GoKawiilI just got back from PyCon US. It was delightful; I saw old friends, met new ones, gave a tutorial on decorators, and spoke at the education summit. I’m a PyCon US sponsor, which means that I also had a booth, giving out T-shirts, books, stickers, and flyers about the LernerPython platform.
Of course, the topic on everyone’s mind was AI. From the education summit (where my talk was about “vibe teaching”) to the new AI track to hallway conversations, I don’t think that 10 minutes passed without someone raising the topic. Everyone is simultaneously excited, confused, and worried.
As I’ve written elsewhere, I believe that agentic coding (aka AI coding, aka “vibe coding”) isn’t the future. It’s the present. This raises lots of legitimate questions, from where agentic coding is (and isn’t) appropriate, how best to configure it to ensure it produces quality software, the increasingly large budgets that AI tokens are demanding, the privacy and security of data sent to commercial LLMs, and how we’ll train the next generation of software developers.
These are all legitimate and important questions. But I’ve been thinking about another question: How relevant is Python to the future of agentic coding?
It might seem absurd to worry about the future of Python. After all, Python is enormously popular and important among everyone working with data. Data analysis, data engineering, and machine learning are all largely implemented using Python. Between the standard library and PyPI, Python offers an almost embarrassingly large treasure trove of packages that handle everything from data to calendars to package delivery. The community of Python coders, including contributors to the language and third-party packages, is huge, and continues to grow. And because Python has been around so long, there is a great deal of online Python code that LLMs can use in their training.
In other words: AI is implemented using Python. Lots of people are using Python. It’s easy for LLMs to produce Python. Which is good, both for AI (which has more experience/training to draw on) and good for the people reading and examining the code.
But over the last few weeks, I’ve started to wonder about several things.
Why ask AI to write its output in a dynamic language with relatively slow execution (i.e., Python)? In order for AI to write in Python, someone needs to be maintaining the language, standard library, and popular packages on PyPI. Who will do that, when the “customer” will be AI? Who will learn Python, if coding will go through AI?
I want to go through each of these, describing where things currently stand and where they might go.
Why output Python?
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