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.

Jeremiah Lowin, founder of Prefect , returns to the show to discuss the seismic shift in the data and AI landscape since our last conversation a few years ago. He shares the wild origin story of FastMCP, a project he started to create a more "Pythonic" wrapper for Anthropic's Model Context Protocol (MCP).

Jeremiah explains how this side project was incorporated into Anthropic's official SDK and then exploded to over a million downloads a day after MCP gained support from OpenAI and Google.

He clarifies why this is an complementary expansion for Prefect, not a pivot , and provides a simple analogy for MCP as the "USB-C for AI agents".

Most surprisingly, Jeremiah reveals that the primary adoption of MCP isn't for external products, but internally by data teams who are using it to finally fulfill the promise of the self-serve semantic layer and create a governable, "LLM-free zone" for AI tools.

I'm back, and give some notes from the road, thoughts on choosing tools and vendors, having a plan B for tools, and more.

There's no shortage of technical content for data engineers, but a massive gap exists when it comes to the non-technical skills required to advance beyond a senior role. I sit down with Yordan Ivanov, Head of Data Engineering and writer of "Data Gibberish," to talk about this disconnect.

We dive into his personal journey of failing as a manager the first time, learning the crucial "people" skills, and his current mission to help data engineers learn how to speak the language of business.

Key areas we explore:

• The Senior-Level Content Gap: Yordan explains why his non-technical content on career strategy and stakeholder communication gets "terrible" engagement compared to technical posts, even though it's what's needed to advance.
• The Managerial Trap: Yordan's candid story about his first attempt at management, where he failed because he cared only about code and wasn't equipped for the people-centric aspects and politics of the role.
• The Danger of AI Over-reliance: A deep discussion on how leaning too heavily on AI can prevent the development of fundamental thinking and problem-solving skills, both in coding and in life.
• The Maturing Data Landscape: We reflect on the end of the "modern data stack euphoria" and what the wave of acquisitions means for innovation and the future of data tooling.
• AI Adoption in Europe vs. the US: A look at how AI adoption is perceived as massive and mandatory in Europe, while US census data shows surprisingly low enterprise adoption rates