AI boom

The AI boom is a period of rapid growth in artificial intelligence capabilities, investment, adoption, and public attention, driven especially by advances in generative AI.

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Transformer: A neural network architecture that uses attention mechanisms to process relationships among sequence elements. Transformers underpin many language models that helped drive the boom.
Artificial intelligence: The field of building machines that perform tasks associated with human intelligence. The boom is a recent expansion within this much older field.
GitHub Copilot: An AI coding assistant that suggests code and related text within software development environments. It illustrates how generative AI entered routine software work.
AI investment: Capital spending and financing directed toward artificial intelligence companies, research, and infrastructure. Large investments fund the chips, data centers, and companies scaling the boom.
Artificial general intelligence: A hypothetical AI system capable of performing a broad range of intellectual tasks at or beyond human level. Disagreement about its feasibility and timing shapes expectations about the boom's endpoint.
Large language model: A neural network trained on large text datasets to predict and generate language. The rapid improvement of these models made generative AI a public-facing force.
Deep learning: A machine-learning approach that trains multilayer neural networks to learn representations from data. Deep learning enabled the capability gains behind modern AI products.
AI agent: A software system that uses AI to pursue goals through actions in an environment. Agent products extend the boom from content generation toward delegated tasks.
AI productivity: Changes in output per unit of labor or other inputs associated with using artificial intelligence. Whether adoption raises productivity is a central test of the boom's economic value.
AI hallucination: A generated AI output that is false or unsupported but presented as if it were reliable. Hallucinations limit the safe use of generative systems in consequential settings.