Julien Simon has spent years explaining cutting-edge tech to the world — first at AWS, then Hugging Face, and RCAI.

In this conversation, he shares how he moved from CTO roles into tech evangelism, what it was like pushing for honest benchmarking inside big tech, and why trust matters more than marketing.

We get into:

• The real role of a tech evangelist
• Why benchmarking caused internal friction
• The flood of AI-generated content and what it means for trust
• Whether software engineering as we know it is over
• How AI coding assistants are changing workflows
• Why young engineers may want to rethink their career paths

Julien also talks about his move into private equity and helping portfolio companies adopt AI at scale.

This one is candid, sharp, and forward-looking.

Adam Davis has been in content marketing for over a decade, and he’s spent the last year testing AI tools full time.

We talk about how AI is changing content workflows — not by replacing marketers, but by forcing them to rethink process and positioning.

We cover:

• Why SEO isn’t dead, but evolving
• What GEO actually means
• How Reddit influences AI training data
• Why authenticity is becoming the only moat
• How small startups should think about branding differently than big companies

We also talk about the difference between creating content and managing content in 2025.

If you’re building online, this conversation is grounded and practical.

Roey Zalta has worked at the forefront of enterprise AI deployment, including multi-agent systems in production environments.

In this episode, we discuss:

• What “agentic companies” really mean
• Multi-agent systems deployed in Hebrew
• Why evaluation (evals) is the hidden key to enterprise AI
• The risks of automating broken business logic
• Voice AI agents replacing call centers
• Microsoft’s long-term AI strategy

We also zoom out to the broader questions:

• How AI affects education and human learning
• Whether LLMs will reshape social interaction
• The privacy tradeoffs of Apple Intelligence
• And what happens when AI-generated content dominates the internet

It’s part technical, part philosophical, and very grounded in real-world deployment.

Greg reconnects with former DataRobot “Director of Competitor Annihilation” Thomas Dinsmore to unpack what’s happened in the AutoML world, the rise of AI agents, and whether generative AI is truly disruptive or just another buzz cycle. They discuss Domino’s governance-first platform, the future of predictive analytics, AGI skepticism, and why “don’t trust—verify” still applies in the AI era.

In this episode, Greg and Razi revisit their DataRobot roots and discuss what still hasn’t been solved in data science. FeatureByte’s data science agent tackles the messy 90% of the ML workflow: feature engineering, pipeline management, and production deployment. They debate tabular foundation models, agentic frameworks, and whether feature stores are actually being used in the real world.

Greg reconnects with Andrew Engel to talk about leaving the LLM SaaS wave behind and building something tangible. After years in the data science startup ecosystem, Andrew is now co-founding Nyst.ai, using computer vision and movement analysis to detect concussions from a mobile phone. They discuss startup realities, prompt engineering skepticism, remote work, and what it’s like to move from employee to founder.

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.