Fallback video processing stuck at 99.9% — critical architecture flaw causing computer + user time waste
I used ChatGPT to process a video project (3 short videos for a TikTok-style cooking video for my dogs, Chloe and Charlie). I followed the recommended flow: uploaded the first video (~118 MB), requested mute, background music, and light trimming.
- The video was processed successfully — ChatGPT confirmed this.
- The fallback upload process initiated — I monitored progress for over 3 continuous hours, keeping the chat session alive.
- The upload reached 99.9% and stalled in “final write / finalization” phase.
- I left it overnight. In the morning, I returned — no link was posted, confirming that fallback upload had stalled and the processed video was silently lost.
This is the second time I experience this exact failure — earlier attempt with 3 combined videos also stalled.
I understand fallback media handling is not a core feature. But this architecture flaw is unacceptable:
- The video was fully uploaded → full bandwidth and computation time already spent.
- The processed video existed temporarily on internal storage — but was lost because fallback upload failed without guarantee of persistence.
- I actively checked the process — this was not user abandonment.
Wasting computer + bandwidth + 3+ hours of my time due to lack of a simple “must complete if >99.5% done” flag is unacceptable for any serious media pipeline.
It is standard practice in professional video pipelines to:
- Guarantee persistence of processed file until fallback upload completes or user retrieves it.
- Allow user-provided upload destination (Dropbox, OneDrive, etc.) to bypass unreliable fallback paths.
I suggest urgently:
- Implement a “must complete if >99.5%” flag — no silent kill or cleanup allowed at this stage.
- Implement guaranteed retention of processed file until fallback upload succeeds.
- Optionally allow users to provide an external upload folder (Dropbox, OneDrive, Google Drive) — to fully bypass fallback path limitations.
I spent over 3 hours managing this project carefully and professionally — this outcome is deeply frustrating and entirely avoidable.
If ChatGPT is going to attract media/creative users (which it is), this architecture must improve or this workflow will drive users away.
I am a long-time ChatGPT Plus user and college professor of engineering, experienced in managing media workflows. I invested over 3 hours monitoring this project and carefully documenting the architecture weakness to provide constructive feedback. I hope this example helps improve the system for all users who want to explore ChatGPT’s creative/media potential.
Thank you.