There’s been a lot of discussion recently about how expensive AI video generation is compared to text, and it feels like this is more than just an optimization issue.
Text models work well because they compress meaning into tokens. Video doesn’t really have an equivalent abstraction yet. Current approaches have to deal with high-dimensional data across many frames, while also keeping objects and motion consistent over time.
That makes the problem fundamentally heavier. Instead of predicting the next token, the model is trying to generate something that behaves like a continuous world. The amount of information it has to track and maintain is significantly larger.
This shows up directly in cost. More compute per sample, longer inference paths, and stricter consistency requirements all stack up quickly. Even if models improve, that underlying structure does not change easily.
It also explains why there is a growing focus on efficiency and representation rather than just pushing output quality. The limitation is not only what the models can generate, but whether they can do it sustainably at scale.
At this point, it seems likely that meaningful cost reductions will require a different way of representing video, not just incremental improvements to existing approaches.
I’m starting to think we might still be early in how this problem is formulated, rather than just early in model performance.
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