Here's a blog post draft based on the podcast with MIT’s Ana Bell, tailored for an audience interested in why Python—and programming more broadly—remain relevant in the age of AI:
In a world increasingly shaped by AI tools and low-code platforms, it’s fair to ask: Do we still need to learn how to code? According to Ana Bell—MIT lecturer, author, and programming advocate—the answer is a resounding yes. And not just because of career prospects, but because of something deeper: agency, creativity, and trust.
Ana Bell sees programming as a foundational skill—akin to math. Not everyone becomes a mathematician, but everyone learns math because it's vital to navigating the modern world. Similarly, you don’t need to become a software engineer, but learning to code gives you the ability to build, understand, and verify the digital tools shaping your life.
This is especially important in the AI era. While GenAI tools can generate code, they don’t always get it right. Ana summarizes the key principle in one phrase: “Trust but verify.” Even basic programming knowledge allows you to evaluate and adjust what AI produces. Without it, you risk being a passive consumer of AI-generated output—unable to correct mistakes, unable to innovate independently.
Ana began with Java as a kid, but now teaches Python at MIT—and for good reason. Python’s readability and simplicity make it ideal for beginners. But don’t let that fool you: Python powers some of the world’s most advanced systems, from machine learning frameworks to backend APIs. It’s a gateway language—accessible for newcomers, yet robust for professionals.
Ana's teaching philosophy blends rigor with creativity. Her flipped classroom model encourages hands-on practice in real-time—right during lectures. Students don’t just passively absorb syntax; they write code, make mistakes, and debug—all key to internalizing programming concepts.
And yes, rubber ducks are involved.
Ana teaches a classic debugging method called rubber duck debugging—where students explain their code line-by-line to an inanimate duck. The goal? To surface hidden assumptions and catch logical errors. (Judgmental friends not required.)
This whimsical but effective method reflects a broader truth: programming is deeply creative. Whether designing an algorithm, solving a bug, or building an app, you’re constantly inventing, iterating, and imagining new possibilities.
Ana readily uses GenAI tools herself—but sees them as collaborators, not substitutes. They provide a starting point, not a solution. The real value lies in what you do after the AI gives you code: testing, verifying, adapting, and extending it. That process requires human understanding.
Without foundational coding skills, users become over-reliant on AI and unable to troubleshoot errors—leading to frustration, inefficiency, and sometimes dangerous outcomes.
Whether it’s building a personal project, automating a boring task, or evaluating AI output, programming gives you control over technology—not just exposure to it. It’s not about becoming a full-time developer. It’s about choosing your own digital adventure.
As Ana puts it: “I don’t want everyone to become a computer scientist. I just want to empower them.”
And in a world where AI is everywhere, that empowerment begins with something as simple—and as powerful—as Python.