We’ve been working our way through the future, focusing on those sections of society with the most impact on our daily lives. So far we’ve covered transportation, healthcare, longevity, retail, advertising, entertainment, and education. In Part Three, we’ll broaden our scope to the wider topics of energy, the environment, and government. But what about everything in between? In short, what about the future of the rest of your life?
That future will certainly be different from the past. Technological acceleration appears to be leaving no stone unturned. From architecture and artistry to aviation and accounting, dozens of industries will soon be transformed. Yet, in the final two chapters of Part II, we’re going to turn our attention to four specific areas—insurance, finance, real estate, and food.
We’ve chosen this quartet for a number of reasons. For starters, three of these sectors—finance, insurance, and real estate—are top ten industries in America. In fact, when coupled with what we’ve already covered in Part II—excluding government and security, two topics we’re saving for Part III—these sectors round out our look at the main ways Americans make a living. In this chapter we’ll examine the first three. Then, in the next chapter, we’ll close out Part II with a look at the future of food, because it’s both foundationally important and wildly accelerating. Thus, in total, the sectors we’ll review in this chapter are the front runners in the race to reinvent the rest of your life.
Let’s dive in.…
In 1680, Edward Lloyd arrived in London. He was thirty-two years old and hunting for opportunity. He found one in coffee. Fueled by this then novel beverage, London’s coffeehouse scene was exploding. Over three thousand java shops were already scattered throughout the city. Was the marketplace too crowded for another competitor? Lloyd didn’t think so. In 1686, he launched his own establishment, Lloyd’s Coffee House, on London’s Tower Street.
At the time, London was driven by two economic engines: shipping and finance. Lloyd’s was located at the epicenter of both, tucked into a tiny area between the Tower of London and Thames Street. Given this location, from the beginning the shop was popular with merchants, sailors, and shipowners.
Back then, coffeehouses earned patron loyalty by offering a mix of caffeinated beverages, the latest news and information, and the opportunity to participate in heated intellectual debate. Lloyd’s offerings went further than most. To provide reliable and accurate shipping news to his customers, he established a network of correspondents in ports across Europe and published the news they gathered on tip sheets. Spending time at Lloyd’s meant gaining access to detailed intelligence on ships, cargo, and foreign events.
This big data/big caffeine combo was a big winner. By 1691, business was booming and Lloyd needed to expand. He moved his operation to 16 Lombard Street, across from the Royal Exchange and into the heart of the merchants’ quarter. This newer, much larger venue was tricked out with wall-to-wall blackboards and a central pulpit. The blackboards replaced the tip sheet. The central pulpit provided a place to announce maritime auction prices and real-time shipping news. And here, amidst black coffee and blackboards, Lloyd turned an idea first invented by the Babylonians into the foundation of the modern insurance industry.
Nearly four thousand years ago, the Babylonians developed a strategy for merchants sailing the Mediterranean. If a merchant took out a loan to fund a shipment, they would pay an additional sum in exchange for a guarantee: Should his goods be stolen or lost at sea, the lender would cancel the loan. In the fourth century BCE, loan rates differed according to the time of year. Prices were cheaper during the calm summer seas versus the more dangerous winter swells—that is, the Babylonians developed a risk-based pricing idea similar to the foundation of modern insurance.
Some two millennia later, this notion of risk-based, data-driven maritime insurance reached new heights in the confines of a certain London coffeehouse. The bankers who frequented Lloyd’s were willing to collect premiums in exchange for taking shipping risks. They dubbed this process “underwriting,” as bankers would literally write their names on the blackboard under the name of the ship and a list of the trip’s details: its cargo, crew, weather, and destination.
Today, some 320 years later, this idea of “underwriting” has grown into a multitrillion-dollar insurance industry. Lloyd’s humble coffeehouse evolved into the famed Lloyd’s of London, which generated 33.6 billion pounds’ worth of insurance premiums in 2017. Yet, driven by forces very similar to those that originally helped sculpt Lloyd’s—an upsurge in information and collaboration—the insurance industry is again about to be completely transformed.
Three major changes are under way. First, by shifting the risk from the consumer to the service provider, entire categories of insurance are being eliminated. Next, crowdsurance is replacing traditional categories of health and life insurance. Finally, the rise of networks, sensors, and AI are rewriting the ways in which insurance is priced and sold, remaking the very nature of the industry.
But let’s start with a simple question: If you’re riding in an autonomous car as a service, and there is no driver, do you need insurance?
Insurance is a game of averages. The industry’s basic business model is assess risk and set premiums—or, covering this much risk will cost this much money. With a large enough number of customers and long enough stretches of time, this averages out to a profit for the underwriter. Car insurance premiums, for example, are currently calculated according to the age and history of the driver, traits of the car itself, and where the driver and that car live. Get enough drivers involved, stay in business long enough, and the result is massive profit. But what happens over the next decade, when autonomous vehicles take to the road and change every aspect of that calculation?
Right now, human error sits at the center of auto insurance. People—distractible, emotional, and sometimes irrational—are responsible for 90 percent of the 1.2 million traffic fatalities a year. Yet, without humans in the driver seat, 90 percent of that danger gets removed. For an insurance industry built on assessing risk, this alone is a huge change.
Now take it a step further. Today, we insure the stuff we own. But autonomous cars shift us from car-as-property to car-as-service, removing the need for consumer-facing auto insurance altogether. Which is why the accounting firm KPMG predicts the car insurance market could shrink by an astounding 60 percent by 2040.
This shrinking has already begun. Waymo automatically provides passengers with insurance every time they step inside one of their vehicles. And it’s an assessment made with a confidence that comes from big data.
In 2018, Waymo vehicles had autonomously traveled some 10 million miles on public roads, and an additional 5 billion miles inside of a simulation. All of those trips were data-gathering missions, with the resulting information being used to train Waymo’s AI. The big deal here is both safety and a nearly unassailable market advantage. All that data puts Waymo far ahead of the competition. It means that our autonomous car transition hasn’t really begun and already traditional insurance companies are years behind the curve.
When we combine autonomous vehicle technology with smart traffic systems and sensor-embedded roads—two developments that have already begun rolling out—transit risks don’t just plummet, they mutate. For instance, if the LIDAR sensor that’s helping steer an autonomous car goes on the blink and causes an accident, who do you blame? Not the passenger. Maybe the carmaker. Maybe the LIDAR supplier. Or, who’s fault is it if your Waymo loses its 5G connection and suddenly can’t drive? Is it Alphabet, who owns the car; Verizon, who manages the connection; or OneWeb, who owns the satellite that provides that connection? What if an autonomous vehicle gets hacked or stolen?
While these questions remain to be answered, and certainly the scenarios they describe sound dangerous, it’s helpful to remember that today, we routinely give testosterone-laden teenage boys control of two-ton vehicles. Not to mention the fact that roughly 1 million people are arrested for DUIs each year. In other words, just like the old tech, the new tech comes with trade-offs. But this time around, one of those trade-offs might be the end of auto insurance as we know it.
Before the arrival of exponential technology, size was the ultimate insurance advantage. Again, it comes down to averages or, more technically, what it takes to calculate an average in the first place.
Statistically accurate actuarial tables demand a ton of data. To gather that ton, you need an army of customers. To find those customers, you also need an army of salespeople. To analyze the data generated by these two armies, you need, no surprise, another army, of statisticians. Managing all these armies takes another army. And, up until now, this law of large armies guaranteed insurance was a game played by giants.
It was also a game of statistics. In both health and life insurance, the premiums of the healthy cover the costs of the unhealthy. But the healthy end up paying unnecessarily high premiums for this privilege, making them the consistent losers of this particular game.
So what happens when the ultra-healthy tire of this arrangement and decide to use social media to find other ultra-healthy folks, share data, and self-insure? All it takes is a few people raising their digital hand and saying, “Hey, look at my genes, see how much I exercise, check out my Oura ring data, my Apple Watch data. If anyone else is healthy like me, let’s come together and do this.”
In the insurance game, when the lowest risk clients opt out, the statistics stop working. With the ultra-healthy gone from the pool, the risk curve shifts dramatically. To cover costs, either everyone’s rate increases or the insurance company goes bankrupt. But if everyone’s rate goes up, then everyone goes elsewhere for insurance, and, once again, the insurance company goes bankrupt.
Which is exactly what’s happening.
Introducing decentralized, peer-to-peer insurance, or what’s become known as “crowdsurance.” Crowdsurance eliminates the middleman. Instead of an insurance company, there’s a technology stack—an app connected to a database connected to an AI-bot. The stack oversees a network made up of people who pay premiums and file claims which the network then approves. To put it differently, the stack removes three out of the four armies needed to create an insurance company. And the only remaining army? The customers—the ones left to decide what to do with all the money they just saved on health and life insurance.
Take the New York–based Lemonade, arguably the best funded of today’s crowdsurance startups. Via an app, Lemonade brings together small groups of policyholders who pay premiums into a central “claim pool.” Artificial intelligence does the rest. The entire experience is mobile, simple, and fast. Ninety seconds to get insured, three minutes to get a claim paid, and zero paperwork.
Adding more technology to this arrangement, companies like the Swiss firm Etherisc sell “bespoke insurance products” on the Ethereum blockchain. Because smart contracts remove the need for employees, paperwork, and all the rest, all sorts of new insurance products are being created. Etherisc’s first offering is something not covered by traditional insurers: flight delays and cancellations. Individuals sign up via credit card, and if their plane is more than forty-five minutes late, they’re paid instantly, automatically, and without the need for any paperwork. And they’re only one example. The crowdsurance space is exploding. Brand new micro-insurance categories—boat-hull insurance, chihuahua insurance—are moving out of the planning stages and into the marketplace. To return to our historical terms, it’s as if the sailors who frequented Lloyd’s started making deals directly with the blackboards, and everybody else was just left to drink their coffee in silence.
Founded in 1937, Progressive Insurance started out by trailblazing a niche nobody wanted: high-risk drivers. Then, true to their name, they maintained their edge via technology. Progressive was the first insurance company to have a website, the first to allow customers to purchase policies through that website, and the first to dress up that site with high-quality video content and voice-over-internet tools. By pioneering apps for policy purchasing and management, they were also first into mobile. All of these developments helped modernize insurance and made Progressive one of the more profitable corporations in America. But in 2004, they became first into a different category, and this decision was a little more than progressive. In an actuarial kind of way, it was downright revolutionary.
Not at the beginning.
At first, all Progressive did was ask customers in Minnesota to volunteer for a research program, “TripSense.” Literally a black box, TripSense plugged into a car’s diagnostic port and tracked three variables: mileage, speed, and travel times. When completed, volunteers shipped the box back, and Progressive sent them twenty-five bucks for their trouble.
In 2008, this pilot went mainstream. Renamed “Snapshot,” this upgrade was designed to collect a single piece of information: vehicular speed at one-second intervals. Progressive then used that information to calculate two additional data points: miles driven and “hard-braking events,” such as a driver slamming on the brakes to avoid hitting a cat. Why? Because Snapshot had morphed from a pilot project into a radical idea: price auto insurance according to driving habits rather than driving history.
The technical term for this is dynamic pricing. The paranoid term: Big Brother is always watching. Either way, Progressive helped replace the traditional auto insurance model with sensors. Everything from driving speed and braking habits to radio volume and the number of other cars on the road can impact your rate. Drivers now get insured based on car usage rates (the less you drive, the less you pay), good driving trends (you consistently stay within the speed limit), and low-risk driving times (your daily commute does not take place after midnight).
This same trend is creeping into home insurance. Pricing used to be based on the state of the home at policy purchase, but 30 percent of all home claims are from water damage that occurs long after the policy is sold. Now insurance companies get real-time metrics using in-pipe temperature sensors and in-wall water detectors, and home owners are notified about potential problems well before they occur.
Thanks to all the data from our wearables, this same shift will soon arrive in health insurance. Insurance companies will suddenly have the opportunity to prevent disease before it happens rather than swoop in post-op to clean up the mess. The upside will be cheaper insurance for healthier living, the downside is Big Brother. Do rates go up when you sneak a cigarette? Do they go down when you eat your vegetables?
The term McKinsey coined to describe this kind of AI-driven, sensor-laden insurance is “pay-as-you-live,” morphing the traditional “detect and repair” role of an insurance company into “predict and prevent.” Your rates fluctuate with your choices in an almost entirely automated process. By 2030, the number of humans required to process a claim will drop by 70 to 90 percent, while processing times will shrink from weeks to minutes. This points toward a future where insurance companies become front-line guardians of the health of society, and quite a change from the days of Lloyd, his coffee, and his blackboards.
Look at the skyline of São Paulo, Hong Kong, or New York. Check out the tallest buildings, those iron monsters. Who owns that high-flying real estate? Insurance companies and financial firms. Why? For the same reason that Willie Sutton allegedly said he robbed banks—“Because that’s where they keep the money.”
Since we’ve already covered insurance, we’ll shift focus to the changes in banking and finance, where exponential technologies are steamrolling both industries, completely altering this business of money. We got a peak at this transformation earlier, when we decoded the dollars pouring into crowdfunding, ICOs, venture capital, and sovereign wealth funds. To understand what else is coming, let’s start with a simple question: What, exactly, do we do with our money?
We store it, of course. Mostly in banks. We also move it around, sometimes transferring cash between companies, other times borrowing or lending among individuals. Next, we invest it, trying to use our money to grow more money. Finally, since the time coins were conch shells, we trade it for the stuff we want. Thanks to converging exponentials, each of these areas is being reimagined, with bits and bytes replacing dollars and cents, and neither economics nor the way we live our lives will ever be the same.
Gunnar Lovelace grew up poor. He was raised by a single mother in an intentional community in California, and never forgot his family’s struggle to meet basic needs, chief among them food and money. Lovelace went on to become a serial entrepreneur, and while his first three startups were in tech and fashion, he poured those profits into his fourth venture, Thrive Market, or his attempt to solve the struggle for food.
A purpose-driven venture, Thrive uses eco-friendly packaging, zero-waste shipping, and nontoxic ingredients to deliver high-quality organic food at lower prices directly to the door of over 9 million consumers. But Thrive only solves the first of Lovelace’s problems, there’s still the struggle for money to consider. This brings us to his fifth company, Good Money, which is using this same value-driven approach to take on the storage side of the traditional banking industry.
Right now, most of our money sits in banks, and mostly, we’re abused for the privilege. On average, we pay $360 a year in banking fees. The larger banks, meanwhile, average $30 billion a year in overdraft charges alone. But where things go truly sideways is what the banks do with our money.
Banks get to invest our money, typically at significant profit, wherever they see fit. This often includes projects that don’t align with customers’ values. Wells Fargo, for example, lost a ton of business when they were outed as major backers of the controversial Dakota Pipeline. So while the bank is making bank, not only is your money not working for you, it might actually be working against you.
Good Money does the opposite, in a half-dozen different ways. Technically a mobile wallet, Good Money lives on your phone and holds both regular and crypto currencies. It can be used at any ATM, with zero annual fees, no ATM charges, and an interest rate a hundred times larger than most banks. Customers also become owners. Put money into Good Money and you get an equity share in return, while the company funnels 50 percent of their profits into impact investments and charitable donations.
With this strategy, Good Money is targeting people who prefer value-driven companies, and the 40 million Americans who have been driven out of the traditional banking system by overdraft fees and blackball lists. But the largest mobile market is a third category entirely, the unbanked, those without any place to store their money.
The issue is infrastructure, especially in poorer countries, where the cost of building and maintaining banks simply exceeds the value they can generate. The economics are upside down. Yet, while over two billion people in the world still lack bank accounts, nearly all of them have mobile phones. And this brings us to a Vodafone executive named Nick Hughes and one of the more stubborn of all international economic problems: microfinance.
At the 2002 World Summit for Sustainable Development, Vodafone’s Nick Hughes gave a presentation about risk. His goal was a long shot: convince large corporations to help poorer nations by allocating research dollars for high risk/high reward ideas. An official from the UK’s Department for International Development was in the audience. Afterward, he approached Hughes with an even more unusual proposition.
The Department had begun paying attention to mobile phone usage and noticed that in parts of Africa, people were treating mobile phone minutes as a quasi-currency, trading them for goods and services that would normally require cash. They saw potential here and, more importantly, had a million dollars to invest. If Vodafone agreed to match funds, the Department agreed to finance a pilot project.
Since borrowing money is one of the largest problems faced by the unbanked, their initial pilot project idea was microfinance. A micro-loan for a cow, motorbike, or sewing machine—that is, the startup costs for a small business—is often the start of the end of the cycle of poverty. By giving people a way to withdraw and repay their loan via cell phone minutes, the Department suspected they might jump-start entrepreneurship in countries that needed it most.
The result of this collaboration was M-Pesa, which was initially rolled out in Kenya in 2007. Without bank branches or ATMs, M-Pesa relies on an ancient technology: people. Individual agents sell cell-phone airtime in local markets, trading minutes for cash and vice versa. Customers load the airtime to their SIM card, then to their phone, turning the minutes into money, which can be sent to another via text message.
While microloans initially generated this plan, remittances turned it into a force. Being able to transfer money without banking fees allowed workers in the city to send money to relatives in the countryside—aka, remittances—which both saved them the 12 percent charged by the likes of Western Union and replaced the older method: giving a bus driver an envelope of cash and hoping for the best.
Eight months after launch, a million Kenyans were using M-Pesa. Today, it’s nearly the entire country. According to research done at MIT, 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.
Nor is it just Kenya. M-Pesa now provides banking services to over 30 million people in ten different countries. In places rife with corruption, it’s become a way for governments to protect against graft. In Afghanistan, it’s how they now pay the army. In India, it’s pensions. And it’s no longer just M-Pesa offering such services.
In Bangladesh, bKash now serves over 23 million users; in China, Alipay serves just shy of a billion. And like Good Money, Alipay has become a force for social good. More than 500 million customers play “Ant Forest,” earning points for making environmentally friendly decisions in their daily lives. These points are then redeemed for real trees planted in the real world. It’s become something of a national obsession. To date, more than 1 million trees have been planted.
Importantly, these developments upend the traditional arc of technology. Typically, cutting-edge ideas pioneered in Silicon Valley move from West Coast introduction to East Coast adoption to European investigation and finally, eventually, into the rest of the world. But this process has inverted, with developing world innovation becoming developed world disruption.
And more disruption is coming.
Banks occupy a rare spot in the economic ecosystem: All the infrastructure that money flows through belongs to them. As the trusted central repository, whenever anyone wants to move money around—lend it, transfer it, even give it away—banks can insert themselves into that process. Or, at least, until blockchain came along.
With blockchain, since trust is built into the system, the system is no longer necessary. Take a stock trade. Right now, to execute that trade, there’s a buyer, a seller, a series of banks that hold their money, the stock exchange itself, clearinghouses, etc.—roughly, ten different intermediaries. Blockchain removes everyone but the buyer and seller. The technology does the rest.
In an attempt to hold on to their thinning slice of the pie, every major bank is rushing into blockchain. Yet arguably moving faster are the thousands of entrepreneurs using blockchain to disrupt these same banks. Consider R3 and Ripple, two examples of developing world disruption impacting developed world businesses. In both cases, these companies are using blockchain to replace the SWIFT network, the standard protocol overseeing international banking transactions.
This inverted flow of disruption isn’t going to end any time soon. Over the next decade, 4 billion people, the rising billions, will gain access to the internet. Since all of them will need basic banking services, the opportunity is massive. But thanks to converging technology, the downstream result of Nick Hughes’s long shot is that nearly everyone but banks will end up capitalizing on it.
“Fintech” is the term for the convergence of technology and financial services. First colonized by networks and apps, it was then radicalized by AI and blockchain, and now serves as a global wealth redistribution mechanism. Think Robin Hood with a smartphone, taking cash away from banks and putting it back into the hands of customers.
Wherever large amounts of customer frustration encounter large piles of money, opportunity lurks. This gave rise to a company called TransferWise. By matching customers who have, say, pesos they want to turn into dollars with customers who want to change dollars into pesos, TransferWise is using a modified dating app to take on the entire foreign currency exchange market. In fact, because it’s easier to match people looking to exchange currency than it is to match people looking to date, the company reached a billion-dollar valuation in under five years.
Built on networks and apps, TransferWise is also an example of fintech’s colonization wave. The radicalized wave arose when AI entered the picture. Consider the age-old practice of “Buddy, can I borrow a dollar,” otherwise known as peer-to-peer lending. Traditionally, this is a high-risk practice—which is to say, Buddy rarely gets his dollar back. This problem only gets worse with scale. As villages turned into towns, towns expanded into cities, and cities began to sprawl, neighborly trust broke down. That’s where banks came into play—they added trust back into the lending equation.
But who needs trust when there’s data?
With AI, huge groups of people can come together, share financial information, and pool risk, becoming the peer-to-peer market now known as “crowdlending.” Prosper, Funding Circle, and LendingTree are three examples in a market expected to grow from $26.16 billion in 2015 to $897.85 billion by 2024.
A different example is the Smart Finance Group. Created in 2013 to serve China’s massive unbanked and underbanked population, Smart Finance uses an AI to comb a user’s personal data—social media data, smartphone data, educational and employment history, etc.—to generate a reliable credit score nearly instantly. With this method, they can approve a peer-to-peer loan in under eight seconds, including microloans to the unbanked. And the results speak for themselves. Roughly 1.5 to 2 million loans are taken out every month via Smart Finance.
AI is also making an impact on investing. Traditionally, this game was played by the wealthy because it’s a game of data. Financial advisors had the best data, but you needed to be wealthy enough to afford a financial advisor to access it. And advisors are picky. Since it can take more time to manage small investors than large investors, many wealth managers have investment minimums in the range of hundreds of thousands of dollars.
But AI has leveled the playing field. Today, robo-advisors like Wealthfront and Betterment are bringing wealth management to the masses. Via an app, clients answer a series of initial questions about risk tolerances, investment goals, and retirement aims, and then algorithms take over.
Actually, algorithms have already taken over. Every day, roughly 60 percent of all market trades are made by computer. When the market turns volatile, this can climb to as high as 90 percent. All robo-advisors have done is make the process available to the customer, and save the customer money as a result.
With no humans in the chain, fees are slashed. Instead of the typical 2 percent of profits charged by a wealth manager, most robo-advisors take around .25 percent. Investors are responding. As of January 2019, Wealthfront had $11 billion under management, while Betterment was at $14 billion. While robo-advisors still account for only roughly 1 percent of total U.S. investment, Business Insider Intelligence estimates that number will climb to $4.6 trillion by 2022.
Finally we come to our last category, using money to pay for things. But we already know this story. When was the last time you dropped coins into a toll booth? Or paid cash for a cab ride? In fact, Uber and Lyft allow us to get around a city without a wallet. Couple cashier-less stores like Amazon Go with services like Uber Eats and these wallet-less ways are about to become the new normal.
Denmark stopped printing money in 2017. The year prior, in an attempt to expand mobile banking and demonetize the country’s gray-market economy, India recalled 86 percent of its cash. Vietnam wants retail to be 90 percent cashless by 2020. Sweden, where over 80 percent of all transactions are digital, is almost there.
Economists often point out that two of the main factors that drive economic growth are the availability of money—the stockpiles we can draw upon—and the velocity of money, or the speed and ease with which we can move that money around. Both of these factors are being amplified by exponential technologies. The result is acceleration, a profound shift in who owns the real estate that dominates our skylines. In fact, in turning to real estate itself, we will see that those changing skylines are only the start of this transformation.
Our story begins in the Great Recession of 2008, where the reckless behavior of two familiar players—big banks and big insurance—plunged the country into chaos. Real estate was especially hard hit. The bottom fell out of the market, and kept on falling. The housing crisis ensued, and things went from bad to worse to downright miserable. Which is when Glenn Sanford had what really should have been a stupid idea: start a real estate company.
But it was a company built for the times. In the face of rising overhead costs and decimated revenues, Sanford decided not to do what real estate agents had always done: open an office. Ditching the brick-and-mortar model entirely, he created eXp Realty, the first cloud-based national real estate brokerage.
Devoid of real estate, Sanford built a fully immersive mega-campus using a virtual world platform called VirBELA (which eXp now owns). Today, eXp Realty’s campus is home to sixteen thousand agents from all fifty US states, three Canadian provinces, and four hundred major real estate markets—all supported without a single office.
Instead of coming into work, agents and managers stay home. Using either a VR headset or their laptop, they gather virtually, on a campus replete with a lobby, library, theaters, meeting rooms, and a sports field. Sanford, meanwhile, attributes at least $100 million of his company’s $650 million market cap to money saved via reduced infrastructure and overhead.
While Sanford’s reinvention has had significant consequences for the real estate industry, it was merely the result of a trio of convergences: computation, networks, and VR. Pile on what’s coming: AI, 3-D printing, autonomous cars, aerial taxis, and floating cities, and everything changes fast. And that includes the one component of the industry that Sanford left intact: the real estate broker.
Most people, if they’re lucky, get to buy a home. Typically, it’s once in a lifetime. Often, it’s the largest purchasing decision they’ll ever make, the biggest check they’ll ever cut, and for many, the most terror they’ll ever feel. We’re about to tell you a story about how AI is changing this process, but before we get there, it’s helpful to remember this is really a story about nervous people making hard decisions in the real world.
Truthfully, AI has been helping people make hard real estate decisions for a little while now. Zillow, Trulia, Move, Redfin, and many other companies have invested millions in the technology. Already, property searches, evaluation, consulting, and management are easier, faster, and more accurate than ever before. Investors now have analytical capabilities far beyond any human, blending established real estate variables such as rent, occupancy rates, and local school data, with novel inputs: Web clickstream data, satellite imaging, geolocation tracking, etc. And like the AI arms race in other industries, in real estate too, the companies with the best data will end up dominating the market.
If AI is already doing all this on the research side of real estate, why not let it handle brokering as well? Or, more specifically, why not let the convergence of AI, VR, and sensors become your broker?
In a future where always-on preference tracking is a standard feature of any personal AI, why hire a stranger to sell you real estate? Your AI already knows you, via your likes and dislikes, possibly better than you know yourself. We’re not long from the day when most of your real estate search—home, apartment, office, whatever—will be conducted from your couch, via a VR headset, with the help of your personal AI. It’ll be like talking to Siri if Siri were an interior designer: “Industrial modern loft, concrete floors, close to a Whole Foods,” etc. Your real estate AI will offer up selections that fit your criteria, while the VR headset makes taking a tour a 24-7 proposition.
As a seller, this means potential buyers can come from two miles to two continents away. As a buyer, each immersive VR tour is an AI learning experience. Advanced eye-tracking software follows your gaze, while voice recognition algorithms listen for pleasure or aversion in your vocal tone. Both add to your list of likes and dislikes, with the AI recommending better and better properties as your search goes along.
Curious what this living room might look like with a fresh coat of blue paint? No problem. VR programs can modify environments instantly, changing countless variables, from flooring to wallpaper to the sun’s orientation. Keen to input your own furniture into a VR-rendered home? Advanced AI could one day compile all your existing furniture, art, and books, then impose them on any virtual space, providing certainty in a place where we’ve never had much before. In essence, AI-driven, VR real estate platforms will allow you to explore any property on the market, do any remodel you’d like, then figure out if the home you dreamed up is really the home of your dreams.
“Location, location, location” is the real estate mantra, but there’s an important addition to this story: “Proximity, proximity, proximity.”
The value of your home is partially measured in its proximity to a half-dozen locations: a central shopping district, the best schools, your place of work, favorite restaurants, the homes of your closest friends, etc. But over the next decade, with the transportation transformation that’s coming, we’re changing the relationship between here and there. So what happens when driverless cars, flying cars, and the Hyperloop make proximity a possibility for all?
If Las Vegas to Los Angeles becomes a half-hour Hyperloop commute; if upstate Vermont to middle-of-Boston becomes a flying car ride away; if remote Virginia into Washington, DC, is an hour-long nap in an autonomous Uber, why not buy twice-the-house for half the price in outlying areas? Travel time can now be repurposed any way you like—sleep, meditation, conversation, take your pick. When previous geographically undesirable locations become easy to reach, proximity itself becomes democratized.
The point is that our definition of “prime real estate” is going to start to shift over the next decade. But this isn’t just about altering the relationship between here and there—it’s also about making more “here.”
Enter: floating cities.
Floating cities are a proposed solution to a trilogy of modern problems: rising seas, skyrocketing populations, and threatened ecosystems. In the five hundred coastal cities now threatened by global warming, they could offer what we need most: flood-proof, tsunami-proof, and hurricane-proof living. Plus, close to 40 percent of humanity already lives near the ocean, so, if successful, floating cities will create an abundance of prime real estate in places where none has existed before.
Earlier versions of this notion met considerable resistance, but in 2019, in the face of mounting climate change dangers, the United Nations decided the technology was worth a second look. One idea they’ve been considering is Oceanix City, a zero-waste, energy-positive design by Tahitian entrepreneurs Marc Collins and Itai Madamombe. It consists of a series of hexagonal islands arranged in a circle, with each of these self-sustaining 4.5-acre platforms able to house three hundred people, with the complete 75-acre property capable of sustaining up to ten thousand.
A second design by the San Francisco–based Seasteading Institute is being tested in the waters off French Polynesia. Known as the Floating Island Project, the idea here is less a floating city and more a test platform for the designs of future floating cities. With a hundred acres of beachfront property and a special economic zone for inhabitants, this project is aiming to have a dozen structures erected by 2021.
In both locations, sustainability is key. Water capture technologies provide drinking water; an array of greenhouses, vertical farms, and fish farms supply the food; and sunlight, wind power, and wave energy power the whole lot. Electric boats, or soon, autonomous flying cars, will zoom inhabitants to work on the mainland. Or, more importantly, maybe not. With drone delivery for supplies, and avatar commuting for business meetings, you may never have to leave the island.
Exponential technology is dematerializing, demonetizing, and democratizing nearly every aspect of real estate. Corporate infrastructure has gone virtual; brokers aren’t far behind. Location and proximity, the twin pillars of the industry, are next, arguably demonetized before this decade’s done. Today’s scarcity of bespoke addresses for the lucky few will become tomorrow’s prime real estate at affordable prices for the average many.
When you couple the changes in real estate to the remaking of finance and insurance, we’re seeing a drastic shift in both the skylines of cities and the business of business. By making these processes faster and cheaper, cutting out the middleman (or woman), and making these opportunities available to all, we’re reimagining three of the largest wealth creation engines in history. Which is, once again, another reason that the future will be faster than you think.