The AI Stack After LLMs: Local Inference, World Models, and Logic | Dr. Matthew Molineaux

0:00 - Matt Molineaux intro 1:30 - Agents before LLMs 3:23 - Why Matt hated LLMs at first 5:34 - Why AI (and LLMs) need world models 13:05 - The problem with LLMs' nondeterminism & the case for adding logic 21:49 - How & why open-weight local models could win 25:22 - Who owns AI-generated work? 31:17 - Can governments control LLM outputs? 35:30 - Mythos and AI danger messaging 41:16 - Whose ethics govern AI? 48:37 - Filtering the internet through agents 56:03 - Controlling AI is short-hand for controlling humans 1:01:43 - How & why decentralization builds resilience 1:06:48 - Can AI solve coordination problems? 1:16:12 - "Personal lens": Aligned local AI project 1:25:15 - Why LLMs still need logic 1:34:30 - The stack after LLMs A conversation with Delegance co-founder Dr. Matthew Molineaux on what comes after LLMs, why AI needs world models and logic, and how open-weight local inference could shape the next AI stack. 🌐Learn more about Delegance at delegance.ai 🌐Learn more about Vora at vora.io