Technology or Monetary System: What is the key to progress? — Part II: The case for the Monetary System

Is money really the most important innovation of society?

Keith Patarroyo
30 min readJul 2, 2021


This is the second of a series of posts where I debate whether a monetary system or technology is the key to growth in society.

In the previous post, I argued that technology has historically exploited layers of smaller and smaller phenomena. During this process, fundamental discoveries in the nature of matter, energy, and information have allowed for the construction of very productive economic niches. These niches are not uniformly distributed geographically, regardless of the monetary system. This implies that having a diverse pool of available technologies is predictive of growth at the country level. Of course, this analysis is incomplete because we assumed all the time that we are immersed in a functioning monetary system.

In this post, I’ll argue the opposing view and argue why having a functioning monetary system is the key to the growth of modern society. Moreover, I’ll claim that the discovery of money is an evolutionary transition of such importance that it may be compared with early transitions in the very first life to arise on earth or the emergence of human intelligence compared to other animals.

1. What is Money?
1.1 What is Money really?
2. Intelligent Animals
3. Origin of Life
4. Economics
A. Appendix

What is Money?

In this post, I’ll be blunt and I’ll attempt to answer one of the foundational questions in economics. What is money? You might wonder, why this apparently simple question has startled so many minds and has avoided a concise answer for many years. We all know what money is, right? It is the dollar we have in our pocket, the euro number in my bank website, my silver coin, my bitcoin in my crypto-wallet, the cigarettes when my friend was in jail, secured loans, treasury bills,… As you see things start to get fuzzy really quickly, so in this case, people start to say what are the common characteristics of these objects. From these we get the common definitions of money:

  1. A universal medium of exchange, store of value, and unit of account.
  2. Six characteristics of money: durability, portability, acceptability, limited supply, divisibility, and uniformity.
  3. a social device for moving value across space and time
  4. an extension of your mind to think about value, you think in your currency
  5. is energy, it represents a claim on all other forms of energy…
  6. is a way to reduce entropy and a signal of information…

Some of these definitions describe what money does, other answers how do we use the money for? Some answers are more satisfactory than others, and some are really loosely described.

In this post, I’m less interested in the How or the What, but rather I’m interested in the Why and a certain form of When? So the questions are Why does money work at all? and what was unlocked when money first raised?

What is Money really?

In order to solve this problem I’ll have to recall three different concepts:

  1. Evolution by Selection: It is the steady accumulation, through natural selection, of such differences, when beneficial to the individual, that gives rise to all the more important modifications of structure.
  2. Evolution by Combination: Given a set of building blocks, new blocks are generated by combining the starting set of blocks. Each block exploits an effect or phenomenon, usually several.
  3. Information Bottleneck: Given a random variable X and an observed relevant variable Y, the information bottleneck problem study is how well Y is predicted from a compressed representation T compared to its direct prediction from X.

Having defined the previous concepts I’ll define Money as, the bridge between the human world of combination and the market world of selection. By the way, note that in this definition, I highlighted the words, human and markets. I remark on this because evolution by selection and combination is not only seen in markets or in human technology respectively. They seem to be “universal” laws that apply in different scales and systems. Two fundamental examples that we’ll use later in the post are, combination in chemistry, fundamental for the origin of life, and selection in biology, fundamental for biology and the rise of intelligence.

Although I defined money in the previous paragraph, the definition is very sloppy. What do I mean by a bridge? Here is where the concept of information bottleneck comes in handy. So if we imagine a network of services and goods X from a person, company, city, country. This network has a great amount of information(materials, supply chain, user manuals, …). Then we have a relevant variable Y, which can be any kind of variable used in a social algorithm(decision making, allocation of resources, error correction,…). Money is the compressed representation T that allows a useful prediction of Y without knowing all the details about X.

The mechanism just described can also be thought of as an alternative way to understand the supply and demand economic model. This model defines the price of an asset as the equilibrium between subjective demands of humans against the objective supply of resources. This notion of objectivity I claim is related to the combinatorial nature of technology and the subjectivity with the selective nature of social systems. Money is the bridge between these two apparently incompatible worlds.

This mechanism also allows us to answer the related question: why money works? Well, because money effectively solves the information bottleneck problem between a network of objects evolved by combination and a network of objects evolved by selection. Money has been the de-facto compressed representation that we use to predict the behavior of social systems.

The role of a compressed representation, like money seems to be also a mechanism by which not only humans, but nature builds hierarchies of complexity. These hierarchies are not fully understood, but a crucial element that seems to be of fundamental importance is called: Coarse-graining as a downward causation mechanism.

The two concepts here are Coarse-graining and Downward Causation. Both are pretty simple, first to coarse grain means to compress, so our compressed representation T is also a coarse-grained representation. Downward causation is the exact properties by which our information bottleneck works, these are:

  1. Aggregate properties T are predictive of the future state of the system Y (slow variables).
  2. Aggregate properties T are robust to small perturbations 𝛿X.
  3. Estimates of aggregate properties T are used nearly universally by all components to tune decision-making.
  4. Components largely agree in their estimates of these properties T.
  5. As estimates converge there should be an increase in mutual information between the random variable X and the relevant system variable Y.

So we can think of money with these lenses:

  1. Money is predictive of the future of social variables(decision making, allocation of resources, error correction,…)
  2. If you value a machine at T dollars, it is going to be the same T amount of dollars if you use a screw with a different brand and supply chain 𝛿X. The network X is totally different, but T changes very little.
  3. From producers to consumers, to companies to cities, to countries everybody uses the money to tune their decision-making.
  4. Although a mechanism of price determination is less universal. We can think that locally there is an agreement in parties of what is the “right” price for an asset.
  5. Similarly, when these local estimates converge we have a high correlation between supply and demand. In other words, correct pricing is fundamental for a party to survive in the market.

Although we analyzed the downward causation in terms of human agents with a sense of agency. This framework can also be used, as we mentioned before, by nature, where it constantly does downward causation in order to create hierarchies of complexity. In this case, sometimes people differentiate nature coarse-grained quantities with endogenous coarse-graining. This is to differentiate human-generated coarse-grained quantities like money, Gini index, inflation, or even physical quantities like entropy.

Consequently, we can think of money as a way to create larger hierarchies of complexity, in this case, we start from the human world of objects to the market world of decisions. Therefore we can answer the second question when money was conceived what was unlocked? Money seems to be a piece that allows for more complex objects to appear by selection.

Although it is subtle, we are saying more than: the mechanism of money is downward causation. We are adding the fact that the objects from X come from a combinatorial origin, and the resulting variables Y come from a selection level. So in some sense money allows objects in X to evolve by selection mechanisms because of its coupling with Y. This coupling I claim is not just a property of money, but it has occurred once in the origin of life and reversed in the origin of human intelligence.

I’ll discuss these connections in the rest of the post, and I’ll claim that this coupling is a possible foundation for the field of artificial intelligence(Deep Learning). The information bottleneck could be also foundational for the fields of artificial life, cultural evolution, and economic complexity.

Intelligent Animals

Next, we’ll move to the realms of biology and human intelligence. These are complicated subjects, however, we’ll start with a simple unrelated question: what is the proper computer data structure of an image?

This question is very weird, I still remember when somebody in college asked me this question, I answered, well obviously RGB right? Well, I think I had physics confidence back then. Moreover, I was wrong! In reality, a camera is a complicated device, if we think of a CCD sensor(digital camera), it is an array of detectors in a grid structure. Every time a photon hits the detector an electron is generated. So by cleverly analyzing the electron current released by each detector of the CCD sensor, we can infer the energy and thereby frequency of the photon. Hence we can recover an image. Of course, this is all a cartoon picture, the important thing here is that we are obtaining a spectrum of photons(say 32 frequencies) at each point of the grid, each with a different frequency. Now comes the real question why do we only care about the RGB intensities and not all the rest 32 frequencies in the spectrum?

So we can answer this question in two different ways, the first one is the biological one and the second one is the information bottleneck one!

  1. Also as a cartoon explanation. In the retina, we have cone and rod cells that are photoreceptive cells. In particular, the cone cells are the most mostly sensitive to colors near Red, Green, and Blue. Therefore all of our human colors are really a combination of these three colors.
  2. However, we can explain this phenomenon without knowing the mechanism behind it. We can say that for us humans, the RGB color data structure solves the information bottleneck problem in a way that we can make useful predictions with an image of only these three colors.

Moreover, we can see that an RGB image satisfies the downward causation properties if we think of human image recognition:

  1. We can recognize objects perfectly with an RGB image.
  2. If we change pixels randomly inside an RGB image, we can still recognize the object inside. See image encryption and adversarial images.
  3. We use image recognition universally in our life and in technology.
  4. Most of the time we coincide with what objects are really on the image. See the infamous blue dress.
  5. When consensus arrives, we can be sure that the original multispectral image is highly correlated with the objects inside. Therefore the colors are highly correlated with the material of the objects the image is capturing.

This idea of compressed representations is not new, moreover, the idea that we humans do this process in our brain is the defining paradigm of deep learning. In this framework, we have a multi-layer neural net, and at each layer, the network learns more abstract representations of the data. As a simple example, we can take the auto-encoder neural net architecture.

In this case, the auto-encoder is learning how to generate from the reduced encoding a representation as close as possible to its original input. This encoding can be used for dimensionality reduction, anomaly detection, machine translation, etc. There are different types of autoencoders(sparse, denoising) and they also have been used for generative modeling.

Although the difference is subtle. Note that the autoencoder compressed representation is a layer of complexity on top of the compressed representation of RGB images. Usually, these images are the input of the neural net. Therefore what this is really telling us is that the compressed representation is really all about the specific application.

So we can postulate that this process of information bottleneck or compressed representation is ubiquitous in a lot of our biology. We can think that our other senses(hearing, olfaction, taste, touch,…) are to some extent also a compressed representation of highly complex signals. So we have developed a series of compressed representations in our minds, moreover, this seems not only to be a guess but rather to be a universal behavior in biology. Several species use these compressed representations to take decisions.

So perhaps in some sense, it is not surprising that these compressed representation techniques have had a lot of success in recent years with deep learning. With these we have achieved absolutely remarkable results: the processing of natural images, speech, natural language. But what do all these domains have in common? First, they are compressed representations of very complex signals: light, sound, and communication systems. Second, we have been able to develop robust computer data structures that can store information that can be decoded by humans. So we humans are living proof that these data structures are useful. If we can decode the content embedded in them, then a computer can do it as well.

Note that the information bottleneck from human technology to markets is a transition of different nature than the ones we have been talking about in this section. Here we considered X to be variables coming from signals that were in some sense inspired by nature(except language) and how the compressed representation T helps us to implement a social algorithm Y. On the contrary, money allows us to generate a compressed representation in the principle of any human-generated technology, and even some natural processes, thereby extending the Darwinian evolutionary mechanism to more objects.

Also, note that the compressed representation techniques in artificial intelligence are single-purpose technologies. They have in some sense reverse-engineered the biological information bottleneck that we humans use in several of our modules of pattern recognition. These are parts of the puzzle of human intelligence, however, it is not sufficient to generate a full human-level artificial multi-purpose intelligence. Another big piece of the puzzle that is very related to the module of language is our ability to plan. Moreover, there seems to be something quite special about planning for the future that allowed us to achieve so much as a species. In fact, it seems as if the communication of this planning to other humans was what naturally leads to the necessity of language.

Planning, therefore, seems like a fundamental ingredient of human intelligence. Moreover, the idea of a sequence of events that lead to the future seems to be foundational for our construction of tools. We divide big tasks into smaller tasks that can be performed one after the other to achieve the original big task(combinatorial evolution). However, this sequentialization seems to be a way in which we humans perceive the world. Not necessarily all natural processes can be decomposed in a series of events one after another. In fact, many processes in biology seem to be computed collectively or concurrently, each part of the process is computing and competing with each other for resources. It is this competition for resources that I currently consider to be the key element of Darwinian Evolution. In this view, different individuals might be selected for different characteristics, sexual selection in the case of sexual individuals, monetary selection in the case of companies, and perhaps oxygen or solar energy selection in the early stages of life.

Note that technology itself does not share this competition element, but rather a combination element. Each sub-piece is not in constant competition for resources. An important difference is that a competition mechanism is a useful element in organisms made of unreliable or noisy components. On the contrary technology in most cases is built from reliable components. Additionally, it is the fine-tuned cooperation or orchestration of the pieces’ outputs and inputs that allow for a functional piece of technology. This orchestration can have many processes running in parallel, but it is the well-defined sequence of outputs and inputs that is a trace of its combinatorial nature. In this case, it is rather the merging of technology plus market, that allows the competition between technologies themselves using monetary selection to decide the dominant technology.

It is not clear if the characteristic of sequentialization can be analyzed in terms of an information bottleneck. For this we will have to ask, what do we use sequentialization for? Well, we use it for explanation or understanding, if we can understand how the output of a piece affects the behavior of the next piece in order to produce a final result, we say we understand something better. For this process, we first need to understand what each piece does(bounded computation) and only then we think about how pieces are linked together(causality). The first is known as descriptiveness and the second as co-explanation. So we could put a score to explanation and coarse-grain with this variable. Then looking at natural phenomena we could generate an explanation or story in order to build technology out of it.

This explanation score although is a high-level feature of human thinking can also be analyzed with downward causation,

  1. A good explanation eventually leads to the construction of robust technology.
  2. Good explanations are supposed to be useful in a variety of different physical situations.
  3. We use explanations in all aspects of our human decision-making.
  4. Humans largely agree on what a good explanation is.
  5. The better an explanation of the same phenomena, the easier is to build technologies out of it.

As we just saw, as a consequence of our ability to define a well-ordered sequence of events, nature devised a way to evolve combinatorially instead of competitively. Next, we’ll argue that the opposite process also occurred in the past, we claim that a combinatorial evolution element was of fundamental importance in the origin of life. Finally, we’ll discuss what this transition of combinatorial to competitive can tell us about the current transition of technologies to economies.

Origin of Life

The topic of the origins of life is an open problem in science today, so by no means, this section is meant to contain the definitive explanation of how life first arose on earth. Having said that, we do know about certain ingredients that might have been very important in the origin of life. I’ll not get into much of the detail of the biochemistry of the origins of life, mainly I’ll present some high-level ideas of how early life evolutionary transitions can be seen with our information bottleneck framework.

I’ll start with two suppositions, first I’ll consider a metabolism first approach to origins, and second I’ll consider that life is not a property of objects or organisms, but rather it is a planetary process. So let’s unpack these two ideas briefly:

  1. Metabolism first hypothesis: some aspect of modern metabolism existed naturally in the environment before genes and before proteins.
  2. Life is a planetary process: Life is not a property inherent to things. The property of being alive is defined by participating in the order of the biosphere, all the things that build it up, and all the things that break it down. And things like cells and viruses are a form of individuality.

So to understand the first point, we must consider first how can we know what were the characteristics of the first life that arose on earth. One way is to look at the genetic code of different individuals and trace what genes are shared among different individuals. So it is postulated that since these genes are shared across different species in a variety of domains, they must have been inherited from a common ancestor.

The individual that may have contained these shared genes is denoted as LUCA(Last Universal Common Ancestor). There are two sub details we must address here, first, we are talking about genes with LUCA, in our first point above, we said that metabolism came first than genes, so why are we talking about genes? Well, this was an example to show evolutionary logic in play, this logic tells us that widely shared traits are probably the oldest. The second detail is to mention that genes are a form of information storage, but in order to generate life we don’t only know what are the pieces of the puzzle, but how do we put them together. This is, how can we construct complicated molecular structures like RNA, DNA, a cell, a tissue, etc.

If we think about the constructability of life, then it makes much more sense to consider metabolism first than genes. Thinking about the chemicals and the reactions that allow energy generation, protein construction, and waste elimination that are shared across all individuals might give us a clue of what is needed to construct life. One such metabolic cycle that is very promising for the origin of life is the reverse citric acid cycle. It's a cycle of 11 simple molecules(in the image below the color dots are carbons, the smallest has only two carbons and you go around as many as six carbons — that is citric acid). Note that here we are in the realm of chemistry, this is a world where possibilities are combinatorial. More complex molecules are assembled in reactions where several smaller molecules get combined. That is, we are in the realm of combinatorial evolution!!

What is remarkable is that all molecules that are used to construct the entire biosphere originate in one of four or five compounds in the cycle. All the diversity we see now comes after, as a combination of these. It is at first very surprising to think that a metabolic cycle is a biological individual like a human. This naturally leads us to the second point, it is rather the entire biosphere that contains the property we call life, and biological individuals are the ones that play a part in the order of the biosphere. So the question is not whether this is alive and this is not, but rather how these individuals differ? or how much individuality does this biological thing have?

One possible answer is to think about how different combinations of environmental influences and internal dynamics can predict a system’s future states. A recent paper defines the following three types of individuality:

  1. Organismal Individual: An entity that is shaped by environmental factors but is strongly self-organizing. Nearly all of the information that defines such an individual is internal and based on its own prior states. Mammals and humans are in this category.
  2. Colonial Individual: This entity involves a more complicated relationship between internal and external factors. Individuals in this category might include an ant colony or a spiderweb — distributed systems that are “partially scaffolded” by their environment but still maintain some structure on their own.
  3. Environmental Determined Individual: This entity is driven almost entirely by the environment. If the scaffolding is removed, the entity would fall apart. It’s like a tornado, which dissipates under the wrong temperature and moisture conditions. The very first life to arise on Earth was probably like this.

So taking this lens, we can see that a metabolic cycle is a type three individual. We can think that at the beginning of life, this cycle was only able to rise in certain places like hydrothermal vents. If we were to remove the vents, then the cycle would not be able to keep going, it needs the chemical gradient to survive. On the other extreme, we have mammals that are able to survive even if several elements of the environment are removed. As proof of this fact, humans have survived in space, outside the biosphere.

However, are humans only one individual? Well, this is not clear. We can think that we are the union of several individuals like cells, tissues, organs. So at this scale, we look more like a colonial individuals. In the same way, we can think of companies, cities, countries, even the whole earth as only one individual. The earth individual would be somehow robust because even in the hypothetical case where we remove the sun, some life would still survive. For example, biological organisms that use the earth's core heat as an energy source would survive.

Thinking of the biosphere as only one life on earth bears the question. Why didn't a different type of life arose? For example, we can think of a different metabolic cycle as the main shared feature of all common ancestors, perhaps with different chemicals and molecules. We can propose three solutions to this conundrum, interestingly enough the first two come from Darwin:

  1. Darwin’s experiment: Take a square plot of land, and carefully remove all visible living things from the soil. Uproot all the plants, sift to remove insects, and left the plot alone to see how it would be recolonized. What we observe is that the plant species that recolonized the plot are first of the fast-growing unstable variety, that a whole bunch of weeds and bugs spread over the new area. Then, over time, other more hardy species slowly took over from the weeds, until, many months later, the plot was indistinguishable from the remaining land in the lot.
  2. Life preempts life: ” But if (and oh what a big if) we could conceive in some warm little pond with all sorts of ammonia and phosphoric salts, light, heat, electricity etcetera present, that a protein compound was chemically formed, ready to undergo still more complex changes…at the present day such matter would be instantly devoured or absorbed which would, not have bee the case before living creatures were formed“ — Charles Darwin, in a letter to Joseph Hooker (1871)
  3. Life is inevitable: Given the chemical and geological cycles that the earth has been constantly generating, one necessarily gets a background of chemical regularity. Then individuals arise in the “paths of least resistance” defined by the concentrating mechanism of organic chemistry, the cycles in metabolism, and the earth’s energy sources.

At the core, the first two explanations capture a fundamental aspect of the Darwinian evolutionary: competition. While the first applies to our human scale, the second talks about the very origin of life. So in most likelihood at the beginning of life on earth, there might have been different metabolic cycles, however only one passed the test of time. In this competition, both local and external factors may have contributed to establishing a dominant life-building block set.

The first two observations somehow imply that the life we have is a series of coincidences that are contingent on some historical factors that we cannot predict. On the other side, the third observation claims that the life we have is actually inevitable, that given the geological and chemical cycles on earth we can predict what is actually the chemistry of life. Then what is it? Is life an accident or a necessity?

Well, this is one of the open questions in the origins of life. However, if I could tell a convincing story, it would be as follows: It is not only chance or only necessity, they are intertwined together. For example, the amino acids we use in our genetic code are the easiest chemistry we can form with the carbon backbones coming from the reverse citric acid cycle. With these pieces, combinations or chains of them can be formed and form proteins. At some point the proteins compete and evolve, producing a more precise class that can survive, which eventually catalyze the formation of nucleic acids (among other things), and learn to store data for later retrieval in nucleic acids. The nucleic-acid protein complexes then compete more and learn to store data in DNA, for permanent storage (since DNA is much more stable).

In summary, there are regularities at all stages of competition. These regularities to some extent limit the number of possibilities that the competing structures can evolve to. Having said that, I do believe there is a threshold that occurs as soon as a system can store large amounts of information spontaneously and has interactions that are capable of forming a computer. At this point, I believe that Darwinian evolution takes over, then the history of life really becomes important and competition is fundamental.

So what would be the system that can store large amounts of information and capable of computation on the origin of life? well, that would be the biosphere. The biosphere first had to accumulate a big amount of biomass, then when the proteins in a region of the biosphere were capable of storing information and had the interactions to form a computer, then the Darwin struggle started. In this struggle, individuals that are stable in the regularities of the biosphere survived and evolved into more complex individuals.

At the moment the competition started, there might have been different traits that selected some individuals over others. Perhaps a property that might have been predictive for future survival in a certain environment was of special importance in this stage of the history of life. This might have given rise to an important evolutionary transition in early life. On this property, we could in principle use our information bottleneck framework.

Since we don’t know all the details of the exact environment of early life, we can only speculate on what might have been this property. Something a little bit more concrete is to analyze what are some of the known important transitions that allowed more complex individuals to evolve. In this we find two important transitions:

  1. Photosynthesis Transition: Structures in the individual were able to harness the energy from sunlight to turn carbon dioxide and water into oxygen gas and sugars, which they could use for energy.
  2. Endosymbiosis Transition: An individual capable of photosynthesis was “swallowed” by another pre-historic individual. In doing so the pre-historic individual acquired its own photosynthesis mechanism. This combined organism is believed to be the ancestor of modern plant cells.

Although these two transitions sound like very interesting alternatives as fundamental transitions for early life, it is currently believed that the individuals that played a role in these transitions were ancestors of Cyanobacteria. Although these bacteria are “simple” individuals since they don’t have a nucleus like a cell, they contain incredibly complicated molecular machinery that was likely generated after several important evolutionary transitions. Having said that they also give some insight on what are the consequences of big evolutionary transitions

Clearly, we can think of the first property, the amount of energy harnessed by light, to be of incredible importance compared with organisms that only gain energy from the earth's heat or other chemicals in the environment. Furthermore, the individuals that resulted from this transition started to produce oxygen from the reactions that generated energy from sunlight. This not only started filling the atmosphere with oxygen but unleashed a mass extension due to the toxicity of oxygen in early life. In a similar way, the individuals that could adapt to this new oxygen environment also were selected next.

A transition of similar importance was the generation of complex cells by endosymbiosis. In this transition is interesting to consider that the energetics of scaling might have played an important role in this transition. Once a structure like a cell is already formed, it would be only energetically efficient to grow to a certain size. Then it is more efficient energetically to get individuals with multiple cells. This transition is of incredible importance, in the family tree above Archaea, Bacteria, and Eukaryota most of the individuals are single cells organisms.

Next, we consider some of the lessons of the evolutionary transitions of intelligent animals and the origins of life to analyze the consequences of the transition that money is currently having in our economy.


Social algorithms occur at all scales in human society, from a partnership to a household, to a company, to a neighborhood, to a city, to a county, to a state, to a country, and even to the whole earth. Although it seems now that these hierarchies are ubiquitous, this was not always the case. In fact we humans started small, with small hunter-gatherer communities, and slowly we spread to the whole earth, then transition slowly to agriculture and establishing bigger and bigger communities in a region of land.

The transition from hunter-gatherers to agriculture was a transition that lasted for several thousands of years and to some extent is analogous to the evolutionary transitions in the origin of life. We first had to spread to different places in a region, at first we were nomads and we collected fruits in several places, we dependent strongly on our environment, just like the environmental determined individual we described above. Slowly we learned to harness the power of fire and started to hunt, then with the propagation of knowledge of crops and seeds(Most of the calories we consume today stem from about 15 different crops that humans domesticated in this time) we started staying at a single place, so similarly to the photosynthesis transition and great oxygenation event. So in summary we first spread in a big region(accumulation of biomass), then with the discovery of fire we could cook animals instead of only recollecting fruits(the harnessing of sunlight by the cyanobacteria ancestors), next the propagation of knowledge of seeds and crops(slow production of oxygen through millions of years) both allowed for villages to form and almost extinguished nomadic societies(Oxidative ecosystems and the oxygen extinction).

I believe that the strong individuation of humanity happened when we started created villages, in fact, there are several reasons to believe this is the case. First the longest continuously inhabited human institutions are cities, there are cities that have been continuously inhabited since 5000 years ago. Second, there is consensus in the community of historians that the start of the Human era started around 12000 years ago with the establishment of the first temple. Soon after Jerico, the oldest known city has traces of inhabitation around 11000 years ago. This doesn’t mean that we don't have any more hunter-gatherer societies or nomadic communities. We still have today anoxic(reductive) individuals in the deep ocean.

I also believe that it is at this city-scale that the universal adoption of money makes the most sense, at this stage is easier to make money universal(at least locally) and more importantly, it allows us to build complicated structures even if we have unreliable components. You don’t really care about any specific detail as long as you get paid. In societies without money, you would have to find a way to assess in another way if the unreliable components are satisfying their minimum requirements, generating conflict. Even in societies with money but not universally adopted, you’ll have trouble making sense on choosing what institution is being more “successful”. Lastly, in cities a local universal medium of communication(language) is also easier to establish, thereby making business and trade much simpler.

It is also at this scale that it makes sense to have a division of labor. Although there is a fractal structure within cities, each neighborhood has its own school, supermarket, hospital, there are things that are only repeated in cities. Each city has its own police department, transportation system, airport. There is a well-defined individuality in a city. It makes sense to specialize if you live in a city, not in the countryside. Of course, the division of labor is not only seen in a city, it is seen in a company or even in a university. But these institutions are generators of only a certain type of products, whereas a city is capable of generating a big pool of processes for which more complex structures can be built on top.

In some sense, we can think of a city like a hydrothermal vent, a process that is constantly generating biomass, or in the case of the city, technologies. Just like in the origin of life, there are things that can be predicted and other things are pure chance. For example, given the building blocks of the wide availability of cars, smartphones, internet communication & payment, and a GPS location module it could be predicted that you could get a company that, via websites and mobile apps, matches passengers with drivers of vehicles for hire. However, the exact company that may result from this combination is not predictable, since this is mostly a mechanism of competition.

One last thing quite interesting to analyze is the notion of scarcity or limited supply that is fundamental to money or food. To some extent, markets do not play well with things that are not scarce. Commonly known as public goods are a source market failure and if we think in our information bottleneck terms when we consider public goods money is not predictive of social algorithms. If we see at the history of life, both sunlight and oxygen were fundamental in the transition to more complex life-forms but after they became widespread, almost all individuals were using them, so they provided no competitive advantage.

So what can this tell us of goods that were initially scarce, but then became widespread? Well, we have as an example the Internet, and how it has converted scarce goods like music to universally available. While in antiquity music could only be played live, figures like Socrates considered music a second-rate art, since it faded away through time. This was the rule for most of the human era, only in the last century, musicians could package a piece of music in a piece of material, a record, and mass produce it. Although the music in a record could be mass-produced, it remained a private good that had a great profit, because the marginal cost of production was very small. Moreover, there was still a finite amount of records in the market. However today with the internet, music transmission has almost zero cost, and because of the universal internet accessibility, each song value is practically zero. Hence there is no space for profit. This is a really big problem for musicians because basically, almost nobody can generate profits on these platforms.

So what kind of lesson can we learn from nature? Well, not much really. What nature did was basically provide this element as a universal asset just like universal education or universal healthcare. Should musicians become priest-like figures, with no space for profit in their motives, just to admire and venerate the beauty of music? Well, I don’t think so. There is an innovation that recently changed this landscape, Bitcoin and the blockchain. In some sense what bitcoin did was reverse the process of private goods to public goods by introducing scarcity to a software asset, e.g., there are only 21 million Bitcoins. Moreover, we can trade these coins in a decentralized way, without having to rely on any big tech company. Can the blockchain save the musicians? Only the future will tell.


Something I didn't address in this post is how do we account for a compressed representation T changing through time t. In economic terms, we would talk about inflation. Technically this is an increase over time of a hypothetical measure of overall prices for some set of goods and services, in an economy during a given interval (generally one day), normalized relative to some base set. In some sense, the definition of money we gave above is the price of a variable X at an instant of time. Additionally, inflation is something dependent on the monetary policy of a country so it is a property that is local and is meant to make sense of the change of prices year to year.

In the previous post, I actually mentioned how can we update a combinatorial model to be updated over time. In summary, you update the network of services and goods X(t) for each year. In this case, we would have to also update the relevant variable Y(t) for the preferences of the social algorithm. Then at a time t, we would obtain a compressed representation T(t). However, a question remains, how can we make sense of two compressed representations at different times T(t₁) and T(t₂). This is a difficult question because the economy of a country is an open system. So is yearly inflation comparing oranges with apples? Moreover, inflation is related to the money supply that is declared by a central bank and other factors. I’m not an expert on these calculations, but it is something that I definitely plan to consider in the future.

Moreover, if currencies like Bitcoin become mass-spread in the future, maybe there would not be a necessity for a central bank to order an inflationary rate on the population because of the constant update of the bitcoin network. Because Bitcoin although highly volatile, may regulate the price in a decentralized way at every transaction. I definitely need to learn much more about Bitcoin and crypto in general, but it is very cool that something new is finally taking off.


  1. #176 — Robert Breedlove: Philosophy of Bitcoin from First Principles. Podcast Notes
  2. Ron Maimon (, Is Stephen Wolfram’s NKS, an attempt to explain the universe with cellular automata, in conflict with Bell’s Theorem?, URL (version: 2014–11–22):
  3. Jason Smith, The price mechanism as information bottleneck, URL(version: 2017–10–05):
  4. Flack Jessica C. 2017. Coarse-graining as a downward causation mechanism. Phil. Trans. R. Soc. A.3752016033820160338
  5. Tishby, N.; Zaslavsky, N. Deep learning, and the information bottleneck principle. In Proceedings of the 2015 IEEE Information Theory Workshop (ITW), Jerusalem, Israel, 26 April–1 May 2015; pp. 1–5.
  6. Smith, E., & Morowitz, H. J. (2016). The origin and nature of life on earth: the emergence of the fourth geosphere. Cambridge University Press.
  7. What Are the Six Characteristics of Money?
  8. 72 | César Hidalgo on Information in Societies, Economies, and the Universe
  9. An Interview with Eric Weinstein
  10. New Theories on the Origin of Life with Dr. Eric Smith
  11. Inevitable Life?
  12. What Is an Individual? Biology Seeks Clues in Information Theory.
  13. How a single-celled organism almost wiped out life on Earth — Anusuya Willis
  14. The mysterious origins of life on Earth — Luka Seamus Wright
  15. What Is The Metabolism-First Hypothesis For The Origin Of Life?
  16. P. N. MALANEY, The Index Number Problem: A Differential Geometric Approach, PhD Thesis, Harvard University Economics Department, 1996.
  17. Gabriel Peyré, Mathematical Foundations of Data Sciences.(2019)
  18. Goodfellow, Ian, et al. Deep learning. Vol. 1. №2. Cambridge: MIT press, 2016.
  19. Arthur, W. Brian. The nature of technology: What it is and how it evolves. Simon and Schuster, 2009.
  20. Judson, O. The energy expansions of evolution. Nat Ecol Evol 1, 0138 (2017).
  21. A New History for Humanity — The Human Era
  22. When Time Became History — The Human Era
  23. Why Civilization Is Older Than We Thought
  24. The Growth Lab at Harvard University. The Atlas of Economic Complexity.

This is the end of this series on money and technology. When I started this journey I didn’t really know what money was nor what I was going to find. Curiously the discussion about Bitcoin first started this series of posts, I never really mentioned it until the end, but I was not satisfied with the explanations online. However now, I’m quite satisfied with the answer I have. I hope this series of posts gave you a bit of insight into the nature of money.



Keith Patarroyo

My research interests include Hierarchical Assembly, Computational Design and Digital Fabrication.