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Ever feel like you've been lied to? In Scott’s Tots, Michael Scott promises a class of third graders he’ll pay their college tuition if they finish high school. Ten years later they’ve held up their end, and he shows up to tell them he's broke. A lot of people feel like those kids right now. They got the degree, learned to code, and followed the career plan they were handed, only to find a job market that feels empty and hollowed-out. In this episode I use Scott’s Tots to look at who made these promises, why most of them were sincere but wrong, and what the consolation prizes look like today.

In this episode, I sit down with Doug Meil, software veteran and author of The Rise and Fall of Explorys and IBM Watson Health. A decade ago, IBM Watson was the undeniable face of the modern AI revolution. Fresh off its historic Jeopardy win, IBM promised Watson would revolutionize entire industries and cure cancer. Instead, it became one of tech history’s most high-profile corporate implosions. Doug lived through the heart of it. His healthcare data startup, Explorys, was acquired in 2015 to serve as a foundational pillar for IBM Watson Health. What followed was a masterclass in what Doug calls the "paradox of inorganic growth"—an unravelling driven by corporate hubris, aggressive internal "blue washing," brutal cloud lock-ins, and leadership prioritizing marketing slogans over iterative product engineering. We dig deep into the untold engineering and product side of the acquisition, why enterprise mergers crush agile technical teams, and the enduring lessons today’s AI founders and leaders must take away before jumping headfirst into enterprise AI hype.Get Doug's book here: https://link.amazon/B05ATbGBL

A lot of people ask me for career advice. My latest advice is learn the fundamentals, build useful things that interest you, and stop outsourcing your direction to everyone around you. But originality doesn’t automatically pay the bills, copying is part of learning, and “do what AI cannot” is lazy advice. In this episode, I unpack the advice, its limits, and how to find your own way without betting your livelihood on it. 🏂⛷️ Join us at Practical Data Summit — January 28–29, 2027, in Salt Lake City, Utah. We're bringing together around 300 of the brightest minds in data and AI for two days of candid conversations, practical talks, hands-on sessions, and meaningful connections. This isn't another massive vendor conference filled with sales pitches. We're building the kind of event we'd actually want to attend: a smaller, more intimate gathering of practitioners, builders, and leaders sharing what they've built, what worked, what didn't, and where the industry is heading. We'll have some incredible speakers, plenty of opportunities to meet people who are shaping the industry, and of course, the beautiful Wasatch Mountains right outside our door. Stick around for skiing, snowboarding, and après-ski fun over the weekend. Early bird tickets are $1,000 (regularly $1,300), and space is limited. Learn more and grab your spot at practicaldatasummit.com.

The data industry rarely moves in a straight line. We swing from centralized to decentralized, batch to real-time, general-purpose to specialized, abstraction to control, and governance to self-service. In this episode, I unpack why these cycles keep repeating, what forces push the pendulum in each direction, and why every successful architecture eventually creates the conditions for its own backlash.

We see a lot of hype cycles in our industry - Data mesh. Generative AI. Agents. Now context. Every cycle brings a new label and a familiar promise: this will finally make your organization’s data useful. The technology has improved enormously. So why do the same problems keep coming back? In this episode, I explore how communication, incentives, and organizational politics shape what our tools actually deliver. Will context finally deliver the silver bullet to solve our problems? Is buying the next platform easier than resolving the messy organizational problems we all deal with? Listen to get my thoughts.

Hannes Mühleisen (co-creator of DuckDB) joins me to chat about the past, present, and future of DuckDB, Ducklabs (recently acquired by AWS), and more. Note - parts edited due to AWS's request.

In this episode, I'm joined by Rob Collie, CEO of P3 Adaptive, early pioneer of Power BI and Power Pivot at Microsoft, and author of the new book Fair Game: Customizing AI to Your Business is Easier Than You Think. Most business leaders play with off-the-shelf ChatGPT or Claude and think it's magic until it completely face-plants on their actual company data. Why? Because the LLM is just one Lego brick. Delivering real value requires the unsexy, "boring" work: structured context, reliable data modeling, and robust semantic layers.Rob and I dig into why 15 out of 16 people bounce off Excel, why semantic layers are hot, if dashboards are dead, and much more.

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

This week at dbt Summit, Tristan Handy (co-founder of dbt) asked me why I write books. It's a good question, especially today when reading is declining and any answer is seemingly at your fingertips. In this Freestyle Friday, I discuss why I write books, why they still matter, and much more. If you're interested in writing a book, this episode is definitely a must-listen.

I talk to executives and practitioners around the world about AI and what comes next. Everyone has predictions and pontifications, and everyone seems to think someone else has the answers. But the more conversations I have, the clearer it gets that nobody knows what's next. In this episode, I explore that uncertainty and how we move forward anyway. Because in the end, nobody knows...

In this episode of The Joe Reis Show, I catch up with my good friend Christophe Blefari, co-founder of NAO, to dig into what's actually happening on the front lines of AI agents and data engineering. We talk about the evolution from building SQL IDEs to agentic analytics harnesses, how prompt-driven workflows are reshaping data teams, and why age-old problems like data discovery, modeling, and documentation are suddenly roaring back. We also dive into the risks of "token slop," whether data and software engineering roles are destined to merge, Christophe’s take on DuckDB’s acquisition by AWS, and the realities of building AI tools across Europe and the US.Nao: https://getnao.io/

In this episode, I sit down with Sami Hero, CEO of Ellie.ai, to talk through the evolution of data modeling and why foundational conceptual modeling is more critical now than ever. With the explosion of AI agents, LLMs, and semantic layers, many teams assume they can bypass data modeling and just let an AI infer meaning directly from raw tables. We dig into why that mindset leads to bad decisions, how companies end up with 15,000 Snowflake tables and thousands of unmaintained dashboards, and why defining core business concepts is the real prerequisite for reliable AI. We also discuss the resurgence of ontologies, lessons from old-school methodologies like MERISE, and how to balance speed with sound architecture.

AI agents are being pitched as the “death of the data team,” with CEOs supposedly able to do the work themselves. We’ve heard versions of this promise before, from self-service BI to low-code analytics. In this episode, I revisit that history and ask what agents actually change. Who prepares the data, resolves conflicting definitions, and checks the answers? What does the hiring research tell us about demand for experienced people and opportunities for juniors?

In this episode of the Joe Reis Show, I sit down with David Aronchick, co-founder of Expanso and one of the early pioneers behind Kubernetes, Kubeflow, and the CNCF. We dive deep into the evolving data landscape and discuss why the architectural pendulum is swinging back from pure cloud centralization toward distributed edge compute. David breaks down the real operational nightmares of edge data collection, why treating your bronze tier as a "toxic waste dump" breaks downstream pipelines, and how applying schema and context upstream makes your data truly AI-ready. We also get into the critical need for data provenance, data bills of materials, and what agents actually need to communicate reliably.Website: https://expanso.io

I get asked all the time whether I’m still “hands-on.” The short answer: not in the traditional sense. My work has shifted toward writing, teaching, advising, investing, building companies, and working with the people building what comes next. In this episode, I argue that technical relevance isn’t about staying trapped in the same operating mode forever, and that AI is drastically changing what “hands-on” even means. ------------------------ Sponsor: Fivetran With the rise of AI and agents, having centralized, trustworthy data is the absolute foundation for building tools and training models. Fivetran automates your data pipelines, removing the need to build fragile connectors so your data arrives clean and reliable. By handling the messy infrastructure behind the scenes, Fivetran allows your team to focus on building the future. Visit ⁠⁠⁠⁠⁠fivetran.com⁠⁠⁠⁠⁠ to learn more. The Joe Reis Show is invitation-only. Unsolicited guest pitches and PR outreach are not considered.