AI models represent a paradigm shift in programming. Although the artifact is still an executable algorithm, the logic is not explicitly coded by humans.

Traditional software development involves compiling human instructions into machine code, which ultimately translates into binary. This process is deeply rooted in human logic, where developers conceive algorithms and translate them into a language machines can execute. Essentially, it’s about compressing human reasoning into executable instructions.

In contrast, the AI model development process shifts the focus from human-designed algorithms to data management. Instead of writing explicit instructions, developers now manage vast amounts of input data. For large language models, this data undergoes a pipeline where it is prepared, featurized, trained, and evaluated. The outcome is a compressed matrix of numbers, representing the distilled essence of the input data, akin to how compiled software compresses human logic.