AI has actually made the lives of chip designers building AI accelerators harder, not easier. On the design side, AI does offer some productivity benefits for chip designers, such as vibe coding RTL. However, quite paradoxically, silicon for AI applications requires increasingly complex architectures and compute requirements. There are also several other problems chip designers have to deal with, including: - Verification, which has historically been the most labor-intensive step in chip design - Multiphysics challenges with off-chip thermal/SI/PI/packaging - Late-stage firefighting in power closure due to bursty, unpredictable LLM workloads in systolic arrays I went to @DACconference, the heart of design automation for chips, and noticed that most experienced silicon engineers do not completely trust AI to design chips quite yet. In my latest deep-dive post, I cover these issues so that a broad audience can understand why from first principles.