Why Sora Failed: What Actually Works in AI Video Editing Right Now

OpenAI shut down Sora this week. Disney pulled out of their deal. And honestly? I'm not surprised at all.

I've been editing video professionally for about 5 years, and I spent the last 12 months testing every AI video tool I could get my hands on. Sora included. Here's what I learned — and what I think actually matters going forward.

The Demo-to-Reality Gap

Sora's launch demos were incredible. Photorealistic cityscapes, smooth camera movements, consistent lighting. The problem was that none of that translated to actual production work.

When I tried using Sora for a client's product demo back in January, the results were unusable. Character faces morphed between shots. Lighting shifted randomly mid-scene. I generated the same 10-second clip maybe 40 times trying to get two consecutive shots where the main character looked like the same person. Never got there.

This wasn't just a Sora problem — Runway, Kling, Pika, they all have it. Text-to-video generation sounds revolutionary until you need to produce something a client will actually pay for.

What Killed Sora Specifically

Three things:

Compute costs were brutal. Every generation burned through GPU time that OpenAI needed for their core language model business. When you're spending millions on inference for a product that most users treat as a toy, the math doesn't work.

No moat. Google's Veo, Runway's Gen-3, Kling 3.0 — the space got crowded fast. Sora had first-mover hype but not first-mover advantage. By the time it launched publicly, cheaper alternatives existed that produced comparable output.

The use case was wrong. Sora targeted the "type a sentence, get a video" market. But professional editors don't want to type sentences. They have footage already. They need help with the tedious parts of working with that footage.

Where AI Actually Saves Me Time

Here's the thing nobody talks about in the AI video hype cycle: the boring applications work. They've been working for over a year now.

Auto-transcription and captioning. I used to spend 45 minutes manually transcribing a 10-minute interview. Now it takes 30 seconds and the accuracy is above 95%. This alone changed my workflow more than any generation tool.

Rough cuts from script markers. I work on interview-driven content. Being able to feed in a script and have the tool pull matching segments from 3 hours of raw footage — that saves me an entire afternoon per project.

Color matching between cameras. Multi-cam shoots where one camera is slightly warmer than the other used to mean 20 minutes of manual adjustment per scene. AI handles this in seconds and gets it right maybe 85% of the time. The remaining 15% still needs manual tweaking, but 85% automation on a tedious task is genuinely useful.

Smart audio cleanup. Background noise removal has gotten scary good. I had a client shoot an interview next to a construction site — two years ago that footage would've been unusable. Ran it through AI noise removal and it sounded like a studio recording.

Tools like NemoVideo have been leaning into this practical direction — focusing on the editing workflow rather than generation from scratch. You tell it what you want done to existing footage instead of trying to conjure something from a text prompt. It's less flashy than "here's a video of a cat riding a skateboard through Tokyo" but it's what actually ships to clients.

Where This Goes Next

I think we're about to see a real split in the AI video space:

The generation side (text-to-video, image-to-video) will keep improving but stay limited to social media content, prototyping, and creative experimentation. It won't replace professional production workflows for at least another 3-5 years. The consistency problem is that fundamental.

The editing assistance side will quietly become standard. Within 18 months, I expect auto-transcription, smart rough cuts, and AI color grading to be built into every major NLE. The standalone tools that got there first — the ones that focused on making editors faster rather than replacing them — will either get acquired or become the new standard.

Sora's failure isn't proof that AI in video doesn't work. It's proof that the industry was building the wrong thing. The editors I know don't want AI to make their videos for them. They want AI to handle the 40% of their job that's repetitive so they can spend more time on the 60% that's creative.

That's a less exciting pitch than "generate anything from text." But it's the one that actually works.