Episode 64: Data Science Meets Agentic AI with Michael Kennedy (Talk Python)

Vanishing Gradients

We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software, data, and AI.

In this episode, Michael joins Hugo to dig into the practical side of running Python systems at scale. They connect these ideas to the data science workflow, exploring which software engineering practices allow AI teams to ship faster and with more confidence. They also detail how to deploy systems without unnecessary complexity and how Agentic AI is fundamentally reshaping development workflows.

We talk through:

• Escaping complexity hell to reduce costs and gain autonomy
• The specific software practices, like the "Docker Barrier", that matter most for data scientists
• How to replace complex cloud services with a simple, robust $30/month stack
• The shift from writing code to "systems thinking" in the age of Agentic AI
• How to manage the people-pleasing psychology of AI agents to prevent broken code
• Why struggle is still essential for learning, even when AI can do the work for you

LINKS

• Talk Python In Production, the Book!
• Just Enough Python for Data Scientists Course
• Agentic AI Programming for Python Course
• Talk Python To Me and a recent episode with Hugo as guest: Building Data Science with Foundation LLM Models
• Python Bytes podcast
• Upcoming Events on Luma
• Watch the podcast video on YouTube

Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (35% off for listeners): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav