A definitive framework for when to trust AI — and why humans must remain in control.

Vasant Dhar, Ph.D., Professor at New York University's Stern School of Business and the Center for Data Science and one of world’s foremost AI pioneers, today released his highly anticipated new book, Thinking With Machines: The Brave New World of AI, through publisher John Wiley & Sons, with the foreword by Scott Galloway.

Drawing on decades of groundbreaking research and practical application, Dhar provides readers with a lucid and actionable framework for navigating the accelerating AI revolution, with the goal of helping today’s leaders, policymakers, educators, students, and citizens alike determine when to trust machines, when to question them, and how to ensure human judgment remains at the core of intelligent decision-making.

Thinking With Machines is not about what to think about AI, but how — providing readers with both questions and guidelines to help them navigate the present and future of an AI-informed world. As AI systems become increasingly integrated into everyday life, the stakes have never been higher. Dhar’s research spans AI governance, finance, healthcare, sports, and consumer technology — providing a comprehensive framework for understanding and governing AI responsibly.

“I wrote Thinking With Machines because that’s the reality that you and I, like it or not, will be facing,” said Dhar. “The decisions we make today about AI governance, transparency, and accountability will define how free, fair, and humane our societies remain. This book is both a mirror and a map — a way to reflect on what it means to stay human while building a future where we think with machines.”

Dhar, who brought machine learning to Wall Street in the 1990s and whose work has influenced global discussions on AI governance, responsible innovation, and algorithmic trust, distills years of academic and applied research into a book that bridges the gap between technical rigor and public understanding. Across compelling case studies and deeply researched insights, Thinking With Machines explores:

• The impending bifurcation of humanity, and how to stay on the right side of the divide
• When to trust AI predictions and when human judgment is necessary
• How AI is transforming industries from healthcare to finance to education
• What the future of work looks like in an era of intelligent automation, for entry-level and senior workers alike
• How to govern AI responsibly, and the role that new and established institutions can play in the establishment of global, national, and local guardrails for this pivotal technology

"Every so often, an editor encounters a manuscript that feels urgent, necessary, and perfectly timed — Thinking With Machines is that book," said Judith Newlin, Editor, Finance & Accounting at Wiley. "Vasant's ability to cut through both the hype and doom provides readers with a better understanding of how to thrive in a world where machines are increasingly making decisions that affect their lives. This book should serve as required reading for anyone who wants to understand and shape the AI-powered future we're all creating together."

“Thinking with Machines will make you smarter about AI, more skeptical of the hype, and better prepared for the choices ahead,” said Galloway, host of the Prof G and Pivot podcasts. “Read it, learn from it, and for God’s sake — don’t give it the keys to the castle.”

Thinking With Machines is now available at bookstores and online. For more about Vasant Dhar and Thinking With Machines, visit vasantdhar.com.

About Vasant Dhar

Vasant Dhar, Ph.D., is a Professor at NYU's Stern School of Business and the Center for Data Science, and one of the world's leading authorities on prediction, data science, and trust in AI. He is also the creator and host of the “Brave New World” podcast and newsletter, where he interviews Nobel laureates, CEOs, and thought leaders about technology, ethics, and humanity. His research has been featured in The New York Times, The Wall Street Journal, Financial Times, Wired, and MIT Technology Review.

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