and remarried, had a couple more children, and then at age forty, came out as transgender and changed her name to Martine. With all this going on, Jenesis started to become more and more ill. Martine made the bold decision to leave Sirius XM and take that money to desperately try to find a cure for her daughter. Martine describes it so: “I felt like my only purpose in life now was not to help move to the stars with satellites and stuff like that. It was to save Jenesis. So I just stopped everything I was doing” (Harrington, 2016).

Through her research, she found that there was a molecule/orphan drug for pulmonary hypertension that might have been able to help. The issue was that the big pharmaceutical company GlaxoSmithKline owned the molecule, but like many orphan drugs, the diseases they would be curing were so rare that it wasn’t profitable for them to develop a drug for them. Glaxo initially wouldn’t hand over the molecule to someone like Rothblatt who isn’t a scientist for safety reasons, but nonetheless, Martine organized a scientific team to try to convince Glaxo to allow her to purchase the rights. Ultimately, all Martine received from Glaxo was a tiny bag of powder that, in a few tests with rats, showed some signs that it might be effective.

At the time Martine got the dubious molecule from Glaxo, United Therapeutics was founded. The first task for Martine was to make a medicine out of what little she had gotten from Glaxo. Just like the doctors who told Martine she would never get a cure in time, many expert chemists said without a doubt that the Glaxo molecule would never become an effective treatment. A couple of years later, as Jenesis was on the brink of death, the medicine was FDA-approved as Remodulin. To the surprise of almost every medical expert, her daughter is now thirty seven years old and healthier than ever. The drug that saved her daughter is now a moneymaking, lifesaving drug that generates billions of dollars a year in revenue for United Therapeutics (Ticker Symbol UTHR). When Martine started this mission to cure pulmonary hypertension, there were two thousand cases, and now there are over forty thousand (Butrous, 2020).

While it is a medical miracle in itself that Martine could create an effective drug for PAH, it was only able to improve the condition of patients, not cure it. The only way to cure PAH, cystic fibrosis, COPD, or other related diseases is to get a lung transplant. The problem with that is there is an immense shortage of lung transplants, as only a couple of thousand per year become available for use. Because of the shortage, seventeen people die each day waiting for one, every nine minutes someone is added to the transplant waitlist, and about half a million people die from tobacco-related deaths, many of whom could have used a transplant (HRSA, 2021).

With that being said, Martine is now attempting to manufacture an unlimited number of organ transplants. To do this, she started with the basics. The reason close to 80 percent of donated lungs are unable to be used is because of damage from toxic chemicals (Senior, 2018). In order to preserve lungs once outside of the body, Martine worked on a process called ex vivo lung perfusion to attempt to save thousands of lungs and lives. However, this didn’t completely solve the problem, so Martine went after the organ shortage through xenotransplantation: the process of transplanting organs or tissues between members of different species. Since pig organs are similar to human organs, using genetically modified pig organs, they can manufacture lung transplants that don’t get rejected as transplants.

United Therapeutics has continued to work on growing organs in the laboratory from scratch using tissue regeneration. In this approach, scientists take pig donor lungs, strip them of their own cells, and use the remaining collagen structure as an organ scaffold (Terry, 2019). This is then repopulated with billions of human cells, completely replacing the pig collagen with human DNA-directed collagen. The result, if it comes together, is essentially a completely human lung grown in a laboratory. Using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, a tool for editing genomes, it could kill all the virus-ridden genes so it could produce usable pigs. Next, United Therapeutics is trying to get rid of the genes that lead to human organ rejection, which would result in an unlimited supply of transplants, saving thousands of lives. To accomplish this difficult task, Martine is 3-D printing artificial lung scaffolds from collagen and turning them into living lungs with stem cells.

The last complication in Martine’s mission to get more people transplants is that it takes far too long for an organ to get from its location of origin to the patient—another top cause of faulty transplants. Martine has invested in a flying car from Beta Technologies that will use Beta’s aircraft as an efficient, eco-friendly distribution system to bring new organs to in-need patients. The flying car prototype can travel over 250 miles per hour carrying over six thousand pounds, which in itself would help harvest far more lungs (Alcock, 2020). All in all, Martine was able to miraculously fight pulmonary hypertension through 3-D printing, genomics, AI virus detection, and even electric vehicles, but most importantly, she had the courage to take on the task.

Martine Rothblatt’s latest idea is that she wants the rights to create cyber-humans, who would one day hold all your thoughts, dreams, experiences, and online presence, living for an eternity as you. Basically, Rothblatt is creating a bodiless robot named Bina48 that is just a head but has facial recognition software that can scan your profile (Neely, 2014). This idea of having a human and machine as one is extremely hard to process, but it is something Elon Musk, Ray Kurzweil, and other futurists have pondered. Martine has accomplished a lot in her life, always overcoming the doubts of medical experts, so I wouldn’t doubt this either. Martine hopes that in the near future, death from organ failure and the days of waiting for a transplant will be over. With Martine’s drive, intelligence, and love for her daughter, she has saved tens of thousands of lives.

The US Health Care System is Broken

Profit comes before people, and costs comes before care; the United States has a broken health care system even though 18 percent of the GDP is spent on it (O’Brien, 2021). Rarely on any issue can a majority of Americans agree, but on health care, 70 percent of Americans describe the current US health care system as being “in a state of crisis” or having “major problems.” Furthermore, every year since 1994, between 65–78 percent of Americans have said the United States has “major health care problems” (Rege, 2019). Indeed, we do have major problems; “every day, ten thousand people globally die because they lack access to affordable health care.” And if they don’t die, many become incredibly poor, as “each year one hundred million people are forced into extreme poverty due to health care costs” (Oxfam, 2021).

So why is it so bad? First of all, the health care system is currently centered around making reactive decisions rather than being proactive. For example, today, most money and resources are spent on technologies used to diagnose a patient once the disease is already manifesting, which results in the sick having to throw lots of money at late-stage treatment (think cancer, heart disease). We have the technology to lessen the blow and even prevent diseases, and with all the increased collection of personal health and lifestyle data (think Apple Watch, ECG Devices, WHOOP Watch), big data and improved analytics are giving us what we need.

Reactive measures of treating patients are much more difficult than proactive measures because the damage has already been done, resulting in doctors scrambling to figure out the severity and next steps. Figuring out the next steps (correct treatment) has proven to be a failed experiment. Analyzing medical death rate data over an eight-year period, Johns Hopkins patient safety experts have calculated that more than 440,000 deaths per year in the United States are caused by medical error, making it the third leading cause of death just behind cancer and heart disease. Even worse, 10 percent of medical errors aren’t even reported, so the actual number is much higher (Sipherd, 2018)! Why isn’t this talked about more?

This is happening because when you have to provide health care reactively, it causes two major issues: (1) possibility of wrongly determining the state of the patient’s condition (e.g. you overprescribe a patient because disease spread is unknown) or (2) surgery complications, which will soon be solved with surgical robots, as people are tired of malpractice. Also, you can see the faultiness of the system just from the fact that in the United States, fear of legal liability has doctors spending $220 billion per year on treatments or procedures that patients don’t need whatsoever. In the famous words of Ben Franklin, “an ounce of prevention is worth a pound of cure,” or in other words, it’s far easier to halt something from happening in the first place than to repair the damage after it has occurred (Ringwall, 2021).

The health care system can shift to a more preventative approach by leveraging innovative tracking technologies, advanced tests, and machine learning algorithms to not only stratify risk but also predict and stop disease before it becomes too late. For example, tobacco use, alcohol consumption, unhealthy eating, and insufficient physical activity are all big risk factors that lead to a majority of chronic diseases. We have devices that can accurately measure patients’ current health levels, and thus, we are able to take preventative action to step in before the patient’s condition snowballs. If we want to fix health care, lets design a system in which everyone is equipped with personal health trackers and can receive real-time guidance on how to better their health.

Health care research is also flawed. For drug discovery and implementation of drugs, on average, a measly five out of every five thousand drugs introduced make it to human trials, and then only one of the five thousand actually get approved (MedicineNet, 2021). This is why it is so hard to receive medicine for rare diseases. On average, it takes thirteen years to develop a drug, costing around $2.5 billion. This failure in drug research is part of the reason why the United States spends $11,582 per person every year on health care—by far the most in the world (CMS, 2020).

This is just scratching the surface of all the issues the system has, but one thing is certain—the industry is changing through the implementation of emerging technologies. Technologies such as health wearables, personalized medicine, AI, genomics, robotics, sensors, and 3-D printing are all coming together to change the reactive health care system that we know into a personalized, proactive, and transparent health care system. Let’s have a look at one of these technologies, AI-powered automation.

Automating Routine Health Care Tasks

When people often think of how AI can positively change health care, we often allude to the awesome projects helping to cure cancer, push drug discovery, design bionic limbs, build robotic surgeons, create AI radiologists, etc. And while those are all great, the greatest opportunity for AI to make an impact in the short term may be allowing algorithms and computers to automate routine tasks. Exorbitant administrative tasks and paperwork are an annoying burden on modern medicine. It is estimated that a whopping 14 percent, or $91 billion, of wasted health care spending comes from inefficient administration. The promising part about AI is that it can do all the administrative muck work that is cluttering our health care system and hurting doctors with the stress of administrative work.

Doctors should be able to solely focus on providing patients with the best care possible, but unfortunately, they are handicapped with a lot of extra administrative work. Something as simple as providing every physician with a smart device like an Amazon Alexa or another artificially intelligent virtual assistants would save a lot of time for tasks like pulling up medical records, data entry, explaining insurance coverage, scheduling appointments, etc. The average appointment scheduling call takes about 8 minutes, office staff waste 74 minutes a day tracking down information, and the typical doctor’s visit consumes 121 minutes of the patient’s time (travel, forms, and visit). Patients are frustrated about these issues and others, including poor communication, long wait times, and lack of digital tools. However, many of these problems can be addressed with automation and further improved with AI (Prabhu, 2020).

Next, telemedicine in which patients can talk with doctors via video, phone, email, or another platform offers more convenient access to follow-up care, specialists for quicker diagnosis, and telemedicine treatment. Because of that, hospitals will reveal fewer readmissions, complications, overnight stays, and lower higher cost treatments. As a result of the COVID-19 pandemic, there was a forced pivot to telemedicine, which was largely successful. Patients were able to avoid traveling and waiting to see a doctor; doctors were able to meet with far more patients throughout the day; and caregivers, like a child’s mom or dad, were easily able to listen in from anywhere. Also, going back to the administrative costs problem, health systems saved immensely on administrative costs because with telemedicine, there are no clipboards full of information to fill out by hand, and no one needed to spend fifteen minutes typing out the answers on a computer—all the patient’s data is pre-stored on the platform.

On the flip side, the COVID-19 pandemic, along with introducing telemedicine and automation of administrative tasks, will lead to significant medical job displacement. Essentially, the pandemic—along with telemedicine—have caused a “double disruption” because taking everything virtual has pushed hospitals to speed up the adoption of new technologies to cut costs, enhance productivity, and be less reliant on human labor. The World Economic Forum (WEF) concluded in a recent report that “a new generation of smart machines, fueled by rapid advances in artificial intelligence (AI) and robotics, could potentially replace a large proportion of existing human jobs in the health care sector.” Further, WEF estimates that automation will cut about eighty five million jobs by 2025, with health care cuts being the second most impacted (Kelly, 2020).

Of course, concerns of automation causing job losses, specifically in health care, have been floated around for a while. One side often argues that it’s all nothing to worry about because automation will just create better health care jobs and eliminate the need for as many health care workers doing the tedious administrative tasks. Talks of surgeons, radiologists, and pharmaceutical technicians being replaced by AI is definitely possible, even probable. However, the idea of doctors and nurses being subbed in for robots is farfetched and won’t be happening anytime soon. In fact, automation in health care is a great thing because it will free up time for physicians to focus more on the care side of health care, leaving AI to do the diagnosis.

Health care has many problems, but the future is very promising; we are going to live longer, better, and cure many diseases along the way. “This is a disruption unlike any we’ve seen in decades,” said Ani Turner, the co-director of sustainable health spending strategies at the Altarum Institute. Next, we will look at how AI is making health care more accessible for underserved populations.

Democratizing Health Care

Just like how the United States has a broken health care system that isn’t accessible for everyone, so do other countries like India. At Google/Verlify, which is Alphabet Inc.’s research organization, a team of doctors and engineers are exploring ways to use AI to screen for diabetic retinopathy and solve other health care problems in rural regions of India that typically lack access to quality care. It has started to use the AI technology in countries like India first because physicians are in short supply, which leads to a health care system that is inefficient, especially for those in rural, low-income areas. In fact, according to Google project manager Kasumi Widner, there’s a drastic shortage of more than “one hundred thousand eye doctors, and just six million of the seventy two million diabetics are screened for eye disease” (Miliard, 2019).

Meet Santhi: she is the mother of four children in rural India. Santhi does not have an easy life; she comes from little money and has to raise four children all on her own, as her husband passed away from cancer years ago. However, the thing that is making life the most difficult for her is diabetes. Santhi’s diabetes is affecting her vision, as she has diabetic retinopathy. She wakes up with constant headaches, eye strain, and blurriness. She has been told before that if she does not receive medical help soon, she will almost certainly go blind. In fact, diabetic retinopathy is one of the leading causes of blindness worldwide, and she knows that it is particularly true in India due to the shortage of eye doctors. To illustrate India’s eye doctor shortage some more: in the United States, there are about seventy four eye doctors for every one million people, but in India, there are only eleven (Metz, 2019). So just keeping up with the volume of patients, let alone giving them the necessary attention and care they need, is extremely overwhelming for doctors there.

To help diabetic retinopathy patients receive medical attention in time to prevent vision loss, Google built an AI-enabled eye scanner that is being used instead of human eye scanners. Google is training an AI model that can help read eye scan images and decrease the number of doctors required to do this task. The team uses the same kind of machine learning that allows us to organize our photos or tag our friends automatically on Facebook: image recognition.

How image recognition works: first, models are trained using tagged images of things like cats or dogs. After looking at thousands of examples, the algorithm learns to identify new images without any human help. For the diabetic retinopathy project, over one hundred thousand eye scans were graded by eye doctors who rated each eye scan from a scale of one to five, from healthy to diseased. These images were then used to train a machine learning algorithm. Over time, the AI learned to predict which eyes showed signs of disease far more accurately than a team of doctors could (Metz, 2019).

The AI eye scanner in India works like this: A patient walks into the room and gets pictures taken of the back of each eye. The images are then uploaded to an algorithm immediately, and once the algorithm performs its analysis, it sends the results back to the system, along with a voice-enabled recommendation (essentially, a machine speaks to you about what you should do about your results). What is important is that because the algorithm works in real time, you can instantaneously get an answer from the machine, and that real-time answer goes directly back to the patient. Using AI algorithms to diagnose diseases makes it almost like hopping on a scale to take your weight; it happens within seconds and gives you a direct answer.

In the past, Santhi’s condition could have taken months to diagnose, if at all. By the time an eye doctor would have been able to see her, Santhi’s diabetes might have caused her to go blind. But now, with the help of machine learning and technology, she can take an hour-long bus ride to the clinic for same-day treatment. In a broader sense, now thousands of patients who may have waited weeks or months to be seen can get the help they need before it’s too late. Technologies like an AI retinopathy scanner may not be the flashiest technology we see in the headlines but very well may be the most impactful.

AI is democratizing health care by making it more accessible and more affordable for the underserved. This is just one example of what the future of health care will look like. In the near future, every doctor, using massive amounts of data and images, will be able to leverage AI to predict diseases before they happen. Take cancer for example: we can take a sample of somebody’s blood and look for minuscule amounts of cancer or tumor DNA in that blood. There are many use cases for AI in health care, but what we will see first is disease detection.

Although this case is from India, it is important to note that in 2018, IDx Technologies was the first company to gain clearance from the FDA in the United States for an autonomous AI diagnostic device, IDx-DR, which deploys an algorithm to help detect the disease (FDA, 2018). And it’s already in trial use in some US health systems. Once every US health care system is able to use technology like this to detect diseases, we can finally change health care from being more reactive to being more preventative. Also, machine learning is revolutionizing the way we think about medicine. If anything can solve the hard problems in medicine, its AI. Let’s take an example like heart disease. No single human being can have in their head all the knowledge it takes to understand heart disease, but a computer can. At this point, with all the data collected, AI can actually diagnose diseases much better than highly trained humans.

A common trend with technology is that it’s often started off the idea of trying to democratize health care. One thing we use often is phones. Alexander Graham Bell’s mother was deaf, and his invention of the telephone came out of his experience with the deaf community. More recently, audiobooks were invented as a way for the blind to enjoy literature. Video captioning was invented to make content compliant with the Americans with Disabilities Act (ADA) but now is regularly relied on by anyone watching videos when they can’t listen to the audio (Snow, 2019). Innovating and developing new technologies can help democratize health care by becoming more inclusive toward a segment of our population that for long has often been left out of quality health care.

Chapter 9

Health Drugs, Robotic Surgeons, and COVID-19

Not Your Typical Drugs

With a pounding head and a bad cough, a pneumonia-infected individual heads over to the medicine cabinet to grab some Zithromax. The label says “take as directed,” so they take a couple and wait twenty to thirty minutes with the hope it provides some relief. The individual with pneumonia—like millions of other sufferers—postulates that the pills are safe and effective, suggested to them by their trusted doctor. What many never think about is what it took to make the drug, or any other prescription medicine for that matter.

The reality is that the drug development process is a one-size-fits-all approach that is extremely long, costly, and for the most part, ineffective. According to the Tufts Center for Drug Development, developing a brand-new drug takes on average ten to fifteen years and costs between $2.6 billion and $12 billion to get from idea to pharmacy shelves (Anderson, 2020). Also, some 86 percent of all drugs fail in clinical trials, according to a recent MIT study (Hale, 2018). When you combine the outrageous costs of drugs, the lengthy amount of development time, and the low approval rating of drugs, it is apparent that drug development today is badly in need of change.

Stepping back for a moment, the drug development process involves thousands of calculated steps and work from many different scientists in the fields of biology, chemistry, medicine, and pharmacology. When a pharmaceutical company decides to create a new medicine, they usually have a couple of key decisions to make. Either they spend years negotiating patent rights in an attempt to modify an existing drug or they go on the lookout for naturally occurring sources like fish oil or tree extract that may have cancer-fighting chemicals. Both of these routes are riddled with complex processes and take years of effort all to have a small chance of getting FDA approval. In short, drug discovery is a long and inefficient process that the science community is confident will soon be sped up by AI.

Related to drug discovery, most of the drugs we take one day are going to be personalized and based on a person’s unique genomic sequence to help prevent and treat diseases (personalized medicine). Our bodies are all unique with many different variables; someday, it will become common to sequence your genes and take diagnostic tests to identify biological markers, which will then help set the foundation for giving someone personalized medical treatment. Mark Cuban described the future of drugs best when he said, “I’ve got a five-year-old son. By the time he’s twenty-five, the idea of going to a drugstore and buying over-the-counter medication will seem barbaric. My son is going to say, ‘Dad, what do you mean? You bought this medicine, and on this over-the-counter medicine, there was a warning that said you might be the one unlucky schmuck that dies from this. And you actually bought it and paid for it?’” (Whitten, 2015). More on this later.

Robotic Surgeons

In Star Wars: Revenge of the Sith, Anakin/Darth Vader had his arm cut off and his legs burned by lava. He had to have emergency surgery, and it was done by robots. In Empire Strikes Luke Skywalker had his arm reconstructed by a team of robots. Robots performing surgery are still in the first inning, but based on many tests and the rate at which robotics is growing, in the future, it will be like Star Wars where almost every surgery is completed by a robot. Why? Because robots performing surgery is safer, quicker, less painful, and results in less blood loss.

On a high level, there are two central problems in health care, cost and effectiveness of treatment, both of which can be addressed through artificial intelligence. In fact, AI and robotics are being infused all across the health care system, from genetic testing to surgery. AI decreases the need for paid staff, operates continuously without needing breaks or sleep, and maintains a constant level of productivity, unlike humans. On top of this, AI is very precise and reduces the amount of human error that occurs in hospitals. In many cases, autonomous robots have outperformed humans on routine procedures and surgeries (Paddock, 2016).

Robots have been used to manufacture products since 1954 (GlobalTranz, 2014), and their introduction into medicine coincided with the rapid demand for minimally invasive surgery in the 1990s. Today, the da Vinci surgical system by Intuitive Surgical has performed nearly seven million surgical procedures. In fact, every thirty seconds, the da Vinci robot is in use for minimally invasive surgery (Intuitive, 2019)! This is just one particular surgical robot, but there are many more being developed, from the likes of Intuitive Surgical, Titan Medical, Stryker, J&J, etc. The da Vinci robot has arms similar to a human but with greater range of motion, dexterity, and stability. Surgical robots provide a few benefits, but safety is far and away the biggest one, as humans have a high error rate.

Robots performing better than humans is key, and we should be investing much more into the development of surgical robots. You’ll rarely hear it mentioned as a problem, but over “4.2 million people worldwide die within thirty days of surgery every year” from complications (Nepogodiev et al., 2019). Surgery is extremely difficult because within a surgery, a doctor goes through a ton of physical and mental stress, along with having to follow specific technical procedures. Consistency with humans is hard, and everyone makes mistakes. With a robot, once you get the system to work, the ways in which it can fail are almost zero.

Another prominent surgical robot is the STAR robot (Soft Tissue Autonomous Robot) from the Children’s National Health System, which developed a robot that was “able to independently complete repairs on a pig’s intestine—notably, with greater accuracy than a human surgeon” (Bondereud, 2021). For humans, repairing human tissue is mucky and bloody. It requires the utmost attention to detail and accuracy to ensure patient safety. Because soft tissue surgery is so complex and because doctors vary in skill level, there is no certainty you’ll get a safe surgery experience. You won’t get a fast one either. On the other hand, “STAR sews tissue ten times faster than a human” with laser-focused precision (Brown, 2017). While human surgeons can make mistakes and require hundreds of hours of training, AI does not need extensive training. STAR has shown that it is completely viable to have autonomous robots perform critical tasks like soft tissue surgery without much worry.

After the success of robots used in soft tissue surgery, the use of robots for rehab, prosthesis, bionic limb procedures, and home health monitoring will begin to ramp up. On the bionic limbs front, companies like Atom Limbs have created a mind-controlled artificial arm that leverages cutting-edge AI to listen to your nerve signals. And it all happens in real time; you think, and the bionic arm moves. Plus, it’s noninvasive; just put the arm on, and its sensors listen on top of your skin, no surgery required (Atom Limbs, 2021). This is just one small example of AI making health care better for all, and in the next few years, we will see a wave of innovation in health care that will save many lives.

Picture yourself as a patient getting ready for surgery. You might ask the surgeon, “How many times have you performed this type of surgery?” You would want to hear that you are getting the surgeon with the most experience, as they are most likely the best at doing it. I can guarantee eight years from now when you are told you need surgery, you’ll request to have the robot do it. At least I will.

AI Spotting COVID-19 Before Humans

COVID-19 has killed nearly four million people worldwide (Jin et al., 2021), it has brought on long-term effects for people who’ve had it, and it has subsequently destroyed many small businesses across the world. There is no doubt that COVID-19 caused many problems for all of us, but what if this could have been prevented earlier by relying on artificial intelligence?

Meet the Canadian software company BlueDot. In 2014, BlueDot received $9.4 million in funding for their SaaS infectious disease targeting technology, which uses AI and human intelligence to alert people of potential disease outbreaks, thus helping prevent them from spreading. Today, BlueDot has over fifty employees—consisting of programmers and physicians who formulate the disease-monitoring analytics (Niiler, 2020).

BlueDot’s predictive technology works by using machine learning to train computer algorithms to spot “150 pathogens from a plurality of data sources including news reports (by using natural language processing to go through news reports in 70+ languages), medical bulletins, climate data, livestock health reports, human population data, and more.” When an anomaly in the data is detected, the algorithm uses airline ticket sales data from over four thousand airports around the world and other data points to predict the spread of the disease. BlueDot is able to accurately and quickly analyze large amounts of information every fifteen minutes of every day (BlueDot, 2021).

“What we have done is use natural language processing and machine learning to train this engine to recognize whether this is an outbreak of anthrax in Mongolia versus a reunion of the heavy metal band Anthrax,” said BlueDot CEO Kamran Kahn. After the automated data mining is complete, human analysis of the situation takes over, and that is when epidemiologists make scientific conclusions based on the specific situation and then report their findings to their government or business client.

As you would expect, the coronavirus was a big test for the company. In the middle of the night on December 30, BlueDot spotted a pocket of “unusual pneumonia” cases appearing around a wet market in Wuhan, China, and alerted it as a potential problem. It had flagged the coronavirus, nine days before the WHO sent out a statement alerting people to the virus that had emerged. So, through the use of AI detection and analysis, BlueDot was the first in the world to note and alert people about the up-and-coming risk from COVID-19. In addition, it was the first to “publish a scientific paper outlining and accurately predicting eight of the first ten cities” to get the virus. Since its alert, COVID-19 has crushed economies, disrupted society, and taken the lives of millions globally.

More than just detecting infectious disease risks as early as possible, BlueDot’s mission is to understand how these infectious diseases may spread to different regions of the world and subsequently figure out the consequences of the spread.

In an interview in the middle of the pandemic, Khan said what he learned from COVID-19 is that we must trust the science and use emerging technologies to anticipate rather than be reactive. “If we rely on government agencies to report information about infectious disease activity, we may not always get that in the most timely way,” he stated.

In BlueDot’s history, it has been very successful in detecting diseases early on. In 2014, it accurately identified importation risks from the Ebola outbreak in West Africa. A couple of years later, BlueDot successfully predicted the spread of the Zika virus to Florida six months before official reports. Not only is this impressive technology-wise, it is important work that it does in keeping the world safe. There are many novel diseases and small outbreaks that most of society never hears about, and that’s because of the great work of companies like BlueDot that help keep an eye on everything so that we can act swiftly (BlueDot, 2021).

Overall, using AI to monitor diseases is a great use of its power. There is a lot of talk from people about AI’s harm, but this is a great example of AI making the world a better place. Humans are imperfect, they make mistakes, they cannot work twenty-four hours a day, and they get distracted. Having AI that can work every minute of every day, keeping tabs on diseases around the world is important, especially in times like the coronavirus pandemic, when everyone is fixated on one disease.

Working with AI

As jobs disappear everywhere, having your health care linked to your employer or employment will become increasingly unsupportable. Experts are starting to realize the need for not only a new insurance model but also a new approach to health care altogether. One thing I believe will happen is that a new sector of health care jobs will appear, specifically jobs for younger adults. What will happen is instead of twentysomethings going to school for eight to twelve years of college, they will trim that by four years or so and be sent to rural or underserved areas to act as a health care provider. They will work alongside AI technologies, thus lowering the skill level required, but these younger professionals will handle the human elements and more skilled parts of the job.

This new sector of care providers will be able to monitor and give greater attention to people with chronic conditions like heart disease, cancer, obesity, and diabetes, whereas right now, they aren’t getting personalized attention and care. AI will soon be at a point where technology in conjunction with a less-educated medical professional could offer the same quality of care as an experienced doctor in the vast majority of cases. In one study, IBM’s Watson made the same recommendation as human doctors did in 99 percent of one thousand medical cases and made suggestions human doctors missed in 30 percent of them (Lohr, 2016). AI can reference more cases than the most experienced physician while keeping up to date with the latest journals and studies.

Predictably, doctors have lobbied against nurse practitioners and unsupervised specialists. But this change would make health care much more widely available, open up a new employment category for smart and empathetic college graduates who genuinely want to spend time with patients, and eventually lighten the burden on individual physicians.

Chapter 10

Longevity

“In the future, ninety will feel like fifty. There will come a time when you can’t tell how old someone is.”

—David Sinclair, professor of genetics and co-director of the Center for Biology of Aging Research at Harvard Medical School (PodcastNotes, 2019)

Jeanne Louise Calment was the oldest human to ever live, as she made it to 122 years old (Champigneul, 2019), though there are some skeptics who claim her daughter did an identity change to avoid paying taxes. Nevertheless, people have definitely lived to 117 years old, and as old as that seems, that will soon be the norm. If you ask David Sinclair, he’d argue that 117 is just middle age. And there is a whole field of people working on making that extended life a reality (PodcastNotes, 2019).

Sinclair, a Harvard professor who made list of the one hundred most influential people in the world, is the most respected and accomplished scientist in the field of aging (Agus, 2014). David, along with myself, is of the belief that growing old isn’t a natural process of life, it’s just a disease that needs a cure. Sinclair has conceded that everyone has to die eventually, but before that happens, we can more than double our life expectancy and live healthy, active lives until death. Scientists are saying to hell with the previous hypothesis about aging, and they have already uncovered several health actions that you can take right now to suppress—and in some cases, reverse—the aging clock.

In 1950, the US average life expectancy was sixty seven, and today it’s about seventy nine years (Macrotrends, 2021). New research has conservatively suggested that humans may “one day live well past the age of one hundred, even to the ripe old age of 150!” (Baker, 2021) People in general are getting older; in fact, over eighty five years old is the single largest growing age group of Americans (ACL, 2019). We are seeing a transformation in how society looks at death, specifically from millennials and Generation Z. Younger generations are deviating from the traditional style of health care where medicine and treatment are so generalized. Instead, things like personalized medicine and taking a more holistic view on personal health are happening.

Younger generations also have far more self-awareness of whole-person wellness—not just their diagnosed medical condition but how it interplays with sleep, nutrition, fitness, and mental health. This is something the health care industry has historically never seen. At the end of the day, obviously, people still age and die. However, by combining AI, genomics, personalized medicine, cloud computing, nanotechnology, sensors, and much more, we are starting to understand why exactly we age and why we don’t have to.

Before looking at the developments in the longevity space and what we can do to live longer, let’s look at what’s killing us today.

First, genomic instability: this is when DNA is damaged and doesn’t replicate the way it should. The breaking of chromosomes is the number-one culprit in speeding up the aging process, along with causing cancer. In layman’s terms, this is like a “broken copy machine but instead of printing blank or unreadable pages, it produces diseases like cancer and ALS” (Diamandis, 2020). Using X-rays, microwaves, and flying all increase radiation, thus breaking down DNA.

Second, deregulated nutrient sensing: according to David Sinclair, this includes “insulin insensitivity, Type 2 Diabetes—which is very bad for aging, probably the worst one we know of. The way to combat that is: don’t be overweight, keep exercising, and lift weights.” (Sharoni, 2020). Our bodies rely on many different types of nutrients. Cells need to recognize the various nutrients, but as we age, they start to break down.

Third, loss of proteostasis: this is when the body loses the ability to recycle proteins. The loss of protein homeostasis happens because of misfolded proteins. Dr. Sinclair recommends fasting as a diet strategy to help maintain cellular proteostasis (Sharoni, 2020).

Other reasons we age are telomere shortening, mitochondrial disfunction, and cellular senescence. The latter is when senescent cells cause inflammation in the body and actually work to prevent cell division, and thus, cancer. Senescent cells are something billionaires like Jeff Bezos and Peter Thiel are targeting through their wealth. Both have invested millions into Unity Biotechnology (UBX), which is a biotechnology company that works on developing drugs to target senescent cells to “address the root cause of many diseases of aging, such as retinopathies, neurodegenerative disorders, fibrosis, and cancer.” Unity has proven this by successfully killing senolytic cells in mice (Unity Biotechnology, 2021). The next step is beginning human trials. I’d keep tabs on them as Unity moves forward.

The Worm, the Whale, and the Jellyfish

The roundworm, Caenorhabditis or C. has been a part of many studies related to longevity. Numerous scientists have won Nobel prizes from this research because it really is the first step in progressing toward gene sequencing and subsequently living longer (Van Der Linden Lab, 2021). C. elegans was the first organism to undergo gene sequencing, having its whole genome analyzed and its brain diagrammed (Mitani, 2017). But even with this big breakthrough in gene sequencing, many experts believe C. amazing aid to antiaging and longevity research is still in its early days, as the roundworm is the first animal to face death and continue to live past its typical life-span.

Normally, a C. elegans worm has a life-span between twelve to eighteen days (Zhang et al., 2020). But eight years ago, scientists at the Buck Institute went on a mission to halt the C. elegans from dying and see if they could make it live longer. Historical research had shown they could remove a gene named RSKS-1 or remove DAF-2. Lead scientist and Buck faculty member Pankaj Kapahi, PhD, said single mutations in RSKS-1 usually result in a 30 percent life-span extension, while mutations in DAF-2 often result in a doubling of life-span in the worms; added together, they would be expected to extend longevity by 130 percent (Buck Institute, 2020).

After more research and debate, they decided to see what would happen if they cut out both RSKS-1 and DAF-2! To the excitement and surprise of the scientists, the worms were still alive at one hundred days—more than a 500 percent increase in life-span (Buck Institute, 2020)! They blew out all scientific expectations by a moon shot and sent the longevity field into a frenzy. Obviously, humans aren’t like worms, but if possible to apply this gene editing to humans, this would be like us living four hundred to five hunded years.

Next, whales, the longest living mammals on Earth, have been under constant examination, with researchers trying to see what the secret to longevity is. The University of Liverpool sequenced the genome of a bowhead whale with great curiosity because whales have one thousand times more cells than humans (Casey, 2015). Conventional wisdom would expect them to die quicker and be more susceptible to death. Yet, after the sequencing, it found that whales have about eighty genes that are different from humans that help protect them from cancer and disease (Casey, 2015). Because the costs of DNA sequencing are declining significantly, more and more research is being done into studying long-living species. This is important because we can use the findings produced from studying these impressive mammals to help identify mechanisms to improve human life and repair cellular damage.

Another interesting creature is the Turritopsis dohrnii jellyfish. First discovered in the 1880s, it is the only species known to mankind that is biologically immortal (AMNH, 2015). These cool jellyfish are able to turn back time and revert to an earlier stage of their life cycle. This cellular process is called transdifferentiation—something scientists have tried to understand to see how it could apply to other mammals. I only bring this example up because it’s a bit cool to imagine if we could find a way to apply it to humans. We are in the first quarter of longevity, and with every study, every small breakthrough, whether it be increasing the life-span of a worm or discovering drugs to prevent Alzheimer’s, we are marching toward living a lot longer.

Although big breakthroughs are happening, it’s imperative to understand that it isn’t one technology or one gigantic breakthrough that will result in us slowing the aging process or living longer; it’s the combined utility of all these technologies that is powering us to a new wave of health care.

Immortality

Ray Kurzweil, a man who predicted the perfect time line and impact of the internet and the capabilities of AI and hundreds more technologies, now believes that immortality in humans will be achieved in the year 2045. Even before that, he thinks we will “reach a point around 2029 when medical tech will add one additional year, every year to your life expectancy. By that, I don’t mean life expectancy based on your birthday, but rather your remaining life expectancy” (McCue, 2016).

That is remarkable. In order to achieve this immortality, he sees a few big breakthroughs in science happening before that. First, computers routinely passing the Turing Test by 2029, meaning a computer is now at human-level intelligence. Also within the next decade, Kurzweil predicts nanobots will be able to “cure practically any disease and heal any wound, including those in the brain. Nanobots will also spontaneously create food by simply reorganizing existing matter” (Johnson, 2021). The nanotechnology the longevity field is so excited about “science, engineering, and technology conducted at the nanoscale, which is about 1 to 100 nanometers” (Nano.gov, 2021).

Nanobots are tiny robots sent into our bloodstream to perform microsurgery and repairments. One day, we will likely be implanting molecular-sized robots into our bodies in order to destroy pathogens, fend off cancers, viruses, diseases, clots, tumors, and correct DNA errors. This would make humans virtually immortal. And we aren’t too far away from nanobots: “James Friend, a professor of mechanical engineering at UC San Diego who focuses on medical nanotechnology, thinks that we’re only five years away from being able to effectively use brain nanobots, for example to prevent epileptic seizures” (Miles, 2015).

Another dimension to Kurzweil’s prediction is that by the 2030s, we will be able to bring our own human intelligence and consciousness to the cloud. This will lead to technological singularity by 2045, where AI essentially takes over humans, as everything is connected to their systems. The way this is possible is from the idea that technology is getting smaller and smaller at an exponential rate and increasing in price performance—twenty-five years from now, computers and AI will be a billion times more powerful per dollar, and they’ll be one hundred thousand times smaller, about the size of blood cells (McCue, 2016).

Hypothetically speaking, if this were to happen, AI could be sent inside the brain to connect the neocortex to the cloud wirelessly. This would clearly lead to a massive expansion of the human intellect at the expense of everyone being part-robot. All this likely sounds crazy, but it truly is a possibility. Russian billionaire Dmitry Itskov agrees with Kurzweil that we will be immortal by 2045 because our brains will be tapped into robots or holograms (Pettit, 2019). Another longevity expert, Aubrey de Grey, believes, “The first person who will live to be one thousand years old is already among us.” Additionally, the idea of a robot with a human personality like Star Wars’s C-3PO is “not crazy, but difficult” (Pettit, 2019).

*Graphic showing the exponential growth in computing power over time

Take these predictions on the future for what they are, just predictions. I tend to keep an open mind by researching and forming my own opinion, and it is clear that Kurzweil’s vision on the future is not coming too soon, but it is much closer than you’d think. Billions of dollars are being thrown at making this happen because the one thing in life you can’t buy or get back is time. It goes to show why Jeff Bezos, Peter Thiel, Elon Musk, and many more have invested tens of millions of dollars into companies trying to extend human life.

Chapter 11

The Future of Real Estate and Insurance

The year 2008 was a dark time in US history. The economy was in free-fall, and the housing market was at the center of it all, as the market experienced its biggest disruption and fall in value since the Great Depression. US properties saw a 33 percent drop in value, many had their wealth wiped out, jobs were lost, and it was a true wake-up call that there needed to be more oversight on the financial activities of banks and expansive change (Lerner, 2018).

In the aftermath of the housing collapse, as the industry was starting to recover, new innovative tech-centered real estate companies like eXp Realty, Opendoor, Zillow, and Redfin emerged to reshape an industry that had seen little change in the past fifty years. Real estate had long lagged (and still does to a certain extent) in innovation and other industries. It is ripe for disruption; funding for real estate tech startups hit an all-time high the past year, as it is clear the process of buying and selling a home needs change. More technology being intertwined into the industry is something realtors may remain stubbornly resistant to, but consumers are ready for change. Just how retail got overtaken by e-commerce and online selling, so is real estate. Online transactions are convenient, cheaper with lower fees, and much quicker to complete a deal. Emerging technologies are finally coming to real estate.

Virtual Reality (VR)

Just a year after the financial crisis of 2008, Glenn Sanford had the seemingly crazy idea to start a real estate company. Money was tight for everyone in real estate, so the thought of opening offices and taking on high overhead cost amid crumbling revenues was not on his mind. Instead, Glenn founded the first cloud-based nationwide brokerage company, eXp Realty, which allows people to buy and sell homes just like any other conventional brokerage, but it’s almost exclusively online. What’s unique about eXp is it’s not just a digital view/buy/sell platform; it utilizes VR technology to onboard thousands of employees and agents to work inside a virtual reality campus.

The VR campus looks like a video game where your real estate agent avatar “enters a virtual reception and starts engaging with others in the digital realm. Agents can use VR headsets, or even just 2-D, to collaborate. eXp was rated as a top place to work because although agents work virtually, they feel like they are actually there in person (eXp World Holdings, Inc., 2021). The virtual campus includes everyone having their own meeting room, conference rooms, a lobby, and even sports fields where employees can connect with one another in a fun way. Importantly, they are likely saving millions of dollars in overhead and office space costs by choosing VR.

During the pandemic, eXp began 3-D virtual tours to essentially bring a buyer or seller into a home without having to be there physically. Using a virtual mode of touring homes, buyers can view many more properties without spending countless hours driving from home to home. In addition, VR allows buyers to plan renovations on the spot by changing the look and feel of the property using superimposed AI-generated home features on a virtual platform (eXp Life, 2020). It is likely that within the next decade, almost everyone will be using VR and AR in the home shopping experience. Agents today are showing potential buyers empty homes with not much furniture or ambiance. If they used VR or AR, they could show the same property except it’s decked out with an appealing, relatable feel. Which experience is better?

No Need for a Broker

Imagine all the work that goes into the process of buying a new home. You’ll likely need a broker to sell your existing property (if you have one). You might also need a broker to search for a new place and then a mortgage lender to finance the buying of the home. You’ll need indemnity insurance in the form of title and home insurance. Lastly, you’ll need a moving company or a generous friend with a trailer to help move your stuff. Wouldn’t it be nice to get the whole process all from one company and have the process happen much quicker and without the exorbitant fees? Several disruptive companies like Opendoor, REX, Redfin, and Zillow are doing just that.

Opendoor is what is called an “iBuyer,” where it makes cash offers for your home at an algorithmically determined price, often buying as-is condition. It allows you to “take the money, buy your next home, and move out at whatever date works best for you. The transaction closes in a matter of days” (Andrews, 2019). Opendoor, which focuses on homes in good conditions, will make any quick fixes to the home and then sell it on the open market. The process is (1) The user submits the property information, (2) The AI-driven algorithm of Opendoor will give an estimated price for the house, (3) The user schedules a video walkthrough of the property, and (4) Opendoor will make a final offer often determined by an AI algorithm (SeekingAlpha, 2021). The turnaround time from selling your home to buying a new one is remarkably fast at the expense of a slightly lower closing value.

Another innovative company is REX—a residential real estate hopeful that uses AI and big data to cut out the middlemen (agents) to provide a better experience for buyers and sellers . . . all for a fraction of the cost. The way it works is REX’s AI analyzes hundreds of thousands of data points to search for likely buyers for a specific home and targets buyers with personalized digital ads. Along with VR, REX deploys robots at open houses to answer any questions a person might have. These robots are able to answer over seventy five questions, even questions the typical agent wouldn’t remember off the cuff, like “When was the siding last done? How far away is the nearest school? When were the last renovations completed?” Critics of this AI-focused way of brokering a deal may argue that since buying/selling a home is such an emotional experience and important decision, having a robot versus a human would not be comforting. Because it cuts out the middlemen, it charges only a 2 percent commission instead of the typical 5 to 6 percent that real estate agents charge (Zhao, 2018).

Obviously, everyone wants lower fees, but a majority of homes sold still go through the traditional broker-agent process with higher commissions because people still don’t trust a computer to help make one of the biggest choices a person will make. Although not likely for decades, I’d be willing to bet that converging technologies like AI, VR, and blockchain will eventually displace a great majority of the two million real estate agents out there today. In agreeance, an Oxford University study estimates that artificial intelligence has a 98 percent chance of replacing real estate agents in the future (Paine, 2017).

Next, blockchain is yet another technology disrupting the need for a middleman. Through blockchain, smart contract assets like homes can be tokenized and traded, sort of like how bitcoin is. It allows people to buy and sell property quickly, securely, and transparently all for a small fee. Here are five of the main ways blockchain smart contracts will disrupt real estate as we know it.

First, blockchain will solidify real estate being on an online marketplace that connects buyers and sellers, similar to Zillow. With blockchain, you can trade real estate, just like you can with stocks. One company, ATLANT, is building a blockchain-facilitated platform that allows sellers to tokenize your property and subsequently liquidate your property via token sale.

Second, as mentioned previously, blockchain eliminates the intermediaries like brokers and agents through the digital ledger (Atlant.io, 2021). Blockchain can handle listings, payments, and documentation and can do so at a cheaper cost with the middlemen cut out.

Third, real estate has long been an illiquid asset, meaning it is not easy and fast for a transaction to conclude. With cryptocurrency tokens, it all happens in seconds.

Fourth, fractional ownership is possible on blockchain. It allows everyday people to get their beaks wet into investing in real estate, not having to be barred by big down payments and the time-consuming nature of investing in a property.

Fifth, blockchain is a completely decentralized and transparent technology. All housing data (property costs, home history, who owns the property) is stored publicly on the digital network. This is key when recollecting on the 2008 housing bubble crash when transparency and greed led to grave consequences.

Blockchain, like AI, will make real estate more efficient and cheaper at the expense of appraisers’, agents’, and brokers’ jobs.

Insurance

Welcome to the year 2030 as imagined through a middle-aged man named Ryan. His Alexa digital assistant calls up a Tesla robotaxi vehicle with full autonomous capabilities for a dinner with his friend. As he gets into the car, Ryan chooses to flip on the “drive mode” instead of staying in autonomous mode. The Tesla punches in the most efficient route and then relays that information to Ryan’s insurer. In just under a minute, his insurance assistant suggests a different route that has a lower chance of getting into an accident. Along this whole journey, his transportation insurance premiums are changing in real time based on the route, number of cars, driving or autonomous, and much more.

As he drives up the parking ramp near the restaurant, Ryan slightly hits the side of a pillar. He immediately throws it in park while the car’s internal sensors analyze the severity of the car damage. His digital assistant instructs him to photograph the front bumpers, the pillar, and the surrounding area a few times. In less than a minute, the dash-mounted screen notifies him of the accident damage detected by the AI and certifies that the claim has been approved; now Tesla robotaxi will either be transported to the shop for repairs or he can keep driving it based on the drone’s assessment.

While this story may seem totally distanced from our future reality, over the next decade or so, stories like this and the technology they feature will start to come to fruition, especially considering how all the aforementioned technologies or processes already exist and most could be in the hands of consumers today. With advancements in deep learning techniques such as neural networks, artificial intelligence and machine learning have the potential to be better insurance agents and support staff with perception, learning, reasoning, and problem solving.

In the age of AI, the insurance industry will undergo a transformation from its existing broken condition of being reactive to becoming proactive in real time, altering every aspect of the industry. Because insurance has so many moving components, adoption will be slow at first, but as agents, brokers, customers, banks, insurers, and suppliers grow more eager and capable of adopting technology, adoption will accelerate along with cheaper insurance.

The insurance industry is adopting AI quicker than most industries. A Genpact report found that “AI adoption is accelerating rapidly among insurers, with 87 percent of carriers investing more than $5 million in AI-related technologies each year. This is more than both banking (86 percent) and consumer goods and retail companies (63 percent). Plans for AI are becoming more extensive, and all our respondents expect to adopt AI within the next three years” (Genpact, 2021). Also, the disruption from COVID-19 is a leading reason the time lines for the adoption of AI dramatically accelerated digitization for insurers because as soon as the lockdowns started, insurance companies had to adjust to accommodate remote workforces, expand their digital capabilities, and upgrade their online platforms. While most companies (especially smaller ones) likely didn’t allocate much capital to AI during the pandemic, the increased use of digital technologies and willingness to change processes put the insurance industry in better shape to start to leverage the power of AI.

History of Insurance

Around 1750 BCE, the fundamentals of insurance were created by the Babylonians for merchants that engaged in trade via the Mediterranean, especially with Chinese traders. In the Code of Hammurabi (Babylonian law), a merchant who needed a loan to finance a shipment across sea would pay the loan lender a fee with the caveat that the loan would be cancelled if goods were lost or if the shipment got stolen (Lloyd’s, 2021).

Fast forward to the fourth century: rates and risk levels varied depending on the weather. There was greater risk in the winter when the winter seas caused havoc, thus rates were higher. And of course, loan rates were lower in the calm summers. The next key component to making insurance less risky to shippers was to “pool” the risk among all shippers because on the shipper side, a string of bad weather could bankrupt them. There was not much insurance innovation following the Babylonians until the 1600s when shipping between the New and Old World was established. Here is where the process of underwriting was created for the London coffeehouses, Lloyd’s of London—the establishment for shippers, merchants, and others wanting insurance. The underwriters at Lloyd’s would write out all the risks on the ship, weather, number of crew, length of trip, type of cargo being shipped, etc. Today, this very process of assessing risk through underwriting is now done through AI that can accurately assess risk with thousands of data points (Lloyd’s, 2021).

In 1927, Massachusetts became the first state requiring car insurance, but it wasn’t until the 1970s that insurance became ubiquitous across all states (Sims and Walker, 2021). That brings us today to an industry where autonomous cars and incredible safety threaten a $740 billion dollar industry (Borasi, Chhabra, and Kumar, 2020). If you’re riding in an autonomous car as a service with no driver present, do you need insurance?

Car Insurance

Insurance is essentially the weighted probability of certain events occurring. For car insurance premiums, an underwriter calculates them based on age, driving history, type of car, and location. Using the vast amounts of data they have on consumers, insurance companies work it out to receive a net profit across thousands of customers. Industry leaders Geico and State Farm have long made out like bandits with lots of drivers in an industry that hasn’t seen much change. With 1.35 million traffic deaths globally every year, it is evident that there is a great deal of risk in driving (CDC, 2021). But, as mentioned earlier in the book, autonomous cars will be safer and essentially remove the customer from the insurance equation since the car technology itself is the sole risk. This is why the accounting firm KPMG predicts the car insurance market could shrink by an astounding 60 percent by 2040 (Insurance Journal, 2017). In addition, on the premium side, auto insurers brought in “$200 billion of premiums, about a third of all premiums collected by the property-casualty industry last year. But as much as 80 percent of the intake could evaporate in coming decades,” say some consultants.

Since autonomous driving is both safe and cost-effective, it’s causing a big disruption in how insurance is calculated. Car companies like Tesla and Waymo have had thousands of test drives where they have been accumulating massive amounts of data. This data is key because they will then use that for insurance (the risk, premium, etc.). These old-school insurance companies are so far behind that they will likely never be able to catch up with the companies themselves, which is exactly why Tesla is creating its own insurance. Companies like Waymo and Tesla have plenty of technologies like lidar sensors, 5G, and computer vision to collect data points from, but if those technologies happen to fail, the fault is on the company. Waymo has decided to automatically insure passengers every time they take a Waymo trip.

Autonomous driving will also essentially eliminate the over 1.5 million people who get arrested for DUIs every year (BACtrack, 2021). There are just so many variables with emerging technologies like self-driving cars, so it is easy to see how cross-sectional industries like insurance will be changed forever. Auto insurance companies like Allstate, MetLife, and others will probably be extinct in seven or so years, and we will be left with insurance from the car companies themselves, or little to none. Until then, carriers will need to understand how the emergence of robotics in everyday life and across various industries will alter risk pools, change customer expectations, and enable new products to be conceived.

Peer-to-Peer Insurance & Crowdsurance

One big problem with health and life insurance is that healthy people are forced to pay insanely ridiculous premiums that ultimately are just covering the costs of the unhealthy people. In the averages “game” that is insurance, the healthy are often the losers in the model. Imagine a model where people who are proven to be very healthy decide to team up with similar health-conscious people to share data, pool risk, and self-insure?

This can happen, especially with the influx of wearable technology. Whether it is an Oura ring, Apple Watch, WHOOP watch, running watch, or simply an iPhone, almost everyone has access to data that shows their health. A group of healthy people could very well compare health data and pool their health insurance risk. That is a big problem for the current insurance system because when the low-risk people are gone, the law of averages on which the insurance model is built no longer works, as rates would go up. This model is being tested through a decentralized, blockchain-run insurance called crowdsurance that unites people in a community to minimize their potential financial risks.

With crowdsurance, there are no brokers, no intermediaries, and no lengthy claims processes. It utilizes artificial intelligence to analyze various situations and make decisions in minutes. By eliminating the middlemen and utilizing AI-chatbots instead, crowdsurance is able to control the entire pool of people that pay premiums and also file claims. The AI handles every part of the insurance process through a mobile experience that is simple and fast—ninety seconds to get insured, three minutes to get a claim paid, and zero paperwork.

The best example of this is a newly publicly traded company, Lemonade (LMND). Lemonade, all from an app, provides renters’, homeowners’, car, life and pet insurance to groups of people who pay premiums into a centralized pool. It’s a great model that does away with the traditional profit-driven insurance model where providers collect ridiculously high premiums (unpaid claims) as revenue. Its 100 percent digitized process works like this: If you insure your home or apartment with Lemonade, the premiums you must pay to the company are combined with other homeowners or renters (“peers”). Lemonade takes a flat fee of about 20 percent out of that pool. If anyone in your group makes a claim, money is paid out from the pool to repair their home or replace belongings. After a calendar year, the remaining premiums are donated to a charity chosen by you (Giveback policy). All this is facilitated through artificial intelligence and chatbots; a Lemonade bot even set a world record for processing a claim in three seconds (Lemonade, 2021).

Big Data for Insurance

Whether it’s an Apple Watch, VR Goggles, Oura ring, or WHOOP watch, wearable technology and computer vision sensors are on the rise, as experts estimate there will be up to one trillion connected devices by 2025 (Takahashi, 2018). Besides these being awesome devices, they will also provide an enormous amount of data to personalize how you pay insurance. Insurance carriers will be able to gain greater insights into clients, insure new products, personalize pricing, process claims in minutes, and help eliminate fraud. The advantage of this influx of technology and data into insurance is that it will without a doubt make it cheaper for healthy and responsible individuals. Do rates go up when you sneak a cigarette? Do they go down when you eat healthy? They should.

One way I see wearables and health care crossing paths is in promoting healthy actions. Right now, people may get a gift card or something futile through work programs, but imagine if we took this a step forward by incentivizing or penalizing health behavior with precise accuracy. For example, people who smoke cigarettes cost self-insured employers $6,000 per every employee who smokes. We have the technology to incentivize people to quit (Petsko, 2018). There are companies actively working to allow smokers to blow into a breathalyzer device to measure smoking levels daily, and they pay lower insurance or receive checks from their company for decreased smoking levels. At any rate, the future of insurance is all about using AI and technology to make “smarter risk selection and pricing decisions, root out fraud, settle claims more quickly, produce more accurate reporting and forecasting, and—most important of all—radically transform the customer experience” (Genpact, 2021).

Part 3

What’s Next?

Chapter 12

The Future of Work in the Age of AI

“I am very close to the cutting-edge technology in AI, and it scares the hell out of me. It is vastly capable of far more than anyone knows.”

—Elon Musk

So far, we have looked at how AI and emerging technologies are coming together to rapidly change several industries. With that change, like every tech revolution before, comes job displacement. Now, as touched on previously, we have always been able to create more jobs than we displace, but with AI, it’s much more disparate. The debate among experts about whether AI will destroy more jobs than it creates is evenly contested.

A Pew Research Center study asked 1,896 experts about the impact of emerging technologies. The results were that “half of these experts (48 percent) envision a future in which robots and digital agents will have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order” (Smith and Anderson, 2014). Even more prominent than the experts, “73 percent of US adults believe artificial intelligence will ‘eliminate more jobs than it creates,’” according to a Gallup survey (Reinhart, 2018).

So, while nearly half of all AI experts and three-quarters of US adults believe AI will eliminate more jobs than it will create, if you do a simple Google search, you’ll see almost every article saying otherwise. Further, a recent report from the consulting firm Deloitte found that among more than one thousand surveyed American executives, 63 percent agreed with the statement that “to cut costs, my company wants to automate as many jobs as possible using AI,” and 36 percent already believe that job losses from AI-enabled automation should be viewed as an ethical issue (Ford, 2019). Other than a few outspoken people like Elon Musk, a majority of those in Big Tech and the media have downplayed the significance of automation replacing people—often likening it to sci-fi fantasies.

For example, Steven Mnuchin when he was Donald Trump’s treasury secretary made the incredibly ignorant and irresponsible statement: “In terms of artificial intelligence taking over American jobs, I think we’re so far away from that that it’s not even on my radar screen. . . . I think it’s fifty or one hundred more years away” (Hatmaker, 2017). That statement is so preposterous it’s not even funny. When we have extremely powerful decision-makers like Mnuchin brushing off the accelerating artificial intelligence revolution, it creates a dangerous situation. As a country, we need to be proactive rather than reactive in terms of AI. We need a federally mandated plan to reskill millions of workers into different jobs before it is too late. We need policy-makers in Washington who understand AI and other emerging technologies and how best to utilize them in the 2020s and beyond because if we don’t, technology powerhouse countries like China who have been investing heavily into AI will have a huge global edge.

Further, leaders of this nation along with every corporation need to actively work on overhauling the education system to start to skill future workers with the right skills. For people in the workforce, there needs to be more investment in retraining programs, as, so far, retraining has flopped. Take a truck driver who is thirty five years old. When they get displaced, where do they go next? Yes, there will be some jobs that oversee the trucking fleet, but nowhere near the number of drivers out of a job. It is estimated that between one to three million truckers could lose their jobs in the coming years (Transportation.gov, 2021). Andrew Yang famously said, “You can’t turn truck drivers into coders,” and he is 100 percent right. Taking people who are vulnerable to being automated and retraining them with a couple of online courses makes sense in theory, but the data reveals that it doesn’t work; MOOCs (massively open online courses), the most common pathway to retaining, have shown that “90 percent of participants drop out within two weeks,” with a sample of forty one leading online courses (Rivard, 2013).

Other programs such as the US Department of Education’s Adult Dislocated Worker Program found that the evidence suggests that training funded by the Adult and Dislocated Worker Programs does not have positive employment impact in the thirty months after retraining (Forston, Rotz and Burkander, 2017). Most people who think we can turn truck drivers into coders have never done either job. This is part of the reason I argue we will see more jobs displaced than created over the next thirty years; many of the projections and studies assume that most workers who are displaced will be able to transition into a different skill or job. However, it’s not that smooth, considering how a majority of the people who will be displaced first have low skill levels, thus people with low educational attainment are most at risk.

Also, there is an issue with the projections that say X number of jobs will be created. It is very hard to quantify the advancements of AI, sensors, robots, and other technologies and their impacts on the workforce because they are not only in their early stages but they are constantly developing. It is much easier to project which jobs are going to be displaced because we can analyze existing technology and then match it to existing job responsibilities.

On the flip side of all this, Tesla CEO and technology visionary Elon Musk, who arguably has access to the latest technology, contradicted Mnuchin’s statements by saying that “robots will be able to do everything better than us.” Not only will automation become omnipresent, he noted, “It is the biggest risk that we face as a civilization. . . . There will certainly be a lot of job disruption” (Clifford, 2018). It is a bit concerning to me that a White House cabinet member is saying automation is nothing to fear while the richest and arguably most successful person in the world is saying it’s civilization’s biggest risk. It just shows why this topic is crucial to talk about; nobody really knows what will happen, but we need to be prepared for any outcome.

AI Displacement in Action

Throughout this book, we have looked at emerging technologies and how they will change various industries such as finance, health care, education, etc., but now, let’s look at how AI is already changing the workforce and what’s ahead.

Automation is already happening, but one thing to be clear about regarding this automation is that severe job displacement won’t happen until the latter part of the decade, as technology adoption is still in its early stages. Jobs in retail, sales, and customer service are all employment sectors that are ripe for automation. Examples of occupations likely to be automated include cashiers, waiters, cooks, warehouse workers, cleaners, customer service representatives, and essentially every retail job.

For the broader picture of how this impacts jobs, the overall employment of retail workers and sales account for 9.2 percent of the US workforce, or about 10.8 million workers in all. They have an average income of $11 per hour, or $22,900 per year. A good portion didn’t graduate high school, and even worse, their median age is thirty nine (Anderson, 2020). On the warehouse side, every year, Amazon has grown the use of robots in its distribution warehouses. It has about 200,000 Kiva robots, up from 50,000 in 2018, and this will grow as it continues to open up more warehouses and stores (Heater, 2019).

More and more retailers will also follow suit, as they will otherwise not be able to come close to competing with Amazon’s cost savings and speed. Warehouse picking is the most expensive and labor-intensive component for a majority of e-commerce distribution centers, but it is the least automated. Amazon, among other big companies, are developing robots to do the picking, which “could cut the labor cost of fulfilling online orders by a fifth,” according to Marc Wulfraat, president of consulting firm MWPVL International (Baskin, 2017).

The advantage robots have over humans is that they are immensely productive. They can move large racks and source products for shipping, and they reduce errors by storing large amounts of data in their systems. Not to mention, they can work 24-7 and don’t need insurance or bathroom breaks. Also, with machine learning, they will only get better over time at performing tasks; robots store information in the cloud and constantly improve as they begin to pick up on certain patterns and experiences.

Robots, autonomous transportation, virtual reality, artificial intelligence, machine learning, 3-D printing, drones, and the internet of things (like Amazon Alexa) are beginning to transform how businesses operate and the role human labor has. There is already evidence of this happening with blue-collar jobs, but the automation will soon be felt by white-collar jobs as well. In a recent survey, business executives were asked about their own hiring plans over the next five years in the context of increasing use of robotics. “58 percent of CEOs queried said they planned to reduce jobs, while only 16 percent said they would increase jobs” (Pew, 2019). CEOs of all people would know whether AI will increase or decrease jobs, and so that study does not look good for the argument of robotics creating more jobs than jobs being destroyed. For low-skilled labor, the executives said it will come down to costs and productivity—things like increasing the minimum wage will all but be the final nail in the coffin for human labor.

Some people argue that automation will be more of a tool rather than a replacement. While it is true that a lot of AI will be used as a tool for productivity and improved efficiency, it does so by decreasing the number of workers needed to generate an equal or higher level of output. The fact is, although some new jobs get created, the purpose of adopting new technology has never been to create new jobs; it just funnels people into different sectors, like the service sector, until the service itself can be automated.

Disruption from AI will not happen abruptly, when suddenly one day robots just steal all of our jobs like some people portray this change to be. Alternatively, it will start as job deskilling, in which innovative technology will make what was once good-paying work now a minimum wage or gig economy type job. As Andrew Yang describes it, “The story so far has largely been about the elimination of solid middle-class jobs requiring moderate skill levels and the emergence of lower-paying jobs in the service sector. Indeed, the economy’s propensity to create large numbers of low-wage service jobs has been one of the main factors that has led to the current low levels of unemployment” (Ford, 2019).

Another startling statistic is that in 1965, only 3 percent of men in the United States between twenty-five and fifty four (the age when they have had time to complete education but have not yet reached retirement) was neither working a job or even searching for employment. Today, that number is 12.2 percent (FRED. 2021). In layman’s terms, there are four times more working-age men completely out of the workforce, sitting around doing nothing. What is this number going to hit once AI takes their blue-collar jobs?

Overall, I tend to agree that in ten to fifteen years, 40 percent or so of jobs will be automated, and eventually new jobs that we can’t even imagine today will be created. But in the end, AI will displace more jobs than it will create—starting with mainly white-collar repetitive labor, subsequently followed by blue-collar labor. To give a few job-related time lines, a survey of AI experts by researchers at Yale University and Oxford University found that technical specialists believe a dramatic workforce transformation will take place over the next few decades. As noted in their report, “Researchers predict AI will outperform writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a best-selling book (by 2049), and working as a surgeon (by 2045). These experts believe there is a 50 percent chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years” (Grace et al., 2017).

The robots are coming.

Bottom Line

In the next five to ten years, we will see millions of routine jobs being displaced. And when I say displaced, that could mean 100 percent of the job being automated, or maybe 50 percent of the job. Also, a common talking point among those with a dystopian view of the future of work is that AI automating routine jobs means blue-collar factory workers. While it is true that blue-collar jobs will experience some automation, the bulk of routine jobs that can be automated are actually white-collar. That is because of the fact that artificial intelligence has proven capable of performing tasks that involve planning, predicting, learning, and problem solving—common descriptions of routine white-collar jobs.

The Federal Reserve has conglomerated about sixty two million jobs as routine—or approximately 44 percent of total jobs. The Fed calls the removal of these low- to medium-skilled jobs as “job polarization,” signifying that the rest of the jobs will be low-end service jobs and high-skilled human-centered jobs with not much in between (Shroff, 2019). This idea is right on par with the weakening of the middle class today and insanely rampant wealth inequality in the United States. And all you need to replace a white-collar worker is an algorithm or software.

In comparison, in order to displace a blue-collar worker, you need robotics, dexterity, and precise mechanical work. So when I say routine jobs are likely to be automated first, that is any job where there is a heavy amount of repetitive work involved—say back-office administrators, compliance workers, some HR specialists, entry-level computer programmers, copywriters, executive assistants, analysts, traders, researchers, salesmen, customer service representatives, and pretty much all tasks an intern does today. On the flip side, after routine white-collar jobs are displaced, blue-collar service workers will experience automation—grocery store workers, vehicle drivers, cooks, assembly line and warehouse workers, etc. It is important to note that historically, after every technological revolution where routine jobs were displaced, new routine jobs were created, so there was always room for people to jump into a new job. But with automation, the entire point is that eventually, all routine jobs will be replaced, so there will be too few routine jobs for all the people seeking employment.

The reason reskilling is so vital to keep our future society prosperous for the masses is that AI will create new jobs, but it won’t create new routine jobs because if it did, AI would just automate it. So, unlike the past, where if your job got automated, you could jump to another routine job, you will instead have to learn a brand-new, non-automatable skill that requires retraining.

My prediction is that from 2025 to 2035, millions of white-collar routine jobs will be displaced along with repetitive blue-collar workers. Then from 2035 to 2040, as AI technology improves, almost every routine job will be automated, and nearly 40 percent of existing blue-collar jobs today will no longer exist. In the 2040s and beyond, superintelligence/general intelligence will come within ten years of being possible, but by this point, society will adapt and create millions of new jobs for people to thrive in; life will be unbelievably different, yet much better. So, short term (five to twenty years), we will see automation of white- and blue-collar repetitive jobs followed by jobs with a human touch starting to get displaced, but then in the long term, America will solve these job displacement woes and continue to be the greatest place for opportunity.

Inequality

Right now, around the world and most prominently in the United States, income inequality is growing. Here is the proof—the wealth gap between America’s richest and poorest families more than doubled from 1989 to 2016, and middle-class incomes have grown at a slower rate than the upper-tier incomes in the past fifty years (Schaeffer, 2020). More shockingly, the world’s richest 1 percent have more than twice as much wealth as 6.9 billion people, and shockingly, about half of the world lives on less than $5.50 a day (The World Bank, 2018). In order to combat wealth inequality, you’ll often hear “taxing the rich more,” raising the minimum wage, and investing more in education. Of those three, none are going to solve the problem.

First, the money potentially gained by taxing the rich or instituting a wealth tax is difficult to measure since a majority of the top 1 percent hold and make their wealth through capital markets where capital gains laws allow for investments to not be taxed until you realize the gains; essentially, if you change the tax structure of capital gains, it will hurt everyone.

Second, raising the minimum wage is a positive policy change in the short term to close the wealth gap and get people back to work post-pandemic. However, looking ahead, the benefits of raising it would be short-lived since a lot of minimum wage jobs are going to be automated; it would lead to people pursuing jobs that won’t exist. Also, raising the minimum wage pushes companies toward automation; a National Bureau of Economic Research study found that based on CPS data from 1980 to 2015, “increasing the minimum wage decreases significantly the share of automatable employment held by low-skilled workers, and increases the likelihood that low-skilled workers in automatable jobs become non-employed or employed in worse jobs” (Lordan and Neumark, 2018).

Third, the long-term answer to wealth inequality will not be resolved with regulation, stimulus checks, or taxation, but by our capacity to give everyone an education system built for the age of AI. This means a complete overhaul of education from kindergarten through college and not relying on measly online retooling courses to retrain people. We have to be preventative rather than reactive to automation by looking for ways to incorporate AI into everything we learn. We need to ask: How do we teach individuals to utilize the power of emerging technologies to innovate and thrive? How do we better teach critical thinking and constant learning?

In order to improve retooling, companies facilitating learning and retooling programs from within will be crucial to stave off massive job loss; companies like JPMorgan and AT&T are doing a great job of working with schools to update the curriculum they teach as well as helping current employees upskill into important jobs in data science and analytics. “Ninety percent of maturing companies expect technology enabled automation, but only 44 percent are adequately preparing for it” (Caminiti, 2018). As the gap between highly skilled workers and others continues to increase, the existing problems of inequality are going to increase exponentially with artificial intelligence.

Automation will continue to exacerbate the inequality gap due to the algorithmic nature of our workforce, where there are far fewer opportunities for career advancement. We often hear inspiring stories of the self-made man or woman who went from rags to riches. The past fifty years or so, this type of vertical advancement has always been at least feasible. Former McDonald’s CEO Charlie Bell started as a burger flipper, Doug McMillon of Walmart unloaded trucks at its distribution center, the CEO of Planet Fitness started as a part-time front desk worker fielding calls, and former Harley Davison CEO Jim Ziemer started out as a freight elevator operator (Hardesty, 2011).

Now think of the rise of the gig economy and outsourced labor; will any of them ever have the opportunity for advancement in their careers? How many DoorDash workers will ever get a salaried job? In automation-heavy companies, the separation between the C-Suite and the entry level temporary workers has never been greater. Take any sector where automation is taking place—financial services, supply chains, or consumer services—and what we are seeing is they are beginning to be run by a smaller number of well-paid employees with the assistance of automation and low-paid, highly replaceable labor on the outside looking in.

Over the past couple of decades, we have seen technology advances be a component of inequality because they tend to lead to a small handful of winners. For example, a recent study from UCLA investigated how technology affects wages over a period of years. What they discovered was that technology coincided with an increase in wages across the labor market, whereas the average employee saw wages rise by just 2.3 percent, those in managerial positions saw a 9 percent rise, and those in the boardroom saw an even more impressive 19 percent boost to income (Gaskell, 2019). With artificial intelligence displacing labor and subsequently wealth, it is very easy to see how inequality will continue to rise. Big tech company executives at Amazon, Google, Apple, Facebook, etc. will still have big paychecks, but they are actively working at automating all their low-wage earners.

Universal Basic Income (UBI)

We are years away from massive job displacement, but if or when it occurs, trying to come up with solutions on how to structure our economy and give people prosperous lives will be one of the biggest challenges the country will have ever faced. If my theory that more jobs will be displaced than created holds, UBI or some form of stimulus check will in all likelihood be the go-to solution. In fact, many business leaders and politicians are already foreshadowing the effects AI will have on the working class; recently, Andrew Yang, Elon Musk, Bill Gates, Barack Obama, and even Warren Buffet have stated that someday we will likely need some form of income payment to Americans (Domanska, 2018). This is often called universal basic income (UBI)—which is similar to the stimulus checks that were provided during COVID-19—to counteract the effects of AI-caused job displacement.

There are pros and cons to UBI, but I’d be willing to bet UBI will be a policy enacted by the United States. Proponents of UBI believe it would be a great way to help low- and middle-class wage earners who are seeing wages decrease. The common argument against UBI is that it will turn everyone (more people than today) into loafers who just live off the government and people will simply blow all that money on gambling or entertainment rather than on something productive. This is because of a phenomenon called the Peltzman Effect, which is coined after research from a professor, Sam Peltzman, at the University of Chicago who studied auto accidents.

Peltzman discovered that when you establish more safety features like seat belts or blind spot mirror sensors, the number of fatalities and injuries stays the same. This is because people compensate for it (risk compensation). People become less careful when they feel more protected, so they take more risks (The Decision Lab, 2021). With UBI and receiving additional economic comfort, more people will use that added income to do things that benefit themselves and society, like starting that business they always wanted to, investing in stocks, and taking more risks overall. UBI would also allow those with low-wage jobs or no jobs to maintain some purchasing power, thus keeping demand for goods and services.

However, going back to the counterarguments to UBI—a part of me believes giving someone a check every month is not a sufficient solution to job displacement or income reduction because of two things: (1) poor spending and (2) loss of purpose.

First, numerous people who have had a tough break in life would really use this added income in a productive way, but there is also evidence that many of the people struggling today just aren’t spending this extra income how we would think. In the United States, the average person making less than $13,000 a year is spending 9 percent of their income, or a whopping $1,200, on lottery tickets (Blodget, 2012). Further, government welfare for the most part hasn’t worked as intended. Many supporters of UBI make the claim that people will use their UBI checks to make art and do productive things, but that isn’t what people have been doing with their welfare, disability, or stimulus checks. For example, the rise of the terrible opioid epidemic and the drug and alcohol problem in the United States is concentrated mainly in the same demographic that heavily receives checks from the government and also the same people who are at highest risk for automation.

Second, when people get laid off or displaced from their jobs, they also lose a sense of purpose. Contrary to cultural belief, I truly believe most people don’t just want to sit back and receive a check and not work. At the end of the day, a sense of purpose is about happily adding value to the world. The loss of purpose in the future comes from two sources: (1) people working jobs where the work is deemed pointless and (2) people being unemployed, struggling to find work, and losing all sense of purpose no matter the stimulus checks being thrown at them. UBI on its own will not give people a sense of purpose; it is in having a career along with UBI that people can find meaning through their labor.

For example, with the recent COVID-19 stimulus checks, many of my friends used the money to invest in Roth IRAs or tax-advantaged retirement accounts. They all work full time, but after student loan bills and basic necessities, there just isn’t a whole lot to tuck away for investing. Their COVID-19 checks compounded at 7 percent until retirement age will be worth nearly $1.5 million—essentially setting them up nicely later in life. This quite possibly never would have happened at such a young age had it not been for the stimulus.

Even today, a lot of people have adopted nihilism as a philosophy for life where everything is “woe is me, nothing matters, I’m not going to pursue my dreams.” In the movie Fight Tyler Durden said something that describes much of society today: “We’ve all been raised on television to believe that one day we’d all be millionaires and movie stars and rock stars, but we won’t, and we’re slowly learning that fact, and we are pissed off” (Fincher, 1999). It’s the cop-out when times get tough that “nothing matters, so I’m not going to push forward, and that if I’d never been born, no one would care.” A perfect example is church attendance declining and atheism exponentially rising—maybe in part because of this nihilistic approach. The point is that people now more than ever are longing for purpose. And once this displacement hits, our top priority needs to be amplifying individual autonomy and purpose.

Technology is Advancing Faster than Anything Before

In 2012, Google built a neural network, which mirrors the working of a human brain, that in just three days was able to spot pictures of cats. It stunned the tech world. A couple of years later, AI could transcribe speech, defeat the world’s best Go players, and much more. Today, machine learning is excelling at complex video games, powering autonomous vehicles, and talking to us every day though Alexa. As you can see, AI development is accelerating fast, so fast that “every three months, the speed of artificial intelligence computation doubles, according to Stanford University’s 2019 AI Index report.” In addition, the report found that the speed of artificial intelligence is outpacing Moore’s Law, which states we can expect the speed and capability of our computers to increase about every two years, and we will pay less for them (Saran, 2019).

The advances in AI, especially at this pace, are occurring exponentially faster than technologies before it. The reason the rate at which AI is accelerating is so crucial is because many fear that it is coming so fast that we won’t have the time to retrain and adopt to the automation. Do we have a plan in place for the fifty five-year-old truck driver who gets booted from his job and for whom it’s likely too late to spend a couple of years learning a new trade yet but doesn’t have enough income to retire?

The short-term goal of AI is to keep improving narrow intelligence, whereas the long term “goal” is general intelligence, or strong AI. This is where AI has the cognitive ability to surpass human intelligence. This type of AI is what we are used to seeing in sci-fi films where sentient robots are roaming the streets. The question on everyone’s mind is, When will this happen?

Trying to predict when world-changing technological feats such as superintelligence will happen is simply a shot in the dark. Some argue it won’t happen in the twenty-first century while others say it will happen in the 2030s. Based on hundreds of hours of research and interviews with some of the world’s top technologists, I am confident that we will see AI pass the Turing Test in 2033. Regarding technological singularity, or the point at which technology is uncontrollable, I don’t see it happening in 2045 like Kurzweil predicts; I’d guess that this will happen in the 2060s because human beings have gradually been merging themselves with AI for over a decade now—smart watches, computers, smartphones, digital assistants, etc. At some point this century, as both individuals and as a society, we will be more AI than human for the purpose of extending life.

The possibly of the singularity and strong AI prompted Stephen Hawking to proclaim “the development of full artificial intelligence could spell the end of the human race” (Cellan-Jones, 2014). It is quite possible that even if we were capable of creating general AI, it would be regulated and shut down immediately. The ethical concerns alone are likely some barriers, and the conversation about automation and AI ethics is an extremely important one to have. At the rate AI is accelerating, who is to say we can’t have a serious AI problem one day?

The past does not represent the future, especially at this time in human history. The exponential growth of technology makes the interval between technology singularity points closer and closer. The birth of life on Earth was 4.2 billion years ago, followed by the birth of reptiles 330 million years ago, then the invention of agriculture 10,000 years ago, the industrial revolution 250 years ago, computers 70 years ago; the next big change is coming soon.

How to Prepare for the Future: Upskilling, Learning, Career Advice

The best jobs in the future will be ones that are not repetitive and involve humans interacting with each other in a creative way. Jobs that will most likely be safe in the short term are data scientists or AI engineers writing AI software; then, eventually, the AI will just write its own software. It’s not exactly clear what specific jobs will be automated, but building a skill set around problem solving and critical thinking is the best way to prepare.

Learning: As touched on earlier in the book, education today as we know it is completely broken. If you want to be successful today, you’ll require continual learning far beyond the classroom. Don’t fall to the pressure of societal norms to automatically enroll in college, but if that is the path you take, consider STEM (Science, Technology, Engineering, and Math), as those careers have high wages and are relatively safe from the fears of automation. As far as industries go, the health care and social assistance sector is expected to grow the most at a projected annual rate of 1.9 percent, or around 3.8 million total new jobs over the decade. That is about one-third of all the positions expected to be created (West, 2018). I think working in health care will be the safest and most valuable industry of them all, specifically in nanotechnology, synthetic biology, and personalized medicine.

Reskilling: Start with taking one to two Coursera or Udemy courses on data science, artificial intelligence, basic programming, and data analytics. I know online courses aren’t the most enjoyable learning experience, but it is the best we’ve got; taking them will help you at least develop a cursory understanding of the future. Back in 1920, machines took over jobs with hands. But today, AI is taking over jobs with brains. That’s why the future of job displacement is different today; you must be a learner to compete.

Career Advice: This may offend some, but in today’s economic environment, if you can’t find a decent job that satisfies you, then it is all on you. Coming out of the pandemic, companies are feverishly trying to fill job openings, and job displacement is still a few years away. At least in the current economy, I’ll never buy the excuse, “Nobody is hiring” or “There are no jobs I want.” Yes, thousands of companies are hiring, likely for the role you are seeking, but the problem is the approach to job searching.

Career counselors in college will tell you to visit job postings, send in a nicely formatted resume online, and hope for the best. Don’t do that. The best way to apply for a job is to network and cold email the recruiter or even CEO of the company you want to work for. Every job and business opportunity I’ve ever received has come from cold emailing someone or social media, but mainly cold email; it is a powerful tool. Keep the messages short and personalized, have an opening greeting, then convey the value you can bring in one sentence and consider offering to work for free for a month to prove your value. A message to the unemployed desperate for a nice job: send two hundred or even one thousand cold emails to the CEO and head HR recruiter at the company you want to work for, and I can guarantee you’ll get interviews and likely job offers.

In a time when we have a global pandemic, high unemployment, divisive politics, increasing substance abuse, broken health care, climate change, and surging crime, it may feel like the world is going to hell, but it’s not. In the past century, there has been a huge amount of progress that has taken place, and many people have taken it for granted. I encourage everyone to keep an open mind when it comes to AI and other emerging technologies, as they will greatly enhance society. There is no doubt AI will take jobs and likely cause harm to the broader society in some form for a couple of decades as we adapt to the change. However, I have no doubt the United States under a capitalistic society will continue to be the best place on Earth for opportunity and freedom to live a fulfilling life.

The future depends on what you do today, and nothing is guaranteed! If you are not willing to adapt and learn, you will be left behind. Be a winner, be an optimist, be a learner, be a kind person, and always live life with no regrets.

Acknowledgments

First and foremost, I’d like to thank everyone who read this book. My whole goal in writing this was to inform and inspire people to become aware of how technology is rapidly changing the world; I hope I did that.

In creating this book, I had the unique opportunity to interview CEOs, industry leaders, professors, and many people in the technology ecosystem.

To all the Super Donors who contributed to my book campaign: Kevin S., Mitch P., Allie Johnson., Eric K., John J., Lauren B., and Dan D.

Finally, thank you to the team at New Degree Press, and especially Eric Koester for reaching out to me about writing a book. It was a lot of work, and I learned a lot, but it never could have happened without a great team. Thank you.

Appendix

Introduction

• Bukszpan, Dan. “Here’s How Amazon Robots Could Make the Deliveryman Extinct.” Last updated May 1, 2019.

• Copeland, B.J. “Artificial Intelligence.” Encyclopedia Last updated August 11, 2020.

• Manyika, James, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi. “Jobs Lost, Jobs Gained: What the Future of Work Will Mean for Jobs, Skills, and Wages.” McKinsey & Company. November 28, 2017.

• Smith, Aaron, and Janna Anderson. “AI, Robotics, and the Future of Jobs.” Pew Research Center. August 6, 2014.

• Tencer, Daniel. “85% of Jobs that Will Exist in 2030 Haven’t Been Invented Yet: Report.” HuffPost July 14, 2017.

AI and the History of Jobs

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• Garner, Robert. “Early Popular Computers, 1950 - 1970.” ETHW. June 2021

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AI Today

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• Callaham, John. “What Is Google Duplex and How Do You Use It?” Android May 21, 2021.

• Cimino, James D., Richard G. Stefanacci, and Matthew Pakizegee. “Alexa, Can You Transform Healthcare?” Managed HealthCare Executive. February 26, 2020.

• Cutsinger, Paul. “Mark Cuban: Voice, Ambient Computing Are the Future. Developers Should Get in Now.” Alexa Skills Kit March 25, 2019.

• Dadashi, Nastaran. Automatic Surveillance and CCTV Operator Workload. PhD diss., University of Nottingham, 2008.

• Demush, Rostyslav. “A Brief History of Computer Vision (and Convolutional Neural Networks).” Hackernoon (blog). February 27, 2019.

• “The History of Face Recognition.” Accessed June 26, 2021.

• Gorey, Colm. “AI Is Now Better Than Human Doctors at Predicting Death or Heart Attacks.” Silicon Republic, May 13, 2019.

• Hartford Healthcare. “Startups in Digital Health CT Cohort.” Accessed June 26, 2021.

• Huddelston, Tom. “Here’s What Bill Gates Said about the Internet in a Microsoft Internal Memo 25 Years Ago Today: It’s a ‘Tidal Wave’.” CNBC. Last updated May 26, 2020.

• Islam, Kh Tohidul, and Ram Gopal Raj. “Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.” NCBI. April 13, 2017.

• Leadership 4iR (blog). “AI Robot Candidate to Run for Mayor in Japanese City.” April 19, 2018.

• Lyons, Kim. “A College Student Used GPT-3 to Write Fake Blog Posts and Ended up at the Top of Hacker News.” The Verge, August 16, 2020.

• Metz, Cade. “In a Huge Breakthrough, Google’s AI Beats a Top Player at the Game of Go.” Wired, January 27, 2016.

• Novet, Jordan. “Facebook Is Using AI to Help Predict When Users May Be Suicidal.” Last updated February 22, 2018.

• Petroff, Alana. “Google CEO: AI Is ‘More Profound Than Electricity or Fire.” CNN Business, January 24, 2018.

• Werner, Fredric. “Artificial Intelligence: the music industry’s newest producer.” ITU News, May 17, 2019.

The Future of the Truck Driving Industry

• Baker, Linda. “Study Finds Tusimple Trucks at Least 10% More Fuel Efficient than Traditional Trucks.” Freightwaves, December 18, 2019.

• BLS. “Worker Displacement News Release.” Last modified July 15, 2016.

• Bui, Quoctrung. “The Most Common* Job in Every State.” NPR, February 5, 2015.

• D’Orazio, Michael, Cornelius Bittersohl, Max Rasovsky, and Raul Kraus. Refueling Autonomous Southfield: Umlaut, February 2020. https://www.umlaut.com/uploads/documents/2020-02-06-Refueling-automated-and-autonomous-trucks.pdf

• Edmondson, Catie. “What Does a Trucker Look Like? It’s Changing Amid a Big Shortage.” New York Times, July 28, 2018.

• Hernandez, Richard. “The Fall of Employment in The Manufacturing Sector.” August 2018.

• Hoopes, Stephanie. ALICE in Pennsylvania: A Financial Hardship Report. United Way Pennsylvania: March 2019.

• International Trucks. “Navistar and TuSimple Form Strategic Partnership.” May 2021.

• Kitroeff, Natalie. “Robots Could Replace 1.7 Million American Truckers in the Next Decade.” Los Angeles Times, September 25, 2016.

• Solon, Olivia. “Self-Driving Trucks: What’s the Future for America’s 3.5 Million Truckers?” The Guardian, June 17, 2016.

• Viscelli, Steve. “Driverless? Autonomous Trucks and the Future of the American Trucker.” UC Berkeley Labor Center, September 2018.

Self-Driving Cars

• Adams, Rod. “Congressmen Wanted to Control Gasoline (Parable about Atomic Power).” Atomic Insights. February 23, 2016.

• Alonzo, Isaiah. “Tesla Q3 Safety Report: Autopilot 10x Safer Than Any Car in Us, Reduces Accident by 90%.” Tech Times, November 1, 2021.

• Brenan, Megan. “Driverless Cars Are a Tough Sell to Americans.” Gallup, May 15, 2018.

• Brown, Laura Sky. “Was Tesla Model S in Texas Crash on ‘Autopilot’? Musk Car and May 10, 2021.

• Davies, Alex. “An Oral History of the 2004 Darpa Grand Challenge.” Wired, August 3, 2017.

• Fagnant, Daniel, and Kara Kockelmanm. “Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations for Capitalizing on Self-Driven Vehicles.” University Texas at Austin. 2015.

• “Average Annual Miles per Driver by Age Group.” Last updated March 29, 2018.

• Ford Media “Ford Targets Fully Autonomous Vehicle for Ride Sharing in 2021; Invests in New Tech Companies, Doubles Silicon Valley Team.” August 16, 2016.

• Genovese, Daniella. “Tesla’s Elon Musk on Why Self-Driving Cars Are Not Mainstream Yet.” Fox April 30, 2021.

• Johnson, Ben. “The Great Horse Manure Crisis of 1894.” Historic Accessed June 24, 2021. https://www.historic-uk.com/HistoryUK/HistoryofBritain/Great-Horse-Manure-Crisis-of-1894/

• Kolodny, Lora. “Elon Musk Claims Tesla Will Have 1 Million Robotaxis on Roads Next Year, but Warns He’s Missed the Mark Before.” April 22, 2019.

• NTSB. ”Preliminary Report.” May 12, 2021. https://www.ntsb.gov/investigations/AccidentReports/Reports/HWY21FH007-preliminary-report.pdf

• Peng, Yachun. “Automotive Experience 2020.” Accessed June 2020. https://www.behance.net/gallery/36956749/Automotive-Experience-2020

• Preston, Benjamin. “Here Are Automakers’ Plans for Adding More Electric Vehicles to Their Lineups.” Consumer Last updated June 16, 2021.

• Rapier, Graham. “A Waymo Executive Explains Why the Self-Driving Startup Wants Its Rides to Be ‘Amazingly Boring.’” Business Insider, October 23, 2019.

• Reuters Self-Driving Vehicles Cover 20 Million Miles on Public Roads.” Reuters, January 7, 2020.

• University of Cambridge. “Electric Cars Better for Climate in 95% Of the World.” March 23, 2020.

• Wong, Samantha. “Estimated Annual Cost of AV Ride-sharing and Car Ownership in 2017 and 2027.” March 19, 2021.

• Wood Mackenzie. “700 Million Electric Vehicles Will Be on the Roads by 2050.” February 8, 2021.

• World Health Organization “Road Traffic Injuries.” June 21, 2021.

Flying Cars (eVTOLs)

• Bellamy, Woodrow. “eVTOL Investments Will Continue Billion Dollar Trend in 2021.” Avionics International, February/March 2021.

• Joby. “Joby Aviation Named to Fast Company’s Annual List of the World’s Most Innovative Companies for 2021.” March 9, 2021. https://www.jobyaviation.com/news/joby-aviation-named-to-fast-companies-annual-list-of-the-worlds-most-innovative-companies-for-2021/

• Kimmelman, Michael. “Taking Parking Lots Seriously as Public Spaces.” New York January 6, 2012.

• Kotler, Steven, and Peter Diamandis. “How Innovation Can Bring Us a Solution to the Stress of Sitting in Traffic Wisdom.” Thrive Global, January 31, 2020.

• Kuhn, Brittany. “Unleashing eVTOL Technology.” Inside Unmanned Systems, July 14, 2020.

• Nice, Karim, and Jonathon Strickland. “Gasoline and Battery Powered How Stuff Works. Accessed June 22, 2021.

• NSC Injury Facts. “Odds of Dying, Brief.” Accessed June 28, 2021.

• NSC Injury “Odds of Dying, Data Details.” Accessed June 28, 2021.

• Stuckey, Alex. “NASA to Test Uber’s ‘Flying Car’ Concept in Dallas.” San Antonio Express Last updated May 9, 2018.

• UN. “68% Of the World Population Projected to Live in Urban Areas by 2050, Says UN.” May 16, 2018.

• USHST. “Slight Change in Total US Helicopter Accidents During 2019.” AviationPros, February 4, 2020.

• Weisfeld, Daniel. “Peter Thiel at Yale: We Wanted Flying Cars, Instead We Got 140 Characters.” Yale, April 27, 2013.

Finance and Cryptocurrency

• Bajpai, Prableen. “Countries Where Bitcoin Is Legal and Illegal.” June 12, 2021.

• Burton, James. “How Digital Wallets Provide Investors with $4.6 Trillion Growth Potential.” Fintech News, May 17, 2021. https://www.fintechnews.org/how-digital-wallets-provide-investors-with-4-6-trillion-growth-potential/

• CoreLedger (blog). “Land Registry on Blockchain.” January 22, 2020.

• Economic Inclusion. “2017 Household Survey Results.” 2017.

• Finclusion. “Senegal: Financial Inclusions Insights from Kantar.” Accessed June 28, 2021.

• Hamid, Sarah. “Helping the 1 Billion People Without Proof of Identity.” Veriff. October 30, 2019.

• Isidore, Chris. “Machines Are Driving Wall Street’s Wild Ride, Not Humans.” CNN Business, February 6, 2018.

• Locke, Taylor. “Mark Cuban On Blockchain: ‘It’s like the Early Days of the Internet’ When a Lot of People Thought We Were Crazy.” Last updated February 12, 2021.

• Lydon, Tom. “Digital Wallets: A Generational Opportunity for Fintech Investors.” ETF Trends. January 27, 2021.

• Matheson, Rob. “Study: Mobile-Money Services Lift Kenyans Out of Poverty.” MIT News, December 8, 2016.

• Nova, Annie. “Banks Will Collect More Than $30 Billion in Overdraft Fees This Year. Here’s How to Avoid Them.” Last updated December 1, 2020.

• Our Cultures (blog). “Are You a Part of Serious Unethical Banking Practices? We Have Alternatives for You.” Accessed June 22, 2021.

• The World Bank. “2: The Unbanked.” In The Global Findex Database 35-41. 2017.

• The Economist. “The Quest for Secure Property Rights in September 10, 2020.

• Town, Phil. “50 Warren Buffett Quotes on Investing, Life & Success.” Rule #1 Finance Blog. Rule 1 Accessed June 26, 2021.

• Young, Martin. “BTC Was Best-Performing Asset of past Decade by 1,000%.” March 15, 2021.

The Future of Education

• All-In “America is Failing Math.” May 21, 2021. Video, 25:53.

• Batelle, John. “Max Ventilla of AltSchool: The Full Shift Dialogs Transcript.” Newco Shift, July 13, 2016.

• Capelli, Peter. Will College Payoff?: A Guide to the Most Important Financial Decision You’ll Ever Make. New York: PublicAffairs, 2015.

• Deift, Percy, Svetlana Jitomirskaya, and Sergiu Klainerman. “America Is Flunking Math.” The Persuasion (blog). May 17, 2021.

• Esquivel, Paloma, Howard Blume, Ben Poston, and Julia Barajas. “A Generation Left Behind? Online Learning Cheats Poor Students, Times Survey Finds.” Los Angeles Times, August 13, 2020.

• Fleischner, Victor. “Stop Universities from Hoarding Your Money.” New York Times, August 19, 2015.

• Gladwell, Malcom (@Gladwell). “I was going to donate money to Yale. But maybe it makes more sense to mail a check directly to the hedge fund of my choice.” Twitter. August 19, 2015.

• Henchinger, John. “The Troubling Dean-to-Professor Ratio.” Bloomberg, November 21, 2012.

• Institute for the Future. Emerging Technologies’ Impact on Society & Work in 2030. IFTF, 2017.

• Jimenez, Laura, Michael Rothman, Erin Roth, and Scott Saragrad. “Blueprint for Accountability Systems for Alternative High Schools.” American Progress. June 15, 2018.

• Johnson, Joseph. “Internet Usage in the United States.” April 27, 2021.

• Johnson, Sydney. “California Math Curriculum Spurs New Controversy about Accelerated Learning.” Edsource. May 2021.

• Kelly, Jack. “US Lost Over 60 Million Jobs—Now Robots, Tech And Artificial Intelligence Will Take Millions More.” Forbes, October 27, 2020.

• Manyika, James, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi. “Jobs Lost, Jobs Gained: What the Future of Work Will Mean for Jobs, Skills, and Wages.” McKinsey Global Institute. November 28, 2017.

• McDonald, Tim. “Humanics: A Way to ‘Robot-Proof’ Your Career?” BBC, January 27, 2019.

• NAICU. “41% of Recent Grads Work in Jobs Not Requiring a Degree.” February 18, 2020.

• NYSUT. “Fact Sheet 19–17: Teacher Shortage in New York State.” November 12, 2019.

• OECD. “Chapter 4. How Did Countries Perform in Pisa 2018?” Accessed June 20, 2021.

• O’Shaughnessy, Lynn. “Federal Government Publishes More Complete Graduation Rate Data.” Cappex (blog). Accessed June 6, 2021.

• Partelow, Lisette. “What to Make of Declining Enrollment in Teacher Preparation Programs.” American Progress. December 3, 2019.

• Phan, Trung T. “Meet Synthesis: The Edtech Startup Scaling Elon Musk’s Ad Astra School.” The Hustle, April 12, 2021.

• Puckett, Katie. “The Future of Education.” The July 2017.

• Schwartz, Jeff. “Making Alternative Work a Meaningful Opportunity.” SHRM Executive Network Blog. March 16, 2021.

• Soave, Robby. “In the Name of Equity, California Will Discourage Students Who Are Gifted at Math.” May 4, 2021.

• Sonnrad, Nikhil. “Tim Cook Doesn’t Seem to Understand that Good Programmers Have to Learn English.” Quartz, October 12, 2017.

Health Care: Fixing a Broken System

• Alcock, Charles. “United Therapeutics Digs Deep to Back Longer-Range eVTOL Aircraft.” Futureflight. December 15, 2020.

• Butrous, Ghazwan. “Pulmonary Hypertension: From an Orphan Disease to a Global Epidemic.” NCBI. April 30, 2020.

• “NHE Fact Sheet.” Last updated December 16, 2020.

• “FDA Permits Marketing of Artificial Intelligence-Based Device to Detect Certain Diabetes-Related Eye Problems.” April 11, 2018.

• HRSA. “Organ Donation Statistics.” Last reviewed April 2021.

• Harrington, Rebecca. “How a Millionaire Saved Her Daughter’s Life—And Tens of Thousands of Others in the Process.” Business Insider, May 5, 2016.

• Kelly, Jack. “US Lost Over 60 Million Jobs—Now Robots, Tech And Artificial Intelligence Will Take Millions More.” Forbes, October 27, 2020.

• Lieberman, Marvin and Joanne Y. Zhang. United UCLA: May 1, 2015.

• MedicineNet. “Drug Approvals—From Invention to Market . . . A 12- Year Trip.” Accessed June 28, 2021.

• Metz, Cade. “India Fights Diabetic Blindness With AI.” New York Times, March 10, 2019.

• Miliard, Mike. “Google, Verily Using AI to Screen For Diabetic Retinopathy in India.” Healthcare IT News, February 26, 2019.

• eely, Tucker. “Martine Rothblatt: She founded SiriusXM, a Religion and a Biotech For Starters.” Washington Post, December 12, 2014.

• O’Brien, Jack. “Athenahealth Exec: Healthcare Tech Will Build on Telehealth, Remote Care.” HealthLeaders, June 23, 2021.

• Oxfam. “5 Shocking Facts about Extreme Global Inequality and How to Even It Up.” Accessed June 28, 2021.

• Prabhu, Nagi. “Use AI to Improve Administrative Operations First.” Medical Economics, September 10, 2020.

• Rege, Alyssa. “70% Of Americans Believe Healthcare Is in ‘State of Crisis,’ Poll Finds.” Becker’s Hospitals Review, June 15, 2019.

• Ringwall, Kris. “BeefTalk: An Ounce of Prevention Is Worth a Pound of Cure.” Accessed June 27, 2021.

• Senior, Melanie. “Beating the Organ Clock.” Nature Biotechnology. June 6, 2018.

• Sipherd, Ray. “The Third-Leading Cause of Death in Us Most Doctors Don’t Want You to Know About.” Last updated February 28, 2018. https://www.cnbc.com/2018/02/22/medical-errors-third-leading-cause-of-death-in-america.html

• Snow, Jackie. “How People with Disabilities Are Using AI to Improve Their Lives.” January 31, 2019.

• Stevenson, Abigail. “Highest Paid Female CEO: Race to Save My Daughter.” Last updated May 19, 2015.

• Terry, Mark. “United Therapeutics’ Martine Rothblatt Envisions Abundant Supply of Lung Transplants.” BioSpace, June 17, 2019.

Health Care: Drugs, Robotic Surgeons, and COVID-19

• Anderson, Leigh. “FDA Drug Approval Process.” Last updated April 30, 2020.

• Atom Limbs (website). Accessed June 28, 2021.

• BlueDot (website). Accessed June 28, 2021.

• Bondereud, Doug. “What Does the Future Hold for Robotic Surgery?” HealthTech, February 4, 2021.

• Brown, Allan. “Smooth Operator: Robot Could Transform Soft-Tissue Surgery.” AABME. August 14, 2017.

• GlobalTranz. “The History of Robotics in Manufacturing.” October 6, 2014.

• Hale, Conor. “New MIT Study Puts Clinical Research Success Rate at 14 Percent.” Center Watch. February 5, 2018.

• Intuitive. “Intuitive By the Numbers.” May 22, 2019.

• Jin, Wu, Allison McCann, Josh Katz, Elian Peltier, and Karan Singh. “The Pandemic’s Hidden Toll.” New York Last updated February 9, 2021.

• Lohr, Steve. “IBM Is Counting on Its Bet on Watson, and Paying Big Money for It.” New York Times, October 17, 2016.

• Nepogodiev, Dimitri, Janet Martin, Bruce Biccard, Alex Makupe, and Aneel Bhangu. “Global Burden of Postoperative Death.” The Lancet 393, no. 10170 (Feb 02, 2019): 401.

• Niiler, Eric. “An AI Epidemiologist Sent the First Warnings of the Wuhan Virus.” Wired, January 25, 2020.

• Paddock, Catharine. “Robot Performs Surgery on Soft Tissue Better Than Human Hands.” MedicalNewsToday, May 6, 2016.

• Whitten, Sarah. “This Is the Next Big Thing According to Mark Cuban.” CNBC. Last updated May 29, 2015.

Longevity

• ACL. 2019 Profile of Older May 2020.

• Agus, David. “David Sinclair.” Time, April 23, 2014.

• “The Immortal Jellyfish.” AMNH. May 4, 2015.

• Baker, Nashia. “New Research Suggests That Humans Could Live to 150, Twice the Current Average Life Expectancy.” Yahoo News, May 26, 2021.

• Buck “More Details on 500% Life-span Extension Highlight Buck’s Role in Moving Science Forward.” January 21, 2020.

• Buck “Five-Fold Life-span Extension in C. Elegans by Combining Mutants.” December 12, 2013.

• Casey, Michael. “Could a 200-Year-Old Whale Offer Clues to Help Humans Live Longer?” CBS News, January 5, 2015.

• Champigneul, Francois. “Jeanne Calment’s Unique 122-Year Life Span: Facts and Factors; Longevity History in Her Genealogical Tree.” Rejuvenation Research 23, no. 1, (February 17, 2020): 19–47.

• Diamandis Peter H. “Why We Age.” Peter H. Diamandis (blog). June 14, 2020.

• Johnson, David. “Ray Kurzweil’s Crazy Yet Somewhat Precise Predictions about the Future.” The Great Course Daily, June 1, 2021.

• Macrotrends. “US Life Expectancy 1950–2021.” Accessed June 27, 2021.

• McCue, Duncan. “Futurist Ray Kurzweil Predicts Computers as Small as Blood Cells.” Last updated January 25, 2016.

• Miles, Kathleen. “Ray Kurzweil: In the 2030s, Nanobots in Our Brains Will Make Us ‘Godlike’.” Noemag, October 1, 2015.

• Mitani, Shohei. “Comprehensive Functional Genomics Using Caenorhabditis Elegans as a Model Organism.” NCBI. October 11, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743858/

• “What Is Nanotechnology?” Accessed June 27, 2021.

• Pettit, Harry. “Russian Billionaire ‘100% Confident’ He Can Make Humans Immortal by 2045.” The Last updated July 30, 2019.

• “David Sinclair, PhD – The Joe Rogan Experience #1349.” September 19, 2019.

• Sharoni, Erin. “Q&A: How Harvard’s David Sinclair is Fighting Aging—and How You Can, Too.” Inside Tracker (blog). May 28, 2020.

• Unity Biotechnology. “The Science.” Accessed June 28, 2021.

• Van Der Linden Lab. “Why C. Elegans?” Accessed June 26, 2021.

• Zhang, Siwen, Li Fei, Tong Zhou, Guixia Wang, and Zhou Li. “Caenorhabditis elegans as a Useful Model for Studying Aging Mutations.” Frontiers In. October 5, 2020.

The Future of Real Estate and Insurance

• Andrews, Jeff. “Tech Companies Want to Fix It.” Curbed, March 21, 2019.

• Atlant.io (website). Accessed June 27, 2021.

• BACtrack (blog). “DUI Statistics.” Accessed June 27, 2021.

• Borasi, Pramod, Monica Chhabra, and Vineet Kumar. “Auto Insurance Market Outlook.” Allied Market Research. September 2020.

• CDC. “Road Traffic Injuries and Deaths—A Global Problem.” Accessed June 27, 2021.

• eXp World Holdings, Inc. “eXp Realty Named One of Glassdoor’s Best Places to Work; eXp Founder and CEO Listed on Swanepoel Power 200 List.” Intrado GlobeNewswire, January 13, 2021.

• eXp Life. “VirBELA’s Software Makes Remote Working Come Alive.” February 22, 2022.

• Genpact. “The Dawn of the Age of AI in Insurance.” Accessed June 27, 2021.

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