Search engines are the primary way most people access information today, but entering a few keywords and getting a list of results ranked by some unknown function is not ideal. A new generation of Artificial Intelligence-based information access systems, which includes Microsoft's Bing/ChatGPT, Google/Bard and Meta/LLaMA, is upending the traditional search engine mode of search input and output. These systems are able to take full sentences and even paragraphs as input and generate personalized Natural Language responses. AI systems like ChatGPT and Bard are built on Large Language Models (LLMs). The LLMs-based systems generate personalized responses to fulfill information queries. However, there are plenty of downsides, as well. In this talk, we will focus on AI-based information access systems, weighing on their advantages and disadvantages from the developers', as well as the users' viewpoint. Valia Kordoni is a Deputy Chair of Computational Linguistics at the Department of English at Humboldt University Berlin. She is an active researcher in Language Technology (LT), Data Science and Artificial Intelligence (AI). Her research interests include multilingual Robust Natural Language Analytics, Computational Semantics, Discourse and Human Cognition Modeling, as well as Machine Learning for the automated acquisition of knowledge, especially concerning multiword units and figurative language and their impact in Natural Language Processing, spoken and written. She has been the president of the ACL (Association for Computational Linguistics) SIGLEX's (Special Interest Group on Lexicon) MWE (Multiword Expressions) Group. She was the Local Chair of ACL 2016 - The 54th Annual Meeting of the Association for Computational Linguistics. She has coordinated and contributed to many projects funded by the EU, the DFG (Germany), the BMBF (Germany), the DAAD (Germany), as well as the NSF (USA).