Where Does My AI Store Its Knowledge?

An ongoing question that confounds even the AI gurus: where exactly does a large language model store its knowledge? The reality is we don't know or understand how these systems do what they do.

What? But you AI guys wrote the program, right?

Yes – but:. The AI guys designed neural networks that were then used to TRAIN the neural network to learn to give correct answers.  What nobody realized (experts included) was how effective this modeling of the human brain would to be.

So the fact that we have no clue about how AI stores information shouldn't surprise us. We don't have a clue how the human brain stores information either. We know that neurons communicate with each other—some neurons trigger the firing of other  neurons, some  inhibit firing. (Think about how learning a physical skill is very much learning what not to do). In artificial neural networks this biological dance has its equivalent in gradient descent, the algorithm which raises or lowers the weight of neuronal connections based on how well the model performs.

So, asking where ChatGPT "stores" its knowledge about Shakespeare is just like trying to pinpoint  where the memory of your first bicycle ride lives. Can’t be done.

Consider this: if I ask you to describe your first bicycle ride, you don't retrieve a pre-written paragraph from some mental filing cabinet. Instead, you generate words on the fly from fragments—images, feelings, sensory memories—that come together to support your verbalization. When we converse with an AI, we read coherent sentences, but those sentences are the end product of a similar process: patterns distributed across millions of parameters interacting and coalescing into language. Both human memory and AI responses emerge from the dynamic interaction of countless connections, not from stored text waiting to be retrieved.

Is AI the end of Computer Science?

The rise of AI coding assistants like GitHub Copilot, Claude, and ChatGPT has sent ripples of anxiety through computer science departments and entry-level developers alike. These tools can generate functioning code from natural language descriptions, debug existing programs, and even explain complex algorithms in plain English. For many, the immediate question isn't whether AI will change programming—it's whether there will be any programming jobs left for humans, especially those just starting their careers.

This transformation raises profound questions for computer science students. Should they abandon their studies in favor of prompt engineering? Is learning data structures and algorithms still relevant when AI can implement them automatically? Are four years of computer science education becoming obsolete in the face of tools that seem to make programming accessible to anyone who can describe what they want in natural language?

The answer is emphatically no—computer science is not dead but still relevant. However, the simple focus on coding must evolve to emphasize the skills that become even more critical when working alongside AI coding assistants. These way to get an AI help you solve problems The fundamental ability to decompose complex problems into manageable components remains essential, perhaps more so than ever. When an AI generates code, someone still needs to understand whether that code solves the right problem and whether it does so efficiently and correctly.

Functional programming concepts become particularly valuable in an AI-augmented world. Understanding pure functions, immutability, and higher-order functions helps developers write code that's easier to test, debug, and reason about—qualities that become crucial when integrating AI-generated components. Similarly, object-oriented programming principles for defining classes and creating objects provide the architectural thinking needed to structure systems that incorporate AI-generated code into larger, maintainable applications.

Most critically, the ability to test AI-generated code through both white-box and black-box testing methodologies becomes a core competency. AI can write code, but it cannot guarantee that code is correct, secure, or optimal. Computer science students who understand testing frameworks, edge case identification, and verification techniques will find themselves more valuable than ever, serving as the quality assurance layer between AI output and production systems. The future belongs not to those who can code the fastest, but to those who can think the clearest about problems, architect robust solutions, and ensure that AI-generated code actually does what it's supposed to do.

Bottom Line: Don’t freak-out just because a bot can code. Understand your problem. Break it down and using your basic coding skills to understand and iterate to get the best out of the AI.