Jimeng Studio: Don't ask if AI can make movies yet

Jimeng Studio: Whether AI Can Make Movies Depends on Project Workflow First

I compared Jimeng AI's "Studio" projects with traditional film pre-production outsourcing and actually ran through it. Conclusion first, depending on the situation. It suits those who use AI as a pre-production accelerator and trial-and-error tool, not those expecting to turn a single sentence into a distributable feature film.

Recently, I saw Jimeng AI launch a film content label "Jimeng Studio," opening calls for long-form projects targeting film companies, professional teams, and AI content creators, with key support for movie projects and series no shorter than 90 minutes. I've been doing compiler optimization on Ascend 910B for the past month, so I'm used to looking at inputs/outputs first, then bottlenecks. So I tried a three-minute technical explainer script. My experience shows the page looks more like a project submission form, requiring clear type, duration, distribution direction, and organized scripts or storyboard ideas. It wants a complete project package; a few prompts are just the starting point, and "prompt" means instruction.

First stuck on cinematic language. After throwing in the oral script, AI could expand it but couldn't judge which parts needed PPT, live-action, or whitespace. Then stuck on character consistency. I wrote two virtual presenters, asked it to supplement shot descriptions based on the script, and later visual descriptions started drifting in clothing and lip-sync. Short clips can pass, but longer ones reveal flaws. Has this operator fusion happened yet? Operator fusion in compilers combines multiple calculation steps to reduce data movement. I don't think so. At least in the Studio workflow, it seems more about explicitly exposing problems.

Surprise is it turns generation into project initiation. Many AI video tools are like scattered operators: give prompt, spit clip. Jimeng Studio is more like adding an IR layer to the project. IR can be simply understood as an intermediate format describing programs; here it unifies creativity, scripts, storyboards, and distribution goals. What you submit is an intermediate representation evaluable by human teams; prompts are just part of it. Friendly for engineers: workflow visible, responsibility traceable. After organizing a script version, it indeed saved time finding outsourcers to align requirements.

Drawbacks are obvious too. It doesn't solve copyright, aesthetics, directorial expression, or post-production refinement. 90 minutes is hard to solve by piling compute power; it's more about memory bandwidth bottlenecks, meaning states like context, assets, character relationships, and pacing need long-term transfer—one break causes collapse. So-called AI resilience, if looking at revenue models, film companies' difficulty is turning one-time creativity into stable delivery. Jimeng Studio might help small teams make samples, proposals, but theatrical or online distribution targets remain tough. Regular novices wanting to make a movie with one sentence will be disappointed; professional teams using it for pre-production rehearsals, storyboard validation, and project submissions will feel it has potential.

I previously wrote that judging whether software companies will be disrupted by AI depends on billing points and call volumes. Similar for film. AI might first disrupt middle-tier roles earning from template storyboards, simple editing, and asset splicing, then slowly touch roles requiring judgment. Labels like Studio fundamentally pull AI from toys into production workflows. But whether it can truly compete depends on whether delivery quality can consistently pass audits; slogans don't count.

Ultimately, whether models generate visuals is surface-level; the bottleneck lies in whether project workflows can retain human judgment as auditable nodes.