Innovation requires a super-charged combinatorial hub, 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 or evolutionary perspective.
One common analogy is the Cambrian explosion, 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 ecological niches. In short, lots of new, successful critters, then their predators and then co-evolution.
Lex Sokolin, a CoinDesk columnist, is Global Fintech co-head at ConsenSys, a Brooklyn, N.Y.-based blockchain software company. The following is adapted from his Fintech Blueprint newsletter.
Looking at more recent examples, we can cite tech 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 network effect. It is just the right soup to take a particular capitalist risk. More importantly, everyone else thinks 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 U.S. dollar as a reserve currency.
If your startup 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 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 among 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.
What does this mean for the future of money?
When economists try to figure out the best shape of a monetary system, 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.
See also: Lex Sokolin – The Smart Money Economy
Sometimes, history really does provide useful experiments. Take the free banking era from 1837 to 1864, when the 50 states in the U.S. each ran slightly divergent financial policy. It used to be that central banking was quite controversial in the U.S. and that each state localized the issuance of credit and money.
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. Some states like New York actually showed very low loss rates on bank notes. Others, including 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 three 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 dollar COVID-19 social programs and always-rising stock markets, it would be super prudent to try out different policies experimentally.
The money accelerator
The evolution of our money machines is stuck at a local maximum (the point at which one needs to step out of a system to get beyond it).
They are incumbent and hegemonic. They are gargantuan and monolithic, moored and 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 (1837-1864), 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 is referred by the industry as “open finance” (short for open-source finance), or decentralized finance (DeFi).
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 like that community of Cubists in the 1910s 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 percentage 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.
See also: Lex Sokolin – Valuing Open Source: Principles for Acquiring DeFi Projects
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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