Data Day with Greg Michaelson

In this episode of Data Day, I sit down with Bhaskar Sunkara, co-founder and former CTO of AppDynamics, to unpack the evolution from application performance monitoring to AI-driven business analytics.

We go deep on how AppDynamics redefined monitoring by focusing on business transactions instead of infrastructure metrics, why self-serve was a game-changing GTM move, and the behind-the-scenes story of their near-IPO turned Cisco acquisition.

Then we shift to what comes next. Bhaskar shares how his new company is rethinking analytics with always-on agents that detect, explain, and act on business signals before you even ask the question. We dig into what actually makes AI systems useful in production, where coding agents are already replacing engineering work, and why the real bottleneck has shifted from writing code to designing the right system.

If you’re building in AI, data, or modern SaaS, this is a clear look at where things are going and what actually matters.

I sat down with Todd Persen from Hydraulics to unpack observability, log data, and what actually happens behind the scenes when your apps, streams, and infrastructure are running at scale. We get into how systems are monitored, why logs are exploding in size and complexity, and why LLMs struggle once the data gets truly massive. The conversation takes a turn into the future, where agentic traffic may outnumber humans, and what it means to tell good bots from bad ones. We also get into real-world use cases from CDNs, streaming, and even what happens when AI starts doing your grocery shopping for you.

In this episode of Day to Day, Greg reconnects with Darius Sabas to unpack his journey from DataRobot to leading analytics teams across European marketplaces. Darius shares lessons from scaling data organizations, building modern data stacks with tools like Snowflake and Mixpanel, and navigating the transition from basic reporting to predictive modeling and AI-driven products.

The conversation dives into the realities behind marketing mix models, the challenges of balancing data rigor with business politics, and why marketplaces are a goldmine for data science use cases. They also explore the growing role of LLMs in both work and personal life, from agentic workflows to the future of personalized AI, data privacy, and trust in an AI-generated world.

Saad Ansari has had one of the more unusual paths in AI.

He shares what it was like serving as an advisor inside the Saudi Ministry of Hajj and Umrah, helping guide digital transformation for one of the largest recurring events in the world. We talk about how AI intersects with government systems, why execution looks different across countries, and what it takes to modernize legacy infrastructure.

Then we shift to his new company, Partake.ai, where he’s focused on reinforcement learning that optimizes for human outcomes rather than engagement metrics.

We cover:

• Why today’s AI systems optimize for attention
• What “human-aligned” AI might actually mean
• Pokémon Go as the most successful fitness app ever
• Whether AI can nudge people toward healthier behavior
• Edge inference and privacy tradeoffs
• And whether companies can build something that is both profitable and good

It’s a thoughtful conversation about AI, incentives, and what kind of systems we actually want to live with.

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.