Can AI tell me what is wrong with my video?
Short answer: yes, for everything measurable, and surprisingly well. Here is exactly what AI can catch in your video, what it cannot, and how to read the result without taking it as gospel.
By Thomas, founder of CutScore · Updated June 2026
You already know something is off. The video feels a little flat, a little amateur, and you cannot put your finger on which thing did it. So you do the reasonable thing: you ask a friend, who says "looks great," because they are being kind and they watched it once on their phone. That feedback is worth almost nothing, and deep down you know it.
Here is the part that stings. You are the worst judge of your own video, and so is your friend, just for different reasons. You watched it forty times in the edit and your brain filed the quiet audio under "normal." Your friend has no targets to measure against, so all they can offer is a feeling. Neither of you can read the actual numbers buried in the file.
That is the real reason to ask a machine. Not because it has better taste than you, it does not, but because it can measure things you can only feel. A meter does not get tired, does not want to spare your feelings, and reads −19 LUFS the same way at 2pm and 2am. I have shipped videos that I was sure were fine and a meter would have saved me in ten seconds. Let me show you the line between what AI nails and what it cannot touch.
What AI can tell you is wrong with your video.
Anything that lives as a number in the file is fair game. These are the problems a tool reads straight off your video and audio, with a target it can hold you to.
| What AI checks | How it knows | The problem it catches |
|---|---|---|
| Loudness | ≈ −14 LUFS | Reads the integrated loudness and tells you the video is too quiet or too hot for the feed. |
| True peak | ≤ −1 dBTP | Spots peaks that will crackle and distort once the platform re-encodes your file. |
| Voice vs music | voice on top | Detects when the music is sitting over the speech, the most common amateur tell. |
| Exposure + colour | no clipping, neutral | Measures blown highlights, crushed shadows and a white balance that drifted green or orange. |
| Focus + sharpness | subject sharp | Flags soft footage that reads as a mistake rather than a choice. |
| Stabilisation | no drift or jelly | Catches shake and rolling-shutter wobble that pull attention off the subject. |
| Pacing · shot length | fits the genre | Counts cuts and average shot length to tell you it drags or it is too frantic. |
| The hook | earns 3 seconds | Reads the opening and flags a slow logo sting where a reason to stay should be. |
| Captions + safe zones | readable, in-frame | Tests text size, contrast and whether it drifts under the platform interface. |
| Export settings | matches platform | Checks resolution, frame rate and bitrate against what the platform actually wants. |
Hand CutScore the file or a link. It reads every check above in one pass and hands back the exact problems, with timestamps and the fixes, so you stop staring and start editing.
How does AI know what is wrong?
It decodes the file, then measures it
The first thing a real tool does is open your video properly, frame by frame and sample by sample. That is the step a chatbot skips. To say your audio is too quiet, it runs a loudness meter over the whole track and reads back a number, say −19 LUFS against a −14 target. To say a peak will distort, it checks the true peak and finds it touched −0.2 dBTP. There is no guessing. The file either says it or it does not.
It counts the things you stopped noticing
Pacing is the clearest example. You have watched your edit so many times it feels fast. The tool does not care how it feels: it counts every cut and works out your average shot length, then compares it to what your genre usually runs. A tutorial can breathe. A short cannot. If one shot is held three seconds too long, twenty times over, that is where your retention quietly leaks, and a jump cut often fixes it without a reshoot.
It reserves AI for the parts that are not numbers
Loudness is a meter. Whether your hook earns the first three seconds is a judgement, so that is where machine learning earns its keep. The model reads your opening, notices you spend six seconds on a logo and a throat-clear, and flags it. Same with filler words: it transcribes the speech and counts the "ums" so you do not have to. The split matters: measure what is measurable, and only use AI for the genuinely subjective calls.
Here is a real CutScore report on an everyday vlog: every problem above, found and scored, with the timestamp and the exact fix next to each one.
What AI cannot tell you.
A good tool is loud about what it measures and honest about what it does not. Here is the line, because pretending it does not exist is how you end up trusting a robot about your own jokes.
Can ChatGPT just tell me what is wrong?
Not really, and it is worth being clear about why, because it is the question I get most. A general chatbot is brilliant at reacting to a description or a handful of frames you paste in. Ask it "is my intro too slow" and it will give you a thoughtful, confident answer. The problem is that it never opened your file. It did not measure your loudness, it did not read your true peak, it did not count a single cut. It is reasoning about a story you told it, not about the video.
So it will happily tell you the audio "sounds fine" when it is sitting at −22 LUFS, because it cannot hear it. For the real problems, the ones a viewer feels but cannot name, you need a tool that decodes the video and audio and measures them, and only then uses AI for the subjective parts. That is a different job from chatting about it, and it is the job CutScore was built to do. If you only ever ask a chatbot, you are getting an opinion about your memory of the video, not the video.
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