Generative AI

Generative AI is a class of AI systems that produce new content — text, images, code, audio, or video — rather than only classifying or predicting from existing data. The shift is that the model outputs a sample from a learned distribution instead of a label, so the same system can write a paragraph that has never existed. Different architectures serve different media. Transformer-based large language models such as OpenAI's GPT, Anthropic's Claude, and Google's Gemini generate text and code token by token, while diffusion models including Stable Diffusion and Midjourney start from noise and refine it into an image. Music and speech generation follow similar principles. The commercial impact has been fastest where a draft is valuable and review is cheap: marketing copy, code completion, customer support replies, and image concepts. The pitfalls are well documented. Models hallucinate, stating false claims fluently; they reproduce biases present in training data; and the provenance of that training data is the subject of ongoing copyright litigation. Output that reads well is not verified, which is why serious deployments pair generation with retrieval, citations, and human review rather than shipping raw text.