Data Day with Greg Michaelson

What happens when you apply agent-native AI to one of the most fragmented industries in America? Greg and Satadru explore the evolution from enterprise AI at DataRobot to building automation infrastructure for home service providers. They discuss workflow automation, AI-generated quoting and payments, cold-start marketplaces, and why “point solutions” fail in complex industries.

In this episode of Day to Day with Greg Michaelson, Greg sits down with Ray Mi, former DataRobot leader and AI solutions expert, to explore how data science is evolving in the age of generative and agentic AI. They discuss AI-native companies, digital twins, virtual simulations, privacy, education, and what the data scientist role looks like over the next decade. If you’re wondering where AI is really headed and how to stay relevant, this conversation is for you.

In this episode, Greg sits down with longtime colleague John Forrest to talk about what really matters in technical customer support. Drawing on years of experience across startups and enterprise software, they explore trust, customer relationships, AI, vector databases, agentic memory, and why human judgment still matters even in an AI-driven world.

On this episode of Data Day, Greg Michaelson sits down with longtime friend and former DataRobot colleague, Dennis Oleksyuk, co-founder and CTO of AirCon, to unpack what it really takes to put AI agents into production in a weird but massive industry: air freight.

Dennis shares his unconventional path from growing up in one of the coldest inhabited places on earth, to applied math in a Soviet-style university, to telecom engineering, and eventually into machine learning and AI infrastructure. He explains how AirCon’s agents read freight emails, build viable multi-leg routes, talk to a zoo of carrier APIs, and autonomously generate and book quotes for freight forwarders, all while dealing with a million messy corner cases that never appear on the public internet.

Greg and Dennis dig into the reality of agentic coding in production: why generic agent frameworks often fall apart, why software engineering fundamentals matter more than “prompt wizardry,” how context windows really work, and why fine-tuning is usually limited by training data, not tooling. Along the way they touch on hidden operational knowledge in every industry, the myth of microservices as a default, and Dennis’s infamous “get on a plane, B” bug story.

If you care about AI agents, real-world automation, infrastructure, or just want a peek behind the curtain of how your stuff actually gets around the world, this one is for you.

Notebooks for data haven’t meaningfully changed in more than a decade, so we rebuilt them completely, based on how data work is actually done. We're introducing a major evolution of the Zerve app in a clean, streamlined UI that brings the fluidity of notebooks together with the power of agentic execution, reproducible canvases, and real cloud compute.

Join the founders, Greg Michaelson, Phily Hayes, and Jason Hillary, for a live look at what’s new, why we built it, and how it unlocks a smoother, more intuitive workflow for everyone who works with data. We'll demo the new app, answer your questions live, and share behind-the-scenes insights.

What You'll Learn

• How the new Zerve Notebook improves stability, collaboration, and speed
• Understand why Zerve is blending notebook familiarity with agentic execution and reproducibility
• Watch a live demo showing faster workflows, better clarity, and smoother iteration
• Hear directly from the founders about the design decisions, roadmap, and what’s coming next

Notebooks were made for people, not for AI. Zerve rebuilt them for both. In this livestream, Greg Michaelson, Zerve Co-Founder and Chief Product Officer, chats with data science guru Joel Grus (of “I don’t like notebooks” fame), and unveils the latest Zerve release: a new kind of notebook where agents and humans collaborate in real time to go from question to deployed solution 10x faster. Learn how Zerve carries ideas from exploration to live apps and APIs — no rewrites, no orchestration tools, no context switching.

Data science isn’t the same as software engineering. The difference is context. In this livestream, we’ll unpack what “context” really means, why it matters for data science, and how it transforms how teams work. You’ll hear how leading companies use context-aware workflows to cut wasted effort, streamline collaboration, and move faster from idea to impact. We’ll also demo Zerve, the first AI development environment for data science, with context at its core.

Key takeaways:
- Why context is the missing ingredient in traditional AI coding assistants
- Real-world examples of context-aware workflows boosting productivity
- How context-aware AI agents keep hypotheses, data, and code aligned to avoid wasted cycles
- A live demo of Zerve’s context-first, agentic approach