The Artificial Superintelligence Alliance has launched ASI:Chain public DevNet, providing early developer access to a Layer 1 blockDAG infrastructure specifically engineered for autonomous agent coordination and decentralized artificial general intelligence applications.
The announcement came at Web Summit Lisbon on November 13, where SingularityNET Foundation and its partners Fetch.ai and CUDOS unveiled both ASI:Chain DevNet and Hyperon AGI Framework Alpha 1. The timing targets a critical inflection point where autonomous agents are transitioning from research projects to production systems across enterprise operations.
Dr. Ben Goertzel, CEO of SingularityNET and the ASI Alliance, positioned the release as addressing fundamental limitations in existing blockchain platforms. "The ASI:Chain architecture provides a novel approach to solving the blockchain trilemma," Goertzel explained.
The network achieves scalability through sharded architecture with different consensus mechanisms optimized for specific purposes - high-frequency trading shards use one mechanism, while mesh networks for regions with unreliable internet connectivity deploy another.
What Happened
ASI:Chain distinguishes itself through three core technical innovations addressing constraints that have emerged across AI infrastructure: compute bottlenecks, security vulnerabilities from excessive centralization, and data sovereignty concerns.
The platform combines blockDAG architecture with MeTTa, a programming language developed over years by SingularityNET as part of the Hyperon AGI framework. MeTTa enables developers to implement complex reasoning patterns and agentic logic directly onchain through its focus on declarative and functional computations over knowledge graphs.
MeTTa smart contracts compile largely into Rholang, a language pioneered in the RChain blockchain and mathematically optimized for concurrent processing and distributed systems. Rholang's foundation in rho-calculus provides the first model of concurrent computation with reflection, ensuring autonomous agents can execute coordinated actions at scale.
The compilation also produces large-scale graph operations executed against MORK, the MeTTa Optimal Reduction Kernel. MORK functions as a state-of-the-art hypergraph processing engine and specialized zipper-based multi-threaded virtual machine, providing what developers describe as among the most efficient in-RAM graph database technology currently available.
The DevNet phase offers early-stage developer access for gathering real-world feedback before advancing to testnet. Rather than relying on simulations, developers can deploy applications in a live environment, validate the blockDAG consensus model, stress-test infrastructure under genuine workloads, and provide critical feedback to refine the ecosystem.
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Why It Matters
The launch addresses a growing misalignment between enterprise autonomous agent adoption and available blockchain infrastructure. Gartner forecasts that within 36 months, enterprise software incorporating agentic AI will rise to 33 percent, up from less than 1 percent in 2024. Most major enterprises have already deployed agentic solutions for production workflows across finance, supply chain, and operations.
Traditional blockchains were architected for token transfers and basic smart contracts, lacking native support for high-performance agent orchestration and dynamic reasoning patterns. They also lack the modular infrastructure needed for decentralized AI services at scale.
ASI:Chain's sharded architecture enables customizable consensus mechanisms and modular execution layers tailored for different AI workloads. The platform provides developers with comprehensive documentation, GitHub repository access, a block explorer for transaction visibility, and a faucet distributing test ASI tokens.
The alliance has positioned itself as the largest open-source, independent entity in decentralized AI research and development. The technical stack aims to accelerate advancement toward decentralized AGI and ultimately Artificial Superintelligence.
Final Thoughts
The DevNet release arrives as centralized AI development faces mounting concerns about concentration of computational power and decision-making authority. ASI:Chain's decentralized approach offers an alternative infrastructure where independent AI systems can cooperate safely and openly.
Dr. Goertzel emphasized the framework's orientation toward open-ended intelligence and autonomous agents rather than simple chatbots or software tools. The Hyperon AGI Framework provides what he describes as a "place for developers and researchers to experiment and collaborate in building the next generation of intelligent systems, using a variety of approaches: deep neural nets but also logic systems, evolutionary learning and whatever else their imagination cooks up."
Validator node setup and shard configuration tools are available for those interested in participating as network operators. The alliance plans broader community testing as ASI:Chain moves toward wider public access, with rapid iterations of Hyperon based on developer feedback driving the roadmap forward.
The technical foundations draw from decades of research in mobile process calculi, cognitive architecture design, and distributed systems programming, now entering a critical testing phase with real-world developer deployment.
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