ConsenSys Creates Guide for Ethereum’s Shanghai Upgrade

  • The platform explained that the upgrade would help stakers, the ETH staking ecosystem, and DeFi.
  • The upgrade would also reduce the liquidity risks of ETH staking.

ConsenSys, the private blockchain software technology company, published an ultimate guide to ETH staking withdrawals, focusing on the impact of the Ethereum blockchain’s “hardfork”, the Shanghai/Capella upgrade on the staking ecosystem as well as on Decentralized Finance (DeFi).

On Tuesday, the Ethereum developers successfully launched the Shanghai upgrade on the Sepolia testnet, focusing to allow validators to withdraw ether upon the completion of the update on the mainnet.

Following the update, ConsenSys wrote on its official Twitter account the details about the blockchain’s new upgrade and the staking withdrawals:

Notably, the platform expounded that the upgrade has several implications for “stakers, the Ethereum staking ecosystem, and DeFi”. In particular, ConsenSys explained that the stakers would be able to access funds locked for years, stating:

Partial and full withdrawals will give long-term stakers access to funds that have been locked for upwards of two years. Early stakers however, have demonstrated their belief in Ethereum, and may be more likely to stake this newfound liquidity, rather than take profits.

In addition, the platform pointed out that the withdrawals would “encourage increased participation by validators”, guaranteeing the security of the Ethereum network. Furthermore, ConsenSys assured that the upgrade is expected to reduce the “liquidity risks of staking ETH”, quoting:

By reducing the liquidity risk of staking ETH, withdrawals could inspire confidence in liquid staking protocols and make ETH staking a more attractive opportunity in general, especially for typically risk-averse institutions.

Moreover, the company added that the stakers who make use of third-party staking services would have an opportunity to re-evaluate the specifics of ETH staking; they could study it based on factors such as “reward, maximization, validator performance, simplicity of the user”.

Related Articles

Back to top button