Interesting pattern emerging across the video model landscape: different architectures tend to hit similar walls. Whether it's latency issues, hallucination artifacts, or resource bottlenecks, the failure modes look surprisingly alike.

This convergence suggests the bottlenecks aren't arbitrary—they're structural. The underlying constraints (temporal coherence, computational limits, training data bias) affect everyone building in this space, regardless of their approach.

Worth watching how builders adapt when facing the same ceiling. The real differentiation might come from how elegantly teams solve around these inevitable failure points rather than avoiding them entirely.