Introduction

The creation of EigenLayer, built on the Ethereum blockchain, is an innovation for the future of work that has the capacity to nurture human potential for decentralized innovation, creativity, collaboration, and trust. The following article will discuss how EigenLayer functions; how it impacts human task specialization through increased efficiency; how validators will incentivize better human performance; how the trust leveraged from the Ethereum blockchain will enable EigenLayer to optimize human oversight while not wasting time on things that are easily automated; how human autonomy can be increased through greater choice and opportunities to simultaneously be involved in separate and distinct networks; and future implications for ZKML (zero-knowledge machine learning). The ultimate goal is to illustrate how this technology can improve utility for a better future of work, the blockchain, and AI innovation.

EigenLayer Overview

In brief, EigenLayer aims to increase security, sovereignty, and scalability. The symbiotic relationship between EigenLayer and Ethereum functions such that EigenLayer utilizes the strong trust and security of Ethereum, while simultaneously aiming to solve the main issues associated with Ethereum including: cost efficiency; no inherent alignment of protocol interests; and a lack of trust between protocols. It does so by providing an infrastructure that allows for staked ETH to be re-staked on other blockchains, protocols, and middleware. The metaphor of a shopping mall has been used by multiple writers on this topic, and does a good job illustrating how these issues have been playing out, their relation to one another, and how EigenLayer serves to fix these problems. 

Imagine that the Ethereum blockchain is the mall, dApps and middleware are the shops, consensus mechanisms are the elevators, and the trust network is the concrete floor. The mall (Ethereum) has provided a space for shops (dApps and middleware) to serve customers and offer a product. However, the mall (Ethereum) does not have one elevator (consensus mechanism) that connects all of the shops (dApps). Rather, each shop (dApps) is required to build its own elevator (consensus mechanism). Obviously, the way this is set up will be very expensive for all of the shops in the mall, and doesn’t increase trust or a relationship between the different people working in this somewhat shared space. Therefore, EigenLayer is paving the way forward for the Ethereum blockchain and protocols alike, through the application of restaking. Here, ETH already staked on the PoS (proof of stake) chain is downloaded by ETH validators, which run the node software, thus validating both the ETH PoS chain and AVS (actively validated services, or dApps) at the same time (those using the EigenLayer contract). 

(EigenLayer, 2023)

Human Task Specialization

EigenLayer’s ZKML (zero knowledge machine learning) use case, EigenDA, is a data availability layer that allows Ethereum to offload data without having to go off-chain, remaining within the security of the Ethereum ecosystem. Therefore, the future of collaboration between ZKML and EigenLayer can take many forms, all of which should improve security, capital efficiency, value alignment, and indeed restructure the ways in which we work as humans moving forward. Most importantly though, it will empower people via task specialization, a choice in the security networks that they work with, as well as what projects they want to give their time to. 

ZKML  enables independent validation of AI-generated content, further reinforced with trust and security via EingenLayer. ZKML can be broken down into two parts to better understand how this field of research is making a significant difference in cryptography. The first constituent, ZK, or zero-knowledge cryptography, is important because it is a technology that allows developers to build both scalable and private applications. The structure of blockchains, in which each node has to verify each block by running it independently, is inherently limited in its computational power. This is why ZK proofs are increasingly useful, as they enable computations off-chain, which can then be verified on-chain. Starknet, Scroll, and Polygon Zero are all examples of this. Furthermore, ZK proofs allow for increased privacy, with applications such as Aztec, that is building a scalability solution for Ethereum in which the public cannot see users’ balances or transactions. Since ZK has the capacity to both minimize and make computations private, the outcome has been the creation of smaller, easily verifiable, L1s such as Zcash and Mina. 

The second constituent, ML, or machine learning, in conjunction with AI, is a rapidly developing field in which computers autonomously learn and adapt from data. GPT-4, Bard, Midjourney, and Stable Diffusion are all either large language models or text-to-image models that are rapidly advancing. As they become more sophisticated, they will continue to revolutionize the ways in which humans both live with and alongside technology. Therefore, the coupling of EigenLayer and ZKML has the capacity to simultaneously leverage Etheruem’s trust network, while also unbundling this network so that components can be re-delegated more efficiently.

Human oversight of automated systems

The more people automate work that they don’t want to be doing, the more opportunities there are for work oversight instead of drudgery. AI will not automate everything away, but can enable system oversight so that people can be involved where they are excited to be involved. For example, EigenLayer requires human oversight for crucial security and governance, addressing the main issues of similar systems in the past such as merged mining. Here, validators were not being held accountable for neglecting to run sub-block systems properly. EigenLayer therefore monitors whether users are simultaneously running identical validating keys in two or more places. If they are, they run the risk of being slashed. Another realm in which human oversight will be required is operator collusion, which is the main fear anticipated by the EigenLayer team. This involves a case in which stakers are also restaking their AVSs, and the total profit would make the system crypto-economically insecure. Such an instance would also result in penalization.

Human performance rewards

EigenLayer validators are rewarded for proper functioning, which in turn incentivizes participation on a myriad of levels. Slashing is a penalty, so doesn’t directly make people better workers, but will eliminate sub-par performers. Prior issues associated with the cost, trust, and speed of innovation associated with the Ethereum blockchain are therefore ameliorated through improved performance, with slashing acting as the main incentive mechanism. The penalty of slashing on Proof of Stake (PoS) networks also gives participants the choice to eject an offending validator for various ways of failing to perform their job properly. Essentially, with the heightened benefit that comes with staking yields on both Ethereum and an additional protocol, comes the opportunity-cost of the slashing mechanism. This system thereby incentivizes optimal human performance because users will trust more via this worker penalization mechanism, and workers will perform better to meet user expectations.  In the case that slashing is in question because an honest user has lost funds due to a bug in the system, a multisig veto committee will be able to veto the slashing of the user in question.

Human autonomy and work opportunities

Systems function best when workers choose specific tasks to contribute to and earn rewards based on good performance. One of the main problems that developed with Ethereum's modular blockchain technology was a decoupling of trust and innovation. As, trust lay in the Ethereum chain, while the majority of innovation occurred within the dApps. Therefore the autonomy of workers was challenged because they needed to spend more time on building trust in order to be innovative, thus detracting from pure innovative potential. In turn, this led to performance issues, because sales development representatives (SDR) and business development (BD) are performance-based positions in which people want autonomy and to feel like they are doing work which is both meaningful and enjoyable. 

A system is only as strong as its weakest layer, so because the TVL (total value locked) of Ethereum is too high to easily attack, individual protocols became much easier targets, leading to the many hacks that have occurred over the past few years. Therefore, applications and middleware have had to focus on security, which diverts the potential for innovation and capital. Simultaneously, protocols that are not EVM-compatible cannot benefit from the Ethereum network’s benefits. Therefore, the bridge that has been built between these two systems through the innovation of EigenLayer has enabled people to work together creatively rather than merely protecting existing systems. 

Summary

In the last decade, people are increasingly waking up to the idea that they do not want to be cogs in a capitalist machine. Rather, innovators who are specialized in domains rather than tasks, and empowered through doing this work. With the emergence of Web3 and blockchain technology, there is now more and more choice in security networks, investment opportunities, and projects to collaborate with or even found. Therefore, our world is calling for the sort of freedom of innovation, coupled with trust, that EigenLayer is providing through its emergent technology. Organizations have also had to become both more transparent as well as more optimal employers, making autonomous employment, whether in task or location, and therefore raising the bar for the future of work. 

EigenLayer allows validators to choose which apps they participate in, simultaneously solving security, data, and slow or decreased innovation issues. Essentially, this marketplace for decentralized trust overcomes the obstacle of fractured trust on the Ethereum blockchain. It is an innovative means by which to stake both on the Ethereum blockchain as well other dApps, using a single collateral and securing multiple layers of trust. 

With the application of EigenLayer, it is not only that a connection has been made between previously incompatible systems, but that humans are now given the choice and opportunity to simultaneously be involved in separate and distinct networks. Perhaps this marriage of trust (ie Ethereum’s network) and decentralized innovation (dApps and middleware) is EigenLayer’s most valuable function, as it provides for the flourishing of human creativity. Thereby, making a considerable step forward for the future of how we imagine, engage with, and accept what our relationship with work should be like. 

EigenLayer and other blockchain protocols are, inherently, human systems. Ideally they are ones in which banal tasks are automated. Automation of such tasks, in combination with leveraging the trust network that already exists within the Ethereum blockchain, will further enable the flow of capital, trust, and innovation between dApps, middleware services, and Ethereum. The two mechanisms which make this possible are pooled security and free-market governance. In turn, with more protocols using EVM’s security, supported by this strong base, a higher rate of development will occur.

This article sought to illustrate the potential for Eigenlayer to develop in relation to AI as an additional driver of work opportunities, in so far as AI enables the capacity to select and curate databases, build models, and apply these to specific use cases. Existing AI and data services that make use of EigenLayer can also be employed to increase efficiency and innovation. This technology demonstrates a meaningful shift in the ways in which humans work in and with technology as well as one another, for EignenLayer is not only about staking and security, but most importantly, about increasing human creative potential.