Vasant Dhar, a professor at NYU's Stern School and one of the first academics to teach machine learning in a business school, offers a synoptic account of artificial intelligence drawn from forty years inside the field. The book runs from the symbolic AI of the 1970s and 1980s through the rise of statistical machine learning, the deep learning revolution of the 2010s, and the recent emergence of large language models and generative AI. Dhar uses his own research and consulting career—building expert systems for Wall Street, working on early data-mining projects, advising tech firms on AI deployment—as a thread through the history. The book's central concern is not whether AI is impressive but how humans should organize work, regulation, and judgment around systems that increasingly take on cognitive tasks. Dhar argues for what he calls 'thinking with' machines rather than thinking they will think for us: assigning AI to the parts of a problem where it has structural advantages while reserving the framing of questions and the evaluation of consequences for humans. The book covers AI in finance, medicine, education, transportation, and creative work, and is aimed at general readers and managers trying to make practical decisions about a fast-moving technology.