Don't fire your software developers yet

Software developers are obsolete! I’d never written a line of code before, and I was able to create an app in a week using AI!

As LLMs continue to improve, I have seen people make this kind of statement more and more. Often the one making the statement is incredulous at their software developer friends’ seeming lack of interest in AI. But I think there’s something that those who have “never written a line of code before” don’t realize.

I’m not just talking about the fact that software development is much more than the mere act of typing code, though that is certainly true. There has been enough discussion on that topic already that I don’t feel like I need to cover it in much detail here. Suffice it to say that creating software involves much more thinking and problem solving than it does typing. However, even if someone knows this, I think there is another reason that they might come to the above conclusion of just how much an LLM can really do: they do not have the experience in software development needed to gauge someone else’s skill level.

To explain what I mean, consider the Dreyfus Model of Skill Acquisition. We often reference this at my company because it is a helpful tool for understanding how a person’s thought processes and capabilities change as they gain experience, especially in high-skill professions like software development. According to this model, a person progresses through these stages as they learn:

1. Novice

2. Advanced Beginner

3. Competent

4. Proficient

5. Expert

Someone in the first couple of stages relies primarily on following rules and step-by-step instructions. As a person gains experience, they rely more and more on intuition, and do not need to consciously apply rules. Importantly, Experts do not merely work faster than a Novice or Advanced Beginner. The way they think is entirely different, and they are capable of much, much more.

An Advanced Beginner may be able to throw an app together quickly. So can an Expert, though for more complex domains. Unlike an Advanced Beginner, an Expert can also maintain, update, and expand that app over the course of several years, because they will build it in a way that allows for modification. This requires managing the inherent complexity of the domain through good code architecture while avoiding introduced complexity from bad architecture.

Many people never make it past Advanced Beginner in a given field. I think this is often the case for hobbies—one might not be able to put in the time needed to advance to higher levels (it really does take around 10,000 hours to become an Expert). But more relevant to the AI discussion is the other reason someone might fail to achieve the later stages: being blind to how much they have left to learn.

When someone reaches the Advanced Beginner stage, they have already learned a lot and are much more capable than when they were a Novice. The danger is that this can trick the Advanced Beginner into believing that their skills are greater than they really are. They might even believe that they are Experts, ceasing to learn because they think there is nothing left to learn. Even worse, though, is how that person is perceived by someone else who is a Novice, because to a Novice, an Advanced Beginner looks like an Expert.

And this, I think, is the mistake so many people make with AI. By their own admission, having “never written code”, they are Novices. They see an LLM writing code that they have neither the skill to write nor to understand, and conclude that the AI is an Expert, when in reality it most likely is not. Then they go post about it on social media.

LLMs still need to prove that they can maintain an app long-term, and do it well, without continually increasing the time and cost to add features. Proving this will take time, though we can get some early indication by evaluating the quality of their code, particularly for how well they do when the subject is not found in their training data (please stop extrapolating from todo apps). So far, the hype comes mostly from those without coding experience, while those with experience tend to find that LLMs still have significant gaps in their abilities.

If you try to replace your software developers entirely now, you could be left with an app that an LLM can’t maintain, and no humans to correct it. You could try keeping only your senior developers, but if AI never becomes capable enough to replace them, you’ll be stuck trying to keep increasingly rare and valuable resource. Experienced developers will be in short supply at any company that isn’t willing to invest in growing their own talent.

Now, I am not at all saying that LLMs are useless, but I do think the hype is unreasonably optimistic, and often pushed by people with something to sell. Those instances where someone who is not a software developer has used one to build a system are quite impressive, and I think there will be many cases where that is sufficient. I also think they are valuable tools in the hands of an experienced practitioner, and can boost productivity in many ways. On the other hand, I have personally seen them be a detriment to junior developers who do not yet have the experience to validate their output.

I think it’s clear that while LLMs have some impressive capabilities and will continue to transform software development, they still cannot entirely replace human expertise. Maybe they will one day, and maybe they’ll prove capable of the long-term thinking necessary to build sustainable systems. Personally, I think that day is still a long way off, if it comes at all, and the smart move is to continue to invest in capable humans. Though maybe that’s just what I want to believe because I enjoy software development. Either way, I don’t plan to let the computers have all the fun.