What is Generative AI?

Generative AI refers to AI systems trained not just to understand or classify existing content, but to create new content. These models are trained on massive datasets — billions of web pages for text models, hundreds of millions of images for image models — and learn the statistical patterns of that content well enough to generate novel examples that resemble their training data.

The breakthrough that enabled modern generative AI was the Transformer architecture (introduced in a 2017 Google research paper) and the subsequent scaling of these models to billions of parameters trained on internet-scale data. GPT-4, Claude, and Gemini are examples of large language models (LLMs) — the text-generation branch of generative AI. Stable Diffusion, Midjourney, and DALL-E are examples of diffusion models for image generation.

Generative AI is rapidly being embedded into existing software products: Photoshop has AI image editing, Word has AI writing assistance, GitHub has AI code completion, Salesforce has AI CRM summaries. The trend is toward AI becoming a pervasive feature of professional software rather than a standalone product — which means exposure to generative AI is increasingly unavoidable regardless of what tools you use.