Eigenlayer: the verifiable agent universe ———————————— Eigenlayer has the universe of autonomous verifiable services: AVS (zkTLS, DA, Oracle, AI - data, models, benchmarking, inference, training, DB, bridging, event driven actions). These AVS will make agent actions verifiable. Eigenlayer will power the universe of autonomous verifiable agents!

We have a fleeting opportunity to build AI systems on crypto rails, embedding decentralization, transparency, and fairness. Here are my notes for 22nd @eigencloud episode with @sreeramkannan of @eigencloud on @therollupco, co-hosted by @andyyy and @robbie_rollup. How Decentralized AI Fits into Coordination and Innovation Successful complex systems rely on two essential layers: 1. The Coordination Layer: This provides the foundational rules and structure (e.g., governments, constitutions). 2. The Innovation Layer: This allows free agents to experiment and innovate within the boundaries set by the coordination layer (e.g., free markets). Advances in AI and technology have significantly accelerated the innovation layer, empowering individuals (or agents) to innovate more rapidly. However, the coordination layer has not evolved at the same pace. It still relies on old structures like constitutions, double-entry accounting, and traditional money systems. The AI Dilemma AI poses challenges to safety and coordination: - Some fear AI could break existing safety mechanisms. - The current solution often proposed is regulation through traditional systems like governments, which operate on outdated coordination frameworks. A Crypto-Native Solution Instead of relying on slow-moving regulators, a crypto-native approach could upgrade the coordination layer using blockchain technology. This could involve creating mechanisms where AI systems are governed by decentralized rules. For example: - Binding AI to Coordination Conditions: Using blockchain, AI actions could be subject to human or decentralized autonomous organization (DAO) approval. - Extreme Example: An AI cannot act or can even be terminated unless a human DAO approves. - Incentivizing Decentralized Collaboration: a. Encourage data owners to work with AI model developers. b. Promote collaboration between different AI models to create more powerful "meta-models." - Blockchain-Controlled AI: Use blockchain to ensure AI "loyalty," where a model only acts based on its master’s instructions, similar to mechanisms already being explored (e.g., Sentient’s loyalty concept). However, these ideas introduce novel challenges, such as: - How to effectively bind AI models to blockchains. - Designing incentive structures for decentralized collaboration. - Building automated, self-enforcing, and globally coordinated systems. Instead of relying on regulators to address AI challenges, the goal is to upgrade our coordination systems. This could lead to a global, automated, and self-enforcing system that adapts to technological advancements, fostering innovation while ensuring safety and order. The Clear Case for Crypto’s Existence @sreeramkannan highlights that crypto's fundamental purpose becomes increasingly evident when addressing one of society's most urgent challenges: How can we decentralize AI and bind it to commitments that are not just "knowable" but also verifiable and self-enforcing? This capability of verifiable, self-enforcing commitments is crypto's unique superpower. It offers a critical solution for ensuring AI operates within a decentralized and accountable framework. According to @sreeramkannan, there is a short window of opportunity for the crypto space to leverage this superpower. Historically, transformative technologies like social media were built without decentralized frameworks. For example: - Imagine if Facebook had been built on crypto rails from the start.. it would be fundamentally different in terms of ownership, governance, and trust. @sreeramkannan notes that we are in a similar pivotal moment today. We have the chance to ensure that the best AI systems in the next 3 to 5 years are built on crypto rails. This would embed decentralization, transparency, and accountability into the foundations of AI development, creating a more equitable and trustworthy technological future. The Transformative Potential of Crypto and AI Crypto has two key roles to play in shaping the future of AI: 1. First, it can serve as an incentive and tracking layer for contributions. For instance, if one person provides data and another builds a model, crypto enables smart contracts to fairly distribute rewards, ensuring transparency and accountability. 2. Second, crypto offers verifiability; AI outputs can be bound by mechanisms that ensure accuracy, which is crucial for applications like intelligent DeFi and proof of personhood, especially as autonomous agents grow more sophisticated. AI, by its nature, is decentralized. People are scattered across the globe, and crypto provides the perfect infrastructure to harness this decentralized intelligence. With crypto, we can incentivize global data contributions and ownership, solving one of AI's looming challenges: running out of quality data. At the same time, AI can help address some of crypto’s own pain points. For example, smart contracts today are complex and prone to errors that can result in massive losses. A promising future involves contracts written in plain English, which AI could translate into code automatically. This approach could improve usability and reduce vulnerabilities, with tools like large language models already showing promise. However, the mismatch between AI’s massive computational needs and blockchain’s limited throughput remains a challenge. Solutions from the modular blockchain paradigm, such as having one node handle computation while others verify, could bridge this gap. The synergy between these fields holds untapped potential to address shared limitations and create groundbreaking applications. The episode is packed with insights into coordination and innovation, role of crypto in AI’s future, user-owned AI, and more. Check it out (link in the reply)!

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