AI won't kill programming, but it might kill how juniors learn to code
Yes, and
Carson Gross, a computer science professor at Montana State University, answers whether AI makes programming a dead-end career with a resounding 'Yes, and...' He argues that while AI can generate code, juniors must still write code to develop a visceral understanding of it—otherwise they risk creating systems they can't control. AI works best as a teaching assistant, not a code generator, and skills like communication, business understanding, and software architecture will become more valuable.
Yes, AI can generate the code for this assignment. Don’t let it. You have to write the code.
- ibejoeb
It's the architecture. That's the correct answer here. The current models produce good implementations. They're also quite good at identifying and planning for edge cases. But, today, a successful software project requires picking the right atoms for the job.
Some of the calculus for that picking will change, since volume of code that must be produced becomes less of an issue. And I don't doubt that models next year and year after will be able to make better formative architectural choices. But as it is now, I'm certain that actual systems handling real workloads require a human designer.
Someone who knows what good software looks like is empowered with agents. Someone without that knowledge isn't going to create a high quality system yet.
- layer8
> Is Coding → Prompting like Assembly → High Level Coding? […] I do not agree with this simile. Compilers are, for the most part, deterministic in a way that current AI tools are not.
It’s not quite about the determinism. It’s about being able to reason about the relationship between source code and compiled program with formal precision. You can predict which changes in the source code will lead to which exact changes in the behavior of the compiled program. The same isn’t the case about changes to an LLM prompt and the LLM’s output.
You could make an AI deterministic by fixing its source of randomness. That still wouldn’t allow you to reason about how its output will change when (for example) you add or remove a word in the prompt. The only way to find out is to run the LLM (= have the prompt run through the model and observe what comes out).
That is the fundamental difference. Changes to source code have predictable and reason-able outcomes. You generally don’t have to compile the code and test it to know how precisely the change will affect the behavior of the compiled program according to the semantics of the programming language. That’s the case even if the compiler uses some probabilistic heuristics for trade-offs in code generation, and hence isn’t deterministic on the machine code level.
To repeat, the difference is how you can reason about a compiler’s behavior versus an LLM’s behavior. Programming languages are designed such that you can reason about it. With LLMs […]
- johsole
I disagree with the author. I think as LLMs get better at coding it will be more likely that that fewer devs will be required to keep systems running and progressing, the bottleneck at my company is already new revenue generating ideas. We've seen a roughly 30% increase in speed of new features, so the same number of devs are building a lot quicker. I expect that to continue to increase. I also see a lot of Devs simply trusting that the code is correct, they are losing touch with the code.
I don't think I would encourage my kids to get involved with programming, instead I would encourage them to become entrepreneurs who might use some coding.
- tengbretson
> I explain that, if they don’t write the code, they will not be able to effectively read the code. The ability to read code is certainly going to be valuable, maybe more valuable, in an AI-based coding future.
I'm not certain of this. Thinking back to when I first started in my career after graduation- I remember feeling like my ability to write code had improved greatly during my time in school. Meanwhile, my ability to read code felt like it had barely improved at all. Even now, after over a decade in the industry, while both skills have improved tremendously, I still feel like my ability to read and internalize code is not at the level I would like or assume it to be simply as a result of my experience.
It could very well be that reading and writing are two separate (though related) skills that require intentional practice and honing on their own. I can't speak for everyone, but reading code as a skill, for me, only really began to develop once I had a job where it was expected of me.
Maybe it's possible to learn to read code without learning to write it. It certainly feels like its possible to learn to write it without learning to read it.
- ivanjermakov
Programmers make computer programs. LLMs make making computer programs more affordable and efficient, resulting in more computer programs and higher demand in programmers. We don't know what programming would be like in 10 years, but why would demand in well functioning computers go down?