Long Take: What 1840s "Free Banking" and the 1910s Cubist movement suggests about DeFi and economic machine evolution

Hi Fintech futurists --

There are a few bread and butter topics we could cover this week, but will push to the Short Takes instead. They are large and interesting, but not profound. Those include: (1) Goldman’s Marcus becoming a $100 billion deposit neobank, after a few billion in spend, (2) the SPAC boom surging ahead with a new ETF backed by Morgan Creek and a former Credit Suisse CEO as well as a $16B United Wholesale Mortgage transaction, (3) Capital One getting a $390MM fine over AML failures between 2008 and 2014. Each of these could be a chunky discussion — but we want to stick to deeper questions

This week, we look at:

• The nature of innovation hubs, and how close groups of actors within a particular environment can be massively, fundamentally productive. Take for example the 30 million years of the Cambrian explosion.

• The difficulty of experimenting with banking and money frameworks, the limits of traditional econometrics, and an overview of “free banking” in the 1840s.

• How evolutionary theory can help us think about selection of economic models, and the hyper-competition and hyper-mutation that we see in crypto. DeFi protocols, like BadgerDAO and ArcX among hundreds of others, are experiments in designing different monetary policies and banking regime experiments in real time.

For more analysis parsing 12 frontier technology developments every week, a podcast conversation on operating fintechs, and novel food-for-thought essays, become a Blueprint member below.

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Featured

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• Podcast Conversation: Embedded Finance as a $7 trillion market opportunity for banks in the business of suffering, with Simon Torrance

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Long Take

Let’s talk about money, central banking, and experimentation in the economy. There are several ideas we flag upfront:

1. First, innovation, art, and new ideas incubate and are born in recursive hubs.

2. Second, evolution happens through mutation, experimentation, and a variety of probabilistic outcomes.

3. Equilibria can be stable for some time, even centuries or millennia. But they are local optimizations over time. New designs and systems — a yield from the prior two points — inevitably replace the old ones.

A Cambrian explosion requires a super-charged combinatorial hub, and an environment where hyper-competition filters for the best ideas and allows them to proliferate. When we say best, we mean the most *fit* for their context, likely to sustain into future generations and outcompete from a multi game-theoretical / evolutionary perspective.

The actual Cambrian explosion was a period between 540 million and 520 million years ago that was responsible for exponential rise in the biodiversity of the Earth. It was caused by an interplay of multiple physical and biological causes, which were self-reinforcing, and created a pressure cooker of competition across various ecological niches. In short, lots of new successful critters, then their predators, and then co-evolution.

Looking at more recent examples, we can cite the various start-up hubs around the world. Silicon Valley is what it is because of the hardware, telecom, and high tech history of its location, the abundance of venture capital, and a massive reputational network effect. It is just the right soup to take a particular capitalist risk. Even more importantly, everyone else thinks that it is the right place to take that risk. That belief in the belief is what gives the idea power. Consider as a comparison the status of the US dollar as a reserve currency.

If your start-up is born in Silicon Valley, you are an organism of a certain type. Or alternately, if you are an organism of a certain type, you likely end up *in* Silicon Valley. Maybe not the actual one — but rather one mediated by Twitter communities, and Zoom, and the recent migration into Clubhouse. You filter into a tribe of people whose attributes are fruitful for you to emulate, and then you compete in the games of their environment. The start-up game has very particular rules, no different from the rules that an arthropod must observe deep in the ocean.

With some low probability, you may win, and turn into the “PayPal mafia”. This is an example of intergenerational survival and proliferation. As this type of organism acquired more resources, it spread its DNA (i.e., agile product development, software eating the world mission) and proliferated through angel investing.

Perhaps less cliche are examples of artists, poets, and revolutionaries. Take any artistic movement — say the early Cubists in the 1910s in Paris. Pablo Picasso did not develop the style in isolation, no more than Satoshi Nakamoto conceived every derivative of a blockchain-based network. Rather, there was an interplay between Picasso, Georges Braque, Juan Gris, Jean Metzinger, Albert Gleizes, Robert Delaunay, Henri Le Fauconnier, and Fernand Léger. These artists responded visually to the industrial machinery of their time, with photography unmooring art from physical representation towards emotion and symbolism.

Cubism’s germ may have come from Paul Cézanne, was caught and applied in Paris, and then went on to transform into other art fashions like Futurism, Suprematism, Dada, Constructivism, Vorticism, De Stijl and Art Deco.

We could apply a similar analysis to the interplay of ideas at the start of the American revolution and the narrative of the “Founding Fathers”. An emerging economy, combined with the distance of the oceans, French democratic experiments, and colonial geopolitics, gave a group of American rebels requisite mana for their governance experiment. The presence of such a hub — often with an associated manifesto, a Declaration, a way of looking at the world — is the soil in which explosive seeds grow.

Central Banking, the United States, and Experimentation

The next thing we need is experimentation.

Nature provides ample opportunity for mutation and selection, but on a slow time frame. Once we move into the real of the human mind and its ideas, time speeds up from millennia to centuries. We can organize various concepts into bodies, play them through in our social games, and select the ones that hold sway for future permutations.

If we narrow further on the idea of value and money, things get trickier because they are moored to the real world. Yes, the markets incorporate our animal spirits of speculation. But they also reflect actual companies with cash-flow fundamentals that work with objects in the “real” world. Monies, as units of account and so on, are expressions of sovereign power projected onto subject societies for the orderly benefit of those societies. Sometimes this is in the guise of an assumed social contract. But money is the flowing blood of a nation — or perhaps its ancient limbic system. A utility curve singing the song of exchange.

When economists try to figure out the best shape of a monetary system, whatever that may be, they are severely disadvantaged. Unlike scientists in other disciplines, who have labs and experiments to run, economists are stuck in history. Normally, you wouldn’t be able to hold all population variables constant and switch on and off from John Maynard Keynes to Friedrich Hayek. That would require a revolution and a seizure of the means of production and regulation. In peaceful times, perhaps it would require wildly political appointments to a Central Bank. Further, a wrong turn or a bad model would lead to a destructive effect on the financial lives of millions of people.

So what do you do? After getting a PhD from Chicago and practice in a lot of formal mathematics, you might turn to historical aberrations. You find “naturally occurring experiments”, and deploy the statistical econometrics toolbox to figure out which levers did what in that environment. You design 50 page papers with deep analytical underpinnings and hundreds of footnotes full of multivariate equations, and hope for the best.

Sometimes, history really does provide useful experiments. We here at the Fintech Blueprint are not qualified to do a literature review, but can point to some clear examples. The different approaches to economic regimes across various countries is one such set of data. There are about 200 countries in the world, but they largely follow a few clumps of economic theory, of which China (capitalism & authoritarianism), the United States (capitalism & democratic republic), and Europe (capitalism & democratic socialism) are the familiar examples. Others are on the way to these equilibria, and the Soviet experiment (collectivism & authoritarianism) failed out. Forgive us, astute reader, for the oversimplification.

If we take these economic theories and run them through the last few centuries of human events, we can find examples that suggest what interest rates do, and whether we should have money backed by gold or the credit of nation states. Financial shocks and recessions create measurable events that tell us about the levers available to control these economies. See Bretton Woods, the Asian financial crisis, the Great Recession, and so on.

The other example we can think about is the 50 states in the US, each running slightly divergent policies. Of particular interest is the free banking era from 1837 to 1864. It used to be that central banking was quite controversial in the US, and that each State localized the issuance of credit and money. The papers linked here and here, and the images below, provide a summary of this temporal experiment.

Private companies in these States were permissioned to issue bank notes that would function like currency (or a cash equivalent), and be redeemable into collateral held by the bank. The collateral ranged widely in quality, from currencies to other liabilities like State-issued bonds. The notes themselves would trade at different discounts depending on the State you were in, your counterparty, and the market conditions.

A bank run would involve many people wanting to redeem the notes at a bank at the same time, which in turn would often blow up the underlying institutions — either because they were over-levered, or held poor/fraudulent collateral. In the chart above, you can see that some states like New York actually showed very low loss rates on bank notes. Others, like Indiana and Wisconsin experienced much more volatility and bank closures.

Yet, today we have in place an orthodoxy about the right way to do monetary policy, which involves the close regulation of banking for the purpose of managing the money supply and the economic cycle. That means even less space to do experiments, such as implementing nominal GDP targeting for 3 years and then reverting, or running several simultaneous policies side by side as an A/B test. Given that we have entered a truly bizarre, strange phase of the economy epitomized by negative interest rates, $2 trillion Apples and Amazons, multi-trillion COVID social programs, and always-rising stock markets, it would be super prudent to try out different policies experimentally.

But we can’t.

The Money Accelerator

The evolution of our money machines is stuck at a local maximum.

They are incumbent and hegemonic. They are gargantuan and monolithic, moored ad tied into the physical economy. The crypto money machines are not yet in such a position.

Let’s consider them — the protocols on programmable blockchains — as a type of animal. Like the State banks in the free banking era, the protocols are collateralized with certain capital assets. Rather than obligations of States backed by taxes, that capital is often digital capital of another sort. It can be the store-of-value function of Bitcoin, or the computational rent of Ether, or the derivative promises of various Compound, Aave, Uni, or Yearn pools and vaults. In crypto language, collateral is “locked”, which then generates a particular structured note / receipt token. This is not much different as a mechanism from free banking, and in fact is referred by the industry as “open finance” (short for open source finance), or “decentralized finance”.

Nothing is new, dear reader.

A run on the collateral would similarly be a familiar sight — an unwinding of interconnected positions across the DeFi ecosystem. However, one major difference is that the entire thing does not have the embedded uncertainty of prior eras. The actual exposures are etched directly into all of the financial systems. We know exactly how collateralized all the positions are — and many industry participants can derive this number from easily available data and analytics services.

Further, the process of doing the work of collateralizing bank notes in 1850 and 2021 are pretty different. DeFi is blazing fast. In months, you can engineer and launch an entire economic system humming along on the latest financial software available to human kind. In minutes, you can re-price your risk and swap out your collateral. In fact, the robots will do all this for you.

The community of DeFi is also like that community of Cubists in the 1910s or the early Silicon Valley, passing ideas back and forth to engineer an innovation, a style, a fashion that will be the root of how we think about the financial world for years to come. It sits on a Manifesto about what money and finance should do for the individual, accessible anywhere in the world.

And it is full of rapid experiments that economists can only dream about. Those experiments compete for capital and reproduce through software forks. Many unfit versions of these experiments die out, while the fit ones re-combine and evolve.

We were inspired to write up this framing by the recent launches of projects like BadgerDAO and ArcX. You can think of them as individual instances of free banks, operating under different collateralization and issuance rules. Badger generates a synthetic elastic price asset called DIGG, which is pegged to the price of Bitcoin. Its arithmetic token count automatically adjusts to make sure that the peg holds (with your % position of the money supply held stable), and its value is driven by the price and demand for a stable Bitcoin-like asset on Ethereum, as well as the liquidity provision in certain automated market makers. ArcX allows users to take various synthetic assets (created from other collateralized experiments), which are equivalent to our previously discussed bank notes, and then use those as collateral to further mint/create a new financial asset called STABLEx, which in turn opens up various algorithmic savings rates.

There are many more other novel ideas in the space as well. These are just our chosen examples of 400+ different projects reproducing at the moment. To be clear — most of these will die, and some are destructive rather than collaborative in spirit. Some are multi-level marketing schemes, or wrong in their mathematics and code. But we have never before had such acceleration in the design space of the economic machine, subject to evolutionary pressures, built by a closely-wound nexus of developers. It is a fortune for the curious.

Most economists and bankers are allergic to this newness. Instead, we should be thankful for the opportunity to run such experiments, learn from them, and find new and better constructs for our economic world.

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For more analysis parsing 12 frontier technology developments every week, a podcast conversation on operating fintechs, and novel food-for-thought essays, become a Blueprint member below.