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

The world of data is being reset by AI, and the infrastructure needs to evolve with it. I sit down with streaming legend Tyler Akidau to discuss how the principles of stream processing are forming the foundation for the next generation of "agentic AI" systems.

Tyler, who was an AI cynic until recently, explains why he's now convinced that AI agents will fundamentally change how businesses operate and what problems we need to solve to deploy them safely.

Key topics we explore:

• From Human Analytics to Agentic Systems: How data architectures built for human analysis must be re-imagined for a world with thousands of AI agents operating at machine speed.

• Auditing Everything: Why managing AI requires a new level of governance where we must record all data an agent touches, not just metadata, to diagnose its complex and opaque behavior

• The End of Windowing's Dominance: Tyler reflects on the influential Dataflow paper he co-authored and explains why he now sees a table-based abstraction as a more powerful and user-friendly model than focusing on windowing.
• The D&D Alignment of AI: Tyler's brilliant analogy for why enterprises are struggling to adopt AI: we're trying to integrate "chaotic" agents into systems built for "lawful good" employees.

• A Reset for the Industry: Why the rise of AI feels like the early 2010s of streaming, where the problems are unsolved and everyone is trying to figure out the answers.

I still see some companies acting sheepish with AI, too scared to even try it out. That's a massive mistake. Now is the time to play offense with incorporating AI into your company and reimagining what it can become.

Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents.

We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.

Join us as we cover:

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• The Shift from Dashboards to Agents: We discuss why dashboards can create a high cognitive load and fail users in complex scenarios , and how conversational agents in the flow of work (like Slack) provide targeted, actionable insights and boost adoption.
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• What is Product Telemetry?: Irina explains how telemetry is evolving from a simple engineering observability use case to a critical data source for AI, machine learning, and recommendation systems.
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• Why Standard RAG Fails in the Enterprise: Irina shares why typical RAG approaches break down on dense, entity-rich corporate data (like Salesforce's help docs) where semantic similarity isn't enough, leading to the rise of Graph RAG.
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• The New, Measurable ROI of Data: How moving from BI to agents allows data teams to precisely measure impact, track downstream actions, and finally have a concrete answer to the ROI question that was previously impossible to justify.
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• Data Teams as Enterprise Leaders: Why data teams are uniquely positioned to lead AI transformation, as they hold the enterprise "ontology" and have experience building products under uncertainty.

It's all about acquisitions, acquisitions, acquisitions! Matt Housley joins me to tackle the biggest rumor in the data world this week: the potential acquisition of dbt Labs by Fivetran. This news sparks a wide-ranging discussion on the inevitable consolidation of the Modern Data Stack, a trend we predicted as the era of zero-interest-rate policy ended.

We also talk about financial pressures, vendor exposure to the rise of AI, the future of data tooling, and more.