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/
Just launched your Substack? Great! Here’s what to do next.
This episode covers the realities of writing long-form in public, the traps that cause most writers to stall, how to build consistency, and how to grow an engaged audience from day one.
Data engineering is undergoing a fundamental shift. In this episode, I sit down with Nick Schrock, founder and CTO of Dagster, to discuss why he went from being an "AI moderate" to believing 90% of code will be written by AI. Being hands on also led to a massive pivot in Dagster’s roadmap and a new focus on managing and engineering context.
We dive deep into why simply feeding data to LLMs isn't enough. Nick explains why real-time context tools (like MCPs) can become "token hogs" that lack precision and why the future belongs to "context pipelines": offline, batch-computed context that is governed, versioned, and treated like code.
We also explore Compass, Dagster’s new collaborative agent that lives in Slack, bridging the gap between business stakeholders and data teams. If you’re wondering how your role as a data engineer will evolve in an agentic world, this conversation maps out the territory
Dagster: dagster.io
Nick Schrock on X: @schrockn
The days of easy entry into data jobs over. Maggie Wolff joins the show to discuss the new reality of the data career landscape. We dive into why the bar is higher than ever and why "cold DMing" on LinkedIn is a terrible strategy.Maggie also breaks down her secret strategy for networking as an introvert: treating events like a game or role-playing a more extroverted friend. Plus, we discuss the rise of AI in education, the problem with "lazy" learning , and why companies replacing humans with AI are making a mistake.
Matt Housley joins me for our monthly round-up of topics. This time, there's danger everywhere - The AI Bubble, how vibe coding is evolving, AI slop, and more.
After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.
In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it.
We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.
In this conversation, Dr. Cecilia Dones and I discuss the social skills we're losing as AI becomes more integrated into our lives. We explore the erosion of social norms, from AI companions joining Zoom calls without consent, endless enshitified content, to my son's generation calling AI girlfriends "clankers".Is there hope? We break down the "rage currency" that dominates media and the positive AI stories that go unheard. The biggest takeaway: as the world becomes more synthetic, "showing up" in person will become the ultimate "premium value."
In conversations I've been having with leaders and practitioners, there's some open-ended questions about the impact of AI on vendors and open-source projects. If you don't have a moat, you need to start thinking about how AI coding tools will erode the edges of your product. And what about getting users and traction? I cover this and much more in this episode. Enjoy!
Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.
They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.
We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.
Matt Turck (VC at FirstMark) joins the show to break down the most controversial MAD (Machine Learning, AI, and Data) Landscape yet. This year, the team "declared bankruptcy" and cut over 1,000 logos to better reflect the market reality: a "Cambrian explosion" of AI companies and a fierce "struggle and tension between the very large companies and the startups".
Matt discusses why incumbents are "absolutely not lazy" , which categories have "largely just gone away" (like Customer Data Platforms and Reverse ETL) , and what new categories (like AI Agents and Local AI) are emerging. We also cover his investment thesis in a world dominated by foundation models, the "very underestimated" European AI scene , and whether an AI could win a Nobel Prize by 2027.
https://www.mattturck.com/mad2025
I travel a TON, and the most frequent questions I get relate to traveling: Why I do it and any tips I have for traveling.
Here, I answer those questions and more.