In one of our special “2x” episodes of 16 Minutes (32ish minutes;) — our show where we quickly cover the headlines and tech trends, offering analysis, frameworks, explainers, and more — we cover the algorithm that powers TikTok, the short video-sharing platform that grabbed massive marketshare in cultures and markets never experienced firsthand by the engineers and designers in China, beating out other apps in the United States. Now, with talk of U.S. ownership/partnership for TikTok, what happens if the algorithm isn’t included in the deal? And what can we learn from the “creativity network effects” flywheel of TikTok; for “algorithm friendly” product design; and more broadly, about the future of video?
The news: Given the U.S. government calling for TikTok’s business to be sold to U.S. owners last month, and several bidders coming in since, the latest news was that Oracle Corporation and Bytedance are hammering out an agreement for the former to be TikTok’s “trusted tech partner” in the U.S. This could include (as reported by Axios) their exclusive ability to oversee all tech operations for TikTok in the U.S., including access and control of U.S. user data; ability to review source code and all updates to software for security vulnerabilities; and separate boards and entities for ensuring compliance with CFIUS/ U.S. policies (and for allowing ownership stakes for Oracle, with Walmart). The deal hasn’t been approved yet [as of September 18, 2020].
The episode: But since this show is focused on where we are on the long arc of innovation, and what’s hype/ what’s real when it comes to tech trends & the news, where does the source code (and more specifically, the “For You Page” algorithm) — which may or may not be included in the deal due to China’s revised export controls — come in? Yet it’s not just about if TikTok is really TikTok without it, or whether “the algorithm” and machine learning training data can be recreated… the real question is: How does the “creativity network effects” flywheel work between video creation and distribution — from origination to mutation to dissemination? It boils down to the idea of “algorithm friendly design”, observes Eugene Wei, who has written a series of deep dives on TikTok, and formerly led product at Hulu, Flipboard, and video at Oculus, among other things. So what does TikTok, regardless of deal outcome, suggest about the future of product development, and more broadly, the future of video? All this and more in this 2x+ long explainer episode of 16 Minutes.
Sonal: Hi everyone welcome to 16 Minutes, I’m Sonal, your host; and this is our show where we tease apart what’s hype/ what’s real when it comes to the headlines, the tech trends, and where are on the long arc of innovation — And so this episode is all about the short video-sharing platform TikTok (which has been in the news a lot lately), but is also about the future of entertainment and especially video; we also cover “creativity network effects” from creation to distribution; the concept of “algorithm friendly” product design; and much more.
For those who are new to this show, I do one of these deep-dive, kind of “2x” explainer episodes — so about 32ish minutes;) — every so often, where we talk about what’s in the news, but really dig into — with the top experts — the key underlying concepts. And our expert today is Eugene Wei, who has written a series of deep dives about TikTok, and formerly led product at Hulu, Flipboard, and video at Oculus, among other things. (As a reminder, none of the following should be taken as investment advice; for more important information, please see a16z.com/disclosures.)
And for the quick news context before we go into the discussion:
- TikTok has obviously been in the headlines with the administration calling for its sale and majority ownership of it in the U.S. last month, with multiple companies bidding since;
- The latest news, as reported by Axios, is that Oracle and Bytedance are hammering out an agreement for the former to access and control U.S. user data; to have the ability to review source code and all updates to software for security vulnerabilities; and have independent boards for compliance)
- But all of this is yet to be cleared by both governments
So our focus in this episode will be around the evergreen and key question of where the algorithm (as if it were a single thing!) does and doesn’t come in — given talk of removing it from the equation. And more specifically, the “For You Page” algorithm, which, Eugene, you wrote about recently as quote-“the most important piece of technology” that Bytedance introduced to Tiktok, and you also called it “the hardest part” — which allowed a team of people who’ve mostly never left China to crack the cultural code and grab massive market share in places they’ve never experienced firsthand… So what do YOU make of the news that this sale or partnership or whatever it ends up technically being, may or may not include this algorithm?
Eugene: Yeah, I think in a lot of talk about TikTok’s algorithm (and I’m partially responsible), the dialogue’s gotten a little bit breathless, around the algorithm — it’s become like the magical MacGuffin in a film; the uh you know suitcase of whatever in “Pulp Fiction” (or something like that).
And, while I do think the algorithm is important, I actually think that people may be overstating just like the power of the algorithm in isolation, whether it comes along in a deal or not. If you ask machine learning researchers around the world, if they think ByteDance has some algorithm that nobody has, I doubt they would agree; the algorithm is based off of very conventional research, and conventional thinking in terms of recommendations algorithms. What matters is actually the combination of the algorithm itself, and then the training data that you can train it on — and it’s the combination of the two that’s super powerful.
But, what makes TikTok different from other spaces (like visual AI or text AI), is that there isn’t a large corpus of just publicly available training data. And so the magic of TikTok in a way is that it’s a closed-loop ecosystem: It’s an app that encourages its users to create the training data that it then trains its algorithm on. And that’s I think, the magic.
Sonal: Can you quickly actually just walk us through the history of how TikTok actually did get that training data and then combine the algorithm to create this phenomenon where “it was able to run circles around U.S. video apps”, from YouTube, to Facebook, to Instagram, to Snapchat — How did they do that? Because anyone could have theoretically, you know gathered training data and come up with a different algorithm; like there’s something specific here.
Eugene: Yeah. Well, it’s ironic because it starts with the app Musical.ly, in many ways.
Musical.ly was a video app created by Alex and Louis, who had worked in the U.S., but were in China, and had pivoted from a short video education app. And, they launched it in both China and the U.S. — and it actually became more successful in the U.S., especially among American teenage girls, who used it to do lip sync and dance videos — then ByteDance cloned Musical.ly essentially, in China, in an app called Douyin. The irony of that is actually that the clone of Musical.ly ended up launching in a larger market, and becoming a larger app with a larger user base. And so eventually, they bought Musical.ly after its growth had stalled out in the U.S. And that’s when they rebranded Musical.ly into TikTok.
So it’s this weird you know “multi-hop” mutation of the app that like <chuckles> — built in China; did well in the U.S.; got copied in China; and then China bought the U.S. version — it just kept hopping back and forth across the ocean.
Sonal: Well now the hop is kind of funny because it could go the other direction <yah>, where part of it could be divested to a sale in the U.S.!
Eugene: Yah… it just keeps going back and forth.
But, all of that wouldn’t have mattered if nobody was making videos on the app, right; they actually had to build an app that made it possible for people to create a new type of video.
Sonal: Could you break down a little bit more into the tools? You come at this from the vantage point of someone both in *tech, and who’s also been to *film school, and is a huge lover of multimedia. What specifically — let’s talk a little bit more about what makes the tools — because frankly, there’s a lot of apps in the U.S. (like YouTube and others) who easily have the capabilities of putting these tools together.
Now they didn’t — so that’s part of the point — but what specifically about these tools or the combination about them is really part of this flywheel?
Eugene: Yeah. That’s where the app is a little bit underrated in terms of its creation tools. It has a really great set of camera tools; editing functions; filters that take certain high-production film techniques, and make them really accessible to a broad audience. Even licensing the music tracks was a huge thing for Musical.ly to do: Previously, if you wanted to lip sync to a pop song, you had to get like a pirated copy (or just do something that might get pulled down for copyright and trademark violations). Them signing the deals with the music labels now allow teenagers to lip-sync to the actual version of the song that they wanted to lip sync to.
Sonal: That’s a great example of a tool that really makes something easy and fast, that was previously hard.
Eugene: It’s two things; one is, the creation tools are really taking features and functions that traditionally you would have to use like the Adobe Creative Suite to do, on your laptop — and making it possible to do a lot of that just with your phone. That’s a huge thing because first of all, a lot of people can’t afford Adobe Suite tools, and the learning curve on them is significant; if you didn’t go to film school, you don’t know how to use After Effects. But TikTok essentially integrates those into kind of their camera suite.
The second thing I think — and this is less about the tools — there are network effects on the creativity side, when it comes to TikTok, and that’s really underrated.
In your podcast library, you probably have a ton of episodes that are all about all different types of network effects; the important thing to think about when it comes to this example though is just that: Does every additional creator on TikTok, make the rest of the community more creative? That’s what I mean by creativity network effects. And I actually think it’s very rare to find this form of network effect in the wild, but TikTok has achieved it, a couple ways:
…So the hardest thing for any creator, on any app, is to just think about what to create. You know, if you are presented with a blank canvas or the blank page as a writer, can you come up with something from scratch. And the truth is, most people can’t originate ideas.
But TikTok — because of the distribution, because of their discover page making what’s trending very salient — essentially allows you to just remix someone else’s idea. Most TikToks that people make, are actually just riffs on someone else’s idea. And so they solve that sort of blank page problem for you. You can go on TikTok and find a whole bunch of ideas, from other people.
…The second thing is they actually structurally make it possible for you to physically riff off of the other person’s idea. So, you could do —
Sonal: Oh you’re talking about Duets, yeah.
Eugene: — a duet; yah you could do a duet with someone where just like one half of the video with someone else.
You can easily grab a component of their video to reuse in your own — like maybe you just like the music track, and the music track is the meme that you want to make; now you can just grab it, reuse it. And sometimes people upload original audio; so someone just records a TikTok video from scratch, you can even just use their audio, in your own TikTok.
…And, the last thing is just really, I think there is a shared inspiration in the community — they make sure that if someone comes up with an inspired idea, it’s distributed really broadly. And then the sort of ethos of TikTok is that you pay it forward, everybody can borrow somebody else’s ideas.
Sonal: So, it’s really interesting because you in your original post described,“TikTok is such a fertile source for meme origination, mutation, and dissemination”.
So we’ve talked about the origination, which is like the creative tool suite. You’re now talking about the mutation, which is this remix, taking bits and pieces — I feel like a broken record because I often talk about “combinatorial innovation”, which is such a buzzword — but it is sort of this idea of remixing bits and pieces, Lego blocks, composability in software; there’s many ways to describe this phenomenon.
But specifically on the mutation side, it makes it very easy for people to be creators without having to be quote-“creators”. What do you make of challenge culture within that too, and hashtags, and some of the other specifics within TikTok, that kind of make the mutation work? Because again, remix culture is nothing new; in fact, when I think of the early web, the story of it is remix culture. So like what do you think specifically about TikTok really advanced the mutation… wheel?
Eugene: Yeah. I think that’s where the algorithm actually really comes into play — because the algorithm determines kind of who sees what. So, there’s a way in which you are incentivized to participate in certain challenges because you know the algorithm happens to be amplifying that particular meme and trend a lot right now.
If you didn’t have the algorithm, and things had to organically find an audience, that whole challenge culture thing would work so slowly that it might not actually achieve critical mass. In a way, what TikTok is, is a mix of a free market — but also a managed economy.
Sonal: Ooh, interesting.
Eugene: So on the Discover page (which is a tab that you can go to), they will post what are the challenges that they’re featuring at the top: What is the hashtag; what is the you know musical track that fits with it; and what are people doing for that challenge. And you know as a creator then, that if you make something on that challenge, you have a chance to hit the top of the Discover page because it’s being featured.
So that’s the managed economy part of it, where they actually can coordinate the entire community, and create common knowledge about what is going to be promoted. And it’s the same with hashtags, right; the hashtags that you can search on, you can see how many views each hashtag is getting right now, and try to attach yourself to the ones that have the highest velocity and momentum.
Sonal: Right and as a quick point of contrast for those who are not as… as, in TikTok <chuckles>; in contrast, when you think about most other social networks and the trending hashtags, you actually don’t know which is more– the weighting of them at all, they could be arbitrary for all you care; <Right> it could be five people trending, it could be whatever.
And then similarly, one of the biggest complaints people have had about YouTube is that you CAN go viral, but it’s very rare, and it’s very loaded towards very established people, as a mature established platform, because you’re essentially quote-“gaming the algorithm”. And so what you’re kind of saying in a weird way here as you can game but not game the algorithm, <yah> on TikTok.
Eugene: And it does feel meritocratic in that way. You’ll sometimes click into a profile, of a creator who’s made a viral video — and you’ll see that all their other videos actually have very low view counts. They’ve sort of removed that old money effect that I describe in other social networks, where the creators who’ve been there the longest, have such an advantage over new creators.
Sonal: Right; they’ve accrued the quote-most “status” in that network…
Eugene: …Exactly, exactly. So if you even see like the Meteor/Meatier pun video this week — which is about the extinction of the dinosaurs — that one was great, because she was kind of a newish creator who finally just had that first big hit.
<Sonal: Ah that’s great> And that also helps on the viewer side, right — because you’re not getting decreasing economies of scale, where the same creators videos keep getting shown to you, even if they’re no longer any good. You are always being shown stuff that they have determined, has entertained some test audience, at some you know part of the network.
Sonal: It’s almost like evolution; it’s constantly testing for fitness <right> of this creator, essentially in this, in this model.
Eugene: Right! We know from evolutionary theory that the stronger the fitness function or the selection pressure, the better the output on the other side.
And I view TikTok as an “assisted evolution” ecosystem: It’s not purely leaving everything up to chance — they do put their finger on the scale sometimes in terms of hey, we have a corporate partner that wants to do this challenge; we’re going to feature it, and that’s going to give it more prominence — but for the most part, no matter how popular you are as a creator, they’re gonna let your video sink or float based on how it does with that first test audience they show it to.
Sonal: So when you talk about assisted evolution, it’s like a combination of this managed economy and free market dynamic, which is fabulous. <yah> Okay.
So, so far then these are all the kind of features that now we’re kind of wrapping up on this idea of mutation. So TikTok being the most fertile source for origination with the creative tools, and, those allow some more of these creative network effects. The mutation, which allows this interaction of the community, the discovery; the fitness of creators — so you’re always getting fresh, and not only going with only the mature creators — and other kind of dynamics to play in this assisted evolution as you describe it.
So now let’s talk about this “fertile source” for dissemination — and by the way, I don’t mean to cut these apart as if they’re three discrete things; they’re obviously on a continuum, and interact — but let’s talk about dissemination and really, distribution.
Eugene: Yeah. So, the algorithm essentially sits at the center of all this; the algorithm is going to determine who gets shown what videos. And creators are only going to go typically, to a network where they feel like they have a chance to get disproportionate distribution of their content.
And, the way that TikTok has sort of like short-circuited that process and accelerated it, is by using an algorithm rather than a social graph, as the primary axis of distribution.
Sonal: Say a little bit more about what that means just for our listeners who are not in the weeds of, social networks.
Eugene: Right. So in a typical social network, like Facebook, or Twitter, or Instagram, you start posting content, and then you try to acquire followers — and this builds out kind of a social graph, right; it’s an interconnected web of people. And based on who chooses to follow you, you will get distribution of your content to them. And then eventually if the network gets really big, they’ll put some algorithmic feed into place, where not everything you create will be shown to the people that follow you.
I always think of this as the very traditional path of social graphs, where the follower graph kind of determines the pathways through which content travels.
Sonal: Which is then very path dependent, shaping the future of that social network.
Eugene: Exactly. And so, if you don’t build up enough of a following, eventually your content gets no distribution; you’ll churn out of the network, or maybe just become a viewer, where you only look at other people’s work.
TikTok doesn’t go through that process at all. They have the ability for you to follow creators, but, that content is put into a secondary tab, the Following tab — which gets like just a fraction of the traffic that the FYP tab gets.
Sonal: Which is the For You Page.
Eugene: The For You Page. Essentially, they use the algorithm to determine what you see. And that just allows you to see content from people that you don’t follow, that you would enjoy otherwise. And I call this just you know TikTok basically fast-forwarding to the interest graph and bypassing the social graph.
Traditionally, our large social networks in the West have consistently used a social graph to approximate an interest graph. But that gets them into problems.
Sonal: Yeah… In fact, if you look at the history of original recommender algorithms, I actually met the guy who got the original patent on he used to work at Xerox PARC. And one of the things that’s fascinating about that is that he had this really cutting-edge insight [at the time] that one of the ways to recommend things is to look at your friends and find things that you like. But that’s not always true. Like, your friends’ interests do not actually capture your interest. Like, I’m your friend, and I love your views on film and you’re really into movies and books; I have those interests in common with you — but you’re also really into sports, and I have no interest in sports. And so if you were suddenly tweeting a bunch of sports things, I wouldn’t be interested in following that segment of your timeline.
Eugene: Right, so we’ve seen this happen again and again in other social networks: On Facebook, they pivoted from, hey here’s photos from your friends, to hey here’s someone sharing like a political news story. And it’s the same on Twitter where you might follow someone who has a lot of interesting thoughts on something that you care about. But then, yes, they suddenly start posting about their favorites home sports team, or, something that you don’t care about — and then you’re stuck in this bind, because the entire feed, and the entire graph, is built off of that social following. And you start to get a higher noise to signal ratio in your feed. And that can lead to churning, or losing interest in that.
So TikTok is like you know what, we’re not focused on that at all: We just consistently want to know what’s entertaining you right now. And we’re going to keep showing you more of it.
Sonal: I’m just gonna read something from your post that’s super relevant, because you talk about how they notice everything. And if you like a video featuring video game captures, “that is noted”. If you like videos featuring puppies, “that is noted”. Like, “it is known”, it is noted, it is noted. So they notice everything basically, and they do all the work, so you don’t have to explicitly tell the algorithm by who you’re following… it just decides for you and serves things up to you.
Eugene: The thing that’s really interesting, is that they epitomize an idea that I first read about in James Scott’s Seeing like a State.
James Scott writes a lot about hey, you know a lot of modern governance and everything was built around this idea of, we have to make certain phenomenon more legible in order for us to take actions on them. For example, if you want to tax people, if you want to conscript people, you need to actually know like how many people live in your country, what pieces of land do they operate; and so, there came about this idea of just classifying and structuring society in a way that made those units of measurement more legible, so that you could do things like tax people fairly. And we live in such a world where that’s taken for granted now that we almost don’t think about it, but if you think about a previous era, when people didn’t even have last names, it was just really hard to track your citizenry.
I think about TikTok as an app that epitomizes the idea of “seeing like an algorithm” — where if the algorithm is going to be one of the key functions of your app, how do you design an app that allows the algorithm to see what it needs to see?
So, the ByteDance example: They have a huge operations team that when videos are made, are tagging videos with features and attributes — so this video has a kitten in it, this video has a lion in it, this video has soldiers doing workouts in it. All those classifications actually really matter because visual AI hasn’t reached a point where you can determine exactly what the video is about. But because ByteDance invests so much in this, when they serve a video to you in TikTok, the algorithm can already see a lot of what’s in the video, it knows what the video is about.
Next, if you look at the design of the app, what’s striking about TikTok is it only shows you one video, full screen, at a time. And whether it’s by design or accident, this is very very different from social media apps, where there are many items on the screen at one time. So with a Facebook or Twitter, if they show you like four stories on your phone screen at a time and you’re just rapidly scrolling past it, the algorithm has a hard time seeing what you feel; like, what are you even looking at on the screen?
TikTok is different: They show you one video, one video only. And from the moment that video is on the screen, they’re looking at everything you do. And they can attribute all of that to being a clue as to your sentiment on that video. If you flip past that video, before it even finishes, that can be a negative signal. If you instead let the video loop four times, then you share it, then you heart it, then you go and follow the creator, or then you go and look at the musical track — those are all signals of interest.
And so in that way, their feedback loop is super efficient and tightly closed. And that is, I think, a form of design that I refer to as “algorithm friendly design”. You know traditionally, all of the design principles that have guided the Valley for a long time are about minimizing user friction; in this case, they’re actually introducing a bit of friction, right.
It would be faster if they showed me multiple thumbnails on the screen, for me to just scan through a bunch and flip through them; they’re intentionally slowing me down, and showing me one thing at a time. But in doing so they get much cleaner feedback about my sentiment — and that means that the training of the algorithm happens more quickly.
Sonal: Ohmygod what a great explanation. So just to quickly sum up, this idea of “seeing like an algorithm” is critical. And what you really added to this as well — besides that great phrase <chuckles> — is, the fact that the product is designed to support this ability to essentially isolate the variables, in that feedback loop of what you’re studying and what you’re noticing, so that you feed it back to your users.
That explains then the context that we need to know to kind of understand how the algorithm works, and what it is. So now let’s cover the third question of dissemination — and now how does that play into this whole… flywheel of these creator network effects, and then now you have distribution.
Eugene: Yah. So, the problem in the modern age is not that we don’t have enough content… it’s that can that content find its audience. And because TikTok has such a nice closed feedback loop — its algorithm can see what each viewer is interested in, and it can see what each video is about — it can also see how an initial test audience reacts to a video.
It has all the components it needs to match the right video to the right viewer. And that’s the distribution part — not build on a social graph, build out an algorithm that’s just really efficient at matching content, to people who will enjoy that content. And that’s why I referred to it as “The Sorting Hat” from Harry Potter; you know more about Harry Potter than I do.
Sonal: <laughs> I do!
Eugene: Yah, it’s a little mysterious how the Sorting Hat works. But it did seem to pick people with the right disposition to be a Hufflepuff, or a Gryffindor, or a Slytherin.
You know I’m interested in really weird postmodern memes on TikTok, and it consistently serves me some really bizarre things <chuckles>; it feels like magic to me. But I know that it’s very mundane if you break it down how it works.
Sonal: So, just to just to ground the significance of your analogy of the Sorting Hat — Imagine a world of the countless thousands, millions, billions of users out there. And then you have… this ability to essentially identify people who have like-minded kind of interests — again going back to the concept of interest graph — and sorting them into quote-“houses” of shared interest. Because in Harry Potter, the analogy is not just that these people are alike or anything, but that they have shared interests, and personality traits, or things that they like, or whatever it is.
You know one of the interesting things about the internet, is people often talk about how it breaks down geographical barriers… going back to this idea of the Sorting Hat, the significance of this ability to distribute and sort people into houses, and communities, is really significant.
Eugene: The thing that an algorithmic sorting allows you to do is to just scale that sorting function… infinitely. You could have editors at a magazine trying to determine what its readership is interested in, but, it will never be able to keep up with the just sheer infinite variety of its audience. You could have Reddit, which kind of sorts people into subreddits; but you still have to go and find the subreddit yourself and join.
TikTok just allows this to happen organically, without you really having to do much that feels like work. They don’t necessarily force you through a long profiling step; you just jump in and start watching these funny videos. It’s relatively low cost; if you see a bad video or one that bores you, you just swipe past it, and immediately have a new one playing. And as that’s happening, the app is learning about your tastes.
The other thing is people’s tastes change, over time. And so as your tastes evolve, the TikTok algorithm quickly can detect that like oh okay, this week you’re into Draco fan fiction. We’re gonna show you some more of that, because we happen to have plenty of that right now–
Sonal: <laughs> Which you are!
Eugene: –Yah yah; and I’m sure by next week, I’m going to be on to something else. <Right> So it sort of is just closely hewing to your taste profile.
You know, Alex and Louis (who founded Musical.ly), I mean they did work in the U.S.; so it’s not like they didn’t know anything about American culture. But, the fact is that no matter how many people you have working at your company, there’s no way — if you reach hundreds of millions or even billions of users — that you can personalize, manually for all of those users. And, the algorithm here essentially says that you can scale to serve an audience of ANY size, in ANY country. And that’s really powerful.
Sonal: So just as you made the observation earlier that the creators can evolve on this platform, and that the system evolves in identifying them and their skills as they do, so does it work for the consumers who are evolving, which is super powerful.
I love what you said about the subreddits, too, because it’s not just the friction — actually, when you go into any kind of online community, you have to learn these norms. And here, you’re kind of immersed in a community; but, it’s actually not social at all, at the end of the day. Like TikTok, ironically, is not a social network, technically, then. <Right!> How do you kind of define it in your taxonomy of social networks?
Eugene: I call it an entertainment network, where its primary purpose is to match these entertaining videos from creators, to the audience that would enjoy them — that’s its primary purpose. And you can obviously leave comments with creators… And a lot of creators will accept challenges, from their viewers (you can ask someone to make a video of a particular type, and sometimes in a video, they will say, “Hey, this is in response to user X, Y, Z”) —
But you’re right, that the dominant mode of TikTok is not as a social graph. And that’s probably by design, and allows them to avoid the negative economies of scale that come from a social graph, that reaches a really large size.
Sonal: Okay. Now let’s bring it back to the news and the trends; so this show is about covering the long arc of tech trends — we’ve talked about the evolution of recommender systems, the social networks, we’ll talk about video in a second — you’ve started to tease apart what’s hype, what’s real (including some of the hype you yourself may have put out about the importance of the algorithm;) —
To close the loop on bringing it back to the news, where do you stand on this idea, if in the final agreement — and again, who knows what’s going to happen ‘cause this changes every day — the algorithm is or isn’t part of it? Cuz China just updated their export controls to be able to refute the deal if they don’t want it to be in there, the source code. How much of a difference do you think it makes? Do you think if they were to back engineer an algorithm that functions similarly, that noticed everything, given the current product design — do you think they could conceivably still recreate that sort of wheel, given that there is already this critical mass of users on TikTok?
Eugene: Well, earlier, I talked about how I think people are maybe overrating the algorithm in terms of just like you know how unique the algorithm is itself. But: It is certainly true that if you purchase TikTok and it didn’t come with the algorithm, it would take you some amount of time — even if you had all the user data, video metadata, all of that — to sort of rebuild, and retrain, an algorithm of your own.
There’s always a risk with a social network that in that interim period (maybe it takes you months, maybe it takes you a year), that people would find that the app wasn’t as responsive… to their interests anymore, and that they might churn off of it. So, certainly you would rather have access to the full closed loop that allows that information to be fed back cleanly into the algorithm.
The algorithm’s already been trained <chuckles>; the hardest part often with a lot of these algorithms is getting that training data set, and they already have just a massive training data set of these videos with I don’t know, a gazillion hours of view time. You have a lot of users whose tastes are-have already been profiled.
So… yeah; I would say that it is possible to rebuild an algorithm. I think with the right tech companies, you have a lot of the talent here in the U.S. that can do that. But, that process takes time, and that’s risky.
Sonal: Okay so now I’m going to ask you just two last quick questions on sort of the long arc of tech trends, and then one practical question before we switch to that.
As someone who thinks a lot about product, and multimedia, and you know has worked on designing– you’ve actually actively designed many of these things in production, do you have any advice, or what are the implications, of all this — besides the fact that this phenomenon could occur, penetrate into mass market — what do you think about how this affects your thinking for finding product-market fit, or designing products in this… kind of era?
Eugene: Yeah. You know, I think a lot of people have said wow there hasn’t been any big new social network in recent years other than Snapchat, that have come up to challenge Facebook, Instagram, Twitter, those giants. So I think one big learning from TikTok is, hey, there’s an alternative approach that might work — which is to just cut straight to the interest graph.
And… that the way to do that would be to figure out, can you design an experience, a user experience, that allows a machine learning algorithm to get access to a unique set of training data. And I think it is probably possible in other fields and disciplines. I do think it takes a new approach to design, which is this “algorithm-friendly design”.
Sonal: Yeah. “Seeing like an algorithm”.
Eugene: Yeah, exactly. You’re like hey, this algorithm isn’t sitting in this design meeting with us right now; but it’s really important that when we’re thinking about what does the UI look like, what are the feedback loops, that we’re capturing the right data for the algorithm to be able to SEE, and do its work.
So I think that is a novel new sort of design- and product-development paradigm, which TikTok has created. (And you know really ByteDance even used that to develop their first trendy hot news app in China called Toutiao.)
Sonal: ‘K. So then now arcing back up a bit to broad trends, how do you view this in the long arc of innovation when it comes to video, and the future of video? Because one of the recurring themes of your post — which it was kind of a recurring motif — is, we really haven’t figured out video; we’re actually still at the beginning of video; there’s a lot more to be done in video; it’s shocking to me how little people are doing video well. What are your high-level takeaways on that front when it comes to that tech trend and evolution?
Eugene: Two big takeaways. One is, that I think we consistently underrate the degree to which people respond more broadly to video than they do to, for example, text. You know the number of people who are going to read books, all the time, is just a fraction of the number of people who enjoy watching video.
And so, that really matters at scale. When you’re talking about reaching a broader audience. I don’t think we have a medium that can challenge video, in the world. I think the evidence is overwhelming. The second thing is…
Sonal: I mean, I would give you a little bit on audio <Eugene laughs> but we don’t have to go off on that side tangent. Let’s just stick to video <right> Keep going! The second thing.
Eugene: The second thing… is that in order for video to scale as a medium, you do have to do some work to overcome some of the challenges inherent to video. Video is traditionally a little bit harder to scan for conceptual information; you know, it’s harder to understand what’s in a video. Even if you’re watching a video, if someone sends you a video, sometimes people are like, I wish you would just send me the transcript so I can just scan through it really quickly. You know scanning video is even hard.
So, TikTok fortunately, the video is all really short. And they allow additional layers of metadata; you can bring text into the video, really easily. And so video overall as a medium, is a richer medium on TikTok. If you can bring that all to bear, then I think video becomes more relevant in other fields — like, education, or you know if you want to pick a place to go on vacation, or you want to pick a restaurant to go eat at.
Sonal: Yah. Our partner Connie Chan’s actually argued a lot about the power of every commerce will become video, and every video will become commerce, and sort of the intersection of the two.
Eugene: Right… video is really just the bed for a whole bunch of other information to be laid on top of it.
Video is just such a high bandwidth medium. I think we haven’t really taken advantage of that full level of bandwidth in the past. We know that humans are super attuned to body language, to reading another person’s face; you know, one of the downsides of trying to read body language over Zoom, you may have like 15 people in a Zoom, each is just a small thumbnail; you can’t really see anything, other than a blurry version of their face. There is something that is lost when you lower the bandwidth. And video brings that back, and video gets higher fidelity every day. And, you know something like TikTok now is just making more use of that full bandwidth.
Sonal: Great. So then the last question: What do you make of this larger phenomenon, given that the whole point of your post is about how this is the first time a social network from another place has really cracked into a different market. (And we haven’t even talked about India, and Middle East, but it’s also cracked into other markets, not just the U.S.)
The thing that fascinated me about your post, is this idea that there could be this internet layer that crosses regions and cultures. And you share an anecdote at the end of your post where the engineers that you- the office that you visited, they had like all these Hindi lyrics and Bollywood lip synching going on, and not a single person in the office even knew what they were seeing, or could even read Hindi. That is kind of amazing…
Eugene: Right. And that’s the one powerful thing about video; a lot of it doesn’t require you to understand the language. In fact you know a dance video, a little skit, even if they’re speaking in it, often you can just interpret based on what’s happening on screen <yah> what they’re talking about.
That language is international. In a way, it’s more international language than even text. You know a lot of people in America still can’t read a lick of Chinese <mhm>, and a lot of people in China can’t really read English, as well. But when it comes to video, and you show somebody a video on your phone, everybody can understand you know, oh this is a cute baby video, or this is an animal doing something funny. Netflix, for example right, is trying to figure out, hey which shows that we make in one market could carry over to other markets; if we can, we prefer that because it makes our content spend more efficient.
Sonal: All right. So, Eugene, bottom-line it for me. A lot to say, but on this explainer/news commentary episode on to-algorithm or not to see-like-an-algorithm, what is your takeaway on the news, bottom line it for me.
Eugene: Look, I don’t know what’s going to happen with this deal… regardless of that, I think TikTok’s impact will last, in that it provides a model, for how in an age of you know increased use of machine learning algorithms, you might build a new sort of network — that’s really built around algorithmic recommendations, and that shortcuts you to building out the interest graph.
Which ultimately, is probably one of the most valuable graphs in the world. If you think about how social networks make money — trying to determine which ads are relevant to serve to you; on the other side, the advertisers want their ads to reach the right audience — that’s ALL interest graph; that’s not really social graph. And so TikTok came along at a time when everybody was like, well, we’re stuck with these social networks. And they kind of snuck up on everybody from the side. And that’s a remarkable story.
Sonal: Thank you so much for joining this segment of “16 Minutes”, Eugene.
Eugene: Thanks for having me.
image: Eliza Petersen
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