The Soup of AI: A Metaphor for the Black Box

In Eastern culture, the essence of good cooking often lies in the broth. A broth is more than hot water; it is a patient distillation of countless ingredients, simmered until their flavors merge into something deeper than the sum of its parts.

AI, in many ways, is built the same. The training process is like preparing a broth. Vast amounts of text — encyclopedias, books, articles — are poured into the pot. Through long hours of simmering (training), the model’s parameters emerge as the hidden essence, a “soup base” that holds the richness of all the data.

Once the broth is ready, the next step is cooking a dish. Different AI platforms are like different restaurants or chefs: each has its own signature broth, giving every dish a unique character. A customer may not know the exact recipe, but they can taste the difference in the final meal.

This is why AI systems, even when trained on similar data, reveal different “flavors” in their responses. It is the hidden broth — the millions or billions of parameters — that makes the distinction.

For those who obsess over the so-called “AI black box”, perhaps the metaphor offers a gentler perspective: Don’t think of it as a mystery machine, but as a simmering pot. The question is not simply what ingredients went in, but how time, heat, and interaction transformed them into broth.

And maybe, just maybe, to understand the secret of AI, we should go home, lift the lid of our own soup pot, and ask ourselves: Why does a broth taste the way it does?