Claim OwnershipThe Joe Reis Show
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What happens when a best-selling author and "recovering data scientist" gets a microphone? This podcast.
I'm Joe Reis, and each week I broadcast from wherever I am in the world, sharing candid thoughts on the data, tech, and AI industry.
Sometimes it's a solo rant. Other times, I'm chatting with the smartest people I know.
If you're looking for an unfiltered perspective on the state of AI, data, and tech, you've found it.
I'm Joe Reis, and each week I broadcast from wherever I am in the world, sharing candid thoughts on the data, tech, and AI industry.
Sometimes it's a solo rant. Other times, I'm chatting with the smartest people I know.
If you're looking for an unfiltered perspective on the state of AI, data, and tech, you've found it.
343 Episodes
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In this episode, I sit down with Mike Driscoll, founder of Rill Data, to discuss the evolving landscape of business intelligence and data engineering. We explore why the industry keeps "rediscovering" old concepts like the semantic layer and how the rise of AI agents is forcing us to rethink how we structure data.Mike shares his insights on the "shape" of analytics, debating whether conversational interfaces will replace dashboards or simply complement them. We also dig into the growing demand for data engineering, the importance of watermarks and temporal semantics, and why data visualization remains a critical tool for "trust but verify" in an AI world.Rill Data Mike’s Podcast: Data Talks on the Rocks
As I use AI, I'm finding that I create MORE work for myself, not less. One task completed means five more to do. This is the paradox of today - AI might actually mean more work, not less. I talk about this, the Data Day Texas final episode, and more.Check out the review I did of Cube's new analytics agent: https://www.youtube.com/watch?v=p3frGJOUl1E(Thanks to Cube for partnering on the review)
Lak Lakshmanan had a successful career in Private Equity and Big Tech, but he realized he couldn't just "coach the game" while the rules were changing. He had to get back on the field play it. We discuss vertical AI, the "foolhardiness" required to start a company , the reality of the AI technology wave, and why sitting on the sidelines is the biggest risk of all.LinkedIn: https://www.linkedin.com/in/valliappalakshmananGenerative AI Design Patterns (book): https://amzn.to/45v0xBO
In this episode, I talk about how I'm kind of living in a bubble of cool tech and AI, and how the 99% of businesses out there are still grappling with the same old data and tech problems they've always dealt with.I also talk about how me and my friends are using AI to automate the boring stuff and scratch our own itches.
In this episode, I sit down with science fiction author, activist, and journalist Cory Doctorow to unpack his viral concept of Enshitification, the three-act tragedy of platform decay: 1. be good to users 2. lock them in 3. extract value from users to feed advertisers and shareholdersWe also dive into:- The AI bubble: Cory’s case that parts of the sector are propped up by aggressive accounting and incentives, not durable value.- The “Reverse Centaur”: How workers (from Amazon drivers to radiologists) are being reorganized to serve machine workflows, rather than machines serving humans.- Software engineering vs. “vibe coding”: Why autocomplete isn’t engineering, and why AI can’t replace process knowledge and domain context.- The Post-American Internet: What happens when the U.S. weaponizes platforms, and the rest of the world builds alternatives.About Cory Doctorow: Cory is a multi-time international bestselling author, special advisor to the Electronic Frontier Foundation, and creator of the blog/newsletter Pluralistic.If you got value from this conversation, hit Follow and share it with one person who cares about the future of tech.
Tech is full of smart people with smart ideas - enterprise data models, ontologies, data mesh, proprietary AI strategies - that repeatedly fail to gain traction. When they fail, the blame usually goes to "stupid users", "lazy and immature organizations." Perhaps, but I don't think that's the whole story, and if you adopt that mindset, you're sure to keep failing.I think there's more to the story. Listen and find out...
In this episode, I visited the Hex office and sat down with Barry McCardle (CEO of Hex) to talk about the massive shift we’re seeing in the data stack. Countless companies have spent decades buying BI tools in the hope of "self-serve Nirvana," yet most dashboards still raise more questions than they answer. Barry and I dive into why the traditional dashboard is becoming a "jumping-off point" rather than a destination, and how AI agents are finally closing the gap between having a question and getting a sophisticated answer.We also discuss building tools people love, "commitment engineering", Barry's story, and much more.
What status game are you playing? Are you trying to outcompete others, or playing your own game? In this episode, I talk about status games in data and careers in general.
The technology industry is prone to moving fast and forgetting its history. This is a shame because our industry is built on the shoulders of many giants, often long forgotten. Bill Inmon, Roger Whatley, and I discuss the history of technology and computing, covered in their new book, From Stone to Silicon. We talk about the big people and moments in technology and computing, and much more.From Stone to Silicon (book): https://amzn.to/4pLfqat
Welcome to 2026! In this spontaneous Friday AMA, I take listener questions on ontologies, the “leaky abstractions” of AI coding tools, why the “button pusher” era of engineering is a professional dead end, and the shifting landscape of data engineering.I also provides an update on my upcoming book, Mixed Model Arts (launching in March 2026), and discuss the unexpected convergence of library science, ontologies, and traditional data modeling, something not on my 2025 bingo card.Great turnout, especially for no notice. Thanks to everyone who showed up!
Happy 2026! In this episode, I rant about whether vibe coding and AI coding agents makes the Law of Leaky Abstractions obsolete, making your first dollar (or whatever currency), and more.The Law of Leaky Abstractions: https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/If you like this podcast, please take 10 seconds and give it a rating or review on your podcast platform of choice. It will go a long way to giving the show more visibility. Thanks!
2025 is nearly gone, and in this episode, I give some thoughts on what I think might happen in 2026. I also chat about this week's surge of interest in Kimball vs Inmon (and the podcast I tried to organize with them) and much more.
“What I built today might be obsolete tomorrow.”This is something I heard this week from a developer, and this is not uncommon given the warp speed nonstop advancement of AI models every week. We used to measure the rate of change in months or years. Now it’s days or weeks.In this episode, I talk about why writing code is rarely hard part, and why having good taste and shipping things that people love is the most important things we can do.
In this episode, Nik Suresh returns to the show to discuss his first year running a bootstrapped services company. And no, he probably will not throat punch or pile drive you.Nik explains why he moved away from hourly billing to fixed pricing, why writing code is often the least profitable part of a project, and how to spot "status games" in the tech industry. We also dive into the current state of AI, why bad leadership is the real problem behind failed tech initiatives, and trade stories about MMA and boxing.We debunk the myth that starting a business has to be miserable, explore the performative nature of "hustle culture" in Silicon Valley, and break down why engineers often struggle with consulting sales.
Oh yeah...ontologies. In this mini-clip from Matt Housley and I, we chat about why ontologies are super popular now.
Had an interesting discussion with my 15 year old son. He and his friends see white collar work as “cooked.” They see it as a rat race where the work is increasingly insecure, abusive, and meaningless. Then there’s the looming question of AI…Instead, they’re interested in careers they find meaningful and not as exposed to whatever AI does to work. And if they own a company, they’ll just hire “clankers” whenever that moment arrives.I’m excited that these kids are looking at what’s happening right now, questioning if it’s their path, and choosing a life that’s fit for them.More broadly, especially in the age of AI, I think some of the most important conversations we need to have is over what we find valuable and meaningful, making a living and the nature of work, and the nature of community.
It's Friday! Matt Housley and I catch up to discuss the aftermath of AWS re:Invent and why the industry’s obsession with AI Agents might be premature. We also dive deep into the hardware wars between Google and NVIDIA , the "brain-damaged" nature of current LLMs , and the growing "enshittification" of the internet and platforms like LinkedIn. Plus, I reveals some details about my upcoming "Mixed Model Arts" project.
In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O’Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup.The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.Gable: https://www.gable.ai/
I meet a lot of people who want to accomplish major goals next year. Then the year comes and goes and most people are still waiting to get started.It's almost December. Rather than wait until the New Year to get going, use December to plan how you'll execute on "that thing" you're itching to accomplish. Time waits for nobody, so get going.
In this episode, Ciro Greco (Co-founder & CEO, Bauplan) joins me to discuss why the future of data infrastructure must be "Code-First" and how this philosophy accidentally created the perfect environment for AI Agents.We explore why the "Modern Data Stack" isn't ready for autonomous agents and why a programmable lakehouse is the solution. Ciro explains that while we trust agents to write code (because we can roll it back), allowing them to write data requires strict safety rails. He breaks down how Bauplan uses "Git for Data" semantics - branching, isolation, and transactionality - to provide an air-gapped sandbox where agents can safely operate without corrupting production data. Welcome to the future of the lakehouse.Bauplan: https://www.bauplanlabs.com/
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