Core Viewpoint - The article discusses the current state of AI research, likening it to the early stages of physics, specifically the Tycho era, where there is a wealth of observational data but a lack of systematic understanding of underlying principles [1][8]. Group 1: Current State of AI Research - AI research is still in the observational phase, focusing primarily on performance metrics rather than understanding the underlying phenomena [3][9]. - The pursuit of short-term performance has led to a significant "cognitive debt," as the field has bypassed the critical step of understanding [3][9]. - The academic publishing culture favors "perfect stories" or significant performance improvements, which has resulted in the neglect of valuable but fragmented observational work [5][12]. Group 2: Call for a New Approach - There is a need for a more accessible and inclusive phenomenological approach in AI research, which does not prioritize immediate applicability or require a complete narrative [17][21]. - This new approach should emphasize controllability through toy models, multi-perspective characterization, and curiosity-driven exploration [21][22]. - The article advocates for researchers to document observations and collaborate more broadly, moving away from the fragmented nature of current AI research communities [22]. Group 3: Challenges in Phenomenology Development - The development of AI phenomenology is hindered by the high standards for publication, which often only recognize universally applicable or surprising phenomena [15][16]. - Many interesting phenomena are discarded because they cannot be easily structured into a publishable format, leading to a loss of potentially valuable insights [14][22]. - The article highlights the need for a shift in mindset to foster a more robust understanding of AI phenomena, akin to the evolution seen in physics [7][9].