Chapter 2 lays the technological foundations for the analysis developed in the rest of the book. In contrast to Chapter 1, which is largely AI-free, this chapter is deliberately entrepreneurship-light, focusing instead on the technological and socio-economic features of artificial intelligence. After a brief introductory overview of AI, the chapter provides a structured and accessible representation of deep learning technologies, explaining how they operate, what their core capabilities are, and where their main limitations lie. The chapter presents AI as a digital ‘statistical machine’ and examines the inferential architectures underlying contemporary deep learning systems, with particular attention to data-driven learning effects and feedback loops. Emphasis is placed on the socio-economic implications of these technologies for the functioning of markets, including issues related to data, infrastructures, and computational resources. Building on this foundation, the chapter then introduces the functional capabilities enabled by AI – predictive, generative, and agentic – and discusses how they are deployed within the economic system, illustrating the structure of AI supply chains. By isolating the technological dimension from entrepreneurial dynamics, the chapter provides a clear analytical baseline for understanding how AI reshapes economic environments before turning explicitly to entrepreneurship in subsequent chapters.