Artificial Intelligence (AI) and Data Management (DM) emerged as distinct computer science disciplines about 6-7 decades ago! In the first wave of AI, which was symbolic in nature, and which required domain experts to handcraft the knowledge of a domain in production rule systems or expert systems, DM didn’t play a very significant role. Big Data (BD) became a hot topic about two decades ago in both industry and the research community. For the most part, BD developments were initially triggered by the requirements of Web 2.0 (e-commerce and social media) companies. Both technical and non-technical issues continued to fuel the rapid pace of developments in the BD space. Open source and non-traditional software entities also played key roles in the latter. It’s this BD wave, in synergistic combination with hardware advances and AI algorithmic inventions, like neural networks, that has fueled the major strides made in the second wave of AI. In the last decade, AI has reemerged after having been out of fashion for a long time. Initially, it was fueled by developments around Deep Learning (DL). Vast amounts of labelled data were leveraged for training to produce AI models rapidly and with less human involvement than before. Lately, AI is on fire with the popularity of Large Language Models (LLMs) and Generative AI (GenAI) technologies, and with the founding of startups like Anthropic, DeepSeek, OpenAI and xAI. Major vendors like Alibaba, AWS, Google, IBM, Meta and Nvidia have pivoted to focus on AI related work. In this talk, I will survey the historical developments, DM implications of AI and provide a status report on the AI landscape in different regions of the world!