Partly. AI is reliable at finding events that leave a signature in the audio or video: laughter, applause, a volume spike, a scene change, a face appearing, a chat surge in a stream. It is unreliable at knowing which of those you needed, because a highlight is defined by what the clip is for, and no automatic pass knows that. On material where highlight has a stable meaning, like goals in a match or crashes in a race, automatic detection genuinely removes the pass. On a three-hour interview it hands you the loud parts and calls them highlights.
What the tools are detecting
Almost every automatic highlight feature is built on proxies. Audio energy, speech density, sentiment in the transcript, faces, camera cuts, engagement data if the video was live. Each proxy is a guess that interesting things are noisy, emotional or busy. That guess holds up well in sports and reaction content. It falls apart on the quiet stuff, and the quiet stuff is often the whole point: the sentence where someone finally says the number, the ten seconds where a bride's father stops talking.
The failure mode is worth naming, because it isn't a bad clip. A tool returns twelve moments, all defensible, and you have no way to know about the thirteenth it didn't return. On footage you shot yourself you'd notice. On six hours of someone else's webinar you won't.
The work moves, it doesn't disappear
An automatic pass that gives you thirty candidates has changed where the hours go, not how many there are. Reviewing candidates is its own job: you still have to sit through each one, hold the finished piece in your head, and decide. In wedding and event work, choosing which fifteen seconds of a ceremony to use routinely costs more of the day than assembling the finished cut does. Any tool that adds candidates without narrowing them makes that worse.
So the useful question about an automatic feature is how much it removes, not how much it finds. Twelve candidates out of six hours is a real reduction. Two hundred tagged moments is a second archive.
The routes
Watching faster is the unglamorous baseline. Playback at two or three times speed with the waveform visible, marker on every hit. It's slow, it's honest, and you finish with a complete picture nothing else gives you.
Signal-based DIY is cheap and underrated. Chat message rate per minute, audio peaks, applause detection: a short script over the data you already have will give you a ranked list of timestamps for one afternoon of work. It has the same blind spot as commercial highlight features, since it's the same idea, but you know exactly what rule produced the list.
Automatic highlight generators are the packaged version, usually bundled with a clip editor that crops and captions the result. Best case they're excellent for reaction-heavy content going to short form. Worst case you accept their taste without noticing, and everything you publish starts to look like everything else made with that tool.
Then there's the category that gives up on automatic and goes on demand. Instead of asking software what the highlights are, you say what you're looking for and it returns where those moments are in your footage. Vivu is one of these: you describe the moment, and it comes back with the exact timepoints in the material you already have, without cutting or generating anything. Nothing in it ranks your footage as good or bad, which is the part people expect from a highlight button and won't find here.
The trade is real. On-demand search only helps if you know roughly what you want. Automatic detection is what you reach for when you don't.
When you don't need this
If you shot the video and you're clipping it this week, your memory is better than any detector, and you should just make a marker list from memory and check it. If your long videos follow a fixed structure, a run of show or an agenda with timestamps already tells you where everything is. And if you need one clip from one recording, opening the file is faster than evaluating a tool.
How to tell which side you're on
Ask what happens when the automatic pass is wrong. If you'd catch the miss because you know the footage, automatic detection is a fine first draft and worth using. If you'd never know, then you're relying on a tool's taste in material you can't audit, and you're better off spending the same time defining what you're looking for and searching for that instead.