On AI Coding and Its Discontents - Cal Newport
“Writing your own code, slowly but surely, and using LLMs for narrow or particularly annoying tasks (say like writing tests or throw-away scripts), is the best way to produce the highest quality code, since it’s the only way to properly understand it.” […]
This last year has been exhausting. The PR departments of the frontier labs have done an excellent job convincing us that AI developments are occurring at an astounding, world-changing rate. But if you zoom out, it becomes clear that almost every “breakthrough” since last summer has concerned the narrow domains of computer code and math, which are defined by highly structured languages and come accompanied by massive amounts of specialized training data.
And yet, even in this best-case-scenario setting for AI, we’re still struggling to figure out how to actually use these tools in a way that makes sense in the long run.
Writing production code is tricky in that it can not fail. In this engineer’s case it broke production twice.
The thesis of this article is that it’s ok to use AI to write code, which if it breaks does not cause to too much of an issue. Which has been my experience too. I have primarily used Claude to build personal tools, and my website and some scripts and what nots at work. Also, how much of code out in the world is truly code that can not break?
But I know of people who are talking to their agents and having it build things.
The one thing I agree with is - that we are all trying to figure out the steady state of using AI in development work.