This week on The Data Stack Show, the crew welcomes Eric Dodds back to the show as they dive into the realities of integrating AI and large language models into data team workflows. Eric, Matt, and John discuss the promise and pitfalls of AI-driven automation, the persistent challenges of working with APIs, and the evolution from big data tools to AI-powered solutions. The conversation also highlights the risks of over-reliance on single experts, the critical importance of documentation and context, and the gap between AI marketing hype and practical implementation. Key takeaways for listeners include the necessity of strong data fundamentals, the hidden costs and risks of AI adoption, the importance of balancing efficiency gains with long-term team resilience, and so much more.

This week on The Data Stack Show, Eric and John welcome back Kostas Pardalis, long-time co-host of the Data Stack Show and now Co-Founder of typedef. The group discusses the rapid evolution of AI and data infrastructure. The conversation also explores how AI is accelerating industry change, the challenges of integrating large language models (LLMs) into data workflows, and the limitations of current semantic layers. Kostas shares insights on building next-generation query engines, the importance of using familiar engineering paradigms, and the need to make AI seamless and almost invisible in user experiences. Key takeaways include the necessity of practical, incremental innovation, the reality behind AI hype, strategies for making advanced data tools accessible and reliable for engineers and businesses alike, and so much more. 

This week on The Data Stack Show, John and Matt dive into the latest trends in AI, discussing the evolution of GPT models, the role of tools in reducing hallucinations, and the ongoing debate between data warehouses and agent-based approaches. They also explore the complexities of risk-taking in data teams, drawing lessons from Nate Silver’s book on risk and sharing real-world analogies from cybersecurity, football, and political campaigns. Key takeaways include the importance of balancing innovation with practical risk management, the need for clear recommendations from data professionals, the value of reading fiction to understand human behavior in data, and so much more. 

This week on The Data Stack Show, John and Matt bring you another edition of the Cynical Data Guy. John and Matt dive into the evolution of customer data infrastructure, the growing influence of low-code tools like Clay, and the blurred lines around the “engineer” title in modern data roles. They also discuss the trade-offs between SaaS adoption and building custom solutions, the pitfalls of enterprise software buying, and the realities of platform lock-in—using Palantir’s unique business model as a case study. Key takeaways include the importance of simplicity and scalability in data engineering, the need for clear requirements when evaluating tools, and a healthy skepticism toward sales pitches and “art of the possible” features. Don’t miss this month’s Cynical Data Guy.