Author: Patrick Bush, Matthew Sigel Source: VanEck Research Translation: Shan Ouba, Golden Finance
We conclude that the Ethereum Layer 2 landscape is currently crowded and has few winner-take-all characteristics.
Assessment of Layer 2 blockchains through the lens of developer experience, user experience, and technical capabilities.
Shows the assumptions behind the base case valuation of Ethereum Layer-2 reaching $1 trillion in market capitalization in 2030.
Ethereum's dominance in the smart contract space faces a key obstacle: scalability. While the network offers unparalleled security and decentralization, transaction fees and processing times can skyrocket as usage increases. To overcome this, Layer 2 solutions have emerged, and advancements such as the recent fork EIP-4844 are expected to unlock greater scalability for these Ethereum forks. Here, we analyze a range of Layer 2 solutions from the perspective of transaction pricing, developer experience, user experience, trust assumptions, and ecosystem size.
Layer 2 (L2) blockchains are connected networks that run on top of a main blockchain, such as Ethereum, to increase its ability to process transactions. By processing transactions on the main blockchain and then settling them back onto the main blockchain, L2 solutions help expand the functionality of a blockchain without compromising its security or decentralization.
It is well known that Ethereum’s current capacity is insufficient to host all of the world’s financial transactions. More precisely, the world’s financial system needs to handle more than Ethereum’s long-term limit of approximately 19.2 USDC or 6.8 Uniswap transactions per second. However, this is a limitation by design, as Ethereum's managers believe that censorship resistance is best achieved by enabling anyone to cheaply run an Ethereum node.
The result is that Ethereum limits the capacity of its chain to reduce the network requirements, data storage requirements, and computer hardware requirements of its nodes. This effectively limits the number of bytes of data that Ethereum can process in a given time. Since transactions on a blockchain are nothing more than snippets of data that the blockchain considers to be correct, the capacity of a blockchain can be measured simply by how much useful data it can process.
Source: VanEck Research as of March 15, 2024.
To address these limitations, Ethereum’s developers initially proposed a “sharding” solution, which involves splitting the blockchain into 64 smaller, interconnected child blockchains, called “shards.” Each shard would process transactions in its own containerized child blockchain and then submit proof of activity for coordination by Ethereum’s parent blockchain. While this approach looked promising, and some of its components debuted on Polkadot starting in 2020, Ethereum developers ultimately abandoned the sharding initiative, dubbed Ethereum 2.0. This was because they deemed it technically unfeasible and unscalable to Ethereum’s vision of becoming a blockchain for billions of users.
Instead, Ethereum’s roadmap shifted toward utilizing layer 2 (L2) blockchains. These L2 networks process the majority of transactions outside of the main Ethereum blockchain, settling only the highest-value transactions directly on them. This approach reduces the load on the main blockchain, allowing it to process more transactions efficiently. In this dynamic, Ethereum accrues value because the costs of these settlements must be paid in ETH; this strategy also reinforces the value of ETH as the real “oil” that powers the entire interconnected chain ecosystem.
Essentially, Ethereum’s main challenge is its limited ability to process, store, and compute on data in the form of financial transactions. This bottleneck in data throughput can be addressed by moving the majority of data processing and computation to layer 2 blockchains. As a result, Ethereum’s development is now focused on enhancing its ability to integrate compressed transaction data from these L2 blockchains. But how exactly do these interconnected blockchains work, and what are their business models?
Layer 2 (L2) blockchains enhance Ethereum’s capabilities by aggregating multiple transactions into compressed packages, called “rollups.” These “transaction bundles” are published to Ethereum by L2 at different intervals that are designed to balance transaction demand, security, and cost. As a result, Ethereum is becoming a "blockchain of blockchains." Each L2 is typically made up of its own series of smart contracts on Ethereum that track L2 transaction history, facilitate data transfer between L2 and Ethereum, run proof-of-fault or zk validator contracts (more on this below), and act as an escrow for assets between Ethereum and the L2. Very powerful computers called "collators" ingest and sort all the transactions that occur on the L2 blockchain. This is more powerful and cheaper than Ethereum because L2 runs a single very powerful server computer that simply receives transactions and sorts them. This dynamic allows L2 to handle much greater data throughput than Ethereum. In contrast, Ethereum transaction processing involves hundreds of thousands of globally distributed validator nodes sending, interpreting, and agreeing on transaction data. This takes much more time due to the Ethereum consensus process and involves repeating the work of one computer on each of the hundreds or thousands of Ethereum nodes. Logically, a single computer like a sequencer that processes transactions is much cheaper and faster than a globally dispersed, less powerful computer system that collectively uses gigabits of internet bandwidth to send messages and hundreds of thousands of CPUs to process blockchain transactions.
There are two main types of L2s connected to Ethereum: Optimistic Rollups (ORU) and Zero-Knowledge Rollups (ZKU). Both settle their ledger balances or "states" on Ethereum by sending a compressed version called a "Merkle Root." The ORU also publishes a batch of compressed transaction data so that changes to the ledger over time can be verified and tracked.
Settlement in a layer-2 blockchain (L2) can be likened to updating the scoreboard of a baseball game inning by inning, with the transaction data serving as detailed game data. For optimistic rollups (ORUs), they follow the optimistic principle, meaning they are assumed to be accurate until proven otherwise. If an entity (such as a high-frequency trading firm or a mathematically skilled researcher) identifies an incorrect or flawed Merkle root, they can submit a fraud proof (called a fault proof) to Ethereum. The entity monitoring the ORU for fraud has a 7-day window (called a "challenge period") to detect any fraudulent activity after the state is updated. Once that period is over, the transactions within the ORU are considered final. If the fault proof successfully proves fraud, the smart contract that oversees the state of the ORU reverts all transactions to the state before the fraud began. The challenge period is extended for 7 days, after which each batch of transactions is irrevocably finalized.
At the time of writing, only 4 chains out of the 46 L2s we track through l2beat have live fraud proofs. Two of these four fall under the umbrella of Arbitrum, which has the highest Total Value Locked (TVL) of all L2s at $4.31B and only allows fraud proofs from a whitelisted set of entities.
The most popular ORUs are Arbitrum, Blast, Optimism, Manta, Metis, Mantle, and Base.
Zero-Knowledge Rollup (ZKU) operates similarly to ORU, but with one key difference. ORU submits both the transaction data Merkle root and the state Merkle root to Ethereum, while ZKU only sends a zero-knowledge proof of the transaction data. This is because ZKU does not operate under the assumption that the submitted state root is correct. Instead, once the proof is submitted to Ethereum, the smart contract verifies the authenticity of the ZKU transaction package.
Therefore, ZKU has no fault proofs, as proofs are generated for every state update. Unlike ORU, ZKU transaction data is considered final once the proof is accepted on Ethereum, ensuring immediate finality and eliminating the need for challenge periods.
The most important ZKUs currently are Starkware, zkSync, zkScroll, Linea, and *c zkEVM
The underlying economics of ZKU and ORU are very similar to L1 blockchains. Both types of rollups make money when users create activity on their chains and pay ETH fees to Ethereum. Currently, all L2s price transactions in ETH, as this is the token required to settle transaction data to Ethereum.
Whatever the process, it is important to understand that transaction ordering has value, and blockchains can make money by selling the right to order transactions. This diagram illustrates how three different transaction ordering models create different revenue streams.
L2 charges users a fee for including transactions in each block. It is composed of a base fee and a priority fee. Some L2s charge priority fees, such as Optimism. Priority fees enable users to be first in line at the top of a block of transactions. In the past 6 months, the top 10 L2s on Ethereum have earned $232 million in revenue from user transactions alone. This ability to "cut the cord" by paying priority fees benefits users who engage in time-sensitive activities, such as arbitrage trading. Arbitrum uses a first-in, first-out (FIFO) ordering method when transactions arrive. In some cases, users may prefer their transactions to follow specific other transactions on a block. A common strategy called "reverse running" involves placing a transaction immediately after a major transaction to exploit price differences between decentralized exchanges (DEXs) to obtain arbitrage opportunities. More malicious transaction ordering techniques, such as "sandwich attacks," involve strategically placing a buy order before a user's planned transaction and a sell order immediately after it. This manipulation drives up the price of a desired token before a user’s trade executes, forcing them to buy at an unfavorable, artificially inflated price.
On Ethereum, order books are monetized via software added to Ethereum validator software. Called Flashbots, the software allows validators to auction the right to order transactions (and insert their own) to an external entity. This auction generates “maximum extractable value” (MEV), increasing the payoff for both validators and stakers. While L2s have the potential to monetize MEV by auctioning block ordering rights, no L2 has yet formally done so. However, trading firms may already be locating their servers close to L2 servers, similar to how stock and commodity exchanges do.
Looking forward, many L2s plan to decentralize their set of sequencers, which could involve staking tokens — perhaps ETH from the Eigenlayer DA or native tokens from each rollup. Decentralization of sequencers could unlock new revenue streams for MEV. For context, Ethereum’s MEV take-up rate for DEX volume averages about 4 basis points (bps), while other blockchains like Polygon and Solana have rates of 0.4bps and 3.5bps, respectively. These rates likely underestimate the full extent of MEV due to tracking challenges and incentives to conceal profits. By estimating MEV take-up based on DEX volume, if Arbitrum’s MEV is captured at a rate of 3.0 bps, the amount would be $58.9 million — 57% of Arbitrum’s fee-only revenue.
Layer 2 (L2) mainly incurs costs through Ethereum Gas fees because they regularly publish transaction data, settlements, and proofs to Ethereum. But the cost structure of zero-knowledge rollups (ZKU) and optimistic rollups (ORU) is different. While both update their state on L1, ORUs have to pay heavy on-chain data costs, while ZKUs have to spend money on proof generation and verification. In any case, the consequence of relying on Ethereum is that L2's input costs are subject to fluctuations in Ethereum's block space. Most of the time, this cost difference is passed on to users. However, the profits earned by L2 are therefore very volatile.
Before EIP-4844, L2 published settlement data and proofs to Ethereum as a single transaction, with the "message field" of each transaction structure called "call data". This is a "hack" that exploits a component of Ethereum's standard transaction format to hold compressed L2 data. While this is novel, it is very expensive. For example, in February, Optimism paid $5.7 million, Arbitrum paid $7.2 million, and Scroll paid $6.7 million to publish call data to Ethereum. Compared to ORU, the cost component of ZKU is inherently higher because ZKU submits zero-knowledge proofs and call data to Ethereum. While ORU may also involve proof costs, these costs are typically outsourced to third parties who challenge the state when needed, so they do not significantly affect the base cost of ORU. The cost of verifying ZKU's zero-knowledge proofs on Ethereum can be extremely high. Despite Ethereum's optimization efforts, such as using native operation code to simplify zk-proof verification, fees are still high, for example, Scroll's ZKU incurred $1.1 million in proof fees in the first 13 days of March.
Due to the high cost of proofs, ORUs have an average profit margin of 26.7% over the past six months, while ZKUs have an average profit margin of 21%. Logically, rollups can send more transactions in fewer batches to reduce variable batch posting fees. However, infrequent batch postings can also be caused by less transaction throughput occurring on L2. Regardless, the frequency with which L2 batches are posted to Ethereum is a profitability lever that L2 can pull, but at the expense of user experience. In practice, L2s decide to batch post based on a calculation based on the number of transactions they can fit into a block, the Ethereum L1 gas price, and the incoming transaction flow per L2.
Technically, L2s can publish a broader understanding of what is happening on L2s beyond simple "scoreboard" solutions. Price competition between L2s to provide the cheapest transactions to users results in L2s often choosing the most economical data to publish. Typically, this means publishing only "state differences" for ZKUs, while for ORUs it means publishing highly compressed transaction data. Curiously, while ZKUs are not technically required to publish full transaction data, some ZKUs still do so. Starknet and zkSync only publish “state differences,” while Linea, Polygon, and Scroll publish full transaction data. This is done because it can be challenging for things like browsers and wallets to follow the blockchain without transaction data. Another possibility is that publishing full transaction data can increase transparency so that anyone can run a node to follow ZKU.
The way many L2s currently reduce costs is to improve compression efficiency. For example, on February 13, Linea deployed a new compression scheme that increased the compression rate on the chain by 10 times, from around 500 bytes per transaction to around 50 bytes. By 2024, the average transaction size of other L2s (ORU and ZKU) on Ethereum was 300 bytes. While compressing transactions may save L2 data costs, it reduces its potential due to the time it takes to compress transactions.
On March 13, 2024, Ethereum passed the Dencun upgrade, which had several important changes, the most important of which was the creation of the so-called "Blob Space". Prior to this upgrade, the main challenge facing Layer-2 was the high costs associated with publishing transaction data to Ethereum. Recognizing this, Ethereum’s solution was to strategically create a dedicated data layer, colloquially known as Blob Space, designed specifically for L2 data publishing.
This newly established layer provides a targeted transaction environment tailored specifically for receiving data from the L2 network. The innovation of Blob Space lies in its transient data handling - data blobs published here are retained for only four weeks before being deleted, significantly reducing Ethereum’s data overhead. As a result, L2s can choose to bypass the main Ethereum layer and publish directly to Blob Space.
Ethereum’s Blob Space layer has its own gas price, following the same set of rules as Ethereum’s regular execution layer. The result is that transactions publishing data from L2 no longer need to compete with regular Ethereum transactions for block space. The design of a dedicated transaction layer also makes the cost of data much cheaper than publishing data to Ethereum as a call. At the time of writing, Data Blob has reduced L2’s gas usage fees by (-96%).
The first part of the off-chain cost expenditure of Layer 2 (L2) is the sequencer they use to order transactions. This is basically just a high-end server located in a data center. For most L2s, the infrastructure or business entity behind the L2 pays the cost of the sequencer. In the grand scheme of things, the cost of running the sequencer itself is small, with the equipment costing around $1,000-2,000, and perhaps another $3,000-5,000 per month in labor. This cost is consistent for both optimistic rollups (ORU) and zero-knowledge rollups (ZKU).
A less discussed but important cost element of ZKU involves the operation of the prover. Unlike the sequencer, which generates state roots, the prover is responsible for creating zk proofs that are verified on the Ethereum network. This computational process typically occurs on cloud computing platforms such as AWS.
According to decentralized zk prover project Gevulot, the cost of proving will be between “10-20% of Ethereum’s verification costs.” Furthermore, these costs scale with the volume of transactions generated per L2. ZKU faces a balance between cost and user experience, and may choose to reduce the frequency of proofs posted to Ethereum as a potential cost-saving measure. Through a process called recursion, ZKU provers can merge multiple proofs into a single submission, which, while increasing off-chain computational requirements, can optimize economics by reducing expensive proof verification on Ethereum.
At the time of writing, all ZKUs run their own provers and pay for proof generation directly. However, over time, many intend to decentralize proof generation.
In our analysis of key Layer 2s, we use five main variables to measure potential success or failure:
Transaction pricing – transaction costs for users
Developer experience – ease of building products and applications
User experience – simplicity of deposits, withdrawals, and transactions
Trust assumptions – liveness and security assumptions
Ecosystem size – how many interesting things can be done
The main difference in pricing economics between ZKU and ORU is that ZKU has higher fixed costs than ORU. This is because ZKU must pay for proof generation on Ethereum and proof verification on Ethereum. Proof generation/verification is a large static cost that does not increase significantly as each proof covers more transactions. In contrast, ORU must post the full transaction data to Ethereum. Although ORUs employ different compression mechanisms to reduce data costs, posting to Ethereum is very expensive. Since more transactions on an ORU means more data needs to be submitted to Ethereum, the cost of posting to Ethereum increases. However, with EIP-4844, the cost of posting data to Ethereum has been significantly reduced, and these savings have resulted in cheaper transaction pricing for ORUs. Similarly, ORUs can also choose to place transaction data on cheaper data availability blockchains, such as Celestia, EigenDA, and Avail. Currently, Manta Pacific and Aevo post transaction data to Celestia.
In 2024, the cheapest chains by average transaction cost are Mantle ($0.17), zkSync ($0.21), and Starknet ($0.25). Each chain is able to stand out in terms of pricing using different techniques. Mantle is an ORU that is able to keep transactions cheap because it accepts lower than average margins (19.9%), uses its own data availability (Mantle DA) for full transaction batching, and updates its state root to Ethereum with the second least frequent being every 20.7 minutes. zkSync is a ZKU that is able to cheaply price transactions due to its high transaction volume (94.9 million), the highest of all L2s, making its proof system very economical. Meanwhile, the ZKU chain Starknet settles to Ethereum the least frequently among the top 10 L2s, once every 57.8 minutes, while also only publishing state differences in place of full transaction data. These two cost savings result in the least amount of data per transaction settled to Ethereum. Curiously, we estimate that Starknet lost $0.09 per transaction as of March 13, 2024.
Developer experience is another important point of Layer-2's competitive advantage. The simplest fundamental understanding of developer experience is to achieve EVM compatibility. This means that smart contract code, tools, and developer libraries can be directly ported from Ethereum to use on L2. Since Ethereum has a large network of developers, this is believed to bring advantages to each L2. Currently, the vast majority of L2s are compatible with EVM. However, due to the limitations of zero-knowledge proofs, ZKUs often have subtle differences that developers must adhere to.
Some developers also believe that adhering to EVM compatibility is a disadvantage because the EVM places significant restrictions on blockchain functionality while excluding developers who are more familiar with other computer languages. For example, Starknet smart contracts are written in a language called Cairo, which is more efficient for Starknet's zero-knowledge extensions. Of course, this is a trade-off, and anyone deploying to Starknet must understand the intricacies of Cairo. Movement Labs is another L2 developer that allows smart contracts to be written in the Move language, which attracts developers who want to learn Move. For those who are more familiar with Solana's programming language Rust, Eclipse is building a Layer 2 blockchain that runs in the Solana virtual machine.
User experience is another pillar of the competing Layer-2s. The most basic component of this is loading assets and removing assets from L2. In most cases, onboarding between L2s does not differ significantly, but some centralized exchanges (CEXs) allow native assets to be moved to each L2. For example, Kraken allows users to withdraw USDC to Arbitrum and Optimism, while Coinbase allows USDC to be ported to Optimism and Base.
FinalityThe point at which transactions on L2 become irreversible marks a significant difference in the user experience between optimistic rollups (ORU) and zero-knowledge rollups (ZKU). For ORU, finality occurs after the fraud challenge period has ended, while for ZKU, finality occurs after the state root and its proof are published to Ethereum. One of the consequences of the difference in finality is exiting an L2. For ORU, users must pass 7 days before they can transfer their funds back to Ethereum. For ZKU, the same process may take only an hour, depending on how often ZKU issues settlements and proofs, and the security systems of each chain. While zkSync issues proofs every 6 minutes and updates status every hour, due to zkSync's security module, users must have a 24-hour waiting period before their assets can be bridged to Ethereum.
When users interact with L2, familiar tools and interfaces are critical. From Ethereum to L2, adopting familiar wallets and blockchain browsers greatly improves user comfort. This seamlessness is critical, as most L2s adopt an experience similar to Ethereum, ensuring that the learning curve for people migrating across platforms is minimal. In the realm of quantifiable user experience metrics, latency and throughput stand out. Latency refers to the time it takes for a transaction to be confirmed by the network after it is submitted, while throughput measures the network's ability to process transactions per second.
The slowest block time or round-trip time (RTT) - the duration between a user's transaction reaching the sequencer and receiving a confirmation back - generally defines the latency of an L2. For example, Arbitrum has the potential for very low latency of 0.25 seconds, although actual latency may vary based on geographic factors and the user's distance from the sequencer, which is presumably located in a Silicon Valley data center.
zkSync is known for having the highest theoretical throughput, capable of processing up to 434 swap transactions per second. However, both latency and throughput are tunable parameters in L2 networks.
ZKU is currently bottlenecked by how fast its prover can process incoming transactions, while ORUs are limited by how efficiently transaction data can be compressed and the rate at which Ethereum can absorb that data. Currently, L2s voluntarily limit their throughput to align with Ethereum’s capacity. If L2s were to fully utilize Ethereum’s block space (given Ethereum’s current data cap of ~937.5kb per block, plus an additional 375kb from three data blobs), this could theoretically scale to around 1.3MB per block, or 110kb per second.
For a particular L2 like zkSync, which averages 62 bytes per transaction, fully utilizing Ethereum’s block space could potentially balloon to 1,764 transactions per second. In contrast, an ORU like Arbitrum, which averages 255 bytes per transaction, could achieve a processing rate of 429 transactions per second under the same conditions.
Throughput can be further increased by integrating a data availability blockchain such as Celestia. However, this approach raises concerns about compromising user security, as alternative blockchains may not provide the same level of security guarantees as Ethereum. The choice to scale throughput in this way is a delicate one that requires balancing improved performance with the inherent security provided by Ethereum’s robustness.
There are large differences in the security and liveness guarantees that L2s provide to users. Security refers to the property of a blockchain that ensures that only the account owner can access his/her assets, while liveness refers to the safeguards put in place to ensure that assets can be exploited. Since L2s rely on a single sequencer that both orders blocks and "proposes" them to L1 (Ethereum) for settlement, sequencer failure is the biggest concern for L2 users. This is because each L2 currently runs a single sequencer, and if it fails, that L2 cannot process transactions. While assets cannot be stolen in the event of an outage, users will also not be able to access them until the outage is resolved. At the same time, if a malicious entity is able to take over a sequencer, they could potentially create fraudulent transactions to take assets off the L2. The weakness of all current L2s is that they each run only one sequencer, and that sequencer is typically centrally operated by the foundation behind the L2.
L2 manufacturers are aware of the issues presented by sequencer failure or takeover, and some have implemented new types of safety valves. These vary by L2 and its security. Complicating matters is that some of these safeguards open up possibilities for other areas of attack. Some of the guardrails created to protect users include allowing users to delete assets under certain conditions, use L1 hosts to submit L2 blockchain transactions, and even propose L2 blocks. Most of the time, these situations arise when something is clearly broken somewhere in the L2 system.
Some L2s are developing frameworks where anyone can become a sequencer, and allow multiple sequencers to take turns sequencing. This would come with people running sequencers establishing economic bonds (most likely one per L2 native token) to penalize cheaters. Companies like Espresso, Astria, and Fairblock are examples of projects building software for decentralized sequencers. Currently, L2 Metis is the furthest along in pioneering decentralized sequencers on L2. Metis’ community recently passed a governance vote that creates a framework for decentralizing sorters and allowing multiple sorters to exist. The next point of variation in the trust assumptions we discussed above is called “data availability.” ZKUs provide proof that a state update is correct, while ORUs provide proofs that allow anyone to prove that a state update is incorrect. However, in both cases, it is important to know the provenance of the data in order to generate the proof for the ZKU or ORU. Ideally, this data would be easily “available” on L1 (Ethereum) so that anyone can verify the underlying data from which the proof was generated. Blockchains such as Immutable X and Metis keep the full transaction data elsewhere. While ZKU does not require publishing full transaction data, chains like Linea and Polygon zkEVM do, while Starknet and zkSync only publish state differences. Additionally, L2 publishes data to Ethereum, while others publish it to dedicated data availability blockchains (such as Celestia). Publishing data on other chains arguably makes L2 less secure than Ethereum because it introduces new trust assumptions.
Another interesting dynamic with ORUs is that, as things stand, almost none of them are fraud-proof. This means that anyone using them is subject to censorship by the collator (transactions not finalized). Arbitrum is an exception, which allows fraud proofs. But even in Arbitrum's case, only whitelisted entities can submit fraud proofs. ZKU, on the other hand, relies on the prover (a different entity from the collator) to publish proofs. If the ZKU prover fails, some chains allow users to submit their own proofs (just do zero-knowledge math!) in order to have the transaction included in the L2.
Anyway, layer 2s have many issues with trust assumptions. However, they currently have hundreds of thousands of daily active users, so it seems no one will care until something major goes wrong. To simplify our view of the range of safeguards in place at L2s, we ranked them from most risky to least risky and found Arbitrum to be the current (although still insufficient) gold standard.
The most important competitive factor for L2 is the ecosystem created by each L2. Blockchain is a marketplace for services and digital goods. The more useful things are done on the blockchain, the more value it generates through user transactions, demand for its native tokens, and network effects. Unfortunately, metrics that measure blockchain activity do not always translate correctly into the value of that blockchain's ecosystem. Applying Goodhart's Law argues that once a metric becomes important in cryptocurrency, it is more likely to be manipulated. This rule becomes even more ironclad when we take into account airdrop farmers, who are performing meaningless activities to get free airdrops of token value. In general, what matters are users who are willing to bring value to the blockchain and engage in meaningful activities to generate fees. In this regard, Arbitrum, Optimism, and Blast have shown that they have ecosystems that matter to users, as these have bridged $16.3B, $7.85B, and $2.43B to each, respectively. In most cases, Layer-2s generate user interest and activity through airdrops of their native tokens. For example, Optimism has given away nearly 25% of its current floating supply to users in the form of active airdrops. Arbitrum has given away over $1.84B in tokens to individuals who have used Arbitrum. Blast has taken this concept a step further to attract bridge value, with the premise that Blast itself and teams building on Blast may airdrop tokens. Conceptually, Layer-2s compete by giving away free tokens that grow in value as each L2 network grows. By measuring trailing 12 month (TTM) revenue as a multiple of fully diluted valuation (FDV), each L2 trades at a much higher multiple than Ethereum. However, this dynamic changes if we change the multiple to be based on floating token supply rather than fully diluted value. This is a strange disconnect that has to do with the issuance schedule of L2 tokens - most L2 projects have only released a small fraction of their supply. In reality, we see L2s trading more based on speculation about long-term value accumulation rather than current revenue dynamics. We attribute this dynamic to the potential for L2s to have much higher future revenues than Ethereum.
We expect L2 revenues to exceed Ethereum as Ethereum cannot match L2’s transaction throughput or user experience. We are also increasingly seeing cases where the general purpose roller shutter market is consolidated by a few major players. This is due to the network effects of on-chain application composability and shared value. It is also attributed to rollup frameworks such as OP Stack or Arbitrum Orbit becoming dominant and OP/ARB tokens accruing value from other L2s and even Layer-3 (blockchains that submit state to L2s). It is also clear that most rollups will eventually move to zero-knowledge frameworks (ZKUs) due to their many advantages.
In the long run, we still believe that Ethereum block space will be expensive and the result may be that many L2s will merge their proofs into a unified proof layer that “recursively” combines all the proofs of its layer constituents. This is especially true in the case of application and sector-specific rollups. An example of a concept is Polygon’s aggregation. Conceptually, something like an “aggregation layer” could also greatly improve the user experience, as it would be more economical to frequently issue proofs and state roots to allow crossing L2 and Ethereum bridges in seconds rather than hours.
Thus, we see a brutal race among L2s, where network effects are the only moat. As a result, we are generally bearish on the long-term value prospects of most L2 tokens. The top 7 tokens in L2 already have a combined $40B in FDV, and there are many strong projects aiming to launch in the medium term. This means that over the next 12-18 months, there could be an additional $100B in FDV in L2 tokens. This seems like a bridge too far for the crypto market to absorb even the limited supply without a deep discount. Furthermore, while there is reason to believe that some L2 tokens will become valuable, the path to value accrual is less predictable than in other areas of crypto. This is especially the case because L2 tokens are not even the base currency in their own ecosystem.
Beyond the dominance of a few rollups in general-purpose L2s, we predict the emergence of thousands of use-case-specific rollups in the future. These L2s will be segmented by sector, application, or functionality. Enterprises might build rollups explicitly as their own revenue and/or cost center, such as building an asset management layer 2 chain. Other types of chains might be specialized for hosting entire sectors, such as a rollup that hosts a social media network, and applications that want to build products and services for that social media network.
We found a 2030 valuation for the L2 space by applying a FCF terminal multiple to our expectations for future cash flows. We estimate the revenues that feed these cash flows by: Transaction revenue (including transactions on the blockchain) Estimating the TAM of the end-market that can leverage public blockchains Calculating the amount of TAM that actually uses public chains Forecasting the market share of public blockchains in the Ethereum ecosystem Applying a fee to end-market revenue that leverages the Ethereum ecosystem for settlement and transactions Splitting transaction value between Ethereum and L2 MEV (Transaction Ordering on the Blockchain) Estimate the value of assets (including currencies, securities, and digital assets) that will be secured by the Ethereum ecosystem Predict DEX volume in the Ethereum ecosystem by applying asset turnover estimates to our forecasts for the value of assets in custody in the Ethereum ecosystem Multiply DEX volume by MEV occupancy to derive total MEV value Distribute value between Ethereum and its L2s Distribute value between Ethereum and its L2s Source: VanEck Research as of March 21, 2024. Past performance is no guarantee of future results. The information, valuation scenarios and price targets in this report are not intended as financial advice or any call to action, recommendation to buy or sell, or as a forecast of the future performance of Layer-2. The actual future performance of Layer-2 is unknown and could differ materially from the hypothetical results described here. There may be unaccounted-for risks or other factors in the scenarios presented that could impede performance. These are merely simulation results based on our research and are provided for illustrative purposes only. Please conduct your own research and draw your own conclusions.
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MiyukiThe EU AI Act imposes strict regulations on high-risk AI systems, impacting U.S. tech firms with fines up to €35 million or 7% of global revenues.
WeiliangThe "Tech Against Scams" coalition, comprising of Coinbase, Match Group, Meta, Kraken, Ripple, Gemini, and the Global Anti-Scam Organisation (GASO), has united to combat the escalating problem of online fraud and financial schemes.
CatherineMeta assembles a tech titan advisory group to drive AI advancement, yet faces criticism for a lack of diversity in its board. Additionally, Meta's platforms struggle with cybersecurity vulnerabilities and insufficient customer support, raising doubts about its ability to lead in AI responsibly.
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