Generative AI

Overview

Generative AI describes models that create new outputs rather than merely sorting or scoring existing ones. A classifier tells you whether an image contains a cat; a generative model draws the cat. Language models produce text, diffusion models produce images and video, and synthesis models produce audio, each conjuring fresh content that did not exist before, in the style of what it learned from.

How it works

Most of these systems are trained on a deceptively simple objective: predict the next element in a sequence, the next word, pixel, or audio sample. Pursued across vast quantities of data, that single goal forces the model to internalize the deep structure of its domain, and once trained, it can extend a prompt into coherent, original output.

Why it matters

Generative AI is the force driving today's surge in demand for GPU compute. Nearly every enterprise building a generative product, whether a writing assistant, an image tool, or a coding agent, needs substantial hardware to train, adapt, and serve its models. The commercial pull is what has turned GPU capacity into one of the most sought-after resources in technology.