Generative AI
Generative AI refers to AI systems that create new content—text, images, code, audio, video—rather than classifying or analyzing existing data. ChatGPT, DALL-E, Midjourney, and Stable Diffusion are all generative AI. The underlying models learn statistical patterns from training data and produce new outputs that follow those patterns. This is a meaningful shift from the previous decade of AI, which was dominated by discriminative models (spam filters, recommendation engines, fraud detectors) that sorted things into categories. Generative AI crossed into mainstream adoption faster than any technology since the smartphone, reaching 100 million users in two months. But the speed of adoption has outrun the infrastructure for using it well. Most organizations are still in the experimentation phase—running pilots, debating policies, and trying to figure out where generative AI creates value that justifies the cost, the risk, and the organizational change required to use it at scale.
Related terms:
Transformer
The transformer is the neural network architecture introduced in Vaswani et al.’s “Attention Is All You Need” that replaces recurrence with parallel...
Pace Layers
Pace layers separate complex systems into fast-changing parts that learn and slow-moving parts that stabilize, helping teams choose experiments vs. strategy.
Linting
Linting is the automated analysis of code or any structured output to flag errors, enforce style rules, and catch problems before production.