The Joe Reis Show

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.

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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.

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