Pod Insights a16z Podcast Two-Sided Networks in Healthcare - a Founder's Playbook About this episode 5 leading digital health founders - from GoodRx, Headway, Incredible Health, Komodo, and Owkin - on how to build multi-sided networks in healthcare.
Multi-sided network businesses are connecting our fragmented and siloed healthcare system in fundamentally new ways. Founders are using this go-to-market strategy as a creative and efficient way of getting their technology innovation to their target customers — patients, providers, payors, pharma/biotechs, and/or pharmacies. However, it can be complex to execute.
Vineeta Agarwala and Jay Rughani spoke with digital health builders — from GoodRx, Headway, Incredible Health, Komodo Health, Owkin — who are meeting the challenge of setting up these complex but resilient business models.
0:00 Intro & Disclosures
9:50 Which side do you start with?
15:56 How do you win over your early users?
20:00 How do you price your initial product?
21:33 Which metrics should you focus on?
26:53 When should you activate the next side?
28:00 How do you find product-market fit (again)?
33:01 How do you balance customer priorities across each side?
34:08 How do you keep growing both sides?
For more on building digital health companies: https://a16z.com/digital-health-builders/
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 The algorithm is a buzzword that often crops up when discussing social media, creators and many consumer tech products, old and new. But when we say quote unquote, "the algorithm," what do we actually mean from a product perspective? And what does the success of algorithmically driven products like TikTok tell us about the future of product design, the creator economy, and innovation across cultural lines? In this crossover episode from our tech news podcast, 16 Minutes on the News, we go deep into the what, why, and how of TikTok, specifically digging into its algorithm-friendly product design. The conversation features Eugene Wei, former head of product at Hulu, Flipboard, and video at Oculus, and Sonal Choksi.
0:43 Speaker 00 And it was originally released in September 2020, when the Trump administration was considering mandating US ownership of the American version of TikTok. For the transcript of this interview, as well as more experts on product, algorithms, and the evolution of social apps, please check out future.com.
1:01 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 we 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 it's 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 the show, I do one of these deep dive, kind of 2X explainer episodes, so 32-ish minutes every so often, where we go deeper, not only in what's in the news, but really dig into what the top experts, the key underlying concepts. And our expert today is Eugene Wei, who has written a series of deep dives about TikTok,
1:46 Sonal? 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 a61z.com/disclosures. And for 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 US 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 the US 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.
2:31 Sonal? 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, we're talking about the For You page algorithm, which, Eugene, you wrote about recently as, quote, "the most important 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 this news that this sale or partnership, or whatever it ends up technically being, may or may not include the algorithm?
3:12 Speaker 02 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 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 the 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.
3:53 Speaker 02 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.
4:37 Sonal? Can you quickly actually just walk us through the history of how TikTok actually did get that training data and then combined the algorithm to create this phenomenon where it was able to run circles around US video apps from YouTube to Facebook to Instagram to Snapchat? How do 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.
4:58 Speaker 02 Yeah. Well, it's ironic because it starts with the app Musically in many ways. Musically was a video app created by Alex and Lewis, who had worked in the US but were in China and had pivoted from a short video education app. And they launched it in both China and the US, and it actually became more successful in the US, especially among American teenage girls who used it to do lip sync and dance videos. And then ByteDance cloned Musically essentially in China in an app called Douyin. The irony of that is actually that the clone of Musically ended up launching in a larger market and becoming a larger app with a larger user base.
5:43 Speaker 02 And so eventually, they bought Musically after its growth had stalled out in the US. And that's when they rebranded Musically into TikTok. So it's this weird, you know, multi-hop mutation of the app that like, built in China, did well in the US, got copied in China, and then China bought the US version. It just kept hopping back and forth across the ocean.
6:04 Sonal? Well, now the hop is kind of funny because it could go the other direction where part of it could be divested to a sale in the US.
6:09 Speaker 02 Yeah, 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.
6:22 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 US 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?
6:47 Speaker 02 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 Musically 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.
7:27 Sonal? That's a great example of a tool that really makes something easy and fast that was previously hard.
7:31 Speaker 02 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.
8:14 Speaker 02 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 actually, I think it's very rare to find this form of network effect in the wild, but TikTok has achieved it. A couple of 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.
8:59 Speaker 02 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 a duet.
9:33 Sonal? You're talking about duets?
9:34 Speaker 02 Yeah, you can do a duet with someone where it's 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 if 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.
10:18 Sonal? So it's really interesting because you, in your original post, described TikTok as 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,
11:02 Sonal? the story of it is remix culture. So like, what do you think specifically about TikTok really advanced the mutation wheel?
11:08 Speaker 02 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.
11:45 Sonal? Ooh, interesting.
11:46 Speaker 02 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.
12:30 Sonal? Right, and there's a quick point of contrast for those who are not as in TikTok. 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. It could be arbitrary for all you care. It could be five people trending. It could be whatever.
12:44 Speaker 02 Right.
12:44 Sonal? 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 is you can game but not game the algorithm on TikTok.
13:02 Speaker 02 Yeah. 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 described in other social networks where the creators who've been there the longest have such an advantage over new creators.
13:23 Sonal? Right. They've accrued the most quote status in that network.
13:26 Speaker 02 Exactly. Exactly. So if you even see like the meteor meteor 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.
13:40 Sonal? Ah, that's great.
13:41 Speaker 02 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're always being shown stuff that they've determined has entertained some test audience at some, you know, part of the network.
14:02 Sonal? It's almost like evolution is constantly testing for fitness of this creator essentially in this, in this model.
14:08 Speaker 02 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 going to let your video sink or float based on how it does with that first test audience they show it to.
14:45 Sonal? So when you talk about assisted evolution, it's like a combination of this managed economy and free market dynamic, which is fabulous.
14:50 Speaker 02 Yeah.
14:51 Sonal? 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 discreet things or obviously on a continuum and interact. But let's talk about dissemination and really distribution.
15:29 Speaker 02 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.
15:59 Sonal? Say a little bit more about what that means just for our listeners who are not in the weeds of social networks.
16:03 Speaker 02 Right, so on 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 also think of this as the very traditional path of social graphs, where the follower graph kind of determines the pathways through which content travels.
16:43 Sonal? Which is then very path dependent shaping the future of that social network.
16:46 Speaker 02 Exactly, and so if you don't build up enough of 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.
17:14 Sonal? Which is the for you page.
17:15 Speaker 02 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.
17:43 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 it. 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 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 friend's 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.
18:20 Speaker 02 So we've seen this happen again and again on 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 and something that you care about. But then, yes, they suddenly start posting about their favorite 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.
19:04 Speaker 02 We just consistently want to know what's entertaining you right now. And we're going to keep showing you more of it.
19:09 Sonal? I was just going to 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.
19:35 Speaker 02 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,
20:19 Speaker 02 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.
20:41 Speaker 02 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.
21:26 Speaker 02 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
22:11 Speaker 02 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
22:56 Speaker 02 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.
23:12 Sonal? Oh, my God, 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, 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.
23:47 Speaker 02 Yeah, 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, built out an algorithm that's just really efficient at matching content to people who will enjoy that content. And that's why I refer to it as the sorting hat from Harry Potter. You know more about Harry Potter than I do.
24:32 Sonal? I do.
24:33 Speaker 02 Yeah, 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. It feels like magic to me, but I know that it's very mundane if you break it down how it works.
24:59 Sonal? So 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 interests." 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,
25:40 Sonal? the significance of this ability to distribute and sort people into houses and communities is really significant.
25:48 Speaker 02 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 a 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.
26:31 Speaker 02 If you see a bad video or one that bores you, to 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 fanfiction. So we're going to show you some more of that because we happen to have plenty of that right now.
26:57 Sonal? Which you are.
26:57 Speaker 02 Yeah, yeah. And I'm sure by next week, I'm going to be on to something else.
27:01 Sonal? Right.
27:01 Speaker 02 So it sort of is just closely heaving. To your taste profile, you know, Alex and Lewis, who founded Musically, I mean, they did work in the US. 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.
27:37 Sonal? So just as you made the observation earlier that the creators can evolve in 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.
28:05 Speaker 02 Right.
28:06 Sonal? How do you kind of define it in your taxonomy of social networks?
28:09 Speaker 02 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 XYZ. 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.
28:49 Sonal? OK, 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 a 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 because this changes every day, the algorithm is or isn't part of it? Because 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?
29:33 Sonal? Do you think they could conceivably still recreate that sort of wheel, given that there is already this critical mass of users on TikTok?
29:40 Speaker 02 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 purchased 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. And there's always a risk with the 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
30:24 Speaker 02 to the full closed loop that allows that information to be fed back cleanly into the algorithm. The algorithm's already been trained. 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 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 US that can do that. But that process takes time and that's risky.
31:02 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?
31:32 Speaker 02 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,
32:17 Speaker 02 which is this algorithm-friendly design.
32:19 Sonal? Yeah, seeing like an algorithm.
32:21 Speaker 02 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 Tootiao.
32:52 Sonal? OK, 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?
33:19 Speaker 02 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-
33:53 Sonal? I mean, I would give you a little bit on audio, but we don't have to go off on that side tangent. Let's just stick to video, keep going. The second thing.
34:00 Speaker 02 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 videos are 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,
34:44 Speaker 02 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.
34:55 Sonal? Yeah, our partner Connie Chan 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.
35:06 Speaker 02 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.
35:47 Speaker 02 And, you know, something like TikTok now is just making more use of that full bandwidth.
35:53 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 US. 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 syncing going on and not a single person in the office even knew what they were seeing or couldn't even read Hindi. That is kind of amazing.
36:32 Speaker 02 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.
36:45 Sonal? Yeah.
36:46 Speaker 02 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.
36:55 Sonal? Mm hmm.
36:56 Speaker 02 And a lot of people in China can't really read English. 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. On Netflix, for example, right, is trying to figure out, hey, which shows that we make in one market can carry over to other markets? If we can, we prefer that because it makes our content spend more efficient.
37:21 Sonal? All right. So Eugene, bottom line it for me, a lot to say, but on this explainer slash 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.
37:34 Speaker 02 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.
37:56 Speaker 02 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.
38:27 Sonal? Thank you so much.
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