Ethereum Falls Back to Earth. Can it Re-ascend?

The Problem

Ethereum founder, Vitalik notes that distributed networks suffer from a trilemma: the security and decentralization of a system come at the cost of its scalability. As additional nodes are added to a PoW blockchain, so too does the amount of work required to process data across the entire ledger. To compensate the Ethereum network, a participant must pay cryptocurrency referred to as ‘gas’ for each transaction. During the early days of Ethereum these costs were negligible for participants, but as more numerous and complex computational tasks have been transmitted to the network, and as more nodes are added to the system, gas costs have similarly ascended.

Pick Your Favorite Two

While the PoW architecture renders the network more secure — more nodes reduce vulnerability to sybil attack — it does come at the sacrifice of efficiency. Whereas Visa can verify up to 56,000 transactions per second, Ethereum can handle roughly 15. Consequently, a proof-of-work based smart contract platform is ill-suited to process numerous computational tasks, and even worse suited to base an entire monetary system upon. If such a system cannot compete with the entrenched incumbents, it must evidently continue its downward fall to earth.

Verifying Ethereum’s Scalability Issues with Queryable Blockchain Data

As we’ve established the conceptual dilemma facing distributed networks like Ethereum, let’s next verify that Ethereum indeed suffers from a scalability problems, by showing the data. Average daily gas costs provide a useful proxy to gauge scalability bottlenecks, and such costs can be derived from data contained within each Ethereum block. Public blockchain advocates purport that tracking transactions across the ledger is easy and transparent. This has historically been false. Until recently, monitoring transactions across the Ethereum network required firing up a complete node and deep technical expertise to boot. No longer. Having uploaded the entireties of the Ethereum and Bitcoin blockchains onto its cloud platform, Google’s BigQuery allows us to easily track Bitcoin and Ethereum blockchain data via SQL queries. Simply sign up for Google’s BigQuery via your Google Account. Next create a Sql instance and tie that to BiqQuery’s Ethereum dataset. BigQuery’s Ethereum dataset contains all relevant information since the Ethereum genesis block was launched, including each block’s timestamp, hash, parent hash, and all the transactions contained therein. With data being mapped to a traditional relational database structure, we can run a query like the one that follows:

SELECT 
sum(transactions.value/POWER(10,18)) as value, AVG(gas_price*(receipt_gas_used/POWER(10,18))) AS avg_gas_cost, date( blocks.timestamp) as date
FROM
`bigquery-public-data.ethereum_blockchain.transactions` AS transactions,
`bigquery-public-data.ethereum_blockchain.blocks` AS blocks
WHERE TRUE
AND transactions.block_number = blocks.number
AND receipt_status = 1
AND value > 0
GROUP BY date
HAVING date >= '2017-10-16' AND date <= '2018-12-31'
ORDER BY date, value

This query offers three outputs: dates in ascending order, the total amount transacted in Ethereum across the network on that date, and average Ethereum transaction cost denominated in Ether on the same day. From Google’s Biquery, one can export a csv file to Excel and multiply average daily average transaction costs in Ether by daily average price of Ether to derive the average cost per Ethereum transaction on a given date in dollar terms:

Ethereum Hits a Bottleneck

In late December of 2017, Ethereum’s first hit dApp, Cryptokitties clogged the network [Some transaction went unconfirmed for hours on end, while costs of running simple contracts rose precipitously]. For a system intended as the bedrock of an open source ‘internet of value,’ that folks simply sending (adorable) digital cats over Christmas time could test the network’s throughput capacity served to undermine the promise of a distributed financial system. This wasn’t the first, or even the most dire crisis to confront the Ethereum ecosystem. In 2016, the DAO hack would result in a hard fork. Prior to the Dao Hack, smart contracts had not been battle-tested. The hack demonstrated a need for heightened security around smart contracts, the applications built on top of Ethereum. Suddenly, the need for safer, more scalable distributed ledgers became apparent to the world.

In July, despite falling dollar costs of Ether, gas prices again spiked. This time around the culprit was a new feature launched by Chinese exchange, FCoin. Fcoin used poorly programmed smart contracts to allow participants to vote on new tokens to issue to its exchange. Ethereum clearly needs scalability solutions to update its platform.

Someone is salty..

The best solution will safeguard public blockchain’s best features (i.e. security, transparency, and immutability) while eliminating those features that make it ill-suited for global commerce such as excessive energy consumption, poor user interface, and throughput concerns.

The View from Earth

Some predictions on the future of Ethereum and Public Blockchains going forward are as follows:

  • Scalability and Security will be prioritized above Decentralization. ‘Store of value’ cryptocurrencies like Bitcoin and Monero will maintain proof-of-work consensus mechanisms, but the leading smart contract platforms will need to transition to alternative consensus mechanisms. These protocols will place a higher barrier to entry on participating nodes, diminishing the decentralization of such systems. This is a good thing, as in reality PoW blockchains haven’t been truly decentralized since Bitcoin’s early days. Though all nodes must process transactions in a PoW system, in reality the same game-theoretic mechanisms that reduce the chance of a sybil attack in a PoW system are at play in PoS system (it minimizes token value and hence the payout for all nodes when a blockchain’s decentralization is jeopardized by a sybil attack; preventing dishonest behavior from ≥ 51% of the nodes of a distributed system is thus a cryptoasset’s nash-equilibrium).
  • Other Smart Contract Platforms will become market leaders. Ethereum will aim to integrate scalability solutions such as the proof-of-stake consensus mechanism and sharding. However, other platforms like EoS and Tezos that have already implemented Delegated Proof of Stake and are therefore already scalable will become smart-contract market leaders beforehand.
  • Transparency and Immutability of blockchain will be threatened. Our Western democratic political systems are mostly transparent, the outcomes of elections immutable. Moreover, similar to public blockchains, democratic political systems benefit from oppositional forces at play in a public sphere. Some protocols like EoS have established mechanisms that serve to mimic the division of powers found in democratic institutions. Those parties operating nodes containing vital economic information will need to operate transparently. It will therefore depend on public internet forums to hold the guardians of such smart-contract platforms accountable.

In conclusion: the ability of public blockchains to allow us to collectively and transparently track economic inefficiencies in real-time rather than ‘ex post fact’ renders this technology worth being patient with. A system that incentives participation and invites criticism is why even as speculators flee the market, developers continue to build applications on top of Ethereum, and a generation brushes up on some of the more mundane topics of cryptography and computer science. A more transparent economic system for all to analyze, for all to critique, will be the prize.

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