I have long struggled with the logic of applying the aggregator model to price-insensitive goods like luxury, and it was only a year or so ago that I understood the source of my confusion. (The reason is so simple that I—like T. H. Huxley when first grasping the dynamics of Darwin’s theory of evolution—thought to myself: How extremely stupid not to have thought of that!)

Essentially, my realisation was that luxury economics work in reverse to normal economics. While this is what makes them powerful, it also suggests that any aggregator model applied to those economics might also work in reverse.

Useless Veblen goods and the honest signalling of wealth and status

As Thorstein Veblen realised and wrote about in his treatise, The Theory of the Leisure Class, luxury turns everything that we thought we knew about supply and demand on its head: Instead of becoming less desirable as the price goes up, luxury items become more desirable (and often the more so the more expensive they are). I think this is because luxury is synonymous with excess (i.e. the opposite of necessity), so that ‘luxury’ is that which nobody needs. As a result, the consumption of luxury goods says something about the consumer, meaning that the consumption of luxury products serves a signalling function. 

In biology there’s a concept known as ‘signalling theory’, where signals are thought to be either honest or dishonest. Honest signals are the most valuable since they can only be shown by those organisms able to afford it. Examples of such honest signals would be the gaudy males of some sexually dimorphic animal species, where traits like the male peacock’s tail have evolved because females like gaudy males. The reason why females like gaudy males is because the extravagant tail makes the male conspicuous. This conspicuousness further becomes an honest signal of health, since only strong and healthy males could maintain the tail to a high standard and afford to be so conspicuous to predators. (Another example is the warning colouration of venomous animals, where the more venomous an animal is, the more honestly conspicuous it can afford to be.)

When it comes to humans and luxury, the conspicuous consumption of luxury goods communicates to others that the conspicuous luxury consumer has both an elevated social status and that they enjoy superior access to material resources. This is because social status and material wealth are both metrics that humans find attractive and that many people, therefore, want to maximise and flout. As such, the value of a luxury good lies less in the value of the good itself and more in the signalling function that access to such goods provides. Therefore, while only the richest people (at least historically) have been able to afford luxury, the desire for such consumption is near-universal. 

This desire (combined with the efficiency of the signalling), however gives rise to dishonest signalling (which, in the biological world, is known as mimicry), where people without the wealth or resources required to afford luxury good procure these (or something like them) to gain the implied social status. Dishonest signalling however cheapens the signalling function of associated objects, like luxury brands, and for this reason brand equity is best protected by keeping prices high. This is probably also the reason why luxury goods are Veblen goods (i.e. becoming more desirable the more expensive they are), because the more expensive the goods are, the more honest the signals become. 

The world’s luxury brands are therefore likely the monetary beneficiaries of the human need (and demand for) the conspicuous signalling of wealth and status. The desire for such goods creates a supply-demand gradient that the luxury brands can profit from selling into—up to a point, and the CEO of Ferrari China [link] explains the conflict of managing a luxury brand well when he says:

The protection of the residual value, the pre-owned market, for us, is a priority. As the founder of the company always said—and I want to repeat it, because it very clearly explains the philosophy of the company—we should always sell one car less than the demand; we will never sell one car more than the demand. … We never push. Ever. For us, the most important thing is to always have the correct amount of cars in the market. Even if we can sell more, we refrain from selling, because we have to protect the exclusivity of our clients … This is a key element of the business model.

In other words, luxury brands can amplify the demand gradient that they feed on by artificially reducing supply so that it doesn’t meet demand, and to so keep prices (and demand) high. This dynamic is unique to Veblen goods as they are the only goods to see demand scale proportionally with price (rather than inversely). This is because their function is price-dependent in a way that no other types of good are: If a luxury item wasn’t expensive, then it wouldn’t be a luxury anymore. Or, put differently, the point of a luxury item isn’t the item itself, it is signalling power of that item as a function of the price. The higher the price is, the greater the impact of the signalling. As the price goes up, the power of the signalling goes up too. This is the exact dynamic required for reverse price-sensitivity to work, namely, that the price is the real product. This means that the highest-quality ‘products’ are the most expensive. 

Ultimately, the power of the economic curation that is engaged in by luxury brands is rewarded through the erection of deep moats (built from sales discipline and investments in brand equity), all with the intent to keep prices artificially high and to ensure the longevity of demand. When done well, this strategy results in the emergence of outlier (‘star’) brands that have less to do with traditional ideas of luxury (e.g heritage, tradition, artisanship, etc.) and instead exist more as purely cultural phenomena with sufficient momentum to create the positive feedback loop of fame that drives luxury economics: Where the price is high enough so that one’s possession of the item implies a specific degree of material wealth and that others (not just the conspicuous consumer) know just how much must have been paid.

Alice in Wonderland and the reverse aggregator model

Because luxury brands are used for signalling, it also becomes obvious that the better-known a brand is, the more effective the signalling will be. This makes a well-known brand or a logo pretty much required. The Internet can further help to amplify this dynamic, as it’s a medium as-if-built for maximising fame by amplifying reach; allowing previously local brands to become global superstars. Therefore, even if it’s superficially counterintuitive, the cultural relevance and fame of the existing star luxury brands might actually increase once the Internet is thrown into the mix. If so, rather than the Internet acting as the ‘great equaliser’ it has been in every other industry, in the luxury industry it might actually help the pre-existing star brands to grow more and more famous; further cementing their outlier status and allowing them to gobble up more and more of the industry money flows. These are classic free-market dynamics in action, where the rich get richer and where the big (or famous) get even bigger and better-known. 

In every other industry, the Internet has of course powered exactly the opposite development, where generalists and aggregators have scaled to never-before-seen levels of success at the expense of the brands they are aggregating. So, what gives? Of course, the missing piece of the success-equation comes from the upside-down dynamics that characterise Veblen goods: While generalists have thrived in commodified industries (where the lowest common denominator is price and since everyone loves a bargain, the generalists can leverage economies of scale to lower prices and to so win sales), the luxury industry (which, famously, does not thrive on bargains) cannot make use of this dynamic since luxury—by definition—cannot compete on price. In other words, in the luxury industry, the economic benefits of increasing scale (as afforded by the Internet) are likely to accrue to the players that are the best able to compete on fame (rather than price). 

Now, if we were to accept the premise that fame (and not price) is the driver of growth in the luxury industry and then run the resultant argument to its logical conclusion, the Internet could—counterintuitively—make the luxury industry less competitive: Because technological innovation typically focusses on lowering prices (by making more of something, or making it cheaper), the luxury industry will be uniquely hard to disrupt. Instead, disruption in the luxury industry will likely feed on fame (i.e. being able to build it faster and more sustainably or better and more efficiently), but since fame also (famously) begets fame, the luxury disruptors might—provocatively—actually be the incumbent brands themselves as the Internet has magnified their pre-existing autocatalytic potential. If so, the winner-take-all dynamic would likely not accrue to a marketplace or other aggregator (because their power over the brands would be limited), but would, instead, accrue to the star-brands themselves.

Consider, for example, quotes like the following from a transcript of a presentation at Kering’s 2019 capital markets day [link; p. 6]:

When we look at luxury … we do not see the emergence of digital native brands as much as we’ve potentially seen in other industries. When you look at the ranking of the top luxury brands, these are the same brands. … So, less disruption on the brand side … ¶ … net-net, the luxury industry is subject to that digital revolution as well. Probably not as massively as other industries, and probably driven by the fact that we’re creating unique products that are impossible to replace, and also we are controlling our distribution. ¶ … ¶ What we see in [the] online market really mimics what’s happening in offline retail

In other words, in the Alice-in-Wonderland world of luxury, the luxury conglomerates themselves might actually fulfil the role that Amazon and kin has played everywhere else.

Presupposing everything above, the opportunity available to luxury ‘disruptors’ could therefore look very different from the (otherwise generally defensible) copy-paste ‘platform + Internet = success’ arithmetic. As a result, there is the potential for aggregators in this upside-down industry to actually become more reliant on the brands that they are aggregating, rather than less (as has been the case in every other industry with more ‘normal’ economics). If so, the bigger a luxury aggregator becomes, the greater the aggregator’s needs to actively disintermediate the luxury brands would be, almost like an aggregator model running in reverse.

Given this, the most successful strategy for hopeful luxury disruptors might not actually be that of an aggregator, but that of an anti-aggregator (e.g. becoming a brand). For the luxury industry, this would mean that wannabe aggregators would need to give up on the misleading ‘platform = disruption’-playbook to instead roll their sleeves up and acquire the portfolio of brands required to play the game as the game is currently being played. Such a strategy could allow the aggregators to claw back some of the power that they’re otherwise potentially helping third-party brands accrue (which actively, but unintentionally, undermines their own competitive position).

I don’t think it has escaped anyone in the financial services industry that ‘sustainability’ is a hot investment theme, and on the back of this, ESG-flavoured growth stocks like those associated with electric vehicles, hydrogen fuel cells, or renewable energy have done particularly well. Of course, these stocks have recently seen bit of a pullback, but this might be more indicative of a general weakness in the markets than any meaningful change in the ESG investment theme per se? For what it’s worth, while this trend is in full swing, I find myself curious as to where the trend came from and when it came about.

By all accounts, ESG inflows continue to be strong, and a good performance in the sector during the past year has likely whet investor appetites for more. As I’ll discuss in this post, there is also an underlying cultural driver to these flows, which suggests that the excitement about ‘green’ stocks is likely just one symptom of a wider, underlying, cultural trend. As such, something that might be interesting to ponder how durable the trend is, as it relates to markets: Will the interest in ‘sustainable’ investing last beyond the current stage in the market cycle (i.e. is the trend cycle-agnostic), or is the excitement about ESG investing just part and parcel of the current cycle? In the latter case, even if the sustainability sentiment might cool when excitement about the market begins to sag, it remain possible that the underlying cultural driver of the sentiment might remain intact, just that it’s no longer as pronounced in the market. I won’t discuss the correlation between interest in ESG investing as a theme and the level of excitement in the market in further detail in this post, but it’s something that’s worth keeping in mind.

In previous posts I’ve written about how you rarely see a change happening at first (e.g. [link]), as it’s so subtle at first so to be pretty much imperceptible, and that it’s only weeks or months (or even years) later that you take a step back and realise that a trend is already in full swing and you kinda knew but just didn’t notice until now. Therefore, while I try to be more aware of such themes as they’re developing (as it can be useful for you as an investor to tune into these themes early on and capitalise on them as they grow), every day is full of inputs—many of which could be possible ‘themes’ and you’ll just never know. In addition, as I wrote in the previous post, the culture of a society represents the average ‘temperature’ of a great melting-pot of memes, and while some of the circulating memes grow in popularity while others go down, the changes are so distributed as to be hard to track in any meaningful way in real time.

To get back to the discussion of sustainability, I recently attended a (virtual) conference on the same topic as a conference that I’d attended back in 2019. Surprisingly, one of the most meaningful changes between then and now didn’t seem to have anything to do with the underlying technology of the industry that I was researching, but everything to do with sustainability. To wit, while ‘sustainability’ was mentioned in 2019 as one of many industry attractions, today, in 2021, ‘sustainability’ was presented front and centre and then highlighted as the core attraction of the industry.

I found this change in emphasis to be fascinating, and (sadly, you could argue) the most memorable take-away from the conference was this seemingly obvious observation: That sustainability has gone viral. It’s everywhere.

Allow me bit of a digression as we rehash some of the insights from last weeks’ post:

Memes (as popularised BG Richard Dawkins) are emulated behaviours, in the form of ideas, topics, or behaviours, that spread in a population of brains much like a virus would spread in a population of organisms or an invasive species would spread through an ecosystem. While this spread is conditional on several factors, the appeal of the meme, its longevity (staying power), and the receptivity to the meme in the population of brains would be particularly important. For example, a very appealing meme with low staying power would burn through the population rapidly before fizzling out. Indeed, we see this with viral fads all the time. On the other hand, an appealing meme with greater staying power would spread and then stay around for a while, in the form of a trend (effectively, a longer-lasting fad).

The total population of memes circulating in a population of brains would compose a ‘meme pool’. A better name for this meme pool would be something like ‘culture’; the culture of a society being the aggregate population of memes, some spreading rapidly before fizzling out with others having a more lasting impact on collective behaviours and ways of doing things. The deeper a meme drops in the modified Pace layering model shown above, the more fundamental its impact can be reasoned to be.

In this framework, topics like ‘sustainability’ and ‘ESG’ might be particularly appealing because the collective meme pool is overweight similar and related memes. When this is the case, talking about ‘sustainability’ becomes a culturally acceptable thing to do, and, indeed, it’s something that many people have come to actively seek out and enjoy. (My office cannot be the only one that has been inundated with special interest groups intent to use the office as a battleground for saving the planet.) Closer to home, many parts of the world has also recently gone through a period of shoppers asking their supermarkets to get rid of excess plastic, and I don’t think it’s possible to turn over the tray that your favourite health food is sold in without seeing the manufacturer’s assurances that ‘as much plastic has been cut from our packaging as the food-safety regulators allow us to do’.

In other words, enough ‘activation energy’ on the topic has been injected into the culture though each individual sustainability-related meme that caring for the environment is essentially part of the culture now; it’s just something that we all do without thinking and that we try to become better at be a use it’s the right thing to do. In addition, because multiple ‘sustainability’ memes are already circulating in the population, and because each additional such meme makes people find other, similar memes to be more appealing, we can see autocatalytic meme-amplifying mechanisms begin to take root: If you’re into saving the environment, of course you’ll find additional sustainability-related memes appealing. This means that a culture that is overweight sustainability-related memes will make it easier for additional sustainability-related memes to spread. That’s the environment that I think we’re in today when it comes to topics like sustainability and ESG.

To return back to my experience at the conference, realising that the sustainability meme has grown from a nice-to-have to a must-have in the industry marketing decks in the past 18 months also made something ‘click’ into place as I realised that we’re seeing a viral trend well underway with the current sustainability Zeitgeist. No wonder that everyone is talking so much about ESG these days, I thought to myself at the end of the conference (when things clicked for me): It’s the right meme, at the right time, in the right place!

Armed with this realisation, and as the recovered scientist that I am, I turned to Google Trends to see if I could see evidence of this trend emerging in the search data, and, indeed, there is evidence for a viral (autocatalytic) memetic spread in search keywords like ‘responsible’, ‘ESG’, ‘development goal’ and ‘harmonious’. (Conversely, keywords like ‘sustainable’ or ‘sustainability’ or ‘clean’ or ‘green’ and similar might be too generic to show a reliable signal.) Notably, there is also a marked acceleration of the search term popularity (i.e. onset of what looks like an autocatalytic dynamic) sometime around late 2017/early 2018—presumably when the trend became self-sustaining for whatever reason, where talk about ‘ESG’ or ‘sustainability’ begets more talk about ‘ESG’ and ‘sustainability’.

Of course, at this point, my next question was: What happened around this time? 

Late 2017/early 2018 isn’t exactly ancient history, so I racked my brain to remember what might have happened. I didn’t come up with anything at first, but after listening in to some of my colleagues talking about an unrelated topic, it all came back to me: It was the turtles. The turtles is what happened then.

For those of you who are yet to be made familiar with my obsession with people’s obsession with the turtles, this goes way back—at least as far back as just late 2017/early 2018—when Blue Planet II premiered on the BBC. The series places a heavy emphasis on sustainability, highlighting the damaging effects that human pollution and littering has on the oceans and the creatures that live in the sea. At the time, many people were already primed for this message as a video with a turtle with a straw stuck up its nose had gone viral earlier in 2017. The video makes for unpleasant watching, so it’s no surprise that it struck a chord, especially when the culture is already ripe for change with climate change looming and the debate about plastic heating up.

Back in late 2017, when Blue Planet II premiered, I remember people stepping up their game in the war on plastic as the BBC hit series added further fuel to the fire. It’s possible (but I’m speculating here) that Blue Planet II helped push the culture from a state of being overweight sustainability-related memes to a state where sustainability-related memes became self-perpetuating. (To put this into physics terms, at the point the sustainability-related meme—either because of Blue Planet II or something else—became self-perpetuating, the cultural meme ‘symmetry’ had been broken and an outlier sustainability-related collection of memes could begin to compound where the popularity of sustainability-related memes were feeding on the popularity of the sustainability-related memes that had come before.)

At the time, before I started learning about complex systems and realising that culture is a melting pot of circulating memes, I was also woefully naïve, and I remember trying to stem the tide by appealing to reason in an intra-office note:

The popularity and durability of plastics, coupled with a lack of adequate recycling efforts, has allowed plastics pollution to proliferate. Sexed-up nature documentaries (typically) narrated by the honeyed tones of David Attenborough (or local equivalent) have raised awareness of this problem, giving rise to well-meaning social movements to reduce plastic use—while simultaneously failing to ascertain how such change is best affected.

Developed economies are typically high-consumers of plastics—but they are also efficient at collecting these via efficient recycling waste streams. Though much work remains to be done before a truly circular economy becomes reality, what televised vistas of pristine beaches spoiled by droves of discarded plastic fail to convey is that an estimated 64 % of plastic waste in the world’s oceans is derived from a handful of rivers in the developing world.

Indeed, a quick toying-around with some numbers from some twenty countries globally suggests that there is a relationship between a country’s GDP per capita and the efficiency of its recycling stream (see FIGURE 1). This suggests that greater change might be affected by consumers choosing not to eschew pre-packaged produce at the supermarket, but by promoting efforts that reduce poverty—both globally as well as closer to home.

As you can tell from this excerpt, I was a bit snarkier back then. In fact, this episode (and several related ones, for better and for worse) actually did a lot to help me adjust my tone.

Please, indulge me for a bit as I reminisce:

At the time, some co-workers riding high on the anti-plastic crusade decided that we needed to get rid of paper cups and plastic stirrers in the office. Now, this wouldn’t have affected me directly, as I always brought my own mug to work as I found the using and tossing of multiple paper mugs every day to be pretty wasteful. But, I liked having the paper mugs around on the occasions when I left my mug at home by accident (as we didn’t have dishwashers in the office and I reserve the right to find communal workplace sponges to be a bit iffy), and I also felt that it was unprofessional to ask guests to a firm in the financial-services industry to please bring their own mug when visiting (because, turtles). Essentially, the paper mugs had their uses. Long story short, I shared my view on the topic, arguing that we should keep the mugs but maybe encourage heavy users to cut down. (Indeed, some of the managers were self-confessedly too “lazy” to either bring their own mug or recycle the paper ones.) I was a bit too snarky in making this argument, using numbers and pointing out that the coffee (especially if you used milk!) probably had a larger carbon-footprint than the mugs themselves, so if we were really interested in saving the planet, then maybe we should get rid of the communal coffee machine altogether. For some reason, that I had the gall to make this argument on the intra-office messaging board turned out to have been beyond the pale, and so I was eventually called into my boss’s office for an unofficial reprimand: I wasn’t allowed to argue facts (part of the front-office staff as I was) on office-wide channels, he said, before explaining that it ‘wasn’t good for intra-office relations’.

So that’s what happened with the turtles, and that’s’ why I find people’s obsession with them to be so fascinating: One viral video in 2017 led to my unofficial reprimand and a 2020 inbox full of job offers from ‘ESG-themed funds’.

Back to the topic at hand: For any company operating in any industry, it’s a greatmarketing strategy to lean into self-perpetuating cultural trends like these, as a lot of the groundwork has already been done: If you want to market a product, you just tap into the autocatalytic Zeitgeist and say that your product is aligned with x. Such marketing can also become even more powerful if you’re of an analytical bent and once the memetic origins of the trend are understood, as it allows you to ‘ride the wave’ for as long as it’s accelerating, and then get off before the trend has burned itself out. In this way, marketing is no different from investing— as you’re just engaging with a different pool of assets. This is why trends and memes and the collective meme pool itself (e.g. what we otherwise call ‘macro’ in an attempt to differentiate it from more fundamental concerns) is important to track and to understand and it’s something that I think we as (investment) professionals need to become much better at.

Understandably, marketing is also something that everyone has to think very hard about: When doing marketing, you’re basically creating new memes, and once let loose into the world, these memes must be curated and managed in attempts to optimise for the upside of viral marketing while anticipating the negative aspects that a backlash would bring. Ultimately, marketing, for better or for worse, sits at an uncomfortable nexus between these two concerns, and many industries need to find ways to align themselves with the Zeistgeist.

Accordingly, leaning into something like the sustainability or ESG trend is an easy way for investors and asset managers to do this, as the memes are already prevalent in the culture (and presumably, the client population), and so the autocatalytic flywheel keeps spinning and more ESG funds are born because the WSG flows were so strong, and the more funds there are, the more believable the investment theme seems to be, and the greater the inflows become. Ultimately, we’ll just have to wait and see where it all ends up, even if, for now, it probably means that I will be thinking of turtles at my next conference as well.

Popular retellings of past bubbles always seem to give the impression that things were crazy back then, and that stocks were the only thing that people could think about as part of a “mass escape from reality”. While this idea has taken on bit of a life of its own to inform current discussions about the level of speculative excess in markets (or the lack thereof), I want to show in this blog post that ‘crazy’ is a relative term: People in the past were no more or less crazy than we collectively are today. Therefore, it’s essential to have an accurate view of cultural trends (both past and present) for us to maintain an objective view of complex systems like the stock market—that otherwise (blindly) aggregates information from the economic, financial, and cultural spheres.

Culture is what emerges from a great melting pot of memes

Richard Dawkins coined the idea of the ‘meme’ back in 1989, writing in The Selfish Gene that the meme is “a new kind of replicator” that emerged alongside the human brain as it evolved, providing a new, fertile ecosystem for thoughts and ideas to replicate in. A meme, Dawkins explains in the book, is a “unit of cultural transmission … a unit of imitation”, explaining that

Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation. If a scientist hears, or reads about, a good idea, he passes it on to his colleagues and students. He mentions it in his articles and lectures. If the idea catches on, it can be said to propagate itself

Dawkins continues,

Imitation, in the broad sense, is how memes canreplicate. But … some memes are more successful in the meme-pool than others. … [and] particular examples of qualities that make for high survival value among memes … must be … longevity, fecundity, and copying-fidelity. … As in the case of genes, fecundity is much more important than [the] longevity of particular copies. If the meme is a scientific idea, its spread will depend on how acceptable it is to the population of individual scientists … if it is a popular tune, its spread through the meme pool may be gauged by the number of people whistling it in the streets. … Some memes, like some genes, achieve brilliant short-term success in spreading rapidly, but do not last long in the meme pool. Popular songs and stiletto heels are examples.

Ultimately, the ‘fitter’ a meme is, the more appealing it will be, and the more popular it becomes, the faster it will spread throughout the meme-pool that we call ‘culture’. At any given time, culture will be the emergent, collective average of all memes perpetuating through the collective human brain and which occupy their thoughts and habits. In some cases, these memes will be in the form of institutionalised knowledge (facts and paradigms, or ways of doing things), while others will manifest as faster-moving fads or trends. The resultant great melting pot of fast- and slow-moving memes and the churning of the mix over time is perhaps best represented by drawing an analogy with the Pace layering concept, where trends and fads represent the faster-moving fashion-layers, and where facts and paradigms are more fundamental and slower-moving.

In this modified Pace layering structure, memes exist on two axes, one measuring popularity (fads are more popular than reality) and the other measuring realism (reality is more realistic than fads). While popular but loosely anchored memes fizzle out quickly, themes with greater staying power can ‘drop’ into the layers below and increase their odds of being institutionalised as knowledge or enshrined as ways of doing things. 

A vague analogy can also be drawn here between this model and gene-based replicators like viruses, where fast-spreading viruses like SARS-CoV-2 can infect many people quickly, but whose biology means that it can never be more than a perennially circulating virus. Retroviruses like HIV, on the other hand, spread less quickly, but their biology allows them to occasionally be integrated into human genomes where they can be preserved as useless and decaying ‘genetic fossils’ for very long periods of time.

To describe the imitation strength of memes using words like ‘fecundity’ (as Dawkins does) however has the potential to be a bit misleading by taking the analogy between genes (or viruses) and memes too far. Instead, something like appeal (or Dawkins’ ‘acceptability’) might be more accurate, as concepts or ideas (in the form of memes) spread faster the more acceptable they are, and the stronger their appeal is, and the more people who happen to be receptive to the meme. 

The marketing and entertainment industries have of course been aware of this for a long time, as they specialise in generating and spreading memes, often by tapping into and by piggybacking on existing ones. For example, the blockbuster movie concept is built on the idea of near-universal appeal, as a production that is palatable to everyone (young and old, male and female, and across the world) is both de-risked and exposed to a much higher potential upside than any more specialised production (with a smaller audience and lower appeal) would be. Platform companies like Facebook, YouTube, or TikTok have taken this idea one step further by outsourcing the production of content to their users and allowing the users themselves to surface the content that is the most appealing to the largest number of people, while the platform contents itself with raking in the resultant advertising cash. 

The analogy between a Hollywood blockbuster and an outlier stock is also easy to draw, as both are generally acceptable and have a near-universal appeal. Because of this appeal, they also spread readily through word-of-mouth, except stocks are (typically) more likely to be recommended to you by your investor friends and the general industry than by your friends or neighbours. Blockbusters are also successful at the stock office for the same reason as outlier stocks are outliers: Because they are so popular, large numbers of people will be queuing up to be part of the show.

The stock market is Google Trends for stock-related memes

The analogy between blockbusters and outlier stocks and the ‘culture as a meme pool’-heuristic suggest that the stock market would be the ultimate platform for stock-related memes, where investors (individual or institutional) are using their money to cast short-term votes on individual stocks or companies. Despite temporary inefficiencies, where meme stocks like GameStop or Tesla can trade at steep premiums, in the long term—as Graham realised long ago—it is only the most stable and durable businesses will deliver long-term value for shareholders.

Accordingly, the stock market records the popularity of capital-market trends as they develop. In each era, the stock market has its own meme-driven investment trends and meta-trends (themes), and it’s by tracking these that the astute stock-market observer can effectively ‘take the pulse’ on the meme pool to intuit the direction of the economy-wide money flows. Sometimes, these flows are headed into the stock market as part of a thematic meta-trend (i.e. stocks are ‘hot’), while at other times, the flows are headed into other asset classes like housing (i.e. real estate is ‘hot’). During times when a lot of money is making its way into the stock market, mini-themes (trends) will result, where the money flows into the stocks from companies operating in a handful of industries or sectors, like renewables or electric vehicles or IT (software). Each theme will further see its own winners and losers, as some companies are better than the rest at capturing the imagination of investors—either by showing great fundamentals or succeeding at memetic marketing. (The very best stocks will have both great fundamentals and be generating attractive memes.)

Altogether, the ups and down of individual stocks will be recorded as stock-market price curves. While the random movement of these curves up and down are the product of random processes like investors balancing their portfolios or meeting client cash flow demands, the directed movement that develop in these curves (up or down) measure the changing popularity of the instruments in the investor meme pool. Because of this quality, where the trend or direction of a stock’s price changes reflect the aggregate outcome of investors’ capital-allocation decisions, each price curve, and all the market price curves in aggregate, mean that the stock market represents something like a ‘Google Trends’ for stocks. Here, the best-performing stocks are effectively the stocks with the most rapidly growing investor mindshare. These stocks represent the currently most successful and fittest finance-related memes circulating in the cultural meme pool. 

This model, where the stock market tracks the popularity of individual stocks as a proxy for the popularity of stock-related memes circulating in the meme tool also allows us to draw analogy to our modified Pace layering framework. Here, the fastest-changing and most volatile stocks would map onto the ‘fad’ layer (high in popularity and low on realism), showing a rapid price appreciation as the fad spreads through the meme pool and then dissipating, as quickly, as the fad burns itself out. The more realistic an investment thesis is, the slower-moving it is likely to be, as realism is a less attractive but more durable mimetic characteristic. 

As this suggests, our modified Pace layering model can act as a framework with two axes along which to analyse stocks: On the realistic axis, we’ll analyse stocks on the basis on fundamentals (where good fundamentals help seed memes), whereas on the popular axis, we’ll analyse stocks by assessing the attractiveness of their associated memes (where attractive memes help incentivise positive investment flows). Using this model, we’ll find that the price appreciation of some previously inexplicable outlier stocks start making a whole lot more sense: There are both good stocks and popular stocks, and while the ideal stock is both good and popular, there is much to be said for being able to analyse stocks both ways.

Bubbles result from meme pools being overweight stock-related memes

Sometimes the stock market itself becomes a meme. Indeed, when we consider the cultural backdrop across market cycles, we find that the meme pool is typically overweight stock-related memes during particularly frothy periods, and underweight during stock-market lulls. 

Regardless of the prevalence of stock-related memes, the underlying memetic diversity of the meme pool is however typically maintained. Instead, it’s the ratio of stock-related memes to other memes that goes up and down, and just because stocks are particularly popular during some periods and less so during other periods doesn’t mean that people stop enjoying gardening or talking about politics. 

I’m well aware that I’m mixing my metaphors pretty liberally here, but a useful mental model to use when visualising this dynamic (of the rise and fall of thematic memes) might be to draw analogy to how allele (gene variant) frequencies can fluctuate randomly over time in a biological population (gene pool), where an allele’s frequency varies by something approximating a random walk between generations.

The chart below shows the simulated frequency of a collection of alleles (gene variants) in the gene pool over time (I got the diagram from here, [link]). While all alleles start at a frequency of 50 %, random fluctuations between generations (depending on what carriers reproduce and which ones don’t) see the alleles either increasing or decreasing in frequency. The smaller the population is, the more volatile the fluctuations, and the larger the population, the less volatile the fluctuations.

Admittedly, this is a very imperfect way of visualising the frequency of memes in the meme pool—especially as there (by definition) is no autocatalytic element present in genetic drift (which would see the increased frequency of an allele increasing the frequency of that allele further), but hopefully you see what I’m getting at by referring to a modified version of the chart, below:

Here, if the blue line (for example) tracked the popularity of the stock-market meme in a culture (meme pool), it would be low at first (as only a few people would be interested in stocks and investing), but over time, due to random influences (albeit with the important autocatalytic component that’s missing here, where you’re excited by the stock market because all your friends are excited by the stock market), the frequency of the stock-market meme in the meme pool would increase. At the point where an above-critical number of people are host to this meme and are acting on it by investing in the stock market, the market would be experiencing a cyclical upswing. This would increase the prevalence of the stock-market meme further, as more people are ‘infected’ and want to join in on the fun. As this modified diagram also shows, even if one allele/meme is increasing/decreasing in popularity, there are typically multiple additional memes vying for attention, each experiencing its own cyclical up- and down-swings. Adding an autocatalytic element to this dynamic would just make the up- and down-swings more dramatic, as would happen if we were to increase the speed by which information and actions can be logged and processed (e.g. over the Internet, using free chat apps and zero-fees trading). In fact, the Financial Times has a great chart showing retail interest in different equity classes in this article here: [link].

Applying this model to the Pace layering framework above, suggests that fads would be memes with a wide (‘viral’) appeal but that score low on longevity, meaning that they very quickly burn their way through the meme pool before fizzling out to never be heard from again. (Aside from as part of future recollections, e.g. “Can you believe that Bill Gates did the ice-bucket challenge?”) Trends would be less volatile, but still fluctuating over time; never going ‘viral’ but also never sizzling out completely. Instead, some trends could become ‘fixed’ as more deep-rooted themes, while others would remain popular only among the select few. Something like the stock-market meme (where investing in stocks is seem as a sensible and popular thing to do) would be a periodically recurring trend that’s present at a high frequency during some periods and lingering at a low frequency during other periods.

Things weren’t really that crazy back then

Now, let’s return to the original topic of this post: How crazy were things, really, during past market-cycle peaks, and how does this compare to today’s craziness? 

The popular perception is that things were pretty crazy, and if we think back to the 1920s stock-market boom, the story typically goes that everyone was invested in the market. This view is perpetuated by famous stories of investors ‘selling their stock at the point where the shoe-shine boy was giving them stock tips’ because things, at that point, were believed to have gone too far. In reality, the public attitude was more muted, and if we turn to someone like John Kenneth Galbraith who chronicled the 1920s boom and bust in The Great Crash 1929, he says

There is probably more danger of overestimating rather than underestimating the popular interest in the market. The cliché that by 1929 everyone ‘was in the market’ is far from the literal truth. Then, as now [in 1954], to the great majority of workers, farmers, white-collar workers, indeed to the great majority of all Americans, the stock market was remote and a vaguely ominous thing. Then, as now, not many knew how one went about buying a security; the purchase of stocks on margin was in every respect as remote from life as the casino at Monte Carlo.

This view, Galbraith continues to say, is supported by a post-crash “Senate committee investigating the securities market to ascertain the number of people who were involved in securities speculation in 1929”, finding that “only one and a half million people, out of a population of approximately 120 million … had an active association of any sort with the stock market” and that “only 600,000 of these accounts … were for margin trading, as compared with roughly 950,000 in which trading was for cash”. 

Even if we should take Galbraith (who wrote his account of the 1929 crash in 1954) with a bit of salt, his numbers suggest that only about 1 % of Americans were carrying the stock-market meme and acting on it. Regulatory changes (including the introduction of the 401(k) plan in the 1980s) have of course changed this dynamic somewhat, as more investors are vaguely aware of the stock market today than they would have been in the past (Pew Research numbers from 2020 suggests that over 50 % of US households were invested). That’s pretty crazy (given how risky stocks are), and it shows that the stock-market meme is pretty prevalent (and probably with a higher floor today than in the past, because of the need for so many people to grow their retirement savings). Indeed, the IPO market has been strong for some time now, and the SPAC craze was running hot for a while. In addition, we have been seeing retail investors using the stock market as a way to ‘get back’ at Wall Street. All of these are signs that the market cycle is peaking.

One counter-meme that is symptomatic of a high prevalence of the stock-market meme is the need to justify the market’s good performance. A popular version of this counter-meme is to point to the bears and say that ‘If these people are saying that we are in a bubble, then we cannot be in one, as being in a bubble means that everyone is a believer’. This counter-meme is, as I hope to have shown with the quotes above, a direct symptom of the popular misconception that things need to be as crazy as the tales of bubbles past to signal that we are in one. This, of course, isn’t true, and popular retellings typically compress a decade’s worth of stock-market history into a sentence or two, like: ‘A booming economy led to growing investor enthusiasm, which eventually turned into euphoria. Investors got ahead of themselves and traded too much on margin, and when the economy started dipping, the stock market crashed.’ The choice of the word ‘euphoria’ is particularly damaging to perception, as it brings to mind investors cheering the market on with champagne, something that we don’t really see around ourselves today (or I’ve been spending time with the wrong people…). 

Instead, a more accurate definition of ‘euphoria’ is the growing feeling that the market cannot go down. This feeling typically develops after years of reliable stock market returns, where the market seems to go only up and up, and more reliably so for each year that passes. Over time, the idea that the market can go down fades out of the popular consciousness as people forget. Looking at the market, such short-sightedness can be understandable: When the market is going up, it’s hard to realise how volatile stocks can be. Over time, reliable returns breed complacency, and complacency encourages investors to take on more and more risk because they don’t think they’ll end up paying for it. This is euphoria. 

One sign that euphoria has taken root is the strong conviction that it’s advisable to ‘buy the dip’. In the short term, this is sensible advice, as the further along the market gets to its peak, the more common rocky patches become. These drawdowns (typically of 20 – 40 % at a time) quickly revert as there are plenty of investors being willing to ‘buy the dip’ and to prop the market up before propelling it to greater heights. At some point, this reserve buying-power however gets increasingly exhausted, and at the point where investors are tapped out and exhausted with the stock-market meme, they will start looking to invest their dollars in other assets. That way, when a ‘correction’ comes, there are too few people to prop the market up, and it starts dipping—and then declining further. And the further it falls, the less likely people are to put their dollars at risk, as other asset classes start looking more attractive (and other memes start increasing in prevalence at the expense of the stock-market meme). 

Past experiences of bubbles popping also tell a story of extremely anti-climatic market peaks: The peak is typically not accompanied by much fanfare, and often pass quietly, without much comment even by the bubble-callers themselves. For example, Galbraith writes in The Great Crash that

On 3 September, by common consent, the great bull market of the nineteen-twenties came to an end. Economics, as always, vouchsafes us few dramatic turning points. Its events are invariably fuzzy or even indeterminate. One some days that followed – a few only – some averages were actually higher. However, never again did the market manifest its old confidence. The later peaks were not peaks but brief interruptions of a downward trend. ¶ On 4 September, the tone of the market was still good.

It was only later (admittedly a day or so), when stocks dropped more dramatically, that people started to get worried. 

John Cassidy in his (surprisingly enjoyable) 2004 book on the dotcom boom and bust, dot.con, tells a very similar story, where, even as the market was getting increasingly worried in early April 2000 (the NASDAQ peaked in late March), there were still differences of opinion and plenty of people who were willing to ‘buy the dip’.

James Cramer claimed the worst was over … Business Week rejoined the stock market boosters with a cover that asked: ‘WALL STREET: IS THE PARTY OVER?’ The answer, also emblazoned on the front page, was unequivocal: “High-tech stocks are undergoing a much-needed correction. But relax, the overall market probably won’t tank. What we’re seeing looks more like a healthy flight to quality.

Justifications and excuses accompany stocks on the way up

Because not everyone will be ‘infected’ with the stock-market meme, even at the market peak (as even the most popular meme will fail to ‘infect’ every single brain), there will—at both early and late stages of the bubble—be plenty of people willing to point out the excesses of other market participants. Towards the end of a market cycle, it’s sometimes even the people with the most to lose who will make the most candid comments. 

What sets today apart from other market peaks is that there was a general feeling of insecurity in the market before it peaked in both 1929 and 2000, that was making even long-time bulls uneasy. Other parts of the historic narrative however fits well with what we’re seeing today, where even market cheerleaders like ARK Invest’s Cathie Wood are warning of the potential for near-term corrections:

Rejecting talk of an equity market bubble led by many of their high profile holdings, Wood said she expects innovative companies will grow into their high equity valuations as demand for their products and services expands exponentially over the next decade. Aside from Tesla, Wood reckons the broad adoption of digital wallets will benefit the payments groups Square and PayPal, for example. Wood said a stock market correction will provide Ark with an opportunity to buy more “high conviction names” and companies it believes will be “winner-take-all” in a fast-expanding new industry. “A correction is a great time to determine what are our high conviction names,” she said.

Just for fun, let us compare what Wood is saying to statements made by Morgan Stanley’s Mary Meeker towards the end of the dotcom bubble (in May 1999):

In a research report, Morgan Stanley’s Mary Meeker said she expects “more weakness” from Internet stocks, which will likely make investors more cautious about the sector.

“It isn’t uncommon this time of year for prices of technology stocks to contract when catalysts are insufficient,” Meeker wrote.

Meeker also noted she wouldn’t be surprised if Morgan Stanley’s Internet index — already down 33 percent from its April high — fell another 20 percent.

Superstar sell-side analysts also admitted during the dotcom boom that some growth stocks were “extremely expensive” as early as in 1998, but kept finding various ways to justify their increasingly ambitious price targets. Merrill Lynch’s Henry Blodget, for example, wrote that

The overall Internet stock phenomenon may well be a ‘bubble’, but at least in one respect it is very different from other bubbles: there are great fundamental reasons to own these stocks … the companies underneath these stocks are (1) growing amazingly quickly, and (2) threatening the status quo in multiple sectors of the economy.

Here’s Blodget again (from dot.con, like the previous quote):

When Yahoo! went public [in 1996], it looked like the biggest joke in history … Investors the world over (understandably) crowed about manias and insanity, but [Yahoo!] was actually trading at an insanely cheap 10X Q4 1998 annualised earnings. Investors who failed to ask themselves two questions—(1) how big the company could actually be, and (2) how fast it could get there—missed the boat. With these types of investments, we would also argue that the real ‘risk’ is not losing some money—it is missing a much bigger upside.

While written in 1998, these words are almost verbatim from multiple conversations that I’ve personally had with growth investors over that past couple of years. ‘Valuations’, they say, ‘are justified’, one way or another, whether it’s through ‘embracing uncertainty’ or ‘imagining the upside’ or even making value-based appeals like ‘corporate earnings have never looked so good’. Of course, this type of behaviour (especially when coupled with strategies like running your winners, however high they soar into the stratosphere) is perfectly justified when lots of stocks are going up and even dart-throwing chimpanzees can pick a market-beating portfolio of stocks.

What this lesson from history shows is that just because some people are calling the market expensive, or even as being in bona fide bubble territory, this is not, by itself, evidence that we are notin a bubble. In fact, bubbles are easy to spot—even by the people who are the most invested in them. Talk about bubbles are therefore not a symptom of a healthy market. Instead, the bubble meme (another counter-meme) grows more prevalent the more prevalent the market-meme itself becomes.  

Below are two charts of the NASDAQ Composite, on which we can map the statements above.

Something that’s not tracked in any of these charts are the drivers of each market cycle, which dependend on unique, era-dependent factors. (The stock market, like Google Trends, tracks only the symptom of memes spreading in the population, and not their exact nature of what precipitated their spread.) Notably, in March 2000, the market was already skittish following a growing number of penetrating analyses of cash-losing Internet stocks; analyses that showed that the times were already turning, and that the stock-related memes were burning out. Therefore, when the market started dipping, there were fewer people infected with the market meme to be around and actively buying the dip: Because the meme was losing its appeal, stocks just weren’t as cool anymore as they’d been before. Instead, more money started rotating into more tangible assets like real estate (potentially seeding the housing bubble), but also into tangible assets (which led to higher inflation, which spooked the Fed). 

It doesn’t feel like we’re quite at that point yet, but then again, if we’re in a bubble today, it is a more grown-up bubble: We haven’t seen quite the level of excess that we saw during the dotcom era and a lot of the existing growth stocks have been around for much longer than the year-old companies that were IPOing during the height of the dotcom boom. So maybe things actually were crazier back then, after all? But then again, the dotcom boom saw AOL become the most valuable entertainment company in the world (which allowed them to acquire Time-Warner), and there were also loss-making delivery operations that used student bicyclists to deliver items of convenience to residents in NYC. Both of these fateful business ideas are 20-year old premonitions of some of the excesses that we’re seeing in markets today. 

Ultimately, I guess we’ll have to wait a while longer for history to make its verdict on how today’s craziness is stacking up.

I think the last week on financial twitter has been very educational, as the GameStop saga has grown in popularity from the occasional Tweet to seemingly being the only thing that people are tweeting about. This, I think, makes for a great case-study of how bubbles are formed, and how they rise (and eventually fall). Understanding how the GameStop story (a bubble in fast-forward) was born and unfurled, can help us to better understand those bubbles that take a bit longer to fully play out.

First, I however need to be up-front about not having followed the GameStop story at much depth, at first: Because there are so many things vying for your attention every day (and because you rarely know what’s worth paying attention to until after the fact), I’m not sure exactly how the GameStop story started, and it’s probably not even important: Every bubble is different, and instead it’s what they have in common that is most useful to think about. 

The boilerplate version of the story however goes something as follows, where GameStop (as an Old Economy company) has been disrupted by gamers’ move to streaming. This left the company struggling, and because a struggling company is nectar to short sellers (the predators and vultures of the investing world*), short interest in GameStop accumulated over time. Eventually, the stock ended up on ‘most-shorted’ lists.

(* As an ex-biologist, I feel compelled to point out here that vultures and other predators play a very important part in biological ecosystems, and I don’t think short sellers are different or less important. Indeed, the negative sentiment that many people harbour over shorts and short-selling activities feels artificial and almost anti-Malthusian in sentiment.)

Being a gaming-related stock, GameStop was also a natural fit for a contemporary market frenzy—a frenzy that is (and has been) building for years on the back of what I’d describe as a futuristic and partially science-inspired sub-culture (that includes gaming, crypto, the financial independence-movement—all combined with a fascination for certain electric-vehicle companies’ CEOs). This, more than anything else, probably made GameStop a natural fit for subreddits like r/WallStreetBets, alongside other tech-heavy ‘cult’ stocks like AMC and BlackBerry.

To this mix, we however also need to add the growing frustration that some young people have with ‘The Establishment’, where they feel that their parents’ generation have given them the wrong advice (e.g. get a degree and you’re guaranteed a good job), without much understanding of how different economic conditions require different strategies for success. This frustration has led to a kind of ‘soft rebellion’ (perhaps best characterised by the ‘OK, Boomer’ meme), where many young(er) people feel that ‘the system’ has failed them, and that they need to take things into their own hands. This sort of frustration, when shared (especially with other common interests also in the mix), can lead to quite a strong cultural cohesion over time.

Together, these are also all ingredients that help to explain how sentiment on, for example, a subreddit can start clustering together with the ambition to extort some kind of good-natured ‘revenge’ on the idols of the financial system (e.g.hedge funds and other Wall Street ‘suits’). Once stirred, such sentiment can also easily be whipped up and concentrated through the action of charismatic leaders, to produce quite a powerful force—once unleashed on a target and equipped with the tools to make this happen (e.g. low-cost apps and the ability to buy on margin). In this way, a group of Reddit users could easily start to push up the price of a dying company’s stock with the goal of squeezing shorts out of their positions.

While journalists have subsequently been trying to make sense of the story along the lines of ‘retail investors take on Wall Street’, I think the action has more been powered by people enjoying a bit of lockdown fun and a sense of comraderie (perhaps similar to sentiments expressed in cult classics like Ready Player One). Of course, the GameStop story has taken many turns since (including trading apps like RobinHood finding that ‘democratising trading’ on margin can lead to regulatory squeezes requiring some less-customer-friendly actions), with saga eventually attracting the ire of politicians on both sides of the political aisle.

Regardless of how the saga plays out, what all observers have in common is that they’re struggling to make sense of what’s happening: Early on, on-lookers were first expressing sentiments along the lines of ‘surely, this has to be illegal?’, with whispers of ‘is this market manipulation?’ being thrown around. In the time since, the sentiment has instead shifted to something more aligned with the traders themselves, with stories being told of a brave collective David taking on the capitalist Goliath.

Initially, I was also very much in agreement with those thinking that this is only the latest episode in a centuries-long drama of market manipulation (because at this point it seemed so obvious that the redditors were just hoping to make a quick buck), but the more attention I’ve paid to this story, and the more I’ve thought about it, the more I’ve come to realise that what we’ve seeing was really a bubble developing in fast-forward. That is a lot more interesting than any old, boring form of market manipulation, as it’s a dynamic story of animal spirits and online friends ganging together for a bit of blowout fun.

I’ve previously written on this blog about the anatomy of a bubble [link], and how you can find patterns in markets (where markets crash and recover in very similar ways, [link]). With GameStop, all the ingredients are also there: We have the initial spark (the exact nature of which are likely already lost to history) that ended up with a small band of retail investors banding together and deciding to trade GameStop stock, making the price go up. From this point on, the higher the price went, the more people wanted to join in on the fun, and the more people who were having fun, the more the price went up, and the more fun people were having. This is textbook autocatalysis is action.

This autocatalytic element is also crucial to declaring something a ‘bubble’, as without it, you wouldn’t have a bubble, by definition. Indeed, a bubble absolutely requires an autocatalytic element, as this process is what drives the share price far away from its fundamental value at sufficient momentum to break through whatever efficient barriers that the market has in place, as the price has taken on a life of its own. After this point, the share price is just a number, as it doesn’t mean anything anymore: For as long as an autocatalytic process is running, valuations don’t matter. It’s just a price, a number, and it’s going up.

In bubbles, the autocatalytic element is also powered by a speculative temperament. One of the most important lessons to take away from the GameStop story is however that speculation is not always about money. Instead, speculative temperaments can be fuelled by anything—as long as it allows people to feel as if they’re part of something. (A feeling of community is a strong unifying force and being part of a group allows you to give your individualism to become part of a new, greater, emergent whole.) While onlookers have tried to cast the GameStop saga in epic terms (retail investors taking on Wall Street), I really think the unifying force with GameStop was the feeling of having a few days’ fun with your friends while pushing the limits of the financial system as if you were a carefree teenager again.

Tracy Alloway (a Bloomberg reporter who’s also active on Twitter and worth a follow) summarised this quite well when she wrote on Bitcoin, calling it the “perfect post-modern asset”, as

Its value isn’t tied to any single real thing, which means that it’s driven by narrative and networking. Because the narratives aren’t limited by reality, there tend to a lot of them. This abundance of Bitcoin ‘stories’ means it can appeal to more and more people. You want to make money? Bitcoin is going to the moon. You want to protect your portfolio from inflation? Bitcoin is digital gold. You want to avoid government scrutiny? Bitcoin is (supposed to be) anonymous and decentralised.

There’s a story for everyone, and that make it attractive to a larger number of people who then put their money into it and give it value though sheer force of belief. So the value is in the stories, and the ability of those stories to mobilise a network of people willing to buy. [emphasis mine]

Altogether, this also tells us something important about bubbles and how they form: They are not really about the money. Instead, the money (in the form of rising prices, say) is a symptom of an underlying temperamental or sentimental fervour, and we can see the same processes happen in many other systems, with or without money being involved:

(When these processes run, not in markets, but in the social sphere, we call them ‘fads’. In this way, the Beanie-Baby fad was a kind of ‘bubble’, as were memes like the ice-bucket challenge. If other people are doing something, it’s only natural for us—as human animals—to want to join in.)

With GameStop, the money wasn’t what motivated people (which means that it cannot have been market manipulation). Instead, it was the community, and the sense of banding together on a mission, that motivated people to push the share price upward, towards greater heights. In other cases (e.g. the dotcom boom), the animal spirits have of course been more smitten with the (more typical) idea of becoming rich beyond their wildest dreams, but that’s because ‘getting rich’ was the mimetic motivator. In this sense, all bubbles are different (because they’re motivated by different sentiments), but they’re also the same (because the sentiments, however different, all fuel the same processes): The sentiment is the seed (and it’s always different), but the mimetic processes that they result in all unfurl in the same way.

At its core, this type of behaviour is also contagious; taking the form of intentionally mimicked behaviour (either consciously or unconsciously), and once it’s take root, it will spread between people to amplify the popularity of whatever is in vogue. When the mimicry is strong enough, it can spill over into other topics or geographies. With GameStop, we saw this happen with other highly-shorted stocks like AMC or BlackBerry or iRobot (and last I saw, there are reports of similar short-squeezes being engineered in markets as far away as Malaysia).

Regardless of what sentiments or desires underlie the imitation games that give rise to autocatalytic processes in markets and other human networks, they all however end the same way, with a rapid deflation of interest (and prices) once the mimetic autocatalysis comes to a halt. Indeed, you wouldn’t be able to have a bubble without a crash or deflation at the other end: Without a crash, it just wouldn’t be a bubble as it suggests that no runaway autocatalysis was at play. In this way, the GameStop saga is not new or different, and all these stories also end the same way: Once the autocatalysis has come to a halt, the energies of the animal spirits will deflate, and the price (that was just the symptom of their frenzy) will follow suit. Sometimes, this marks the end of an era, while for others, the fun will go on, just in a different place.

In this post I’m going to build on my previous post on how autocatalytic processes can give rise to bubbles in financial markets, starting with a brief discussion of the ‘Boredom Market Hypothesis’, and propose that US retail investors putting their stimulus checks into the stock market is a predictable consequence of the current market frenzy.

After this, I’m moving on to the role of speculation in financial markets and economies; drawing parallel between speculative bubbles as they happen in both financial markets and other inflationary situations, as it’s possible that inflation and bubbles are both fuelled by similar speculative tendencies—only that one takes the form of asset-price inflation while the latter takes the form of goods-price inflation.

In the end, I conclude that the importance of speculative elements to the run-on of inflationary processes suggests that we shouldn’t expect inflation to accelerate in an economy where an autocatalytic inflationary process is already raging as there simply might not be enough speculative temperament around to fuel both processes at the same time.

*

In yesterday’s edition of Matt Levine’s Money Stuff(which is a newsletter worth reading if you’re interested in an informative and humorous take on capital markets news [link]), there was a section on ‘The Stimmy Markets Hypothesis’, where Levine provided some examples of how Americans are using their stimulus checks to stimulate not the economy, but the bull market that’s raging in equities:

Here’s what happened in the market around the time the government sent people $600 earlier this month. Penny share volume mushroomed. […] Coincidence? Maybe — though a lot of people doubt it. They can’t help notice how tiny traders with money to spend keep turning up in the vicinity of almost every market spectacle these days. Now, more federal aid may be on the way, and Wall Street pros are bracing for what comes next.

“If the additional $1,400 goes to the same income levels it did before, we are highly likely to see additional speculation in stocks, which could continue to inflate an already-existing bubble,” Peter Cecchini, founder and chief strategist of AlphaOmega Advisors LLC, said in an interview. …

The sums would be hitting bank accounts at a time of full-blown mania in the market. Volume in penny stocks regularly tops 40 billion shares a day lately, up sixfold from a year ago, with day traders venturing off-exchanges and into the speculative over-the-counter markets. The options market saw the second-busiest day ever for bullish equity calls this week. Meanwhile, Goldman Sachs data show that a basket of retail-favored stocks has surged 10% since the end of December, beating both the S&P 500 and returns on hedge-fund favorites by more than 9 percentage points.

And:

Given the number of Americans eligible to receive the payments, some of the $1,400 checks will inevitably land in the pockets of people who will either save it or invest it, rather than spend it on essentials. Such was the case with 23-year-old Ava Frankel of Boston, who works in the financial services sector.

“I told my friends, if you’re going to spend your stimulus check on shoes, you might as well just put it in Robinhood instead,” Frankel said in an interview. “The $600 check was just something extra I didn’t need so I just threw it in the stock market.”

Levine proposes that this is a “version of the boredom market hypothesis”, which he has expanded on in previous newsletters, where he proposes that:

people will trade stocks to the extent that (1) trading stocks is fun and (2) other things are less fun; it suggests that stocks have gone up a lot in a pandemic because it’s now hard to see friends or do stuff, so trading stocks on Robinhood is now relatively more entertaining. You don’t need shoes if you never leave the house, so a lot of people who would otherwise spend their stimulus money on goods and services are spending it on penny stocks instead. In a pandemic, perhaps, a lot of fiscal stimulus goes to inflating asset bubbles, but not in the assets that professional investors like. The combination of economic stimulus and having nothing to do means that people are spending their stimulus checks on the experiences that are still available, specifically the experience of buying SPACs.

I’m citing Levine (and his citations) at length because I find the Boredom Market Hypothesis interesting (and amusing; the Money Stuff newsletter is one of few financial market newsletters that regularly have me laughing out loud), but I’d also like to take this hypothesis further.

In my previous post on the blog [link], I wrote about the sort of self-organising, autocatalytic processes that we can see at play in financial markets:

[Here] we have the beginnings of a simple model for the formation and persistence of speculative bubbles in markets, where, (1) they form when economy money-flows favour financial markets over other parts of the economy (creating a strong flow of money into the market); (2) they pick up steam as they start growing by attracting more money into the market, which allows them to persist; and (3) they deflate (gently or catastrophically) when there is not enough money entering the market for them to continue to grow.

[…]

From the first part of our simple model, we see that market bubbles are formed when money-flows favour the market instead of other parts of the economy. (Of course, money-flows favouring non-market assets can lead to bubbles forming in non-market parts of the economy.) Normally, you’d think that economy money-flows would balance themselves out, where too much money flowing into one part of the economy would incentivise less money to flow into those parts in the future. There are, however, circumstances when this is not the case, and both economic and social factors can incentivise the disproportionate flow of money into one part of the economy, creating the conditions for a bubble to start to form.

(Click through on the link above to read the rest of the post, where I draw parallel between financial market bubbles, hurricanes, and other ‘organisms of physics’.)

In this previous post, I also alluded to the possibility that part of the current US financial market might be host to at least one of these autocatalytic processes. The implication of such an autocatalytic process going on is that the ‘bubble’ so-formed has the potential to divert money-flows from elsewhere in the economy, and for these money-flows to go into the bubble and keep feeding it. This model maps onto the Boredom Market Hypothesis as cited above (where Levine suggests that stock prices are booming because retail investors are bored, putting their money into their Robinhood accounts rather than goods), but with the added twist that if we’re in a bubble, then it’s likely that the bubble is ‘sucking’ money from the rest of the economy as if it was an ‘economic vampire’, of sorts. The reason for why a bubble can suck money from the economy is (ergodicity economics aside), that people will be putting their money where they think their investment will yield the greatest return.

In an efficient economy, good investment opportunities will be spread reasonably well across the economy, which will avoid money pooling in any particular part thereof. However, in the situation where good-enough investment opportunities are not available at a high-enough frequency, the few opportunities that remain will show a higher propensity for going autocatalytic and maturing into bona fide bubbles. These bubbles will be hard to predict ahead of time, but once started, they will contribute to an increasing misallocation of assets throughout the economy, as they’re attracting far-greater investments than what is justified by the underlying fundamentals. These resultant investment inflows are however themselves justified on the secondary level, because the more money that is invested in the bubble, the bigger the bubble will grow, and the greater the potential returns for the brave investor who’s willing to give the bubble a ride.

As such, when there is a bubble raging in the economy, it is likely to attract a disproportionate amount of marginal money flows. Indeed, it would be silly for people to forego investing in a bubble when it’s happening, because the potential returns (if you ride the bubble well) will far outweigh any returns that you would have made by investing more risk-aversely. This, however, means that giving out stimulus checks to people who don’t need such checks to survive (i.e. by using the money to buy goods), you won’t be ‘stimulating the economy’ as much as you’re feeding the bubble-vampire. For this reason, if you were to want to use stimulus checks as a tool for stimulating the economy, you may not wish to do so when an autocatalytic process is running, as it otherwise becomes likely that your stimulus money will just end up there and not in the economy proper (which would effectively defeat the purpose of the stimulus in the first place).

(Interestingly, this dynamic might also help explain why we’re seeing increasing levels of wealth inequality across large swathes of the industrialised world, where the more money is being sucked up by bubbles (in whatever market), the less money should be expected to circulate through the economy and make its way back into the pockets of people at lower, more productive levels in the economy [link].)

Furthermore, this also prompts the interesting question of under what conditions that the economy would be ‘stimulated’, and intriguingly, the bubble dynamic does map beyond financial markets and onto the economy in the form of inflation, where, if you go rooting around the academic literature at the intersection of physics and economics, you do find the occasional paper (e.g. [link], [link]; both titles available on arXiv.org) that suggests that inflation shares quite a few similarities with other autocatalytic speculative processes. Now, this has been shown to apply mainly to hyperinflationary regimes, but this also suggests that normal inflation would be to hyperinflation what normal stock-market price-appreciation is to bubble-driven stock-market price-appreciation.

This view of inflation (that hyperinflation is an autocatalytic process) traces its roots all the way back to 1956, when the economist Phillip Cagan suggested that (hyper)inflation happens as the result of ‘inflationary expectations’ that develop when the observed inflation rate exceeds the expected inflation rate. As a result of this subjective divergence, people start to over-compensate and end up expecting more inflation to happen; effectively allowing the inflationary regime to not only continue, but also to pick up momentum. Over (not even long periods of) time, even mild inflation can, in such cases, develop into more rapid inflation. What this implies is that inflation is a form of speculation in the price of goods, and from this, it should be clear that the speculation in prices that happens during inflation is much like the speculation in prices in a bubble regime. 

For this reason, it’s probably not a coincidence that bubbles are characterised by inflation in the price of assets rather than goods. Indeed, if we were to run with this model, and recall how bubbles are vampires that ‘suck’ money up from elsewhere in the economy, we shouldn’t expect an inflationary regime (in the broader economy) to take place when a bubble is raging in one corner of the economy: For one, there might not be enough fuel (as money is limiting), and the different inflationary regimes (in goods vs assets) also require opposing speculative mindsets; with inflation requiring people to expect the purchasing power of money to keep going down (as goods-prices are appreciating) and bubbles requiring people to expect that the purchasing power of the asset-based quasi-currency to keep goingup (as goods-prices are going down in comparison). 

With regard to the factors that kick-start these speculative processes, I covered some of them in my previous blog post, pointing to uncertainty as being an important factor (where, the greater the uncertainty in asset-prices becomes, the greater asset-prices should be expected to appreciate). This type of uncertainty would also be particularly common during times of New Economy-type thinking or during periods of rapid social change.) This applies to inflationary regimes in the broader economy as well. 

Indeed, the periods in which high inflation or hyperinflation have occurred, have all been preceded by large shocks to the economy in the form of wars, the collapse of empires or nation-states (e.g. the fall of the Weimar Republic in 1920s), or large price-shocks to the economy (e.g. the 1973 oil crisis). When these large, economy-wide events occur, the uncertainty that is injected into the economy as the result of the external shock, is enough to unbalance the economy and allow speculative processes to fester. (While in my previous blog post I floated the possibility that the 2016 election of Donald Trump caused stock prices to start appreciating and seeding the bubble that we’re likely in the midst of, where COVID has poured further fuel on the fire, exogenous shocks like OPEC dramatically raising the price of oil in the 1970s allow similarly strong speculative processes to take root and spread through the economy as the impact of a price shock in oil feeds into all goods where oil is a meaningful input.) Seen from a physics perspective, the speculation that underlies inflationary processes in economies and markets also fulfils an important purpose as the speculation is the process by which the economy regains its balance and returns to a new equilibrium state, eventually allowing the economy to settle on a new price level and proceed from there. 

Altogether, this interpretation of the inflationary dynamic also helps explain why the money supply doesn’t always correlate with the level of inflation in the economy. While in standard economic theory, we’d expect the price of goods to equilibrate with the amount of money in the economy (where more money equals higher prices, with everything else staying the same), inflationary periods have however rarely coincided with increases in the money supply historically, and even popular examples of such situations (e.g. the 1600s price revolution) turn out to be more complicated once you take a more holistic view (e.g. [link]).

As such, when we see low levels of inflation in the economy, we should consider this a sign of potential certainty; where people feel comfortable that prices will not be going up (and the low levels of inflation that we’d still be seeing being the result of low ‘economic noise’). As a result, it’s possible that low interest rates don’t give rise to either high or low levels of inflation on their own, and, indeed, if you start digging into the really esoteric parts of economic and financial history, you will soon find that interest rates (and inflation) seems to equilibrate around 1 – 2 %—whether you’re looking at interest rates in Italian city-states during the Renaissance [link], interest rates over the last 700 years [link], or interest rates across today’s developed economies. Instead, we should probably see these low rates as an indication that things are going well (as interest rates typically scale with the inflation rate), and that we’re living in a world where the major industrialised economies are enjoying low levels of uncertainty.

Of course, if you’re a believer in Schumpeterian ideas like the power of creative destruction, perhaps these certain times are also indicative of a more general economic and innovative lull. Obviously, the world’s financial markets would disagree with this interpretation somewhat, but what’s going on there will have to form the topic of a blog post some other day…

For the last few years (and this year in particular!) it has felt a bit like financial markets have gone mad: The economy is struggling, but stocks are reaching all-time highs (while some of the top winners are bleeding cash). Digital currencies seem to have a shot at becoming the new gold. Investors trying to value companies on fundamentals have been underperforming for years. Working from home seems to have renewed thinking about the Internet as a ‘New Economy’. A new generation of stock-trading apps have ‘gamified’ the trading of stocks. And so on. 

Of course, many people have noticed that 2020 is not normal—and I’m not just talking about the coronavirus pandemic, but also that market sentiment seems to be getting out of hand with 2020 seeing a revival of the SPAC; bankrupt companies getting a new lease on life; and with the IPO market getting increasingly frenzied with loss-making companies doubling on their first day as if there simply wasn’t enough stock to go around. Mind-boggingly, it looks like everything (except for the traditional safe havens) is rallying.

At the same time, surrounded by this weirdness and New World-thinking, there are plenty of people who suggest that we must be close to the end of this market cycle:

The Financial Times, for example, quoted Jeremy Grantham, the founder of the investment group GMO, in an article on the ‘everything rally’, where he

reckons [that] markets have smashed past the “full bull” stage and are in a late-bubble “melt-up” phase that rivals the two biggest bubbles of the past century. … There is as much craziness now as there was in late 1999 or 1929 … It is bewildering, impressive, and for financial historians like me, exciting. This is the real thing … It looked like we were in a bubble mode this summer, but the real craziness has come out in the last few months.

Similarly, Matt Maley, chief market strategist at Miller Tabak & Co., is quoted in Bloomberg (via an Almost Daily Grant’s) that “The action in these [IPO] names is definitely a concern for us … Experience tells us that froth in the IPO market tends to be a ‘leading indicator’ for an important top.”

Intuitively, it feels like these people must be right: Surely, the current rally just cannot go on? Yet, haven’t people been saying this for years, while the market has just kept on going up? 

Indeed, if we look to the US market, the Dow Jones Industrial Average is up 85 % in the last 5 years and having increased almost 12 % this November (its best monthly showing since 1987). Similarly, the NASDAQ 100 is up almost 200 % since the end of 2015 and increasing 11 % last month. Internationally, the MSCI World Index also increased over 12 % during the same period (making November its best-performing month since 1975), with the index up almost 80 % in the last 5-year period . How (the reasonable among us ask) can we reconcile these stellar market performances with the vague feeling that it all—surely—should have ended long ago? 

What I’m going to argue in this post is that these observations are not as conflicting as they might first appear: It is possible for financial markets to post increasingly stellar results long, long after contrarian investors have started feeling uneasy. For example, if history is any guide, markets never crash when things are looking down. Instead, crashes happen when the market is at its peak—suggesting that Mr. Maley’s experience might not be too far off: That irrational exuberance can be a harbinger of more painful times to come. As it happens, there might be a simple explanation for these somewhat contradictory observations, where, the higher markets go, the more unstable they also become, with the risk of a crash increasing alongside. 

This dynamic starts to make sense once we start thinking of financial markets as complex systems, with behaviours that emerge from the interactions of multiple independent agents using the markets as a vehicle for capital exchange. Complex systems such as these are also nonlinear, meaning that their overall behaviour emerges from the rolling interaction of its component parts. For example, if you were to break a financial market down and to place all of its component money flows and buyers and sellers and their different motivations and economic contents into separate buckets, you wouldn’t be able to recreate the market’s behaviour by just putting them all together again. In this way, a market is more like an organism: Just putting different parts of an animal together does not a living, breathing thing make. Therefore, while it’s valuable to form a bottom-up understanding of a complex system (by dissecting its parts), this understanding must be paired with a top-down understanding of its behaviour as well. When it comes to financial markets, this requires us to understand the processes that the market is home to, and how small fluctuations in these can be amplified by the system to generate out-sized effects.

Speculative bubbles are hurricanes, but in in financial markets

At their most simple, the speculative bubbles that sometimes occur in financial markets can be understood as runaway processes that, once started, can drive valuations increasingly far away from fundamentals. Once you start looking into the mechanisms of how bubbles are born and how they grow, you’ll also start to realise that they are a form of ‘self-organising system’. While the physics underlying the emergence of such systems can be devilishly complex, I find that they can be relatively easy to understand conceptually. For example, self-organised systems can be found everywhere—ranging from the ‘convection cells’ that arise in heated fluids or in organised storm systems like tornadoes or hurricanes, to processes like life itself (in the form of organisms). Therefore, if we can understand the emergence of such diverse systems, we can start to build a mental model of the emergence of self-sustaining systems like bubbles in financial markets as well. 

The first thing that we need to understand to understand self-organising systems is that they’re powered by the flow of energy. Energy flows from regions of high energy to regions of low energy (like from a hot reservoir to a cold reservoir), and wherever this flow is strong enough (because the gradient is large enough), a self-organising system (an ‘organism of physics’) can emerge. The emergence of such a self-organising system ultimately helps dissipate the underlying energy gradient faster than the alternative where no self-organised system was present. These systems can also persist for as long as the underlying flow of energy remains strong enough, and they only ‘die’ when the underlying energy flow has been depleted.

Thermodynamics textbooks commonly use the example of a convection cell arising in a heated fluid when discussing self-organising systems, but I find weather phenomena to be more intuitive. (Both convection cells and weather systems are also examples of heat engines, which are powered by temperature differences.) To give a brief introduction to the weather, the sun shining on something like the Earth’s oceans creates a temperature gradient, where the ocean surface is relatively hot, and the atmosphere’s higher layers are much colder. This heating of the surface causes water at the surface to vaporise. The heat-energy contained in this moisture is then absorbed by the hot air at the ocean’s surface, and the hot air begins to rise. As the air rises, it starts to cool down, eventually releasing the moisture as rain and some of the latent heat-energy that it has been carrying is released into space. While the now-dry and cold air will start falling towards the surface again and the cycle repeats for as long as the underlying gradient persists, the ultimate result of this process is that the high-quality energy from sunlight is lost into space as low-quality heat. A hurricane is simply an extreme version of this process, where the heat-energy of the rising, moisture-laden air is high enough to create an updraft so powerful that it reaches a high-enough wind-speed for the process to be classified as a hurricane. In other words, a hurricane is just an extreme form of atmospheric mixing and an efficient way for the temperature gradient between the hot surface and the cold atmosphere to be dissipated. 

So, that’s all very well and good (I hear you ask), but what does this have to do with bubbles in financial markets? 

Quite a lot, I think!

Let me explain:

While hurricanes form in the atmosphere and ‘feed’ on a temperature gradient that exists between the hot surface and the colder layers of air above, speculative market bubbles form in financial markets where they feed on energy in the form of money flows. 

To be more precise, let us map the dynamic of a financial-market bubble onto the dynamic of an atmospheric heat engine like a hurricane: First, while the hurricane forms from a strong-enough temperature gradient between the surface and the atmosphere, market bubbles form from a strong-enough money-gradient between the economy and the market. Second, while the hurricane feeds on the underlying temperature gradient (sucking more air in) and, as a result, dissipates heat into space, the financial-markets bubble feeds on money entering the market (sucking more money in) and dissipates the gradient by reducing the purchasing power of the currency (in market units). Third, while the hurricane dies down when the underlying temperature gradient is weakened, the market bubble dies when the underlying money-flows have been depleted. 

From this, we have the beginnings of a simple model for the formation and persistence of speculative bubbles in markets, where, (1) they form when economy money-flows favour financial markets over other parts of the economy (creating a strong flow of money into the market); (2) they pick up steam as they start growing by attracting more money into the market, which allows them to persist; and (3) they deflate (gently or catastrophically) when there is not enough money entering the market for them to continue to grow. 

With this model in hand, let us look at some dynamics that can favour the emergence (and growth) of bubbles in financial markets.

Misallocated money flows can cause asset values to increasingly deviate from fundamentals

From the first part of our simple model, we see that market bubbles are formed when money-flows favour the market instead of other parts of the economy. (Of course, money-flows favouring non-market assets can lead to bubbles forming in non-market parts of the economy.) Normally, you’d think that the sum total of money-flows in the economy would balance themselves out, where too much money flowing into one part of the economy would incentivise less money to flow into those parts in the future. There are, however, circumstances when this is not the case, and where both economic and social factors can incentivise the disproportionate flow of money into one part of the economy and creating the conditions for a bubble to start to form. 

First, uncertainty can play an important part in the misallocation of economy money-flows by making some assets seem more attractive than they are: While all assets have a ‘fundamental value’ (i.e. what the asset would be worth given perfect knowledge of life, the universe, and everything), nobody has perfect-enough knowledge of the asset or the future to be able to value assets with perfect accuracy. This makes the fundamental value of an asset more of an idealisation and an approximation, rather than something that can ever be known in real life. This value can also be thought of as an ‘equilibrium value’, which the real-time value of an asset—even if imperfect—would fluctuate around; updating bit by bit as more information about the asset or the future gradually becomes available. Therefore, these approximate fundamental values serve as good placeholders for the never-to-be-fully-known real fundamental valuation of an asset. 

That being said, the approximate fundamental value has two significant flaws in being sensitive to both the uncertainty of the inputs and the value of money in the economy:

The quality of the inputs matter because the output of the valuation formula is very sensitive to even slight changes in the input values. This is because the value of an asset is determined based on cash flows, where the value of an asset is the present value of all the future cash flows to result from the asset. Since these cash flows can never be known, they must be estimated, meaning that the more accurate the estimates are, the more accurate the final valuation will be. During periods of great social or technological change, the uncertainty surrounding these estimates is however greatly increased. This uncertainty ultimately reduces the accuracy of the estimated cash flows, allowing the resultant approximate value to start deviating from its underlying fundamental value by an amount that scales with the underlying uncertainty. (It can help to think of this amount as an ‘uncertainty component’ of the valuation.) The positive skew of asset-price returns (limited downside and unlimited upside) and the human tendency to overestimate the impact of changes in the short run and to underestimate the impact in the long run (Amara’s law) also implies that these deviations from fundamental values are likely to be positive.

(Importantly, the uncertainty component emerges naturally from use of the discounted cash flow formula, making it natural for asset prices to deviate from their fundamental values. Indeed, given the underlying uncertainty of the inputs and their future values, the situation where an asset is valued at its fundamental value should be considered exceptional, as this would represent a very unlikely event. The larger the uncertainty around future cash flows, the larger the deviation from fundamentals will become. This also means that we shouldn’t be surprised to see valuations start to deviate significantly from fundamentals during periods of great social, technological, or economic change, as this would cause the uncertainty component would start to make up an ever-greater proportion of the asset’s valuation. In this way, the sensitivity of the formula to the uncertainty of inputs helps to explain why asset prices are more likely to deviate from fundamentals during times when New Economy-type thinking is prevalent, as this is when the uncertainty of the inputs would be at its highest.)

Economically, the discount rate also plays a role: When we value a risk-free asset, we adjust the cash flows for growth using the risk-free growth rate and then discount these back to the present using the risk-free discount rate. To adjust the formula for risk, we then simply add in a risk premium in the form of a risk-adjusted growth rate and a risk-adjusted discount rate. However (as the famous mathematician John von Neumann realised in the 1950s), in a perfectly balanced economy, the growth rate would equal the interest rate (and thus the discount rate). In this situation, the discounted cash flow formula would break down, as the risk-adjusted growth rate would equal the risk-free rate, meaning that we’d be dividing by zero and asset values would go up to infinity. In economics, this is known as the ‘growth stock paradox’. At the point where this happens (where the risk-adjusted growth rate is equal to or larger than the discount rate), the value of money isn’t enough to stabilise the economy. 

When the value of money isn’t enough to stabilise the economy, money-flows can be diverted and misallocated because it becomes favourable to borrow money and to use this borrowed money to buy assets earning a more effective rate of return. Like the uncertainty dynamic described above, this destabilising dynamic also allows values to deviate from fundamentals as it encourages the misallocation of money; allowing more of it to become concentrated in a small part of the economy. Wherever this happens (and for whatever reason), self-organising systems in the form of asset-price bubbles have the potential to form as the underlying money-flows become strong enough to push asset-values away from fundamentals and to sustain this mis-valuation over time. 

Historically, the onset of many bubbles in financial markets has also been traced back to misbalanced economic growth rates and interest rates in this way, perhaps most famously (and disastrously) in the 1920s. Of course, plenty of additional examples abound, like the Japanese land bubble in the late 1980s; the dot-com bubble in the late 1990s; the US housing bubble in the late 2000s; the Chinese stock-market bubble of the early 2010s; and so on. Today, it’s possible that we’re seeing a similar dynamic at play—perhaps triggered by the favourable tax implications of Donald Trump’s win of the US Presidency in 2016 and exacerbated by the uncertainty surrounding the economic impact of COVID-19 and the work-from-home trend.

Investor herding allows market returns to accelerate during bubbles

While uncertainty can make some assets seem more attractive than they really are (and favour the allocation of money to these) and misaligned interest rates can cause money to lose its value (and pool more locally in the economy), the local herding of investors into some areas is an additional factor that can help create (and feed) asset-price bubbles by strengthening the underlying money-flows. 

During periods of low uncertainty (when data is plentiful and accurate), investors are afforded the luxury of independence of thought, courtesy to their access to diverse sources of information. As a result, the insight they generate is likely to be accurate, which allows for the efficient functioning of the market as investors collate their individual pieces of information into the market ‘library’ as they’re hoping to profit. As a result, the market valuation of an asset is likely to be a good approximation of its fundamental value, as the approximation has been derived from a large collection of heterogenous and independent sources of high-quality information. (The market acting as a repository of collective information is also further reminiscent of how Benjamin Graham likened the market to a ‘weighing machine’ in the long run. This also makes it unlikely for an individual investor to be able to ‘beat’ the market, as the market ‘knows’ more in aggregate than any individual investor does.) In this efficient market, investors will also use any new information that they come across to improve on their estimated fundamental valuation of asset, meaning that the approximate fundamental valuations of an asset will evolve as the market’s collective understanding of it evolves. This allows valuations to remain true to fundamentals for as long as enough data is available. 

When the level of uncertainty is high and data is scarce, investors however often have to make do with lower-quality sources of information—like looking over their shoulder to observe the behaviour of other investors. When this happens (and the behaviour of one investor influences the behaviour of another investor), an ‘imitation game’ is initiated that reduces the heterogeneity of the market. This decreasing heterogeneity can exacerbate the misallocation of money flows as the misallocation of money in an uncertain environment begets more misallocation in an uncertain environment. 

In the short term, the increasing misallocation of money-flows means that investor returns will accelerate as investor herding in an uncertain environment begets increasingly irrational exuberance, where the more money is going into the market, the greater the investor returns to result, and the more other investors will be incentivised to join in—adding more money into the market to push returns up further, and so on. (This dynamic also helps the bubble to achieve an ‘autocatalytic’ state, where the bubble sucks up more investor monies the longer it persists; allowing market valuations to grow ever-more-divergent from the underlying fundamentals.) This makes autocatalysis an important component of the bubble dynamic, since it’s symptomatic of a state where growth begets even more growth, allowing returns to compound faster and faster. As a result, we should expect returns to scale with the duration of a bubble: expecting returns to increase slowly at first, but growing more and more for as longer as the bubble goes on. 

Counter-intuitively, the autocatalytic process also means that the greatest returns are earned at the point where the bubble is at its most unstable, as it’s growing at a pace faster than the underlying money-flows can support. A Swiss professor, Didier Sornette, has further shown in a book called Why Stock Markets Crash that bubble-induced increases in market capitalisations can be tracked mathematically by fitting a power-law trendline to the underlying data. Diagnostically, an accelerating power-law exponent could therefore be symptomatic of an underlying autocatalytic process, meaning that a bubble can be suspected to be underway in such markets.

In the long term, the increasing misallocation of money flows into local parts of the economy will however burn itself out as the autocatalytic process supporting the bubble’s growth will eventually require more energy than what is available to the system. Theoretically, if we were to treat the bubble as an abstract ‘heat engine’—but at work in financial markets—we would be able to calculate the theoretical upper bound of the price-appreciation trend based on simple thermodynamic formulae (much like how we can calculate the maximal wind speed of a hurricane using temperature and moisture data). In reality, the numbers we’d need to plug into these equations (including the total energy available to feed the system) would however be hard to know, even as it would be interesting to see more economists try. Experience however tells us that the total amount of money available to feed financial market bubbles is always much larger than you’d think at first glance—allowing bubble-induced market returns to keep compounding for much longer than any sensible person would think. While this is what underlies the warning to short sellers that “the market can stay irrational for longer than you can stay solvent”, understanding how bubbles die and how markets collapse could however be helpful for short-sellers to time their positions better and to minimise losses in the lead-up to the market’s final growth-spurt.

Structural instabilities lead to crashes in financial markets

Market returns increase faster the closer the market gets to the point where the market’s continued growth requires more energy than what is available to the system. This point would more accurately be called a ‘critical point’ as the closer the market gets to this point, the more unstable the market becomes. That market returns scale with increasing proximity to this point suggests that investors are compensated for the risk that they’re shouldering: As the risk of a crash increases, the greater the return that investors will require to assume the risk of staying invested in the market. As further discussed in Why Stock Markets Crash, this dynamic also suggests that it’s rational for investors to stay invested throughout a bubble as the returns are attractive and the risk of the bubble ending in a crash is less than 1 (meaning that there is a probability that a crash will not happen and the market will gently deflate instead; giving investors ample time to exit). Indeed, data available to Sornette when he wrote Why Stock Markets Crash in 2003, suggests that 60 % of autocatalytic bubbles end in crashes—with the remaining 40 % of bubbles ending in ‘deflation’. (It can however be argued that deflations are slow-motion crashes, as both events commonly see market capitalisations decrease by 30 – 70 % from the market peak.) Importantly, both deflations and crashes however see market capitalisations returning to something closer to their underlying fundamental values. 

The structural instability that builds up in a market undergoing the autocatalytic growth of a bubble can also be exacerbated investor herding, further increasing the risk of a crash. This is because as investors herd (because they’re starved for high-quality data and are forced to play the imitation game), they organise into increasingly homogenous clusters. The larger theses clusters become, the larger the risk then becomes that a large group of investors will have the same ‘sell!’-reaction to an unexpected piece of news, with such a large sell-order also carrying the risk of oversaturating the market’s ability to absorb the order, causing the market to crash catastrophically. As such, crashes happen when the incoming money-flows are too weak to compensate for the market outflows, forcing the market to return to a ‘lower level of complexity’ (as outlined in a previous post on this blog). Whenever we see market returns accelerating beyond reason, we should therefore brace ourselves and be ready for something to happen. Some parties simply cannot go on indefinitely, even if it’s uncertain exactly how they will end.

The dependence of catastrophic market crashes on the underlying autocatalytic behaviour of the bubble also means that the nature of crashes is not part of the ‘normal’ behaviour of markets. Indeed, autocatalytic processes birth ‘outlier’ phenomena (which are not normal, by definition). Instead, ‘normal’ in financial markets is the ‘random walk’ that features in investing classics as Burton Malkiel’s A Random Walk Down Wall Street. The random walk of markets is the result of investors making random trading decisions like model adjustments or investing/redeeming client cash flows. These decisions (which are noisy and non-informational) altogether cause market values to fluctuate randomly up and down; sometimes deviating positively and sometimes negatively from fundamentals, allowing value investors to capitalise on the stock market’s tendency to regress to the mean over moderate time-spans. The autocatalytic behaviours described above are however very different and do not form part of the market’s random walk. This means that they can both persist for a very long time and that they can see rapid reversals.

One of the main theses of the book Why Stock Markets Crash is further that autocatalytic behaviours underlying the growth of bubbles allows the death of the bubble to be timed with a fair degree of accuracy. While the random movements that result from ‘normal’ market behaviours are truly random (and therefore impossible to predict), the autocatalytic process underlying the growth of bubbles is much more predictable. As a result, bubble-induced corrections are not the ‘black swans’ that have been popularised by Nassim Taleb, but are instead much more predictable outlier phenomena. Indeed, Sornette claims that the appearance of log-periodicity in the accelerating power-law curve applied to autocatalytic markets allows observers to determine the timing of the market’s critical point, as the log-periodic oscillations contain information about the market’s underlying homogeneity and implied instability. However, again, this information might not be useful to investors, if we were to assume that they are compensated for the risks that they’ve assumed by staying invested.

Yet, something about this sits uneasily with me (as it does with Sornette, as outlined in the introduction to his book) as we live in a very strange time in history when an increasing proportion of the social contract requires us to stay invested in the market to pay for our future spending in the form of education, healthcare, and pensions. This need to stay invested (and the dearth of safer investment opportunities offering us enough compensation to accept the risk of not investing in markets) creates interesting regulatory problems, as it’s becoming increasingly expected of regulators to avoid any catastrophic market meltdowns. Understanding the mechanisms underlying the accelerating returns in markets is therefore crucially important, as this allows investors to make decisions that fit their long-term risk-appetite.

Financial markets are organised and complex—and risky

All things considered, a growing emphasis on the riskiness of financial markets would ultimately allow us to develop much healthier social attitudes in terms of the risks that come with staying invested in financial markets over long periods of time—as well as better recognising the increasing need of stimulating capital investments in other part of the economy to ensure the economy (and its cumulative investments) are as safe and diverse as can be. Indeed, if there is one message you should take away from this blog post it’s this: That complex systems like financial markets can be extremely unpredictable in the long term, as their behaviour unfurls over time, feeding on inputs that reach far into the past. As mentioned above, financial markets share these characteristics with other complex systems like the weather, and while the either can typically be forecast with a reasonable degree of accuracy, this accuracy declines the further we look into the future. Of course, this also applies to financial markers, and it means that the age-old wisdom of diversifying our investments remains sound—regardless of how attractive current market returns may happen to be. For some of us, this understanding will be little more than a curiosity (but hopefully one to help us with our own work and investments), but for others, it will help us to make the most of the opportunities (and challenges) that undoubtedly lie ahead.

Finally, this understanding will however also help us make better sense of recent events and developments, where loss-making companies are generating unprecedented returns for brave investors and the working-from-home trend seems to have renewed New Economy-type thinking and enthusiasm. Furthermore, this understanding also helps us to make sense of the anecdotal wisdom of more wizened investors as the only thing that doesn’t change in financial markets are the human agents who stay invested in them. After a few market cycles, you start seeing the patterns in markets in remarkably new ways.

Recognising talent is hard, and it’s harder the more qualitative or long-term the project or job or industry is. Indeed, the more complex the challenge, the harder ‘talent’ is to recognise as it becomes an increasingly emergent quality; being built from and composed of more and more details, none of which constitutes singularly reliable evidence on their own. In other words, the qualitative nature of talent often scales with the complexity of the task, and so, the challenge of recognising talent also scales in parallel.

At the same time, recognising talent is often an important component of success. In the workplace, recognising talent is an important part of hiring and attracting the right people, as well as ensuring that opportunities are given to the best people for the job. Being able to recognise talent is also important when dealing with juniors (be they children, students, or new recruits), and the attention paid to those with the biggest potential has the power to compound over many years and to produce outlier results. (A dollar invested in a bright student at an early age has the potential to yield millions down the line.) For example, being able to pick the most-promising candidates from a pool of recruits can be paramount to the success of a company, as the quality of the work often will depend on the quality of the people doing the work. This, of course, comes with the added benefit of rewarding those people who would be the most valuable to retain. 

For investors, the ability to recognise talent is similarly useful. First, the job of the investor is, in some ways, to recognise talent, by looking at collections of companies and sieving the most promising candidates from the mix. The better an investor is at recognising talent on the company level (either managerial or organisational), the earlier they will be able to pick outliers and to so compound their returns over time. Second, the task of recognising company talent can also be made easier if the manager can surround themselves with a group of talented colleagues who can bring their own perspectives, insight, and experience to the job. This, of course, prenecessitates the manager being able to objectively recognise talent in both themselves and their colleagues; to identify blind spots and to elevate and promote those people who are likely to help fill these in to allow the join investor group to become more talented than any individual investor can be on their own; effectively allowing the group to take meaningful positions with greater conviction at an earlier time.

All of these benefits are however dependent on finding answers to the original question: How do we recognise talent, even if it is hard?

An easy answer would be that it’s easy to recognise talent if you, yourself, are already talented, but that’s also an answer that’s not very useful and that would prove hard to scale. Instead, a better option is to look for proxies; to look for traits that are easier to identify than the ambiguous ‘talent’, but that nonetheless correlate with it. This, however, only helps in kicking the can further down the road: Assuming that we can find good proxies for recognising talent, how do we know what proxies to use? (Especially when these proxies themselves become more complex and qualitative the more complex the talent we’re trying to identify.)

The easiest answer to this question has often been singular traits like ‘intelligence’ or ‘creativity’ or ‘years of experience’, but complex problems are characterised by having no easy solutions. Indeed, I think that intelligence or creativity make poor proxies for talent since (I find) it likely that they’re often one and the same (making them useless as talent-proxies). For example, if we suppose that—depending on the task in question—that intelligent or creative people would be the most talented (or that intelligence or creativity is talent), wouldn’t this presuppose that intelligence or creativity are actually equally hard to identify? In my experience this is often the case, where ‘intelligence’, ‘creativity’, or ‘talent’ are like other qualitative concepts like ‘success’ or ‘potential, which is to say, that they are relative. (It’s hard to point to one person or accomplishment and say: “This is what success looks like”, and then be able to apply that situation wholesale to anything else, because ‘success’ doesn’t look like that, because success is many things.)

Instead, it makes more sense to look to more quantitative metrics that can be used as checklists or templates or recipes; where you’re unlikely to find all of them represented at the same time, but where you’re hoping to find many of them represented at the same time, and that this co-existence can be used as a proxy for talent, success, potential, or whatever qualitative metric you’re hoping to measure (much like the trinity of opportunity + competitive advantage + management quality can be used to benchmark predictions about an organisation’s outlier potential). In terms of identifying ‘talent’, such an emergent proxy could therefore be envisioned to include everything from the obvious but hard-to-recognise (e.g. intelligence and creativity and potential) to more-easily recognisable qualities (e.g. being hard-working and a quick learner) and traits (e.g. being open-minded and insightful). 

Within the services sector (where we’d find industries like finance and professions like investment management), a lot of ‘recognising talent’ boils down to ‘identifying’ these kinds of low-quality, quantitative traits. Indeed, many people look to checklists like grades and universities to assess the intelligence of new recruits (while also recognising that such proxies are inherently flawed and therefore perhaps most useful as filters to whittle down the pool to more manageable numbers). Once someone has been welcomed into an organisation, the assessment of ‘talent’ often further boils down to cookie-cutter metrics like ‘did they follow the task to the letter?’ (instead of the spirit), and ‘do they do the job to our liking?’ (regardless of whether this is actually representative of a job well-done). Over time, even these performative and imperfect proxies are whittled down even more, until we reach the situation that I think most of us are familiar with: Where ‘number of years served’ becomes the ultimate proxy of quality. 

A challenge with these emergent proxies is however that while they’re better than the wholly qualitative, that they’re still not perfect: An individual can be both hard-working and open-minded and still not be very good at the tasks they’ve been set, while another might be neither open-minded nor hard-working and be really good. Such proxies are also hard to measure: How do we measure ‘hard work’ (is it speed or work ethic) and how do we keep track of people’s open-mindedness (especially when they belong to a large organisation and our interactions with them are few and far between)? For this reason, the attraction is often to resort to wholly quantitative metrics, however imperfect these might be as proxies or however easy they might be to game.

To some extent, I think the financial services and investment management industries are uniquely bad in using ‘number of years served’ as a talent proxy—perhaps because the job is reflexive by nature and therefore hard to score. Indeed, while investment returns make for good proxies, the ever-changing nature of technologies and companies and markets means that the tide can turn at any time and that someone who’s performed well in the past might perform less-well in the future (and reverse). Instead, ‘years of experience’ makes for a more neutral proxy, where we assume that just because someone has survived the industry for x number of years, that they have to be good at what they do (because if they were not, they wouldn’t be there). 

This sort of thinking is however extremely dangerous since it leads us on a path where some of the best proxies available to us (number of years served) also become the worst (as they keep young talent—which is perhaps the most important talent—from being recognised). Instead, professional success in the financial services industry becomes a function of time: If you just put the time in (we tell our young ones), you’ll eventually be recognised. This is damaging to the enthusiasm of those who otherwise could—with the right support and encouragement—become the next generation’s shining stars. Indeed, it’s a common saying within the STEM fields (be this the engineering professions or life-science academia) that “the best ones get away”; alluding to the tendency for many organisations to push away the very people who they should be working the hardest to keep. A lot of the time, this pushing-out of the most talented comes from the poor incentives structures built into these organisations, where ‘talent’ (imagined or otherwise) isn’t actually the metric by which people come to succeed. Instead, as in investment management, we place the emphasis on time.

Jeff Bezos has alluded to this as a kind of ‘folly of the commons’ when he’s written about company cultures in the past [link]:

A word about corporate cultures: for better or for worse, they are enduring, stable, hard to change. They can be a source of advantage or disadvantage. … It is created slowly over time by the people and by events – by the stories of past success and failure that become a deep part of the company lore. If it’s a distinctive culture, it will fit certain people like a custom-made glove. The reason cultures are so stable in time is because people self-select. Someone energized by competitive zeal may select and be happy in one culture, while someone who loves to pioneer and invent may choose another. The world, thankfully, is full of many high-performing, highly distinctive corporate cultures. We never claim that our approach is the right one – just that it’s ours

Now, if you’re working for an organisation that places the emphasis on time served. What does that tell us about the culture of that same organisation? To me, it echoes sentiments expressed by Dan Wang [link], when he writes on the societal danger posed by risk-averse college students:

Because acts of youth are more easily recalled, our future elites will be made up of people who’ve managed to keep their records unsullied. What happens when most records of our life are accessible via Facebook, Snapchat, Twitter, or blogs? I think that makes it so that our future leaders will be selected for whether they were willing to be really boring in their 20s, who have no recorded indiscretions that might derail a Senate confirmation. Are these the people we want to be governed by?

In the same vein, it is worthwhile for the employee of any organisation to ask themselves: Is my organisation selecting for the most talented people, or simply the ones who’ve managed to keep their heads down enough to avoid being pushed out? Often, I find, it’s hard to answer the first part of such questions in the affirmative. Indeed, during my tenure in finance, I have realised that one of the most humbling insights that you can have as a portfolio manager is that if we can’t recognise talent in the juniors who we work alongside every day (and to so incentivise the most interesting, talented, and creative ones), how can we look at companies and choose in which to invest when those decisions are made on less-useful information than the information by which we recognise and reward talent within our own organisations?

These questions are doubly relevant to ask when contemplating that most organisations are meritocracies and that what differentiates them is the organisational definition of merit, i.e. what constitutes ‘merit’. While we’d like to believe that ‘merit’ is defined as ‘talent’ or ‘potential’ across the board, this is rather naïve. Instead, when we look to the people who become promoted within organisations, we often find that ‘merit’ is defined very differently from ‘talent’, where, in some organisations, the meritous are those with the sharpest elbows and those with the loudest shout, while, in other organisations, it is those with the strongest networking skills or political savvy. (This is of course not to say that any of those definitions of merit are wrong as cultures are only good or bad in the sense that they help or don’t help an organisation to achieve its stated objectives.) Instead, it is only very rarely that an organisation is organised in the way required for talent to be both meritous and recognised early on.

Of course, this brings us back to the problem with proxies all over again. And perhaps the point was never to find a perfect proxy (or collection thereof), but instead that we need to start having the conversations within our organisations of what proxies that we value and how we should track these over time. In many cases, there will be simple gains to make in giving up on the idea that talent is something that we can easily recognise or perhaps even engineer. Instead, we should probably think more about our organisational structures, where, if we can’t recognise talent or potential, how do we create the organisations that would allow us to select for these people without being able to recognise them ahead of time?

Here, it would of course be the most important to properly articulate the organisational idea of ‘merit’ (e.g. What do we consider meritous, and what behaviours would we like to incentivise?), as this will form the first pillar of a wider framework for informing incentives and aligning these with organisational objectives. Often, the answers to these questions will also be very different from the type of thinking that we’ve engaged in in the past. (For example, if we were to want to incentivise insightful research, we should probably worry less about how well a checklist has been ticked if the task has been completed in the right spirit, which is the bane of many a young analyst.)

After all, the beauty of most work worth doing is that it’s only by asking the right questions that we can start taking the first steps toward generating good results. And to do so successfully, it helps to first figure out what we would consider a ‘good result’ and then work backwards from there. So too, I would argue is also the case with recognising talent and building organisations hoping to capture as much of it as possible from early on.

In a previous blog post [link], I tried to explain why America feels so broken by applying non-linear (qualitative) tools developed in ecology and ecology-based economics. This post is looking to contextualise these tools further by focussing on contemporary identity politics and making the argument that the contemporary politic is just another symptom of an economy (society) that is growing increasingly (energetically) stressed. To this end, I’m hoping to explain politics from a more economy-energetic-point-of-view by introducing the concept of ‘political energetics’, which hopes to explain politics using a more thermodynamic point of view. To do so, I’ll focus on one branch of contemporary identity politics, the ideology of Social Justice.

Social Justice is an interesting political and cultural phenomenon because its goals differ so markedly from those of the traditional civil rights and social justice movements. While liberalism (which includes the civil rights and social justice movements) seeks to further the Enlightenment project by providing everyone with the equal opportunities owed to them through the recognition of a common humanity (and doing so using the tools afforded to them by science, empiricism, humanism, and reason), the ideology of Social Justice seeks to actively undo and work against these liberal processes to erect a more cynical and activist world-view in their place. 

Indeed, rather than acknowledging humanity as simultaneously fallible and flawed—but also capable of great progress and improvement—Social Justice sees humanity through a more cynical lens, painting us as inherently bigoted creatures who are flawed beyond redemption. As such, Social Justice prescribes that the only way to reform society is to tear down its current institutions and to re-build something more ‘equitable’ in their place. Under this structure, the equal opportunities valued in a meritocratic society would be replaced with the right of equal outcome in a perfectly representative society. Here, instead of people being judged on the content of their character, they would be reduced to groups and ‘identities’, each expected to conform to their own internal narratives and to be represented by their prevalence in the population. While this view of the world is simplistic enough to represent nothing but a caricature and pipe-dream, it is the world that a small—but vocal—proportion of the population is working towards. To do so, they’re hoping to discredit and tear down objective ways of knowing like science and empiricism, so their—more cynical—methods can take their place.

This juxtaposition between liberalism and it’s emphasis on universal human rights and the much-less charitable emphasis of contemporary Social Justice is contrasting enough to merit further interest: How come that the civil rights project of the 1960s and all of its successes since—working tirelessly to further the extension of equal rights to women and minorities, regardless of race and sexual orientation—has become increasingly superseded by a movement seeking to undo these unprecedented successes of humankind to install a grotesque caricature of civil rights in its place?

My own interest in this question led me from today’s Social Justice movement to the critical theories that become popular in humanities scholarship in the 1980s, and from there, I noticed that there were significant similarities between the 1980s’ critical theory and postmodernism. At this point, I however abandoned the search for an explanation of how critical theory came about as I realised that I’d need to read up on postmodernism itself and there were other projects that seemed more deserving of my time.

I was therefore very pleasantly surprised to learn a few months ago that Helen Pluckrose and James Lindsay had a book on the topic—Cynical Theories—coming out, which promised to trace the origins of Social Justice back to critical theory and, ultimately, to postmodernism. Having waited for the book to be released, I have since spent the past two weeks and some digesting their work. I was very pleasantly surprised with the book overall: It is a good introduction to the topic, tracing the evolution of critical theory from its postmodern roots all the way to its current form in the guise of Social Justice.

While the book is a more academic work, it is still highly accessible. It is academic only in the sense that it is rigorously objective; avoiding excessive moralising but calmly extending the arguments for and against. This is however also my strongest criticism of the work, since it’s only if you already accept liberalism and recognise that it’s a process that—like democracy—is flawed but still represents the best process developed so far that you’ll accept their conclusion that liberalism is a superior project to that of Social Justice. Indeed, because of the objective tone, many of the Social Justice claims that are introduced in the book (e.g. of blurring boundaries to undo stereotyping and to fight equal opportunity to replace it with processes ensuring more equal outcomes) come across as superficially sensible. (I can, for example, think of a few acquaintances who would find enough support for Social Justice in the book to reject its conclusion that liberalism should be better, somehow.)

That criticism aside, I consider Cynical Theories to be one of today’s most important and accessible political books: Because it explains the framework that people interested in Social Justice operate by, it explains why they act the way that they do (e.g. ‘problematising’ discourses and going over opinions and long-forgotten Tweets with a fine-tooth comb). For this reason, the book is a highly recommended read for anyone interested in better understanding the conflicting roles of liberalism and Social Justice and some of the stranger aspects of the contemporary social discourse. For me, it helped provide a lot of interesting context to observations that I’ve made over the years, effectively tying together several different strands of critical theory into a very coherent historical picture.

I don’t intend to use this post to review Pluckrose and Lindsay’s book beyond this, as their premise is really quite simple: The book effectively suggests that Social Justice—in its current guise—represents a ‘third phase’ of postmodernism. According to this historical hierarchy, postmodernism evolved from an episteme that questions the existence of objective truth (this representing postmodernism’s ‘first phase’), into the toolkit used by critical theorists. Critical theory would thus represent a form of ‘applied postmodernism’, which is equipped to find problems regardless of them being imagined or real (this would represent postmodernism’s second phase). This ‘problematising’ then further evolved into a bona fide ideology, built on the assumptions of critical theory (that our social systems are irredeemably harmful and problematic) and instead treating them as Truth (representing postmodernism’s third phase).

Therefore, instead of reviewing Cynical Theories in more detail, I want to build on the authors’ work to show how the conversation around the conflict between liberalism and Social Justice fits into the ecosystem view of economies, where economies are complex systems organised around energy flow. I introduced this idea in the previous post—linked here and above—but to quickly summarise, economies are ecosystems that are built around the flow of energy proxies like money. This follows from the Second Law of thermodynamics, which suggests that ‘Nature abhors a gradient’. Therefore, when gradients arise (for example through a supply and demand mismatch), there is an increasing likelihood that they will be satisfied and collapsed. The collapse of gradients is catalysed by physical processes of varying complexity, of which companies (in economies) would be an example. (In this interpretation, companies arise naturally with the purpose of satisfying supply-demand gradients, if these gradients are strong enough.)

As gradients collapse, energy (or energy-proxies) will flow. This flow of energy is ultimately what supports the formation (and continued existence) of the system (much like the collapse of a supply-demand gradient liberates money, which can be used to grow and sustain a company). An ecosystem is then simply a network of such complex, gradient-collapsing, and energy-powered systems. As such, the size of an ecosystem is determined by the level of energy flow within it (like how the size of the gradient-collapsing system itself is dictated by the amount of energy flow it can capture). The level of energy flow also scales with the energy-harvesting potential of the system. Therefore, ecosystems with a productive energy-capturing layer can grow bigger than less-productive ecosystems (e.g. compare the growth of a manufacturing economy to a service-based economy). Ecosystems can also grow by retaining more energy (by exporting more and importing less), which effectively sets a direction for their ‘progress’, to allow ecosystems to grow larger and more efficient until they have reached an optimum that’s dictated by parameters specific to each ecosystem.

If these ecosystems are economies, the economies with the biggest ‘producer-layers’ (e.g. manufacturing) will be able to grow larger and more stable than economies with smaller producer-layers because are supported by higher levels of energy flow (as more energy is harvested and fed into the system to support it). A given level of energy flow can therefore support a given size of an ecosystems or economy. In other words, the size of an economy is given by the level of flow, with bigger flows resulting in bigger economies. (Indeed, GDP is measured as money flows.) As such, any reduction in flow (because of lower productivity or because of lower retention) will cause the ecosystem to grow smaller in size; to regress. In my previous post, I suggested that such a process of ‘economic regression’ might represent a contributing factor to why countries like the USA seem to be growing increasingly politically unstable, because they’re losing energy and therefore in the process of reverting back to a lower-level energy state.

In this view, it follows that economic growth is something that requires a continuous influx of energy: Therefore, if more energy is infused into a system (through technological innovation, for example), it will grow. In the absence of innovation, the Second Law of thermodynamics (which also concerns itself with the availability of energy) will however see that less energy is available to the system as more of it is lost. As such, it is a constant struggle to keep a given level of energy flowing through an economy, and the higher the level of energy flow becomes, the harder the struggle to keep it at that level becomes. Instead, the ‘invisible hand’ of thermodynamics acting on the economy will see the energy dissipate away, which would cause the economy to deflate. Instead, the economy would be moving closer to the equilibrium state (which is the state that the Universe is moving toward), which is the state where zero energy is available. In other words, without the constant struggle to grow and become more complex, systems will regress to a state of lower complexity, like a balloon deflating on its own when no more air is being blown in. In the extreme case, like with the balloon, this regression would continue until the system deflates. Accordingly, the process of no growth means stagnation, a loss of complexity, and, ultimately, death. The feeling that the USA is struggling—if not failing outright—is therefore a likely symptom of a complex system losing energy and reverting back to a more-simple state. To put it differently, America is currently undergoing a process of negative progress. It’s an economy (and country, and society) in regress.

Reading Pluckrose and Lindsay’s book, I recognised much of this process in many people’s growing distrust of science and liberalism. To wit, liberalism (and the wider Enlightenment project) is a process of moving away from the more-simple and less complicated state of political tribalism. As such, the development of liberalism can only proceed in lock-step with economic growth (as both liberalism and growth are symptoms of more energy flowing through the system). This is probably why the development of Enlightenment values has coincided with periods of strong economic growth, and why the fight for civil rights peaked in the period between the 1960s to the 1980s, when the economies of the West were firing on all cylinders, having recovered from the devastation of the Second World War and reaping the economic gains of the wartime innovation of new technologies.

In the time since the Second World War, processes like globalisation have however seen energy gradually draining from the economies of the West. While this has led to the expansion of economies in the East, the lack of compensatory innovation in the West means that the producer-layer in these economies has been shrinking gradually. Today, we’re therefore living through a period when not enough energy seems to be flowing through the Western economies to support them at their current size. As such, they are reverting to a lower-energy state, and for the duration of this process, political upheaval will result. 

To this, I however think that we also must add the undoing of the Enlightenment project: As economies are losing steam, there doesn’t seem to be sufficient energy flow available to support liberalism. As a result, peace and the fight for equal rights and opportunities is increasingly regressing closer to equilibrium, which—in the human political system—would be conflict and tribalism. In other words, we’re moving towards a state which we’d previously left behind. With its emphasis on tribalism and identity politics and its open rejection of science and liberalism, Social Justice facilitates and promotes this progress of social regress. However, given the economical context, I don’t think Social Justice is as revolutionary as it is reactionary. Instead, I think the ideology has arisen in reaction to the deflating powers at work elsewhere in the economy, with the ideology now working to accelerate these.

Altogether, this demonstrates how an ecosystem (and thermodynamic) view of economies can be helpful in making sense of (and predicting the outcome of) contemporary political trends. This view hints at the existence of something I personally like to call ‘political energetics’, it being the study of how the political landscape is just another symptom of the underlying energy flows. As I’ve outlined in this post, political energetics suggests that when more energy is flowing and thus made available to an economy (either though increased technological innovation or greater wealth redistribution), the political landscape will progress, moving away from the tribal equilibrium and towards liberalism and civil rights. In the reverse scenario, where less energy is flowing and being available to the economy, the political landscape will regress, with the zeitgeist eschewing the lofty ideals of liberalism and moving towards simpler solutions like tribalism and authoritarianism.

While Cynical Theories is a really good book, as outlined above, think its conclusion that we can fight Social Justice with an increased emphasis on liberalism misses the mark, as the undoing of liberalism is not the work of Social Justice (or other illiberal ideologies). Rather, the rise and popularity of these illiberal ideologies are symptoms of a more general state of economic stress, where declining energy flows cause people to turn inwards—to prioritise survival—and away from lofty goals like social progress, the scientific project, and technological innovation. This is understandable, since Cynical Theories is a book chronicling the history and ideology of Social Justice, introducing the movement and tracing its roots back to postmodernism. As such, Cynical Theories is not (and should not be) a book on political energetics. 

However, what all of this means is that the fight for the continued development of the Enlightenment project and the concomitant emphasis on (non-capitalised) social justice, civil and equal rights, science, empiricism, meritocracy, and liberty is—at its heart—an economic and energetic project. This changes the discourse on how best to support progressivism significantly, since it is only when economies—and the people who operate within them—are exposed to healthy levels of energy flows that they keep building a better tomorrow, elevating themselves towards science and away from illiberalism. 

In other words, political energetics suggest that to build the society that we all want to live in—where everyone is happy and healthy and living their best life—that we need to place a bigger emphasis on creating the economic reality that makes all of this possible. Indeed, if we look at something like the West of the past, where technological innovation kept infusing growth into the economy, or contemporary Scandinavia, where wealth is heavily redistributed, it suggests that the methods by which we choose to accelerate the pace of energy flow (creation or redistribution) in the economy doesn’t matter. What does matter is that the energy itself flows. Therefore, to create the rich and equal and humanitarian and happy society that we’d all like to live in, we need to make sure to keep the money flowing. The very future of the Enlightenment project and the scientific worldview might depend on it.