Ethereum & other Smart Contract Networks

This is a chapter from the book Token Economy (Third Edition) by Shermin Voshmgir. Paper & audio formats are available on Amazon and other bookstores. Find copyright information at the end of the page.

Ethereum was a game-changer in blockchain protocol development in many ways. It created the first general-purpose blockchain network on which any type of smart contract could be easily developed at the application layer. It also transitioned from Proof-of-Work to Proof-of-Stake, paving the way toward a more modular blockchain architecture. Ethereum’s system architecture has been subject to both criticism and mimicry over the years, inspiring many other blockchain networks to create similar smart contract infrastructures or complementary Web3 protocols that meet the needs of increasingly complex decentralized applications.

Ethereum was conceptualized by Vitalik Buterin with the idea of expanding the capabilities of the Bitcoin protocol with a more versatile scripting language, which would allow the development of any type of decentralized application beyond currency transfers. The first version of the Ethereum white paper, titled “Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform,” was written and circulated by Vitalik Buterin around November 2013. This document was shared privately with a small group of early crypto enthusiasts and developers to gather feedback. It laid out the core vision for Ethereum as a general-purpose blockchain with a built-in Turing-complete programming language to support a wide range of decentralized applications. Vitalik publicly presented the Ethereum concept at the Bitcoin Miami conference in January 2014, helping to attract attention from potential collaborators and early contributors like Gavin Wood, who authored the Ethereum Yellow Paper that was published in April 2014. This yellow paper was a more formal technical specification that defined the Ethereum Virtual Machine and gave developers the low-level blueprint for how the network would function. A final version of the white paper, dated December 2014, is referenced on Ethereum’s official website. This version consolidated and clarified earlier drafts.

Unlike Bitcoin—which was launched by an anonymous creator without a formal fundraising process and was developed solely through voluntary code contributions—Ethereum’s development was funded through a token sale later in 2014, in which 18 million USD paid in BTC were raised. In this process, approximately 60 million ETH were issued and sold to investors. An additional 12 million ETH were issued and allocated to early project contributors and the Ethereum Foundation, which was established as a non-profit organization to manage the raised funds and oversee protocol development. The funds were used to pay developers and for other necessary operations. The network was launched on July 30, 2015, with the release of the Frontier version. This marked the beginning of Ethereum as a live blockchain network.

From a technical perspective, Ethereum was significant as it was the first general-purpose blockchain network that allowed smart contracts to be created with just a few lines of code—eliminating the need for developers to build their own special-purpose blockchain infrastructure from scratch. Over the years, the Ethereum ecosystem has contributed a considerable amount of innovation to the crypto space at both the protocol and application level. Ethereum developers also created the first token templates for various types of token contracts. This sparked a wild west period of peer-to-peer fundraising where people would use the Ethereum infrastructure to issue their tokens and sell them to crowd-investors, which in turn incubated many Web3 projects, some of which have profoundly shaped the crypto landscape. Ethereum also spurred innovation in non-fungible tokens and decentralized financial applications and played a key role in the evolution toward a modular blockchain architecture.

Ethereum Ecosystem

The concept of smart contracts, consensus mechanisms, and the evolution of scaling solutions were already discussed in previous chapters of this book. This subchapter summarizes the general concepts of Ethereum's technical, economic, and social design that were not previously discussed. It also describes how the system architecture transitioned over time.

Network stakeholders: From a socio-economic perspective, the Ethereum network is complex and dynamic with a vibrant and diverse ecosystem of independent stakeholders that have driven its evolution over the years. Vitalik Buterin, the living founder, continues to play a pivotal role in shaping Ethereum’s vision. Other key players include core developers, such as those employed or contracted by the Ethereum Foundation, as well as independent client developers, wallet developers, and applications developers. Full node and staking node operators secure the network under Proof-of-Stake and are incentivized to do so with newly minted protocol tokens. Layer 2 solution providers, other Web3 protocols competing or interacting with Ethereum, as well as investors and users all influence the platform’s robustness and market dynamics. External policymakers also influence the ecosystem by shaping the legal and regulatory framework governing ETH as an asset class, which in turn affects its adoption and the long-term viability of its economic system.

Ethereum Virtual Machine (EVM): From a technical perspective, Ethereum was a game-changer because it merged the concept of a virtual machine with P2P networks, which enabled the decentralized processing of much more complex applications beyond currency transfers. Virtual machines are software-based emulations of physical computers, serving as isolated environments to run software on a physical server. Before Ethereum, virtual machines were primarily used for cloud computing, software testing, and enterprise virtualization, but only in centralized environments. Ethereum introduced a decentralized virtual machine that operates across thousands of independent network nodes. This decentralized execution allows smart contracts to be securely and consistently executed by all network participants without relying on a central server. Unlike Bitcoin, which only supports predefined operations through its limited scripting language, Ethereum’s EVM enables general-purpose computation, making it the foundation for much more complex and versatile decentralized applications. Over time, the EVM’s capabilities have been continuously improved to enhance performance, security, and user-friendliness.

Turing completeness: The Ethereum Virtual Machine is designed to be “Turing-complete,” meaning developers can write code capable of executing a broad range of computational tasks. Turing-complete programs can theoretically solve any problem that a traditional computer can solve, given sufficient time and resources. In the context of blockchain networks, Turing completeness enables the execution of complex smart contracts that automate conditional logic, loops, and other programmatic behaviors essential for decentralized applications. However, more complex contracts require more network resources from node operators to execute. Bitcoin, by contrast, is not Turing-complete. Its scripting language is intentionally limited to ensure predictability and security, reflecting its narrower goal of facilitating P2P currency transfers.

EVM-compatible networks are blockchain networks that are designed to be compatible with the Ethereum Virtual Machine. This means that they can execute Ethereum-based smart contracts and run decentralized applications in the same way that Ethereum does. The primary reason for a blockchain protocol to be EVM-compatible is to leverage Ethereum's developer tools, existing codebase, and the vast ecosystem of decentralized applications. EVM-compatible networks can easily port over Ethereum-based projects, reducing the friction of application developers of building on a new blockchain infrastructure. Ethereum, on the other hand, benefits from interoperability with these networks, the more networks choose to be EMV-compatible, ensuring a steady demand for network resources and including the protocols currency ETH. These network effects also benefit Layer 2 solutions and dapp development. While in early years, many new blockchain networks chose to be EVM-compatible, in recent years the rise of non-EVM blockchain networks have diverted some attention and resources away from Ethereum.

Network currency (ETH): The native token, ETH, is the primary currency of the Ethereum network. Similar to Bitcoin, the protocol uses newly minted ETH to reward node operators for providing security services by verifying transactions and executing smart contracts. Users also need ETH to pay for network services each time they interact with an Ethereum-based application. However, while Bitcoin prices transactions based on their size in bytes, Ethereum prices transactions according to the exact computation required, since the cost of using network services can vary greatly depending on the complexity of the executed tasks. Simple token transfers consume fewer resources than complex operations involving one or multiple smart contracts.

Unit of Account (gas): To account for these differences, Ethereum introduced a unit of account referred to as “gas,” which measures the computational steps required to execute a transaction or contract. The total cost of an operation is calculated as “Gas Used” multiplied by “Gas Price.” Gas prices are denominated in gwei, where 1 gwei equals one-billionth of an ETH. Gas and gwei are not currencies themselves but units of measurement used to price computational effort—similar to how kilowatt-hours measure electricity usage and are priced in a local currency—making them essential for ensuring that users pay proportionally for the resources they consume. The gas mechanism also plays a crucial role in protecting the network from denial-of-service attacks. Without network costs, malicious actors could flood the network with computation-heavy transactions, potentially overloading the system. By requiring gas fees, Ethereum ensures that every computation has a cost, which discourages abuse and promotes fairness in the use of network resources.

Gas limit: When interacting with a smart contract, users must set a gas limit, which is the maximum amount of gas they are willing to allow the Ethereum Virtual Machine to consume during execution. If the gas limit is set too low, the EVM halts the operation once the gas runs out, reverts all changes made during the transaction, but still charges the user for the gas that was consumed up to that point. The gas limit is a critical safeguard because Ethereum smart contracts are Turing-complete—meaning they can, in theory, run forever if not properly constrained. To prevent this kind of “runaway computation,” Ethereum requires users to explicitly define how much computational effort they are willing to pay for. By imposing a gas limit, the network ensures that all computations eventually stop, even if the logic in a smart contract is flawed. While the gas limit sets a cap on resource usage, the gas price determines how much the user is willing to pay per unit of gas.

Gas wars & gas prices: Each block also has a block gas limit, which is the maximum amount of computation (gas) that can be included in a single block. This limit is what causes transactions to compete for space when demand is high. Users can choose how much they are willing to pay per unit of computation, which helps validators decide which transactions to include in the next block. Users who offer higher gas prices (not higher gas limits) can prioritize their transactions by making them more appealing to validators who process them in exchange for fees. When many users try to get their transactions processed at the same time, this can lead to a “gas war,” where users compete by offering higher gas prices to prioritize their transactions. In the early years of Ethereum, users sometimes paid several times the average fee just to get included in the next block during peak demand, effectively outbidding one another for faster processing. Gas wars still occur during periods of extreme network congestion. However, recent protocol upgrades such as EIP-1559, which introduced a base fee mechanism, have helped reduce their frequency and intensity. Under EIP-1559, users can also include a small ‘tip’—called a priority fee—to further incentivize validators to include their transactions quickly.

Gas-fee burden: Having to think about gas fees when using a decentralized application complicates usability. To reduce the burden of gas fees on users, many dapp developers have implemented various solutions, such as meta-transactions. In this model, a third party—typically the dapp operator—covers the gas fees and submits the transaction on behalf of the user, allowing the user to interact with the dapp without needing to hold ETH or understand gas mechanics. Another approach involves gas tokens, which allow users to pre-purchase and store gas at lower prices during periods of low network demand, then redeem them during high congestion to save on fees. Most gas token mechanisms have become obsolete due to protocol changes such as EIP-2929, which made gas refunds less favorable. Both methods aim to simplify the user experience by abstracting away the complexity and unpredictability of gas pricing, while still enabling interaction with decentralized applications. On a technical level, developers can furthermore adopt gas-efficient coding practices by optimizing smart contract logic to minimize computational steps and storage operations—reducing overall transaction costs. Layer 2 scaling solutions—such as rollups—have been another way to reduce the gas fee burden by processing data off-chain on more scalable and cheaper networks, settling only the final results on Ethereum’s mainchain.

Monetary policy of ETH: Unlike Bitcoin, Ethereum does not have a hard cap on its total supply of ETH. Its monetary policy is flexible and can be adjusted through Ethereum Improvement Proposals (EIPs) and on-chain governance by the Ethereum community. The total supply of ETH in circulation can be calculated by the sum of all pre-issued ETH allocated at project genesis and all newly issued ETH since the network launched. Approximately 72 million ETH were pre-mined before Ethereum went live in 2015, distributed to early contributors, the Ethereum Foundation, and crowdsale participants. Following the launch, ETH issuance occurred through block rewards—initially under a Proof-of-Work consensus mechanism. The Proof-of-Work block reward began at 5 ETH per block, later reduced to 3 ETH (in 2017 via EIP-649) and then to 2 ETH (in 2019 via EIP-1234) to moderate inflation. With Ethereum’s transition to Proof-of-Stake, completed in September 2022, issuance mechanics changed significantly. There is no longer a fixed block reward. Instead, ETH issuance now depends on the total amount of ETH staked: the more ETH is staked, the higher the aggregate issuance, though returns per validator decrease proportionally. Currently, the average reward per block proposer is estimated between 0.1 and 0.3 ETH, but this amount fluctuates based on staking participation and network conditions. In addition to the drop in issuance, EIP-1559, introduced in August 2021, fundamentally changed Ethereum’s fee model by introducing a base fee burn mechanism. With each transaction, a portion of the fee (the base fee) is permanently burned, reducing the total ETH supply. When network activity is high and burned fees outpace new ETH issuance, Ethereum becomes deflationary—a dynamic made possible by the combination of Proof-of-Stake and EIP-1559. As a result, Ethereum’s monetary policy is now shaped by both validator-driven issuance and usage-driven fee burns, resulting in a flexible but increasingly deflationary supply model. At the time of writing, the total ETH supply is approximately 120.52 million.

Application tokens: In addition to its network currency ETH, the Ethereum infrastructure allows anyone to issue and distribute their own application token via smart contracts (aka token contracts). At the time of writing this book and since the network went live, over 1.5 million different application tokens have been issued over the Ethereum network. Their use cases can vary greatly—from stable tokens to crypto-collectibles, tokenized art, tokenized real estate, tokenized KWh, tokenized entry tickets, tokenized driver's licenses, to purpose-driven tokens that incentivize protocol contributions. The technical, economic, legal, and ethical aspects of various token use-cases as well as their design, issuance, and distribution—are discussed in greater detail in part two and part three of this book.

Application economics: Each time an application token is issued or transferred over the Ethereum network, or another smart contract operation is triggered, every node in the network collectively performs the necessary computations to validate and record the transaction. As more applications are built on top of Ethereum and their usage increases, the demand for ETH tends to rise, since all transaction fees must be paid in ETH. As ETH’s value increases, validator rewards also tend to become more profitable, both in nominal and real terms. However, the relationship between Ethereum’s transaction fees and the market price of ETH is dynamic and context-dependent. In general, periods of intense network usage—such as during the token sale boom of 2017, the rise of DeFi in 2020–2021, and the NFT surge in 2021—have pushed transaction fees higher due to increased competition for block space. This fee pressure often correlates with rising demand for ETH, as ETH is the native currency required to pay for deploying or interacting with application-layer tokens and services. In these moments, ETH functions not only as a utility token but also as a gateway to access high-demand network activity. However, this relationship is neither linear nor guaranteed. Other factors can also influence the price of ETH. For example, the token sale boom of 2017 also drove demand for ETH because it was the currency in which token sales were conducted. Furthermore, extremely high fees can price out smaller users and developers, leading to a decline in on-chain activity or a migration to Layer 2 networks and competing ecosystems. While such transitions alleviate mainnet congestion, they may also reduce direct demand for ETH used in transaction fees—although many Layer 2s still settle and pay fees in ETH on Ethereum’s base layer. In parallel, speculative activity, staking dynamics, and macroeconomic factors can decouple ETH’s market price from on-chain fundamentals. As a result, ETH’s valuation is influenced not only by usage-based demand but also by broader investor sentiment, ETH’s role as a yield-bearing asset in staking, and Ethereum’s evolving monetary policy post-Merge. In this light, application-layer activity contributes to ETH’s value long-term. Especially short and mid-term, it is only one element within a much larger, reflexive system of incentives, expectations, and protocol-level dynamics.

Transition from Proof-of-Work to Proof-of-Stake: Although it was initially developed as a Proof-of-Work network, the Ethereum protocol eventually transitioned to Proof-of-Stake. The transition was planned from the beginning and became a complex, multi-year effort marked by extensive preparations and numerous protocol upgrades. The reason Ethereum was launched as a Proof-of-Work network was to leverage a proven security model—crucial for its early growth and stability. This allowed developers more time to refine the more complex and untested Proof-of-Stake mechanisms and address concerns such as the “nothing at stake” problem. The gradual transition also allowed for a fairer initial ETH distribution via Proof-of-Work mining, which was essential because starting as a Proof-of-Stake network might have disproportionately allocated tokens to staking node operators, thereby influencing the network’s economic power structure. The transition process involved changing Ethereum's consensus mechanism while the network was still operational, posing numerous technical challenges and risks—particularly concerning security vulnerabilities and potential disruptions to existing services. The adoption of the new protocol rules, with new types of stakeholders, a new incentive mechanism, new monetary policy, and new fee policy required all nodes to simultaneously update the protocol to maintain the network's integrity and security. Any misstep could have led to significant disruptions or losses for users. The process began with the deployment of the new Proof-of-Stake-based network—the 'Beacon Chain' on December 1, 2020, operating in parallel with the original Proof-of-Work network. Initially, the Beacon Chain did not handle mainnet transactions or smart contracts, but it managed the registry of validators, their stakes, and the new consensus mechanism and was upgraded in 2021. The culmination of these efforts was in September 2022, when the Ethereum mainnet fully merged with the Beacon Chain under the new Proof-of-Stake consensus rules. With “The Merge” in September 2022, the Beacon Chain, which handled Proof-of-Stake consensus during the transition period, was fully integrated into Ethereum’s architecture. It is no longer referred to as a separate blockchain but became responsible for consensus functions, reflecting its role in validating blocks and finalizing the ledger data under the Proof-of-Stake mechanism. Ethereum’s transition to Proof-of-Stake also reduced the network's energy consumption by 99.95 percent.

Modular blockchain thesis: Early blockchain networks, like Bitcoin and Ethereum before the transition to Proof-of-Stake, had a monolithic architecture where each node was responsible for validating transactions, executing contracts, creating blocks, and maintaining the entire state. As previously explained, this approach made the system slow and difficult to scale, as every node had to process every transaction and perform all the tasks. The Ethereum ecosystem began evolving toward a more modular architecture by splitting functional components so that each node could—in theory—contribute to one system function only (consensus, data availability, execution, and ordering of transactions), reducing the workload on each individual node. The move toward a modular system coincided with Ethereum’s shift to Proof-of-Stake. While monolithic networks handle all functions on the same network layer (L1), a modular approach separates functions across distinct network layers. For example, consensus and data availability could be operated by L1 nodes, while execution could be offloaded to the nodes of a Layer 2 network. These second-layer solutions including sidechains are typically decentralized (“Arbitrum,” “Optimism,” “StarkNet,” “Polygon,”) although centralized Layer 2 also exists (“Base.”) The idea was that the separation of tasks across layers allows each module to be optimized independently, improving scalability and efficiency, without conflating all functionalities into a single system. Although modularity was initially pursued to address scalability, it also tackled some other issues such as the lack of direct incentives for maintaining ledger data and transaction validation, as well as privacy and interoperability. Ethereum is one of the most prominent blockchain ecosystems to adopt a modular approach. Its transition to a more modular blockchain architecture has been embraced by some blockchain ecosystems and shunned by others, because modularity affects a wide range of technical and operational aspects such as the power structure within the network, security mechanisms, and various performance metrics.

Ethereum’s post transition system architecture: After the transition to Proof-of-Stake, the Ethereum L1 remained a fully functioning blockchain network that can operate independently of any Layer 2 system. The Ethereum mainnet, also referred to as Layer 1 (L1), represents the base layer of the Ethereum ecosystem that can handle all system functions—execution, data availability, consensus, and ordering transactions. However, Layer 2 networks can now also build on top of the mainnet and inherit its security—leveraging its consensus and data availability. In such a setup, L1 nodes validate the final state of Layer 2 transactions and ensure the availability of their associated data. Layer 2 nodes, in turn, process transactions and smart contract logic off-chain. Sequencers on L2s, notably in rollup solutions, are critical in managing transaction ordering and execution, substantially reducing reliance on L1 for these functions, thereby enhancing throughput and efficiency. Sequencers on L2s order and batch transactions before they are submitted back to L1 for finality and settlement. * Data Availability* refers to ensuring all data relevant to smart contract operations is accessible for verification. On Ethereum L1, both full nodes and validator nodes play critical but distinct roles. Full nodes maintain the entire history and state of the ledger, preserving long-term integrity and enabling independent verification. Validator nodes ensure that recent blocks and transaction data are available to the rest of the network, particularly during the block proposal and attestation process. Some L2 solutions, particularly zk-rollups and validiums, process a substantial portion of data off-chain but still rely on L1 to store or verify critical compressed data or proofs to ensure overall data integrity and availability. While future protocol upgrades aim to modularize and scale data availability, as of the current writing, full L1 nodes primarily bear the data availability burden. *Consensus* is performed exclusively by validator nodes on Ethereum L1. Validators propose new blocks and attest to their validity under the Proof-of-Stake mechanism. L2 solutions, particularly those using Optimistic or zk-Rollups, introduce localized consensus mechanisms for internal transaction ordering and execution validation. However, these mechanisms operate under the security umbrella provided by L1, ensuring that any local consensus achieved on L2 aligns with the overarching consensus rules of Ethereum. *Execution* can be managed by Ethereum L1, where smart contracts are deployed and run natively. To alleviate congestion and improve scalability, application developers can opt to offload transaction execution to L2s, particularly rollups, such as Optimistic Rollups and zk-Rollups. These L2s bundle and compress transactions, posting the data back to L1 for finality and data availability. This modular execution model allows Ethereum to scale effectively by reducing the operational load on L1, thereby facilitating faster transaction processing and lower gas costs. *Sequencing* involves collecting, ordering, and batching transactions for inclusion in blocks. In Bitcoin and pre-Merge Ethereum, sequencing was handled implicitly through P2P coordination in the mempool, where miners or validators select unconfirmed transactions, typically prioritizing those with higher fees. With the advent of rollup-based Layer 2 solutions, sequencing has become an explicit system component executed by specialized nodes or centralized services. These L2 sequencers enhance transaction throughput by managing transaction order efficiently before these transactions are finalized on L1. Currently, L2s often designate centralized sequencers responsible for ordering transactions before submitting them to L1 for finality. This role increases efficiency and throughput but also introduces potential risks such as front-running, transaction censorship, and opaque MEV extraction. Depending on the L2 design, various sequencing policies exist: some follow a First-In-First-Out model, others allow fee bidding similar to L1 mempools, and some aim to be MEV-resistant by enforcing deterministic or encrypted ordering schemes. The transition to L2s has introduced complexities in Ethereum’s MEV landscape. Emerging solutions aim to maintain the efficiency benefits of rollups while mitigating the adverse effects of centralization and rent extraction.

Ecosystem nodes: Ethereum’s base layer includes two primary node types: full nodes and validator nodes. Layer 2 networks feature more varied architectures, where execution and sequencing may be managed by either a single node or separate node types, depending on the protocol design. *Validator nodes* are crucial for achieving consensus on the validity of new blocks and the state of the ledger. They must stake ETH to participate, which they risk losing if they act dishonestly. Validators face penalties (slashing) for malicious actions like double-signing or submitting invalid blocks, and may also be penalized for failing to ensure data availability. In return for their services, they receive staking rewards and transaction fees from the blocks they propose, proportional to the staked ETH. Validators are also responsible for ensuring that block data is accessible to the network, a requirement that becomes increasingly important with the rise of L2s relying on L1 for finality. *Full nodes* are pivotal in data availability and trustless verification. They store the complete ledger history, validate new blocks, and independently verify state transitions. Although full nodes do not receive protocol-level rewards or transaction fees, their operation is typically motivated by goals such as enhanced privacy, security, and decentralization—particularly for those running infrastructure, wallets, or services that require verified chain data. *L2 nodes* are responsible for executing and sequencing transactions off-chain. Depending on the specific architecture of a Layer 2 network, these nodes may perform all L2 functions or only specific tasks. Incentives for L2 node operators can vary widely and are defined by the respective protocol or are driven by market dynamics.

Economics of modular approach: In monolithic blockchains, all transaction fees and economic value directly benefit the base layer's node operators and token holders. However, Ethereum’s shift to a modular architecture introduces a more complex economic dynamic. When Layer 2 networks execute transactions off-chain and submit only compressed transaction data back to Layer 1, a significant portion of user fees goes to L2 operators instead of directly to Ethereum L1 validators. This redistribution raises concerns about economic fragmentation. As L2 networks develop their own infrastructure, user bases, and sometimes independent token economies, not all value necessarily flows back to the Ethereum mainnet. Critics worry that if fewer transactions occur on L1, the fee revenue and corresponding ETH burn—introduced under EIP-1559 to create a deflationary dynamic—could diminish. For long-term ETH holders, this reduction in burned ETH may weaken the narrative of increasing scarcity, potentially putting downward pressure on ETH’s price. Nonetheless, L2 networks are not fully independent of Ethereum. They settle on Ethereum L1, pay for L1 data availability, and rely on L1 consensus for finality and security. Thus, ETH is still used for paying these settlement fees, and many L2s continue to denominate fees in ETH, at least partially tying their economic activity to the base layer. Emerging designs like shared sequencing layers, enshrined rollups, and ETH-aligned incentive mechanisms may help rebalance value flows between L2s and Ethereum’s core protocol in the long run. Proponents of the modular approach argue that scalability necessarily implies decentralization of value capture. In their view, some fragmentation is not a flaw but a feature—part of building a broader, interoperable ecosystem. They assert that the alternative—constraining all execution to L1—would limit Ethereum’s global usability and increase barriers to entry. At the time of writing, the debate remains ongoing. Ethereum’s economic model is evolving in parallel to its technical roadmap, and future protocol upgrades may further define how value is distributed across the stack.

Scalability, privacy, interoperability & MEV challenges: Significant advances have been made in scalability and privacy with the adoption of L2 solutions such as zk-Rollups and Optimistic Rollups, which not only enhance transaction throughput but also offer improved privacy features. Ongoing research into sharding and various protocol improvement proposals promises to optimize data availability and reduce gas fees even further. Regarding interoperability, the Ethereum ecosystem has developed wrapped token standards and bridging solutions to facilitate seamless communication between Ethereum and other blockchain networks. L2 networks have created bridges such as “Polygon Bridge,” “Arbitrum Bridge," or “Optimism Gateway,” enabling seamless transfers of assets between Ethereum and Layer 2 networks, some of which are developing into general-purpose interoperability solutions. However, Ethereum still faces MEV challenges, where miners and validators can extract profit by prioritizing or reordering transactions. The introduction of centralized sequencers on L2 can mitigate certain MEV risks by controlling transaction order within their batches and limiting the visibility of transactions until they are finalized. However, they can also introduce new risks if they exploit their position to manipulate transaction order for profit. The shift towards sequencers in L2 changes the dynamics of MEV and reduces the reliance on mempools for transaction ordering, but it doesn't entirely eliminate MEV risks or the utility of mempools. Solutions such as “Flashbots” and “MEV-Boost” have been introduced to mitigate these issues, but MEV remains a concern for fairness and decentralization, as it can lead to front-running and transaction censorship, undermining the integrity of the network.

Other Smart Contract Blockchains

Inspired by the idea of creating a public infrastructure that facilitates more complex decentralized applications, many protocol developers set out to create their own smart contract networks. Ethereum's code was either copied and modified, or the system architecture was completely changed—creating a range of blockchain solutions with different trade-offs between decentralization, scalability, security, transaction costs, and privacy. Many Ethereum competitors were developed over time, not all survived or could develop significant traction.

“NEO,” originally named “Antshares,” was launched in 2014, around the same time Ethereum was conceptualized. It initially offered non-Turing-complete smart contracts. Following Ethereum's success, NEO adapted Turing-complete smart contract capabilities. It was hyped as the "Ethereum of China," but struggled with scaling and eventually lost relevance in the competitive blockchain space that started to emerge. “EOS,” launched in 2017 after a record-breaking token sale that raised 4.1 billion USD, was initially hailed for its scalability and zero transaction fees. Among others, it struggled with centralization and governance issues and did not become a dominant player. “Cardano,” founded in 2017, also promised scalability and security with its academic foundation and Proof-of-Stake mechanism. While it faced challenges in early application adoption, it has survived and continues to build momentum with ongoing protocol upgrades and significant network traction. “Tezos” founded in 2017, stood out with its alternative governance features, such as a self-amending ledger and on-chain governance. However, it struggled with management issues, adoption, and progress, remaining a niche network despite surviving. “TRON,” established in 2017, was highly controversial from the beginning due to centralization and governance issues, which have hindered its broader success, in spite of being fast and providing low transaction fees. Still, Tron dominates network rankings in terms of total value locked on-chain. This is largely driven by one decentralized application only—the stable token Tether (USDT) which has a big transaction volume because of its usage. Tron, however, lacks a diverse application environment as seen on other blockchains like Ethereum or Solana. “Rootstock,” launched in 2018, is a Bitcoin sidechain adding smart contract functionalities to the Bitcoin ecosystem. As of now, it continues to evolve, facing challenges such as enhancing security features and increasing adoption.  “Polkadot,” launched in 2020, stood out for its interoperable multi-chain framework, which gained significant traction in the DeFi space due to its scalability and decentralized governance model, and is still an important blockchain ecosystem, but it has a much smaller developer community than Ethereum.

At the time of writing, the established L1 blockchains with the most developer activity, dapp development, or transaction volume besides Ethereum, are “Solana,” “BNB Chain,” and “Avalanche.” The most promising upcoming L1s seem to be “Sui” and “Aptos” due to their innovative approaches to scalability and transaction efficiency, although long-term developer and user adoption still need to be proven. Many more L1s exist, which are also considerable market players capturing significant market share, transaction fees, and developer activity. Unfortunately, a detailed analysis of the technical and economic complexities of each blockchain network—both on L1 and L2—is far beyond the scope of this book as it could fill an entire book, if not several. For the purposes of this book, four L1s (Ethereum, Solana, BNB Chain, Avalanche) and one L2 (Polygon) have been selected for a high-level analysis and comparison.

• BNB Chain (formerly Binance Smart Chain) was launched in 2020 by the operators of the crypto exchange Binance under the leadership of founder Changpeng Zhao. It was initially developed with an Ethereum-compatible codebase to allow developers to easily port their decentralized applications from Ethereum to BNB Chain, luring them with lower fees and faster transactions compared to Ethereum’s mainnet at the time. However, the system architecture was later modified, transitioning to a Proof-of-Staked Authority (PoSA) consensus mechanism that blends Delegated Proof-of-Stake (DPoS) with Proof-of-Authority (PoA) to achieve faster block times and lower fees. The native network token, BNB, originally had a supply of 200 million tokens, but Binance committed to reducing this number over time through a burning mechanism to 100 million tokens. The supply is not capped in a fixed way but is continuously reduced through burns. Research and development of the protocol were carried out by Binance, as was the initial funding of protocol development, with no formal token sale. However, Binance Smart Chain did issue a native network token, BNB, which is used for staking and transaction fees on the network. The governance of BNB Chain is heavily centralized under Binance, which is responsible for ongoing protocol development and other governance decisions. Network performance is high, and fees are low because there are only 21 active validators which are selected based on a combination of staking and Binance's reputation. They are operated by Binance and other validators that have been voted in by the community, but Binance’s central role in the ecosystem makes it a more centralized network compared to others like Ethereum or Solana. The blockchain network has been able to foster a vibrant dapp ecosystem, attracted by low fees, high throughput, and quick block times, but its centralization remains a key criticism. Like many public blockchains, it suffers from limited privacy and is susceptible to MEV issues and occasional security vulnerabilities. The key security vulnerabilities stem from its centralized validator structure of only 21 validators making it prone to 51% attacks. Additionally, like many other blockchain networks, smart contract exploits and cross-chain vulnerabilities pose significant security risks.
• Solana was conceptualized by Anatoly Yakovenko and launched in 2020 by Solana Labs. Its native token, SOL, was distributed through a combination of venture capital funding and a public token sale, with pre-launch allocations granted to founders, early investors, and community investors. Venture capital investments included major backers like Andreessen Horowitz and Polychain Capital, among others. The Solana network uses a unique consensus mechanism, combining Proof-of-History (PoH) with Proof-of-Stake, for faster processing speeds and higher throughput directly on the mainchain without the need for Layer 2 solutions. The total supply of SOL was initially set at 511 million. Its monetary policy was designed to be inflationary in the early years, with the total supply increasing annually through staking rewards for validators. The network's token supply rate was designed to gradually decrease to a fixed rate of around 1.5 percent per year. Solana also employs a fee-burning mechanism to remove tokens from circulation, which can offset inflationary pressures over time. This strategy was chosen to incentivize network participation and staking early on, while also aiming for long-term sustainability. The Solana Foundation was created to oversee development and operations. The network has more than 5000 nodes—validator nodes and RPC nodes—that have different roles in the network. However, Solana’s node distribution appears to be more decentralized than it actually is because the native network token SOL, which is needed for operating a staking node, is concentrated in the hands of a few early investors and large institutions (crypto exchanges, institutional investors, and infrastructure providers), which means that they have disproportionate advantages in a Proof-of-Stake based system. Furthermore, the high hardware requirements of running a validator node make participation less inclusive, and therefore less decentralized. Solana’s dapp ecosystem has flourished since its launch, but the network has faced several outages and performance issues, including disruptions caused by spam attacks and resource exhaustion in the past. These issues have led to concerns about its stability compared to Ethereum but seem to have come under control. Despite these challenges, the network has managed to attract a robust and diverse developer base, driven by Solana’s speed, low fees, and ease of use. However, the concentration of power and control in the hands of a small number of validators and influential stakeholders has drawn criticism, especially in comparison to more decentralized networks like Ethereum. Like many other smart contract networks, it faces MEV issues, and privacy features remain minimal as the platform prioritizes efficiency over transaction confidentiality. Although interoperability is addressed via emerging bridge solutions, Solana has been primarily focused on network performance rather than advanced cross-chain connectivity.
• Avalanche was founded in 2020 by Emin Gün Sirer, a computer science professor at Cornell University who developed a unique Proof-of-Stake-based mechanism to enable high scalability and low-latency transactions while enabling interoperability. Its architecture consists of a primary network with three built-in blockchains: X-Chain for asset transfers, P-Chain for validator coordination and subnet management, and C-Chain, an EVM-compatible smart contract platform. Beyond these core chains, Avalanche supports Subnets, allowing developers to create customizable blockchains with independent rules and consensus mechanisms while still benefiting from Avalanche’s security and infrastructure. The project was initially funded via venture capital by some of the same VCs who invested in Solana—Andreessen Horowitz and Polychain Capital—as well as a token sale that included allocations of pre-mined AVAX tokens for founders, early supporters, and ecosystem development. Ongoing protocol development and operations are governed by the Avalanche Foundation. The network token, AVAX, has a capped supply of around 720 million. Its issuance is designed to be deflationary through token burns over time. When the network was launched, its primary advantages were its high scalability, low fees, and specialized subnets, which offer developers the ability to create specialized blockchains for specific use cases. The network gained significant traction among dapp developers in its early years but also faced challenges—such as potential centralization issues linked to validator distribution and subnet management, ongoing security scrutiny as the ecosystem matures, and standard concerns over privacy and MEV, as transaction details remain publicly visible without advanced privacy enhancements. Around 1,400 active validator nodes are spread across the globe, incentivized to secure the network by staking AVAX tokens, which is much less than Ethereum. Avalanche is still seen as a strong competitor to Ethereum, with its high throughput being a key selling point. As high-throughput blockchain ecosystems are becoming more available, Avalanche must continue to build out its ecosystem to maintain its position in the market.
• Polygon, originally known as “Matic Network,” was founded in 2017 by Jaynti Kanani, Sandeep Nailwal, and Anurag Arjun. It is governed by the Polygon Foundation, which oversees protocol development and strategic decisions. While Polygon originally launched as a sidechain for Ethereum with the aim of resolving Ethereum's scalability issues at the time, it has since evolved into a multi-chain framework with bridging solutions that support various Layer 2s and other blockchains connected to the Ethereum ecosystem. As a sidechain, it is a Layer 2 solution with its own consensus mechanisms, using Proof-of-Stake where validators are selected based on the amount of MATIC tokens they stake. This is somewhat similar to Delegated Proof-of-Stake (DPoS), but with a focus on MATIC staking rather than community voting for specific validators, which makes the network fast. Different types of nodes (Bor & Heimdall) exist that have different roles in the system. The project raised development funds through a token sale of its native network token, MATIC, with a capped supply of 10 billion tokens. Like other blockchain networks, the monetary policy was designed to be inflationary, driven by staking rewards for validators and delegators. To counteract inflation, a deflationary mechanism through periodic token burns from transaction fees was also designed. The dapp ecosystem on the Polygon infrastructure has rapidly grown and is flourishing. However, Polygon has been criticized for its relatively centralized validator structure, as it operates with a relatively small number of validator nodes compared to other public blockchains. Although the exact number fluctuates, the network is primarily operated by a select group of validators, with many validators being large exchanges or infrastructure providers. For example, prominent exchanges like Binance and Coinbase may control a significant portion of the validator nodes, which gives them more influence over the network's consensus process. Others are concerned about its dependence on Ethereum, which detracts from its ambition to be a fully independent blockchain network. As an investor of the token, this dependence needs to be taken into account. At the time of writing, privacy on Polygon is limited to the inherent privacy features of the Ethereum mainchain. Additional privacy features are handled at the application layer, rather than the protocol level, but the protocol developers seem to be exploring privacy solutions.

The table on the next page compares Ethereum with some of the networks mentioned in this chapter. The numbers were sourced from various block explorers and industry analyses, and in some cases, have been approximated where exact figures were unavailable. The purpose of the table is to provide an overview of how blockchain networks can be compared. Many additional metrics exist and are discussed in the next section.

Blockchain Metrics

Blockchain metrics are essential for assessing the health, adoption, and overall value of a blockchain network. They provide insights into performance, adoption trends, and economic viability and are crucial for users, node operators, and network policymakers, as they inform both individual investment decisions and broader economic policy strategies. Different types of blockchain metrics exist, each focusing on distinct aspects of the network: (i) Technical metrics measure infrastructure performance, including transaction speed, block times, network uptime, and node distribution, which indicate a network’s scalability, decentralization, and efficiency. (ii) Community metrics assess user engagement and developer activity, tracking the number of active users, node operators, app developers, and Layer 2 developers to reflect how much innovation, real-world adoption, and decentralization a blockchain attracts. (iii) Economic metrics track network usage and financial activity, including the number of transactions, total value locked (TVL), transaction fees, staking participation, and gas expenditures, all of which indicate network security, demand, and long-term sustainability. Together, these metrics provide a comprehensive picture of a blockchain’s ecosystem, guiding developers, investors, and policymakers in understanding its strengths, weaknesses, and future potential. While the metrics presented are broad in scope, they are neither exhaustive nor fully refined, as measuring the technical and economic productivity of blockchain networks and applications remains an evolving science.

• Price of network currency: The price is determined by the anticipated supply and demand for block space, serving as an economic indicator of network utility.
• Block space refers to the maximum amount of data that can be stored in each block of transactions, directly impacting throughput and transaction fees. Higher demand for block space typically drives up fees and can potentially boost the value of the network’s native token.
• Transaction payload refers to the core data included within a transaction—such as sender, recipient, amount, and any instructions for smart contracts—necessary for processing and recording asset transfers. It does not include auxiliary data used by smart contract networks to handle more metadata-intensive operations.
• Auxiliary data space refers to additional storage space allocated for non-critical data that supports complex smart contracts without burdening the core transaction layer. In Ethereum, this extra space is called “blobs,” whereas in Solana it is known as “account storage.” Like block space, auxiliary data space is limited and plays a significant role in balancing throughput, resource allocation, and operational costs.
• Nakamoto coefficient quantifies the degree of decentralization in a blockchain network or decentralized organization by measuring the minimum number of network nodes required to collaborate to potentially achieve control over the system in a 51 percent attack: validator stakes (for proof-of-stake blockchains), or mining power (for proof-of-work blockchains like Bitcoin), or other types of governance influence (for application-level organizations/networks/protocols). A higher coefficient indicates a greater level of decentralization, reflecting greater resilience against attacks or collusion within the network. The value starts at 1 (highly centralized, where a single entity controls the majority) and increases as the network becomes more decentralized. A higher number indicates a more robust and decentralized network, reducing the risk of single points of failure or control. The coefficient can vary widely depending on the network's architecture and the distribution of control among its nodes. It has been criticized for certain limitations and may require further refinement.
• Block time is the average duration required to produce a new block of transactions. A shorter block time increases transaction throughput but requires faster node processing, which may reduce inclusivity and security. In contrast, a longer block time allows weaker nodes to participate but slows transaction confirmations. For example, Bitcoin’s block time is approximately 10 minutes, while Ethereum’s is around 15 seconds.
• Transactions per second (TPS) measures the number of transactions a blockchain network or its Layer 2 can process within a given time period.
• Backlog & network congestion: Network congestion refers to the backlog of unconfirmed transactions when demand exceeds available blockspace and auxiliary data space. Greater congestion leads to higher transaction fees and slower processing times.
• Time until finality: This metric indicates how long it takes for a transaction to be irreversibly confirmed by the network. Depending on the decentralized application, faster finality may be more or less critical.
• The block reward is the absolute amount of protocol tokens a node operator can receive for performing network services, which vary depending on the consensus mechanism.
• Block rewards profitability refers to the return on investment for operating a mining or validating node. It depends on the number of tokens earned, the costs of operating a node, and the exchange rate of the network token. In modular blockchains, nodes with additional roles also calculate profitability–the rewards just have different names.
• Transaction fees are the cost that users or dapp developers pay for transaction execution/smart contract operations. Lower fees can stimulate dapp development, while higher fees may incentivize node operation. During high-demand periods, fees rise due to competition for limited blockspace, impacting network economics on both the supply and demand sides.
• Client diversity measures the variety of blockchain software implementations available. A diverse ecosystem reduces the risk of single points of failure and enhances decentralization, as users can switch to alternative implementations if one encounters issues.
• Number of core developers, reflects the number of active, independent core developers who contribute protocol creation, improvement and maintenance. A higher count generally indicates a stronger development community indicative of a network’s innovation and resilience.
• Number of GitHub commits, reflects the frequency and volume of code contributions on GitHub, serving as an indicator of active development within a blockchain ecosystem.
• Number of node operators, indicates network inclusivity and the level of decentralization. Lower technical and financial barriers typically lead to more independent node operators.
• Weekly active wallets measure the number of active wallets interacting with the network over a week, providing insights into user adoption and engagement. The time frame of measurement can be shorter or longer.
• Dapp on-chain is a metric representing the number of decentralized applications built on a blockchain network, which is indicative of the demand for blockspace and auxiliary data space, contributing to overall network value.
• Dapp transaction volume captures the total number of transactions processed by decentralized applications, reflecting the scale of the usage of a decentralized application, and can help determine the dapp’s importance/dominance on the blockchain network.
• Dapp fee expenditure measures the total amount of transaction fees paid by users when interacting with a dapp over a specified period (daily, weekly, monthly, or yearly).
• Inflows track the net movement of assets into the network, reflecting user adoption trends and investment activity.
• Market capitalization reflects the total value of all network tokens in circulation at the current market rate.
• Spot market trading volume measures the volume of network tokens exchanged in a given period, indicating liquidity and market activity.
• Derivatives market trading volume measures trading activity in derivative instruments (e.g., futures, options) related to the network’s native token.
• Total Value Locked (TVL) refers to the total value of assets locked in a blockchain network or dapp for purposes such as lending, staking, or liquidity provision, effectively removing them from circulation. It is calculated by multiplying the amount of locked tokens by their current market price.
• Bridged TVL represents the value of assets transferred between blockchain networks via cross-chain bridges. Bridged TVL is often included as part of a network’s overall TVL if those assets are used within DeFi protocols on the destination chain.
• Aggregators volume measures the transaction activity facilitated by decentralized exchange aggregators, which find the best prices and liquidity across multiple exchanges. For native tokens like BTC, ETH, or SOL, high aggregator volume indicates strong liquidity and robust demand for blockspace, as these trades often involve multiple smart contract interactions.

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