1) Feel free to use $26k. My main issue was that you didn’t ask me for my viewer minutes for TikTok (EDIT: didn’t follow up to make sure I give you the viewer minutes for TikTok) and instead used a number that is off by a factor of 10. Please use a correct number in future analysis. For June 15 - Sep 10, that’s 4,150,000 minutes, meaning a VM/$ of 160 instead of 18 (details here).
A) Your screenshots of google sheets say “FLI podcast”, but you ran your script on the entire channel. And you say that the budget is $500k. Can you confirm what you’re trying to measure here? The entire video work of FLI? Just the podcast? If you’re trying to get the entire channel, is the budget really $500k for the entire thing? I’m confused.
B) If you use accurate numbers for some things and estimate for others, I’d make sure to communicate explicitly about which ones are which. Even then, when you then compare estimates and real numbers there’s a risk that your estimates are off by a a huge factor (has happened with my TikTok numbers), which makes me question the value of the comparisons.
C) Let me try to be clearer regarding paid advertising:
If some of the watchtime estimates you got from people are (views * 33% of length), and they pay $X per view (fixed cost of ads on youtube), then the VM/$ will be: [nb_views * (33% length) / total_cost] = [ nb_views * 33% length] / [nb_views * X] = [33% length / X]. Which is why I mean it’s basically the cost of ads. (Note: I didn’t include the organic views above because I’m assuming they’re negligible compared to the inorganic ones. If you want me to give examples of videos where I see mostly inorganic views, I’ll send you by DM).
For the cases where you got the actual watchtime numbers instead of multiplying the length by a constant or using a script (say, someone tells you they have Y amount of hours total on their channel), or the ads lead to real organic views, your reasoning around ads makes sense, though I’d still argue that in terms of impact the engagement is the low / pretty disastrous in some cases, and does not translate to things we care about (like people taking action).
3. I think the questions “who is your favourite AI safety creator” or “Which AI safety YouTubers did/do you watch” are heavily biased towards Robert Miles, as he is (and has basically been for the past 8 years) the only “AI Safety Youtuber” (like making purely talking head videos about AI Safety, in comparison, RA is a team). So I think based on these questions it’s quite likely he’d be mentioned, though I agree 50 people saying his name first is important data that needs to be taken into account.
That said, I’m trying to wrap my head around how to go from your definition of “quality of audience” to “Robert Miles was chosen by 50 different people to be their favorite youtuber, as the first person mentioned”. My interpretation is that you’re saying: 1) you’ve spoken to 50 people who are people who work in AI Safety 2) they all mentioned Rob as the canonical Youtuber, so “therefore” A) Rob has the highest quality audience? (cf. you wrote in OP “This led me to make the “audience quality” category and rate his audience much higher.”)
My model for how this claim could be true that 1) you asked 50 people who you all thought were “high quality” audience 2) they all mentioned rob and nobody else (or rarely nobody else), so 3) you inferred “high quality audience ⇒ watches Rob” and therefore 4) also inferred “watches Rob ⇒ high quality”?
4. Regarding weights, also respectfully, I did indeed look at them individually. You can check my analysis for what I think the TikTok individual weights should be here. For Youtube see here. Regarding your points:
I have posted in my analysis of tiktok a bunch of datapoints that you probably don’t have about the fact that my audience is mostly older high-income people from richer countries, which is unusually good for TikTok. Which is why I put 3 instead of your 2.
“you’re just posting clips of other podcasts and such and this just doesn’t do a great job of getting a message across” → the clips that end up making the majority of viewer minutes are actually quite high fidelity since they’re quite long (2-4m long) and get the message more crisply than the average podcast minute. Anyway, once you look at my TikTok analysis you’ll see that I ended up dividing everything by 2 to have the max fidelity tiktok have 0.5 (same as Cognitive Revolution), which means my number is Qf=0.45 at the end (instead of your 0.1) to just be coherent with the rest of your numbers.
Qm: that’s subjective but FWIW I myself only align to 0.75 to my TikTok and not 1 (see analysis)
“Again, most of the quality factor is being done by audience quality and yes, shorts just have a far lower audience quality.” --> again, respectfully, from looking at your tables I think this is false. You rank the fidelity of TikTok as 0.1, which is 5x less than 4 other channels. No other channels except my content (TikTok & YT) has less 0.3. In comparison, if you forget about rob’s row, the audience quality varies only by 3x between my Qa for TikTok and the rest. So no, the quality factor is not mainly done by audience quality.
On 1, with your permission, I’d ask if I could share a screenshot of me asking you in DMs, directly, for viewer minutes. You gave me views, and thus I multiplied the average TikTok length and by a factor for % watched.
On A, yes, the FLI Podcast was perhaps the data point I did the most estimating for a variety of reasons I explained before.
On B, I think you can, in fact, find which are and aren’t estimates though I do understand how it’s not clear. We considered ways of doing this without being messy. Ill try to make it more clear.
On C, how much you pay for a view is not a constant though. It depends a lot on organic views. And I think boosting videos is a sensible strategy since you put $ into both production costs (time, equipment, etc.) and advertisement. FIguring out how to spend that money efficiently is important.
On 3, many other people were mentioned. In fact, I found a couple of creators this way. But yes, it was extremely striking and thus suggested that this was a very important factor in the analysis. I want to stress that I do in fact, think that this matters a lot. When Austin and I were speaking and relying on comparisons, we thought his quality numbers should be much higher in fact, we toned it down though maybe we shouldn’t have.
To give clarity, I didn’t seek people out who worked in AI safety. Here’s what I did to the best of my recollection.
Over the course of 3 days, I asked anyone I saw in Mox who seemed friendly enough, as well as Taco Tuesday, and sent a few DMs to acquaintances. The DMs I sent were to people who work in AI safety, but there were only 4. So ~46 came from people hanging out around Mox and Taco Tuesday.
I will grant that this lends to an SF/AI safety bias. Now, Rob Miles’ audience comes heavily from Computerphile and such whose audience is largely young people interested in STEM who like to grapple with interesting academic-y problems in their spare time (outside of school). In other words, this is an audience that we care a lot about reaching. It’s hard to overstate the possible variance in audience “quality”. For example, Jane Street pays millions to advertisers to get itself seen in front of potential traders on channels like Stand-Up Maths or the Dwarkesh podcast. These channels don’t actually get that many views compared to others but they have a very high “audience quality”, clearly, based on how much trading firms are willing to pay to advertise there. We actually thought a decent, though imperfect, metric for audience quality would just be a person’s income compared to the world average of ~12k. This meant the average american would have an audience quality of 7. Austin and I thought this might be a bit too controversial and doesn’t capture exaxctly what we mean (we care about attracking a poor MIT CS student more than a mid-level real estate developer in Miami) but it’s a decent approximation.
Audience quality is roughly something like “the people we care most about reaching,” and thus “people who can go into work on technical AI safety” seems very important.
Rob wasn’t the only one mentioned, the next most popular were Cognitive Revolution and AI in context (people often said “Aric”) since I asked them to just name anyone they listen to/would consider an AI safety youtuber, etc.
On 4, I greatly encourage people to input their own weights, I specifically put that in the doc and part of the reason for doing this project was to get people to talk about cost effectiveness in AI safety.
On my bias: Like all human beings, I’m flawed and have biases, but I did my best to just objectively look at data in what I thought the best way possible. I appreciate that you talked to others regarding my intentions.
I’ll happily link to my comments on Manifund 123 you may be referring to for people to see the full comments and perhaps emphasize some points I wrote
@ I want to quickly note that it’s a bit unfair for me to specifically only call you out on this or rather, that this is a thing I find with many AI safety projects. It just came up high on Manifund when I logged on for other reasons and saw donations from people I respect.
FWIW, I don’t want to single you out, I have this kind of critique of many, many people doing AI safety work but this just seems like a striking example of it.
I didn’t mean my comments to say “you should return this money”. Lots of grants/spending in EA ecosystems I consider to be wasteful, ineffective etc. And again, apologies for singling you out on a gripe I have with EA funding.
Many people can tell you that I have a problem with the free-spending, lavish and often wasteful spending in the longtermist side of EA. I think I made it pretty clear that I was using this RFP as an example because other regrantors gave to it.
This project with Austin was planned to happen before you posted your RFP on Manifund (I can provide proof if you’d like).
I wasn’t playing around with the weights to make you come out lower. I assure you, my bias is usually against projects I perceive to be “free-spending”.
I think it’s good/natural to try to create separation between evaluators/projects though.
For context, you asked me for data for something you were planning (at the time) to publish day-off. There’s no way to get the watchtime easily on TikTok (which is why I had to do manual addition of things on a computer) and I was not on my laptop, so couldn’t do it when you messaged me. You didn’t follow up to clarify that watchtime was actually the key metric in your system and you actually needed that number.
Good to know that the 50 people were 4 Safety people and 46 people who hang at Mox and Taco Tuesday. I understand you’re trying to reach the MIT-graduate working in AI who might somehow transition to AI Safety work at a lab / constellation. I know that Dwarkesh & Nathan are quite popular with that crowd, and I have a lot of respect for what Aric (& co) did, so the data you collected make a lot of sense to me. I think I can start to understand why you gave a lower score to Rational Animations or other stuff like AIRN.
I’m now modeling you as trying to answer something like “how do we cost-effectively feed AI Safety ideas to the kind of people who walk in at Taco Tuesday, who have the potential to be good AI Safety researchers”. Given that, I can now understand better how you ended up giving some higher score to Cognitive Revolution and Robert Miles.
1) Feel free to use $26k. My main issue was that you didn’t
ask me for my viewer minutes for TikTok(EDIT: didn’t follow up to make sure I give you the viewer minutes for TikTok) and instead used a number that is off by a factor of 10. Please use a correct number in future analysis. For June 15 - Sep 10, that’s 4,150,000 minutes, meaning a VM/$ of 160 instead of 18 (details here).A) Your screenshots of google sheets say “FLI podcast”, but you ran your script on the entire channel. And you say that the budget is $500k. Can you confirm what you’re trying to measure here? The entire video work of FLI? Just the podcast? If you’re trying to get the entire channel, is the budget really $500k for the entire thing? I’m confused.
B) If you use accurate numbers for some things and estimate for others, I’d make sure to communicate explicitly about which ones are which. Even then, when you then compare estimates and real numbers there’s a risk that your estimates are off by a a huge factor (has happened with my TikTok numbers), which makes me question the value of the comparisons.
C) Let me try to be clearer regarding paid advertising:
If some of the watchtime estimates you got from people are (views * 33% of length), and they pay $X per view (fixed cost of ads on youtube), then the VM/$ will be: [nb_views * (33% length) / total_cost] = [ nb_views * 33% length] / [nb_views * X] = [33% length / X]. Which is why I mean it’s basically the cost of ads. (Note: I didn’t include the organic views above because I’m assuming they’re negligible compared to the inorganic ones. If you want me to give examples of videos where I see mostly inorganic views, I’ll send you by DM).
For the cases where you got the actual watchtime numbers instead of multiplying the length by a constant or using a script (say, someone tells you they have Y amount of hours total on their channel), or the ads lead to real organic views, your reasoning around ads makes sense, though I’d still argue that in terms of impact the engagement is the low / pretty disastrous in some cases, and does not translate to things we care about (like people taking action).
3. I think the questions “who is your favourite AI safety creator” or “Which AI safety YouTubers did/do you watch” are heavily biased towards Robert Miles, as he is (and has basically been for the past 8 years) the only “AI Safety Youtuber” (like making purely talking head videos about AI Safety, in comparison, RA is a team). So I think based on these questions it’s quite likely he’d be mentioned, though I agree 50 people saying his name first is important data that needs to be taken into account.
That said, I’m trying to wrap my head around how to go from your definition of “quality of audience” to “Robert Miles was chosen by 50 different people to be their favorite youtuber, as the first person mentioned”. My interpretation is that you’re saying: 1) you’ve spoken to 50 people who are people who work in AI Safety 2) they all mentioned Rob as the canonical Youtuber, so “therefore” A) Rob has the highest quality audience? (cf. you wrote in OP “This led me to make the “audience quality” category and rate his audience much higher.”)
My model for how this claim could be true that 1) you asked 50 people who you all thought were “high quality” audience 2) they all mentioned rob and nobody else (or rarely nobody else), so 3) you inferred “high quality audience ⇒ watches Rob” and therefore 4) also inferred “watches Rob ⇒ high quality”?
4. Regarding weights, also respectfully, I did indeed look at them individually. You can check my analysis for what I think the TikTok individual weights should be here. For Youtube see here. Regarding your points:
I have posted in my analysis of tiktok a bunch of datapoints that you probably don’t have about the fact that my audience is mostly older high-income people from richer countries, which is unusually good for TikTok. Which is why I put 3 instead of your 2.
“you’re just posting clips of other podcasts and such and this just doesn’t do a great job of getting a message across” → the clips that end up making the majority of viewer minutes are actually quite high fidelity since they’re quite long (2-4m long) and get the message more crisply than the average podcast minute. Anyway, once you look at my TikTok analysis you’ll see that I ended up dividing everything by 2 to have the max fidelity tiktok have 0.5 (same as Cognitive Revolution), which means my number is Qf=0.45 at the end (instead of your 0.1) to just be coherent with the rest of your numbers.
Qm: that’s subjective but FWIW I myself only align to 0.75 to my TikTok and not 1 (see analysis)
“Again, most of the quality factor is being done by audience quality and yes, shorts just have a far lower audience quality.” --> again, respectfully, from looking at your tables I think this is false. You rank the fidelity of TikTok as 0.1, which is 5x less than 4 other channels. No other channels except my content (TikTok & YT) has less 0.3. In comparison, if you forget about rob’s row, the audience quality varies only by 3x between my Qa for TikTok and the rest. So no, the quality factor is not mainly done by audience quality.
On 1, with your permission, I’d ask if I could share a screenshot of me asking you in DMs, directly, for viewer minutes. You gave me views, and thus I multiplied the average TikTok length and by a factor for % watched.
On A, yes, the FLI Podcast was perhaps the data point I did the most estimating for a variety of reasons I explained before.
On B, I think you can, in fact, find which are and aren’t estimates though I do understand how it’s not clear. We considered ways of doing this without being messy. Ill try to make it more clear.
On C, how much you pay for a view is not a constant though. It depends a lot on organic views. And I think boosting videos is a sensible strategy since you put $ into both production costs (time, equipment, etc.) and advertisement. FIguring out how to spend that money efficiently is important.
On 3, many other people were mentioned. In fact, I found a couple of creators this way. But yes, it was extremely striking and thus suggested that this was a very important factor in the analysis. I want to stress that I do in fact, think that this matters a lot. When Austin and I were speaking and relying on comparisons, we thought his quality numbers should be much higher in fact, we toned it down though maybe we shouldn’t have.
To give clarity, I didn’t seek people out who worked in AI safety. Here’s what I did to the best of my recollection.
Over the course of 3 days, I asked anyone I saw in Mox who seemed friendly enough, as well as Taco Tuesday, and sent a few DMs to acquaintances. The DMs I sent were to people who work in AI safety, but there were only 4. So ~46 came from people hanging out around Mox and Taco Tuesday.
I will grant that this lends to an SF/AI safety bias. Now, Rob Miles’ audience comes heavily from Computerphile and such whose audience is largely young people interested in STEM who like to grapple with interesting academic-y problems in their spare time (outside of school). In other words, this is an audience that we care a lot about reaching. It’s hard to overstate the possible variance in audience “quality”. For example, Jane Street pays millions to advertisers to get itself seen in front of potential traders on channels like Stand-Up Maths or the Dwarkesh podcast. These channels don’t actually get that many views compared to others but they have a very high “audience quality”, clearly, based on how much trading firms are willing to pay to advertise there. We actually thought a decent, though imperfect, metric for audience quality would just be a person’s income compared to the world average of ~12k. This meant the average american would have an audience quality of 7. Austin and I thought this might be a bit too controversial and doesn’t capture exaxctly what we mean (we care about attracking a poor MIT CS student more than a mid-level real estate developer in Miami) but it’s a decent approximation.
Audience quality is roughly something like “the people we care most about reaching,” and thus “people who can go into work on technical AI safety” seems very important.
Rob wasn’t the only one mentioned, the next most popular were Cognitive Revolution and AI in context (people often said “Aric”) since I asked them to just name anyone they listen to/would consider an AI safety youtuber, etc.
On 4, I greatly encourage people to input their own weights, I specifically put that in the doc and part of the reason for doing this project was to get people to talk about cost effectiveness in AI safety.
On my bias:
Like all human beings, I’m flawed and have biases, but I did my best to just objectively look at data in what I thought the best way possible. I appreciate that you talked to others regarding my intentions.
I’ll happily link to my comments on Manifund 1 2 3 you may be referring to for people to see the full comments and perhaps emphasize some points I wrote
Many people can tell you that I have a problem with the free-spending, lavish and often wasteful spending in the longtermist side of EA. I think I made it pretty clear that I was using this RFP as an example because other regrantors gave to it.
This project with Austin was planned to happen before you posted your RFP on Manifund (I can provide proof if you’d like).
I wasn’t playing around with the weights to make you come out lower. I assure you, my bias is usually against projects I perceive to be “free-spending”.
I think it’s good/natural to try to create separation between evaluators/projects though.
For context, you asked me for data for something you were planning (at the time) to publish day-off. There’s no way to get the watchtime easily on TikTok (which is why I had to do manual addition of things on a computer) and I was not on my laptop, so couldn’t do it when you messaged me. You didn’t follow up to clarify that watchtime was actually the key metric in your system and you actually needed that number.
Good to know that the 50 people were 4 Safety people and 46 people who hang at Mox and Taco Tuesday. I understand you’re trying to reach the MIT-graduate working in AI who might somehow transition to AI Safety work at a lab / constellation. I know that Dwarkesh & Nathan are quite popular with that crowd, and I have a lot of respect for what Aric (& co) did, so the data you collected make a lot of sense to me. I think I can start to understand why you gave a lower score to Rational Animations or other stuff like AIRN.
I’m now modeling you as trying to answer something like “how do we cost-effectively feed AI Safety ideas to the kind of people who walk in at Taco Tuesday, who have the potential to be good AI Safety researchers”. Given that, I can now understand better how you ended up giving some higher score to Cognitive Revolution and Robert Miles.