TAKEAWAYS - What is Data + AI Observability and Why It's Part of Your Competitive Moat with Barr Moses

Barr Moses, CEO & Co-Founder of Monte Carlo, challenges the notion that models alone create competitive advantage, arguing instead that the real moat lies in how organizations manage their proprietary data and ensure end-to-end reliability. Tim and Juan chat with Barr to get the Honest, No-BS scoop of what AI observability is (hint, it’s really data + AI) and how organizations can build resilient AI applications.

Barr Moses, CEO & Co-Founder of Monte Carlo, challenges the notion that models alone create competitive advantage, arguing instead that the real moat lies in how organizations manage their proprietary data and ensure end-to-end reliability. Tim and Juan chat with Barr to get the Honest, No-BS scoop of what AI observability is (hint, it’s really data + AI) and how organizations can build resilient AI applications.

Fresh from the TED conference, Tim Gasper and Juan Sequeda share the most thought-provoking ideas that caught their attention: What are you most excited about in the world of AI? What are you concerned about? How do you find a balance?

Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch, AI & Data Leader, reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.

Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch, AI & Data Leader, reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.Data teams often emerge from executive FOMO – chasing AI trends or vague "data-driven" aspirations, but Blake Burch reveals most remain stuck in setup mode, creating dashboards nobody uses. Team members rarely understand how their work impacts business outcomes, leading to data graveyards instead of value, with success measured by vibes rather than revenue. Join us as Blake proposes embedding data experts within business units, developing "full-stack" practitioners, and designing initiatives that begin with clear business actions. Is it time to rethink your data team? Pour yourself a strong one – this conversation might sting.

Hugo Bowne-Anderson, Independent Data & AI Scientist, joins us to tackle why most AI applications fail to make it past the demo stage. We'll explore his concept of Evaluation-Driven Development (EDD) and how treating evaluation as a continuous process—not just a final step—can help teams escape "Proof-of-Concept Purgatory." How can we build AI applications that remain reliable and adaptable over time? What shifts are happening as boundaries between data, ML, and product development collapse? From practical testing approaches to monitoring strategies, this episode offers essential insights for anyone looking to create AI applications that deliver genuine business value beyond the initial excitement.

Hugo Bowne-Anderson, Independent Data & AI Scientist, joins us to tackle why most AI applications fail to make it past the demo stage. We'll explore his concept of Evaluation-Driven Development (EDD) and how treating evaluation as a continuous process—not just a final step—can help teams escape "Proof-of-Concept Purgatory." How can we build AI applications that remain reliable and adaptable over time? What shifts are happening as boundaries between data, ML, and product development collapse? From practical testing approaches to monitoring strategies, this episode offers essential insights for anyone looking to create AI applications that deliver genuine business value beyond the initial excitement.

Hala Nelson, Professor of Mathematics and Author of "Essential Math for AI", explores how digital twin technology and AI deployments are reshaping organizational systems while challenging conventional wisdom. We will also discuss establishing a new "standard of care" in human-centered engineering and examine how these technological shifts are transforming the workforce. Join us as we untangle real-world AI implementation strategies that prioritize both technological innovation and human experience.

Hala Nelson, Professor of Mathematics and Author of "Essential Math for AI", explores how digital twin technology and AI deployments are reshaping organizational systems while challenging conventional wisdom. We will also discuss establishing a new "standard of care" in human-centered engineering and examine how these technological shifts are transforming the workforce. Join us as we untangle real-world AI implementation strategies that prioritize both technological innovation and human experience.

Patrick Cuba, Snowflake Architect, explores the fundamental connections between Data Modeling, Data Vault, and Knowledge Graphs—revealing how these approaches all center on the same core elements: business entities, their relationships, and their historical states. Patrick unpacks why, despite the AI revolution, human expertise remains irreplaceable for accountability and real business value. If you're wrestling with cognitive overload in the face of data explosion or wondering how different modeling disciplines can complement each other, this episode delivers the practical insights you need.