It's a really important topic when we try to evaluate an L1 because they are the main source of revenue to sustain the network. However, it's difficult to justify their valuations based on the current data we have, and other revenue sources like MEV or considering the main asset as a monetary asset are emerging, which significantly diminish their importance, making it almost marginal.

Ethereum reached $9 billion in revenue in 2021, while in 2024 it seems likely that it will struggle to surpass the $2.4 billion it generated in 2023.

Tron positions itself as the leading L1 in revenue, while the positive news is that Solana has consolidated revenues of $150 million.

However, the positive news is that the costs are truly marginal, as we explained in an article we wrote about Proof of Stake networks. This is not the case for Proof of Work networks, such as Bitcoin.

This allows us to think that in a competitive market, these revenues could be adjusted much more, given that there is room to do so. However, the smart contract service is not a commodity; some networks offer greater security and neutrality, which are difficult to assess as costs.

It's not easy to quantify what that cost is or how much demand in a more competitive environment would be willing to pay that premium. Decentralization, which would be the value proposition for a more expensive space, is even beginning to erode due to the economies of scale imposed by these networks, making it difficult to escape.

The first question we asked ourselves in the title of this post is:

In this context, are the fees paid to use these L1s really important, or should we be looking for profitability in other aspects?

Computation-based fee model Vs Financial fee model

One of the differences between DeFi and the traditional financial system is that fees are calculated based on the computational and memory requirements needed for the financial operation. The transaction fee is based on the computational and memory resources that the blockchain needs to utilize. For example, performing a multiplication and storing a 6-digit number. This means that a transfer has a fee that does not depend on the amount being sent; the cost of sending 100 million dollars is the same as sending 1 dollar. The cost will be, for example, higher if we use a transfer that can store some type of message that will need more memory resources.

Financial fee models usually define a percentage of the capital involved in the transaction. For example, a 0.01% fee could be applied to the transfer amount. In many cases, there are minimum fees or even special conditions if the transaction exceeds a certain capital threshold.

EVMs cannot differentiate the type of activity being performed, and ultimately, they provide general blockspace that can be used in whatever way the application requires.

Transactional costs tend to have a marginal cost, and theory suggests that in a competitive environment, prices will tend to approach this marginal cost. However, the decentralization characteristics of blockchains have imposed a scarcity in that space, leading to very high prices for these computational resources. Over time, technological advancements should gradually make this process more efficient. Nevertheless, the limitations are imposed by the need to make the network resilient, which means that scalability currently faces significant and complex challenges without compromising that decentralization.

However, there are models on these networks that revert to the financial model, such as MEV or Miner Extractable Value, which potentially offer more significant revenue, especially with high-capital transactions.

These models are similar to those used in HFT, an industry with substantial returns. They may not be the only highly profitable models within these blockchains.

Digital Economies or World Computer

The value proposition of projects like Ethereum lies in creating a neutral environment for digital private property, where digital activities are growing exponentially. Within this system, we can find next-generation corporations that coordinate under this legal framework. It is an environment where new digital capital is safeguarded by the system's inherent characteristics.

Being part of this economy means paying for the computational services executed as smart contracts. In essence, we are paying a sort of cloud technology provider with limited space that auctions off these activities. This provider is beginning to offer us different options for participating in this economy and should gradually address these availability issues.

The question we might need to ask ourselves is whether this digital environment should be sustained by technology fees or by a fee on economic activity.

Can we truly sustain these L1s with a computation-based fee model?"