Discussing L1 Value Capture Through Two Proposals from Solana

Author: Max Resnick, Chief Economist at Anza

Compiled by: Jiahua, ChainCatcher

I am publishing 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 specifically a commentary on these two proposals; I have already left comments under the specific proposals on GitHub. Here, I mainly discuss the problems these proposals attempt to address and their relationship to the valuation of L1.

Part One: Establishing Asset Pricing Theory {#article-toc-33064-2}

Before the formal establishment of asset pricing theory, investors were not lacking in methods for company valuation. Some focused on hard assets and liquidation value, while others emphasized profits, dividends, growth, management quality, or market psychology. There were many indicators available, but what was truly lacking was a rigorous method to explain which indicators determine value and how these indicators should be weighed against each other.

By the late 1920s, just before the Great Depression, this ambiguity had become quite dangerous. Investors could list facts such as profit growth, market expansion, new technologies, and improvements in corporate governance, but these facts were often only used to justify market prices rather than to infer asset values.

Graham and Dodd (1934) later described that period as one where analysis gave way to "potential and prophecy." Even when data was presented, it often turned into "pseudo-analysis used to support various fantasies of the time."

Those who frequently scroll through Crypto Twitter should find this scene familiar. The current discussions surrounding L1 tokens are similarly filled with potential, prophecy, and pseudo-analysis that once appeared in the stock market of the late 1920s.

The number of people involved in blockchain development has reached record highs. Trading activity has hit historical peaks. Tokens are set to become currencies, collateral, digital oil, or a bullish option on the future financial system.

Some of these claims may be true and might even imply that the underlying networks have room for appreciation. However, if it cannot be explained how these factors translate into residual value for token holders, they cannot form a coherent valuation framework.

John Burr Williams was the first to push asset pricing towards rigor. In "The Theory of Investment Value," Williams (1938) argued that value is "the present value of future dividends of stocks or the present value of future coupon payments and principal of bonds."

Gordon (1959) later expressed the same idea more succinctly: "Like other assets, stocks are purchased because they can provide expected future income."

Equity has value not because a company is impressive, active, important, or technically irreplaceable. It has value because equity grants shareholders a claim to future income.

For L1 tokens, value can be aggregated in two ways. The first is through fee burn, which is economically equivalent to a buyback. The second is distributing fees to stakers, which is economically equivalent to paying dividends.

Reliance on inflationary staking rewards is different. It is neither income generated by the network nor a cost borne by the network. The protocol creates new tokens and distributes them to stakers while diluting the holdings of non-stakers.

This mechanism may be necessary for securing the network and may determine who gradually owns the network over time, but from the perspective of all token holders, it neither creates value nor causes value loss.

A blockchain can handle millions of transactions yet create almost no value for token holders because these residuals may be taken by users, applications, validators, or other intermediary participants. Conversely, a chain with lower activity may be more valuable if it can convert a larger proportion of economic activity into value for token holders.

However, not all fees have the same value. The R in ARR stands for recurring, meaning sustainable and repeatable income. Dichev, Graham, Harvey, and Rajgopal (2013) pointed out that high-quality profits should be "sustainable and repeatable" when discussing profit quality. The same standard applies to L1 fees.

A dollar fee generated from long-term financial activity is not the same as a dollar fee generated from airdrops, meme coin frenzies, cascading liquidations, or temporary network congestion.

Some fees come from users' ongoing demand for scarce block space. Others are merely the exhaust of speculative cycles. Once incentives disappear, volatility decreases, or users run out of money, these activities will also fade away.

Fee quality depends on sustainability and defensibility.

Are users paying because this chain provides long-term economic utility, or because some short-term activity happens to occur on this chain? Can the protocol continue to collect these fees without driving users, applications, or order flows elsewhere? Can tokens continue to capture these revenues, or will this value ultimately be siphoned off by validators, applications, seekers, block builders, users, or other chains in competition?

In the past, crypto investors often held two opposing misjudgments regarding income quality.

On one hand, they overestimated income quality because much crypto activity is speculative, reflexive, and episodic.

On the other hand, they underestimated income quality because they did not fully recognize the strength of L1 network effects. Liquidity, applications, wallets, infrastructure, users, developers, assets, and order flows reinforce each other.

These network effects may make certain fees harder to take away from competitors than they initially appear. They also suggest that mainstream blockchains like Solana and Ethereum may have stronger pricing power than the market generally believes, and thus may benefit from higher transaction fees.

Part Two: Accounting Standards for Revenue, Inflation, and Total Supply {#article-toc-33064-3}

Next, it is essential to clarify a set of basic L1 fundamentals that can derive valuation multiples.

It may be premature to call it the "standard model" now. There is currently no widely accepted standard model for L1 valuation. However, the following classification is the form I believe the standard model should take. It intentionally aligns with the methods used by stock analysts for company valuation.

The reason for explicitly writing these down is that there is not even consensus on the most basic accounting objects.

I have discussed this framework with some of the smartest people I know, but they often have disagreements on some fundamental issues. Are validator rewards funded by inflation considered costs? Should foundation expenditures be viewed as operating expenses? Should unused foundation token shares be counted in the token supply? Should MEV paid to validators be counted as protocol income, validator income, or neither?

Some confusion may stem from the fact that there can be more than one correct way to write a valuation model. Accounting classifications are not unique. As long as the corresponding offset items are adjusted accordingly, one can move an item from one side of the ledger to the other while keeping the model correct.

But this flexibility is also dangerous. Many models are internally consistent, and many are not. The existence of multiple correct methods does not mean there are fewer incorrect methods.

Inflation rewards are the simplest example.

Staking rewards funded by inflation are essentially rewards that token holders pay to stakers through dilution. From the overall perspective of all token holders, the two will offset each other. The protocol does not gain income when it mints new tokens, nor does it incur real external costs when distributing those tokens to stakers.

You can establish a correct model that views inflation rewards as costs, but only if you also account for newly issued tokens as a source of value to offset this cost. Otherwise, the model will yield absurd conclusions, such as Solana being unprofitable because it pays out substantial staking rewards.

The following classification is the benchmark scheme I propose. It separates three objects: income, costs, and total supply.

I believe this definition is closest to the model that stock analysts are already familiar with, making it easier to understand and reason.

Other classifications may also be correct, but there must be clear reasons for deviating from this classification. Unique models incur two costs.

First, they are harder for others to understand. Second, it is easy to overlook the dependencies between items.

For example, if foundation expenditures are classified as costs, then unused foundation token shares cannot simultaneously be counted in total supply, or it will lead to double counting. If inflation rewards are classified as costs, newly issued tokens must also be treated symmetrically.

Part Three: Supply, Demand, and Price {#article-toc-33064-4}

Recently, several blockchains have explicitly raised fees with the goal of increasing revenue. However, revenue equals price multiplied by quantity.

Raising fees will increase revenue contributed by the transactions that remain, but it will also cause some transactions to disappear. Therefore, how raising fees will ultimately affect revenue is uncertain and depends critically on the price elasticity of transaction demand.

To understand the logic behind these adjustments, I communicated with some decision-makers from these blockchains. They believe that the original fees of these chains were too low, and thus, within this price range, demand is relatively inelastic.

This statement may apply to specific situations but does not hold universally.

In the past, I conducted some research utilizing the randomness in EIP-1559 pricing. The analysis showed that the price elasticity of gas demand is approximately between 0.6 and 0.8. In other words, a 10% increase in price would lead 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 programs.

Thus, a uniform fee rate is a rather clumsy revenue tool. 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 create different total residuals and have different willingness to pay.

Note: The blue dots represent the same fee payer who initiated over 250 transactions during this period, indicating they are more likely to be bots and thus have higher price elasticity. Bots typically operate on thin margins, so once prices rise, they tend to significantly reduce resource consumption.

Protocols want to charge higher fees for transactions with higher willingness to pay. Calculating fees is a step in that direction, but it is not thorough enough.

For financial activities, nominal transaction amounts often reflect willingness to pay better than computational load. This is also why exchanges typically charge fees based on basis points.

Token programs can provide a way to charge fees based on transaction amounts. By modifying the token program, a small proportionate fee can be charged during token transfers. This way, even if a high-value transfer uses similar computational resources as a low-value transfer, the high-value transfer still needs to pay more in fees.

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