Instagram, TikTok, and the Three Trends
Instagram, TikTok, and the Three Trends ——
Back in 2010, during my first year of Business School, I helped give a presentation entitled “Twitter 101”:
My section was “The Twitter Value Proposition”, and after admitting that yes, you can find out what people are eating for lunch on Twitter, I stated “The truth is you can find anything you want on Twitter, and that’s a good thing.” The Twitter value proposition was that you could “See exactly what you need to see, in real-time, in one place, and nothing more”; I illustrated this by showing people how they could unfollow me:
The point was that Twitter required active management of your feed, but if you put in the effort, you could get something uniquely interesting to you that was incredibly valuable.
Most of the audience didn’t take me up on it.
Facebook Versus Instagram
If there is one axiom that governs the consumer Internet — consumer anything, really — it is that convenience matters more than anything. That was the problem with Twitter: it just wasn’t convenient for nearly enough people to figure out how to follow the right people. It was Facebook, which digitized offline relationships, that dominated the social media space.
Facebook’s social graph was the ultimate growth hack: from the moment you created an account Facebook worked assiduously to connect you with everyone you knew or wish you knew from high school, college, your hometown, workplace, you name an offline network and Facebook digitized it. Of course this meant that there were far too many updates and photos to keep track of, so Facebook ranked them, and presented them in a feed that you could scroll endlessly.
Users, famously, hated the News Feed when it was first launched: Facebook had protesters outside their doors in Palo Alto when it was introduced, and far more online; most were, ironically enough, organized on Facebook. CEO Mark Zuckerberg penned an apology:
We really messed this one up. When we launched News Feed and Mini-Feed we were trying to provide you with a stream of information about your social world. Instead, we did a bad job of explaining what the new features were and an even worse job of giving you control of them. I’d like to try to correct those errors now…
The errors to be corrected were better controls over what might be shared; Facebook did not give the users what they claimed to want, which was abolishing the News Feed completely. That’s because the company correctly intuited a significant gap between its users stated preference — no News Feed — and their revealed preference, which was that they liked News Feed quite a bit. The next fifteen years would prove the company right.
It was hard to not think of that non-apology apology while watching Adam Mosseri’s Instagram update three weeks ago; Mosseri was clear that videos were going to be an ever great part of the Instagram experience, along with recommended posts. Zuckerberg reiterated the point on Facebook’s earnings call, noting that recommended posts in both Facebook and Instagram would continue to increase. A day later Mosseri told Casey Newton on Platformer that Instagram would scale back recommended posts, but was clear that the pullback was temporary:
“When you discover something in your feed that you didn’t follow before, there should be a high bar — it should just be great,” Mosseri said. “You should be delighted to see it. And I don’t think that’s happening enough right now. So I think we need to take a step back, in terms of the percentage of feed that are recommendations, get better at ranking and recommendations, and then — if and when we do — we can start to grow again.” (“I’m confident we will,” he added.)
Michael Mignano calls this recommendation media in an article entitled The End of Social Media:
In recommendation media, content is not distributed to networks of connected people as the primary means of distribution. Instead, the main mechanism for the distribution of content is through opaque, platform-defined algorithms that favor maximum attention and engagement from consumers. The exact type of attention these recommendations seek is always defined by the platform and often tailored specifically to the user who is consuming content. For example, if the platform determines that someone loves movies, that person will likely see a lot of movie related content because that’s what captures that person’s attention best. This means platforms can also decide what consumers won’t see, such as problematic or polarizing content.
It’s ultimately up to the platform to decide what type of content gets recommended, not the social graph of the person producing the content. In contrast to social media, recommendation media is not a competition based on popularity; instead, it is a competition based on the absolute best content. Through this lens, it’s no wonder why Kylie Jenner opposes this change; her more than 360 million followers are simply worth less in a version of media dominated by algorithms and not followers.
Sam Lessin, a former Facebook executive, traced this evolution from the analog days to what is next in a Twitter screenshot entitled “The Digital Media ‘Attention’ Food Chain in Progress”:
The coming fall of the Kardashians in context of how entertainment is evolving… (aka why they are so pissed about tiktok) pic.twitter.com/wtYrvxbS35
— sam lessin
(@lessin) July 26, 2022
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Lessin’s five steps:
- The Pre-Internet ‘People Magazine’ Era
- Content from ‘your friends’ kills People Magazine
- Kardashians/Professional ‘friends’ kill real friends
- Algorithmic everyone kills Kardashians
- Next is pure-AI content which beats ‘algorithmic everyone’
This is a meta observation and, to make a cheap play on words, the first reason why it made sense for Facebook to change its name: Facebook the app is eternally stuck on Step 2 in terms of entertainment (the app has evolved to become much more of a utility, with a focus on groups, marketplace, etc.). It’s Instagram that is barreling forward. I wrote last summer about Instagram’s Evolution:
The reality, though, is that this is what Instagram is best at. When Mosseri said that Instagram was no longer a photo-sharing app — particularly a “square photo-sharing app” — he was not making a forward-looking pronouncement, but simply stating what has been true for many years now. More broadly, Instagram from the very beginning — including under former CEO Kevin Systrom — has been marked first and foremost by evolution.
To put this in Lessin’s framework, Instagram started out as a utility for adding filters to photos put on other social networks, then it developed into a social network in its own right. What always made Instagram different than Facebook, though, is the fact that its content was default-public; this gave the space for the rise of brands, meme and highlight accounts, and the Instagram influencer. Sure, some number of people continued to use Instagram primarily as a social network, but Meta, more than anyone, had an understanding of how Instagram usage had evolved over time.
In other words, when Kylie Jenner posts a petition demanding that Meta “Make Instagram Instagram again”, the honest answer is that changing Instagram is the most Instagram-like behavior possible.
Three Trends
Still, it’s understandable why Instagram did back off, at least for now: the company is attempting to navigate three distinct trends, all at the same time.
The first trend is the shift towards ever more immersive mediums. Facebook, for example, started with text but exploded with the addition of photos. Instagram started with photos and expanded into video. Gaming was the first to make this progression, and is well into the 3D era. The next step is full immersion — virtual reality — and while the format has yet to penetrate the mainstream this progression in mediums is perhaps the most obvious reason to be bullish about the possibility.
The second trend is the increase in artificial intelligence. I’m using the term colloquially to refer to the overall trend of computers getting smarter and more useful, even if those smarts are a function of simple algorithms, machine learning, or, perhaps someday, something approaching general intelligence. To go back to Facebook, the original site didn’t have any smarts at all: it was just a collection of profile pages. Twitter came along and had the timeline, but the only smarts there was the ability to read a time stamp: all of the content was presented in chronological order. What made Facebook’s News Feed work was the application of ranking: from the very beginning the company tried to present users the content from their network that it thought you might be most interested in, mostly using simple signals and weights. Over time this ranking algorithm has evolved into a machine-learning driven model that is constantly iterating based on every click and linger, but on the limited set of content constrained by who you follow. Recommendations is the step beyond ranking: now the pool is not who you follow but all of the content on the entire network; it is a computation challenge that is many orders of magnitude beyond mere ranking (and AI-created content another massive step-change beyond that).
The third trend is the change in interaction models from user-directed to computer-controlled. The first version of Facebook relied on users clicking on links to visit different profiles; the News Feed changed the interaction model to scrolling. Stories reduced that to tapping, and Reels/TikTok is about swiping. YouTube has gone further than anyone here: Autoplay simply plays the next video without any interaction required at all.
One of the reasons Instagram got itself in trouble over the last few months is by introducing changes along all of these vectors at the same time. The company introduced more video into the feed (Trend 1), increased the percentage of recommended posts (Trend 2), and rolled out a new version of the app that was effectively a re-skinned TikTok to a limited set of users (Trend 3). It stands to reason that the comany would have been better off doing one at a time.
That, though, would only be a temporary solution: it seems likely that all of these trends are inextricably intertwined.
Medium, Computing, and Interaction Models
Start with medium: text is easy, which is why it was the original medium of the Internet; effectively anyone can create it. The first implication is that there is far more text on the Internet than anything else; it also follows that the amount of high quality text is correspondingly high as well (a small fraction of a large number is still very large). The second implication has to do with AI: it is easier to process and glean insight from text. Text, meanwhile, takes focus and the application of an acquired skill for humans to interpret, not dissimilar to the deliberate movement of a mouse to interact with a link.
Photos used to be more difficult: digital cameras came along around the same time as the web, but even then you needed to have a dedicated device, move those photos to your computer, then upload them to a network. What is striking about the impact of smartphones is that not only did they make the device used to take pictures the same device used to upload and consume them, but they actually made it easier to take a picture than to write text. Still, it took time for AI to catch up: at first photos were ranked using the metadata surrounding them; only over the last few years has it become possible for services to understand what the photo actually is. The most reliable indicator of quality — beyond a like — remains the photo that you stop at while scrolling.
The ease of making a video followed a similar path to photos, but more extreme: making and uploading your own videos before the smartphone was even more difficult than photos; today the mechanics are just as easy, and it’s arguably even easier to make something interesting, given the amount of information conveyed by a video relative to photos, much less a text. Still, videos require more of a commitement than text or videos, because consuming them takes time; this is where the user interaction layer really matters. Lessin again, in another Twitter screenshot:
FB’s has an interface problem, not an algorithm problem… because people won’t affirmatively click on the things they actually deep down want to watch pic.twitter.com/QfEiaOf13j
— sam lessin
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I saw someone recently complaining that Facebook was recommending to them…a very crass but probably pretty hilarious video. Their indignant response [was that] “the ranking must be broken.” Here is the thing: the ranking probably isn’t broken. He probably would love that video, but the fact that in order to engage with it he would have to go proactively click makes him feel bad. He doesn’t want to see himself as the type of person that clicks on things like that, even if he would enjoy it.
This is the brilliance of Tiktok and Facebook/Instagram’s challenge: TikTok’s interface eliminates the key problem of what people want to view themselves as wanting to follow/see versus what they actually want to see…it isn’t really about some big algorithm upgrade, it is about relesing emotional inner tension for people who show up to be entertained.
This is the same tension between stated and revealed preference that Facebook encounted so many years ago, and its exactly why I fully expect the company to, after this pullback, continue to push forward with all three of the Instagram changes it is exploring.
Instagram’s Risk
Still, there is considerably more risk this time around: when Facebook pushed forward with the News Feed it was the young upstart moving aside incumbents like MySpace; it’s not as if its userbase was going to go backwards. This case is the opposite: Instagram is clearly aping TikTok, which is the young upstart in the space. It’s possible its users decide that if they must experience TikTok, they might as well go for the genuine thing.
This also highlights why TikTok is a much more serious challenge than Snapchat was: in that case Instagram’s network was the sword used to cut Snapchat off at the knees. I wrote in The Audacity of Copying Well:
For all of Snapchat’s explosive growth, Instagram is still more than double the size, with far more penetration across multiple demographics and international users. Rather than launch a “Stories” app without the network that is the most fundamental feature of any app built on sharing, Facebook is leveraging one of their most valuable assets: Instagram’s 500 million users…Instagram and Facebook are smart enough to know that Instagram Stories are not going to displace Snapchat’s place in its users lives. What Instagram Stories can do, though, is remove the motivation for the hundreds of millions of users on Instagram to even give Snapchat a shot.
Instagram has no such power over TikTok, beyond inertia; in fact, the competitive situation is the opposite: if the goal is not to rank content from your network, but to recommend videos from the best creators anywhere, then it follows that TikTok is in the stronger relative position. Indeed, this is why Mosseri spent so much time talking about “small creators” with Newton:
I think one of the most important things is that we help new talent find an audience. I care a lot about large creators; I would like to do better than we have historically by smaller creators. I think we’ve done pretty well by large creators overall — I’m sure some people will disagree, but in general, that’s what the data suggests. I don’t think we’ve done nearly as well helping new talent break. And I think that’s super important. If we want to be a place where people push culture forward, to help realize the promise of the internet, which was to push power into the hands of more people, I think that we need to get better at that.
There is the old Internet AMA question as to whether you would rather fight a horse-sized duck or 100 duck-sized horses. The analogy here is that in a world of ranking a horse-sized duck that everyone follows is valuable; in a world of recommendations 100 duck-sized horses are much more valuable, and Instagram is willing to sacrifice the former for the latter.
Meta’s Reward
The payoff, though, will not be “power” for these small creators: the implication of entertainment being dictated by recommendations and AI instead of reputation and ranking is that all of the power accrues to the platform doing the recommending. Indeed, this is where the potential reward comes in: this power isn’t only based on the sort of Aggregator dynamics underpinning dominant platforms today, but also absolutely massive amounts of investment in the computing necessary to power the AI that makes all of this possible.
In fact, you can make the case that if Meta survives the TikTok challenge, it will be on its way to the sort of moat enjoyed by the likes of Apple, Amazon, Google, and Microsoft, all of which have real world aspects to their differentiation. There is lots of talk about the $10 billion the company is spending on the Metaverse, but that is R&D; the more important number for this moat is the $30 billion this year in capital expditures, most of which is going to servers for AI. That AI is doing recommendations now, but Meta’s moat will only deepen if Lessin is right about a future where creators can be taken out of the equation entirely, in favor of artificially-generated content.
What is noteworty is that AI content will be an essential part of any sort of Metaverse future; I wrote earlier this year in DALL-E, the Metaverse, and Zero Marginal Content:
What is fascinating about DALL-E is that it points to a future where these three trends can be combined. DALL-E, at the end of the day, is ultimately a product of human-generated content, just like its GPT-3 cousin. The latter, of course, is about text, while DALL-E is about images. Notice, though, that progression from text to images; it follows that machine learning-generated video is next. This will likely take several years, of course; video is a much more difficult problem, and responsive 3D environments more difficult yet, but this is a path the industry has trod before:
- Game developers pushed the limits on text, then images, then video, then 3D
- Social media drives content creation costs to zero first on text, then images, then video
- Machine learning models can now create text and images for zero marginal cost
In the very long run this points to a metaverse vision that is much less deterministic than your typical video game, yet much richer than what is generated on social media. Imagine environments that are not drawn by artists but rather created by AI: this not only increases the possibilities, but crucially, decreases the costs.
These AI challenges, I would add, apply to monetization as well: one of the outcomes of Apple’s App Tracking Transparency changes is that advertising needs to shift from a deterministic model to a probabilistic one; the companies with the most data and the greatest amount of computing resources are going to make that shift more quickly and effectively, and I expect Meta to be top of the list.
None of this matters, though, without engagement. Instagram is following the medium trend to video, and Meta’s resources give it the long-term advantage in AI; the big question is which service users choose to interact with. To put it another way, Facebook’s next two decades are coming into sharper focus than ever; it is how well it navigates the TikTok minefield over the next two years that will determine if that long-term vision becomes a reality.
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