Scott is direct about where AI video generation currently lives: it takes you most of the way there. The photo-realistic environments, the establishing shots, the atmospheric footage that makes a story feel grounded in a real world — the tools handle that with a speed and flexibility that traditional production can’t match.
Consider what it takes to produce a short film requiring three distinct international settings — airports, city streets, hotel interiors across multiple continents — each demanding authentic environments and the kind of visual specificity that traditionally puts a location scout on a plane. Captured traditionally, a project at that scope means a full production crew, months of pre-production logistics, location permits across multiple countries, and a travel budget that rivals the creative budget. Built with AI-assisted generation and finished by hand where the tools fell short, the same creative scope was recently delivered by a two-person directorial team on a tight conference deadline. No flights. No permits. No months of pre-production. The locations existed in the world — the production didn’t have to go to them. That’s the capability shift. Work that wasn’t viable at that budget and timeline now is.
That last 25%, though, is where the craft lives — and it demands a filmmaker’s eye. Crowd scenes default toward uniformity unless directed specifically for diversity in age, body type, and wardrobe. Scale relationships between objects break down in ways that read as immediately wrong to anyone trained to notice. Custom aspect ratios — the widescreen panoramic formats that dominate large-scale event production — fall outside what most platforms output cleanly, pushing sequences back into traditional post-production regardless of how the rest of the piece was built. File management compounds all of it: iteration is fast, volume is high, and tracking which version of which generated clip is the right one requires organizational discipline the tools themselves don’t supply.
Understanding those limits before you promise a client something the pipeline can’t deliver isn’t a criticism of the technology. It’s the condition for using it well.