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

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

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