this post was submitted on 09 Aug 2025
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Again, you use exact same tools and processes to evaluate software whether it's written by a human or a model. The reality is that people make mistakes all the time, people write code that's as bad as any LLM. We have developed practices to evaluate code and to catch problems. You're acting as if human written code doesn't already have all these same problems that we deal with on daily basis.
Yes, every programming abstraction comes with trade offs. Yet, it's pretty clear that most people prefer the trade offs that allow them to write code that's more declarative. That said, using approaches like genetic algorithms coupled with agents could actually allow automating a lot of optimization that we don't bother doing because it's currently too tedious.
It's relevant because the key skill is being able to understand the problem and then understand how to represent it formally. This is the skill that's needed whether you have agents fill in the blanks or you do it yourself. There's a reason why you do a lot of math work and algorithms on paper in university (or at least I did back in my program). The focus was on understanding how algorithms work conceptually and writing pseudo code. The specific language used to implement the code was never the focus.
What you're talking about is a specific set of skills degrading because they're becoming automated. This is no different from people losing skills like writing assembly by hand because the compiler can do it now.
There's always a moral panic every time new technology emerges that automates something that displaces a lot of skills people invested a lot of time into. And the sky never comes crashing down. We end up making some trade offs, we settle on practices that work, and the world moves on.
I don't see how that follows. You appear to be conflating the skill of learning the specific syntax of a language with the skill of designing algorithms and writing contracts. These are two separate things. A developer will simply do work that's more akin to what a mathematician or a physicist does.
LLMs don't allow the programmer to bypass every stage of the learning process in both an academic and professional capacity. That's simply a false statement. You cannot create quality software with the current generation of LLMs without understanding how to structure code, how algorithms work, and so on. These are absolutely necessary skills to use these tools effectively, and they will continue to be needed.
Again, this was said many times before when development became easier. What's really happening is that the barrier is being lowered, and a lot more people are now able to produce code, many of whom simply would've been shut out of this field before.
I see no basis for this assertion myself, but I guess we'll just wait and see.
Nobody is suggesting trusting LLMs here. In fact, I've repeatedly pointed out that trust shouldn't be part of the equation with any kind of code whether it's written by a human or a machine. We have proven techniques for verifying that the code does what was intended, and that's how we write professional software.
Even if this technology stopped improving today, which there is very reason to expect, it is already a huge quality of life improvement for software development. There are plenty of legitimate real world use cases for this tech already, and it's not going away.