This podcast explores how the semiconductor industry is adapting to the surge in AI, with a particular focus on NVIDIA's leading position and the future of AI scaling. While some suggest that the dramatic advancements from pre-training are beginning to plateau, hyperscalers are still aggressively constructing massive data centers and pouring resources into AI infrastructure. This push is fueled by the growing need for inference-time reasoning, which demands significantly more computing power than pre-training and enables new capabilities that validate the hefty investments. The conversation also considers competitors like AMD and Google's TPU, examining their respective strengths and weaknesses, as well as the rising significance of high-bandwidth memory (HBM). Looking ahead to 2025, the forecast remains optimistic, with sustained high spending expected. However, 2026 may bring a reality check, hinging on the continued enhancement of models and ongoing revenue growth in AI applications.
