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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.
361 Episodes
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In this episode, I sit down with Gowtham Chilakapati, an analytics veteran of 18 years and Executive Director at Humana , to pull back the curtain on the reality of Agentic AI in the enterprise.We dive deep into the recent wave of tech layoffs—like the news of Block cutting 40% of its workforce —and debate whether AI is truly driving these decisions or simply serving as a convenient excuse for broader management failures.Gowtham shares his firsthand experience navigating an astounding $1 billion AI investment during the early adopter rush of 2024. He details the chaotic first six months of that initiative and the multi-dimensional framework his team developed to measure true return on investment beyond the traditional, and often flawed, software implementation mindset. From the massive risks of pasting PII into LLMs to how AI prototyping is finally bridging the historic gap between product and engineering teams, this conversation is a masterclass in pragmatism for anyone looking to cut through the AI hype, especially in highly regulated industries.
The new Practical Data Community Pulse Survey for March 2026 just came out, and I unveiled some of the findings at yesterday's Undercurrent event in San Francisco. The short version is: AI is here to stay. Everyone's using it, but the hard parts we've always dealt with as an industry still remain unresolved. Listen and find out why.
In this episode, I sit down with Jake Ward, founder of the Application Developers Alliance. We dig into the AI "Frankenact," aka the EU AI Act, and why policymakers regulating tech they fundamentally misunderstand creates a cold wind for software innovation.Jake drops some harsh truths about why giving developers a voice in Washington is harder than it looks, why collective bargaining and developer unions probably won't work, and how bad policy is forcing companies to build for compliance rather than ship great products.
The data job market is evolving, but it's still there. In this episode, I give my thoughts on the data job market, ways to navigate it, going solo and having a Plan B, and more.
In this episode, I sit down with Demetrios Brinkmann (godfather of the MLOps Community) to talk about the absolute Wild West of AI right now. We cover how fast coding agents are changing the game, the reality of "vibe coding" your own CRM , and how Demetrios's community saved $20,000 just by ditching bloated enterprise tools.But we don't just talk tech. We get into the weeds on the content creation pipeline, from the bizarre rise of AI OnlyFans to the "Doorman Paradox" of automated content. Finally, we spill some serious inside baseball on the tech sponsorship game, calling out the sheer audacity of heavily-funded startups expecting free labor from communities , and why protecting your reputation is worth more than any quick paycheck.
In this episode, Matt Housley and I reunite for a Friday catch-up, bringing back some of that classic Monday Morning Data Chat energy. We dive into the absurdity of the "buzzword industrial complex," and why declaring it the "Year of Context" is mostly just industry hype, per usual.We also tackle the chaotic reality of deploying AI agents (including the ultimate YOLO, OpenClaw) without proper data governance, the Anthropic class action lawsuit regarding copyright, and why regional conferences like DataTune are awesome. Finally, we discuss the shifting landscape of media, the death of traditional book publishing models, and the rise of the independent, niche creator.
The white-collar tech industry isn't what it used to be, and anyone could be on the chopping block at a moment's notice. With tens of thousands of highly skilled people getting laid off from Big Tech on a seemingly bi-weekly basis, competing in the traditional job market is brutal right now.In this episode, Jody Hesch and I discuss why building a freelance data consulting business isn't just a career pivot—it is a necessary Plan B. We break down the exhaustion of constantly reinventing yourself and navigating new team dynamics every time you switch full-time roles. We also explore the counterintuitive reality that by going freelance, you only have to build your network and reputation once to create a repeatable motion. Whether you are actively looking for an exit or just realizing that the gig economy is coming for data engineering, this conversation covers the realities of making the jump.
In this conversation, Paul Blankley and Ryan Janssen, founders of Zenlytic, drop in to discuss the massive shift in how we build software and handle data. We trace their journey from studying early NLP and Transformers at Harvard right when the BERT paper dropped, to building a company that relies on cutting-edge LLMs. As far as I know, they're the first to use LLM's for analytics.We dive deep into the reality of the agentic era: engineers are no longer writing the bulk of the code; they are managing agents, verifying outputs, and maintaining ridiculously high standards. We also explore why the industry needs to embrace "net negative scaffolding" as models get smarter, and why having good "taste" might be the ultimate human moat left in tech.Bonus: To prove that software development is changing faster than ever, we literally "vibe coded" a brand-new CRM called "Slop Force" in 20 minutes during this episode. Zenlytic: https://www.zenlytic.com/
We often hear about the AI skills gap, where people need to get training on the latest AI tools. There's also the AI competence gap, where people might not have the skills or competence in a field, and use AI to mask over those shortcomings. The results are what you expect - chaos. In this episode, I unpack these two gaps, and do my usual ranting about learning the fundamentals and investing in oneself.----------🚨Also, if you happen to be in San Francisco on March 26th, please join me at Undercurrent, a small and tech-focused conference for data engineers and architects. No sponsors, no salespeople, no bullshit. Just great technical discussions all day.Register here: https://cnfl.io/3ZCTaVx
In this conversation, I sit down with Tim Delisle and Chris Crane, co-founders of 514, to discuss bridging the gap between software development and data engineering. We cover their experience leading global data engineering at Nike and why software teams are increasingly taking ownership of heavy analytical workloads.We also dive into how they are building the Moose Stack to give developers a local-first, code-first analytics experience. Finally, we explore how AI co-pilots are acting like an "army of interns" to fundamentally change how we write code , and why the "personal data lake" might be the future of privacy and local compute.Check out 514 & The Moose Stack: https://www.fiveonefour.com/
Sadie St. Lawrence joins me to unpack her concept of the "AI Orchestrator," explaining how it shifts our mindset from being a musician to a conductor in the age of AI. She shares insights from her work at the Human-Machine Collaboration Institute (HMCI), detailing how her team is building AI-powered solutions and tackling complex problems. We also chat about the common pitfalls in AI adoption, from unfounded fears to "work slop," and why foundational systems thinking remains paramount.
This week, I published an article called "2028, the Great Data Reckoning," which got a ton of response. Although I originally meant it to be satire, when I re-read it I felt like it was actually a glimpse into what's happening in our field right now. In this episode, I chat about the implications of the Great Data Reckoning on practitioners, leaders, and founders. Article: https://joereis.substack.com/p/2028-the-great-data-reckoning----------🚨Also, if you happen to be in San Francisco on March 26th, please join me at Undercurrent, a small and tech-focused conference for data engineers and architects. No sponsors, no salespeople, no bullshit. Just great technical discussions all day.Register here: https://cnfl.io/3ZCTaVx
In this episode, I sit down with Prashant Sridharan, a 30-year veteran of developer marketing who has shaped go-to-market strategies for tech giants like Sun Microsystems, Microsoft, AWS, Facebook, and Twitter, and currently runs product marketing at Supabase. We dive deep into the origins of DevRel and how marketing to developers has evolved in an increasingly noisy, AI-saturated landscape.Topics covered:- Transitioning from massive tech companies to the fast-paced startup world - How to genuinely measure the success of Developer Relations without ruining communities - Using AI tools like Claude to accelerate mechanical marketing tasks while preserving authentic storytelling - The shift from traditional SEO to GEO (Generative Engine Optimization) for developer tools - The thrill of live, unscripted coding demos and stories from sharing the stage with Steve Ballmer - Prashant's upcoming fiction novel, The Midnight Coders Children, and the craft of writing Find more from Prashant at StrategicNerds.com and check out his non-fiction book, Picks and Shovels: https://amzn.to/4cJ2TRO
For 40+ years, the data industry has tried to teach good practices and get adoption, often in the same way. And for 40+ years, that approach keeps failing over and over. Based on the recent Practical Data Community Survey, practitioners face challenges like time pressures, lack of direction, and lack of clear ownership. Do we need to try something else as an industry? Or do we continue to be the poster child for the definition of insanity - doing the same thing over and over, yet expecting different results? I hope not.
Why are we still using row-based protocols like ODBC and JDBC in a column-oriented world? In this episode, I sit down with Ian Cook, co-founder of Columnar and a long-time Apache Arrow contributor, to discuss the critical infrastructure changes needed to speed up modern analytics and AI.We dive deep into the technical bottlenecks of legacy standards - specifically the "serialization tax" of converting columns to rows and back again - and how ADBC (Arrow Database Connectivity) solves this by keeping data columnar from end-to-end. Ian also shares his insights on the intersection of tabular data and LLMs, why AI agents need better access to OLAP systems, and the tension between vibe coding speed and the stability required for critical open-source infrastructure.
The 2026 Practical Data Community State of Data Engineering dropped this week. It's full of some obvious and very counterintuitive information about the state of data engineers around the globe, in all sizes and types of organizations. Check it out!Also, I talk about the book writing process, where I messed up on this latest book, it's progress toward publication, and more.Survey: https://joereis.github.io/practical_data_data_eng_survey---------------------This episode is brought to you by Ellie.aiEllie makes data modeling as easy as sketching on a whiteboard—so even business stakeholders can contribute effortlessly. By skipping redraws, rework, and forgotten context, and by keeping all dependencies in sync, teams report saving up to 78% of modeling time.Check out Ellie: https://ellie.ai/
I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from StreamKap to catch up on where the world of streaming data is heading—and things have changed fast since we last spoke.We dive into the concept of "vibe coding" and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they’re flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and StreamKap’s upcoming move into the Snowflake native app ecosystem.Streamkap: https://streamkap.com/
This week was a doozy with new AI releases, the stock market, and more. It really feels like this was the first tremor in AI's impact on the SaaS market. What's do I think is next? Listen and find out.
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)
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