AI Agents are not predictable nor procedural
That’s why they won’t take over traditional coding needs
In the world of artificial intelligence (AI), the boundaries of what is possible continue to be pushed every day. From self-driving cars to chatbots that can carry on human-like conversations, AI is revolutionizing various sectors. However, one area that AI is unlikely to fully conquer anytime soon is traditional coding. This might seem counterintuitive given the advances in AI, but it has more to do with the inherent nature of AI agents and coding needs.
The Unpredictability of AI
AI is inherently unpredictable. Even the most sophisticated models, like GPT-4 by OpenAI, are fundamentally probabilistic in nature. They generate outcomes based on a distribution of probabilities, not through a pre-programmed set of instructions.
This unpredictability can be an asset in certain contexts. For instance, in the world of creative writing, the ability of AI to generate novel combinations of words can result in some truly inspired prose. However, when it comes to programming, this unpredictability is more of a hindrance than a boon.
Imagine writing a function in a program where the output is critical. You would want that function to behave predictably, to always return the same output for the same input. An AI model can’t guarantee this level of consistency, which makes it unsuitable for many programming tasks.
The Non-Procedural Nature of AI
AI models like GPT-4 aren’t procedural, which is another reason they aren’t suited for traditional coding. In computer science, procedural programming is a paradigm based on the concept of “procedure calls,” where different procedures are defined, and programs call these procedures to perform a task.
In contrast, AI models learn from data using statistical techniques. They don’t follow a set of pre-defined procedures to generate an output. Instead, they analyze a vast amount of data and use patterns in that data to generate outputs.
Again, this can be an asset in certain contexts. In language translation, for instance, an AI model can use its ability to detect patterns to translate text from one language to another. However, in programming, this non-procedural nature can be a disadvantage. Without a clear set of procedures to follow, an AI model can’t be relied upon to consistently perform the same task in the same way.
Traditional Coding Needs Precision
Traditional coding is about precision. Every line of code, every function, every variable is there for a reason. The code must be clear, consistent, and predictable. It must follow a logical structure and perform a specific task in a specific way.
This is why, despite the advances in AI, traditional coding is not going anywhere. The unpredictability and non-procedural nature of AI make it ill-suited to the precise, procedural requirements of traditional coding.
AI in Coding: A Tool, Not a Replacement
While AI might not replace traditional coding, it can certainly augment it. Already, AI is being used to assist with tasks like bug detection, code completion, and even some level of code generation. In this way, AI can be seen as a tool that can help make coding more efficient, not a replacement for coders.
AI is changing the world, but it’s important to remember that it has its limits. Despite the hype, AI won’t replace traditional coding anytime soon. Instead, AI and traditional coding will continue to coexist, each with its own strengths and weaknesses, and each playing a crucial role in the future of technology.
