The Joe Reis Show

In this episode, Tristan Handy and I sit down to unpack a massive shift coming to the data industry: over the next 12 months, the primary consumers of data won't be humans. They will be AI agents. We dive deep into what this means for data infrastructure, compute costs, and the tools we use every day. We also talk about processing high-volume agent queries, building "context stores", and why the industry shouldn't just build "horses with wheels" when designing agentic data engineers. We also take a fun detour comparing the current AI landscape to the early days of dial-up modems and Mosaic browsers , and discuss why stepping away from the screen and going old-school might be the ultimate productivity hack.

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.Freestyle Friday, May 15, 2026Walking around Salt Lake City and unpacking the April 2026 data modeling survey results (334 respondents). Across three surveys now: January's State of Data Engineering (1,100), March's AI usage poll (193), and April's data modeling deep-dive. Not surprisingly, the same two pain points keep surfacing: time pressure and lack of clear ownership.90% of respondents have a data modeling pain point. When asked what would actually help, only 4.8% wanted better tools. Training, business requirements, time, and ownership crushed tooling in the rankings. Will AI improve things or make them worse? Time will tell...Also covered:Why physical data modeling has become the default (and why that's a problem)Data modeling vs. schema design - they're not the same thingSemantic layers (yay or nay?), Lloyd Tabb, and MalloyConway's Law, Reis's Law, and what changes when org charts get flattened by AIWhy leadership is under more pressure than everThe June half-year survey is coming

Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:The evolution of cloud data warehouse pricing and why it breaks traditional budgets.How AI agents are causing massive, unpredictable spikes in compute spend.Real-world horror stories of ""lift and shift"" cloud migrations.Why database benchmarks focus on speed but ignore the actual ROI of data.The future of open table formats (Iceberg) and multi-engine routing.

Zach Wilson and I happen to be in Stockholm, Sweden, this evening. In this Freestyle Friday chat, we talk about what it takes to be a data engineer in 2026 and much more.

Josh Wills has spent 25 years writing data pipelines, with a career spanning Cloudera, as Director of Data Engineering at Slack, on the dbt DuckDB adapter, and now training foundation models at Datology AI. He uses coding agents every day. And he keeps running into the same wall: the agents jump to conclusions, fix the wrong thing, and ship pipelines no one understands.In this conversation, we unpack why AI agents struggle with the messiest, highest-stakes parts of data work, and what it means for the engineers managing them.We get into:- Big Data is back- Why AI agents jump to conclusions on benchmarks and complex bottlenecks- The $200K vibe-coded pipeline problem nobody wants to talk about- Why there's no training data for the gnarly enterprise pipelines that actually power businesses- "We're all managers now" - managing unreliable agents like managing unreliable people- Wicked problems and the limits of intelligence- Why politics is the last human endeavor to fall to LLMs (the data is never written down)- Whether classical ML still has a place (yes)- What Josh would tell a new grad starting in data today

Stop Tokenmaxxing and step off the AI hamster wheel. Welcome to another Freestyle Friday! What's the overwhelming vibe in the AI zeitgeist? "If you aren't maxing out AI every second, you're going to be left behind." Therefore, Tokenmaxxing is the way, right?I strongly disagree. We're burning ourselves out with fake productivity and a graveyard of abandoned AI-generated projects.In this episode, I talk about my new minimalist travel setup, why I'm purposefully trying to minimize my AI usage for deep cognitive work, and what skills will actually get you left behind (hint: it's not missing the latest model release).Solve real problems. Focus on the fundamentals. Take a walk. You're going to be fine.

Shinji Kim, founder of SelectStar (acquired by Snowflake in December), joins the show to discuss the deal, the integration into Snowflake's Horizon catalog, and where data cataloging is actually headed.We get into the weeds on a claim Shinji makes early: in a few years, we may stop calling these things "data catalogs" at all. The category is evolving into an AI context layer, a living surface that combines metadata, semantic models, business glossaries, and ontologies, continuously updated by both humans and agents. Shinji walks through how SelectStar built toward this with semantic model management, MCP server support, and an AI agent that started serving data analysts and eventually answered business users' questions directly.We also dig into where data catalog implementations go wrong (spoiler: it's almost always adoption, not tooling), why marketing teams are an underrated ETL persona, and what it actually took to get acquired by Snowflake after three years as a premier partner.Plus: if Shinji were starting SelectStar today, what would she do differently? We talk about distribution in the AI era and how the startup playbook is mutating.Connect with Shinji:LinkedIn — https://www.linkedin.com/in/shinjikim/

AI has completely inverted how we build and scale software, which begs the question: What exactly is a moat anymore? In this Freestyle Friday, recovering from jet lag and hiking through the beautiful hills of Salt Lake City, I'm breaking down a recent conversation with a VC friend about defensibility in the era of coding agents. I also look at this through Charlie Munger's lens of "inversion" to figure out what isn't a moat anymore (spoiler: thin foundation model wrappers, "AI", and feature velocity are dead).I also dive into what is defensible today, from mission-critical systems of record like DuckDB and Postgres, to personal branding, to shifting SaaS pricing from per-seat to per-token.

Are vendors trying to lock down your data? In this episode, George Fraser breaks down why the "modern data stack" has evolved into "open data infrastructure". We discuss why data gravity is the most overrated concept in data management, how egress charges are often misunderstood due to poorly designed pipelines, and why companies must insist on having a true replica of their own data.George also shares his hands-on experience with AI coding agents, including how he manages his USTA tennis team with bots like OpenClaw and NanoBot.

Walking through Tokyo and breaking down the reality of the AI revolution. In this Freestyle Friday from Shibuya Crossing, I look past the current AI hype cycle to examine the real bottlenecks of AI adoption. Is the current AI boom just a repeat of the dot.com bubble? Why is simply buying Copilot subscriptions for your team failing to move the needle?Drawing parallels to the 40-year adoption curve of the electric grid, I discuss why most AI projects fail to get traction in the enterprise. Hint: it's not the technology, it's the organization. Plus, a look at the danger of firing employees before capturing their tacit knowledge, and how to actually rewire your business to be AI-native.

In this episode, I sit down with Bob Seiner, a true pioneer who has been working in data governance since before it was even called governance. We dive into why he calls BS on the trendy term "data enablement" and how his trademarked approach, Non-Invasive Data Governance, formalizes what organizations are already doing without beating employees over the head.We also unpack his latest concept, The Data Catalyst Cubed, and get into a fascinating discussion about the precarious state of data security in the age of LLMs and autonomous AI agents like OpenClaw. Plus, Bob shares some great war stories about building the T-DAN newsletter using Microsoft FrontPage back in 1997 and drops his best advice for standing out and building a personal brand in the noisy data industry.Where to find Bob:KIK Consulting: kikconsulting.com LinkedIn:   / robert-s-seiner-445313  Books: Non-Invasive Data Governance and The Data Catalyst Cubed

Do fundamentals still matter, or are we all just "vibe engineering" our architectures now? Coming to you live and sweating from the hillsides of Phuket, Thailand, this week's Freestyle Friday dives into the tension between chasing the newest tech and mastering first principles. After a recent LinkedIn debate suggesting teams "don't have time" for fundamentals anymore, I had to set the record straight.I cover why building data platforms without a theoretical framework is like building a house on a Thai hillside without a geologist (spoiler: it ends in a mudslide), the limits of Kimball, and why the rise of AI actually guarantees that data engineering is going to become more critical, not less.Plus, an update on my upcoming book, Mixed Model Arts, and where you can catch me keynoting around the world in the coming months.Links to my Upcoming Events:April 29: Agentic Analytics Summit (Cube)May 6-8: Data Innovation Summit (Stockholm, Sweden). Catch my Keynote on May 7th and my Mixed Model Arts workshop on May 8th!May 18-20: Current (London, UK)

Wes McKinney is back to discuss his complete transition from AI skepticism to becoming heavily "locked in" on coding agents.Wes shares how he overcame his initial "existential dread" about the future of software engineering and completely rebuilt his personal productivity stack using tools like Claude and Codex. We dive deep into the reality of coding agents, why he believes Go has become the ultimate language for AI agents, and how he manages massive, multi-agent workflows to build production-level software without touching DevOps. Wes also breaks down his mission to fight the platform decay of services like Gmail by 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.----------

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----------

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)

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.