Any fool can write code that a computer can understand. Good programmers write code that humans can understand. ― Martin Fowler.

The above quote resonates and may provide comfort to coders, especially following the introduction of Copilot and the new ChatGPT. However, the question of whether AI will replace coding jobs is causing concern. There are doubts that AI may entirely replace human coders since it can automate certain coding jobs. But what factors prevent this from happening?

The truth is more nuanced. While AI can automate certain coding tasks, it does not necessarily mean that all coding jobs will become obsolete. Rather, AI is likely to create new opportunities for human coders with a range of skill sets.

Deep learning, machine learning, and natural language processing are some of AI’s most significant capabilities. AI can already perform many coding tasks with a high degree of accuracy. For instance, it can generate code based on a high-level program description and optimize existing code by identifying and eliminating inefficiencies. AI can offer several benefits for time-consuming and repetitive coding jobs.

However, coding is not the only task for a developer. While this is true, it misses the mark. It’s like saying a cook’s job is to make food. But not every chef is eligible for a Michelin star. Coding is straightforward, but designing for scalability, meeting client requirements, and working within pre-existing design constraints are considerably more challenging.

What technologies are AI coding using?

AI coding technologies are revolutionizing the coding industry by generating boilerplate code. These codes have minimal variations and help eliminate low-value tasks such as searching for answers on Stack Overflow, writing unit tests, and looping over data. Instead of managing time, coders now manage their energy, and these technologies assist them by handling repetitive coding tasks. As a result, developers can now focus more on complex challenges that offer better pay and job satisfaction.

AI vs Coders…

AI has benefits for specific coding tasks but won’t replace human programmers entirely. AI algorithms are limited by the data they’re trained on and cannot understand human emotions, culture, and values. Thus, programming roles requiring a complex understanding of such factors are less likely to be automated. Tasks like designing user interfaces, building chatbots, developing new algorithms, and creating innovative software solutions need human expertise.

There are several reasons for this:

Originality 

AI algorithms cannot match the unique perspective and creativity that human coders may bring to the coding process.

Biased opinion 

Humans can make decisions that cannot be quantified due to their intuition and judgment.

Awareness 

Humans can understand the context of a project and write code that is tailored to that context, which can be difficult for AI algorithms.

Client-specific 

Human programmers can create user-friendly apps by considering the wants and preferences of the user.

Collaboration 

Human programmers can collaborate with other programmers and AI algorithms to improve their code, create new applications, and enhance the functionality of existing ones.

As much as we might hope for it, current AI technology is not capable of thinking and reasoning like humans. While intelligent systems can perform tasks like identifying faces or predicting coding errors, they cannot replicate the complexity of the human brain. Despite this, the demand for developers is expected to rise over the next decade, with the most sought-after and lucrative roles centred around developing, running, optimising, and testing AI. If AI were to replace coding entirely, all jobs would become outdated, not just coding. While coding is an artistic field, the way we approach problem-solving has evolved, requiring human interpretation, prioritisation, and storytelling skills, which are currently beyond the capabilities of AI technology.

So, will we have a situation where bots are modifying codes and outcomes through some form of “self-learning”, to creating sentient intelligence – with potential human detrimental outcomes? Well not yet, and not for a long while from what it looks like. Sentient capabilities are still a long way from becoming reality. Further, while specific routines and functionalities may be auto-coded, the overall design will still remain a “Human” endeavour. Design, contextualising, originality among others are coding requirements where the human lateral thinking will continue to out-perform AI. 

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.