The Joe Reis Show

Here's a new show where I quickly cover a mental model that applies to your work as a data leader or practitioner.Kicking things off is Conway's Law.

It's been a crazy summer for AI. AI enthusiasm remains high, but the market is starting to ask far harder questions. Semiconductor stocks are in a bear market. IBM’s shares fell sharply. Data-center projects are facing political and community resistance. Enterprises are adopting AI, but many are struggling to turn experimentation into measurable returns.In this Freestyle Friday episode, I look at why these signals do not necessarily mean an AI winter. Instead, they suggest that AI is moving from a "possibility market" into an "accountability market", where capability, economics, infrastructure, and organizational reality finally have to reconcile. Put another way, things are going to get real.-----------------------Sponsor: FivetranWith 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.-----------------------Sponsor: Revefi Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure. Start saving money today at ⁠⁠revefi.com/ai-dba

A short rant based on my new article this week, "The Database Is Not the Data Model""In discussions with data practitioners, I keep seeing the same confusion. Someone pulls up a DDL file, a folder of dbt or SQL, or an ERD reverse-engineered from a Postgres instance and says: “Here’s our data model.”Not to be pedantic, but that’s a schema. Schemas are great. But data modeling is more than just schema design."Let’s dive into the difference in this podcast and the article.Article: https://practicaldatamodeling.substack.com/p/the-database-is-not-the-data-model

In this episode, I chat with Steve Brown, an AI futurist, independent consultant, and former professional at Intel and Google DeepMind. Steve shares his insights on helping global leadership teams decode the future of artificial intelligence. We dive into the common pitfalls of corporate AI adoption, including why so many AI initiatives fail due to poor communication and a lack of cultural integration. Steve outlines a definitive three-step path to AI transformation, emphasizing the critical difference between 20th-century cost-cutting and 21st-century labor amplification. The conversation also explores how AI is reshaping middle management, the distinct differences between "AI-first" and "AI-native" businesses, and the staggering acceleration of physical AI and humanoid robotics.

In this Freestyle Friday episode, I break down results from the June 2026 Pulse Survey on organizational dysfunction among data engineers. I also dig into why data product ownership (or lack thereof) is one of the fundamental issues standing between companies and success with data and AI.-----------------------Sponsor: FivetranWith 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.-----------------------Sponsor: Revefi Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure. Start saving money today at ⁠revefi.com/ai-dba

I walk through exactly how I use AI for idea generation, review, editing, and drafting on my upcoming book, Mixed Model Arts. This is also the first in a new Wednesday format where I answer listener questions about what I'm building.--------------------Mixed Model Arts will be out soon. If you want to get a good deal on the e-book and on-demandcourse, please go to Practical Data Modeling.: https://practicaldatamodeling.substack.com/

Kirill Bobrov, a senior data engineer at Spotify and author of the blog Luminousmen, joins me to talk about his viral "Drunk Post" repost, whether the AI boom is real infrastructure or another bubble, why engineering judgment can't be automated away, and the process of writing a technical book on concurrency.Luminousmen: https://luminousmen.com/

Just a heads up on some new weekly content formats coming out.Monday Mental Models - One durable concept each week, related to data engineering, architecture, AI/ML, modeling, etc.Wednesday Builder’s Log (plus optional office hour listener questions) - Behind the scenes of what I’m working on, experiments, etc.More random rants.This is in addition to Freestyle Fridays/newsletter and podcast interviews with guests. Will be an audio podcast (maybe occasional video), and an accompanying Substack post. Posts will be either on my personal substack or practical data modeling.Also, been stuck in the bat cave finishing the book and course (might be some good material for the Wednesday show?), so expect more drips on that very soon :)

With AI reshaping employer expectations, junior candidates in data are facing real anxiety about the job market. I share the technical and personal skills I'd focus on, my top five traits I look for in a candidate, and how to build a network from scratch.-----------------------Sponsor: Revefi Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure.With a five-minute, zero-touch setup, it deploys 18 specialized agents across your data estate to automatically manage FinOps, performance tuning, and data quality. If you want to cut your cloud costs by 30% to 70% and get back to actual data architecture, check out what they are building at revefi.com/ai-dba

Maxime Beauchemin, creator of Apache Airflow and Superset, joins me to talk about his path from data orchestration to the frontier of AI. We cover his "Claude Code moment," his new agentic workflow tool Agor, and what a software-utopia future might actually look like.

Standing on the high plains near South Pass, Wyoming, where the Oregon and Mormon trails once crossed, I look at the remnants of the 1800s gold rush and draw a direct line to the current AI boom.

Prukalpa Sankar, founder of Atlan, joins me to unpack the piece most AI projects are missing: context. We get into building an enterprise "second brain," why agents get abandoned in testing hell, and what it actually took to rebuild Atlan as an AI-native company.

Juan Sequeda joins me straight off a month of travel to unpack the real state of AI agents in the enterprise, the danger of a "semantic swamp," and why pragmatism beats pedantry every time in data architecture..🎙️ SPONSORSRevify - surprise Snowflake bills? One customer cut theirs 50% in 48 hours.→ https://revify.com/demo

Ryan Dolley and I record from the historic Guardian Building in Detroit to talk about building a data career away from the coastal AI bubble, and why Detroit's comeback energy makes it a city worth watching.🎙️ SPONSORSRevify - surprise Snowflake bills? One customer cut theirs 50% in 48 hours.→ https://revify.com/demo

Back from months on the road across Asia, Europe, and the US, I unpack the mood I kept running into: uncertainty. Energy shocks, supply chain chaos, and AI upending the playbook for vendors and practitioners alike. Plus updates on my book and an upcoming Salt Lake City conference.------------------This episode is sponsored by Revefi, who gives you full cost and performance visibility for Snowflake by warehouse, user, and workload. One team cut Snowflake costs ~50% across 711 warehouses in under 48 hours. Book a demo at revefi.com/demo.------------Timestamps0:00 — Intro vendors facing existential questions and forced to rethink everything (Atlan pivot, DuckDB agent idea)10:14 — AI's impact on workers at every level — Senior practitioners gaining superpowers, juniors worried about jobs, leaders expected to do more with less17:51 — Key takeaway: everyone feels behind — Even top AI insiders are uncertain; give yourself grace, upskill, and consider building something for yourself20:38 — Announcements — Book drops July 27th, course coming, Practical Data Community Newsletter live, fall travel schedule (London, Paris, possible Salt Lake City conference)

Chris Tabb, founder of LEIT Data, joins me live at Confluent Current London to talk honestly about how AI agents are reshaping the consultancy business model, from billing structure to internal knowledge management.

Eric Weber makes the case that the feeling of falling behind in AI is mostly manufactured. We're miscalibrating what's true for the top 0.1% in San Francisco against everyone else. We get into judgment as the real bottleneck, and why so many people are quietly leaving corporate roles.

Tristan Handy and I unpack a shift coming to data infrastructure: within 12 months, the primary consumers of data won't be humans, they'll be AI agents. We dig into compute costs, "context stores," and why the industry shouldn't just build horses with wheels.

NOTE - Sorry for the edits in this video. I used Descript to edit out the umms and uhhs, and it was a bit too aggressive. Will make it less jarring in future videos. Thanks.Walking around Salt Lake City, I break down results from the April 2026 data modeling survey: 90% of respondents have a modeling pain point, and only 4.8% think better tools would fix it. We cover physical vs. logical modeling, semantic layers, and Reis's Law.🎙️ SPONSORSFivetran - stop cobbling pipelines together. Set it, forget it, scale as you grow.→ https://fivetran.comRevify - surprise Snowflake bills? One customer cut theirs 50% in 48 hours.→ https://revify.com/demo

Sanjay Agrawal, CEO of Revefi and co-founder of ThoughtSpot, joins me to talk about how AI agents are quietly driving unpredictable spikes in cloud data spend, and why most FinOps practices are built for a world that no longer exists.

Zach Wilson and I happen to be in Stockholm on the same night, so we sat down to talk about what it actually takes to be a data engineer in 2026.

Josh Wills has spent 25 years writing data pipelines. He uses coding agents daily and keeps hitting the same wall: they jump to conclusions and ship pipelines nobody understands. We get into the $200K vibe-coded pipeline problem, and why politics is the last job LLMs will ever touch.

Everyone says if you're not maxing out AI every second, you're getting left behind. I disagree. I talk about minimizing AI use for deep cognitive work, my new minimalist travel setup, and the skills that will actually get you left behind.

Shinji Kim, founder of SelectStar (acquired by Snowflake), joins me to talk about the deal, and why in a few years we may stop calling these things "data catalogs" at all as the category evolves into a living AI context layer.

Recovering from jet lag and hiking the hills of Salt Lake City, I break down a conversation with a VC friend about what's actually defensible in the age of coding agents, and what isn't.

George Fraser, CEO of Fivetran, breaks down why the "modern data stack" has evolved into "open data infrastructure," and why data gravity is the most overrated concept in data management.

From Shibuya Crossing in Tokyo, I make the case that the 40-year adoption curve of the electric grid is a better analogy for the AI boom than the dot-com bubble, and why most AI failures come down to the organization, not the technology.

Bob Seiner has worked in data governance since before it had a name. We dig into his Non-Invasive Data Governance approach, why he pushes back on the term "data enablement," and the state of data security in the age of autonomous AI agents.

From the hillsides of Phuket, Thailand, I push back on the idea that teams "don't have time" for fundamentals anymore, and make the case that AI actually makes data engineering more critical, not less.

Wes McKinney returns to talk about his complete reversal from AI skepticism to being fully locked in on coding agents, why he thinks Go has become the best language for AI agents, and how he's building his own local data sovereignty tools.

In this Freestyle Friday episode, I catch up with Eric Weber after our recent walk through downtown San Francisco. We dive deep into the very real fear and identity crises sweeping through the tech industry as AI accelerates. We discuss how packing a year of change into a single week is disorienting workers and how the constant hustle culture in SF might finally be hitting its threshold.We also get into the darker side of this shift, including the "reverse centaur" effect where humans are reduced to parts of a machine. Are white-collar engineers about to face the Amazon warehouse treatment through token consumption leaderboards? Eric also shares why he took a step back from leadership, his focus on writing, and the importance of genuine human connection right now.Eric Weber: https://www.linkedin.com/in/ericweberdata/

I recently sat down with Amit Prakash, the brilliant mind who co-founded ThoughtSpot and led AI teams at Google and Microsoft, to talk about a massive shift happening in the data world.For decades, we’ve been forcing the "messy reality" of business into rigid database tables, losing about 90% of the actual information in the process.Amit is now building Ampup to flip that script. We dive deep into how he’s using dynamic ontologies to extract high-fidelity insights from unstructured data, like the nuances of a 60-minute sales call—to drive massive ROI.In this episode, we explore:- The "SaaSpocalypse" and the Future of Agents: Why the early stages of the sales cycle might soon be a "dance" between AI buying and selling agents.- Sales as Athletics: Why high-stakes negotiation is more like football than a desk job.- The "Business Brain": Moving beyond simple CRMs to a central strategy engine that understands every department’s unstructured data.- Human-to-Human Trust: Why large contracts will always require a human touch, even in an AI-saturated world.- Amit’s perspective on how AI can deliver real value to the GDP by fixing the "distribution bottleneck" of innovation is a must-listen for anyone in tech, data, or leadership.

In this episode, I sit down with Chris Gambill, a data strategy and engineering leader, fractional consultant, and career coach. We dive into the realities of the data engineering job market in 2026, exploring what it takes to stand out, the massive shift AI coding tools are causing, and why mastering the fundamentals of data engineering remains crucial.Chris shares his unfiltered thoughts on coaching career switchers into data engineering , why finance professionals make great data engineers , and the exact resume and portfolio strategies hiring managers are actually looking for. We also get into the weeds on the latest AI development tools, comparing GitHub Copilot, Claude, and Codex. If you're looking for solid, no BS advice on the field of data engineering in 2026, this is a great discussion!Gambill Data Engineering: https://www.gambilldataengineering.com/LinkedIn: https://www.linkedin.com/in/databasemanagement/

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/