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

Panel: State of the Data And AI Market | Apoorva Pandhi, Matt Turck, Chris Riccomini, Chad Sanderson | Shift Left Data Conference 2025

Artificial Intelligence is reshaping the landscape of software development, driving a fundamental shift towards empowering developers to take control earlier in the development lifecycle—known as "shift left." In this panel, venture capital leaders and industry experts will explore how emerging trends in AI and data technologies are influencing investment decisions, creating new opportunities, and transforming development workflows. Attendees will gain valuable insights into the evolving market dynamics, understand the strategic significance of shifting left in today's AI-driven world, and discover how organizations and developers can stay ahead in this rapidly changing environment.

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, "Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications." Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Summary

The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process

Interview

Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it?

At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape?

As the data landscape matures, how have you seen that influence the types of projects/companies that are founded?

What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive?

The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve?

Is there a different architectural paradigm that you see as growing to take its place?

How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape?

Contact Info

Website @mattturck on Twitter MAD Landscape Comments Email

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform

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