Economics of Absurdity

  1. The Basics
Photo by Lanju Fotografie on Unsplash

The Basics

Within the ivory towers of proper economics, one of the canon laws is the Efficient Market Hypothesis — the premise that any and all publicly available information about a company is already reflected in that company’s stock price, so no amount of reasoning can give any investor an edge over another. The theory rides on the assumption that hyper-efficient predictors, such as quants and hedge funds, are constantly scanning for mis-priced assets and arbitraging them until the mis-pricing disappears. In normal times, this is a fair assumption, but when abnormal events force drastic changes in both the economy and the minds of market participants, the Efficient Market Hypothesis breaks down. The markets throughout the early 2020’s have been characterized by a series of bubbles, triggered by the shock of economic disruption and sustained by positive feedback loops percolating through social media. These loops are uncontrollable, unpredictable, and responsible for most of the market volatility seen over the past two years. Let me prove it to you.

The disruption to both economic and social life which began in early 2020 drove people indoors, glued them to their phones, and generated unhealthy attitudes toward financial risk. In turn, the inability to leave home for extended periods forced many of them to view the outside world through the lens of social media, which amplified and coordinated their risk-taking behavior. This amplification was generated entirely by positive feedback network effects, and as such was uncontrollable, unpredictable, and targeted toward seemingly random assets. In the end, the assets which won this lottery experienced violent volatility, culminating in a series of waves which saw their prices explode as they temporarily dominated feeds, then taper off as new foci emerged. This volatility continued as long as various forms of lockdown were still in effect, and will remain amplified as remote work becomes more widely accepted.

If you’re having trouble believing that social media is the fulcrum around which financial markets now hinge, don’t worry. Over the following sections, I will break the argument down in detail. We’ll start with a rundown of the decision making process, focusing on how irrationality originates and the conditions under which it can override rational judgement. Next, we’ll explore the world of social media, justifying its allure and describing how it magnifies some irrationalities through positive feedback. Finally, we’ll go over the major bubbles of the last couple years, explaining step-by-step how these irrationalities drove billions of dollars into some of the most flagrant violations of the Efficient Market Hypothesis in modern memory.


To further my argument, I will try to find the sweet spot of just enough scientific evidence to bore the casual reader, but not quite enough to satisfy the scientific one. I will also shamelessly shill two of my favorite behavioral psychology books. There will be lots of sloppy metaphors stretched over concepts like clothes on a snowman, but hopefully they help get my points across. You don’t have to forgive me for any of it. Let’s get started.

Bubble Chart
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The Almighty Heuristic

In this section I will try to convince you that most of your decisions are basically irrational. Before you get angry and say something like “no, YOU’RE IRRATIONAL”, hear me out. I’m not saying your decisions are wrong. More importantly, the way most people come to their decisions is not a syllogistic path from knowledge to conclusions, but rather a complex soup of emotional reactions, remembered experiences, and cognitive biases simmered in the hindbrain and finally served with a garnish of rationality to make it more presentable. And frankly, there’s no better way to do it.

The Two Systems

Throughout the final decades of the 20th century, renowned behavioral psychologist Daniel Kahneman performed a series of experiments dedicated to determining how we make decisions. His work, and that of others like him, was collated for popular consumption in his best-selling book Thinking Fast And Slow. His conclusions are both wide-reaching and profound, and all revolve around a central theory: the decision-making brain is divided into two parts, creatively labeled System 1 and System 2. Each system has its own strengths and weaknesses, and interaction between them is responsible for much of our cognition.

Let’s start with System 2. Despite being named second, it is the set of processes which typically come to mind when we consider decision making. In your head, attempt to add the numbers 25 and 26. Not too hard, right? Now try adding 4356 and 8890. If that was easy, try multiplying them. System 2 consists of those processes which we actively call to perform arbitrary operations on arbitrary data. Importantly, most of the tools of rationality (logic, math, etc) fall into this category. More importantly, System 2 operations are hard. If you actually tried multiplying the above numbers — and you aren’t ridiculously savant — you’ll notice that holding all of the intermediate bits in your head took energy and left you feeling relieved when it was over. System 2 has limited working memory, and its operations are slow and clunky. For this reason, System 2 would prefer not to be activated, and will work very hard to make frequent operations second nature — think how much easier adding 7 and 3 is then when you were in grade school. Which brings us to System 1.

In contrast to System 2, System 1 operates at a nearly subconscious level and takes so little effort that we almost don’t realize it’s there. Think of the work ‘vomit’. If your brain immediately conjured a set of associated concepts — sickly colors, foods you don’t like, that one time back in college — that’s System 1. It consists of an associative network responsible for both generating simple concepts and checking their validity. When a set of concepts is activated, it returns the set of easy associates. Additionally, it attempts to generate narrative connections between those concepts, if one exists. Finally, it checks those connections for consistency, triggering surprise if it isn’t there. System 1 is vastly more powerful than System 2. It processes the gigabytes of data entering through the senses effortlessly, while its counterpart struggles with adding three digit numbers. On the flip side, System 2 is not capable of arbitrary operations, only association, narrative, and checking. When none of these operations apply, it is extremely vulnerable to error.

The two systems work very well 99% of the time — lazy System 2 delegates most activities to automatic System 1, leveraging its processing power and saving energy. In exchange, System 1 calls on System 2 whenever a task requires operations for which it is ill suited. The problem — and the part most relevant for this argument — lies in the 1% where the loop breaks down. This usually happens when System 1 is presented with a task it wasn’t designed to solve, and then solves it wrong. The rest of this section will describe a few of these breakdowns,. They take two forms — heuristics, in which a question is answered in an imprecise but easy way, and biases, in which the answers contain predictable error. Biases first.

What You See Is All There Is

As an associative network, System 1 is very good at determining which concepts are relevant to an idea and reacting on them. This mechanism, however, is fundamentally biased — it is extremely easy, by lack of contrary evidence, to convince System 1 that something is true. For instance, think about those friends with the Instagram-perfect relationship. Always together, always smiling, presenting a united front to the world. God, why are they so perfect for each other? And why are they both so hot? This is an example of What You See Is All There Is (WYSIATI). Their carefully crafted social media image bombards your System 1 with positives, gently convincing it that they really are happy and oh so in love. They may also have nasty, hair-pulling fights which break furniture, though that’s unlikely to cross your mind unless you live below them. Without that evidence, it’s easy to forget about possible negatives next to tangible positives. The same is true in reverse — think of the politicians not of your persuasion. Unless they are literally Hitler, odds are their positive and negative characteristics are fairly balanced, but since your exposure is almost universally through issues about which you disagree, they look corrupt and slimy overall.

The Narrative Fallacy

System 1 is notoriously bad at statistics. If you’ve ever read Kahneman’s book, you’ll remember the numerous, almost redundant experiments designed to prove that estimated probabilities are often wildly off the mark. This is because System 1 is purely associative, and makes measurements based solely off strength of association, so when two things are frequently associated, especially in the presence of complexity, it is often tempted to assume they will always be associated. To justify this, it retroactively generates a causal chain between them, then checks it for consistency. If consistency is found, it generally accepts the explanation, even when it there is no other evidence for causation. This is Hume’s Problem of Induction, in which all notions of cause are predicated on the idea that those actions are always paired. This is the fallacy which causes people to retroactively justify random stock market moves after the fact, and over which Taleb tears his hair out (metaphorically) throughout most of his Incerto series. The world is way easier to understand through causation and narrative — even when neither applies. This leaves us vulnerable to sudden breakdowns in the chain — Black Swans.

Social Proof

On to heuristics. Social proof — a term popularized by Robert Cialdini in his hit Influence books — is a phenomenon in which decision makers, bombarded by far too much information and unable to choose rationally between seemingly equal options, tend to select whatever the majority of their peers have selected. It’s not hard to imagine why. I recently had to buy a grill pan on Amazon, and needless to say, there were several options. Despite the many banners proclaiming resilience to wear and superior craftsmanship, pretty much the only thing differentiating one from another was the size and placement of the brand logo. That, and the fact that one — the Amazon’s Choice — had about five times the review count of its closest competitor. That’s the one which now grills my paninis, all because some component of my decision making center felt very comfortable knowing that a large number of my peers had made the same choice.

Cialdini expounds several reasons why social proof works, with the most compelling (and relevant for this post) being uncertainty and expertise. Uncertainty provides the backdrop for all of human existence. No matter how much we read, study, or experience there will always be phenomena beyond our understanding. Indeed, according to Taleb (a recurring character here), many of the systems with which we frequently interact are intrinsically incapable of being grasped beyond untrustworthy and non-analytic probability models. To avoid getting lost down a PSPACE-complete rabbit hole, we piggy back on the larger collective computing power of the group, maintaining a pretense of rationality while tacitly sacrificing our autonomy to the fickle gods of consensus. There may also be a component of schadenfreude hidden here — if the decision we all make is bad, at least we all suffer together. The hedonic baseline is communal.

The other major aspect of social proof is expertise. There’s a funny trait shared by almost everyone besides the very delusional — low-grade imposter syndrome. This isn’t to say we all wake up every day and wonder how we’re qualified for our jobs (though I’m the first to admit this does happen). Rather, we tend to take other people’s appearances at close to face value, while fully aware that under our own facade lies a tangled web of unsolved questions and Sisyphean partial self-development. It’s easy to assume that anyone who acts confident is an expert whose opinions should be followed. The more experts — or expertly crafted facades — in our environment, the more likely we are to find ourselves carried along by a crowd of confident knowledge-bearers just as secretly uncertain as ourselves.

The Comfort Zone

The other often overlooked aspect of decision making is inertia — colloquially called the Comfort Zone. Cialdini categorizes it under the Principle of Consistency, but it goes deeper than that. Imagine that the cost of my favorite brand of Ice cream goes up. A normal economic model would state that demand for this product (proxied by my likelihood of switching to a new one) would change proportionally. If the price increase was 10%, my switching likelihood might be 2%. If it was 20%, my likelihood might be 4%. This model fails to account for the fact that this brand has my favorite flavors, comes from my local grocery store, and buys its cream from local farmers I know. My incentive to switch away from it given a 20% price increase is a lot smaller than if none of these were true, providing a locally inelastic pricing model. A naive modeler might then think that doubling prices — raising them by 100% — might only increase my change of changing products by 20%, per the trend. But no. I’m a human being, and I’d take such a massive hike as a slap in the face, especially if I’d known some consultant was behind it. I’d stop buying that product altogether, and go back to Ben and Jerry’s.

The Comfort Zone can be modeled as a local kink in any supply/demand curve (see above) in which small changes to routine must provide a larger return per unit cost than large ones to justify adoption. For those with economic training, this implies far lower price elasticity for small changes than large ones. The same is true for risk/reward curves. This inertia creates a buffer against change, insulating many social systems and customs against brief shocks. This benefits society in general by reducing volatility, at the cost of lulling us into assuming that lack of volatility applies all the way up the risk/reward curve. The naive economist assumes my demand for ice cream will behave proportionally to price delta, whether that delta is 10% or 100%.

But this is certainly not the case. Outside the Comfort Zone sandbox lies the Terra Incognita, a land in which personal habits no longer play a significant role and, therefore, elasticity tends to be significantly higher. The Terra Incognita is dangerous not only because it is more volatile, but because the Comfort Zone effect makes its rules far harder to determine. Like the lands beyond the Great Wall, we don’t know what monsters reign there — only that they are merciless and as long as we’re on this side of the wall, it’s healthier to pretend they don’t exist. Unfortunately, we cannot ignore them forever. A sufficiently large shock is more than capable of pushing us beyond the Comfort Zone border, where we suddenly find ourselves unarmed against the ravenous hordes. Our ignorance is no longer harmless but debilitating, and without data from this brave new world, we find we have no good models to use. The economist is shocked when a 100% price hike leads to 95% product abandonment, and even dropping below the original offer can’t restore the lost demand. The company fails.

This brings us to the crux of the Comfort Zone problem — the only way to figure out what lies beyond it is to leave it, and once you’re gone, good luck getting back. After such a shock, we’re forced to navigate the shifting sands of Terra Incognita until a new set of equilibria is reached. We find a comfortable place and begin to construct a new Great Wall. The new Comfort Zone may in time come to be as stable and routine as the old one, though in a completely new place. Perhaps a new ice cream brand from out of town moves in and becomes the new favorite. Still, we have to remember that the monsters are out there, and that the rules of Terra Incognito are shifting in unknown ways so that even before the new customs are in place, everything we learned on our last voyage has become irrelevant. Thus there is no exploring the lands beyond the Wall, merely acknowledging their existence and preparing for the next journey, knowing that even in the most settled of times, Ben and Jerry’s is waiting just beyond the gates.

The Rational Model

Let’s recap. The above biases are the price we pay for leveraging System 1’s computing power and saving ourselves from 8-bit adding our way through an intractable reality. Because of them, human decision making isn’t exact, but rather heuristic. The uncertainty and expertise effects prove to be a double edged sword, providing us with ready-made decisions but leaving us vulnerable when the crowd-mean opinion skews from ground truth. The Comfort Zone effect hides the ways people will respond to real shocks. The Narrative Fallacy creates causal effects where there are none, WYSIATI generates markedly one-sided impressions, and both mask the true complexity of systems beneath easily comprehensible facades.

Now observe any standard economic model, from Adam Smith to Keynes to the Efficient Market Hypothesis. All assume that people act rationally, making calculations of risk and reward before every action. The EMH even goes so far as to ignore information asymmetry, claiming that every market participant can be modeled as fully rational, fully aware of all publicly available information, and equipped with both infinite and instantaneous modeling power. The realities of inertia and social proof are treated as rounding errors, unimportant to the Platonic models who really move the markets. Even Prospect Theory — Kahneman’s own loss-aversion-soaked model — only deviates from these standards by asserting that people suffer more from losses than they benefit from equivalent gains, replacing rationality about value with rationality about value delta. Not good enough.

The prevalence of heuristic decision making and the general failure to acknowledge it creates a situation in which things can go unpredictably and catestrophically wrong. When this happens, the old models will be applied until experience shows that they don’t work anymore. The scientific approach to such an event is to admit defeat, abandon the old model, and move on with the search for a new one, as God and Thomas Kuhn intended. But, as Taleb mourns in The Black Swan, the more common maneuver is to move the goalpost: the model would have worked, if not for The Event. A model that only works most of the time and is liable to break down without warning is itself is just another heuristic, and a dangerous one at that.


The final point to be made about heuristics is their resistance to change. Social proof relies on some ability to gather social data with sufficient clarity to use it for decision making. This requires that it be both fairly unambiguous and representative of our peer group, or at least that it can be transformed until those characteristics hold. The last several million years of primate evolution have given us powerful faculties for subtly gathering vast piles of information from a wide social circle. These faculties include subconscious analysis of body language and conversational nuance, as well as the ability to gather and hold massive quantities of information on people. They rely on certain constants of human behavior, and while several may be culture specific, these constants require generations to develop and are very difficult to unlearn.

If placed into a radically different social environment — one with completely different modes of interaction and cultural norms — it is very difficult to pick up on these subtleties. Without sufficient time to learn the new customs, the brain reverts to those communication channels with which it can still operate. If a person is familiar with a language but not a culture, the channel shrinks to what can be conveyed directly by language, along with those body and facial expressions which are identical to the original culture. If placed in a digital setting, even these latter are removed, and communication is funneled through the extremely narrow medium of language itself. Let’s pause this thought here — it will be extremely relevant later — and restate the theses of this section:

  1. People make most of their decisions based on System 1 heuristics
  2. These heuristics are only effective when social and environmental factors remain relatively unchanged
  3. These heuristics are extremely resistant to adaptation.

These theses form the standard model. They explain why shocks lead to seemingly unpredictable behavior. Now that we have the theory, let’s move on to a description of the landscape as it existed before The Event, and why it served as the perfect catalyst for whipping up an irrational hurricane.

Photo by camilo jimenez on Unsplash

The Social New Deal

I come from a generation on the edge. The late millennial still remembers an era before the widespread adoption of the internet and cellphones, though only as a hazy Eden, where entertainment came from toys and playdates, unmediated by screens. Today, of course, I can hardly imagine an adult life without a phone. It handles not only my scheduling, reading, and media intake, but also entire swaths of the communication landscape which used to require in-person interaction. I can schedule a meeting, organize a trip, buy or sell anything, and even coordinate technical projects, all without hearing a human voice. Indeed, for those interactions in which direct human interaction is necessary, it is often seen as a necessary evil. If you’ve ever bought or sold anything over the internet, think of the exchange when you actually met the other party. There were probably a couple greetings, a little help getting something out the door, and maybe an awkward joke, but in the end you both left the experience thinking some variant of “glad that’s out of the way.”

If this environment, in which digital communication has replaced in person interaction, sounds a little dystopian, that’s because it is. Fortunately, technology offers a solution to that as well, albeit a costly one. “Are you feeling lonely?” it asks, “does your life of emails, texts, and sitting in a white cube feel a little impersonal?” Twirling its mustache slyly, it asks how you would like it if you could have your friends around all the time, like in childhood. Even if you deny it, technology sees the sudden excitement in your eye, and knows it has you. “Well,” it says, “I have just the thing. But it will come at a cost. Your friends will be with you wherever you go, or rather — parts of them will be. You will live forever with the pieces of your social circle which they choose to show the world, the best and most exciting aspects of their lives, always reminding you how boring the real world is. Shall we shake on it?”

Even if you withdraw your hand in horror, technology knows it is just a matter of time. The offer to reconnect at a level beyond the transactional is just too sweet. Sooner or later, we all fall into the glossy embrace of Friendship, Smart Edition.

What I Mean By Social Media

In its broadest sense, Social Media is a collection of platforms designed to allow humans to interact with one another in digital space. According to Merriam Webster, it is “forms of electronic communication (such as websites for social networking and microblogging) through which users create online communities to share information, ideas, personal messages, and other content (such as videos)”. The key words here are ‘create’ and ‘communities’.

Social media sites distinguish themselves from other sites by means of users’ ability to create meaningful content and share it with an interactive community. These characteristics are somewhat interdependent — it is impossible to foster community without some ability to create and share ideas, while creation without community is essentially just backing up local work to the cloud.

For the purpose of this monologue, I’d like to make a further assertion — many sites typically labeled as Social Media are actually Glorified Email. Glorified Email platforms are those which provide functions which could, given a sufficiently advanced protocol, be implemented by email. This includes the likes of Snapchat, Discord or Slack, in which users focus attention on specific serial conversations. It wouldn’t take too much effort to mock up these systems using email, with .mpg attachments for snapchat and long email chains for the message boards, and their value lies primarily in the fact that most people do not want to deal with that.

Those social media which are not Glorified Email are those which aggregate content from a large number of users into a single, partially serialized feed. They are designed to provide a constantly-moving snapshot of the state of a community, and vary by both topicality and social specificity. Let’s take a tour along these axes.

Topicality is determined by how easy it is to go onto a site and find discussion about a specific topic — on the highly topical side is Reddit, with its separation into myriad subreddits reflecting the interests of its sub-communities, while Twitter and Facebook are usually a single mixed feed. On a highly topical site, influence is highly specialized within one sub-community unless that community shares a large number of members with another. For instance, the most actively followed r/chess user may have no recognition on r/WallStreetBets, unless Reddit’s chess and degenerate communities suffer from large overlap. No, I haven’t pulled the numbers.

Social specificity, on the other hand, is determined by how well a user knows those present at a social level — i.e. Facebook tends to be packed with friends, while Twitter and Reddit are full of strangers, with Instagram lying somewhere in-between. On a platform with high social specificity, influence is typically limited to the number of people from high-school you still have hanging around on your feed, so no matter how interesting your life is, how insightful your posts are, or how many people you have enthralled to your multi-level marketing scheme, your Facebook friend count grows at-best linearly with time. Non-socially specific sites, however, tend to follow a power law distribution in terms of influence, as measured by follower count. An active celebrity like Elon Musk may have millions of followers and can command financial markets with a well-placed tweet, while the majority of those followers are happy if their close friends are even on the site.

Thus, if we’re looking for sites with positive-feedback characteristics (and that’s exactly what we’re looking for), we want those on the low end of both social specificity and topicality, with the former being essential, while the latter is simply nice. The best sites for this type of behavior are Instagram, Reddit, and the absolute king, Twitter.

Now that the framework has been established, we can take a look at what these sites actually do. In a world of increasing isolation behind computer monitors, these sites gather those separated by space and screens, fostering a community which (supposedly) rewards creativity and fosters togetherness. This innovation, however, comes at great cost. Let’s look at that next.

The Death Scroll

Socialization makes us happy. According to The Center For Compassion and Altruism Research and Education at Stanford University, social relationships are the most consistent predictor of a happy life (though to be fair, I’d be shocked if they said anything different). It’s a hard fact to debate, as myriad studies have shown that most people — especially when sad — seek solace in companionship. Social media sites offer a nearly-endless social input drip, providing the feeling that our friends are always dimly present, like night-lights in our loneliness.

Social media sites are also incredible sources of novelty. Novelty is defined by Oxford Languages as “the quality of being new, original, or unusual”, and these characteristics apply to virtually everything that shows up on a social media feed. Numerous studies (read ‘anecdotes’) indicate that searching for novelty is a good indicator of general happiness and lifespan, and — more concretely — a study published in Neuron indicates that novel experiences increase both creativity and information retention. In short, we like new things, and that’s exactly what crawls across our feed, again and again and again.

So social media provide us with a continuous barrage of social inputs and novelty. This combination stimulates a continuous low-grade production of dopamine, the chemical responsible for immediate motivation and pleasure, and ultimately addiction. At risk of imitating every popular psychology book ever written, let’s take a look at its effect on rats. In 1953, James Olds and Peter Milner performed an experiment in which they provided rats the ability to press a lever and stimulate the parts of their brain responsible for dopamine production. When given the choice between the pleasure lever and a number of other stimuli, the rats chose the lever every time. In fact, they even chose it over food when hungry and water when thirsty, and in the end, the researchers had to disconnect the mechanism to prevent them from starving themselves to death. This surprising (though retrospectively predictable, otherwise who would remember the study?) conclusion shows the power of dopamine over animal behavior. To the extent that we are irrational beings, the fastest path to dopamine will have the deepest ruts.

While we’re on studies, let’s look at another one which will seem particularly relevant down the line. A 2008 study by Solinas et al. showed that rats which have developed a cocaine habit (one too many f-rat parties, I assume) were able to kick the habit when provided with a richer environment. Access to novel stimuli and socialization with other rats caused those with induced drug sensitivities to become less sensitive, and many seemed to eliminate their cravings altogether. It’s telling that the highest prevalence of addiction in most western societies is among the least wealthy strata, or those with the least access to novel stimuli.

Now the Death Scroll. In human contexts, it is considered bad practice to hook the brain up to a dopamine drip stimulated by a pleasure lever. As fun as it sounds, certain buzz-kills would inevitably object. Instead, clever engineers have provided us with a way of circumnavigating these goodie-too-shoes and triggering low-grade dopamine boosts through a steady feed of content designed to imitate social interaction and novel experience, all from the comfort of our couches. Instead of a lever, we get a scroll. The Death Scroll.

Unlike the rats in Olds’ and Milner’s experiment, we can’t directly stimulate our dopamine centers, so we still get up to do things like eat, drink, and even sometimes go to work. The system isn’t perfect. Nonetheless, a significant portion of our time is spent on these drips, especially when we’re bored or have nothing better to do. This steady drain of time can add up, taking away from personal development or real relationships in the long run, and most of us are completely aware of it. Still, scroll we do, unless provided with a more compelling alternative. In a rich environment — a la Solinas — such alternatives are ubiquitous. However, deny us these alternatives, and we find our selves pulled deeper and deeper into the feed. Like gravity, we rarely notice it, but the tug is always there.

Now that we’ve established why we spend so much time on social media, let’s take a look at what one finds there, and how the concept of curated lives turns this novelty intake into a source of crushing internal turmoil.

None of Your Friends Are Real

I barely remember my first spring break in college. Per the usual stereotype, it involved a crazy, transcendental week on the couch, at home, with my family. Honestly, I was grateful for the rest, and wasn’t jealous that some of my friends had taken the opportunity (and a lot of parental funding) to visit South Padre or Cancún for the week, as I’d spent enough of the previous semester inebriated to know that it wasn’t all that exciting. At least, until they started posting about it. Suddenly, my late winter Wisconsin surroundings seemed bleak compared with boozy camaraderie, scantily clad women, and pervasive smiles amid white sand and cerulean sea. I felt like I was missing out, as if I was the only person I knew who wasn’t having the time of his life. I think my parents noticed, and did their best to make it feel like a “real” vacation, but the damage was done. Only when I got back to college and spoke once again with those friends did I come to a surprising conclusion: my spring break had been better.

First, the phenomenon by which I felt like everyone I knew was off having the time of their lives was a direct result of What You See Is All There Is. The majority, who’d returned to their roots like me for a week of simple relaxation, hadn’t posted about it on social media. Second, of those that went, many had experiences less than ideal, including getting sick off bad water and high-intensity alcohol poisoning. Once again, no posts about that. My feed made their vacations look idyllic and perfect, because rather than reflecting a true sampling of their experiences, it provided a carefully curated projection of a desired image. Aside from a very few exceptions ($5 says somebody specific immediately springs to mind), most people don’t air their dirty laundry on social media. Instead, they carefully curate its content to reflect an image of effortless success, joy, and excitement. Because of What You See Is All There Is, it’s easy to imagine that the whole world is having fun without you.

Moving away from the specific, imagine a hypothetical social media user named Joe. Joe is thirty, has a comfortable white-collar job, and a handful of friends he sees on the weekends for a local foosball club. Once a year, Joe and his girlfriend take a vacation somewhere warm but affordable. Compared to all his ancestors throughout history, Joe’s life is sublime. Those ancestors, however, didn’t live to post about it. Instead, every day, Joe is bombarded with carefully selected imagery from friends and acquaintances which seems designed to make him feel like his life is less fulfilling than theirs. There is no malice here, only the brutal logic of signaling, but Joe’s System 1 doesn’t care. It falls victim to What You See Is All There Is, and he begins to wonder why they’re having so much extra fun. Is it the traveling? The new cars? The dates and dinners out? Either way, he can’t afford to miss out, otherwise he’ll be the one sitting in silence while everybody else at the foosball club talks about their trips to Fiji before driving away in a Ferrari. He casually thinks about how financially stressful it would be to plan two trips this year, and wonders if his girlfriend could take the time off. She’s been feeling down about her recent weight gain (all her friends look great in bikinis), so maybe they should cancel a couple pizza nights…

Fortunately, reality intercedes. Next time his friends come over, they aren’t all talking about their jet-set lives. Despite what his feed makes him believe, Joe’s social circle is just as boringly domestic as he is. This soothes his hindbrain, and when they leave, it quietly rethinks the vacation plan, and remembers that life right now is pretty good after all. His job is easy, he has friends, and his girlfriend is pretty cute. Pizza night is on for tomorrow.

This clumsy example shows the power that What You See Is All There Is exerts through social media. Additionally, it highlights the power of the Narrative Fallacy when combined with these images of excitement and success. Once we assume all of our friends are having a good time, we start asking why, and social media once again provides the answer. Whatever they’re doing in those posts, that must be the secret to a happy and fulfilling life. Mortgage the house, max out the credit cards, leave any relationship that isn’t perfect — it’s all worth it, we promise.

Finally, the above effects explain the phenomenon known as Influencers. An Influencer is anyone with a wide media following who is capable of gently directing that following’s behavior. On socially specific platforms, such as Facebook, influencers are limited in their range. On non-specific platforms, such as Twitter or Instagram, they can achieve followings in the millions. The same pattern holds — these people post carefully curated content about their lives, causing their followers to wonder how they do it. Usually, a company quietly steps in and offers the influencer a deal to shill a product or lifestyle, knowing that with enough iterations, the followers’ hindbrains will come to view it as the answer. This is the brave new world, in which mental heuristics are arbitraged against the non-intuitive statistics of social networks. The ethics of this form of advertising are open to debate, but it seems like — during normal times — most people can connect enough with reality to resist a little. However, when that rich environment is removed, influence runs amok.

If you’ve made it this far, rest assured; the end is near. Now that all the premises are in place, we can finally tackle the big issue — why so much irrational behavior took place in the early 2020's, and how it has to end.

Photo by Marcus Woodbridge on Unsplash

The Conquest


In late 2019, the news first took note of the Thing. At first, it was a side story — pale and transient beside the looming presidential impeachment. Then, as the political drama subsided, rumor grew of a shadow in the East, whispers of a nameless fear, and the titans of digital connection technology perceived that their time had now come.

I remember the brief prelude months of early 2020, when companies started sending employees home for a couple weeks, which became a couple months, then a couple years. When we first ordered takeout, then slowly forgot how to live without it. The last few days of normalcy, while we waited for the first couple cases to show up in our city, stick out in my mind like the green flash of sunset over the ocean. Whether or not we knew it at the time, we were settling in for a Long Night, from which many friends and friendships would never wake up.

The first thing to go was the office. When tech and finance teams started sending their employees home, followed by a far more financially stressful version for service workers, the effect was twofold: the time structure provided by the workplace disappeared, and a lot of people suddenly found themselves a lot poorer. Time structure loss began as a bit of a wolf in sheep’s clothing — with no commute and no boss watching over the cube wall, why force yourself to work eight solid hours before stopping to relax? Instead, why not work when you felt like it, and relax the rest of the time. As long as it adds up to eight hours or so… right? Absolutely not right. The problem is one of smoothing. With a commute to a job, it’s easy to differentiate the time you spend working from that you spend relaxing, with pressure to perform rising from 0 to 1 when you get to work, then dropping back from 1 to 0 when you get home. For the WFH crowd, it transforms into a sort of gentle 0.2 all the time, rising to somewhere near 0.8 when meetings are in full swing and dropping back down otherwise. A constant 0.2 creates a conflict — the lazy brain says that nobody is watching, so I might as well relax a little, while the motivated brain says that I haven’t done enough work today, so I can’t go turn on the TV. Instead, we often find ourselves in a liminal state in which we can’t leave our desks, but we also don’t want to work very hard, so we find ourselves taking ‘breaks’ to sit and scroll through social media. And yes, YouTube counts too. As we’ll see soon, this awkward funnel emptying into the death scroll is a perfect catalyst for social media generated bubbles.

For those who went home without a job, the problem was a lot worse. Without a functioning labor market, many people were forced to spend huge swaths of time stuck and home surviving on minimal resources. While I (thankfully) kept my job throughout the Long Night, many friends did not, and their stories involve days of entertaining themselves with learning new hobbies, trying to pick up side gigs, and yes, hours spent on social media. Like Coke and Mentos in a paper maché volcano, adding financial desperation to the death scroll is a recipe for ruining the science fair.

Finally, and most insidiously, we lost the in-person connections. Like Joe and his foosball club, the ability to talk to people and analyze their experiences firsthand breaks the jealous grip of their curated internet lives. Without it, those who still post (read still do things outside the house) suddenly get double the attention, while the rest of us are stuck indoors feel double the jealousy. There is no opportunity for correction, and the cocktail of loneliness and FOMO slowly overrides our better judgment. Beyond this, we start to feel like Solinas’ rats — with our rich environment taken away, our addiction to those little screens only grows with time.

With finances uncertain, work-life balance deconstructed, and friends and family present primarily through the lens of social media, we began to become unmoored from reality. The softmax-filtered, influencer-ridden, unverifiable constructs of our feeds became our collective nightmare, an inescapable death scroll uninterrupted by the outside world. This new reality followed new rules, rules our primate brains and primate heuristics were not designed to handle.

Blowing Bubbles, Adult Edition

So some time in mid 2020, we found ourselves unwillingly thrust into a Brave New World. It was clear the apocalypse was going to be very boring, and we were in it for the long haul. Two poles began to form.

The first pole concentrated around social media sites with high social specificity — e.g. Facebook. It consisted of groups of friends and acquaintances spreading information around their networks in an uncontrolled and highly random manner, with more mimetically powerful ideas vying for control with more widely-held, but less impactful ones. Those ideas which achieved a critical mass began to enjoy Social Proof, generating a positive feedback effect, until a few ideas drowned out all others. Without the tempering influence of external reality, it was easy to become stuck inside an echo chamber, until WYSIATI made it impossible to see the other side of a political issue as anything other than deeply malicious or irredeemably stupid. Think of the vitriol over the mask mandates, or the anathemas thundered on both sides of the mandatory vaccine debate. Inside the echo chamber, you’re right, and everyone around you thinks so. Only a fool or a lizard person would disagree.

The second pole formed in sites with low social specificity and high topicality — specifically Twitter and Reddit. On these sites, you needed only join the right communities and follow the right voices, and you could build yourself a feed curated to your exact interests without the mess of half-baked political commentary from your extended family. The echo chamber effects of WYSIATI and Social Proof were still present, but in a more controllable and topic-centered form: if you feel curious about what the folks on Wilderness Survival Twitter are saying, you can go check out a few known names in that space. You don’t have to send a friend request to the weirdo living in your backyard.

As the Long Night wore on, people began to find themselves outside their financial comfort zones. Maybe they’d kept their job, and without the costs of travel, nights out, and dates who forget their credit cards at the last minute, they found themselves sitting on new piles of money. Or maybe they’d lost their job, and had watched that down-payment savings wither away while house prices skyrocketed, crushing any dream of owning a home, unless they were willing to do something desperate. When you have nothing to lose, risking everything seems like a ticket to asymmetric upside. Whatever the motivation, a combination of boredom, desperation, and excess funding propelled millions of Americans deep into the world of the stock market.

In the meantime, Jerome Powell, chair of the Federal Reserve and de facto CEO of the US economy was doing everything within his power to prevent irreversible damage. From buying distressed financial instruments to slamming interest rates down so hard that banks were almost paid to borrow, he kept struggling businesses afloat while simultaneously providing a nearly unlimited pool of cheap capital for the rest. Those companies, mainly tech and finance giants and those providing the WFH infrastructure, began using this capital to expand aggressively, ballooning their stock prices until the entire American market began to rise. And rise it did. For nearly two years, while sane economists questioned the likelihood of a new Great Depression, the S&P 500 index climbed to new high after new high, effectively doubling while the world collapsed around it.

All those bored, desperate, and/or highly liquid potential investors began to salivate. But if you’re a first time investor, whose relatives recommend index funds while binge-watching Plandemic, you’re likely not impressed. And while Microsoft may have a guaranteed income stream selling microchips to Pfizer, its mathematically perfect exponential growth chart can only last so long… right? And you have this buddy who keeps gibbering about options, so that’s a thing too. Maybe it’s worth spending some of that ever-growing social media time exploring these areas. Enter Fintwit. Enter WallStreetBets.

Fintwit, a portmanteau of Financial Twitter, is a community once relegated to those working in capital allocation and banking, but now haunted by millions convinced they’re just a couple good trades away from founding their own hedge fund. WallStreetBets (WSB), on the other hand, is the dank Reddit foil, filled with purportedly average people making trades risky enough to double their accounts or empty them. Unlike the decentralized insanity of high-specificity sites, the network effects here revolve around a handful of voices — those accounts with the most followers, or those posts with the most upvotes — and are simultaneously more and less deterministic. They are less deterministic in the sense that your neighbor’s hot take about Iranian politics probably won’t make it outside your community (or hopefully his own head), while a similar claim with a tangential assertion about the Tesla stock price might get anywhere from zero to a million views on WSB. However, they are far more deterministic in that it only takes a consensus among a small group of high-value participants to sway the entire community. These participants, in turn, often have their own equity positions which they seek not only to validate, but to improve. If you own half of Nokia, it’s in your best interest to make sure the internet is chomping at the bit to buy shares. Get some friends on board, alter the sentiment just enough, and pretty soon everyone is getting rich — until they aren’t.

So how does one become one of these important voices? Clearly, in an environment dedicated to making money, you want to be somebody who knows how to do it. Or at least, somebody who looks like they do. Imagine there are twenty people, ten of which invest in some slow growth stock market index, with the other ten investing into various extremely high risk assets. At the end of three months, the first ten will have comfortable 2% appreciation and a dividend. Nine of the others will be down 50%. The last guy, let’s call him Phil, has made 600% by making an outrageous bet on Uber earnings and has gone from an accountant to the guy who hires one. Of all twenty, only one is likely to log into WallStreetBets and post about it. Phil’s success, while anomalous, whets the appetites of those who see it, who learn that the best way to get rich is risky bets on assets you barely understand. Ensconced in our tiny cells, detached from reality, Social Proof effects take over, until Phil looms large in our minds like a degenerate Moses, he who knows the ways of the Lord, whose techniques will lead us across the desert to the Promised Lamborghini. Thus, these communities begin to worship an upper crust of Phils whose ability to get rich quickly depends on either pure luck or the ability to convince others to pay a lot more for something they already own. They are the culmination of Curated Lives — the loudness of success and invisibility of failure, amplified a thousand fold by the statistics of non-socially-specific media.

Now that we understand the incentive structure, let’s look at the market’s reaction. Remember the efficient market hypothesis from all the way back in the intro? This model resides squarely in the Comfort Zone. It assumes that random fluctuations in one direction caused by irrationality should be roughly balanced by similar fluctuations in the other, and most of the time, that’s the case. However, because there is no room for mis-pricing, the market itself becomes the oracle. Rather than the value of a company driving its stock price, the stock price now dictates the value of the company. After all, the market is perfectly efficient, so if it values a company at 4000x current profit, there must be some reason it’s worth that much, even if you and I can’t see it. The market is never wrong. Right? This assumption works as long as we stay within the walls of the Comfort Zone. However, in the turmoil of 2020, as we collectively packed our bags and walked out the gates, many analysts and models didn’t get the memo. They continued to claim mis-pricing was impossible as it swam up from the depths, jaws open.

So now that we have the ingredients, let’s look at the recipe. Take millions of Americans, cut off their family and friends, add half to a pile of new money they can afford to waste, and marinate the other half in desperation, until all have an attitude to risk wildly outside normal bounds. Pour in free Federal money, stir vigorously, then place far enough outside their Comfort Zone that not even seasoned analysts can predict what they’ll do next. Add to a boiling cauldron of social media influencers steeped in luck and moral hazard, add spicy memes to taste, simmer for three months, then watch the whole thing explode.

Wave One: The SPACulators

A Special Purpose Acquisition Corporation, or SPAC, is a company which issues shares on a major exchange in order to raise capital to acquire an existing private company. It is an alternative to going public via IPO, in that participants typically place their faith in the capability of a fundraiser rather than the underlying financials of the enterprise. And it is generally a terrible investment.

In mid 2020, with all that fresh inexperienced capital pouring into the markets, investors and celebrities saw a vast new spring of wealth to enjoy, if only they could find the tools to tap it. SPACs turned out to be perfect. The deal was simple: first, announce a SPAC raising $10 a share, announcing a vague intention to acquire a company in some hot space, like environmental tech. Next, lever clout on social media channels to lure uninformed investors into buying shares, driving prices well above the initial $10, sometimes as high last $30 or $40, using excuses such as warrant rights to justify the vast overvaluation. Finally, announce and acquire a target — or don’t — then watch as reality sinks in and the share price slowly sinks back to something resembling the actual value of the new company. In the meantime, sell off thousands of shares and pile up dry powder for the next round.

The second half of 2020 saw wave after wave of raise and dump, with gullible investors (yours truly included) placing their trust in big names, only to have their hopes dashed. Some of us lost money once, and swore off the game, while others — especially those who recognized these equities as the poker chips that they were — came back again and again like gamblers to the casino. It reached peak in Q1 of 2021, with 96 new SPACS announced that quarter alone and over $125 billion of funds raised, before crashing down in a rolling cascade over the rest of the year. While it’s easy to blame the collapse on market over-saturation, growing aversion to speculation, or a more general tech correction, the reality is much more banal. The retail traders, drunk on the excitement of watching our charts spike up and down all day, had moved on to greener pastures — GME and Crypto.

Wave Two: Planet of the Apes

In late 2019, a little-known r/WallStreetBets user going by the alias DeepF***ingValue posted a several thousand dollar position in Gamestop, the well-known video game retailer trading with ticker $GME. He claimed that the company, which had lost business in recent years to online distributors like Steam, was ripe for a turnaround, and that its stock had become deeply undervalued. Importantly, much of this undervaluation had come from short-sellers, large market participants like hedge funds who “borrowed” stock from brokers and sold it, paying interest while driving the price down under the assumption that they could buy it back lower at a later date. While uncertainty exploded into the world early the following year, DeepF***ingValue bided his time, lurking in the depths of WallStreetBets until — at the turn of 2021 — his time came.

On January 11, 2021, Gamestop announced that it was bringing Ryan Cohen — founder of Chewy — in as a director to revamp the company, transforming it from a failing brick-and-mortar into the “ultimate destination for gamers”. This information circulated through WallStreetBets, doubling the stock price and resurrecting memories of DeepF***ingValue, who turned out to be a likable YouTuber named Keith, and his early position. Even more importantly, it brought to light the fact that numerous large Wall Street funds had taken large short positions against Gamestop, and that if the price could keep rising, they would be forced to cover their shorts at the new value. A few ingenious folks came up with a plan.

A short squeeze is a phenomenon in which a highly shorted equity begins rising in price, causing share borrowers to panic (or see rising rollover costs) and buy back some shares to close their positions. This buying then raises the price more, causing more short-sellers to buy, generating a positive feedback loop. In normal times, the sudden rise of a declining stock prompts other holders to sell off, taking what little profit is left to them, thus driving prices back down and limiting the squeeze potential. However, Gamestop was different. First, there were so many shares shorted that almost every shareholder would have to sell to cover it, and second — these shareholders could take their cues from social media.

So began the MOASS, the Mother of All Short Squeezes. The denizens of r/WallStreetBets, hyping each other up with visions of glory and red with repressed anger at rich financial firms, spilled out into other financial communities, hands forming the IC XC and bearing a message from on high: “Buy more shares of GME,” the Lord said, “And don’t sell them. Ever.” Within a few days, their success became apparent, and the price of GME stock rose from $30 to $50 to $100 to $300 in the span of a week. GME holders spent market hours in a state of euphoria, with social proof barraging them from all directions as their networks grew saturated with hype, and even internet celebrities, like the God of Geeks Elon Musk, joined in. Pretty soon, those who had missed out on the $30 buy-in began scouring the market for other targets, settling on several, particularly Bed Bath and Beyond and theater chain AMC, whose stock then rallied so wildly that its board eventually issued over one billion dollars of new shares, effectively saving the company from bankruptcy. These were the “meme stocks”, incarnate counterpoints to the Efficient Market Hypothesis and beacons of hope to a desperate people. When brokers began limiting trading in GME on January 28th, the stock had reached $347.51 a share, costing short sellers upward of $6 billion and uniting the financial social media world into an immense, self-reinforcing whole. For better or worse, the concentrated influence effects already prevalent on these platforms had augmented and crystallized. The mighty retail investor bloc was here to stay.

Whether or not brokerages were honest about their reasons, the halts on GME and its ilk did effectively kill the squeeze. Prices dropped gently, settling still well above their pre-2021 levels, but allowing battered short-sellers to escape to fight another day. The retail bloc, however, was just getting started. Calling themselves “apes,” as a combined tribute to the “apes together strong” meme and the dear departed Harambe (PBUH), they felt the power of their community and longed for a place where they could exercise it without the restrictions imposed by Big Finance. They began withdrawing their capital from the stagnating meme stocks (except AMC, which spawned its own post-worthy saga) and looking around for greener pastures. With cash in hand and hearts enraged at the short squeeze that wasn’t, they turned to the one asset class which no broker had the power to control — Crypto.

Wave Three: Blockchain and Why We’re Not All Gonna Make It

Blockchain — the most controversial technology of the early 2020’s — has been around in some form or another for decades. It relies on the idea that if thousands of computers are keeping track of the same set of records, inserting a fake record into the system will be computationally intractable. In this way, a universally consistent, decentralized, append-only ledger can be built, and this ledger in turn can form the basis for myriad applications, from recording transactions (the idea behind cryptocurrency) to sealing contracts to verifying ownership. In theory, these records cannot be altered, and the associated transactions are not at the mercy of any third party — this is the fabled “decentralization”. To keep these blockchains running requires a monstrous amount of computing power, for which the owners of that power expect to be incentivized; for the vast majority of chains, this takes the form of a steady trickle of digital currency tokens stored on the chain itself — voila, cryptocurrency. For those still smarting from the seemingly arbitrary halt on GME, this uncontrollable, decentralized realm really felt like striking digital gold.

Around the time GME started taking off, the two major cryptocurrencies — Ethereum and Bitcoin — experienced smaller but significant pumps. Then, as the meme stock rally began to ebb, not only these coins, but others — notably the perennial crypto satire piece turned actual asset called Dogecoin — began to rocket in value. The same channels which powered the previous wave provided the growth engine, and on paper, hundreds of billions of dollars in new value were created in under a month.

With all this new capital moving into the crypto space, it went from a niche techie casino to the forefront of the national debate. This pulled hundreds of blockchain based projects into the limelight, and pretty soon, like flaming debris flying after a bomb goes off, they were exploding into bubbles of their own. This collection of projects and the hype surrounding them became known as Web3, the presumptive heir to the Web2 of interactive web pages, in which blockchain based identity would reign supreme. I will make no secret of my stance — despite trying again and again to like Web3, I’ve found virtually no merit in its claims to decentralization and abhor its brutal energy burn. Nonetheless, my argument is not about the tech itself. Instead, I’ll focus on how many of the financial products associated with Web3 have already proven themselves products of meme-driven hysteria, with fates tied to the vicissitudes of the social media machine.

First off, let’s examine the three bubbliest projects and explain how they work. The first is any of the thousands of low market cap tokens — colloquially called “altcoins” along with some more profane monikers — which circulate using the backing of a more established chain. The technical underpinnings of an altcoin are unimportant — suffice it to say that some project using blockchain technology wishes to get funding, so it creates its own digital coin which it then sells to investors. The majority of these projects come to nothing, but hype around those that do has led these coins to become incredibly speculative assets, with mass social-media-mediated buy-ins driving them up thousands of percent in hours, at which point the original project managers frequently sell off and cause the price to crater. This insane, brief spike is the hallmark of the altcoin, and the get rich or lose everything mentality reflects the twisted attitude toward risk.

The second bubbly asset is the famous NFT. Most of us know NFT’s — or non-fungible tokens — as garish cartoon characters fetching hundreds of thousands of dollars on the internet, nihilistic expressions almost mocking you for the fact that you probably can’t afford it, yet somehow wouldn’t want it if you could. At its root, an NFT is actually just another token on the blockchain, one which records a certain user’s ownership of a specific digital asset (usually at the end of some URI). According to Web3 enthusiasts, this can be anything in virtual reality, such as video game assets or cartoon monkey pictures, or even items in the real world. The point is that, unlike real world assets, the government supposedly cannot take it away by immanent domain, as the blockchain remembers who it really belongs to. The counter to this argument is that what bad actors cannot accomplish within the law, they will often perform by force or fraud, as evidenced by the hundreds of scams and grifts that have deprived NFT purchasers of their assets. Due to the decentralized nature of the platform, once one’s been scammed, one has little recourse — welcome back to the Wild West.

Finally, there’s the most complicated piece of Web3 technology currently employed — the smart contract. This is a piece of code written to run on the blockchain. The technical underpinnings are once again not important: think of it as a publicly visible agreement which cannot be broken — I agree to pay you $10000 in exchange for your copy of Angry Monkey #3 within six months, or my entire account defaults to you. Once it’s on the chain, it’s unbreakable. I pay you, or I lose everything. While in theory, this prevents large and powerful actors from simply refusing to uphold their ends of bargains — think a large art sales gallery deciding they want to give an artist only half the agreed commission — it comes with side effects ranging from annoying to dangerous. The most banal is the gas fee, a nominal quantity of currency charged for each transaction in order to incentivize users not to overwhelm the network. While typically not much higher than a brokerage fee, at times of high traffic they can soar to hundreds or thousands of dollars per transaction — if paying in Ethereum, it’s best to buy your hotdog outside of lunch hours. The most insidious, however, is the poison contract, a token which is dropped into an account with code that states if it’s ever transferred out — or touched in any way that appears on the blockchain — that account’s assets will be transferred to the writer. The poison contracts cannot be removed or reversed, and just sit forever in your wallet like a landmine waiting to explode.

So, how does social media factor into the rocketing valuations of all these assets throughout 2021? For crypto, WallStreetBets took a bit of a side seat, passing its torch to a familiar face and a fresh one — Twitter and Discord.

In the section on social media types, I described Twitter as a platform with both very low social specificity and very low topicality. Topicality can be imposed by choosing who you want to follow, tacitly trusting those figures to post meaningful content in areas which interest you. One such content community is FinTwit. During the SPAC and GME waves, FinTwit stuck mostly to stocks and served as a bit of a sobriety check on the exuberance of WallStreetBets. However, as crypto assets began to take off, the creators of those assets — particularly NFT’s and altcoins — realized what the SPAC founders understood: if you can get enough influencers on board, you can generated a positive feedback loop of hype. Even more, unlike the SPAC founders, they weren’t bound by SEC rules, and could create incentive structures which powerfully rewarded these influencers if they succeeded — in the real world, we call these bribes, but in a space as new and wild as blockchains, it’s merely getting in at the ground floor. By giving Twitter influencers privileged access — free NFT’s, loads of altcoins, etc — they could tap the fresh meme stock billions.

So once you’ve got influencers speaking the gospel of your token, bombarding their followers with messianic rocket emojis, what can you do to seal the deal? Sure, a few folks will buy in just by reading the Twitter zeitgeist, but a lot will feel hesitant. How do you draw those hesitant people in to your sub-community, and once they’re there, how do you keep them long enough for the hype ball to achieve critical mass? Enter Discord. Discord is a chat-based social media platform originally designed to help gamers communicate in real time while playing. Because communities are created by users and their membership controlled by those users, they range across the topicality and specificity spectra. High specificity and low topicality might be your friends who post memes during work, while low specificity and high topicality might be everyone who works in Quantum Computing. For blockchain projects, you want both high specificity and high topicality. Here’s the typical flow.

Let’s say you want to fund a project in the Web3 space. You mint a number of “governance” tokens — essentially a project-specific altcoin — and drop a few in the wallets of your favorite influencer. That person is now incentivized to tout the value of your project, proxied by the value of its altcoin, to as wide an audience as possible. A few curious followers, already jealous of the huge gains made in other coins, buy a few of the new token and are permitted entry into the community discord. Here, the rules are simple.

  1. Don’t speak ill of the project
  2. Recruit as many new people onto the project as possible

Questioning the premise of the project, also known as spreading FUD (Fear, Uncertainty, and Doubt) is the cardinal sin of the Web3 theology. To express doubt, while healthy in the search for truth, is threatens the hype ball forming within the community that will eventually propel the governance token to the moon, at which point whether or not the project is completed is immaterial — the founder can get rich simply by selling tokens and escaping. The latter outcome, called a “rug pull”, is the fate of an ever growing number of projects in Web3, as few mechanisms exist to prevent it.

The dominance of Discord comes from the ability to regulate FUD. If a user expresses too many doubts, simply ban them from the channel. To create a feeling of exclusivity and community, create premium channels and moderator positions for those with more tokens, generating a willing band of henchmen between yourself and the masses. Promote quasi-religious shibboleths designed to ensure a feeling of group unity, and continuously remind users that they bought the tokens, inspiring them to invent their own justifications for continued loyalty. Finally, recruit new members by expounding on the concept of WAGMI — We’re All Gonna Make It — a mantra designed to reassure late accepters that even though they may have missed the biggest gains, there is still more to come. By prohibiting FUD and ensuring positive incentives for everyone involved, it is only a matter of time before the hype ball rises and your net worth shoots for the moon.

What Comes Next (Sung As In Hamilton)

As I write in May of 2022, the Web3 wave shows signs of cresting. Bitcoin is reaching for yearly lows, and indices of publicly traded Web3 companies are cratering. While it’s hard to see the future, rising Federal interest rates and return to pre-Long Night ways of life could be cracking retail investors out of their social media cocoons. In most parts of the USA, life has returned to a fairly consistent 2019 normal, and as we mourn the loss of those years and loved ones who won’t see the dawn, at least we can once again gather with our friends and run our fingertips through the grass.

Will there be another, fourth wave? Possibly, though it won’t achieve the magnitude of the previous three. Instead, as quantitative tightening begins to cool the housing market, and the S&P 500 starts is long march back toward realism, we should turn our attention to how social media inspired finance will affect markets in the long term. While the return to freedom has allowed most of us to settle back into our old comfort zone, we cannot simply resurrect the old models and expect business as usual to resume. The last couple years have caused a tremendous amount of damage. The retail bloc remains vibrant, its gains pared but its sense of community still strong enough to trigger minor squeezes in low cap stocks. New phenomena like SPAC’s, meme stocks, and crypto projects remain relevant, and probably will for some time, as the generation which suffered through the GFC relearns its lessons in the digital space.

If there’s one thing you should take back home with you, it’s an appreciation of the mind-distorting power of social media and the human biases which it magnifies. The itching FOMO caused by viewing a selectively curated reality generates negative emotions where they don’t belong, and inspires us to make risky decisions. The consistent low-grade dopamine drip provided by the Death Scroll robs us of our time, and ensures we keep interacting with the distortions until the real world feels like the fake. To protect our financial futures, it’s imperative that we remember that there is a reality outside the screen, one which follows known rules and suffers far less from the influencer dynamics ascendant on the phone. In an era of remote work, we should be especially vigilant. We must control our exposure to that simulated world, and maybe, when the next wave comes, we can avoid being carried away by it.