Wearing Masks and Asserting Meaning: Insights from the Neurocognitive Science of Cool

By Will McCreadie & Dr. James Giordano, PhD, MPhil. This piece was originally published by Georgetown’s Pellegrino Center for Clinical Bioethics on May 18th, 2020.

Four months ago, people wearing masks stood out. Now it’s those who don’t that often catch a sideways glance. Yet, despite the ongoing risk of infection amidst calls and efforts for relaxing social restrictions, some people are rebelling against wearing protective gear. Just this past weekend, when one maskless family was asked on the street about their lack of PPE, they responded, almost in unison “masks aren’t cool”. At the same time, A-list celebrities like Jennifer Lopez and Alex Rodriguez have been “corona-shamed” and labeled arrogant for not wearing masks. Why the discrepancy?

Research in neurocognitive science suggests that sentiments of “cool” are actually a complex combination of feelings of fear and aspiration. It combines the desire to be differentiated with the need to feel accepted. Studies indicate that deciding something is cool draws on two functional systems of the brain: the default mode network (DMN), and the salience network (SN). The DMN is linked to introspection and the determination of value – the rewards associated with being “cool”, while the SN plays a role in fear (often seeking to balance fears of both the behavior in question, and of being ostracized).

Such patterns of thought, emotion, and behavior are the focus of somewhat new disciplines of neuroeconomics and neuromarketing. The use of masks provides a perfect natural experiment to gauge how “cool” works, because they haven’t been common in our society since the 1918 flu pandemic, and have been now thrust into the social-spotlight.

When deciding if something is “cool,” our brains calculate the relative benefits and costs of that choice. We rarely need to ponder this judgement; we just “feel it”. The human brain takes less than 300 milliseconds to form an opinion, assessing events and consequences in our past, with the current situation, and making predictions about the near-term and future consequences of our decisions and actions. This is the phenomenon of automatic valuation.

Deciding whether something is cool, and worth the “investment” in terms of benefit, burden, and risk, comes down to figuring out what maximizes its – and your – utility. Our brains go through a rapid series of inquiry: Will this choice help me or hurt me? In the near term, or in the future? Is it the best of my available options? People aren’t perfectly rational, so we tend to base decisions and actions upon our beliefs and experience of what’s most useful.

In the case of masks, the obvious tradeoff is freedom versus safety; but considering “cool” in the equation demonstrates that other forces are also at work. What we find “cool” and feel good about depends on the image of ourselves we want to convey. We are strongly social creatures, who are sensitive to the ways we’re regarded. Whether or not you wear a mask conveys a signal (even if you don’t realize it). Social signaling plays a significant role in what we wear, and do.

This partly explains the divide in public stances on PPE. In neuroeconomic terms, masks are an identity good. People who wear masks (or dress up their Twitter profiles with mask pics) may hope to signal their virtue and intelligence, by highlighting the relative sacrifice of their comfort, both for the good of others (and for their own good – both to prevent infection, and to be perceived as socially responsible). People making unusual homemade masks may seek to highlight their resourcefulness and creativity. Those without masks are signaling something else: confidence, rebelliousness, bravery, or foolishness and selfishness — depending on your perspective.

There is also a status system in mask culture. Any recollection of middle school will surely bring to mind the in-group/out-group dynamic that plays a substantive part in determining what we find cool: with people on the “outside” aspiring to copy and outdo people on the “inside” to gain acceptance. Primate studies show this hierarchical behavior to be a side-effect of evolution. Status, and belonging to an in-group were valuable for our ancestors because the chances of survival were higher for a group member than an outcast. This primal need to conform may be a one of the factors in seeking to be “cool”. At the same time, no one wants to feel like a faceless member of the herd. To be cool, we strive for acceptance without homogeneity, and differentiation without alienation from the group.

Almost overnight COVID-19 has created a new in-group: people wearing masks. Like any major trend, there are subgroups within the mask-wearing set. The professional grade mask signals that you either are a “front-line” worker, that you have enough money to afford a scarce item, or that you’ve got good connections. People in different age groups also try to gain status by signaling different things. Teens, for example, may want branded masks (searches for designer masks increased 100-fold from mid-February to mid-April). Part of the reason why a teen covets a Supreme face mask ($450 online if you can get it), while their parents would never wear one, is that their peer groups value different things. Teens tend to want to be edgy and unique.

A significant element of cool in the age of COVID-19 is competitive – and reciprocal – altruism, which is another form of social signaling. Whether it’s the CEO of Flexport sending 3 million masks to Amsterdam, or the CEO of Twitter giving away almost a billion dollars and tracking it in a Google spreadsheet, people and companies are vying to be the most creative and effective responders to the virus. If selflessness wasn’t a valuable social signal, people would make these donations anonymously. Our research has shown that every altruistic act has an egoistic component. It’s “cool to be kind”, and as a result such acts of altruism make the actor feel good.

For many people, masks are an entirely new form of self-expression whose usefulness goes beyond their protective benefit. Simply put, as we strive to re-start our socio-economic engines for the benefit of both individuals and the population at large, masks are a currency of capability and cool. So, whether it’s making a statement of individualism, asserting acts of altruism, or evidencing a stance of responsibility, masks are a medium to represent ourselves in a commitment to each other.

Why The AV Revolution Will Give Southwest Airlines No LUV

Question: Why won’t you want to own regional airline stocks in the future? 

Better Question: Why would you ever fly from Chicago to New York again? 

If I were the CEO of Southwest I’d be terrified of the dawn of self-driving cars.  The average Southwest flight is 757 miles and takes roughly 2 hours.  

Aviation contributes 5% of global emissions, making a growing share of consumers unhappy about flying.  I predict that as self-driving vehicles take over, most people will opt to drive to their destination while they sleep.   

Assume an overnight AV trip takes 12 hours, including an hour to recharge and take occasional breaks.  The current record for a Tesla is 670 miles on a single charge, which can be expected to increase as battery efficiency improves, making driving 11 hours feasible.  An AV going 70 mph (the current interstate speed limit) you could easily travel 770 miles overnight. With no need for controls, and the engine replaced by small electric motors, chairs will be able to recline into lie-flat beds.  Imagine relaxing in business class…in the backseat of your own car. 

In the future, should you wish to travel from Chicago to New York you’ll have a choice. 

Option A: Drive to the airport, check in, wait in security lines with hundreds of other people, experience a delay, eat unhealthy food, experience another delay, all while contemplating why bottled water costs 3x the normal price, and then buying it anyway.  

OR…

Option B: Get in your AV, stretch out, relax, read, work and then lie flat to sleep, only waking up when your car announces that you’ve arrived at your destination. 

Hmm. I’m pretty sure I’ll grab my book, reusable water bottle, laptop and pillow, and head for my AV. 

How far could you travel in your sleep? 

Consider the following:  

Once AV’s are a reality, a traveler starting in San Francisco could go anywhere in the green circle. Start in New York and reach any spot in blue. Begin in Austin and travel to any place in purple radius.  

AV travel will make the most of your valuable time.  Have a full, productive day, get a great workout, plan to eat at your favorite spot, and take your business trip as you rest, while having minimal impact on the environment.  Best of all for younger generations, you don’t need to talk to anyone while you do it.  At an airport, by my count, you talk to 8 people at minimum. 

Southwest is currently investing in increasing short-haul flights (less than 500 miles), where it makes more revenue per mile than longer routes.  In the AV future, those profits will dissipate into thin air. 

You might want to consider other ways to add more LUV to your portfolio. 

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What Changes When No One Needs to Drive?

Imagine trying to ride a horse on the highway.  It’s legal, but quasi-insane.  In the future, we’ll likely feel the same way about letting humans drive cars (although we may still race cars on tracks).  Nearly 25,000 people die in car accidents each week worldwide, the equivalent of a 9/11 scale tragedy every day. Autonomous vehicles (AVs) are projected to decrease auto fatality rates by 80%. The economic arguments for AVs are also compelling.  According to research from Columbia University, if even 10% of driving switches to AVs, $250 billion of annual economic output could be unlocked. 

Combine the potential to save millions of lives with the massive financial rewards of automating transportation (a case of Gekko’s Law) and the multibillion dollar race for AVs makes perfect sense. With that in mind, here are a few ways the world may change when we make the shift.

Space

The magical thing about Uber is not that you don’t need to drive, it’s that you don’t need to park.  Ride-sharing companies like Uber and Lyft provide an excellent roadmap for understanding the AV revolution. Picture Uber without the driver, and you have a window to the future. Many leaders in autonomous driving are working on shared autonomous vehicles (SAVs) to do exactly this.

Parking spaces typically take up 50-60% of the land area of American downtown areas.  Worse, the average personal car is unused for 95% of the day.  With a network of SAVs, you could use far fewer cars and drive constantly, freeing up space used for parking.  This means wider walkways, larger lawns, more parks, and major new urban development opportunities.  

Parking needs for SAVs awaiting maintenance will be minimal. UBS analysts project car ownership will decline by 70% as people join AV ridesharing networks.  If you’re skeptical, check how many DVD’s you’ve purchased since subscribing to Netflix. 

The increase in downtown space will also allow new homes to be built, increasing supply and making cities more affordable.  55% of the world’s population already lives in cities, and the UN projects that will jump to 68% by 2050, making any extra urban space vital. 

Congestion 

While there are likely to be fewer cars, congestion may worsen as SAVs remove the hassle and expense of transportation.  The San Francisco County Transport Authority concluded that half of the 60% increase in the city’s traffic between 2010 and 2016 came from Uber and Lyft.  Ridesharing is cheap and easy, so more people opt for a private car, rather than walk or use public transit.  

On the other hand, congestion may be mitigated by connectivity, our next topic.

Connectivity & Edge Computing

Self driving cars have the potential to be a city’s nervous system.  While most of the focus today is on cloud computing, AVs will depend on edge computing.  With edge computing, thousands of small servers on street corners or in cars themselves will process the data AVs generate as they drive.  It takes far less time to send data to a nearby server than to upload it to the cloud. As anyone who’s tried to stop a car on ice knows, braking a second later could mean stopping twenty feet farther down the road.  Simply put, AVs can’t afford to wait for the cloud. 

Connectivity also enables cars to communicate with each other and the environment.  Vehicle to vehicle (V2V) communication will allow cars to travel faster and know exactly when a light is going to change, whether the car in front intends to turn, and if there is a faster route to take.  Picture Waze on steroids for the entire city, and you have a rough idea of how this will feel. As with the current version of Waze, this may have the unintended consequence of disrupting previously quiet streets with diverted traffic.  

Besides allowing cars to perform rapid calculations, edge computing networks will also enable cities to absorb the staggering amount of data that AVs generate.  Current AV prototypes can generate up to 20 TB of data per day. 20 TB of text, if printed out on single-sided pages, would form a 40 mile high stack (over 7 times the height of Mt. Everest). 

AV data could be used to take the pulse of an urban area: block by block variations in weather and air quality are possible.  It could also be used to track population movements and economic activity. Imagine if Amazon makes a deal with Lyft to get the data from its SAV network, and then serves you a Whole Foods ad.  Its algorithms can then track car movements to see which users and demographics the ad resonated with, and better target you in the future.  

Even if data is anonymized, population movements can be tracked in aggregate.  For example, as David Zipper has suggested, police departments could see a large cluster of people traveling to a house they don’t usually frequent to plan a protest, and then reverse engineer where potential dissidents lived.  Questions like these are already emerging in the legislative battle over the Mobility Data Specification (MDS), a California law which gives cities wide access to rideshare data. 

Other Economic Impacts

Aside from the economic benefits already mentioned, SAVs have several other implications for the economy.  It’s still unclear whether traditional car companies (OEMs) will create separate self-driving systems, or if they’ll all buy AV capability from a software provider like Google or Uber.  It’s also unclear if people will opt to buy their own cars or share them. A network of SAVs seems more likely given the long-term trend away from ownership (which I’ll cover in detail in an upcoming post) and towards services. 

SAVs are also much more economically rational for consumers. 

This chart from ARK reveals two interesting (and horrifying) stats: 

  1. Taxis are almost three times as expensive per mile as private planes. 
  2. Shared autonomous vehicles are projected to be cheaper than walking. 

The projections compare the cost per mile of SAVs to the cost of the calories in a Big Mac that would fuel a human to walk the same distance.  Even if SAVs are twice as expensive as ARK projects, they’ll still beat walking. Some people may still elect to own personal cars, the way some people still download rather than stream songs and movies, but the majority will use SAVs. 

The shift to SAVs will affect more than just OEMs, it also threatens car insurance industry.  KPMG predicts that autonomous cars will lead to a 90% reduction in accident frequency by 2050, resulting in a $137 billion decrease in premiums. 

AVs also promise to disrupt millions of jobs.  There are 3.5 million registered truck drivers in the US, another 5.2 million people who work to support the trucking industry, and an estimated 1.5 million rideshare drivers.  If all of these jobs were eliminated (unlikely, but possible), roughly 6% of the U.S. workforce would be unemployed. 

Some of these are service jobs that would still exist without truck driver, including restaurants that cater primarily to truckers, roadside motels, and mechanics.  However, the above does not include delivery jobs, which are also likely to be automated. 

If labor statistics aren’t your thing, this 2014 chart from NPR helpfully shows the most common job in each state:

While economists often point to the new jobs created after factory work displaced artisans during the Industrial Revolution, finding new, fulfilling work for disrupted drivers remains a poorly addressed challenge. 

Environmental Impact

The environmental impact of self-driving cars rests on whether we make the switch to electric vehicles. Currently, cars looking for streetside parking travel two thirds of the length of the US per parking space per year.  If an all-electric future becomes a reality, the emissions cut by not hunting for parking spaces are enormous. However, if regulation and economics don’t bring an electric AV future, we face a massive increase in pollution as self-driving cars drive nonstop.   

Urban sprawl is another consideration often downplayed by AV enthusiasts.  When transportation becomes easier, urban sprawl increases. For an example, check out this timelapse of Paris’s footprint as road networks develop. The same effect has been shown with the spread of the interstate highway system in the US. 

While long commutes have made us desire downtown real estate close to career opportunities and urban amenities, people may opt to live further away if they don’t need to do the driving.  

Finally, even if AVs are fully electric, current battery technology has a significant environmental impact because of lithium mining.  Alternate batteries will need to be developed to ensure a sustainable future.  

Conclusion 

Given the undeniable potential that self-driving cars have to save lives and increase work and leisure time, all signs point towards the widespread use of AVs. We need to begin designing infrastructure and policy to meet the needs of an autonomous future. 

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A Sardonic Take on CEO Exits

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The Question: What two things do Marissa Mayer, Travis Kalanick, and Andy Rubin have in common?  

The Answer: Paid to Fail

  1. All three executives were fired for failing to perform their corporate duties.  Rubin (Google) was found guilty of sexual harassment, and Mayer (Yahoo!) of managerial ineptness.  Kalanick (Uber) was dismissed for a combination of both.
  2. All three banked tens of millions in severance pay.

The “golden parachute” was originally meant to align managers’ incentives with shareholders’ interests in the event of a merger or acquisition. Even if they lost their jobs without cause in the deal they’d be remunerated. The clause has since evolved into its more controversial form, the “golden handshake”, where a company pays a problematic employee, dismissed with cause, to leave quietly.

In this environment, the best move for many CEOs may be to crash and burn.  Here’s a recipe for getting wealthy from the work of others:

  1. Become an executive at a high-profile firm.
  2. Do something rash, sexist, racist, or generally fail at your job.
  3. Collect a massive severance package in exchange for going peacefully.
  4. Relax and let your shares vest as others work to repair your mess.

Mayer was paid $23 million and Rubin walked away from Google with $90 million, after already receiving $150 million while the allegations were being investigated.

These may seem like outliers, but golden parachutes are common.  

Source: Nat Berman, https://moneyinc.com/largest-golden-parachutes-ever/

These payouts are nothing compared by windfall Travis Kalanick received from public investors after selling $1.4 billion in stock following the IPO.  This is almost as much as the top 5 names in the chart above combined.  

Paul Graham compares starting a company to jumping off a cliff and building an airplane on the way down.  Kalanick jumped off the cliff and dragged $20 billion of VC money with him, but then was handed an airplane by the board and buyers of Uber stock.  While most Uber employees face a lockup period that prevents them from selling shares until November 2019, Kalanick was free to sell on the day of the IPO.  Uber lost over $1 billion last quarter, but that’s not his problem.  Kalanick can leave the difficult work of creating a profitable business, fighting lawsuits from drivers and taxi lobbyists, and competing with Lyft to his successor, Dara Khosrowshahi.

Whether you receive cash in a golden parachute, or freedom like Kalanick, getting fired as a top executive seems like a blessing, not a punishment.  

Attempts at Regulation

Congress has tried several times to combat excessive CEO compensation. The Deficit Reduction Act of 1984 tried to limit severance to 3 times annual salary.  This was intended to be the upper bound. Instead the number became the new normal. This was coupled with an increase in other forms of compensation, like lifetime parking benefits at airports for former American Airlines CEO Jeff Smisek.  Firms also skirted the rule by “grossing up” golden parachutes to cover taxes on the original payment.

The 2010 Dodd-Frank Act also attempted to curb CEO pay, by requiring shareholders to vote on compensation.  This has been shown to have had little effect, and parachute payments actually increased following the law.  

Why have golden parachutes at all?

Defenders of golden parachutes highlight two main arguments: hiring and acquisitions. They argue severance can be used to attract top talent, luring candidates with a large payment the firm may never actually make. Like flood insurance, the parachute is something nice to have that you hope to never use.

Golden parachutes also make it more difficult for the company to be bought out, as the acquirer must pay severance to any fired executives at the smaller firm. As shown below, golden parachutes quickly took off in the 1980s, when junk bonds made it possible for large firms to be taken over by corporate raiders.  

There are two problems with the “acquisition defense” argument.  First, it’s often in shareholders’ best interest for a company to be acquired, as shares typically rise 15-25% on news of a takeover.  The argument also directly conflicts with the golden parachute’s original purpose: to ensure that management will allow the firm to be bought if necessary.

The Better Question: How can companies stay competitive and maintain loyalty without a golden parachute?

Regarding the hiring incentive, there are better ways to attract workers than rewarding poor performance with severance pay.  Stock grants for long term achievement ensure that management benefits when the company does. In an extreme example, Elon Musk’s new pay package awards $2.6 billion in stock, but pays him no salary at all.  Instead, he gets one twelfth of his shares if Tesla’s market cap hits $100 billion, and then another twelfth for every increase of $50 billion in the company’s value.  Market cap isn’t a perfect metric for performance, but it’s a better system than short-term milestones or a golden parachute.

Another option is to mimic Switzerland’s Minder Initiative, which abolished severance packages completely.  This may be the best path.

For now, if you’re a CEO in most parts of the world, it still pays to fail.

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The Five Laws of Futurism (2019)

* I don’t claim these are the only laws, or even that they’re immutable.  My aim is to provide a set of useful, basic tools that can be applied in many fields to make informed decisions about the future. I appreciate any comments, questions, or outraged rants you have about them.  – Will

Laws are useful because they act as maps.  The laws of physics tell us something should be true, even when we haven’t observed it yet.  The laws of futurism are no different. Together, they present a guide to what lies ahead for civilization.

There are five laws worth knowing to forecast the future: Moore’s Law, Carlson’s Law, Metcalfe’s Law, Amara’s Law, and Gekko’s Law.  The first two are based on technology, but have existed for a shorter time, making them less reliable. While they’re powerful forces driving innovation, they should be taken with a grain of salt.  The last three laws stem from human behavior, and are likely to last as long as people exist.

1. Moore’s Law (increasing processing power)

Most people know Moore’s Law, which states that the number of semiconductors that can fit on a chip doubles roughly every two years.

This simple trend has led to most of the modern conveniences of the information age.  One powerful example comes from Andrew McAfee and Erik Brynjolfsson in The Second Machine Age:

“The ASCI Red, the first product of the U.S. government’s Accelerated Strategic Computing Initiative, was the world’s fastest supercomputer when it was introduced in 1996. It cost $55 million to develop and its one hundred cabinets occupied nearly 1,600 square feet of floor space (80 percent of a tennis court)…Designed for calculation-intensive tasks like simulating nuclear tests, ASCI Red was the first computer to score above one teraflop—one trillion floating point operations per second—on the standard benchmark test for computer speed.”

ASCI Red was surpassed in power in 2006 by another device, the Playstation 3, courtesy of Moore’s Law.  Today, a high-end iPhone delivers 5 teraflops, almost three times as powerful as the government’s former juggernaut.

While Moore’s Law has allowed us to make electronics smaller, lighter, cheaper, and vastly more powerful with each passing year, it’s important to remember that it is not a law of physics.  Unlike gravity, if we stop working on computation, Moore’s Law will cease.  This is unlikely because of Gekko’s Law, which we’ll get to.

The other threats to Moore’s Law are the laws of physics themselves.  At a certain point, heat and the speed of light become the enemies of progress, making an infinite doubling of computer power impossible.  But there is strong evidence that this brick wall is still far away. Researchers at companies like ARM are designing 3D silicon wafers, and building purpose specific chips for functions like machine learning.  Further out, they foresee supercooled chips and quantum chips picking up the slack.

While Moore’s law will end, we will likely swing like Tarzan to the next technological vine, and continue to improve computer performance.

2. Carlson’s Law (decreasing genetic sequencing cost)

Source: Wikipedia

Carlson’s Law (it’s actually called the Carlson Curve, but it I’m sticking with law) is the biotech cousin of Moore’s Law, and is equally important.  The curve shows the precipitous decline in the cost of gene sequencing (reading genetic code).

Gene sequencing is the gateway to a host of futuristic technologies, including gene editing methods like CRISPR/CAS9 (a protein) and TALENs (an enzyme).  Research is underway to modify pig organs to aid the 114,000 people currently waitlisted for organ transplants in the U.S, resurrect the wooly mammoth, and make humans resistant to radiation to enable safe space travel (four edits can make bacteria 100,000 times more radiation resistant).  And all of these are projects from just one Harvard lab.

As the graph below shows, the rapid cost decline means biotech is advancing even faster than computation (although it piggybacks on advancements made by Moore’s Law).

The Carlson Curve predicts that gene sequencing will eventually be almost free.  While it may still have a nominal charge, the way that flushing the toilet does, it will be equally quotidian.  We may soon see certain people paid for the rights to their genetic code, as some mutations (like resistance to pain or extreme endurance) will become valuable modifications sold to the highest bidder.

Like Moore’s Law, the Carlson Curve is a recent phenomenon that will guide the direction of the future, although it may eventually fail.  Given the size of the opportunity set (every living thing on Earth) it is unlikely we will exhaust its potential any time soon.

3. Metcalfe’s Law (big networks are strong networks)

We now leave the technological laws for the first behavioral law of the future: Metcalfe’s Law.  Coined by Bob Metcalfe, the creator of Ethernet, the law states that “the value of a network is directly proportional to the square of the number of users connected to the system”.  In other words, if you are the only person in a network, the value is 12 = 1, if you and a friend are in contact, the value jumps to 22 = 4, and so on.  For much of human history, the value of our networks was limited by the Dunbar Number.  This number, roughly 150, was suggested by anthropologist Robin Dunbar as the maximum number of meaningful connections a person can hold in their head.  For this reason, most Neolithic villages were capped at 150. With the invention of writing, our networks could grow further, increasing the value of the human network as trade and information spread.  Finally, in the information age, we smashed the Dunbar number with social networks and marketplaces like Uber. Uber would not be very useful if there were three drivers in New York, but it is immensely valuable with the over 100,000 drivers in the city today.  

The more drivers there are, the easier it is to find a ride, encouraging more users to ride with Uber over a competitor like Juno.  This influx of riders encourages more drivers to join, further strengthening the network and entrenching Uber as the incumbent. The same network effects can be seen in Amazon, iOS, and Facebook.  At a certain point, the network effect grows so strong that it is more advantageous to be part of the network than not. As evidenced by the recent furor over Big Tech in Congress, Metcalfe monopolies become hard to break.

Metcalfe himself suggested that at large numbers, the value of the network is actually n(logn), meaning that networks grow quickly before leveling off in value.  This has been empirically shown with the value of Facebook (blue) and Bitcoin (red):

Source: Bitcoin Spreads Like a Virus, Timothy F. Peterson, CFA, CAIA

Metcalfe’s Law is useful for forecasting because if you can spot a system that attracts and connects users, you can make a reasonable inference about its future value and success.

4. Amara’s Law (believe the hype, just not yet)

Futurist Roy Amara stated:

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”.  

This observation holds pretty much anywhere it is applied.  Artificial intelligence research is one example. When the first AI breakthroughs were made in the 1950s, a wave of excited investment followed.  In 1970, Marvin Minsky declared “from three to eight years we will have a machine with the general intelligence of an average human being”.  Several years later, funding dried up as research failed to deliver a convincing chess-player, let alone a human-equivalent machine.  Processing power, data sources, and theoretical work could not match this grand vision. AI funding regained strength in the 1990s, and excitement again peaked in 1997 when IBM’s Deep Blue beat Garry Kasparov, who was at the time the highest ranked chess player in the world. Funding collapsed again with the dotcom bust, and finally picked up again in 2012 when a team led by Geoffrey Hinton used deep learning to set a new record in Stanford’s ImageNet competition.  

Looking back, it’s easy to see this as a series of booms and busts with little real gain.  But beneath the frenzy, real progress has been made. Our current AI would be shocking to the researchers in the 1950s.  Similarly, our near-term expectations are likely far overblown. Few AI researchers believe we will have generally intelligent machines in the next ten years, but Amara’s law makes them confident about predicting radical advances in the next hundred.

5. “Gekko’s Law” – Reliable Selfishness

In Iceland, huddled around an abandoned WWII airstrip, a collection of warehouses uses more electricity than all of the country’s homes combined to mine Bitcoin.  Worldwide, bitcoin mining uses roughly as much energy as Denmark, making it more energy intensive than some forms of actual mining. This is an example of the principle I call “Gekko’s Law”.

Unlike Wall Street’s Gordon Gekko, I don’t believe “greed is good”, but that greed (selfishness) is dependable.  People tend to do what maximizes their personal gain.

Gekko’s Law: “selfishness is reliable”, applies to everything from colonialism and climate change to bitcoin and AI.  

Classical economists believe in rational choice theory, the concept that humans act out of rational self-interest to maximize their happiness.  Gekko’s Law claims that people act out of self-interest even when it is irrational in the long term.

A classic example is climate change.  We began to understand climate change in 1896, and if we had rationally extrapolated the model, we would have controlled fossil fuel use when it still looked like this:

We could have invested heavily in electric battery technology (invented in 1800).  Instead, we failed stupendously, and our consumption now looks like this:

Like Bitcoin mining, this process is unsustainable, but shows few signs of stopping.  Even though the players involved often acknowledge this, few are willing to change, and if they do, many are clamoring to take their place.  

Gekko’s Law gets scary when combined with the other four Laws of Futurism.  Take genetic engineering as an example. The payoff for gene therapies enabled by Moore’s Law and Carlson’s Law is astronomical.  The gene replacement drug Zolgensma was approved by the FDA recently at a price of $2.1 million per dose.  The proliferation of increasingly radical treatments using gene editing, synthesis (creating oligos, new strings of the DNA “letters” A,G,C, T), assembly (stitching oligos together) seems inevitable.  Germline editing promises to sterilize the anopheles mosquito, ending malaria, and notorious experiments were recently conducted in China to create babies (supposedly) resistant to HIV. Massive potential markets and radical applications ensure that this field will continue to receive a flood of attention and investment.

However, this selfish rush for the next great thing also makes dangerous technology more accessible to terrorists.  Imagine an airborne ebola virus that lived on surfaces for days, or genetic assassination viruses tailored to kill only a certain race or demographic.  We can see these possibilities, but cannot resist the temptation to explore.

Gekko’s Law isn’t all bad though.  As venture capitalists like Collaborative Fund show, predictable selfishness can be harnessed to make the world better.  The trick is to make the new product or solution not only better for the planet, but better for the consumer, so that they will “selfishly” choose to do good.

Most people don’t drive a Tesla purely to help the earth (a bus is a greener form of transportation when you account for the production of lithium-ion batteries).  Instead, people join the cult of Musk because driving a Tesla signals that you value design, care about the environment, and are generally cooler than someone who drives a Ford Fiesta (to all the Fiestistas out there, this is not personal).  The Beyond Burger is climate activism disguised as healthy meat, and CloudKitchens is democratized cooking masquerading as convenient cuisine.  Health and environmental sectors present the most obvious positive examples of Gekko’s Law, but the strategy can be applied to any vertical.

Putting the Five Together

We cannot control the Five Laws of Futurism, but we can use them to forecast and prepare for the road ahead.  Each of them is valuable but limited on its own, but together they provide a powerful framework. While no credible future projection can be more than a collection of estimates, the laws give these projections a direction.  Combine specific knowledge with an understanding that the world will be faster, cheaper, smarter, more surprising than it was today, and anticipate the self-interest of others, and you’ll have a better idea of the future than most.

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