How Bubbles and Megaprojects Parallelize Innovation

Plus! Vaccine Economics and Emerging Markets; Big Tech and the Missing Middle; The Personal, The Political, and Ant; Form, Function, and Ads; More...

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Coming up in the next few weeks, I’ll be talking about: why US and European payment regulations promote different kinds of financial innovation, the theory that “a mine is a hole in the ground with a liar on top” as a long-term driver in natural resource equity returns, a profile of an interesting conglomerate, a writeup of a boring company that can earn far higher profits in a post-Covid world, gold, a general theory of why US equities have outperformed so much of the rest of the world for so long, and the economics of state-backed airlines.

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How Bubbles and Megaprojects Parallelize Innovation

There are two equivalent ways to describe a financial bubble:

  1. It’s when investors pay up for assets without regard to their real  valuation, whether that means high six-figure no-doc loans to  people making minimum wage in 2006, massive checks to the fifth or tenth  online pet food company in 1999, or a meme-themed crypto project in late 2017.
  2. It’s when the flow of money into a market directly or indirectly validates the thesis of investors in that market.

Explanation #1 is simplistic, because it doesn’t posit a mechanism.  It makes calling a bubble easy in retrospect. Just look at what assets  went up a lot and then went down. Explanation #2 is more powerful,  because it offers an explanation and some guidance on when the bubble  can break down. Many famously irrational bubbles turn out to have  internal logic like this. For example, here’s how Paul Graham explains Yahoo’s valuation in 1999:

By 1998, Yahoo was the beneficiary of a de facto Ponzi  scheme. Investors were excited about the Internet. One reason they were  excited was Yahoo’s revenue growth. So they invested in new Internet  startups. The startups then used the money to buy ads on Yahoo to get  traffic. Which caused yet more revenue growth for Yahoo, and further  convinced investors the Internet was worth investing in. When I realized  this one day, sitting in my cubicle, I jumped up like Archimedes in his  bathtub, except instead of “Eureka!” I was shouting “Sell!”

And here’s how Greg Lippmann’s subprime short thesis is described in The Greatest Trade Ever:

[Deutsche Bank quant Eugene] Xu split the country into  quartiles. He discovered that states with the lowest rates of default,  like California, Arizona, and Nevada, also claimed the highest growth in  home prices. The quartile with the highest rates of default, on the  other hand, had the slimmest growth in home prices. Florida and Georgia,  for example, seemed similar in many ways, but Xu’s numbers showed  Florida had a much lower rate of default than its northern neighbor,  which seemed to be due solely to its soaring home prices.

“Holy shit,” Lippmann exclaimed to Xu on Deutsche Bank’s trading  floor while reading over his work, “if home prices stop going up, these  guys are done.”[1]

Let’s talk about another bubble. This one also involves rocketing  market values, scams and frauds, volatile markets, and crazy  self-fulfilling extrapolations. It’s the story of the auto industry. In  the early 1900s, a handful of entrepreneurs in Detroit had a zany idea:  some day, cars would not be driven by eccentric hobbyists; they’d be a  common mode of transportation! Meanwhile, at Standard Oil’s headquarters  in New York, another nutty thesis was brewing: maybe the rise of the  electric light bulb didn’t mean that the oil industry, which up to that point depended on kerosene lamps for its revenue, was doomed.

For either of these to be right, they both had to coincide: for cars  to be a ubiquitous means of commuting, gas had to be available everywhere. Otherwise, a car was just a fancy toy.[2] It would not do to drive a long  distance, run low on gas, and then learn that the automobile revolution  hadn’t reached your destination, and you didn’t have enough fuel to get  back. Gas stations solved this problem, but they came fairly late. The Prize  estimates that in 1920, there were fewer than 100,000 places to buy  gas, and that most of them were “grocery stores, general stores, and  hardware stores” that sold it by the can. The first gas station was  founded in 1907, and by 1929 there were 300,000 stores that sold gas,  almost all of which were gas stations. The same book notes that oil  production rose from 1.0m barrels per day in 1919 to 2.58m in 1929, and  that 85% of 1929’s consumption was gas and fuel oil.

This means an oil wildcatter in the 20s was making a bet on the  widespread adoption of a cutting-edge technology, the car. Meanwhile,  the car manufacturers were making a bet on the continued productivity of  the oil industry. If either industry had failed, both would have: a  shortfall in car production would have flooded the market with oil,  wiping out the (almost always financially overextended) oil entrepreneurs. A shortage of  oil would have made the total cost of ownership for cars prohibitive,  and that would have slowed down the shift towards car-friendly cities.

The internal combustion engine dates back to 1876. Oil is older (it’s  mentioned in Herodotus), but oil as an actively exploited energy source  dates back to the Titusville well in 1859. But the most influential  boom both went through happened decades later, and exactly in sync.

Semiconductors and software had a similar tandem bubble cycle, with  each generation of software justifying the next generation of chips. And  still later, the glory days of ISPs as growth stocks lined up with the  rise of publicly-traded dot-coms: VCs who invested in e-commerce were  indirectly subsidizing AOL and Compuserve, and those companies were  indirectly subsidizing e-commerce.

Sometimes, the bubbles fall slightly out of sync. Fiber optic  infrastructure received far too much investment in the late 90s and very  early 2000s, well before there was a ready supply of streamable  content. By the time streaming started to turn viable, this  infrastructure was still in the ground, and now very cheap. (Not as  cheap as it could have been, since Google was buying it up.)

But in general, the paired-bubble concept is a powerful one. As long as  both sides of the bubble have a lag between when the decision to spend  is made and when the results are realized, they can leapfrog each other:  in period 1, company A invests; in period 2, company B invests in  response; in period 3, company A’s investment creates a broader market  for company B’s product, which comes along in period 4; this success  encourages A to launch another round of spending, repeating the process.

Bubbles aren’t the only mechanism for coordinating parallel  innovation. Scientific megaprojects can, too. One of the impressive  things about the Manhattan Project was how much of it got started on the  assumption that other parts of the project would finish successfully.  The uranium enrichment plant at Oak Ridge, for example, was designed to  use an uncertain enrichment technique at scale. It was possible that the  plant would not be able produce uranium in sufficient quantity and  purity to be useful in the atomic bomb (only a few years before,  estimates for how much uranium would be required varied by an order of  magnitude in either direction). Once completed, the plant would  require electricity, and would, in fact, require the single largest  power plant ever constructed up to that time. So a power plant was built  for a facility that might not function at all, and, if it functioned,  might not do the job it was intended to do, and, if it did that job,  might end up producing raw materials for a project that wasn’t viable  for some unrelated reason.

Atomic theory and the reality of nuclear weapons were linked by a  long chain of technical uncertainties, and resolving them serially would  take too long. So everything got built at once, based on rough  estimates that were rapidly, continuously refined.

Any researcher interested in nuclear weapons in, say, 1935 could have  looked at the information published up to that point and concluded that  such weapons were possible. But penciling out all the technical  problems that would have to be solved to be sure they were viable was  daunting, and building only part of the project was worthless; a  theoretical design for a bomb was pointless when the largest available  samples of pure U-235 were barely visible to the human eye. Refining  larger amounts would have been a ludicrously expensive idea without a  ready blueprint for how to use them. (The entire Manhattan Project ended  up requiring an investment equivalent to the value of the US auto  industry at the time.) A megaproject parallelized a set of tasks that  would never get done serially.

Today, it’s possible to look at trends that have a similar parallelization trend going on.

Every financial mania requires suspension of disbelief, but sometimes  that’s entirely rational. Early twentieth century progress in cars and  late twentieth century progress in computers were both literally  unbelievable to anyone who watched them happen at the time. As it turns  out, sometimes the intersection of finance and technology implies a  double negative: when two industries producing complementary products  embrace a shared irrational delusion, the delusion comes true.

[1] Looking back at these anecdotes, one of the striking similarities  is that figuring out how a bubble works is an emotionally moving  experience (“Eureka!” “Holy shit!”), whether you’re about to cash in your options or buy a bunch  of CDS contracts.

[2] In his excellent Science Since Babylon,  Derek de Solla Price says “Amongst historians of technology there seems  always to have been private, somewhat peevish discontent because the  most ingenious mechanical devices of antiquity were not useful machines  but trivial toys.” Given how many technologies turn out to have older  antecedents that were never put to widespread use, it may be the case  that technology requires the intersection of a toy and a speculative  mania or megaproject.

This piece is adapted from a forthcoming book Tobias Huber and I are working on. Stay tuned for more.

Discussion question: are there other current bubbles that are  parallelizing innovation? I’m opening comments to free as well as paid  subs for this one.


I joined Jordan Schneider on ChinaTalk to discuss vulnerabilities in China’s financial system with Lauren Gloudeman and Logan Wright of the Rhodium Group,  who have both done some very interesting work on the topic. It’s a fun dive into the  quirks of the system, the incentives and information asymmetries of  regulators, and how to track how stressed China’s banks are.

Vaccine Economics and Emerging Markets' Tough Choices

1.35bn doses by year-end 2021, compared to a world population of  7.6bn, forces some difficult choices. One of the hardest will be for the  leaders of emerging markets, who need to handle a more drawn-out period  between when the pandemic’s effects are felt and when it’s no longer a  problem. As The Economist points out  ($), this is especially challenging for India. Their economy “does best  when the rest of the world does well—but not too well. India’s exports  benefit from global growth. But when the world economy gains too much  momentum, interest rates and oil prices can rise uncomfortably high,  hobbling a country that is a net importer of both capital and crude.” In  the rich world, deficit spending is a reasonable way to deal with the  problem of a recession whose catalyst has a known end date, but poorer  countries are constrained in how much they can borrow.

One compelling possibility: immigration restrictions in the US and  travel difficulties everywhere will reduce emigration, meaning that some  of the skilled workers who typically leave India to work in richer  countries will stay behind. That turns a brain drain into a source of  service exports.

Big Tech and the Missing Middle in Emerging Asia

Pondering Durian has an excellent writeup  of how big tech companies will reshape economies in Southeast Asia. In  the rich world, there’s a relatively smooth distribution from many small  companies to a smaller set of mid-sized ones to a handful of huge  corporations. In developing markets, the gap is often wider. As the  Durian notes:

In India & Southeast Asia, there isn’t much of a  middle to hollow out. Given the distribution of employment, the biggest  opportunities come not from digitizing corporations, but from digitizing  SMEs, solidifying the classic bifurcation of big tech & the  micropreneurs they serve. This follows the Chinese examples of Taobao,  Meituan, Didi, and even Douyin to bring the sprawling SME / artisan  class into the 21st century.

The Indian & Southeast Asian ecosystems are following a similar  path; the race to be the preferred partner for SMEs is on. In Southeast  Asia, Shopee, Lazada, Tokopedia, Bukalapak, Sendo, Tiki and more are  jostling to build supplier liquidity on C2C marketplaces. Grab &  Gojek are fighting over drivers and food-delivery. Grab Financial,  GoPay, Momo, VNLife, Mynt, Paymaya, and Truemoney are atop the scramble  to modernize payments, O2O marketing, and digital financial services for  SMEs. B2B marketplaces like Ralali, Telio, Bukalapak Mitra, Warung  Pintar are clashing (or partnering) with PoS / basic accounting tools  like Moka or BukuWarung - striving to own the offline merchant workflows  where they are running smack into eWallets. And the enterprise software  solutions are duking it out, fighting tooth and lol… gotcha, no  enterprise titans :). India is the same story, different names:  Flipkart, Swiggy, Zomato, Google, Facebook, Jio, Paytm and a host of  others striving to embed themselves with the economy’s largest  opportunity - the SME.

Now that we’re all epidemiologists, it’s much easier to throw around  analogies from the world of infectious diseases: the US and Western  Europe have had a long time to develop a reasonable immune response to  big companies, that involves politics (but not too much!), a robust set  of small and mid-sized companies with lots of collective economic heft,  and financial systems that can fuel the growth of challengers when the  dominant firm in an industry gets too lazy. In a more vulnerable  population, the large-company model has fewer obstacles to runaway  growth.

The Personal, The Political, and Ant

The WSJ has a detailed look at how Ant’s IPO came to be undone ($): Xi Jinping was personally furious at Jack Ma’s speech talking up Ant and talking down legacy banks (translated text here).  Xi suggested tighter regulations on fintech, and approved a  stricter-than-originally-planned rule that would require Ant to fund 30%  of its loans.

In one sense, China’s government is legible, because it’s impossible  to coordinate the behavior of almost 1.4bn people in a one-party state   without writing down, in detail, what those people are supposed to do. A  less centralized government, or one that’s less central to people’s  lives, can actually afford to be more vague. But the necessity of many  written rules doesn’t imply the nonexistence of unwritten rules, and  apparently one of those unwritten rules was to avoid criticizing China’s  banks.

Form, Function, and Ads

New companies grow by doing something radically different, but as  they mature they end up converging on the rest of their industry. And  this process works in the other direction, too: incumbents end up  borrowing ideas from challengers, often at scale. Online businesses  built on direct-response marketing, like and Expedia, have  found that the best way to keep growing is to use branded TV  advertising, for example. Two more examples:

Understanding Value’s Underperformance

Lyall Taylor has a long and occasionally quite pugnacious look at why value stocks have underperformed. As he notes:

Examples abound of formerly highly-rated franchises that  were part of the high multiple growth and quality parts of the  investment universe that suffered massive disruption and falls from  grace, and the tendency of a not immaterial minority of highly-rated  quality/growth stocks to fail to live up to their expected potential and  suffer major de-ratings has been a fundamental driver of the long term  underperformance of high multiple stocks as a group.

Normally, value investors are characterized as cynical and  pessimistic: they’re not going to pay 80x earnings for Netflix because  they’re skeptical that any company could be worth such a high price  relative to its current profits. But in a meta sense, value investors  are the optimists: they’re making a bet that the future hasn’t  been figured out just yet, and that the best business models investors  can back today are inferior to the businesses of the future. A growth  investor is a local optimist and a global pessimist, who believes that  Facebook, Amazon, and Apple will continue to do well and nobody will  disrupt them. A value investor is a global optimist, who thinks banks  and energy companies aren’t down for the count just yet, and that every  big tech company today is an IBM waiting to meet its Microsoft.