To make both continuous optimization (deep learning) and discrete optimization (program synthesis) efficient, a universal recipe exists: introduce modularity, hierarchy, and reuse; reuse is fundamentally another word for abstraction, and this principle applies universally to all optimization—deep learning is continuous optimization with modularity and hierarchy hardcoded as stacked layers and architectural patterns like convolution (abstracting spatial position) and RNNs (abstracting temporal axis).