Mehrling’s approach to understanding money and how it functions in the economy can provide actionable insights into how to invest & innovate in digital economies, specifically the aspect of attention. Today I will give one example of how this is the case.

We start from Mehrling’s hierarchical categories of money:

As we recall from Intrinsic Macro #41, the top of the hierarchy is real money, the bottom part is more a form of credit or promises to pay.

When there is economic expansion, the hierarchy gets flattened, and more things seems like money, because securities, stocks, and risky assets go up in value as perceived innovation & speculation increases. This leads to manias & bubbles. When there is contraction, the hierarchy re-asserts itself, as repayments come due, then things that seemed like money show that they were really credit.

We can use this framework to understand the specifically digital economy. In the digital economy, there is a hierarchy from free content all the way up to data, which is what drives the digital economy.

Data is the most money-like not only because it is sold in the marketplace and drives revenues of big tech companies, but because it is the closest to money as a predictor of human behavior & motivations.

Code builds infrastructure for digitals networks by which data can be collected, and it is also used to analyze and aggregate data.

Premium content is gated or paywalled content, which is a productive economic good that is generated by participants of these digital networks.

Free content is what drives attention to the network and converts users to premium.

Conclusions

There are several practical observations that we can draw from this comparison:

1.) This can explain why fake news is so prevalent, because most content we see online is free content, which can have the properties of an IOU, a promise to pay later, but not necessarily. Some free content is reliable, some isn’t.

2.) In a period of what we could call “data expansion,” content becomes more data-like.

3.) Market prices are only one type of data. This suggests that if price has self-organizing properties, then data must have an even greater self-organizing property.

4.) Gold is the ultimate money, yet the banking system largely does not use gold as collateral anymore.

5.) Similarly, while data may be the most money-like thing in the digital economy, data is not scarce. The thing that is scarce is attention or time. And this means that attention gets increasingly economically valuable as there is more and more free and premium content. And therefore, it is possible that attention can serve as a form of collateral.

Whereas neo-classical economics is based mostly on game theory and the concepts of general equilibrium and rational preferences, Mehrling’s approach is based in the older tradition of American institutional economics, which is an offspring of classical political economy.

Rather than flattening all economic activity to the forces of supply & demand in general equilibrium, Mehrling’s view is a realist view, which acknowledges institutional hierarchies that exist the circulation of money. Instead of looking at supply and demand curves, we instead look at assets and liabilities of balance sheets at different levels of the financial hierarchy, to determine liquidity and solvency:

Assets correspond to liquidity, liabilities to solvency.

Academic economics is split between the monetarist-Keynesian views which say that the business cycle needs to be regulated through counter-cyclical interventions, policy frameworks, & redistributions of wealth, and free market-libertarian views that say decentralized planning by market participants & small government / deregulation provides the best outcomes for everyone.

Mehrling’s is more general than both of these views, in that it encompasses both; it is agnostic on the issue of top-down versus bottom-up planning, and simply recognizes that hierarchical institutional structures for proffering money exist, ever since central banking came about.

There is a basic acknowledgement that the banking system in the United States, and in most countries is to some extent socialized. Banks are not really independent of the central bank, they are ostensibly quasi-private corporations, but in reality they have a special relationship with the central bank, and thus, with the state. This became much more clear from the response to the 2008 financial crisis and the 2020 pandemic crisis. Rather than debate about whether this should be the case or not, Mehrling simply takes the realist view that it is the case.

These are notes on Perry Mehrling’s course on the Economics of Money & Banking:

The four prices of money:

The four prices of money show that what we think of as money is actually based on four hierarchical categories.

1.) the interest rate — the price of money today in terms of money tomorrow (future). This is central bank money, the central bank sets the benchmark interest rate.

2.) par — price of one money in terms of another money, today (present). This is commercial bank money.

We take the par price of money for granted. If you have a commercial bank deposit of $107, we take it for granted that the bank has assets of $107 — that it is par. But we see in financial crises, sometimes the par price of money is broken. Bank money trades at a different rate than central bank money. US commercial banks hold central bank money as assets. So there is a hybrid system. Par is key to the payment system. If there is not par clearing between different states, for example; if you buy something from California, and you live in New York, they might charge you 5% extra, if there is not par clearing in the states. Par clearing between states had to be established by the banking system, it was not always there.

3.) exchange rate — the price of domestic money in terms of foreign money.

4.) price level — the price of commodities.

The difference between money and credit:

Money is better than credit. Credit is a promise to pay money. Money is money. Money is qualitatively better. Money is the thing that eliminates debts, the thing that debts are cancelled in. Credit is an IOU.

All text books talk about money and credit like that. In order to emphasize the hierarchy, Mehrling emphasizes four categories of money. There is an ultimate money: gold.

Hierarchy of money & credit:

Gold → National currencies → Deposits → Securities

Strictly speaking, only the first is money, the next three are merely promises to pay money (IOU). Now where do we draw the line between money and credit? If you take a banking point of view, deposits & securities are credit, and gold & national currencies are money. What counts as money and what counts as credit depends on the circumstance. In any individual situation, we can ask ourselves, what things are credit, what things are money? In an expansion, more things become money-like, in a contraction, debts have to be settled.

Hierarchy of financial institutions:

Whenever we are talking about institutions, we are talking about balance sheets.

Central bank → issues national currencies Banking system → takes the national currencies and issue deposits & loans Private sector → takes deposits & loans and buys securities & goods

Everything is credit besides gold or money. Any form of credit is inside money. Gold is no one’s liability, and so it is outside money. All inside money is an IOU, a kind of promise to someone else to pay at a later date.

Dynamics of the hierarchy:

During an expansion the hierarchy of money & credit becomes more flat — the distinction between money and credit is lost. During a contraction, the hierarchy of money is reasserted. In a contraction, all the things that are credit and, not money, lose value, as repayments in money are demanded and liabilities are settled.

The Scarcity of (Ultimate) Money

There is only so much gold in the world. This is the disciplining feature of the money. But counterposed to this is the elasticity of derivative credit. It is denominated in money, but is not itself money.

In a crisis, the principle of discipline smacks you. In a boom, elasticity makes everything seem like money.

The principle of elasticity is credit — if you and I make an agreement, an IOU, then we can expand credit without any constraint of the monetary system. Ordinary people can simply expand the total “money supply” or really the total credit, simply by making an agreement with one another.

And this is where we see how Mehrling’s theory (called the money view) generalized from both left wing and right wing economists. You can build a theory of money from elasticity of derivatives or the scarcity of ultimate money.

Banking principle: monetarism, Keynesianism, is built on the presupposition of more expansion of credit and more elasticity in the system. The money supply can be expanded to intervene in times of crisis.

Currency principle: metallism, charlatism, is built on the presupposition of more discipline and austerity, and less expansion of credit. The market is a disciplining mechanism and the state should allow for bankruptcies of non-functioning companies.

There is always a debate between these two views of economics. At any moment in time, there is always more discipline or more elasticity. It swings back and forth. This can give the impression that economists are fickle or that they don’t know what they are doing. Both sides are capturing something true about the system, but not the whole system.

Hierarchy of market makers:

Central bank → Banking system → Security dealers

Each of the institutions is a market maker.

Securities dealers knit together deposits & securities. This determines price, as interest rate.

The banking system knit together currencies & deposits. This determines the par price.

Central banks knit together nation currencies & international currencies, this determines the exchange rate.

Incentives:

Some actors are focused on profit maximization, the private sector. Others are focused on economic stability, the central banks.

The role of economic stabilization has evolved over time. What do central banks do? One thing that they obviously have to worry about is the exchange rate. They have to defend the exchange rate in the international economy. Sometimes they help banks to maintain par.

Bagehot’s book Lombard Street: A Description of the Money Markets (1873) is about the London money markets. What Bagehot tries to convince the world with this book, is that the Bank of England — a private bank — has been acting as a lender of last resort. It had done this in prior crises. The system had evolved that way without intending to. Bagehot wrote this book to bring this phenomena of “lender of last restort” up to public consciousness, so that people could just accept it.

Bagehot’s solution was, in a financial crisis, we’re going to lend freely, at a high rate against good collateral. It was very controversial when it first came out, but then all central banks embraced it.

But wouldn’t it be better if we never got to the financial crisis? Central banking became about counter-cyclical intervention policy. It comes from the inevitability of hoping you can do something about financial crises to avoid them. Not just counter-cyclical policy, but prudential policy.

The term structure of interest rates

There is the policy rate, set by the central banks. This is the overnight rate, the Fed funds rate in the United States, or simply the interest rate to borrow money from the central bank.

There is also the market rate, which is set by the bond market. This is the rate set by bond traders on instruments such as the 30-year government bond.

Intrinsic Macro is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Introduction

One of the main narrative headwinds against digital assets since their inception has been the claim that they have no intrinsic value. The notion that blockchains do not have revenues like conventional businesses has presented a roadblock to some investors, while others simply consider blockchains as a type of commodity or a social network that doesn’t have or need any intrinsic value.

The main problem with this impasse is that without a fundamental understanding of what digital assets are, it is hard to understand what economic good they produce, how they can be a net benefit for the economy & society, and why they are a better alternative to the existing financial system or internet economy.

Lacking an answer to these questions, many people, including Congresspeople, are convinced that digital assets are actually worse than the existing financial system.

While I don’t entirely agree with these criticisms of the detractors, the proponents have yet to deliver a serious refutation. Perhaps this is because a refutation is not deserved or forthcoming. Or perhaps it is because the next wave of “innovation” must be conceptual rather than technological.

This small treatise is my attempt to offer an answer to what economic good digital assets produce, how they can be a net benefit for the economy & society, and why they are a better alternative to the existing financial system or internet economy.

I will attempt to answer the criticisms by summarizing what I regard as the two primary discoveries about intrinsic value in digital assets- John Pfeffer’s equation of exchange model and Raoul Pal’s network effects model- and showing how these two perspectives are integrated.

Digital assets are best thought of neither as currencies, businesses, social networks, nor commodities, but represent distributions of intrinsic coordination (rather than extrinsic or price coordination).

Equation of Exchange Model

In 2017, John Pffefer built upon Chris Burniske’s model of applying the equation of exchange (M=PQ/V) to digital assets. I will spare the details of M=PQ/V, and attempt to capture the main point of Pfeffer’s argument. Pfeffer argues that for blockchains, the corollary of conventional business revenues is mining fees. Mining fees verify transactions on the network. And they require computational resources. If we were to classify blockchains as businesses, then computational resources are the main revenue source of the network.

Tokens represent computational resources. If there is no way to tie token price directly to computational resources, then there is no real incentive to hold the tokens. Or even if tokens are tied to directly to computational resources, then why would investors hold a deflationary, commodity-like asset like computational resources? Investors do not hold barrels of oil or raw electricity. Since velocity (V) of tokens is likely to remain high, there will be little value (PQ) accrual to blockchain networks.

Although they have different conclusions, Pfeffer’s thesis bears a family resemblance to Ryan Sean Adams’ thesis that blockchains are businesses. The economic good that blockchains produce, is blocks. Blocks are computationally verified transactions on the distributed ledger.

But for Adams, blocks don’t look overvalued, because of all their benefits. To Pfeffer blocks look very overvalued.

Pfeffer is not against digital assets, as he clarifies in a recent podcast. He just doesn’t think they are good businesses. He is skeptical of the value capture of layer 1s & decentralized applications. He argues against network effects models like Metcalfe’s law to value blockchain networks because of the many examples of open source software, that capture little or no value, such as ITP, Linux, and Wikipedia. Even if they are useful products, they aren’t good investments, he argues.

Thus, Pfeffer says that the only thing that can justify the high premium on computational resources is the store of value use case, best exemplified in Bitcoin. Pfeffer’s thesis has been key for the Bitcoin maximalist argument and advantages of proof-of-work over proof-of-stake.

Pfeffer’s thesis may be wrong, but it brings out a few important insights:

1. Like Ryan Sean Adams, “blockchains sell blocks.” Pfeffer’s paper examines the fundamental revenue model for blockchains-as-businesses.

2. Selling blocks secures the network against a 51% attack, which is Kyle Samani’s adaptation of Pfeffer’s thesis combined with the fat protocol thesis. Thus, selling blocks ensures network security, reliable value storage, and risk management.

3. The revenue from owning the network or selling blocks may not have not comparable margins to other profitable businesses. The price / earnings for many layer 1 blockchains can be exorbitant.

Raoul Pal’s Network Effects Model

Raoul Pal published his initial network effects valuation model in January 2021. Pal’s paper explicitly argues against Pfeffer’s equation of exchange model. Pal argues that the MV=PQ of monetary theory has failed to describe reality. The problem is monetary theory’s core assumption- that supply is the key driver of value.

Pal makes the case that demand-side network effects like Metcalfe’s law actually describe value accrual of blockchains in a much more accurate way. Network growth or demand is based on simple liquidity preference, time preference, and risk preference. There is nothing irrational about blockchain network adoption effects.

He sees no real issue that they are purely speculative investments, because in the end everything is. Different networks are just different flavors of value store with different use cases. Pal’s view is not exactly that blockchains are commodities, though it is a comparison he makes with Bitcoin. But L1 blockchains are social networks, like Facebook or Twitter. He doesn’t go into the questions of earnings, revenue, intrinsic value, or value accrual, or the business models for social networks. For Pal, value accrual in blockchains is network effects, plain and simple.

Let’s fast forward from Pal’s January 2021 paper to Pal’s new valuation model, which he published on LinkedIn in March 2022. It goes beyond Metcalfe’s law. Pal gives an incredibly concise & beautiful formulation:

network value = daily transaction volume X daily active users.

Check out how accurately this fits the price movements of $BTC and $ETH:

This formula is astounding in its simplicity, elegance, and descriptiveness.

So what is the problem with it? While it may be a more-than-adequate counter-example to Pfeffer, it doesn’t seem to answer Pfeffer’s basic problem of the intrinsic value or the revenue model, why it should make a profitable business. It doesn’t tell us what economic good blockchains produce, how they are a net benefit, and why they are superior to the existing financial and web2 systems.

Intrinsic Coordination Model

We know that blockchain networks are valuable, but we don’t know why they are valuable. Pfeffer’s perspective is one we are familiar with from trad-fi investors, (especially value investors): “*where are the earnings?” *Pal’s perspective is one we are familiar with from social media: “it’s all about narratives, marketing, & memes.”

I want to suggest a way to go beyond these two perspectives. It is through consideration of the *content monetization models *of web2 vs. web3. I will argue that the content monetization of web2 social media is the advertising revenue model, while the content monetization model of web3 is learning.

What is the economic good that is produced by blockchain network revenues?

Blockchains sell blocks- and blocks are an algorithmic verification of a trustworthy transaction. Verification generates revenues by mutual validation, peer review, & coordinated error-correction. Advertising generates revenues by coercing people into believing an idea. At a conceptual level, advertising revenue and verification revenue could not be more opposed to one another.

To think that verification occurs only at the computational level is wrong.

The intrinsic coordination thesis is that distributed verification applies at the human level as well as at the computational level. People engage in mutual learning and verify one other’s investment hypotheses through peer-review and error-correction. This drives greater network effects to investment and thus, more protocol revenues. This is reflexive, because more mutual learning leads to more reliable confirmation or disconfirmation of the investment hypotheses, which makes the invested networks more secure, which leads to more mutual learning that grows the network.

It is the opposite of a hype-driven, narrative-driven, meme market. It is primarily distributed peer-to-peer learning that drives investment interest. The more meme marketing and propaganda, the more people call crypto a ponzinomics scam. But the more deep content threads and thought pieces, the more outsiders & fence-sitters are convinced of the value.

This is very different form the advertising revenue model, because the system is not based on coercion, but on mutual learning.

Intrinsic coordination describes how digital media as a whole reflects a system of intrinsic & organic content verification. People mutually peer-review and verify the objective truth of each other’s content against their highest criterion, to evaluate validity. Their highest criterion adjusts over time, to get closer to or further away from the objective truth. This is not just for investments, but for any type of content. That is, learning content is its own coordination equilibrium, independent of price.

They do this evaluation not as algorithms, but as organic human beings, using logic, intuitive discernment, & moral standards. Over many years time, the world gradually works out the vast puzzle of objective truth through extended reflection, feedback, conversation, and prayer. The internet is a vast distributed, organic, peer-to-peer learning system. Learning is the human level corollary of transaction verification that happens at the computational level.

This requires such a shift in our perspective and our orientation that I suspect many wolves of blockchain Wall St. will continue along the path of marketing propaganda. But the thing is, they will be unable to compete with objective truth. It’s not possible. In the same way that Bitcoin is unstoppable because of its mathematical / economic truth, objective truth is unstoppable because it reveals the logical, aesthetic, and moral source of existence.

To the extent that web3 groups continue with the web2 advertising model, they cannot make the argument that they offer a meaningful improvement to the financial system or the web2 tech stack.

If web3 is based on learning rather than advertising, then the criticisms of industry corruption & ponzinomics become less valid. The network effects of truth are justified simply because it is the truth. It is not primarily financially motivated. People come to the truth for its own sake, not because they are coerced by marketing or propaganda.

Digital Assets Prove That Content Is More Valuable Than Money(And How Much More Valuable)

My basic thesis is that the premium paid for digital assets above cumulative revenue reflects precisely how much more valuable content is than money. People would literally rather hold part of an open source digital network than the money they use to pay for it. This means that the open source content is more valuable than the money.

It has always been common knowledge that content is more valuable than money. If one can learn effectively, then earning money is trivial. Consider the story about Thales told by Aristotle in his Politics:

He was reproached for his poverty, which was supposed to show that philosophy was of no use. According to the story, he knew by his skill in the stars while it was yet winter that there would be a great harvest of olives in the coming year; so, having a little money, he gave deposits for the use of all the olive-presses in Chios and Miletus, which he hired at a low price because no one bid against him. When the harvest-time came, and many were wanted all at once and of a sudden, he let them out at any rate which he pleased, and made a quantity of money. Thus he showed the world that philosophers can easily be rich if they like, but that their ambition is of another sort.

Similarly, level of education correlates with higher lifetime earnings. Also consider the value of asymmetric information.

Here we can distinguish two complementary equilibria: *intrinsic coordination *or interpersonal coordination by common sense social norms, and extrinsic coordination or coordination by an impersonal price mechanism. These are complementary.

In economics, Ronald Coase’s *The Nature of the Firm *(1937) is the precedent for this distinction. He shows that coordination within the firm is opposed to coordination by the price mechanism.

These are complementary equilibria because one or the other is more appropriate depending on social proximity.

And this is the primary difference between web2 and web3:

In web2, prices coordinate distribution of content or interpersonal norms, because of the advertising revenue model. In web3, content or interpersonal norms coordinate the distribution of prices, through the learning revenue model.

In intrinsic coordination, content is evaluated organically by a highest criterion, which in turn determines distribution of prices & allocation of resources. This is the opposite of the big data approach to content in the advertising revenue model, where prices & statistics algorithmically determine analysis & distribution of content.

This is a conceptual shift rather than a technological one. All we are doing is drawing out the conceptual implications of the technology, and showing its telos. Blockchain technology implies that content is the primary coordinating element that determines prices & allocations of resources. Because one would literally rather hold a piece of an open source software network than hold the money. The generation of content is still completely distributed, but is its own equilibrium.

This is completely different conceptually, from advertising revenue model. If one of the primary questions of digital assets is revenues, then the answer is in how content is monetized. In advertising revenue model, content is monetized in one way, in web3 it is monetized in a different way. Advertising involves imposing a subjective reality onto someone else. Learning involves mutual convergence to objective truth. That is not to say that there is no hierarchy in learning.

NFTs already demonstrate this conceptual difference between learning and advertising- between content determining distribution of prices and price determining distribution of content. NFTs have outperformed other classes of digital assets & are inversely correlated in price to other digital asset classes. This simply reflects that they began to have content that was more valuable than money.

This is a Cryptopunk holder turning down a $9.5 million offer for his jpeg:

“My identity along with the identity of other iconic Punks and apes have value beyond the NFT itself. We have our own brands similar to any other brand and that has value. Because I value my personal brand and identity, this was an easy rejection for me.”

https://twitter.com/richerd/status/1449187165875826693?s=20

NFTs reveal an economic system where valuable content is the primary coordinating element, not price. And this means that NFT content is not just about art, fine art, memes, games, clubs or metaverses, it is about learning. With learning, the content is the end goal, not making money. In advertising, making money is the end goal, not learning the content.

As means to an end, price and interpersonal norms are complementary equilibria, as end goals, there can be no compromise between the two. Either content is more important or money is more important.

Education is the primary resource in any economy, and prices follow from that.

Thus, intrinsic coordination is not a political viewpoint, because its essential claim converges to objective truth; it is theological and not political. And for this reason intrinsic coordination has the same immutable quality as the original distributed ledger.

The political economy that corresponds to blockchain networks is not libertarian or socialist, but distributist. It maintains private property through ownership of digital networks, and at the same time it facilitates more and more direct ownership of capital by labor. It enhances localism rather than anonymous, impersonal, machine-like efficiency. Distributism offers a non-political way to mutually enrich society, without the excesses of market fundamentalism or communism / socialism.

The only content that is both general enough and specific enough to intrinsically coordinate all fields of knowledge more effectively than money is theological. Every humanly designed institution crumbles after some pitiful amount of years. Yet the institution designed by Christ has lived for more than 2,000 years and lives into eternity. According to the witness of St. Thomas Aquinas, God is Simple, Perfect, Good, Infinite, Existent, Immutable, Eternal, and One. He is known by us, He knows in us, He is Just, Merciful, Providential, full of Power. He conquers all evil. The Holy Trinity comes to dwell inside of us to the extent that we will receive it. He alone coordinates all human affairs most justly & fairly by pure Love for us. And we are called by Charity to share this Love amongst one another.

Intrinsic Coordination as Fundamental Valuation Model

This presents an interesting way to combine Pfeffer’s and Pal’s models, in order to get the real fundamental valuation model of digital assets.

A protocol reflects a certain mathematical & economic truth, which drives its reliability of use & how much value is stored in its smart contracts, which reflects how secure the network is, which reflects how trustworthy the network is, which reflects what kind of trustworthy NFT content is associated with that network. And that trustworthy content drives a greater understanding of the network’s reliability at the fundamental mathematical & economic layer.

The mathematical & distributed immutability (driven by computational resources) at the infrastructure layer thus corresponds to a metaphysical & theological immutability at the content layer (driven by human cognitive resources). These reflexively reinforce one another.

Since Pal’s model relies on network effects & transaction volume as one of its key components, we can consider *cumulative revenue (integrated with transaction volume & daily active users). *Cumulative revenue is the total amount of resources that have secured the network against a 51% attack. It is the historical track record & proof of the network’s reliability.

Network security can be analyzed at the protocol layer, as cumulative PoW or PoS revenues, or at the application layer, as TVL or as cumulative liquidity mining fees, or at the NFT content layer, as cumulative royalty fees.

The advantage of incorporating cumulative revenue is that we get a more fundamental model. The cumulative resources that have secured the network against a 51% attack is a metric that demonstrates the historical track record of the networks’ security, validity, and its ability to generate revenue, ultimately its trustworthiness. And cumulative revenue fits proportionally with price data, at least over longer time frames. The cumulative revenue ought to reflexively establish price at a certain multiple, unless network security drops and becomes vulnerable to a 51% attack.

If the P/E or P/S of blockchains is a huge multiple, it is because the economic good that is produced by blocks is so valuable and so lacking in our economy- trustworthiness. We are a world utterly lacking trustworthiness.

The premium or the P/E multiple of network price over its revenue, is precisely how much more valuable content is than money. It reflects the fact that content governs money, and not vice versa. The protocol itself is a kind of open-source content. What we thought would destroy governments instead enables an economy of distributed self-governance through the virtues.

The Problem: Digital media does not fit into the conventional financial model of fundamental valuation.

The dot com era companies had flawed business model assumptions, lacking fundamental value. But even the successful digital media companies show deep market failures: monopoly concentration, censorship problems, privacy issues, fragmentation and polarization of society, mental and physical health issues. The lack of fundamental valuation for digital media causes severe problems for the market and for society. This presents an opportunity for economic and social gain.

Let us look at the conventional finance fundamental valuation model. The Benjamin Graham-Warren Buffet model of fundamental valuation, is essentially:

Low price / high cashflows = good fundamental value investmentwhere,asset price = aggregation of publicly available information (efficient market hypothesis)cashflows = revenues / operating expenses (or demand / cost of supply)

A fundamental value investor looks at the stock price, looks at the financials of the company he is investing in, and does research on how well the business functions. He then invests if there is a low price relative to future cashflows. This concept of fundamental value was a basic principle of American investment for many years.

Idealized value production economy, a system of mutual benefits:

Why doesn’t digital media fit into the fundamental valuation model? The product that digital media produces is content. Content is not limited in supply, only valuable content is limited in supply. Yet there is no consensus definition of what valuable content is or how it is limited in supply. Therefore, the implicit economic consensus is that valuable content is defined by whatever its market price is. However, price is only a small subset of valuable content, and can’t say how content is limited. Therefore, valuable content cannot be a limited good and price cannot be an accurate signal of the value of content. But fundamental valuation requires that price be an accurate signal of value. And this means that fundamental valuation for Google, Facebook, Twitter is inherently problematic.

Why can’t price be an accurate signal of value production in advertising revenue model? The advertising revenue model (Google, Facebook, Twitter) has to assume that price can accurately measure the value of content. The problem with this assumption is that both users and advertisers have different notions of why content is valuable, yet they are both on the same demand side. Advertisers care more about price / ROI of content, not as much about quality. Users care more about quality of content, not as much about price. Price has a consensus economic definition, but content does not. This means that users get the losing end of the deal, economically. They do real economic labor to produce valuable content, and yet they are uncompensated.

Therefore, in a true digital economy, the economic definition of price requires a consensus economic definition of valuable content. And this is what becomes possible with the NFT market.

Digital media economy with advertising revenue model; a race to the bottom:

What is the economic definition of valuable content? With digital assets, the economic definition of valuable content becomes more clear. In digital assets the distinction between user, advertiser, and company, collapses. Thus, the motives of those who produce valuable content versus its opposite, is more clear. Thus, it is more clear that content determines the price of digital assets, rather than price determining content, as in the advertising revenue model. And this applies in the economy more generally. The quality of a good determines its price, the price ought not determine its quality. Valuable content is therefore defined more by how high its quality is, than by how much is paid for it. Then how is valuable content produced, if not by price coordination? Valuable content is produced by internal coordination in small collaborative groups. Internal coordination produces content fundamentals such as engineering, incentives, and design. These are the fundamentals that also determine price in digital media more generally. Therefore, the economic definition of valuable content is: a limited good produced by internal coordination in small group communications.

This economic definition can be derived from Ronald Coase’s “The Nature of the Firm” (1937). Coase shows that internal coordination is **mutually opposed **to price coordination:\

There is a cost to coordination by price mechanism:

The cost of using the price mechanism is less internal coordination (within the firm):

and,

There is an equilibrium dynamic between price coordination and internal coordination:

Coase’s “Nature of the Firm” has a new significance in the context of distributed networks and digital assets. The firm is now small groups of digital collaboration. With Coase in mind, let us return to the original fundamental valuation model:

Low price / high cashflows = good fundamental value investment, where
asset price= aggregation of publicly available information (efficient market hypothesis) cashflows= revenues / operating expenses (demand / supply)

Now, we can take the next step: 

aggregation of publicly available information =
internal coordination / price coordination

Internal coordination could be measured by network size, but network size is only a proxy for price, which does not account for the qualitative aspect that is necessary for true internal coordination.

Conclusion: Revenues and operating expenses (or demand and supply) in conventional finance are analogous to internal coordination and price coordination in digital media. This changes our notion of incentives. In this analogy, revenue is collaboration, and price is an expense burden. The digital economy produces valuable content through internal coordination primarily, and price coordination secondarily, only after the fact. This is what we see in the NFT market. What the conventional economy produces- cars, tables, etc.- is now largely influenced by the valuable content that the digital economy produces. Valuable content is also a limited supply good, but in an altogether different sense than a material good. Valuable content is *limited* because internal coordination is extremely difficult.

Part II introduces the application of this thesis: the Learn to Earn business model.

This piece is available for investors and feedback. Investors and contributors receive monthly dividends on sales and re-sales.