AI Programming and the Hidden Bottleneck: Why Categorization Matters More Than Ever

As generative AI increasingly automates the writing of lines of code, a profound question emerges: Is the future of software development constrained not by algorithms, but by how we classify and organize the very building blocks of computation? According to a widely discussed Reddit thread from the Machine Learning community, the rise of AI-generated code may render traditional programming paradigms—such as object-oriented programming (OOP)—increasingly obsolete, not because they are inefficient, but because they are conceptually outdated. The real bottleneck, the post suggests, may lie not in AI’s ability to generate code, but in our persistent reliance on decades-old taxonomies for structuring data, services, and behaviors.

Programming has long been a laboratory for human cognition. Since the 1950s, the field has evolved from procedural logic to structured programming, then to OOP, functional paradigms, and service-oriented architectures. Each shift represented not merely a technical upgrade, but a reimagining of how humans conceptualize systems. OOP, for instance, introduced the notion of encapsulation, inheritance, and polymorphism—categorizations that mirrored real-world objects and their relationships. Yet, as the Reddit thread notes, even within OOP, there has been a shift: from inheritance-heavy hierarchies to aggregation and composition, and more recently, to defining boundaries via microservices rather than classes. These changes reflect an ongoing, often subconscious, evolution in how we group and relate computational entities.

Now, with AI tools like GitHub Copilot and Amazon CodeWhisperer generating entire functions or modules on demand, the need for human-driven categorization is being challenged. If AI can infer context, structure, and intent from natural language prompts, does it still matter whether a class inherits from another or is composed of multiple interfaces? The answer, according to experts in software architecture, may be yes—because the quality of AI output depends heavily on the conceptual clarity of the prompts and the underlying data models it’s trained on. Poor categorization in training data leads to incoherent, inconsistent, or inefficient generated code—even if the model itself is state-of-the-art.

This is where lessons from seemingly unrelated domains become crucial. Consider the CacheAsBitmapPlugin from GreenSock’s ActionScript library, released in 2014. This plugin didn’t change how animations worked—it changed how the system categorized visual elements for performance optimization. By forcing a DisplayObject to cache as a bitmap during animation, developers bypassed expensive rendering recalculations. The insight? Performance gains didn’t come from faster processors, but from rethinking how visual objects were grouped and treated by the rendering engine. In the same way, future software efficiency may hinge not on more powerful GPUs, but on redefining how code entities are categorized: Are services better modeled as state machines? Are data flows better represented as graphs rather than hierarchies? Could behavioral patterns replace static classes altogether?

Historically, paradigm shifts in programming—like the move from Fortran to Lisp, or C to Java—were driven by human engineers wrestling with complexity. In the AI era, the role of the engineer may shift from coder to categorizer: the architect of conceptual frameworks that guide AI behavior. The most successful systems of the next decade may be those that don’t rely on AI to write code, but on humans to define the ontologies, taxonomies, and semantic models that make AI-generated code coherent, scalable, and maintainable.

The implications are far-reaching. If categorization becomes the new frontier of software efficiency, universities and tech firms must invest not just in AI research, but in formal systems theory, category theory, and cognitive modeling. The next breakthrough may not be a new neural network—but a new way of asking: ‘How do we group this?’