Author: Max Resnick, Chief Economist at Anza
Compiled by: Jiahuan, ChainCatcher
I'm posting this article because SIMD 550 (doubling the rate of inflation decline) and SIMD 553 (resource fees) are about to enter the voting phase. This article is not a direct commentary on these proposals—I've already left my feedback on the specific proposal pages on GitHub. Here, I'm primarily discussing how the issues these proposals aim to address relate to the valuation of L1.
Part One: The Establishment of Asset Pricing Theory
Before the formal establishment of asset pricing theory, investors did not lack methods for valuing companies. Some focused on tangible assets and liquidation value, others emphasized profits, dividends, growth, management quality, or market sentiment. There were many indicators available, but what was truly missing was a rigorous framework to determine which indicators drive value and how to weigh them against each other.
By the late 1920s, just before the Great Depression, this ambiguity had become extremely dangerous. Investors could cite facts such as rising profits, market expansion, new technologies, and improved corporate governance, but these facts were often used merely to justify market prices rather than to calculate asset values.
Graham and Dodd (1934) later described that era as one in which analysis gave way to "potential and prophecy." Even when data was presented, it became "pseudo-analysis used to support the fantasies of the time."
Those who frequently scroll through Crypto Twitter will find this scene familiar. Today’s discussions around L1 tokens are filled with the same potential, prophecy, and pseudo-analysis that once dominated the stock market in the late 1920s.
The number of people participating in blockchain development has reached a record high. Trading activity has hit an all-time high. Tokens will become currency, collateral, digital oil, or a call option on the future financial system.
Some of these claims may be true and could even suggest that the underlying network has upside potential. However, without explaining how these factors translate into value for token holders, they cannot form a coherent valuation framework.
John Burr Williams pioneered the move toward rigorous asset pricing. In The Theory of Investment Value (1938), Williams argued that value is "the present value of a stock's future dividends, or the present value of a bond's future coupons and principal."
Gordon (1959) later expressed the same idea more concisely: “Stocks, like other assets, are purchased because they are expected to generate future income.”
Equity has value not because a company is impressive, actively engaged in business, strategically important, or technologically indispensable. It has value because equity grants shareholders a claim on future income.
For L1 tokens, value can be aggregated in two ways. The first is fee burning, which is economically equivalent to a buyback. The second is distributing fees to stakers, which is economically equivalent to paying dividends.
Staking rewards paid through minting are different. They are neither income created by the network nor a cost borne by the network. The protocol creates new tokens and allocates them to stakers, diluting the holdings of non-stakers.
This mechanism may be necessary for securing network security or determining who gradually gains control of the network over time, but from the perspective of all token holders, it neither creates nor destroys value.
A blockchain can process millions of transactions yet create almost no value for token holders, as the surplus may be captured by users, applications, validators, or other intermediate participants. In contrast, a lower-activity chain that converts a larger proportion of economic activity into value for token holders may be more valuable.
But not all fees are of equal value. The R in ARR stands for recurring, meaning sustainable and repeatable revenue. Dichev, Graham, Harvey, and Rajgopal (2013), when discussing profit quality, noted that high-quality profit should be “sustainable and repeatable.” The same standard applies to L1 fees.
A dollar in fees generated by long-term financial activity is not the same as a dollar in fees generated by airdrops, meme coin mania, cascading liquidations, or temporary network congestion.
Some fees stem from users' ongoing demand for scarce block space. Others are merely the exhaust of speculative cycles. Once incentives disappear, volatility declines, or users run out of funds, these activities will vanish as well.
Fee quality depends on sustainability and defensibility.
Are users paying because this chain provides long-term economic utility, or because a short-term event happened to occur on this chain? Can the protocol continue to collect these fees without driving users, applications, or order flow elsewhere? Can the token continue to capture these revenues, or will this value ultimately be distributed among validators, applications, searchers, block builders, users, or other chains through competition?
In the past, crypto investors often held two opposing misconceptions about the quality of income.
On one hand, they overestimated the quality of revenue, as a significant amount of crypto activity is speculative, reflexive, and episodic.
On the other hand, they underestimated the quality of revenue because they did not fully appreciate the strength of L1 network effects: liquidity, applications, wallets, infrastructure, users, developers, assets, and order flow reinforce each other.
These network effects may make certain fees harder for competitors to undercut than they initially appear. They also suggest that major blockchains like Solana and Ethereum may have stronger pricing power than the market commonly assumes, potentially benefiting from higher fees.
Part Two: Accounting Standards for Income, Inflation, and Total Supply
Next, define the simplest L1 fundamental model that can derive valuation multiples.
It may still be too early to call it the "standard model." There is currently no widely accepted standard model for valuing L1 blockchains. However, the classification below represents the form I believe the standard model should adopt—it is intentionally aligned with the methods used by equity analysts to value companies.
These details need to be explicitly stated because people haven't even reached a consensus on the most basic accounting objects.
I’ve discussed this framework with some of the smartest people I know, but they often disagree on fundamental questions: Are validator rewards paid through inflation considered a cost? Should foundation expenditures be classified as operating expenses? Should unused foundation token allocations be included in the token supply? Should MEV paid to validators be counted as protocol revenue, validator income, or neither?
Some confusion may arise from the fact that a correct valuation model can be expressed in more than one way. Accounting classifications are not unique. As long as the corresponding offsetting items are adjusted accordingly, an item can be moved from one side of the ledger to the other while keeping the model accurate.
But this flexibility is also dangerous. Many models are internally consistent, while many are not. The existence of multiple correct approaches does not mean there are fewer incorrect ones.
Inflation rewards are the simplest example.
Staking rewards funded by inflation are essentially payments to stakers made by token holders through dilution. From the perspective of all token holders collectively, these effects offset each other. When the protocol mints new tokens, it does not generate revenue, and allocating these tokens to stakers incurs no real external cost.
You can build a proper model that treats inflationary rewards as a cost, but only if you also account for newly issued tokens as a source of value to offset that cost. Otherwise, the model will produce absurd conclusions, such as Solana having no profitability because it pays out large staking rewards.
The following classification is my proposed baseline scheme. It separates three entities: revenue, costs, and total supply.
I believe this set of definitions is closest to the model that stock analysts are already familiar with, making it easier to understand and reason about.
Other classification methods may also be correct, but deviating from this framework must be justified by clear reasoning. Unique models incur two costs.
First, it is harder for others to understand. Second, people easily overlook dependencies between projects.
For example, if foundation expenditures are classified as costs, then the unused foundation token allocation cannot also be counted toward the total supply, as this would result in double-counting. If inflation rewards are classified as costs, newly issued tokens must be treated in a symmetric manner.
Part 3: Supply, Demand, and Price
Recently, several blockchains have increased fees with the explicit goal of boosting revenue. However, revenue equals price multiplied by quantity.
Increasing fees would boost the revenue contributed by exchanges that remain, but it could also cause some trading volume to disappear. Therefore, the overall impact on revenue is uncertain and depends crucially on the price elasticity of trading demand.
To understand the logic behind these adjustments, I spoke with key decision-makers on these blockchains. They believe that the original fees on these chains were too low, making demand relatively inelastic within this price range.
This statement may apply in specific cases, but it is not universally true.
I previously conducted research on the randomness in EIP-1559 pricing. The analysis showed that the price elasticity of gas demand is approximately between 0.6 and 0.8. This means that a 10% price increase leads to a 6% to 8% decrease in demand.
Moreover, these data only reflect short-term price fluctuations and do not account for the overall impact of applications migrating off-chain or optimizing their programs.
Therefore, a flat fee is a rather clumsy revenue tool, as different types of on-chain transactions have different willingness to pay.
A small wallet transfer, a large stablecoin transfer, and a liquidation may occupy the same block space, but they generate different total surpluses and have different willingness to pay.
Note: Blue dots represent the same fee payer initiating more than 250 transactions during this period, indicating they are more likely to be bots and thus exhibit higher price elasticity. Bots typically operate with very low profit margins, so they often significantly reduce their activity when prices rise.
The protocol aims to charge higher fees for transactions with greater payment willingness. Calculating fees is a step in this direction, but it is not thorough enough.
For financial activities, the notional trading amount often better reflects willingness to pay than the calculated volume. This is also why exchanges typically charge fees in basis points.
Token programs can provide a way to charge fees based on transaction value. By modifying the token program, a small proportional fee can be charged during token transfers, ensuring that high-value transfers pay more than low-value transfers, even if both use similar computational resources.