"The Bitter Lesson: Scaling Computation and General Methods for AI Success"

"The Bitter Lesson: Scaling Computation and General Methods for AI Success"

Introduction:
In the world of AI research, the bitter lesson learned over the course of 70 years is that general methods leveraging computation are ultimately the most effective. This realization comes from the observation that human-knowledge-based approaches tend to complicate methods, hindering their ability to take advantage of the power of computation. Instead, breakthrough progress has been achieved by scaling computation through search and learning. This article explores the connection between EigenLayer and the bitter lesson, highlighting the significance of general purpose methods and the importance of building systems that can discover rather than contain preconceived knowledge.

Establishing Economic Security in Decentralized Infrastructures:
Developers who build decentralized infrastructures on Ethereum face the challenge of establishing their own economic security. While Ethereum provides economic security for smart contract protocols, infrastructures like bridges or sequencers require their own economic security to enable a distributed network of nodes to reach consensus. Consensus mechanisms are essential for facilitating interactions among these nodes, whether it's an L1, an oracle network, or a bridge bootstrapping a new PoS network is hard.

Challenges Faced by Stakers in Infrastructure Building:
Building a new network requires stakers to overcome several challenges. Firstly, identifying where stakers are located is difficult as there is no centralized platform to find them. Secondly, stakers must invest a significant amount of money to obtain a stake in the new network, often by purchasing the network's native token, which can be volatile and hard to acquire. Additionally, stakers must forgo other reward opportunities, such as the 5% rewards offered by Ethereum. Lastly, the current security model is undesirable as the cost to corrupt any dApp is simply the cost needed to compromise its weakest infrastructural dependency.

Introducing EigenLayer for Economic Security:
EigenLayer provides a trustless and programmable platform that allows stakers to secure infrastructure commitments. By leveraging Ethereum as a platform, EigenLayer connects stakers and infrastructure developers, enabling stakers to provide economic security using any token. The goal is to create a user-friendly interface on top of Ethereum where stakers can secure infrastructure protocols and collaborate to enhance the security of the network.

The Role of TokenPool and Slash Functions:
To ensure economic security, EigenLayer introduces the concept of TokenPool contracts. Each TokenPool has its own unique slashing condition, embedded within the contract. If a staker deviates from their commitment and behaves maliciously, a portion of their stake will be slashed. Other stakers can also stake into the TokenPool, further strengthening the security of the infrastructure. This model allows stakers to pledge stake to specific commitments and ensures their commitment to the smooth running of the protocol.

Enhancing Efficiency with Slasher and Delegation Managers:
To enhance efficiency and enable stakers to participate in multiple commitments without creating new TokenPool contracts, EigenLayer incorporates the use of a Slasher contract and a DelegationManager. The Slasher contract contains slashing logic for each Actively Validated Service (AVS), while the DelegationManager tracks the delegation relationships between stakers and operators. This allows stakers to delegate their tokens to operators participating in different AVSs, streamlining the process and reducing gas overhead.

Native Restaking and the Role of EigenPod:
EigenLayer also offers the option of native restaking, where stakers can participate in EigenLayer using ETH within a validator. Validators can restake their ETH and earn rewards while contributing to the security of other infrastructures. To track validator balances and enable slashing if necessary, EigenPod serves as a virtual accounting system. It monitors ETH balances for each restaked validator and ensures the security and integrity of the restaking process.

The Bitter Lesson and the Power of General Purpose Methods:
The bitter lesson learned from AI research is highly relevant to EigenLayer. It emphasizes the power of general purpose methods, such as search and learning, that continue to scale with increased computation. Rather than building systems that contain preconceived knowledge, EigenLayer aims to create AI agents that can discover like humans do. By leveraging the scalability of general methods, EigenLayer strives to simplify infrastructure building and pool security through restaking, ultimately driving progress in the decentralized infrastructure space.

Actionable Advice:

1. Embrace general purpose methods: When developing AI systems or decentralized infrastructures, focus on scalable general methods that leverage computation. Avoid overcomplicating methods with human-knowledge-based approaches that may hinder long-term progress.
2. Prioritize search and learning: Invest in search algorithms and learning models that can scale with increased computation. These methods have proven to be highly effective in various AI applications, including speech recognition and computer vision.
3. Foster collaboration and security: Encourage collaboration among stakers and infrastructure developers to enhance the security of decentralized infrastructures. By pooling resources and staking commitments, a distributed network of nodes can achieve consensus and ensure economic security.

Conclusion:
EigenLayer, with its focus on economic security and the utilization of general purpose methods, aligns with the bitter lesson learned from AI research. By leveraging scalable methods like search and learning, EigenLayer aims to simplify infrastructure building and enhance the security of decentralized networks. The power of computation, combined with a collaborative approach, can drive progress in the field of decentralized infrastructures and contribute to the broader development of AI systems.