Fetch.ai Inc., one of the founding members of the Artificial Superintelligence (ASI) Alliance, has launched ASI-1 Mini, the first Web3-native large language model (LLM) designed to promote autonomous agent workflow, as reported to Finbold on Tuesday, February 25. The model, powered by the FET token through ASI wallet integration, marks the beginning of the ASI:<Train/> initiative set to democratize access to foundational artificial intelligence (AI) technologies and allow users to invest in, train, and ultimately own their own models. As part of a tiered freemium model, the AI model is immediately accessible to FET holders. Featuring four dynamic reasoning modes — Multi-Step, Complete, Optimized, and Short Reasoning — ASI-1 Mini promises advanced adaptive reasoning and context-aware decision-making. According to Humayun Sheikh, chief executive officer (CEO) of Fetch.ai and chairman of the ASI Alliance, ASI-1 Mini will lay the foundation for a new decentralized ecosystem: ASI-1 Mini is the first major product from the ASI Alliance’s innovation stack, marking the beginning of the ASI:<Train/> rollout and a new era of community-owned AI. … ASI-1 Mini is just the start — over the coming days, we will be rolling out advanced agentic tool-calling, expanded multi-modal capabilities, and deeper Web3 integrations. Instead of relying on a more traditional monolithic approach, ASI-1 Mini dynamically selects specialized AI models optimized for specific tasks. With this system, comprising a foundational intelligence layer (i.e., ASI-1 Mini itself), a specialized model marketplace (MoM Marketplace), and a network of action-driven agents, the model enhances execution capabilities across a wide range of unique applications. ASI-1 Mini is designed to deliver enterprise-grade AI performance while operating on just two graphical processing units (GPUs). The benchmark results of the approach include greater hardware efficiency (up to x8), lower infrastructure costs, and increased scalability. On the Massive Multitask Language Understanding benchmark, ASI-1 Mini was also able to match or outperform leading AI models in domains such as medical sciences, history, and business analytics. Soon, the model will be expanded with an extended context window, allowing it to process larger amounts of information (up to ten million tokens as opposed to the initially supported one million). In addition to tackling performance issues, ASI-1 Mini will also help address the black-box problem in AI. Unlike traditional models, which generate responses without explaining how they came up with them, ASI-1 relies on multi-step reasoning, allowing for real-time self-correction and improved decision-making transparency. This is crucial in industries such as healthcare, where precision and clarity are of utmost importance. As mentioned, ASI-1 Mini plays a central role in the ASI:<Train/> initiative set to empower the Web3 community and encourage end-users to participate in AI development directly. Through a decentralized compute network, users can stake, train, and own their own AI models so that the financial rewards of AI advancements can be distributed more fairly. ASI-1 Minialso promises real-time execution, autonomous workflows, scalable deployment with minimal computational overhead, and enhanced knowledge representation. Users will, therefore, soon be able to deploy AI agents capable of executing real-world tasks ranging from accommodation booking to managing more intricate financial transactions.