Book Club: Code Economy: Finale on the Blockchain

From All you need to know about blockchain

Welcome to our final discussion of Auerswald’s The Code Economy. Today we will be finishing the text, chapters 13-15. Please feel free to jump in even if you haven’t read the book.

After a hopefully entertaining digression about Peruvian Poutine and Netflix’s algorithms*, we progress to the discussion of Bitcoin and the Blockchain. Now, I don’t know anything about Bitcoin other than the vague ideas I have picked up by virtue of being a person on the internet, but it was an interesting discussion nonetheless.

Auerswald likens blockchain to an old-fashioned accountant’s ledger; the “blocks” are the rectangles in which a business’s earnings and expenses are recorded. If there is any question about a company’s profits, you can look back at the information recorded in the chain of blocks.

The problem with this system is that there is only one ledger. If the accountant has made a mistake (or worse, a theft,) there is nothing else to compare it to in order to determine the mistake.

In the modern, distributed version, there are many copies of the blockchain. If on most of these copies of the chain, block 22 says -$400, and on one copy it says +$400, we conclude that the one that disagrees is most likely in error. Like the works of Shakespeare, there are so many copies out there that a discrepancy a single copy cannot be claimed to be authoritative; it is the collective body of work that matters.

“Blockchain” is probably going to get used here as a metaphor for “distributed systems of confirming authority” a lot. For example, “Democracy is a blockchain for deciding who gets to rule a country.” Or “science is a blockchain.”

In Rhodes’s “The Making of the Atomic Bomb,” he recounts the process by which something becomes accepted as “true” (or reasonably likely to be true,) in the scientific community. Let’s suppose scientist M is the foremost authority in his field–perhaps organic LEDs. Scientists L and N are doing work that overlaps M’s, and can therefore basically evaluate M’s work and vouch for whether they think it is sound or not. Scientists J, K, O, and P do work that overlaps a lot with L and N and a little with M; they can evaluate M’s work a little and vouch for whether they think L and N are trustworthy. The chain continues down to little cats scientists A and Z, who can’t really evaluate scientist M, but can tell you whether or not they think B and Y’s results are trustworthy.

This community of science has both good and bad. In general, the structure of science has been extremely successful at inventing things like computers, atomic bombs, and penicillin; at times it creates resistance to new ideas just because they are so far outside of the mainstream of what other scientists are doing. For example, Ignaz Semmelweis, a physician, discovered that he could reduce maternal deaths at his hospital from around 10-18% to 2% simply by insisting that obstetricians wash their hands between dissecting cadavers and delivering babies. Unfortunately, the rest of the medical establishment had not yet accepted the Germ Theory of disease and believed that disease was caused by imbalanced humors. Semmelweis’s idea that invisible corpse particles were somehow transferring corpse-ness from dead people to live people seemed absurd, and further, blamed the doctors themselves for the deaths of their patients. Semmelweis’s tragic tail ends with him being stomped to death in an insane asylum. (His mental ill-health was probably induced by a combination of the stress of being rejected by his profession; and syphilis, contracted via charity work delivering babies for destitute prostitutes.)

Luckily for mothers everywhere, medical science eventually caught up with Semmelweis and puerperal fever is no longer a major concern for laboring women. Science, it seems, can correct itself. (We may want to be cautious about being too eager to reject new ideas–especially in cases where there is clearly a lot of room for improvement, like an 18% death rate.)

But back to the blockchain. In India:

Niti Aayog is working with Apollo Hospitals and information technology major Oracle on applying blockchain (decentralised) technology in pharmaceutical supply chain management to detect spurious drugs, Chief Executive Officer of NITI Aayog Amitabh Kant said here today.

Addressing a gathering through video-conferencing at the inaugural session of International Blockchain Congress 2018 for which Niti Aayog was a co-host, Kant said the organisation was working on applying the blockchain technology to pressing problems of the country in areas such as land registry, health records and fertiliser subsidy distribution m among others.

Further:

Blockchain technology can enable India to find solutions to huge logjams in courts …

With two-thirds of all civil cases pertaining to registration of property or land, the country’s policy think-tank is working with judiciary to find disruptive ways to expedite registrations, mutations and enable a system of smart transactions that is free of corruption and middlemen.

… There are three crore cases currently pending in Indian courts, including 42.5 lakh cases in high courts and 2.6 crore* cases in lower courts. Even if 100 cases are disposed off every hour without sleeping and eating, it would take more than 35 years to catch up, he said. …

On transforming the land registry system using blockchain, Niti Aayog is in advanced stage of implementing proof of concept pilot in Chandigarh to assess its potential to solve the problem of India’s land-based registry system. …

“It’s powerful because it allows multiple parties to collaborate and come to consensus without any need of third party,” he said.

*A crore is an Indian unit equivalent to 10 million.

I probably do not need to review Auerswald’s summary of Bitcoin’s history, as you are probably already well aware of it, but the question of “is Bitcoin real money?” is interesting. In 1875, Jevons, “cofounder of the neoclassical school in economics,” wrote that a material used as money should have the following traits:

“1 Utility and value

2 Portability

3 Indestructibility

4 Homogeneity

5 Divisibility

6 Stability of value

7 Cognizability.”

I am not sure about all of the items on this list; cigarettes and ramen noodles, for example, are used as currency in prisons, even though they are very easy to destroy. It seems like using a currency that you are going to eat would be problematic, yet the pattern recurs over and over in prisons (where perhaps people cannot get their hands on non-consumable goods, or perhaps people simply have no desire for non-consumable ornaments like gold.)

Gold–the “gold standard” of currencies–is a big odd to me, because it has very few practical uses. You can’t eat it. You can’t plant with it, cure parasites with it, or build with it. Lots of people talk about how you’d want a hard currency like gold in the case of societal collapse in which people stop accepting fiat currency, but if zombies were invading, the gas stations had run out of gasoline, and the grocery stores were out of food, I can’t imagine that I’d trade what few precious commodities I had left for a pile of rocks.

People argue that fiat currency is “just paper,” but gold is “just rocks,” and unless you’re a jeweler, the value of either is dependent entirely on your expectation that other people will accept them as currency.

Auerswald writes:

For the past 40 years the world’s currencies have been untethered from gold or any other metal. National “fiat” currencies are nothing more or less than tradeable trust, whose function as currency is based entirely on government-enforced scarcity an verifiability not tethered to its intrinsic usefulness.

I think Auerswald overlooks the role of force in backing fiat currencies. We don’t use Federal Reserve Notes because we trust the government like it’s our best friend from the army who pulled us out of a burning foxhole that one time. We use Federal Reserve Notes because the US government has a lot of guns and bombs to back up its claim that this is real money.

Which means the power of a dollar is dependent on the US government’s ability to enforce that value.

As for Bitcoin:

Bitcoin… satisfies all the criteria for being “money” that William Stanley Jevon set forth… with on exception intrinsic utility and value. That does not mean that Bitcoin will grow in significance as a means of exchange, much less achieve any position of dominance. But with digital transactions via mobile phones–Apple Pay and the like–becoming ever more command the concept of a digital currency not backed by any government gaining rapid acceptance, the prospect of one or another digital currency competing successfully with fiat currencies is not nearly as far-fetched today as it was even three years ago.

The biggest problems I see for digital currencies:

  1. Keeping value–if people decide they won’t accept DogeCoin, then what do you do with all of your DogeCoins?
  2. Ease of entry into the market makes it difficult for any one Coin to retain value
  3. Most people are happy using currencies not associated with illegal activity
You mean you can just make more of these things? Mugabe is brilliant!

The upside to digital currencies is they may be a real blessing for people caught in countries where local fiat currencies are being manipulated all to hell.

Anyway, Auerswald envisions a world in which blockchains (with coins or not) enable a world of peer-to-peer authentication and transactions:

By their very structure, Peer-to-peer platforms start out being distributed. The challenge is how to organize all of the energy contained in such networks so that people are rewarded fairly for their contributions. …Blockchain-based systems for governing peer0to0eer networks hold the promise–so far unrealized–of incorporating the best features of markets when it comes to rewarding contribution and of organizations when it comes to keeping track of reputations.

In other words, in areas where economies are held back because the local governments do a bad job of enforcing contracts and securing property rights, “blockchain”-like algorithms may be able to step into the gap and provided an accepted, distributed, alternative system of enforcement and authentication.

(This is the point where I start ranting to anyone within earshot about communists not recognizing the necessity of secure property rights so that people can turn their property into capital in order to start businesses. Without that seed money to start a business, you can’t get started. Even something simple, like driving for Uber, requires a car to start with, and cars cost money. If you can’t depend on having money tomorrow because all of your property just got confiscated, or you can’t depend on having a car tomorrow because private property is for bourgeois scum, then you can’t get a job driving for Uber. If no one can convert property to capital and thus to businesses, then you don’t get business and you have no economy and people suffer.

Communists see that some people have property that they can convert to capital and other people don’t have said capital, and their solution is to just take everybody’s stuff away and declare the problem fixed, when what they really want is for everyone to have enough basic property and capital to be able to start their own business.]

But back to Auerswald:

Earlier… I alluded to the significant advance in democracy, science, and financial systems that occurred simultaneously during …the Age of Enlightenment. That systems of governance, inquiry, and economics should have advanced all at the same time… is no coincidence at all. Each of these foundational developments in human social evolution is, at its core, an algorithm for authentication and verification. …

It is only because of the disciplinary fragmentation of inquiry that has occurred in the past century that we do not immediately perceive in the evolved historical record the patterns connecting systems of authentication and verification in politics, science, and economics as they have jointly evolved. … Illuminating those patterns has been the point of this book.

Chapter 14 begins with a history of Burning Man, which the author defends thus:

Still, it makes for an interesting case study in the building of cities (and why laws get enacted): Like everything about Black Rock City, the layout is the product of both planning and evolution. Cities are what physicists refer to as dissipative structures: highly complex organisms worse existence depend on a constant throughput of energy. If you were to close down all bridges and tunnels into New your City … grocery stores would have only a three-day supply of food. The same is generally true of a city’s other energy requirements. All cities are temporary, and they survive only because we feed them. …

The evolution of Black Rock is for urbanists what a real-life Jurassic Park would be for a Paleontologist. We really have no idea what the experience of living in humanity’s first cities might have been–whether Uruk in Mesopotamia or Catalhoyuk in Anatolia. And yet all cities also have elements of planning. Where Black Rock City has its Larry Harvey, London had its Robert Hooke and Washington, D.C., had its Pierre L’Enfant.  Each had a notion of how to bound a space, build symmetry and flow, and in so doing provide a platform where the human experience can unfold.

I have a somewhat dim view of “Burning Man” as a communist utopia that’s only open to rich people, filled with environmentalist hippies leaving an enormous carbon footprint in order to get high with a close-knit community of 70,000 other people, but maybe my sight is obscured from the outside.

The question remains, though: will code be a blessing, or a curse? What happens to employment as “traditional” jobs disappear? Will blockchain and other new platforms and technologies make us freer, or simply find new ways to control us?

The advance of code reduces individual power and autonomy while it increases individual capabilities and freedom.

So far, Auerswald points out, there has been good reason to be optimistic:

In 1990, a staggeringly high 43 percent of people in the “developing world,” approximately 1.9 billion people, lived in extreme poverty. By 2010, that number had fallen to 21 percent. …

For the past two centuries, the vehicle for that progress has been the continual capacity of economies to generate more and better jobs. … “Gallup has discovered that having a good job is now the great global dream … ‘A good job’ is now more important than having a family, more compelling than democracy and freedom, religion, peace and so on… Stimulating job growth is the new currency of all leaders because if you don’t deliver on it you will experience instability, brain drain, sometimes revolution…

There is something concerning about this, though. “Jobs creation” is now widely agreed to be in the hands of national leaders, not individuals. Ordinary people are no longer seen as drivers of innovation. People can start businesses, of course, but whether those businesses survive or fail depends on the government; for the average person, jobs are no longer created by human ingenuity but awarded by an opaque power structure.

Thus the liberal claim that “structural racism” (rather than “individual racism”) is the real cause of continued black impoverishment and high unemployment rates. In a world where employment is granted or withheld by the powerful based on whether or not they like you, not based on your own innate ability to make your own economic contribution to the world, then it is imperative to make sure that the powerful see it as important to employ people like you.

It is, in sum, an admission of the powerlessness of the individual.

Still, Auerswald is hopeful that with the rise of the Peer-to-Peer economy and end of traditional factory work, not only will work be more interesting (as boring, repetitive jobs are most easily automated,) but also that people will no longer be dependent on the whims of a small set of powerful people for access to jobs.

I think he underestimates how useful it is to have steady, long-term employment and how difficult it is for individuals to compete against established corporations that have much larger economies of scale and access to far more relevant data than they do. Take, for example, YouTube vs. Netflix. Netflix can use its troves of data to determine which kinds of shows customers would like to watch more of, then hire people to make those shows. This is pretty nice work if you can get it. YouTube, of course, just lets pretty much anyone put up any video they want, and most of the videos are probably pretty dull, but a few YouTubers put up quality material and an even smaller few actually make a decent amount of money. YouTuber PewDiePie, for example, holds the record at 61+ million subscribers, which has earned him $124 million. But most people who try to become YouTube stars do not become PewDiePie; most earn very little. And why should they, when most of them are amateurs low-budget amateurs with no data on what audiences are interested in going up against other TV options like Orange is the New Black, Breaking Bad, and yes, PewDs himself?

I have a friend who is a very talented amateur clothing designer and dressmaker. I have encouraged her to open a shop on Etsy and try sell some of her creations, but can she really compete with Walmart, The Gap, or Nordstrom? Big Clothing has a massive lead in terms of factories mass-producing clothes for sale. (Her only hope would be to extremely upscale–wedding dresses, movie costumes, etc.)

So what does the future hold?

In the next round of digital disruption, tasks that can be automated (the “high-volume, low-price” option resulting from ongoing code-driven bifurcations…) will yield only small dividends for most people. The exception is the relatively small number of people who will maintain the platforms on which such tasks are performed…

The promising pathway for inclusive well-being is humanized work (the “low-volume, high-price” pathway resulting from ongoing code-driven bifurcations…) this pathway includes everything about value creation that is differentiated, personal, and human.

In his Conclusion, Auerswald writes:

To be human is to think critically. To collaborate, to Communicate. To be creative. What we call “the economy’ is one extension of these activities. It is the domain in which we develop and advance code.

From Ray Kurzweil

But the singularity approaches:

We are not at the center of our cognitive universe. Our own creations are eclipsing us.

For each of us, redefining work requires nothing less than redefining identity. This is because production is not something human beings do just to consume. In fact, the opposite is true. We are living beings. We consume in order to produce.

Well, that’s the end of the book. I hope you have enjoyed it as much as I have. What do you think the future holds? Where do you think code is taking the economy? What are the best–and worst–opportunities for growth? And what (if anything) should we read next?

 

 

*An Aside On Netflix and the use of algorithms to produce movies/TV:

…consider the fate of two films that premiered the same night at the 2015 Sundance Film Festival. … One of these films, What Happened, Miss Simone? was a documentary about singer and civil rights icon Nina Simone. That film was funded by Netflix, whose corporate decision to back the film was based in part on insights algorithmically gleaned from the vast trove of data it has collected on users of its streaming video and movie rental services. The second film was a comedy titled The Bronze, which featured television star Melissa Rauch as a vulgar gymnast. The Bronze was produced by Duplass Brothers production and privately financed by “a few wealthy individual” whose decision to back the film was presumably not based on complex impersonal algorithms but rather, as has been the Hollywood norm, on business intuition.

I’ve often wondered why so many terrible movies get made.

A documentary about a Civil Rights leader might not be everyone’s cup of tea (people like to say they watch intellectual movies more than they actually do,) but plenty of people will at least abstractly like it. By contrast, a “vulgar gymnast” is not an interesting premise for a movie. Vulgarity can be funny when it is contrasted with something typically not vulgar–eg, “A vulgar mobster and a pious nun team up to save an orphanage,” or even “A vulgar nun and pious mobster…” The humor lies in the contrast between purity and vulgarity. But gymnasts aren’t known for being particularly pure or vulgar–they’re neutral–so there’s no contrast in this scenario. A vulgar gymnast doesn’t sound funny, it sounds rude and unpleasant. And this is the one sentences summary chosen to represent the movie? Not a good sign.

As you might have guessed already, What Happened, Miss Simone, did very well, and The Bronze was a bomb. It has terrible reviews on IMDB and Rotten Tomatoes. As folks have put it, it’s just not funny.

Davidowitz notes in Everybody Lies that the industries most ripe for “big data”fication are the ones where the current data is not very good. Industries where people work more on intuition than analysis. For example, the choice of horses in horse racing, until recently, was based on pedigree and intuition–what experienced horse people thought seemed promising in a foal. There was a lot of room in horse racing for quantification and analysis–and the guy who started using mobile x-ray machines to measure horse’s heart and lung sizes was able to make significantly better predictions than people who just looked at the horses’s outsides. By contrast, hedge funds have already put significant effort into quantifying what the prices of different stocks are going to do, and so it is very hard to do better data analysis than they already are.

The selection of movies and TV pilots to fund fall more into the “racing horses picked by intuition” category than the “extremely quantified hedge funds” category, which means there’s lots of room for improvement for anyone who can get good data on the subject.

Incidentally, “In 2015… Netflix accounted for almost 37 percent of all downstream internet traffic in North America during peak evening hours.”

Book Club: The Code Economy, Chapter 11: Education and Death

Welcome back to EvX’s book club. Today we’re reading Chapter 11 of The Code Economy, Education.

…since the 1970s, the economically fortunate among us have been those who made the “go to college” choice. This group has seen its income row rapidly and its share of the aggregate wealth increase sharply. Those without a college education have watched their income stagnate and their share of the aggregate wealth decline. …

Middle-age white men without a college degree have been beset by sharply rising death rates–a phenomenon that contrasts starkly with middle-age Latino and African American men, and with trends in nearly every other country in the world.

It turns out that I have a lot of graphs on this subject. There’s a strong correlation between “white death” and “Trump support.”

White vs. non-white Americans

American whites vs. other first world nations

source

But “white men” doesn’t tell the complete story, as death rates for women have been increasing at about the same rate. The Great White Death seems to be as much a female phenomenon as a male one–men just started out with higher death rates in the first place.

Many of these are deaths of despair–suicide, directly or through simply giving up on living. Many involve drugs or alcohol. And many are due to diseases, like cancer and diabetes, that used to hit later in life.

We might at first think the change is just an artifact of more people going to college–perhaps there was always a sub-set of people who died young, but in the days before most people went to college, nothing distinguished them particularly from their peers. Today, with more people going to college, perhaps the destined-to-die are disproportionately concentrated among folks who didn’t make it to college. However, if this were true, we’d expect death rates to hold steady for whites overall–and they have not.

Whatever is affecting lower-class whites, it’s real.

Auerswald then discusses the “Permanent income hypothesis”, developed by Milton Friedman: Children and young adults devote their time to education, (even going into debt,) which allows us to get a better job in mid-life. When we get a job, we stop going to school and start saving for retirement. Then we retire.

The permanent income hypothesis is built into the very structure of our society, from Public Schools that serve students between the ages of 5 and 18, to Pell Grants for college students, to Social Security benefits that kick in at 65. The assumption, more or less, is that a one-time investment in education early in life will pay off for the rest of one’s life–a payout of such returns to scale that it is even sensible for students and parents to take out tremendous debt to pay for that education.

But this is dependent on that education actually paying off–and that is dependent on the skills people learn during their educations being in demand and sufficient for their jobs for the next 40 years.

The system falls apart if technology advances and thus job requirements change faster than once every 40 years. We are now looking at a world where people’s investments in education can be obsolete by the time they graduate, much less by the time they retire.

Right now, people are trying to make up for the decreasing returns to education (a highschool degree does not get you the same job today as it did in 1950) by investing more money and time into the single-use system–encouraging toddlers to go to school on the one end and poor students to take out more debt for college on the other.

This is probably a mistake, given the time-dependent nature of the problem.

The obvious solution is to change how we think of education and work. Instead of a single, one-time investment, education will have to continue after people begin working, probably in bursts. Companies will continually need to re-train workers in new technology and innovations. Education cannot be just a single investment, but a life-long process.

But that is hard to do if people are already in debt from all of the college they just paid for.

Auerswald then discusses some fascinating work by Bessen on how the industrial revolution affected incomes and production among textile workers:

… while a handloom weaver in 1800 required nearly forty minutes to weave a yard of coarse cloth using a single loom, a weaver in 1902 could do the same work operating eighteen Nothrop looms in less than a minute, on average. This striking point relates to the relative importance of the accumulation of capital to the advance of code: “Of the roughly thirty-nine-minute reduction in labor time per yard, capital accumulation due to the changing cost of capital relative to wages accounted for just 2 percent of the reduction; invention accounted for 73 percent of the reduction; and 25 percent of the time saving came from greater skill and effort of the weavers.” … “the role of capital accumulation was minimal, counter to the conventional wisdom.”

Then Auerswald proclaims:

What was the role of formal education in this process? Essentially nonexistent.

Boom.

New technologies are simply too new for anyone to learn about them in school. Flexible thinkers who learn fast (generalists) thus benefit from new technologies and are crucial for their early development. Once a technology matures, however, it becomes codified into platforms and standards that can be taught, at which point demand for generalists declines and demand for workers with educational training in the specific field rises.

For Bessen, formal education and basic research are not the keys to the development of economies that they are often represented a being. What drives the development of economies is learning by doing and the advance of code–processes that are driven at least as much by non-expert tinkering as by formal research and instruction.

Make sure to read the endnotes to this chapter; several of them are very interesting. For example, #3 begins:

“Typically, new technologies demand that a large number of variables be properly controlled. Henry Bessemer’s simple principle of refining molten iron with a blast of oxygen work properly only at the right temperatures, in the right size vessel, with the right sort of vessel refractory lining, the right volume and temperature of air, and the right ores…” Furthermore, the products of these factories were really one that, in the United States, previously had been created at home, not by craftsmen…

#8 states:

“Early-stage technologies–those with relatively little standardized knowledge–tend to be used at a smaller scale; activity is localized; personal training and direct knowledge sharing are important, and labor markets do not compensate workers for their new skills. Mature technologies–with greater standardized knowledge–operate at large scale and globally, market permitting; formalized training and knowledge are more common; and robust labor markets encourage workers to develop their own skills.” … The intensity of of interactions that occur in cities is also important in this phase: “During the early stages, when formalized instruction is limited, person-to-person exchange is especially important for spreading knowledge.”

This reminds me of a post on Bruce Charlton’s blog about “Head Girl Syndrome“:

The ideal Head Girl is an all-rounder: performs extremely well in all school subjects and has a very high Grade Point Average. She is excellent at sports, Captaining all the major teams. She is also pretty, popular, sociable and well-behaved.

The Head Girl will probably be a big success in life…

But the Head Girl is not, cannot be, a creative genius.

*

Modern society is run by Head Girls, of both sexes, hence there is no place for the creative genius.

Modern Colleges aim at recruiting Head Girls, so do universities, so does science, so do the arts, so does the mass media, so does the legal profession, so does medicine, so does the military…

And in doing so, they filter-out and exclude creative genius.

Creative geniuses invent new technologies; head girls oversee the implementation and running of code. Systems that run on code can run very smoothly and do many things well, but they are bad at handling creative geniuses, as many a genius will inform you of their public school experience.

How different stages in the adoption of new technology and its codification into platforms translates into wages over time is a subject I’d like to see more of.

Auerswald then turns to the perennial problem of what happens when not only do the jobs change, they entirely disappear due to increasing robotification:

Indeed, many of the frontier business models shaping the economy today are based on enabling a sharp reduction in the number of people required to perform existing tasks.

One possibility Auerswald envisions is a kind of return to the personalized markets of yesteryear, when before massive industrial giants like Walmart sprang up. Via internet-based platforms like Uber or AirBNB, individuals can connect directly with people who’d like to buy their goods or services.

Since services make up more than 84% of the US economy and an increasingly comparable percentage in coutnries elsewhere, this is a big deal.

It’s easy to imagine this future in which we are all like some sort of digital Amish, continually networked via our phones to engage in small transactions like sewing a pair of trousers for a neighbor, mowing a lawn, selling a few dozen tacos, or driving people to the airport during a few spare hours on a Friday afternoon. It’s also easy to imagine how Walmart might still have massive economies of scale over individuals and the whole system might fail miserably.

However, if we take the entrepreneurial perspective, such enterprises are intriguing. Uber and Airbnb work by essentially “unlocking” latent assets–time when people’s cars or homes were sitting around unused. Anyone who can find other, similar latent assets and figure out how to unlock them could become similarly successful.

I’ve got an underutilized asset: rural poor. People in cities are easy to hire and easy to direct toward educational opportunities. Kids growing up in rural areas are often out of the communications loop (the internet doesn’t work terribly well in many rural areas) and have to drive a long way to job interviews.

In general, it’s tough to network large rural areas in the same ways that cities get networked.

On the matter of why peer-to-peer networks have emerged in certain industries, Auerswald makes a claim that I feel compelled to contradict:

The peer-to-peer business models in local transportation, hospitality, food service, and the rental of consumer goods were the first to emerge, not because they are the most important for the economy but because these are industries with relatively low regulatory complexity.

No no no!

Food trucks emerged because heavy regulations on restaurants (eg, fire code, disability access, landscaping,) have cut significantly into profits for restaurants housed in actual buildings.

Uber emerged because the cost of a cab medallion–that is, a license to drive a cab–hit 1.3 MILLION DOLLARS in NYC. It’s a lucrative industry that people were being kept out of.

In contrast, there has been little peer-to-peer business innovation in healthcare, energy, and education–three industries that comprise more than a quarter of the US GDP–where regulatory complexity is relatively high.

Again, no.

There is a ton of competition in healthcare; just look up naturopaths and chiropractors. Sure, most of them are quacks, but they’re definitely out there, competing with regular doctors for patients. (Midwives appear to be actually pretty effective at what they do and significantly cheaper than standard ob-gyns.)

The difficulty with peer-to-peer healthcare isn’t regulation but knowledge and equipment. Most Americans own a car and know how to drive, and therefore can join Uber. Most Americans do not know how to do heart surgery and do not have the proper equipment to do it with. With training I might be able to set a bone, but I don’t own an x-ray machine. And you definitely don’t want me manufacturing my own medications. I’m not even good at making soup.

Education has tons of peer-to-peer innovation. I homeschool my children. Sometimes grandma and grandpa teach the children. Many homeschoolers join consortia that offer group classes, often taught by a knowledgeable parent or hired tutor. Even people who aren’t homeschooling their kids often hire tutors, through organizations like Wyzant or afterschool test-prep centers like Kumon. One of my acquaintances makes her living primarily by skype-tutoring Koreans in English.

And that’s not even counting private schools.

Yes, if you want to set up a formal “school,” you will encounter a lot of regulation. But if you just want to teach stuff, there’s nothing stopping you except your ability to find students who’ll pay you to learn it.

Now, energy is interesting. Here Auerswsald might be correct. I have trouble imagining people setting up their own hydroelectric dams without getting into trouble with the EPA (not to mention everyone downstream.)

But what if I set up my own windmill in my backyard? Can I connect it to the electric grid and sell energy to my neighbors on a windy day? A quick search brings up WindExchange, which says, very directly:

Owners of wind turbines interconnected directly to the transmission or distribution grid, or that produce more power than the host consumes, can sell wind power as well as other generation attributes.

So, maybe you can’t set up your own nuclear reactor, and maybe the EPA has a thing about not disturbing fish, but it looks like you can sell wind and solar energy back to the grid.

I find this a rather exciting thought.

Ultimately, while Auerswald does return to and address the need to radically change how we think about education and the education-job-retirement lifepath, he doesn’t return to the increasing white death rate. Why are white death rates increasing faster than other death rates, and will transition to the “gig economy” further accelerate this trend? Or was the past simply anomalous for having low white death rates, or could these death rates be driven by something independent of the economy itself?

Now, it’s getting late, so that’s enough for tonight, but what are your thoughts? How do you think this new economy–and educational landscape–will play out?