I struggle to imagine Qf 0.9 being reasonable for anything on TikTok. My understanding of TikTok is that most viewers will be idly scrolling through their feed, watch your thing for a bit as part of this endless stream, then continue, and even if they decide to stop for a while and get interested, they still would take long enough to switch out of the endless scrolling mode to not properly engage with large chunks of the video. Is that a correct model, or do you think that eg most of your viewer minutes come from people who stop and engage properly?
Update: after looking at Marcus’ weights, I ended up dividing all the intermediary values of Qf I had by 2, so that it matches with Marcus’ weights where Cognitive Revolution = 0.5. Dividing by 2 caps the best tiktok-minute to the average Cognitive Revolution minute. Neel was correct to claim that 0.9 was way too high.
===
My model is that most of the viewer minutes come from people who watch the all thing, and some decent fraction end up following, which means they’ll end up engaging more with AI-Safety-related content in the future as I post more.
Looking at my most viewed TikTok:
TikTok says 15.5% of viewers (aka 0.155 * 1400000 = 217000) watched the entire thing, and most people who watch the first half end up watching until the end (retention is 18% at half point, and 10% at the end).
And then assuming the 11k who followed came from those 217000 who watched the whole thing, we can say that’s 11000/217000 = 5% of the people who finished the video that end up deciding to see more stuff like that in the future.
So yes, I’d say that if a significant fraction (15.5%) watch the full thing, and 0.155*0.05 = 0.7% of the total end up following, I think that’s “engaging properly”.
And most importantly, most of the viewer-minutes on TikTok do come from these long videos that are 1-4 minutes long (especially ones that are > 2 minutes long):
The short / low-fidelity takes that are 10-20s long don’t get picked up by the new tiktok algorithm, don’t get much views, so didn’t end up in that “TikTok Qa & Qs” sheet of top 10 videos (and for the ones that did, they didn’t really contribute to the total minutes, so to the final Qf).
To show that the Eric Schimdt example above is not cherry-picked, here is a google docs with similar screenshots of stats for the top 10 videos that I use to compute Qf. From these 10 videos, 6 are more than 1m long, and 4 are more than 2 minutes long. The precise distribution is:
0m-1m: 4 videos
1m-2m: 2 videos
2m-3m: 2 videos
3m-4m: 2 videos
Happy for others to come up with different numbers / models for this, or play with my model through the “TikTok Qa & Qf” sheet here, using different intermediary numbers.
Update: as I said at the top, I was actually wrong to have initially said Qf=0.9 given the other values. I now claim that Qf should be closer to 0.45. Neel was right to make that comment.
I struggle to imagine Qf 0.9 being reasonable for anything on TikTok. My understanding of TikTok is that most viewers will be idly scrolling through their feed, watch your thing for a bit as part of this endless stream, then continue, and even if they decide to stop for a while and get interested, they still would take long enough to switch out of the endless scrolling mode to not properly engage with large chunks of the video. Is that a correct model, or do you think that eg most of your viewer minutes come from people who stop and engage properly?
Update: after looking at Marcus’ weights, I ended up dividing all the intermediary values of Qf I had by 2, so that it matches with Marcus’ weights where Cognitive Revolution = 0.5. Dividing by 2 caps the best tiktok-minute to the average Cognitive Revolution minute. Neel was correct to claim that 0.9 was way too high.
===
My model is that most of the viewer minutes come from people who watch the all thing, and some decent fraction end up following, which means they’ll end up engaging more with AI-Safety-related content in the future as I post more.
Looking at my most viewed TikTok:
TikTok says 15.5% of viewers (aka 0.155 * 1400000 = 217000) watched the entire thing, and most people who watch the first half end up watching until the end (retention is 18% at half point, and 10% at the end).
And then assuming the 11k who followed came from those 217000 who watched the whole thing, we can say that’s 11000/217000 = 5% of the people who finished the video that end up deciding to see more stuff like that in the future.
So yes, I’d say that if a significant fraction (15.5%) watch the full thing, and 0.155*0.05 = 0.7% of the total end up following, I think that’s “engaging properly”.
And most importantly, most of the viewer-minutes on TikTok do come from these long videos that are 1-4 minutes long (especially ones that are > 2 minutes long):
The short / low-fidelity takes that are 10-20s long don’t get picked up by the new tiktok algorithm, don’t get much views, so didn’t end up in that “TikTok Qa & Qs” sheet of top 10 videos (and for the ones that did, they didn’t really contribute to the total minutes, so to the final Qf).
To show that the Eric Schimdt example above is not cherry-picked, here is a google docs with similar screenshots of stats for the top 10 videos that I use to compute Qf. From these 10 videos, 6 are more than 1m long, and 4 are more than 2 minutes long. The precise distribution is:
0m-1m: 4 videos
1m-2m: 2 videos
2m-3m: 2 videos
3m-4m: 2 videos
Happy for others to come up with different numbers / models for this, or play with my model through the “TikTok Qa & Qf” sheet here, using different intermediary numbers.
Update: as I said at the top, I was actually wrong to have initially said Qf=0.9 given the other values. I now claim that Qf should be closer to 0.45. Neel was right to make that comment.