Generative Artificial Intelligence (AI) offers powerful tools that fundamentally change the design of information access systems; however, it is unclear how to use them to best serve the needs of people. At present, Large Language Models (LLMs) process natural language (and multi-modal) input and present credible-appearing but often completely untrue multi-modal output. This opens the door to research into how to produce true, complete, relevant information, where and how to design retrieval augmentation to personalize and ground the system, and how to evaluate beyond relevance for truth, completeness, utility, and satisfaction. The applications of generative AI for information-seeking tasks are broad. In this chapter, we present recent developments in four domains that have been well studied in the information retrieval community (education, biomedical, legal, and finance). We follow with a discussion of new challenges (agentic systems) and research areas that are common to most applications of generative AI to information seeking tasks (credibility and veracity, new paradigms for evaluation, and synthetic data generation). The field of Information Retrieval (IR) is at the leading edge of a transformation in how people access information and accomplish tasks. We have the rare opportunity to design and build the future we want to live in.