This article explores how AI, specifically large language models (LLMs), is being integrated into various industries. It discusses the stages of adoption, from automating existing workflows to creating new products, and highlights the challenges businesses face in this transition.

This article discusses the contrasting approaches of banks and fintechs in adopting AI. While banks leverage existing data and scale for efficiency, fintechs focus on innovation and creating new user experiences. The debate centers on who truly extracts value from AI advancements.

The article argues that artificial intelligence (AI) should be viewed as a standard technology rather than as an extraordinary phenomenon. It highlights the slow pace of AI adoption compared to innovation and suggests that historical lessons from previous technological transitions can inform sensible policies for managing AI's integration into society.

The tech industry often operates within an echo chamber of early adopters, leading to a skewed perception of the pace of technological adoption in the wider world. Many individuals and businesses are still catching up to previous innovations, highlighting a disconnect between rapid development and user readiness. To maintain an advantage, companies must bridge this gap by understanding user needs and focusing on effective customer engagement.