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Evolving programming languages in the AI era
This post is a collection of short ramblings on how programming languages may evolve in the AI era.
It is split into two parts: Reflections and Agentic tooling. The first raises questions about what happens to programming languages, their ecosystems, and their communities when humans are no longer writing most of the code. The second is more concrete and opinionated: how our tools should improve now that coding agents have become users of our languages.
My opinions on these topics will probably change, but this is a reasonable digest of what’s been on my mind lately.
This section explores how programming languages and their communities might change if agents write most of our code. While this remains a contentious topic, it is already the reality for many developers and teams. Therefore, it is our responsibility to explore how this shift may affect us, regardless of how widespread we believe it will become.
At the center of most programming languages, there is a community that rallies around a set of shared sensibilities. Python has its emphasis on an obvious way to do something. Ruby has long cultivated an appreciation for programmer happiness. Lisp communities have traditionally celebrated the ability to reshape the language itself.
But what happens when we stop writing most of the code? And how will that impact our sense of belonging? Is that something we should try to preserve or should communities find out what will be the next thing that glues them together?
Following on the same lines, we build ecosystems around languages to tackle hard problems and create shared abstractions: web frameworks, tensor libraries, data-processing pipelines, GUI toolkits, and so on. Coding agents may affect these ecosystems in two opposing ways.
First, the gap between ecosystems may get smaller. Building all of these frameworks requires a considerable amount of time and effort, much of which agents can potentially trim down. This is especially true when the problem involves implementing known algorithms, translating ideas from papers, or porting existing implementations between languages, allowing smaller communities to catch up with larger ones much more quickly.
On the other hand, if implementing something becomes cheap enough, will people still join efforts and collaborate on the same solution? If I need a library to solve problem X, I might just ask an agent to build exactly what I need.
This creates an interesting tension. Coding agents could dramatically reduce the cost of building an ecosystem while simultaneously weakening one of the forces that causes ecosystems to form in the first place.