Twelve months ago I put every AI editing tool I could find into our actual pipeline. Not a demo project. Real episodes, real hosts, real deadlines. I wanted to know if the machine could take the boring half of my job away.
Here is the one year verdict: about 30 percent of what I do got faster, and the 30 percent that got faster was never the part that made an episode good.
That is not a dunk on the tools. It is a statement about where the value in an edit actually sits. I lost a few weeks learning that lesson, so you can have it for free.
What I actually tested, and how
I ran the tools across three buckets: transcription and search, automated cutting, and generative cleanup. Audio repair, filler word removal, silence trimming, auto reframing for vertical, auto chapter generation, the lot. Every episode that came through the studio got a parallel pass. Human edit on one side, machine edit on the other, then I watched both back at full length.
Watching both back at full length is the part most people skip, and it is the whole test. If you only spot check, the machine looks incredible. If you sit through 58 minutes of it, you start hearing the seams.
The tools are excellent at finding things and mediocre at deciding things. That single sentence covers almost everything I learned.
Transcription won, and it was not close
Search is solved. A year ago I was scrubbing timelines to find the moment a host said the thing about their first hire. Now I type four words and I am there.
This changed how I work more than any cutting feature. I stopped taking notes during the first watch. I stopped building a paper edit in a separate doc. The transcript is the paper edit now, and the timeline follows it.
It also made pulling clips dramatically faster. Finding the twelve candidate moments in an hour long conversation used to take 40 minutes. It takes about eight now. The picking is still human, because picking is taste, but the finding is not a human job any more and I am glad to be rid of it.
Chapter generation is in the same family. The machine reads the transcript, spots topic shifts, and drafts markers. It gets the boundaries roughly right maybe 70 percent of the time, and the titles it writes are limp, so I rewrite them. Still a net win. That is exactly why our show notes and chapters add-on sits at $35 and not more. The machine does the scaffolding, a person does the writing.
Automated cutting is where it fell apart
Silence detection is the headline feature of every tool in this category, and we do not use it. Not on one episode. Not once.
Our whole approach is that a human editor watches the whole episode. That is not a marketing line, it is a production constraint I chose on purpose, and this year is the reason it stayed.
Here is what silence detection cannot hear. A host asks a hard question. The guest goes quiet for two and a half seconds. That silence is the best moment in the episode. It is the moment the listener leans in. The machine sees dead air above the threshold and removes it, and now your guest answers a hard question instantly like a press release.
Silence is not empty. It is where the audience does their thinking. Cutting it out is the fastest way to make an honest conversation sound rehearsed.
Filler word removal has a subtler version of the same problem. Strip every "you know" and "I mean" and you do not get a cleaner speaker, you get an uncanny one. Rhythm is part of how we read sincerity. I now remove fillers only where they cluster, and I leave the ones that carry hesitation, because hesitation is information.
I wrote more about this instinct in a post on why editing is a taste problem, and a year of testing machines made me more sure of it, not less. Machines are very good at applying a rule uniformly. Uniform is exactly the wrong texture for conversation.
Multi-camera switching: the biggest disappointment
I had the highest hopes here and got the worst results.
Auto switching tools generally cut to whoever is talking. Sounds right. Watch 20 minutes of it and you will feel seasick. Real switching is not about who is talking, it is about who you want to watch. The reaction shot before the punchline. Holding on the guest while the host sets up the question. Staying wide when the energy in the room is the subject.
On our multi-cam editing work the machine got maybe half the cuts defensible and none of them interesting. Worse, it never held. Average shot length collapsed, because the trigger was speech and speech alternates constantly. Human editors sit on a shot. Machines twitch.
If you are deciding how many cameras to run in the first place, my argument for the third body is in the piece on why three cameras beat two, and it is doubly true if you were hoping software would do the switching for you. More angles do not rescue a bad switching decision. They just give you more ways to make it.
Where the machine genuinely saved me money
Being fair to the tools, four things stuck and are now permanent:
- Transcript search and rough paper edits. Enormous time saver, zero downside.
- Audio repair. Noise removal, room tone matching, de-reverb. Genuinely better than what I could do by hand in the same time. I have pulled episodes back from the brink that I would have refused two years ago, and I catalogued a few in the post on the worst podcast audio I have fixed.
- Auto reframe as a starting point. For vertical clips it gets the subject roughly centered, then I fix the framing by hand. Saves the first pass, not the last.
- Rough transcript cleanup for show notes. Draft quality, always rewritten.
Notice the pattern. Every one of those is a task with a right answer. Noise is noise. The word is either "operations" or it is not. Where there is a correct output, the machine wins. Where there is a judgment call, it produces something plausible and slightly dead.
The cost question nobody wants to answer honestly
A lot of people asked me this year whether AI would let us drop our prices. The honest answer is that it changed our cost structure less than you would think, and where it did, we spent the savings on the edit rather than the invoice.
Our pricing did not move. Starter is $199 an episode for a single camera 1080p cut with one revision. Studio is $299 for two to four cameras in 4K with unlimited revisions. Both cover up to 60 minutes, with 30 minute shows cheaper and 90 minute shows more. First cut lands in 48 hours, revisions come back the next working day.
What the tools bought us was headroom. The eight minutes I no longer spend hunting for a quote is eight minutes spent on the cold open. That is where the improvement went, and I think that is the correct place for it. If your editor's answer to AI is a discount instead of a better edit, ask what they were charging you for before.
For people running regular shows the packs still do the real work on cost. Four episodes saves 15 percent, monthly saves 25 percent. That is scheduling economics, not software economics.
What this means if you host a show
Three practical things.
First, do not buy a tool because a demo made a 40 minute edit look like a 4 minute one. Demos use clean audio, two people, no crosstalk, and a topic with obvious beats. Your episode has a guest on a laptop mic in a hotel room. Test on your worst episode, never your best.
Second, spend the effort you save upstream instead. Better light and better sound reduce the edit more than any software does, and the podcast lighting guide covers the setup I ask hosts for before a first shoot. Booking sharper guests moves the needle even harder, which is why I keep pointing people at how to book podcast guests. No model fixes a boring conversation.
Third, be careful with generated graphics. Auto lower thirds are getting good enough to be dangerous, in the sense that they are perfectly legible and completely characterless. I have views on this that I put in a separate post about lower thirds that do not distract. On the packaging side, thumbnails are still stubbornly human. Our sister studio ClickTheory does 8 for $250, and the reason that work has not been automated away is that a thumbnail is an argument about attention, not a picture.
My honest prediction
I think the cutting tools get meaningfully better at the mechanical layer and stay bad at the decisions for a long while. Not because the models cannot learn taste, but because taste in an edit is show specific. What works on a chaotic three host comedy show is wrong on a quiet founder interview. The tool has no idea which one you are making.
So the job changes shape rather than disappearing. Less scrubbing, less cleanup, more time on structure, pacing, and the first 90 seconds. That is a better job. I am not nostalgic about the parts I lost.
If you want the longer version of where I think all this lands, I sketched it out in the piece on what podcasting looks like in 2027.
A year in, my summary is short. Use the machine to find things. Use a person to decide things. Every mistake I made this year came from mixing those two up.
If you want a second pair of eyes on your current workflow, or you just want to know what your show would look like cut by someone who watched all of it, write to me at samy@pixelsamy.com. I am in Dubai and we work with hosts everywhere.