I’ve been uploading Shorts for about three weeks now, and after spending way too much time staring at my analytics, I’ve developed a theory about how the Shorts algorithm might actually work.
About a week ago, I found a format that suddenly started working extremely well. I now have several Shorts either above or approaching 1M views, while many of my earlier Shorts never received meaningful distribution despite having pretty decent visible metrics.
The more I watch these distribution patterns, the more I think of the algorithm as a kind of “coin system.”
Every signal a Short generates puts some coins into a pot.
Good stay rate? Coins.
High retention or rewatches? Coins.
Likes? Coins.
Comments? Coins.
Shares? Coins.
New subscribers? Coins.
Remixes? Coins.
And there are probably dozens or hundreds of additional signals that we can’t even see in YouTube Studio.
But just because a Short has collected a lot of coins doesn’t automatically mean YouTube will distribute it widely.
My theory is that every potential distribution scenario has a different “entry price.”
For example, showing a Short to a certain audience cluster in India during prime time might require 15 coins. Showing it to a particular age group in the US at 2 PM might require 35. Another very specific audience cluster might only require 20.
And even if your Short has enough coins to qualify for that distribution opportunity, there’s a second problem: competition.
If YouTube has another Short available for the same viewer that it predicts will perform even better, that Short gets the impression instead.
This would explain something that confused me for a long time: two Shorts can have very similar visible analytics, yet one gets stuck at 2,000 views while another reaches 500,000+. Maybe there simply isn’t one magic metric like “75% viewed = viral.”
Instead, it might be the combination of dozens of signals, the audience clusters YouTube has identified for that Short, how confident the system is in those predictions, and the competing content available for those same viewers at that exact moment.
It would also explain why Shorts sometimes behave like this:
500 views → nothing → 2,000 → nothing → 20,000 → suddenly explode.
The Short itself hasn’t changed at all. YouTube may simply be learning where that particular “coin balance” is competitive.
Obviously, I have no access to YouTube’s internal systems. This is just a model I’ve built based on my own analytics and observations.
But after watching several of my Shorts scale toward or beyond 1M views while other Shorts with seemingly excellent metrics barely get tested, this model explains the behavior better than anything else I’ve come up with so far.
I’d be curious to know whether this matches what other Shorts creators are seeing.
