Tuesday, January 16, 2018


There was some trouble posting this earlier; Blogger did something weird to the formatting. It's still not quite right, but I figured having something here is better than nothing, especially for people who use the RSS feed.

The first US census was taken in 1790. Boston, according to the census, housed 18,320 people. The famous Battle of Bunker Hill, Boston’s main battle in the American Revolution, saw about 2,400 colonial militia face off against at least 3,000 British redcoats.

Let’s put that in perspective. In 1970, Kent State University had about 21,000 students - slightly more than 1790 Boston. The protest which ended with the Kent State shootings drew about 2,000 students.

So, comparing the Battle of Bunker Hill and the Kent State shootings, we see communities of comparable size, and “rebel” forces of comparable size.

In 1775, a few strong writers and orators (e.g. Samuel and John Adams) could rile up the entire city of Boston to the point of armed rebellion. Imagine this today - despite the daydreams of protesters and organizers, it seems pretty unlikely that a major city would be driven to arms by politicians and activists. There’s just too many people to reach them all. But in 1775, the entire city if Boston was only as big as a mid-size modern university. The entire community could be riled up by a handful of writers and speakers.

The comparison between 1775 and today grows even stranger when we think about the battle itself. 2,000 militia - roughly comparable to a campus protest, but with guns - went toe-to-toe with the military of the world’s dominant empire. When the war was over, the student-protesters-with-guns came out ahead, and an entire new nation was founded.

In 1775, a person, a pen, and a soapbox could make that sort of thing happen. Today, no way. Why? Population growth. When the third largest city in the region is smaller than today’s universities, communities were small enough that a few people could mobilize a large fraction of the population. But as populations grew, the methods which once mobilized tight communities no longer worked.

One more piece of perspective, to drive home the point. In the 1790 US census, New York City had a population of 33,131. That’s comparable to today’s Claremont, CA, where I went to college. Claremont isn’t a tiny town - most people don’t know each other. On the other hand, their kids all go to the same high school (Claremont High). That was New York City, the largest city in the US, in 1790: small enough that everybody’s kids would have fit in one modern-day high school.

Some of the teachers at Claremont High have probably met a majority of the people in Claremont. Shaking hands with everyone in the city is quite feasible, and politicians in 1790 New York probably did just that.

And that was the largest city in the nation! When Jefferson called the US a nation of small farmers, he wasn’t waxing poetic or fantasizing. The whole nation had 3.9 million people in 1790; the 24 largest cities housed just 0.2 million. 90% of the population worked on farms - I’ll have more to say about this in a future post.

Friday, December 15, 2017

Bitcoin Future-Spot Divergence

As of last night’s close, the price for a January bitcoin future was $1,171.21 higher than the price for a bitcoin. That means anyone could:
  • Buy one bitcoin for about $16,500
  • Sell a bitcoin future for about $17,500
  • Wait until January
  • Sell off the bitcoin and pay off the future contract
  • Pocket over $1,000.
That’s a return of ~6% in just one month, and you can lock it in instantly - once the bitcoin is bought and the future sold, their values will move in tandem, so there’s no risk of losing money. Normally, we expect these kinds of arbitrage opportunities to disappear quickly - especially for assets like bitcoin, which are easy to acquire and cost nothing to store. So why haven’t the prices converged?

If you want to know why an apparent arbitrage opportunity hasn’t disappeared, an easy way to find out is to try to exploit it, and see what stops you.

In this case, the main issue is that a bitcoin future is a future. Futures require both parties to hold margin: money available to cover their side of the contract as prices move. Usually, margin on a future isn’t huge, since prices aren’t too volatile. But bitcoin? Very volatile. That means very high margin requirements.

As usual, Interactive Brokers has everything you need to know on one page: margin requirement to sell a single bitcoin future is $40,000. Now, that margin can still earn interest while it’s sitting around, so it isn’t a “cost” per se. You don’t need to spend it in order to take advantage of the arbitrage opportunity. But you do need to have it available, and you can only arbitrage one bitcoin per $40,000 available.

This is still a pretty good opportunity, but you can only put so much money into it. That explains why small traders aren’t wiping out this arbitrage opportunity. So, next question: why aren’t the usual big institutions arbitraging away that price difference?

Usually, here’s how the situation would play out. A trader would buy a bitcoin and sell a future. Now, the trader would like to repeat this trade in order to make more money. So, the trader would go to a banker and say “hey, I have this low-risk arbitrage opportunity, I’d like to take out a loan collateralized by my one bitcoin in order to leverage my position.” Banks love collateral, so they’d give the trader a loan, and the trader would make the same trade again. Now the trader has another bitcoin, gets another loan, rinse, lather, repeat. In actual practice, many of the middle steps happen automatically, and the whole process is called “leveraging”.

With bitcoin, this is not so easy. Good luck finding a banker who will make a loan collateralized by bitcoin (i.e., look for a margin account which allows direct bitcoin trading). Even setting aside that issue, a trader would also have to borrow for the futures’ margin requirement. Now, the whole arbitrage together is actually very low risk, so it should be possible in theory to get a loan to do this… but it would require a personal relationship with a banker who understands the nitty-gritty and is willing to dip their toe in untested waters.

Put it all together, and we have a beautiful, persistent arbitrage opportunity limited by liquidity. It will disappear eventually, but it’s going to take time for the bankers to warm up.

Tuesday, December 5, 2017

Lemons, In-Group Signals and Marketing

Professor Quirrell didn't care what your expression looked like, he cared which states of mind made it likely.” - Harry Potter and the Methods of Rationality, chapter 26

Quick, which slogan will yield more sales:
  • “Be smart, buy X!”
  • “Not Your Grandma’s X”
Got a guess? Good, remember it.

This post is going to present some background game theory on signalling, and then talk about what that theory predicts for the slogans above.

The Lemons Game

What can a used car dealer say to convince you it's not a lemon? (image source)

Consider a game with two players: a prospective car buyer, and a seller. The seller begins with either a working car or a broken car - a “lemon” - at random (50% chance for each). The seller knows whether or not the car is a lemon, and considers a working car more valuable. So, for instance, maybe the seller is willing to sell only above $10k if the car is working, but will sell a lemon as low as $5k. On the other side, the buyer is willing to pay up to $12k for a working car, or up to $6k for a lemon.

One little wrinkle: the buyer has no way to check whether or not the car is a lemon before deciding whether to buy. Mechanical problems may not be immediately obvious during a test drive.

What happens?

Well, think it through from the borrower’s perspective. The car has a 50% chance of being a lemon, a priori. Ignoring risk aversion, a buyer would pay $9k for a 50/50 chance of a working car… but at that price, the seller wouldn’t be willing to part with a working car. So if the buyer offers $9k, then she will only end up with either no sale or a lemon! So, the borrower will only bid somewhere between $5k and $6k in the first place - since she’s only going to get lemons anyway, she only offers enough to buy a lemon.

The sad thing is, you may have an honest seller on one side trying to sell a working car for $11k, and a buyer on the other side who would love to buy a working car for $11k… but the deal won’t happen, because there’s no way for the seller to convince the buyer that the car isn’t a lemon. Anything the seller could say which would convince the buyer, a dishonest seller with a lemon could also say.

Cheap Talk vs Signalling
The lemons game illustrates a key concept: even when you let two people communicate freely, it may be impossible to convey relevant information between them.

This problem comes up whenever someone might be motivated to bluff. In the lemon game, a seller with a lemon is motivated to bluff - whatever a seller with a working car might say to sell for $11k, the seller with a lemon will also say in an attempt to get $11k for their lemon. Thus the phrase “cheap talk”: talking can’t actually convey any useful information here.

In the real world, we have various ways around this.

Among the simplest is Carfax: a trusted third party which can tell the buyer whether the car is a lemon. A seller with a working car will happily pay Carfax $50 to certify it. The certified car will then sell somewhere around $11k.

But barring trusted third parties (Carfax isn’t perfect), how else can a seller signal that their car is not a lemon? Remember, the key here is that it must be something which a seller with a lemon could not, or would not, do!

Another simple answer: offer to cover the cost of any mechanical issues for some time after the sale. That would be expensive for lemon-sellers, so they won’t agree to it. Any seller willing to cover mechanical costs must be selling a working car. This is useful, but it creates a new problem: the buyer will be incentivized not to take very good care of the car, since the seller is covering repair costs anyway.

Here’s a more interesting answer: whenever the car needs repairs, the buyer pays for the repairs and then sends the receipt to the seller. The seller takes enough money out of their bank account to cover the repairs, puts the money in a fireplace, and burns it. As before, this is a bad deal for lemon-sellers, so they won’t agree to it. Only sellers with working cars, expecting few mechanical issues, will agree - ideally, this means little or no money will actually need to be burned! What matters is the seller’s willingness to bet on the quality of the car, which signals the car’s quality to the buyer.

Marketing and In-Group Signalling
In the lemons game, the key to effective signalling is that the signal - whether a carfax report, a contract to cover breakdowns, or a contract to burn money in the event of breakdowns - must be very expensive for a lemon-seller, but not very expensive for the seller of a working car. This is critical. Anything which a dishonest lemon-seller could afford to say is cheap talk, and buyers won’t buy cheap talk.

This has interesting implications for in-group signalling.

Suppose I want to signal to my goth friends that I’m in their boat. So, I put on the most over-the-top outfit I can manage, chains and black makeup, the whole shebang - the key being that such an outfit would definitely not fit in any non-goth social circle. (Politics offers better examples, but I don’t want to derail this post.)

If someone wants to signal their membership in a group, then the best way to do that is with something which would be prohibitively expensive for someone outside the group. In these situations, we’re not usually talking about monetary expense. Instead, the “cost” is in social capital with other groups. In other words: the best way to signal membership in an in-group, is to do something which completely ruins one’s chance with the out-group.

Which brings us to marketing.

Truism: it’s better to have 10% of the population 100% interested in your product than to have 100% of the population 10% interested. Nice heuristic, but the model which usually underlies it in practice is in-group signalling. If you can signal that your product is affiliated with some group, then group members will buy your brand religiously. Apple, converse, starbucks… many a household name has made a fortune on this principle. But the all-important key to an in-group product is that it must not target everyone. Like the lemons game, if anyone can send the signal, if the signal is no more expensive for the out-group than for the in-group, then the signal is not a signal at all - it’s just cheap talk. Signals must cost something.

If you want to signal that your product is great for <in-group>, then the best way to do that is to offend <out-group>. The more blatant, the better. “Duck Dynasty” figured this out better than most, and gathered a truly ridiculous following for a show which could generously have been called a non-entity. Part of the key to Starbucks’ success, is all the people who hate it and hate everything a $5 coffee stands for. That’s the beauty of it: offend the out-group’s sensibilities, and you send a strong signal of in-group status. It’s the equivalent of offering to burn money if the car breaks down. (Just make sure to pick an actual in-group first; offending random people is no more useful than randomly burning money!)

Let’s say, hypothetically, you want to get young people to use product X. Easy tagline: “Not Your Grandma’s X”. Conversely, for targeting less-young people, “X for Grownups”, ideally delivered with an ad making fun of teenagers for being idiots (I remember a great Old Spice campaign along these lines). Humans have great intuition for this sort of thing: we see our outgroup mocked, and automatically assume that the mocker is “on our side”.

A few other ideas, to convey the flavor:
  • “Moms love X!”
  • “The X for people who like trucks”
  • “X: for true <sports team> fans only”
Note that these don’t always “offend” the outgroup per se; but they do all but guarantee that nobody in the out-group will ever buy your product. Indeed, the more they discourage out-group members from buying the product, the better they work. Non-moms will almost never buy “X for moms”. By way of contrast, consider a useless slogan like “Be smart, buy X!”. Everyone wants to be smart! Unless your advertising manages to convey a very group-identity-loaded concept of “smart”, enough to actually turn away non-”smart” consumers, it’s going to come off as generic cheap talk and fail to tap into any identity at all.

The takeaway:

  • Signalling should be costly to fake; otherwise it’s just cheap talk.
  • In the case of in-group signalling, the “cost” is usually to push away out-group members.
  • Humans have strong intuition for this stuff.
  • In-group-specific marketing should push away people not in the group.
  • More generally, in marketing, any signal which costs only ad spend dollars will be seen as cheap talk - ad spend is cheap for fakers.

Wednesday, November 22, 2017

Computational Limits of Empire

The tabulation of the 1880 US census took 8 years to complete. As preparation began for the 1890 census, it was estimated that tabulation would not be complete until after the 1900 census began! The computational load was declared to be too great; an alternative approach was needed.

The problem was solved by a mechanical computer based on punch cards. A company was founded specifically to build the contraption; that company would later become IBM.

I was thinking about this story, and I wondered: just how large was the US population in 1890? Did other nations reach that population level before? How did they handle the problem?

The 1890 US census counted 63M people, in total (source). How large did the Roman empire grow? Well, the Roman empire seems to have reached its peak around… 60M people. At this point I really started to get suspicious, and looked up population statistics for the ancient Persian empire and the Chinese empires. 50M people for the Achaemenid empire (Persian). China had 30-85M under the Han dynasty, stabilized around 50M for a few centuries, then grew from 45 to 80M under the Tang dynasty.

Next, I pulled up wikipedia’s list of largest empires and Business Insider’s list of top 10 greatest empires. I had to google around for population stats, many of which were not immediately available, but here are the big ones, excluding empires from 1700 or later:
There were a number of smaller “empires”, mainly the predecessors and/or successors of empires on this list. But on the other end, only the Mongols managed to scrape together an empire of over 100k people, and that empire split within a generation (spinning off the 60M-person Yuan dynasty).

Yes, this is a far cry from systematic. Yes, there’s room to complain about selection. Nonetheless, there is at least a very noticeable tendency for pre-modern empires to max out in the 50-70M population range.

Is the empire population cap due to computational limits in governance? I’m not sure how to properly test that hypothesis, but it does seem awfully suspicious that the founding event of the modern computing industry was triggered specifically by the US passing that 60M population mark.

One interesting question to pursue next: how did other modern nations/empires handle passing the 60M population mark? India and China both achieved sustained growth and built stable nations of over 100M people during the early modern era. Presumably the British empire’s population was also beyond 100M during much of the 19th century. Did these states also face computational blockades? What techniques did they introduce which might explain their ability to overcome the 60M person cap?

Monday, November 13, 2017

The Open-Source Alternative to a College Degree

Five years ago, Massive Open Online Courses (MOOCs) were the hot new thing in higher education. Finally, the time was upon us! The internet was set to upend our outdated modes of education!

Today, that does not seem significantly closer to materializing.

MOOCs failed to usher in an era of cheap, large-scale higher education for exactly the same reason that opencourseware failed to usher in an era of cheap, large-scale higher education ten years earlier: they solve the wrong problem. “Education” is not about learning things, it’s about signalling. People don’t study in school because they’ll need all that knowledge on the job. People study in school to show how smart and hardworking they are, so companies will hire them.

Similarly, the value of college isn’t in making students memorize factoids or formulas. The value of college is in filtering students. Employers hire graduates because colleges filter out weaker candidates, both in admissions and over the course of a four-year degree. College grads are much more likely to make strong employees.

But in shifting from a learning-view of education to a signalling-view, one thing stays the same: college seems like an awful lot of resources to burn. Surely the benefit could be captured without spending four years and two hundred thousand dollars? If anything, it seems like signalling intelligence and work ethic ought to be even less resource-intensive than learning things!

So, what might a viable alternative to college look like? If not MOOCs, then what?

Within software, one answer might be open-source contributions.

Already today, companies are eager to hire large contributors to major open-source projects. And such qualifications seem much more relevant to software engineering than a degree: working on large open-source projects is nearly identical to working on a large project at a software company. A candidate who has contributed lots of code to a popular library or framework will almost certainly be successful writing similar code for a company.

On the flip side, open-source projects are constantly in need of more hands. Even the most popular libraries have long wishlists. There’s no Common Application to get started, just pick a software package, browse the open tickets and go. The filtering comes from project owners, who will review any proposed changes or additions to the code. If your code doesn’t pass muster, re-do until it does - the project owner will likely explain exactly where it falls short. If the owners of a project are unpleasant to deal with, go contribute to a different project - though projects are unlikely to grow large in the first place with unpleasant management.

On the other hand, compare to college. There’s a lengthy admission process of questionable granularity, followed by four years of professors who may or may not be interested in helping you. If you fail, it’s a permanent black mark, even if it’s in some stupid class unrelated to your career. At the end of the day, your incentives, employers’ incentives and colleges’ incentives are not very well aligned.

So why do people still go to college, rather than taking some online programming classes and then working on open-source projects?

One answer, presumably, is that college is the default path. The open-source alternative is non-obvious, especially to people not yet in the software industry. It also lacks the flexibility of a college degree. These both seem like reasonable explanations, but neither is a serious roadblock to wider “adoption” of the open-source alternative. Of course, moronic HR departments are another issue, but that only matters at large companies.

Perhaps the most serious roadblock is simply that nobody is promoting the open-source alternative, so nobody knows it’s there. In this case, there is an obvious group who is incentivized to promote it: owners and managers of open-source projects. If Apache were to promote open-source work as an alternative to a degree, they might find a lot more helping hands.

Am I missing anything here? Is there some other reason why the open-source path would not work? Let me know.

Tuesday, November 7, 2017


Background: This is part of a short series on high-level principles relevant to political/social issues. The previous post discussed depersonalization and scalability of interactions. This post can be read standalone. If you want to understand the modern economy, as opposed to the economies of yore, the one source I recommend most strongly is a short story from the July 1958 issue of Astounding Science Fiction, titled “Business As Usual During Alterations”. It’s roughly a 15 minute read. I’m about to throw out major spoilers, so stop reading here if you want to enjoy the story first.

One morning, two devices mysteriously appear in front of city hall, along with directions on how to use them. Each has two pans and a button. Any object can be placed in one pan and, with a press of the button, a perfect duplicate will appear in the other pan. By placing one duplicator device in the pan of the other, the device itself may be duplicated as well.

Within a span of hours, material scarcity is removed as an economic constraint. What happens in such a world?

People tend to imagine the dawn of a new era, in which human beings can finally escape the economic rat-race of capitalism and consumerism. In the world of the duplicator, a pantry can provide all the food one needs to live. A single tank of gas can drive anywhere one wishes to go. Any good can be copied and shared with friends, for free. All material needs can be satisfied with the push of a button. Utopia, in a nutshell.

The main takeaway of the story is that this isn’t really what happens.

Towards the end, a grocer explains the new status quo eloquently:
“... not very many people will buy beans and chuck roast, when they can eat wild rice and smoked pheasant breast. So, you know what I've been thinking? I think what we'll have to have, instead of a supermarket, is a sort of super-delicatessen. Just one item each of every fancy food from all over the world, thousands and thousands, all different”
Sound familiar?

Of course, that’s just the tip of the iceberg. When it comes to digital goods, like music or videos, the world of the duplicator is exactly the world in which we now live. That’s the obvious parallel, but let’s not stop there.

Over time, the value of raw materials and manufacturing have steadily fallen as a fraction of economic output. Even when looking at material goods, efficiency has shifted the bulk of costs from materials and manufacturing to design and engineering. We are converging to the world of the duplicator, where marginal production costs hit zero, and in many areas we’re already most of the way there!

This hasn’t made economic activity disappear. Pulling from the story again:
“This morning, we had an economy of scarcity. Tonight, we have an economy of abundance. And yet, it doesn't seem to make much difference, it is still the same old rat race.”

I won’t spoil all of the remarkably prescient predictions of the story - do read it yourself.

Badge Value
Here’s one good you can’t just throw on a duplicator: a college degree.

A college degree is more than just words on paper. It’s a badge, a mark of achievement. You can duplicate the badge, but that won’t duplicate the achievement.

Rory Sutherland is another great source for understanding the modern economy. The main message of his classic TED talk is that much of the value in today’s economy is not “material” value, i.e. the actual cost of making a good, but “intangible” or “badge” value. A college degree is an extreme example, but the principle applies to varying degrees in many places.

The sticker price on an iphone or a pair of converse isn’t driven by their material cost. A pair of canvas high-top sneakers without a converse logo is worth less than a pair of converse, because converse are a social symbol, a signal of one’s personal identity. Clothes, cars, computers and phones, furniture, music, even food - the things we buy all come with social signals as a large component of their value. That’s intangible value.

In the world of the duplicator, the world to which our economy is converging, badge value is the lion’s share of the value of most goods. That’s because, no matter how much production costs fall, no matter how low material costs drop, intangible value remains.

In the past, we’ve sold material value because that was a scarce commodity. Now, the shoe is on the other foot, we’ll sell intangible value.

Jobs & Employment
One particularly prescient line from the duplicator story:
“You know, when we first got the word about this thing, this duplicator, we immediately started thinking in terms of pretty drastic retrenchment. Then... it turned out we didn't have much fat to spare. Engineers, draftsmen, designers; we need about six times as many as we have. Nut-twirlers and button-pushers on assembly lines will go; but mechanics, craftsmen who can take a blueprint and turn out a piece to specified tolerance...”
Sound familiar?

We’re already well into the post-scarcity economy, and sure enough, nut-twirlers and button-pushers are disappearing rapidly. Yet every other week, news outlets are running stories about the shortage of STEM workers. The economy of the future, we’re told, needs thinking and creativity rather than repetition and basic labor.

The root cause of all this is the economic equivalent of the duplicator: steady growth of economic productivity, and the consequent reduction of materials and manufacturing as a share of cost.

The duplicator story gets one big thing wrong, however: it predicts that the shift in labor demands will be met by retraining. It’s an elusive dream still chased today, most recently by MOOC advocates. But at the end of the day, learning is not the main purpose of most education - after all, most of what people learn is never used on the job. Education is about signalling, through degrees and grades - badge value. That badge isn’t saying “I know Newton’s laws”, it’s saying “I have handled intellectually challenging problems”. Until we learn to create whatever cognitive capabilities a college degree filters for, retraining is unlikely to turn nut-twirlers into engineers.

And that’s the optimistic case. What if colleges don’t filter for a fixed skill level at all, but instead filter for a relative skill level? Oversimplifying a bit, what if colleges just give degrees to the smartest 20% of people they can find?

Keeping Up with the Joneses
The general problem with badge value, and signalling in general, is that a badge isn’t worth anything if everybody has it. In order for a badge to be worth something, there have to be people without the badge. It’s a zero sum game.

Keeping up with the Joneses is a classic example: people buy things to signal their high status, but then all their neighbors buy the same thing. They’re all back to where they started in terms of status, but everyone has less money.

The prevalence of zero-sum signalling today economically stems from the reduction of material scarcity. If you think about it, zero-sum games are inherent to a so-called post-scarcity society. A positive sum game implies that net production of something is possible. That, in turn, implies that something was scarce to begin with. Without scarcity, what is there to produce?

To put it differently: there’s always going to be something scarce. Take away material scarcity, and you’re left with scarcity of status. If there’s no way to produce net status, you’re left with a zero-sum game. More generally, remove scarcity of whatever can be produced, and you’re left with scarcity of things which do not allow net production at all - zero sum goods.

Today’s world has found a way to get around this problem somewhat: heterogenous cultures. The baristas at SightGlass coffee have very high status among hipsters, but hardly any status with bankers. Janet Yellen has very high status among bankers, but hardly any status with hipsters. Each different culture has its own internal status standards, allowing people to have high status within some culture even if they have low status in others.

Cultural heterogeneity allows net status to be produced, by increasing the kinds of status which one can have. When “hipsters” became a thing, they brought along their own kind of status in addition to all the old kinds of status. Cultural granularization makes status signalling positive-sum. But from another perspective, it just kicks the zero-sum game up to the group level: hipsters as a group compete for status with bankers, in a zero-sum manner. Thus tribal conflict between groups.

Rent Seeking
With all this talk of zero-sum games, the last piece of the post-scarcity puzzle should come as no surprise: political rent-seeking.

Once we accept that economics does not disappear in the absence of material scarcity, that there will always be something scarce, we immediately need to worry about people creating artificial scarcity to claim more wealth. This is the domain of political rent-seeking, of trying to limit market entry via political channels.

One simple way to measure such activity is via lobbying expenditures, especially by businesses. Such spending actually seems to have flattened out in the last decade, but it’s still multiple orders of magnitude higher than it was thirty or forty years ago.

Remove material scarcity as an economic constraint, and what do you get? The same old rat race. Material value no longer scarce? Sell intangible value. Sell status signals. There will always be something scarce.

Between steady growth in industrial productivity and the advent of the digital era, today’s world looks much more like the world of the duplicator than like the world of 1958. Yet many people are still stuck in 1950’s-era economic thinking. At the end of the day, economics studies scarcity. Even in the world of the duplicator, where any material good is arbitrarily abundant, scarcity still exists.

This is the world in which we live: as material and manufacturing costs fall, badge value constitutes a greater and greater fraction of overall value. On the employment side, falling marginal production costs mean less need for assembly line workers, and more need for engineers, designers, and high-skill trades. And politically, less material scarcity means more investment in creating artificial scarcity, through political barriers to market entry.

Welcome to the post-scarcity economy.