In the world of generative AI, 2026 has been defined by the "Video-as-a-Service" boom. Platforms like Google Flow , powered by the Veo 3.1 backbone, have abstracted away the complexity of filmmaking. But as engineers, we know that abstraction always comes at a cost. When you move past the "magic" of a text-to-video prompt, you see an architecture struggling to balance three competing forces: Spatiotemporal resolution , Compute latency , and Token-based memory. 1. The Architecture: Latent Diffusion Transformers (DiT) Unlike early GAN-based or purely convolutional video models, Veo 3.1 utilizes a 3D Latent Diffusion Transformer . The Compression Trade-off: To handle 4K video, the model doesn't work on pixels; it works in a highly compressed "latent space." This involves a Variational Autoencoder (VAE) that squashes video into spatio-temporal patches. The Problem: High compression ratios often lead to "motion artifacts"—the floating, jelly...