This week on The Data Stack Show, Barry McCardel, CEO and Co-Founder of Hex, joins Eric Dodds and John Wessel to discuss the transformative impact of AI on data teams and the broader data industry. The conversation explores how AI is reshaping workflows, team structures, and the very definition of data roles, while also addressing the anxieties and opportunities that come with rapid change. Barry shares Hex’s journey from having a dedicated AI team to fully integrating AI across the product, and offers insights on industry consolidation, the infinite demand for data insights, and the importance of embracing change. Key takeaways include the need for data professionals to adapt and focus on problem-solving, the growing value of context and curation, the competitive advantage for organizations that leverage AI-driven tools and foster a culture of innovation, and so much more.

This week on The Data Stack Show, Alexander Patrushev joins John to share his journey from working on mainframes at IBM to leading AI infrastructure innovation at Nebius, with stops at VMware and AWS along the way. The discussion explores the evolution of AI and cloud infrastructure, the five pillars of successful machine learning projects, and the unique challenges of building and operating modern AI data centers—including energy consumption, cooling, and networking. Alexander also delves into the practicalities of infrastructure as code, the importance of data quality, and offers actionable advice for those looking to break into the AI field. Key takeaways include the need for strong data foundations, thoughtful project selection, and the value of leveraging existing skills and tools to succeed in the rapidly evolving AI landscape. Don’t miss this great conversation.

This week on The Data Stack Show, AI entrepreneur Alberto Rizzoli shares his journey from early computer vision breakthroughs to leading the automation of back-office workflows at V7. The discussion explores the shift from bespoke model training to configurable AI solutions, the impact of automation on business roles, and emerging best practices for integrating AI into enterprises. Listeners will gain insight into how AI infrastructure is moving from labs to everyday businesses, which roles are most vulnerable or secure amid automation, and why future-proofing your career means focusing on creativity, first principles, and continuous improvement. Don’t miss it!

This week on The Data Stack Show, Ryan Dolley joins Eric and John to discuss his unique journey from playwriting to leading product strategy in the data industry. The conversation explores the evolution of business intelligence (BI), the growing influence of AI on analytics, and the shifting skill sets required for data professionals. Key topics include the challenges of adapting to rapid technological change, the importance of embracing engineering practices in BI, and the need for continuous learning. Listeners will gain insights into how AI is transforming data roles, why storytelling remains central to analytics, practical advice for thriving in a fast-changing industry, and so much more. 

This week on The Data Stack Show, Eric Dodds and John Wessel explore how AI is reshaping the data industry, focusing on the ongoing cycles of bundling and unbundling within data infrastructure. They discuss the potential for closed ecosystems like Notion to deliver personalized, integrated experiences and examine recent industry moves such as Fivetran’s acquisitions. The conversation also highlights the challenges faced by both startups and incumbents, the influence of enterprise customers on product development, and the enduring importance of trade-offs when choosing between bundled and unbundled solutions. Key takeaways include the complexity of implementing AI across platforms, the likelihood that market cycles will persist despite technological advances, and the need for organizations to carefully weigh integration, flexibility, and long-term risk when adopting new data tools.

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.