TikTok is an interesting new player in social media because its default feed, For You, relies on a machine learning algorithm to determine what each user sees; the feed of content from by creators you follow, in contrast, is hidden one pane over. If you are new to TikTok and have just uploaded a great video, the selection algorithm promises to distribute your post much more quickly than if you were on sharing it on a network that relies on the size of your following, which most people have to build up over a long period of time. Conversely, if you come up with one great video but the rest of your work is mediocre, you can't count on continued distribution on TikTok since your followers live mostly in a feed driven by the TikTok algorithm, not their follow graph.

Companies in the West, too, have been exploring and adding several services within their apps gradually but not explicitly coming out as a super app. Case in point, Amazon in India, also lets you pay utility bills, book travel, order food, groceries and so on.

Success of super apps in the emerging markets is not a co-incidence. According to a report by Sturgeon Capital, “Super apps are strongly aligned with emerging market governments, not least because of their role in shrinking the grey economy. The Indonesian President fired a cabinet member who appeared to be anti-Gojek and then appointed Gojek’s founder to his next cabinet. This is in stark contrast to the US and EU leaders, who are extremely hostile to the super app ambitions of Amazon, Facebook, and Google.”

Google Maps does not have a connected payment service like WeChat does, and, perhaps, that’s the reason why it’s not boasting a super app status yet.

The striking thing is how the two companies peaked are almost precisely the same time and how that moment (end of 2010) was not related to the entry timing of their nominal disruptor. Recall that both companies cite the iPhone as the factor which caused a shift in the basis of competition. But the iPhone launched in mid 2007 (at the beginning of the graph’s date range.) BlackBerry had 16 quarters of growth after the iPhone and Nokia nearly the same. The stock prices of the companies also prospered for a period long after the iPhone entered the scene validating a do-nothing approach.

The first order consequence of cars was that you could move places faster. But the second order consequence of cars is that new business models became possible: now you could create a store that sold to everybody within 50 miles of you, not one mile. That store probably looks very different than the ones that came before it. It looks like Walmart.

So what does this mean for making predictions today? Start by looking for constraints. There are still a lot of them: food, water, energy; homes, jobs, transportation; safety, education, happiness; constraints are everywhere. What happens if they go away, or change significantly? What current business models, built around those constraints, will suddenly be very out of date? How would brand new business models, free from those constraints, be different? How will the future be different from the present?

Left unaddressed, competitive exposure due to this flaw in their network effect could get Uber into a situation like we see with the airline industry, where there’s razor-thin margins and high sensitivity to price competition. That’s a stark contrast to the high margin, winner-take-most market you normally see with network effects companies. Uber’s ~$80+ billion valuation, not to mention its stated ambition to corner the $12 trillion global transportation market, is based on the assumption that it will end up looking more like a company with true network effects, not like an airline. So it stands to reason that they’ve been doing whatever they can to avoid the commoditized fate of the airlines. Enter reinforcement.

Barriers to entry for ridesharing businesses are relatively low as a result — it’s not hard to get down to 4 minute wait times. New ride-hailing apps can easily gain critical mass on the supply side in a given geo and try to compete, like Juno in New York did. Unlike true marketplaces such as OpenTable or eBay, Uber can’t establish an escalating supply-side advantage with their network effect, and their core business is vulnerable to new entrants. Compounding this are the extremely low switching costs of ridesharing for both the demand and supply side, leading to rampant multi-tenanting. It costs nothing but a couple of seconds for riders to switch from their Uber to their Lyft app on their smartphone, meaning that many riders have both apps downloaded and decide based on price. Drivers can also simultaneously drive for both Uber and Lyft (and some third app) with little to no cost to them.

The funny thing about these situations is that they can be quite hard to see, except in hindsight after they’ve been resolved. For the most part, every individual actor is behaving quite rationally; it’s the overall effect that is both comical and tragic all at once.

The way to get startup ideas is to look for theatres of the absurd: situations we encounter in life that are completely and totally backwards because of some concession to reality that makes no sense.  

Sure, it makes the case that Company X is better than average. But that doesn’t carry much meaning when ‘average’ just means failure, and better than average could very well mean ‘delayed failure’, or ‘failure but only after a bunch of investor money was sunk into it’.

For startups, the default expectation is zero: you’re trying to escape the default with a radical, innovative business model where you know a secret that no one else has picked up on yet. If you’re running a startup (especially a venture-backed one), success isn’t a function of linearly adding lots of little pieces: if stars align in just the right way, you get hyper growth; otherwise, it didn’t work. You’re looking for (radical idea) x (innovation) x (execution) x (super fast growth) to line up exactly the right way and get a huge, explosive result. And here’s the thing: Gaussian distributions don’t describe those kinds of outcomes well at all. Sure, there are a lot of little pieces involved, but the ‘average’ result carries a very different meaning: the average startup is a failure. Non-failures are relatively rare events. We need a different kind of curve to explain what’s happening: something a little bit closer to the F Distribution.

And Microsoft was running the operating system, but this is a world that I could explore to my heart’s content. This is a world that I could go in and not just the computer and Windows, but I think even more importantly, the internet with somewhere that you could go explore. And so something what’s really just fascinating about this world that you can go into is that worlds are meaningful if they contain challenges for you. Easy worlds are boring and people leave, worlds need to have challenges in them and jungle gyms for people to climb on and go explore.

So here’s an exercise to help expose those A+ problems, that single most important thing: Ask yourself, ‘What would be the WORST way for me to proceed? What would the worst possible outcome look like? And then ask: what is the opposite of that?

What is the one single thing you could do whereby: a) if you get this thing right, everything else is just an implementation detail, and b) if you don’t get this thing right, then nothing else will work no matter how good a job you do?

The ecosystem collectively benefits by being lean, but any given participant shouldn’t strive to be. 

The Lean Startup is another one. The Lean Startup described a worldview about innovation that preached, “We don’t know what works, we have to try everything, so run lots of little experiments and iterate on your position rapidly in order to figure out how we ought to build the future.” That feels right, and it is right, except it’s right on the level of the startup community as a whole; not on the level of any given company. 

The thing is, just to repeat this again, Disruption Theory is doing a great job of explaining the forces and incentives that exist at the level of the ecosystem. So it rings true, as it should. The issue is that it’s explaining a kind of gameplay that’s carried out at one level of abstraction higher than what the product and growth people at disruptive tech companies actually care about, or what early-stage investors can measure and finance. So the advice that it spits out feels as though it should be true, but ends up being paradoxically backwards. Now we can understand why.

Christensen devotees might at this point protest, and argue that these companies represent “New Market Disruption”. In retrospect that’s what they were, but in practice that’s not what they looked like. They looked like sustaining innovations! The iPhone was famously lambasted by Christensen as a sustaining innovation on the cell phone. Uber was criticized as a sustaining innovation on livery cars. Almost everything starts out as a derivative of something else that already exists, addressing a market that already exists in some form. If Disruption Theory can only recognize New Market Disruption in retrospect, then it’s not that useful.

That’s key to the mystery: the ecosystem as a whole may be disruptive, but any given piece of this ecosystem does not really want to compete on price or plug-and-play modularity at the low end. They want to deliver the best experience possible, not compete on cost! They want the best users, not the lousy ones! 

The smartphone’s job is “An interface on the whole world, in your pocket” and Instagram’s job is “Find out what your friends are up to, share and signal status”; neither of those compete directly in a JTBD sense with the integrated electronics that came before them. If they had, Apple would look like Sony, Instagram would look like Flickr, and neither would be worth hundreds of billions of dollars. 

The incumbents look like classic disruptees. The problem is that fewer and fewer of the newcomers look like classic disruptors. Upstarts are competing on performance and going after high-end customers, which isn’t at all what disruptors are supposed to be doing.  Instead, we’re seeing ecosystems of many different products and businesses, many of which in isolation look like integrated businesses or a sustaining innovations and compete on high-end, quality user experience. But as they do so, they create an ecosystem of technology that undermines and eventually disrupts the integrated, single-purpose legacy systems that preceded them. 

The second important part of the theory is its explanation for how incumbents respond to this kind of threat. Christensen explains how the rational business decision for incumbents is to retreat upmarket rather than compete head-on against these disruptors, because entering into a low-end competition undermines their own business model. This could be for a variety of reasons. Management could feel that their integrated product, and the high quality, brand, and high margins it bestows, is too important to compromise. It could also be that the business’s cost structure or debt structure does not allow them to compete on price. Either way, the hallmark of disruption is seeing incumbents have “allergic reactions” to this new form of competition. 

Today’s disruption isn’t a story of individual businesses disrupting incumbents, but rather of business ecosystems disrupting incumbents. The result is a framework that feels more relevant than ever, but that generates reliably incorrect advice. 

He breaks down secrets into two categories: secrets about the natural world (more or less things you can patent, be they chemicals, compounds, or algorithms), and secrets about people (aspects of our behaviour and desires that remain unacknowledged or misunderstood). Both can be important vehicles to building durable, profitable companies, although in Thiel’s view secrets about people tend to be more interesting and relatively undervalued.

One central theme of the book is the value of secrets: not secrets that you hide and tell no one, but rather things that you know to be true even though others think you’re crazy. Thiel’s main point is that if you want to build a company that can capture a substantial portion of the value it creates, you’ll need to know something important on which few other people agree with you initially: “What valuable company is nobody building? Every correct answer is necessarily a secret: something important and unknown, something hard to do but doable. If there are many secrets left in the world, there are probably many world-changing companies yet to be started.” –Peter Thiel, Zero to One

The founders who do turn toys into companies are generally the ones who relentlessly push what they've made to users and obsessively improve the toy in response to feedback.

The third thing that goes wrong when you take your toy too seriously is that you immediately start optimizing on the things that you believe serious businesses should - profit and margins. While these things are important in the long run, focusing on them too early injects an impossible set of things for an early startup to do.

The second thing that goes wrong when you take your toy too seriously is that you signal to the bigger and better funded companies already in the marketplace that you are onto something important and profitable. This is bad, because those companies will start paying attention to your toy too early and copy/buy/kill it. Airbnb looked like a doofy hipster thing to hotels for a very long time. And then, when it was too late, they realized that it wasn’t a toy at all. By that time, Airbnb had enough customers, revenue, and funding to survive the attacks of the incumbents.

The first thing that goes wrong is you become unwilling to experiment with ideas that aren’t clearly aligned with making a big company. This means that people building serious things focus rapidly on revenue. They become risk averse and innovation averse. Companies built on new technologies have to capitalize on non-obvious ideas, ones that wouldn’t pass muster in large corporations. Otherwise, the large existing companies would do these things themselves.

So far in 2022, the five largest deals by startups, which account for a fifth of the total venture funding in India, have been led by sovereign wealth funds and pension funds such as Ontario Teachers Pension Plan, Canada Pension Plan Investment Board and Qatar Investment Authority with private equity and venture capital firms taking a backseat.In 2021, the top five deals were led by late-stage private equity and venture capital firms like Footpath Ventures, GSV Ventures, Redbird Capital Partners, Alpha Wave Global and Prosus Ventures.

According to data shared by market intelligence firm CB Insights, venture funding to India-based startups dropped to $3.6 billion in the second quarter of 2022 so far from $8 billion in the January-March quarter and over $10 billion a year back. The data also showed that venture funding slowed for the first time sequentially in the January-March quarter of this year since the October-December quarter of 2020.

Unfortunately, I think it’s unlikely that we will see a product like I described anytime soon. The world’s largest bookstore, most popular eBook reader, and biggest social network for books are all owned by a company that has very little competency in design and user-facing product innovation.

You could even build leaderboards for different topics based on the content of the books and articles you read. Or think about a score that indicated how balanced your reading behavior per topic was (to incentivize users to read takes on political topics from different perspectives).

A related product I’d love to see is Strava for Reading. Imagine an eBook reader that not only tracks how much time you spend reading but also *what* you are reading. Based on these proof-of-(reading)-work mechanisms you could build streaks or GitHub-contributions-like visualizations that incentivize users to read more (and more regularly).

I’m more optimistic about Strava for Learning. While the activity of learning itself might be hard to quantify, you can measure the outcome of learning: knowledge. Has anyone built a multiplayer version of Anki yet? Flash cards would be a perfect proof-of-knowledge mechanism and could easily be turned into a game where you compete against friends. Similar to physical activity in the Strava example, learning is not something that most people enjoy doing. As TikTok founder Alex Zhu points out, education goes a little against human nature. In combination with a strong enough signaling mechanism however, you can get users to participate. It’s kind of the opposite of Chris Dixon’s famous “Come for the tool, stay for the network” strategy. Come for the status, stay for the tool.

What other social networks should we build that could have similar positive feedback loops? And what are their proof mechanisms?

What’s great about Strava is that it reinforces a behavior that’s actually good for you: While the status game that initially got you into the app might be zero sum, the actual physical exercise you have to put in to compete has a very positive, compounding effect.

The cost to participate in TikTok’s status game is a lot higher than Instagram’s (compare a well-made dance choreography on TikTok to your median Instagram travel post) – but its powerful feed algorithms also make discovery easier and thus reward users faster and with more social capital.

When new social networks emerge they have to introduce new proof mechanisms to differentiate themselves from existing incumbents. These can either be novel proof-of-creative-work hurdles or completely new proof-of-x mechanisms.

Social networks are therefore not only signaling distribution (and amplification) networks – they also allow users to prove their signaling messages. The creative proof-of-work is just pretext and helps to boost your post. What’s more important are the additional proof mechanisms that social networks provide. In the case of Instagram those are photos and location tags. Instagram is essentially “pics or it didn’t happen”-as-a-service.

That problem is that the nature of cross-side network effects will ultimately lead to newsletters facing the same dilemma: As long as the number of subscribers increases, so will the number of newsletter publishers. Users’ email inboxes – already full with non-newsletter-emails – will get as crowded as social media newsfeeds. Just wait until Gmail introduces an algorithmic feed for your newsletter inbox.

While blogs could in theory be read by anyone with a browser, the technology that really mattered on the consumer side were RSS readers – and those were never adopted en masse. Social networks on the other hand have become a victim of their own success: The amount of consumers has attracted so many players on the supply side that platforms needed to introduce algorithmic feeds to handle the abundance of content. This is why writers like newsletters so much. As other distribution channels are becoming increasingly crowded, email provides an alternative trade route.

So what then explains the newsletter hype?Simple: Distribution.

What’s special about personal blogs is not just the actual writing, it’s also the design the content is presented in. Newsletters lack the unique design aspect that blogs have. Side Note: I firmly believe that the lack of design customization options is one of the main reasons Medium has never lived up to its potential.

What’s interesting about newsletters is that consumers are willing to pay for them. While blogs have never really figured out monetization (apart from ads), Substack alone claims more than 50,000 paying subscribers. This might partly be a timing thing (blogs were popular during a time when people weren’t used to the concept of paying for digital content yet), but I wonder if it’s also driven by the nature of how newsletters work: You have to wait to receive them – like an Amazon package. Maybe that makes the medium feel more tangible and thus worth paying for?

Bookmarks are great to remember *what* you want to revisit later – but not *why* you saved something in the first place. I would love to be able to add notes to my bookmarks directly in each app so that I have some context on why these objects are important when I return to them later.

Most of us don’t use just one bookmarking app for everything. We use different bookmarking apps or bookmarking features depending on the type of object we want to save for later: Podcasts are usually saved in a dedicated podcast app, for example. Articles are bookmarked in Pocket, books on Goodreads, songs on Spotify, places on Foursquare, products on Amazon … you get my point.

Why isn’t there a digital note taking tool that works like this?

Post-it note reminders are similar to Hey’s Thread Notes in that they are triggered not based on time but on events that don’t have a (forecastable) deadline. They are essentially like notifications that appear when you look at specific objects.

You could write down notes like this in a separate notebook, but then you’d lose the connection to the source they are based on. What makes post-it notes so interesting is the spatial relationship between the notes and their respective context.

One of the reasons I still read a lot of non-fiction in physical book form is because it’s easier to bookmark and annotate passages that I quickly want to find again later. Similar to Thread Notes, sticky note bookmarks help me highlight the most important items in a long list.

Post-it notes serve two of the same functions that Hey’s note features offer: highlights and reminders.

Tinder’s entire business model is built on the assumption that people are willing to spend money on signaling. That assumption seems to be correct: Tinder made a staggering $1.2 billion in revenue last year making it one of the most successful apps world wide.

Instead of monetizing network membership, the software products that monetize most successfully have chosen another strategy: Make memberships free and monetize signal amplification instead.

Signaling can be broken down into signal message, distribution and amplification. “Real world” products are great at visualising a signal message due to their physical nature. However, as a consequence there are also physical boundaries to distribution because there are only so many people you can signal to at once.

From a user perspective, people are starting to talk more and more about the soul-withering effects of playing an always-on status game through the social apps on their always connected phones. You could easily replace Status as a Service with FOMO as a Service. It’s one reason you can still meet so many outrageously wealthy people in Manhattan or Silicon Valley who are still miserable.

Structured properly, social capital incentive structures can serve as an invaluable incentive. For example, curation of good content across the internet remains an never-ending problem in this age of infinite content, so offering rewards for surfacing interesting things remains one of the oldest and most reliable marketplaces of the internet.

As long as we have multiple social networks that don't quite work the same way, there will continue to be these social media arbitragers copying work from one network and to a different network to accumulate social capital on closing the distribution gap. Before the internet, men resorted to quoting movies or Mitch Hedberg jokes in conversation, to steal a bit of personality and wit from a more gifted comedian. This is the modern form of that, supercharged with internet-scale reach.

It's strange to think that social networks like Twitter and Facebook once allowed users to just wholesale export their graphs to other networks since it allowed competing networks to jumpstart their social capital assets in a massive way, but that only goes to show how even some of the largest social networks at the time underestimated the massive value of their social capital assets. Facebook also, at one point, seemed to overestimate the value of inbound social capital that they'd capture by allowing third party services and apps to build on top of their graph.The restrictions on porting graphs is a positive from the perspective of the incumbent social networks, but from a user point-of-view, it's frustrating. Given the difficulty of grappling with social networks given the consumer welfare standard for antitrust, an option for curbing the power of massive network effects businesses is to require that users be allowed to take their graph with them to other networks (as many have suggested). This would blunt the power of social networks along the social capital axis and force them to compete more on utility and entertainment axes.

For the individual user, we've standardized on a few basic social capital accumulation mechanisms. There is the profile, to which your metrics attach, most notably your follower count and list. Followers or friends are the atomic unit of many social networks, and the advantage of followers as a measure is it generally tends to only grow over time. It also makes for an easy global ranking metric.

As with cryptocurrency, it's no use accumulating social capital if you can't take ownership of it and store it safely. Almost all successful social networks are adept at providing both accumulation and storage mechanisms.It may sound obvious now, but consider the many apps and services that failed to provide something like this and saw all their value leak to other social networks. Hipstamatic came before Instagram and was the first photo filter app of note that I used on mobile. But, unlike Instagram, it charged for its filters and had no profile pages, social network, or feed. I used Hipstamatic filters to modify my iPhone photos and then posted them to other social networks like Facebook. Hipstamatic provided utility but captured none of the social capital that came from the use of its filters.

Meanwhile, on Twitter, if one of your tweets somehow goes massively viral, you still have to attach a follow-up tweet with a link to your GoFundMe page, a vulgar monetization hack in comparison. It’s China, not the U.S., that is the bleeding edge of influencer industrialization.

The danger of having a proof of work burden that doesn't change is that eventually, everyone who wants to mine for that social currency will have done so, and most of it will be depleted. At that point, the amount of status-driven potential energy left in the social network flattens. If, at that inflection, the service hasn't made headway in adding a lot of utility, the network can go stale.

Streaks, of course, have the wonderful quality of being unbounded. You can maintain as many streaks as you like. If you don't think social capital has value, you've never seen, as I have, a young person sobbing over having to go on vacation without their phone, or to somewhere without cell or wifi access, only to see all their streaks broken. Some kids have resorted, when forced to go abroad on a vacation, to leaving their phone with a friend who helps to keep all the streaks alive, like some sort of social capital babysitter or surrogate.

If you and a friend Snap back and forth for consecutive days, you build up a streak which is tracked in your friends list. Young people quickly threw their heart and souls into building and maintaining streaks with their friends. This was literally proof of work as proof of friendship, quantified and tracked.

This clarifies Snapchat's strategy on the 3 axes of my social media framework: Snapchat intends to push out further on the utility axis at the expense of the social capital axis which, as we’ve noted before, is volatile ground to build a long-term business on.

A variant of this type of status devaluation cascade can be triggered when a particular group joins up. This is because the stability of a status lattice depends just as much on the composition of the network as its total size. A canonical example in tech was the youth migration out of Facebook when their parents signed on in force.

Fashion is one of the most interesting industries for having understood this recurring boom and bust pattern in network effects and taken ownership of its own status devaluation cycles. Some strange cabal of magazine editors and fashion designers decide each season to declare arbitrarily new styles the fashion of the moment, retiring previous recommendations before they grow stale. There is usually no real utility change at all; functionally, the shirt you buy this season doesn’t do anything the shirt you bought last season still can’t do equally well. The industry as a whole is simply pulling the frontier of scarcity forward like a wave we're all trying to surf.

Many types of social capital have qualities which render them fragile. Status relies on coordinated consensus to define the scarcity that determines its value. Consensus can shift in an instant.

I think network effects are great, but in a sense they’re a little overrated. The problem with network effects is they unwind just as fast. And so they’re great while they last, but when they reverse, they reverse viciously. Go ask the MySpace guys how their network effect is going. Network effects can create a very strong position, for obvious reasons. But in another sense, it’s a very weak position to be in. Because if it cracks, you just unravel. I always worry when a company thinks the answer is just network effects. How durable are they?

One of the common traps is the winner's curse for social media. If a social network achieves enough success, it grows to a size that requires the imposition of an algorithmic feed in order to maintain high signal-to-noise for most of its users. It's akin to the Fed trying to manage inflation by raising interest rates.

This isn’t to say that proof of work is bad. In fact, coming up with a constraint that unlocks the creativity of so many people is exactly how Status as a Service businesses achieve product-market fit. Constraints force the type of compression that often begets artistic elegance, and forcing creatives to grapple with a constraint can foster the type of focused exertion that totally unconstrained exploration fails to inspire.

Whatever the reason, TikTok's creator community is ultimately capped by the nature of its proof of work, no matter how ingenious its creative tools. The same is true of Twitter: the number of people who enjoy crafting witty 140 and now 280-character info nuggets is finite. Every network has some ceiling on its ultimate number of contributors, and it is often a direct function of its proof of work.Of course, the value and total user size of a network is not just a direct function of its contributor count. Whether you believe in the 1/9/90 rule of social networks or not, it’s directionally true that any network has value to people besides its creators. In fact, for almost every network, the number of lurkers far exceeds the number of active participants. Life may not be a spectator sport, but a lot of social media is.

The holy grail for social networks is to generate so much social capital and utility that it ends up in that desirable upper right quadrant of the 2x2 matrix. Most social networks will offer some mix of both, but none more so than WeChat.While I hear of people abandoning Facebook and never looking back, I can't think of anyone in China who has just gone cold turkey on WeChat. It's testament to how much of an embedded utility WeChat has become that to delete it would be a massive inconvenience for most citizens.

Come for the fame, stay for the tool?

Facebook, with its explicit attachment to the real world graph and its enforcement of a single public identity, is just a poor structural fit for the more complex social capital requirements of the young.

Add to that this younger generation's preference for and facility with visual communication and it's clearly why the preferred social network of the young is Instagram and the preferred messenger Snapchat, both preferable to Facebook. Instagram because of the ease of creating multiple accounts to match one's portfolio of identities, Snapchat for its best in class ease of visual messaging privately to particular recipients.

Incidentally, teens and twenty-somethings, more so than the middle-aged and elderly, tend to juggle more identities.

Young people look at so many of the status games of older folks—what brand of car is parked in your garage, what neighborhood can you afford to live in, how many levels below CEO are you in your org—and then look at apps like Vine and Musical.ly, and they choose the only real viable and thus optimal path before them. Remember the second tenet: people maximize their social capital the most efficient way possible. Both the young and old pursue optimal strategies.

While we're all status-seeking monkeys, young people tend to be the tip of the spear when it comes to catapulting new Status as a Service businesses, and may always will be. A brief aside here on why this tends to hold.One is that older people tend to have built up more stores of social capital. A job title, a spouse, maybe children, often a house or some piece of real estate, maybe a car, furniture that doesn't require you to assemble it on your own, a curriculum vitae, one or more college degrees, and so on.

Whatever the mechanisms, social networks must devote a lot of resources to market making between content and the right audience for that content so that users feel sufficient return on their work. Distribution is king, even when, or especially when it allocates social capital.

The same way many social networks track keystone metrics like time to X followers, they should track the ROI on posts for new users. It's likely a leading metric that governs retention or churn. It’s useful as an investor, or even as a curious onlooker to test a social networks by posting varied content from test accounts to gauge the efficiency and fairness of the distribution algorithm.

It's not that the existence of old money or old social capital dooms a social network to inevitable stagnation, but a social network should continue to prioritize distribution for the best content, whatever the definition of quality, regardless of the vintage of user producing it. Otherwise a form of social capital inequality sets in, and in the virtual world, where exit costs are much lower than in the real world, new users can easily leave for a new network where their work is more properly rewarded and where status mobility is higher.

graph-based social capital allocation mechanisms can suffer from runaway winner-take-all effects. In essence, some networks reward those who gain a lot of followers early on with so much added exposure that they continue to gain more followers than other users, regardless of whether they've earned it through the quality of their posts. One hypothesis on why social networks tend to lose heat at scale is that this type of old money can't be cleared out, and new money loses the incentive to play the game.

Young people, with their much higher usage rate on social media, are the most sensitive and attuned demographic to the payback period and ROI on their social media labor. So, for example, young people tend not to like Twitter but do enjoy Instagram.

If a person posts something interesting to a platform, how quickly do they gain likes and comments and reactions and followers? The second tenet is that people seek out the most efficient path to maximize their social capital. To do so, they must have a sense for how different strategies vary in effectiveness. Most humans seem to excel at this.

Thirst for status is potential energy. It is the lifeblood of a Status as a Service business. To succeed at carving out unique space in the market, social networks offer their own unique form of status token, earned through some distinctive proof of work.

It's critical that not everyone can quip with such skill. This gave Twitter its own proof of work, and over time the overall quality of tweets improved as that feedback loop spun and tightened. The strategies that gained the most likes were fed in increasing volume into people's timelines as everyone learned from and competed with each other.

Value is tied to scarcity, and scarcity on social networks derives from proof of work. Status isn't worth much if there's no skill and effort required to mine it. It's not that a social network that makes it easy for lots of users to perform well can't be a useful one, but competition for relative status still motivates humans.

It's true that as more people join a network, more social capital is up for grabs in the aggregate. However, in general, if you come to a social network later, unless you bring incredible exogenous social capital (Taylor Swift can join any social network on the planet and collect a massive following immediately), the competition for attention is going to be more intense than it was in the beginning. Everyone has more of an understanding of how the game works so the competition is stiffer.

If you've ever joined one of these social networks early enough, you know that, on a relative basis, getting ahead of others in terms of social capital (followers, likes, etc.) is easier in the early days. Some people who were featured on recommended follower lists in the early days of Twitter have follower counts in the 7-figures, just as early masters of Musical.ly and Vine were accumulated massive and compounding follower counts. The more people who follow you, the more followers you gain because of leaderboards and recommended follower algorithms and other such common discovery mechanisms.

How is a new social network analogous to an ICO? Each new social network issues a new form of social capital, a token.You must show proof of work to earn the token.Over time it becomes harder and harder to mine new tokens on each social network, creating built-in scarcity. Many people, especially older folks, scoff at both social networks and cryptocurrencies.

The creation of a successful status game is so mysterious that it often smacks of alchemy. For that reason, entrepreneurs who succeed in this space are thought of us a sort of shaman, perhaps because most investors are middle-aged white men who are already so high status they haven't the first idea why people would seek virtual status (more on that later).

I begin with these two observations of human nature because few would dispute them, yet I seldom see social networks, some of the largest and fastest-growing companies in the history of the world, analyzed on the dimension of status or social capital.

Let's begin with two principles:People are status-seeking monkeys*People seek out the most efficient path to maximizing social capital