Author: Decentralised.Co, Translation: Golden Finance xiaozou

If artificial intelligence (AI) requires cloud services, then web3 AI also requires web3 cloud services.

EigenLayer and artificial intelligence are two of the hottest topics in the crypto space over the past year. In this article, we will explore the intersection of the two and learn about the noteworthy projects being built at this critical juncture.

1. What is AVS (Active Authentication Service)?

Let's first understand what the active verification service AVS on EigenLayer is.

The best way to understand EigenLayer is to think of it as a security and compute marketplace.

Blockchain and other cryptographic protocols such as bridge protocols rely on decentralized node operators to process transactions. These node operators maintain the current state of the network and process incoming transactions individually. For a transaction to be verified, a majority of node operators must agree on its validity. Therefore, the more nodes there are, the more secure the network is.

New protocols often have difficulty building a strong and reliable base of node operators due to the cold start problem. Operators are often incentivized by the protocol’s native token. However, in the early stages, without a strong network of nodes to support them, the token’s value may be limited.

To solve this problem, the team may provide more tokens to incentivize node operators. However, doing so may lead to high inflation and token value dilution, which is not an ideal result. In addition, the small number of nodes in the early stage often brings security risks and centralization risks.

EigenLayer solves this problem by helping any blockchain service (Active Verification Service or AVS) bootstrap Ethereum-backed security. The protocol consists of operators that specialize in providing computation and security. Users assign ETH or liquidity stakes to these operators, who then validate one or more AVSs.

If the operators perform their duties adequately, AVS rewards them, and they, in turn, pass these rewards on to their depositors. If the operators fail to perform their duties, their stakes are forfeited.

By validating multiple services with a standard set of operators, all managed by a standard economic layer, EigenLayer simplifies the launch of projects that rely on distributed nodes for security. This advantage has attracted a wide variety of projects, including data availability solutions, bridge solutions, oracles, and ZK (zero-knowledge proof) processors.

2. Artificial Intelligence (AI)

Over the past two years, artificial intelligence has clearly become a focal point in the tech world, attracting the attention of entrepreneurs, investors, and users. Of course, this enthusiasm has also spread to the crypto space. According to Kaito Ai data, AI has accounted for the largest share of narratives in all crypto fields in the past 12 months.

Blockchain operators are essentially computers. When validating rollups, they accept incoming transactions, process them based on the current state, and then output a new state. However, if operators can provide hardware such as GPUs, SSDs, and ZK Prover, this input-processing-output paradigm can be extended to any distributed computing operation. Therefore, EigenLayer can be envisioned as a web3 distributed cloud service provider.

Today, most AI processes are run in the cloud - whether it is a hyperscale enterprise like AWS or a professional cloud service provider like Lambda and Coreweave. These services facilitate training models and inference. This makes EigenLayer, as a web3 cloud, a natural fit for web3 AI applications.

Let’s take a look at some of the noteworthy projects.

3、Ritual

Today, most users and developers access AI services through APIs provided by centralized cloud providers. However, this status quo has several issues, including lack of privacy protection, controversial computational integrity (how can you be sure that the response comes from the model you requested?), and potential censorship issues.

In contrast, smart contracts run in a highly secure, transparent, and trusted environment. In some cases, smart contracts need to interact with AI services. However, it is computationally infeasible to run any AI process on-chain. Existing cloud providers also cannot provide services for smart contracts because this would break their trust assumptions.

Ritual is solving this problem by building an open, privacy-first, censorship-resistant, and verifiable AI layer for blockchain AI services. Their first product is Infernet, which allows smart contracts to request AI model inferences and provide proof of computational integrity. Ritual's future plan is to expand by creating a sovereign chain, Ritual Chain, which will provide extended features such as fine-tuning and training AI models.

Ritual Chain will serve as an AVS developed based on EigenLayer. Operators with specialized hardware (such as GPUs) will perform AI tasks served by the chain. The decentralized validator set will provide high availability and censorship resistance, as each task will be served by multiple operators. In addition, these operators will also provide basic security guarantees for Ritual Chain.

4、OpenLedger

A few weeks ago, we explored the data challenges facing AI and how blockchain protocols can address them. The most important issue we highlighted was the centralization of AI data. Platforms that own valuable data are striking high-value, multi-million dollar deals with well-funded companies, while limiting entry for smaller startups and research firms.

OpenLedger aims to provide a solution by creating an "AI sovereign data blockchain". OpenLedger provides AI teams with: high-quality annotated data to ensure effective training and accuracy; reinforcement learning and human feedback (RLHF) services to enhance models; and tools to evaluate the accuracy, reliability, and security of AI models.

OpenLedger is also being developed as an AVS on EigenLayer. While the exact details of the deployment have not been fully disclosed, we can make some educated guesses. In order to build a distributed, highly available data layer, chain nodes will need large amounts of fast storage. EigenLayer operators are well suited to provide this, as well as basic computation and security services.

5、Sentient

Earlier this month, Sentient announced that it had completed an $85 million seed round of funding, with support from some of the top crypto investors and operators. Their goal is to create an "open platform for AGI development." What does this mean?

Currently, the most advanced AI models are closed source and controlled by a few powerful organizations. This level of control is unhealthy for one of the most important technologies of our time. So, in response, there is a growing open source movement that advocates that the model weights (configuration) be available to anyone, allowing people to run the model on their own hardware or fine-tune it to meet their specific needs.

The problem is that, while the open source model is essential, it is difficult for creators to make money using open source models. Once the weights are publicly available, anyone can host, modify, tweak, and create services based on them without sharing any revenue with the original model creator. This fundamental misalignment of incentives threatens the speed of open source AI development.

Sentient's goal is to "bring ownership to AI development" and aims to create a technology that enables researchers and developers to profit from AI models while maintaining openness and security. When developers use models created by Sentient, they can ensure their validity, just as if they were hosting open source models. However, they also need to compensate the model creators by paying for inference.

Sentient is built using Polygon CDK technology and runs as an AVS based on EigenLayer. While Sentient's exact usage of EigenLayer has not been fully disclosed, we can speculate that it will be similar to what Ritual does. Operators may need to provide computing resources for inference as well as provide security for the chain.

The EigenLayer team mentioned in a blog post last year that AI reasoning is one of 15 potential unicorn ideas that can be built as AVS. Clearly, many teams believe that this potential is real. While EigenLayer and the Web3 AI field are still in their early stages, there is a natural convergence between them. If AI needs cloud services, then Web3 AI also needs Web3 cloud services.

The projects we mentioned above are just initial experiments, and we expect more to come.