Before 2012, computer vision relied on hand-crafted features. This episode untangles how AlexNet exploded onto the scene with deep CNNs: a 60-million-parameter network trained on ImageNet, parallelized across two GPUs, and boosted by dropout and ReLU. We trace how this leap shattered performance expectations, sparked a new era of architectures—VGGNet, GoogleNet, ResNet—and cemented the data-and-compute paradigm that drives AI today. Along the way we reflect on the core ingredients that made the breakthrough possible and what the next convergence in AI might look like.

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