While large language models (LLMs) are rather passive from an economic perspective on their own, AI agents offer a preview of what truly autonomous AI applications can achieve. Fetch.ai aims to create a platform for economic interactions in the AI economy, where participants can provide many different kinds of stake, ranging from purely financial, in the form of cryptocurrency tokens, to utility based, in the form of data sets that LLMs can be trained on. It thus creates a supply chain that links different actors of the AI economy.We were joined by Humayun Sheikh, co-founder & CEO of Fetch.ai, to discuss AI economic models and how LLMs can be integrated by agentic systems as a foundation for autonomous AI apps.Topics covered in this episode:Humayun’s backgroundFounding Fetch.aiMulti-agent systemsAutonomous economic agentBuilding a Cosmos based blockchainIntegrating ML with agent economyScalability & interoperabilityUse cases & partnershipsAI x crypto projectsIncentivising developersAI alignment problemFetch AI roadmapThe future of ML & LLMsEpisode links:Humayun Sheikh on TwitterFetch.ai on TwitterSponsors:Gnosis: Gnosis builds decentralized infrastructure for the Ethereum ecosystem, since 2015. This year marks the launch of Gnosis Pay— the world's first Decentralized Payment Network. Get started today at - gnosis.ioChorus1: Chorus1 is one of the largest node operators worldwide, supporting more than 100,000 delegators, across 45 networks. The recently launched OPUS allows staking up to 8,000 ETH in a single transaction. Enjoy the highest yields and institutional grade security at - chorus.oneThis episode is hosted by Friederike Ernst. Show notes and listening options: epicenter.tv/539