Imagine describing a software task and watching an AI generate the code for you. This scenario is no longer science fiction – it’s becoming a daily reality for developers. AI coding (or AI-assisted coding) refers to using artificial intelligence to aid software developers in writing, reviewing, and improving code. There are several tools available now that act as ‘AI pair programmers’, transforming natural language instructions into working code.

What is AI coding? 

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AI-assisted coding is the use of AI tools to help write and manage software. Instead of manually typing every line, a developer can get suggestions, auto-completion, and even entire functions generated by an AI. These assistants leverage large language models (LLMs) – algorithms trained on massive amounts of code and natural language – to understand prompts and produce relevant code. In practice, they function like an intelligent autocomplete for coding: as you write code or comments, the AI predicts what you need next. For example, a comment saying “// sort a list of dictionaries by a key” might prompt the AI to generate a Python function to do exactly that.

The significance of AI coding is hard to overstate, and while not a completely new idea, it is gaining a lot of traction. Once a futuristic idea, it’s now a reality with a majority of developers embracing these tools. Surveys show that around 70 per cent of developers using AI assistants believe these tools give them a clear productivity boost and help improve code quality. By automating boilerplate coding and reducing tedious debugging, AI assistants allow developers to focus more on design and problem-solving. This has sparked optimism that coding will become more accessible: anyone who can describe a problem well enough might produce working software with minimal manual coding.

How AI coding assistants work 

AI coding assistants rely on advanced machine learning under the hood. At their core is an LLM trained in vast repositories of source code and natural language text. These models learn patterns from millions of code examples, documentation pages, and Q&A threads. As a result, they can take a natural language prompt or partial code context and predict what code should come next. Essentially, the AI doesn’t ‘think’ like a human programmer; it statistically guesses a likely solution based on its training. Yet, with enough training data, those guesses can be uncannily accurate.

English becoming the de facto programming language of AI

One striking aspect of this AI coding revolution is the prominence of English as the language for programming instructions. Programming languages have historically had keywords and syntax derived mostly from English, a legacy of the predominantly English-speaking early computer scientists. AI coding tools take this a step further: now entire logical specifications can be given in English. Tesla AI director Andrej Karpathy quipped that “the hottest new programming language is English,” and this isn’t hyperbole.

English has now been turned into a high-level programming language itself. Instead of mastering the syntax of C++ or Python, one can achieve a result by mastering how to phrase the request to the AI. This shift has implications for accessibility: non-programmers with domain knowledge can now leverage AI tools to create software without needing years of coding experience.

Opportunities for non-English communities 

The dominance of English in AI coding raises an important question: What about everyone else? Not all developers (let alone aspiring developers) are proficient in English. AI coding tools, by and large, work best in English. However, many of the underlying models are fundamentally multilingual. OpenAI’s ChatGPT, for instance, was trained primarily in English but was also exposed to dozens of other languages and can respond in over 50 languages. In practice, a user could write a prompt in Spanish or French and still get a useful answer or code from such a model.

One opportunity to bridge this gap is integrating translation layers. A non-English user could write a prompt in their language, have it translated to English for the AI, and then translate the output code/comments back to their language. We’re also likely to see more region-specific AI

coding tools emerge, tuned for different languages. Ensuring AI understands multilingual nuances will make the promise of ‘AI for everyone’ more of a reality.

Will traditional programming get replaced? 

With AI writing more of our code, some wonder: are we approaching the end of traditional programming as a skill? The consensus so far is no. AI coding assistants are tools that handle easier parts of programming, but they are not inventing novel software architectures or making judgment calls. A human developer is still very much in charge of guiding the AI, verifying the results, and handling complex problems.

Programming isn’t just about writing new code; it’s also about reading and maintaining existing code, something that requires understanding context and intent. AI can assist in generating code, but maintaining a large codebase involves understanding trade-offs, historical reasons for decisions, and non-obvious constraints (like performance or security considerations), which AI lacks.

Traditional programming is not being replaced so much as augmented and redefined. The skill set of a software developer will adapt. Mastery of a programming language’s syntax might become less crucial, while skills like system design, debugging, and critical evaluation of AI-generated output become more crucial. The art and science of programming remain a human endeavour at its core. AI coding will empower more people to create and push the boundaries of what we can build while keeping sight of the creativity and expertise that real innovation requires.

The writer is a tech expert