Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice w/ Sanjeev Mohan

Former Gartner VP Analyst and principal at SanjMo, Sanjeev Mohan, joins me to break down the reality of AI adoption, data foundations, and why enterprises are hitting roadblocks with agents and LLMs. From comparing the current AI boom to the dot-com era to unpacking his new concept, ECL (Extract, Context, Link), Sanjeev explains why moving data around is no longer viable and why "there is no AI stack, only one stack." We also dive into capability overhang, artificial guardrails, the race to standardize semantic context, and why writing books and understanding core primitives still matter in the age of generative models. Timestamps 00:00 – Introductions & Sanjeev’s journey from Oracle to Gartner and SanjMo 04:23 – Dot-com boom vs. the generative AI wave: Are we in a bubble? 09:34 – The reality of enterprise AI adoption & capability overhang 14:56 – The dilemma with AI guardrails: Safety vs. creativity 18:22 – Designing the AI-Driven Data Foundations & why ETL must evolve 20:30 – Introducing ECL: Extract, Context, Link 22:56 – What is the context layer and where does it live? 28:41 – AI as an amplifier: Strategy vs. technical debt 33:05 – Enterprise land vs. product land: Why data ops lagged software 37:25 – "There is no AI stack": AI as a use case of data 39:39 – The core molecule of a modern data foundation 43:42 – Why write a book in the age of generative AI? 51:00 – Governing tables, models, agents, and ephemeral context 55:23 – Token maxing, slop, and babysitting AI agents 01:00:00 – Why human writing beats AI: Finding the "So What?" 01:06:51 – Upcoming conferences & wrap-up