The chapter, “Investing in AI,” explores the complexities and strategies involved in early-stage investments within the rapidly evolving artificial intelligence sector. It addresses the challenge of timing investments in new technologies, drawing parallels with science fiction predictions, and emphasizes the importance of understanding market readiness and integration into existing workflows. The authors delve into key considerations for early-stage AI investors, including proximity to ecosystem drivers, the macro context of foundational models, navigating well-funded competition, understanding user adoption cycles, avoiding hype-driven ventures, and managing valuation expectations. The chapter further categorizes AI investments into computing, foundational models, tooling, and applications, detailing where value accrues in each. It also examines the transformative impact of AI across various industries, from customer success and engineering to professional services and robotics, and discusses the risks and challenges, such as reliance on training data, intellectual property concerns, and the fast-paced nature of the industry. The conclusion offers strategies for building a diversified AI investment portfolio, balancing consumer and enterprise applications, investing across different tools and industries, and staggering investments based on time to maturity, while acknowledging the pervasive nature of AI in modern technology.