True-Cost Multiplier for AI Video Production (Attempts + Audio + Editing)

Advertised per-clip or per-credit costs for AI video tools systematically understate the cost of a finished, publish-ready piece. The true cost is better modeled as:

Finished cost ≈ (generation cost per usable clip × attempt multiplier) + audio engineering + editing

Marketing language like "unlimited" often masks a hard credit cap that, once divided by the per-clip credit cost, yields a fixed and often small number of generations per billing period — see Workflow-Run Cost Basis for Comparing Credit-Metered AI Plans for computing that per-run cost basis.
The attempt multiplier accounts for generations that come out unusable (wrong take, continuity break, artifact) and must be regenerated; one estimate from a 100-second multi-cut commercial put this at roughly 5-10x the naive per-second cost.
AI generation models do not produce sound, so professional audio engineering is a mandatory, non-optional line item for any AI video meant for public use.
AI models do not assemble or merge clips into a coherent sequence; professional editing is required to composite generations into a finished piece — see Take Compositing for Best Performance and Three-Step AI Commercial Workflow (Assets → Setup → Generations) for related assembly work.
Net effect: a project whose raw generation credits might price out at ~$100 can realistically cost 'thousands and thousands of dollars' once attempts, audio, and editing are included — still far cheaper than traditional production of comparable quality (estimated ~$1M for a benchmark 100-second commercial), but well outside reach for individual/hobbyist creators and squarely an enterprise-scale cost.

This multiplier framework generalizes across credit-metered AI video tools, not just one vendor's pricing page, and is a useful sanity check against 'how to make an ad/movie with AI' tutorials that describe method without disclosing the cost of scaling it up.

Из тем: Unsorted, Cost, Risk, and Production Economics