Live AI video is doing too much lifting as a term. Here's a breakdown of what people actually mean.

The term 'Live AI Video' is dominating AI discourse, but a crucial technical breakdown reveals it's being used to describe three fundamentally different capabilities that are often conflated. The first tier is simply faster post-production, where models generate discrete clips more quickly—a throughput improvement, not true liveness. The second is low-latency iteration, allowing users to tweak prompts and regenerate clips fast enough for an interactive feel, though the underlying process remains clip-based.

The third and most technically significant tier is actual real-time inference on a live video stream. Here, the AI model continuously generates frames in direct response to incoming input, operating on a streaming architecture rather than producing discrete clips. This represents a much harder engineering problem. While demos for all three tiers can look superficially similar, only a few companies, like Decart, are genuinely operating in this third category. The conflation is often perpetuated by vendors who benefit from the ambiguity.

For developers and enterprises evaluating 'Live AI Video' solutions, this distinction is not academic. Building a serious application on the assumption of true real-time streaming, only to discover the vendor offers merely fast clip generation, could lead to architectural dead-ends and failed product capabilities. Precision in defining requirements and vendor capabilities is therefore essential for successful implementation.