Field Notes / 2026-09-15
AI Generated Video vs CGI - What Drives the Cost
OnePointFive Studio hears this question from brands weighing two paths for the same brief: AI generated video or traditional CGI. The honest answer is that cost depends on what the shot needs to do, not which label sits on the workflow.
Where the cost actually starts
CGI cost starts with modeling. A product, a character, an environment: each one gets built from scratch in 3D. That build is the expensive part. Texturing, rigging, and lighting come after, and each pass adds time.
AI generated video skips the build step. A trained model or a diffusion pipeline generates frames directly from prompts, reference images, or existing footage. There is no polygon mesh to construct. That is the single biggest reason AI workflows can cost less for certain shots.
What still requires CGI
Precision breaks the comparison. A product shot that has to match an exact CAD file, a logo that must sit at a fixed angle, a package design with exact colorway specs: these need CGI. AI models approximate rather than guarantee pixel-accurate geometry across every frame.
Brand consistency across a long campaign also favors CGI. A 3D asset, once built, renders identically every time. An AI model can drift between generations unless the pipeline is locked down with strict conditioning. Even then, frame-to-frame consistency takes extra passes to enforce.
Where AI generated video costs less
Concept and mood work is the clearest case. A short teaser meant to establish a feeling, not show an exact product, can be generated and revised much faster than a CGI build allows. Revisions are also cheaper. Changing a CGI scene means going back into the 3D model. Changing an AI-generated scene often means adjusting a prompt or a reference frame and regenerating.
Volume work benefits too. A campaign that needs many short variations for different platforms is a different cost equation under AI generation than under CGI. Each CGI variation is a separate render job built on separate scene files.
Where hybrid pipelines change the math
Most real projects are not purely one or the other. A team might build a CGI base asset for accuracy, then use AI tools to generate background variations, motion extensions, or stylistic passes on top of that base. This hybrid approach is common in AI Video Production in NYC work, where a brand needs both fidelity and speed on the same timeline.
The cost impact of a hybrid pipeline depends on where the AI layer sits. Using it for backgrounds and environment extension is cheaper than using it to replace a hero product shot. The hero shot still needs the precision only CGI reliably delivers.
Factors that shift the estimate either way
A compressed deadline forces parallel work. Two artists building separate scene passes at once cost more than one artist working sequentially. This applies whether the pipeline is CGI, AI, or a hybrid of both.
Asset reuse also matters. A CGI model built once can be reused across future campaigns, which lowers cost on the second and third project. AI-generated content does not carry the same reusable asset in the traditional sense. Each new generation starts from the prompt and reference set again, though a locked style or trained model can reduce that gap over time.
Review cycles factor in too. A client who needs five rounds of stakeholder approval will spend more on either method than one who approves early concepts fast. This is less about the technology and more about how many people need to sign off before a shot is final.
Clients include Moët Hennessy USA and TAO Group.
Talk through the specifics
Precision needs, campaign length, and the number of variations in the brief all shape which approach fits a project. Reach out through Contact to walk through a specific brief, or review past work in our portfolio.