The Fundamentals of Crypto's $2T Market Cap

The meaning of "fundamental" means something different with crypto projects than with traditional companies, says our columnist.

CoinDesk Insights
Apr 14, 2021 at 4:38 p.m. UTC
Updated Sep 14, 2021 at 12:40 p.m. UTC

Crypto isn’t magic. It’s math. Two trillion dollars worth of math.

We are still, often, asked incorrect questions about the crypto currency markets, like “but what is the fundamental value?”

You have to unpack the word “fundamental.” That word signals a Warren Buffett view of the world: There are companies out there, they have equity shares well specified by corporate law in a particular jurisdiction, some are expensive while some are cheap, and that bargain-shopping can be determined by a spreadsheet analysis of their cash flows relative to others. It’s so fundamental!

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.

The story of such fundamental truth is anchored in our cultural and social history. We can point to the intellectual tradition of rationalism and classical economics, and talk about the theory of the firm, and its production function. We can point to how these things grew out of governance by religion, and natural rights as granted by a deity, and all sorts of other non-empirical hand waving.

We can talk about supply and demand, and equilibria, and describe some agents in a perfect market with perfectly formed property rights. And some of these agents, surely, will be “good” (i.e., cheap relative to performance) and some will be “bad” (i.e., expensive tulips).

Then we look at the real world and learn that markets are imperfect and deceptive, that humans behave irrationally because of their evolutionary biology programming, that top-down rationalist models don’t square with reality, that some of Warren Buffet’s best investments are in fact political and derive from monopoly market structure, and that the whole machine is careening off a cliff into imagination land.

That doesn’t mean that equities traded on a stock market, as circumscribed by their full supporting human history, can’t be valued relative to each other. On the contrary, they demonstrate that having people agree on a mathematical framework for structuring economic exchange can create that specific economic exchange. But we are living in a system that exhibits complexity, and which appears random not due to some underlying randomness, but because of the exponential interaction of underlying mechanisms. Any math that we put around it is an approximation.

And so the financial models we trade on the markets are not companies; they are beliefs derived from financial models correlated to the promise of legal enforcement. The models represent real world activities, and it is that representation that is being priced, bought and sold.

Let’s assume that we’ve budged your conviction about what is financially real. Turning to crypto networks, we can see that many of the elements of “assets traded on a stock market” do not apply to them. They are not always companies duly organized in Delaware, but often a global smattering of individuals across the Twitterverse. While some deliver cash flows, it is not always the financial attributes that networks seek to grow but economic or operating ones.

So instead of using questions like “How can we maximize profit to accrue to owners?,” they use questions like “How can we get this industry to create a virtuous cycle for storing data for itself on this network?”

This is the type of question that a community manager or online game designer may ask. It is also the type of question that a well-cultured traditional company may ask if it has a Steve Jobs or Jeff Bezos customer orientation, supplanted with an open source ethos.

This is what is worth two trillion dollars.

Sometimes things fail, even though they were the right idea at the time: Morgan Stanley robo-advisers from 2001, machine learning algorithms from the 1970s, video streaming in the mid-2000s. The surrounding infrastructure was not the right soil for that particular idea to grow at the time. All you get is a bunch of salty entrepreneurs.

When the first token offering wave hit in 2017 and 2018, smart people began to establish a formal practice of token engineering, sometimes called crypto economics or tokenomics. It is rooted in rigorous game theory, mechanism design, and mathematical simulation. There was a notable difference in the quality of thinking across the industry. Some teams used terms from this field as if they were magic summoning words, and that in saying those words, correct outcomes would simply appear. Other teams built concepts for the long term with system design in mind.

There was a lot less data around in 2017 about what would end up working. Overly mathematical papers looked like nonsense in an environment where Telegram and EOS were raising over $1 billion each based on business logic and hype. Much of that vapor would dissipate, and the unpopular inventors went onto new frontiers.

Yet, over the long run, there are stark differences between successful and unsuccessful token design. This practice is the closest “truth” we get to the concept of fundamentals in the crypto ecosystem. You can create a network or protocol with incentives that drive usage and value into the ground — a Nash equilibrium with bad pay-offs.

Sometimes those paths to a bad equilibrium come from edge conditions, such as too much demand and popularity. The Fei protocol, which attempts to create an algorithmic stablecoin with punishing mechanics for selling the pegged coin, ran a fund-raise recently that attracted over $1 billion of capital, but quickly lost its mark-to-market value. While we are not endorsing any particular view on this asset, the following threads are instructive in showing how incentive design created the opposite of the desired outcome, and is now resulting in emergency action.

Crypto isn’t magic. It’s math. Two trillion dollars worth of math.

In a counter-example, we can look at Ocean Protocol and The Graph. Both projects had spent years sweating their tokenomics and openly discussing design ideas (not that Fei hasn’t). The result looked over-engineered during the summer of decentralized finance (DeFi) growth and downright unnecessary in the crypto winter of 2018-2019. But it was built for the long run. And when these projects launched, their machines ground into gear, generating purpose-built economic activities.

In a counter-example, we can look at Ocean Protocol and The Graph. Both projects had spent years sweating their tokenomics and openly discussing design ideas (not that Fei hasn’t). The result looked over-engineered during the summer of DeFi growth and downright unnecessary in the crypto winter of 2018-2019. But it was built for the long run. And when these projects launched, their machines ground into gear, generating purpose-built economic activities.

It is imperative to have clarity and insights into what exactly one is trying to grow and optimize. In the parlance of linear programming or machine learning, you have to know your objective function – the exact equation, and thereby outcome, that you want to maximize. And as a corollary, you have to know how that function actually works: its inputs, its outputs, and how the gears turn to generate the outputs. This is the opposite of meme-based investment frameworks, and should be comfort to asset allocators looking for “fundamentals.”

You also need to think about where the incentives are plugging in. Some will be nested into the actual legal and economic system, or equivalently the underlying blockchain network. Others will live at the equivalent level of the firm, and be structured as protocols or platforms. Such projects will have incentives focused on particular digital assets, their adoption and customer behaviors. Yet, other digital assets will be self-contained products, like the art non-fungible tokens now coming to market, and power only the asset itself. 

This approach to growth sounds novel but can be actually found internally at companies with strongly articulated cultures, like Amazon or Bridgewater Associates, accomplished through social and compensation mechanisms. Software has much higher precision around the specification of what agents can do with the software itself. Therefore, network-orchestrated organizations are currently more quantitative rather than the qualitative human social norm. It is this mathematics that is creating the $2 trillion of value now attributed to digital asset ecosystems. And it is this practice that will be a core part of valuing our future economies.

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