AI video's next fight isn't generation — it's the edit, the grade, and the brand

A big market, and a moved goalpost

Luma says the global AI video generator market was valued at USD 716.8 million in 2025, and Luma says it is projected to reach USD 3.35 billion by 2034. Luma also says 75% of marketers use AI for media production. Those numbers describe a market that has already decided generation works. The fight, on Luma's telling, is over what happens after the clip exists: the edit, the grade, and the brand. m:…4156 m:…0765

That is a useful frame even if you discount the source. Luma is a vendor grading its rivals, so every limitation below is Luma's claim, not an independent finding. But the frame itself is worth taking seriously: once clips are cheap, the scarce thing is a workflow that turns clips into finished, on-brand work without manual repair at every step. m:…4156

What Luma says the rivals cannot do

Start with Pika. Luma says Pika has a maximum clip length of 5-10 seconds, and Luma says Pika's character consistency failures make multi-shot campaigns impossible without extensive manual curation. Short clips and inconsistent characters are, on that account, the difference between a demo and a campaign. m:…4156

Runway, Luma says, lacks integrated scripting, voiceover, and multi-scene workflows. Fragmented AI workflows require manual context transfer between generation, editing, and audio tools, Luma says. The complaint is not about quality but about plumbing: every handoff between tools is a place where context is lost and a human has to rebuild it. m:…0765

Higgsfield, Luma says, has output resolution capped at 720p for most workflows and lacks layer-based editing, requiring full regeneration for revisions. Luma also says Higgsfield generates millions of videos daily for short-form content. The two claims sit together awkwardly: volume on one side, a resolution and editing ceiling on the other. That tension is the point Luma is pressing: volume is not the same as a professional workflow. m:…4158

The same argument, in music

The same argument about control is running in music. In a Big Take discussion with composer Max Richter, the argument is that AI systems in music are built on stolen data, and that the proliferation of AI-generated music is diluting the royalty pool for human artists. Record labels are making deals with AI music companies without including artists in the conversation, the discussion says. m:…5638

A parallel suggestion comes from Nicholas Thompson, who suggests that AI companies should compensate publishers for the data used to train their models. And Shane Hegde, reflecting on AI image generation with local craftspeople in Japan, realized that the storyteller is the art, not the piece. The through-line is ownership of the work: who controls the edit, who gets paid, and whose name is on the result. m:…4183

What founders should take from this

For a founder choosing video tools, the honest read is to treat Luma's scorecard as a buying checklist, not a verdict. If a tool's clips are short, its characters drift, its resolution caps at 720p, or every revision means starting over, those are the constraints that will shape a campaign — but only if Luma's claims hold up when tested against the tools themselves. Luma is a competitor, and its numbers describe its own view of the market. m:…4156 m:…4158

The wider lesson is the one the music discussion points to: as generation gets cheap, value moves to control. The edit, the grade, the brand, the royalty — these are the places where a founder's choice of tool becomes a choice about who owns the finished work. That is the fight worth watching, whether or not Luma's particular scorecard survives contact with its rivals. m:…5638 m:…4183