Every field seems to be confronting some form of misalignment with AI; see, for example, mathandai.org/. I am attending ECCV this week, and in my own field of 3D vision, there were serious debates in the workshops about whether explicit 3D representations are still necessary for many 3D-related tasks, such as novel-view synthesis and robotic action. One increasingly influential view is that video generative models have become so powerful that we can simply dispense with explicit 3D. I would not necessarily disagree if the sole objective is to achieve strong performance on benchmarks or practical tasks. Given the limited availability of explicit 3D training data, approaches built around explicit 3D representations can currently be outperformed by alternatives that exploit much larger sources of 2D and video data. But, much like the mathematicians above argue, science is not just about achieving better numbers. Understanding is also extremely important. Explicit 3D, for example, gives us a language in which to define and reason about multi-view consistency. If one of our goals is to understand why a method works and what it has actually learned, I do not see how we can simply throw explicit 3D away. Without such understanding, how can we know when a method will fail, or whether it will behave correctly in corner cases that are poorly represented in the training distribution? Fortunately, I was also very happy to hear several interesting talks at ECCV that emphasized understanding rather than performance alone—for example, Christian Rupprecht's talk at the SFM-DL workshop.