# The Abundance Economy
Your guide to preventing dystopia and transforming the economy
### **THE BOOK**
The book is available to read online for **free** (or as a [Pay-What-You-Wish](#support) model). It can also be downloaded as **PDF**, or purchased on [**Amazon**](https://www.amazon.com/dp/B0CV6X6K1B).
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[Introduction](/introduction)
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### SUPPORT
Support our work via [**Venmo**](https://venmo.com/?txn=pay\&audience=public\&recipients=Abundances\¬e=Creating%20Abundance) or **Ethereum**: [**abundancedao.eth**](#user-content-fn-1)[^1]
All proceeds go toward developing the Abundance Protocol and realizing the vision of an Abundance Economy.
The Abundance Economy - book cover
[^1]: 0xc6FD734790E83820e311211B6d9A682BCa4ac97b
# Introduction
Suppose you’re in a system that evolved over thousands of years. A system that creates opulence out of scarcity and efficiently uses limited resources. It allows millions of people to coordinate their economic activity, and promotes productivity, innovation and enterprise. It is by no means perfect but it is the most powerful and effective coordination mechanism on the planet. And then, over a very short time period, the system begins to show cracks. Crises are emerging everywhere. They build and compound on each other.
When you look for the causes for these crises you discover that they are not external to the system. Rather, they are produced by its own workings. What's more, the crises didn’t result from the system failing, but from it working as intended. As the crises grow, they begin to threaten the very existence of the system and — if that wasn’t enough — of the society you live in.
The system in question is our current economic paradigm. If the crises are intrinsic to our system, the question then is what can we do about it? How can we alter our current trajectory toward dystopia and put ourselves on a path to sustained growth and abundance?
# Chapter 1: The Scarcity Paradigm
Take a look at the world we live in - the internet, AI, smartphones, airplanes, satellites, skyscrapers and the rest of the marvels of modern life. All these were completely alien to our great-great-grandparents. Yet, we do not have any more resources than they did. Come to think of it, we don’t even have more resources than our Stone Age ancestors. The amount of resources on earth simply didn’t change since the dawn of humanity. Perhaps we have a bit more meteor dust, but that’s about it.
With the same resources as our Stone Age ancestors we were able to create everything we see in the world around us today. What sets us apart from our Stone Age ancestors however is not the amount of resources we have but how we use them. We use the same resources much more efficiently.
We as humans are innately curious. We want to explore. Learn about the world around us. Tinker. Try new things. Go where no one has gone before. Leave our mark on the world. This intrinsic motivation propels us forward. A fitting name for the first economic paradigm that drove human progress could therefore be the Curiosity Paradigm.
It is easy to imagine how thousands of years ago progress was made toward where we are today through this Curiosity Paradigm. Over time we learned more about the world around us. We learned about the properties of materials, about agriculture, engineering, extraction and refining techniques, and so on and so forth. By putting this knowledge into practice we progressively used earth’s resources more effectively.
This paradigm held for countless millennia. But over time things got more complicated. And troublesome. Our knowledge increased, technology progressed, human communities grew, and so did the need for more advanced tools. But curiosity alone was not enough to produce these tools.
If you only need one person to produce a tool then not much is required for economic activity to take hold in a community. It’s really quite simple. People in such a community can just barter for goods directly. Alternatively, they can record a debt every time they provide a product or service to someone else. Later on, when the other person provides another product or service of comparable value in return, they can cancel the debt.
Personal debt recording meant that everyone in the community had to know each other in person. If people bartered that too could work. It was simple enough to practice reciprocity in a small community or village. But bartering or using personal debt couldn't work on a larger scale or in a complex economy. Doing so could grind such an economy to a halt.
The question though is how do you practice reciprocity at greater scales? Or, what happens when more people are needed in the chain of production? When you need some people extracting materials, others refining them, yet others fabricating different parts of a tool?
The more complex the tools get the better we need to coordinate people's production efforts. Intrinsic motivation just isn’t enough anymore. Bartering or recording debts also doesn’t scale.
Charismatic leaders could maybe inspire people to work together, but that too can only take you so far. Such leaders could motivate a small tightly-knit group to produce goods for their community but not much beyond that. And certainly not for prolonged time periods, that simply wouldn’t work. After all, people need to satisfy their basic needs. If their work doesn’t help satisfy those needs, they will eventually abandon it. Or worse – rebel.
Perhaps another option is to install a powerful dictator to oversee and micromanage every aspect of the chain of production. But then you'd need a centralized authority to identify every process, component and subcomponent that needs to be produced and to command their production. And since there is no effective incentive structure for people in such a system the task for the dictator would be overwhelming. You'd need people to perpetually monitor any slack throughout the network. You can hope that people are motivated to produce from their intrinsic motivation, but what if they lack such motivation? What if they don't see the benefit of producing or innovating?
Without having an economic incentive to do so you'd need the dictator to perpetually go through every part of the chain and force people to produce more, to innovate, to fill any voids in the system. And that's just for one product. Imagine the dictator having to do this for every product in the economy. That doesn’t sound like a very lucrative job.
What’s worse, the number of people needed to run such a bureaucracy would likely eclipse those who actually do the work. It would simply be impractical if not impossible.
\* \* \* \* \*
Every society, in every time period, has its share of realists and of idealists. Everyone else falls somewhere on the continuum between these two extremes. Idealists focus on what is possible. On what can benefit the common good and make the world a better place. They are driven by curiosity, and less concerned about their own economic interests.
Realists on the other hand focus on what works. They want to understand how the system functions so they can make the most of it to benefit themselves and their families. They are less concerned about the world and believe that if everyone focuses on their own business other problems in society will take care of themselves.
The economy needs both types of people to move forward. Without realists nothing will work or get produced. We’d just have a society of utopian dreamers where little gets done. Without idealists society would stagnate. Nothing new or exciting would ever get created. Nothing would change. When both realists and idealists work together, society can be productive and innovative at the same time.
But what happens when the gap between what exists and what is possible is too great? When what works falls far short of what is possible, but no one knows how to get what is possible to work? When economic self-interest and the common interest diverge. That is when tension between realists and idealists starts to grow. Instead of working together they’re in conflict with each other. The way to resolve this conflict is to find a way to bridge between what works and what is possible with a new paradigm.
Through the Curiosity Paradigm society managed to produce complex tools with the same resources as their Stone Age ancestors. But the same economic paradigm that led to this social and economic growth now began to show signs of strain. To satisfy their curiosity and needs people needed more advanced tools. They had all the knowledge and resources to produce these tools. They just didn’t know how to get people to work together and produce the tools efficiently.
Just like resources were scarce, so was labor. But human wants remained unlimited. And so, a new economic paradigm was needed. A paradigm for scarce resources and scarce labor – a Scarcity Paradigm.
The historic record remains silent on how bad things got before this new paradigm emerged. Was there much societal strife? Were the realists and idealists at each other's throats? Did previously peaceful communities start to fight over resources or subjugate parts of the community? Did strongmen arise to quell the unrest? Or perhaps the transition was seamless. Maybe this new economic paradigm emerged before society turned on itself. We simply don’t know one way or another.
The historic record shows that a Scarcity Paradigm did emerge however, and it was made possible thanks to the introduction of money as a medium of exchange.
What in the world is a “medium of exchange”? A simple way to think of it would be the following: if we take any two scarce products in the economy we can determine an exchange rate between them (i.e., 3 apples for 7 potatoes, 20,000 apples for a car, and so on). We can then conceive of a common denominator that all products and services in the economy can be measured against. This denominator can be used as a unit of account throughout the economy.
What then would make a good medium of exchange? At the most basic level, it would have to be scarce so that it can correspond to the economy’s unit of account. After that it would need to have properties that allow it to facilitate exchange of value in the economy; it would have to be easy and convenient to transact with, portable, divisible, durable, and interchangeable. Obviously it should also be difficult to counterfeit.
Anything could theoretically be considered a medium of exchange – just not necessarily a very good one. But the more closely a medium of exchange follows the criteria described the better it would be in facilitating trade, and therefore the better it would be for the economy.
Money allowed us to replicate what worked in small communities and villages but much more efficiently and on a greater scale. It also allowed us to scale reciprocity beyond village relations.
In the village people reciprocated in the goods they provided to each other. But there was no general agreement on the value of any good, and therefore no easy way to tell which goods have demand in the village. Individuals could exchange goods if they mutually agreed the goods were of comparable value. Or, if people knew each other well they could record a debt for a good and be compensated at a later date.
The new economic paradigm created a dynamic where value could be assigned to goods throughout the economy. What determined the value of a good in this system? Their relative supply and demand. The more demand there was for a good the more it was valued – and vice versa. Conversely, the more scarce the good was – the smaller the supply – the more it was valued.
With money you could scale reciprocity. Instead of exchanging goods directly, you could use money as a medium of exchange. Anyone can reciprocate for a good with money, even complete strangers. Especially complete strangers.
People could exchange the products of their labor for money, which in turn gave them access to the products of other people's labor. This meant that if they wanted access to more products, they simply needed to produce more goods that other people wanted. And so, access to goods was linked to productivity. Which meant that people could be incentivized to produce based on their own economic self-interest.
The Scarcity Paradigm could also determine what people valued, at scale. Based on the demand for different goods in the community, producers could calculate their expected return from producing the goods. They could then figure out how much scarce labor and resources are needed for the production of different goods. The incentive to produce what people valued then led to more efficient allocation of scarce labor and resources.
So the new economic paradigm helped determine what people valued, and allowed complete strangers to transact. It thus drove greater economic activity than was ever possible before. But how could this paradigm solve the problem of getting large groups of people to work together and produce complex tools?
Since the paradigm created an incentive structure based on economic self-interest, people were motivated to produce whatever there was demand for in the market. But demand for a product didn’t just mean demand for a final consumer product. Any producer in such a system could now simultaneously act as a consumer. A consumer of what? Of the products from the previous stage in the production process. Then the producers of the previous stage could likewise be consumers of the products from the stage prior to that, and so on.
This meant that no matter how long or complex a chain of production got, you could always subdivide it into smaller units, and each stage of production could then be the consumer of the products of the previous stage. The laws of supply and demand applied in each stage of the production process.
In theory, such an organization structure could work as long as the process was additive; as long as the producers in each stage could sell the products of their labor for a higher price than what they paid for the inputs to their work. If the same was true in each stage of production, you could then have a well-functioning chain of production.
What’s more, this process had effective feedback loops to drive improvement and innovation. The process was self-correcting. As long as there was an opportunity to make money by improving any stage in the production process, there would be entrepreneurs who step in. On the other hand, if producers at any stage were doing a subpar job, there would be competitors wishing to replace them.
If a work post was necessary for the production process but the compensation was inadequate fewer workers would apply for the position. As the employer was willing to pay more, demand for the position increased.
Likewise, people had the economic incentive to acquire the knowledge and skills needed to be a part of this process, and to meet the demand for the skills needed in the market. The paradigm thus not only used existing scarce labor and resources effectively, it also motivated people to acquire skills, and to be productive and entrepreneurial, thus making the system more efficient as a whole.
\* \* \* \* \*
The principles that applied thousands of years ago to a relatively simple chain of production involving a dozen people also apply in the modern world. The difference is that now our supply chains can involve hundreds of thousands if not millions of people.
Think about how many people are needed to produce something like a smartphone. You need people working on the software, hardware engineering, R\&D, testing, quality control, procurement, assembly, manufacturing, logistics, sourcing, all the way down to raw material extraction. And that's only a fraction of the processes.
Components have subcomponents that people need to engineer and manufacture. Office buildings need to be designed and built. Factories need to be configured with manufacturing equipment, which also needs to be engineered and produced. Personnel need to be educated and trained. Manufacturing and extraction techniques need to be developed and refined. Material properties need to be researched. You also need to develop redundancies, alternative production methods and component suppliers, and so on. Because if any link in the supply chain is broken the product cannot be manufactured properly.
On top of that the production process cannot stay still. Every aspect of the network must continually improve. The process has to respond to consumer needs, to competition with other producers, to availability of materials, and to labor conditions.
While our chains of production grew exponentially over thousands of years and became vastly more complex, the underlying dynamics of the Scarcity Paradigm remained the same.
Does that mean that our economic system hasn’t changed in thousands of years? Of course not. As the Scarcity Paradigm emerged thousands of years ago we only had a vague notion of what money is and how it could be used. It took hundreds if not thousands of years of trial and error to get marginal benefits from it. But over time our understanding of the system grew and our economic outcomes improved. Though our economic system surely advanced over the years, the principles at its core remain the same today as they were thousands of years ago.
Every individual in the system is still motivated to produce, innovate, and work with others for a common economic goal. While some of the people in the network may be doing their job because of some higher purpose, many of them do so simply because the money they get lets them feed their families and gives them access to resources.
Every stage in the vast supply chain still uses as inputs the products of the previous stage. It then produces services and products to be used in the next stage of production until a final product is sold to customers. The process still needs to be additive, and the return from each stage is still determined by market forces. The system’s feedback loops work the same as before also, but now they apply to vast networks and to millions of people.
What makes this approach so powerful then is the fact that it is scalable. It creates reciprocity at scale. It can thus coordinate the actions of thousands and even millions of people. It results in a vast self-assembling network. A network that can also self-correct in response to changing conditions.
And what works in the formation of one product network similarly works for the production of any consumer good or service throughout the economy — no matter how big or small.
The mechanism therefore not only coordinates the actions of millions of people in complex networks in society today, and is responsible for all the prosperity we have today, but also promotes the efficient allocation of scarce labor and resources throughout the economy. This is what makes money the most effective coordination mechanism on the planet.
# Chapter 2: The Problem with Markets
The economic paradigm that worked so well for thousands of years is now starting to fail us.
Once again we seem to be facing a familiar challenge, but this time it is on a much greater scale. Our knowledge is increasing, technology is advancing, human societies dominate the planet, and yet we don't seem to know how to coordinate our efforts at the scale we need. Nor do we use labor and resources as efficiently as we could.
The gap between what exists and what is possible is growing. We have all the resources we need to create the conditions for human flourishing, yet the results we get fall far short of our potential. Instead of creating abundance and prosperity our paradigm now creates conflicts and crises.
We have social networks with billions of users, but instead of getting more empathy and understanding between communities, society is polarized like never before. We have greater access to information than at any time in our history, but instead of having an informed populace we don't know what news sources we can trust. On top of that, science, news media and democratic institutions are facing an unprecedented crisis of trust.
Automation can potentially create incredible abundance in society, yet we’re weary that it will create mass unemployment and lead to economic collapse. AI is getting closer to providing humanity with access to superintelligence and greatly extending our capabilities, yet we fear it could subjugate or destroy human civilization.
What's worse, we can't say that these fears are unwarranted. Such scenarios are not improbable given the current dynamics in our economy.
But how did we get to this point? If the Scarcity Paradigm brought with it the most effective coordination mechanism on the planet, why are we again in a situation where we cannot coordinate scarce labor and resources at the scale we need? Is the Scarcity Paradigm nearing the point where it can no longer drive human progress? Or worse, is it starting to undermine it?
The problem lies with two fundamental and interrelated flaws of the Scarcity Paradigm. These flaws were always a part of the system. The difference is that previously their effect was marginal. In the digital age their effect is growing exponentially and becoming substantial. Maybe even overwhelming.
What are these flaws? Why are their effects so different in the digital age? The main flaw in our economic paradigm is that while there is an effective feedback loop in the market for the creation of products and services, no such effective feedback loop exists for how these affect the rest of society. There is no effective feedback loop to incentivize creating public goods or to disincentivize creating negative externalities.
What does an effective feedback loop look like? We need to look no further than the supply and demand of goods and services in the market. If you have a product that benefits customers they would buy it, and you would get money in return. You can then use that money to produce more of the product and sell more of it to customers. If on the other hand the product you’re trying to sell harms the customers, they wouldn’t want to buy it. If customers don’t want to buy your product you don’t get money for it, and then you won’t have the funds to produce more of it. Producing a good that more people want, or that people want more of, results in greater returns. So producers are always incentivized to improve their product and to make it attractive and valuable to more customers.
This is a basic feedback loop between consumers and producers that incentivizes creating the products and services that the consumers in the marketplace demand. If there is more demand for a particular product this incentivizes producers to put more resources into making more of that product. Resources and labor are then allocated throughout the economy based on the demand for various goods by the population. We’ve already seen how this mechanism is capable of producing coordination between millions of people in incredibly sophisticated supply chains.
Notice also that the feedback loop is intrinsic to the process. It doesn't require any person, organization or government agency to track how much customers like products and then reward or penalize producers accordingly (or anything of that sort). Such a process would be too cumbersome. It would also likely require an incredible amount of resources, making it extremely inefficient. But there is no need for any of that. If consumers and producers use money as a medium of exchange, the feedback loop works automatically. And so we have an effective feedback loop for products and services in the market.
The problem though is that what goes into the production function is more than just the feedback loop between consumers and producers. It also includes the feedback loop for what harms society (aka negative externalities) and the feedback loop for what benefits society (aka public goods). When a producer decides on what to produce they need to take into account all of the above.
Let's imagine then what would happen if there were an effective feedback loop for all of these. How would a producer decide what to produce?
First the producer would consider how they can maximize their impact on society. Then they would look at how to avoid harming others with the product. By taking these into account the producer would be able to get the most return for their work.
If the producer creates a good that has a greater impact on society they would get a bigger return. This would incentivize them to invest in ways to benefit society even more. Conversely, if the good has a minimal benefit to society the return will also be proportionate.
On the other hand, if a good harms society. If it results in negative externalities, the producer would suffer losses. This would incentivize the producer to do their utmost to eliminate the negative externality so that they can recover their losses. With more harm to society the producer will suffer greater losses, thus increasing the urgency to eliminate the harm.
Then, after taking into account the feedback loop for products and services in the market, and the feedback loops for impact and harm to society, the producer would come out with a good that minimizes harm to society, maximizes impact and benefits customers.
At least this would be the case if we had effective feedback loops for public goods and negative externalities. We don't.
Instead we only have an effective feedback loop for products and services in the market and no effective feedback loops for public goods or externalities.
There is no real incentive to maximize impact on society since doing so gives no economic value to the producer. Meanwhile, not only is there no benefit to not produce externalities, but there is an economic incentive at times to produce those. By offloading costs onto the public or the environment, the producer can lower their own production cost.
Does this mean that there is no benefit at all for contributing to the public good and no detriment to creating externalities? Certainly there may be some social benefits to public goods (and cost to externalities). A producer may build their brand or get public goodwill for producing public goods. Their brand may also suffer for public harm. But then this becomes a question of perception; how good the producer is at advertising their supposed virtuousness, and concealing their vice? At the end of the day the producer will have to decide if the economic benefit outweighs the social cost.
Too often it does. Especially when the producer purposely antagonizes one group of people to gain favor with another. This strategy works because a producer does not need every single person on earth to buy from them. It’s enough to have a large base of support. Antagonizing others can galvanize such support and bring more sales. This is yet another example of negative externalities; using divisive messages to polarize people for economic gain.
Of course not having effective intrinsic feedback loops doesn’t mean that there are no feedback loops at all. The main mechanism we have to incentivize public goods production and disincentivize externalities is government. Government can fund projects that are in the public interest, and can penalize companies for public harm or environmental damage.
How effective is government at doing either is questionable. What is clear however is that government is extrinsic to the process. It requires bureaucrats to track how much damage a company is doing to the environment, or analyze the expected impact for funding any given project. These things require resources and labor. The more complex the economy is, the more resources government requires to regulate it. Such a process is thus inherently inefficient.
At the same time government needs legitimacy from the public to operate effectively. Without legitimacy the public would want to curb the government’s ability to fund projects or regulate businesses. This would weaken the already fragile feedback loops that we have.
And this is without even considering that government has its own set of problems, both internally and in relation to the market; the bureaucratic process tends to be inherently inefficient and wasteful. Depending on how government is structured, and whether it has effective institutions to maintain a balance of power, it may lack legitimacy from the people, which would in turn affect people’s confidence in how government funding is allocated or how fairly industry is regulated.
Elected officials also have perverse incentives in funding distribution. Instead of considering what would be most beneficial for the people in the long term, they may favor their allies and supporters or benefit their near-term reelection prospects. For that reason, government may not be impartial when it needs to regulate cases of negative externalities in the market. Conversely, the more money and power businesses have in the economy the more influence they may exert on policymakers. It is also evident that the more involvement government has in the market — both in regulating negative externalities and in allocating funds – the more its perverse incentives are amplified.
\* \* \* \* \*
The dynamics we’ve described so far are not new. They’ve been with us from the very beginning of the Scarcity Paradigm. So what is different about the present moment? How did the digital age change this dynamic?
For that we have to look at what realists and idealists care about in the economy. Let's go back to our production function. The function includes the market mechanism, public goods and negative externalities. This is of course a somewhat simplistic function, but it is sufficient for the point we're illustrating.
What do the idealists in society want to measure? They want to know the overall impact of economic activity on society. The function tells us this impact by taking the benefit to customers of all the goods produced, adding to that the total benefit to the public and subtracting from it the effect of all negative externalities.
What do the realists in society want to measure? They want to know if producers will be profitable. The function gives us this information also. For that we only need to make sure the benefit to customers is positive.
For most of human history there was much overlap between the functions that realists and idealists cared about. That’s because the effect of negative externalities was always understood as inevitable, and was at least partly manageable through government regulations. Meanwhile, for most of the time, and for the vast majority of people, maximizing impact simply meant producing more goods or services.
It’s not that it was impossible to impact society in ways other than producing goods and services. It’s just that those other ways were mostly inaccessible to most of the population.
The portion of the population that could meaningfully contribute to advancing science, engineering, and so on was negligible. It is not just that people didn’t have access to books or to the latest scientific publications of the time to be able to contribute. For most of human history most of the population couldn't even read.
As long as the focus of most of society was on producing scarce goods, the effect of public goods on the production function was minimal. The gap between realists and idealists was thus small as well.
This has been the case when most people worked in agriculture. It’s also been true throughout the industrial age. But what happens when this is no longer the case? What happens when most people can have more impact on society by creating public goods than by producing scarce goods? And more importantly, what if there is no way to make money from creating such goods within the market? This is the reality that the digital age has created.
The Scarcity Paradigm worked so well because it allowed people to exchange the fruits of their labor for the fruits of other people's labor through the medium of exchange. This created a powerful coordination mechanism in the economy that could scale the production process to millions of people. It also created efficiency in the use of scarce labor and resources.
But this process only works as long as the fruits of people's labor are scarce. And as long as they are exchangeable. That’s because while resources and labor are scarce, the fruits of people's labor may or may not be. But customers don't pay for resources or labor, they pay for products and services. They don't pay for the inputs, they pay for the output. So if the output is not scarce, and if it cannot be exchanged, then it has no exchange value in the market — no matter how valuable it is to society.
The digital age created a new class of goods – abundant goods (in the form of digital content). These are goods that require limited labor to create, but are then accessible by virtually everyone. These goods also effectively don't diminish with use, since each good requires practically no resources or cost to store or distribute to billions of people.
Abundant goods still need some infrastructure and energy to maintain, so we can't claim that they are perfect public goods (goods that are by definition nonexcludable and inexhaustible). However we can say that they are nearly perfect public goods. Certainly more so than any kind of good that existed before the digital age.
What's more, as technology improves over time abundant goods can be stored and distributed even more efficiently, so such goods continually use fewer resources and are approaching perfect public goods status (though technically they may never reach it).
From a pure economic perspective this creates a massive headache. Economics is essentially about using scarce resources efficiently to benefit society. What then is a more efficient use of scarce resources than creating an abundant good? A good that requires a fixed amount of labor, is accessible to all and does not diminish with use (or at least nearly so)? Such a good would surely use scarce labor efficiently, wouldn't it?
Not exactly. Just because something is an abundant good doesn't in itself mean that it's beneficial to society. No more than being a scarce good in itself means that the good is beneficial to customers. And if something isn't beneficial to society, how can we claim that it uses resources efficiently? Perhaps the 'good' label is somewhat misleading here.
Being an abundant good only means that its effect on society can be orders of magnitude greater than traditional scarce goods. But this effect can be either positive or negative. If for example an abundant good is in the form of harmful misinformation, that can negatively affect the health or well-being of millions, or cause extreme social polarization, then it's obviously not beneficial to society.
The question then becomes what impact these goods have on the economy and on society. Obviously, the goods that create the greatest positive impact on society are the ones that use labor in the most efficient way. The ones that result in the most harm to society are the least efficient.
\* \* \* \* \*
Abundant goods did not alter the incentive structure of the Scarcity Paradigm. They did however amplify the effect of both public goods and negative externalities on the economy.
If before people had little incentive to create public goods, that didn't change with the introduction of abundant goods. But now the potential impact of such goods has increased exponentially. The problem though is that it mostly remains just that: an unrealized potential.
Surely people still produce such goods, but that goes back to their need to create and explore – to the Curiosity Paradigm. They just don't create them at the scale needed for our economy.
At the same time, people have just as much economic incentive to produce negative externalities. But now negative externalities have a substantial effect on society. An effect that is growing over time as technology advances.
Certainly it would be an order of magnitude more efficient to create an abundant good with a positive impact on society than using the same amount of labor to produce a scarce good that is only accessible to some people and that diminishes with use.
Yet the abundant good in this case, unlike the scarce one, would have no value in the market. Since the abundant good is accessible by everyone, it has no exchange value. Without exchange value it cannot be priced in the market.
To illustrate this point let’s imagine you have a researcher who wants to maximize her impact on the world. After years of work she discovers a cure to a particular disease that can extend the lives of millions of people. Now imagine two alternative scenarios. In the first scenario – let's call it the Scarcity Scenario – the cure is in the form of a pill, and in the second scenario (the Abundance Scenario) the cure is in the form of a combination of carefully measured ingredients that are readily available in stores. In both scenarios the effort to discover the cure was identical. In both scenarios the cure's value to society is the same (identical therapeutic results). The only difference? In the Scarcity Scenario the scientist can sell the pills. In the Abundance Scenario the product is mostly in the form of knowledge, which is much harder to exchange and therefore to profit from.
Obviously the researcher can still write a book about her discovery and make some money from that. If she includes the cure in the book however, anyone would be able to freely copy and distribute that information. And once the information is out, enforcing any kind of intellectual property restrictions would be practically impossible. This suggests that the Abundance Scenario researcher will still have drastically lower returns. It’s possible that she won’t even be able to cover the cost of the research in that case.
To drive this point home, we can modify the scenarios somewhat. What if in the Scarcity Scenario the pill could only extend the lives of thousands instead of millions in the Abundance Scenario. Maybe it is half as effective at that. Maybe the pill also has significant side effects, while the discovery in the Abundance Scenario has no side effects. Maybe the pill production process releases toxins into the environment. Regardless of such differences, the return in the Scarcity Scenario is still likely to be much higher than in the Abundance Scenario.
So it’s possible to have abundant goods that have a tremendous impact on society, and use labor extremely efficiently, yet have little to no value in the market. And at the same time have scarce goods that have relatively little value to society, or don't use labor nearly as efficiently, and yet have a lot more value in the market than abundant goods.
This means that there are economic incentives in the market to create scarce goods that don't use resources efficiently, and at the same time almost no economic incentive to create abundant goods that use resources extremely efficiently. The implication here is that the more the potential impact of the abundant good on society the more inefficient is the market at allocating resources.
Of course, the Scarcity Paradigm's inability to capture the value of public goods was always there. But as long as public goods were rare their effect on the economy was marginal. The emergence of abundant goods in the digital age completely changed this dynamic.
You can imagine the growing rift between idealists and realists that the digital age created. Realists would say that the impact of abundant goods is irrelevant and that if people want to create impact they should do so through the market; or at least they should not interfere with the market that historically worked so well.
Meanwhile, idealists see the potential that abundant goods can have on society, but realize that these cannot be monetized in the market. This leads them to be frustrated and disillusioned in the economy.
For a growing segment of the population maximizing impact no longer means greater production of physical goods. For them, impact maximization means making meaningful contributions in medicine, technology, science, engineering, journalism, software development, the arts, and so on. All areas that can have tremendous impact on society and where the contribution is an abundant good. And yet, such contributions have no comparable exchange value in the market.
When everyone has unrestricted access to the digital content there is no way for the content creator to monetize that content based on its value to society. Realists may say that you can still get revenue from advertising, but getting paid from the popularity of content is very different from getting a return on the content’s value to society. A scientific breakthrough may have incredible value to society but may only catch the attention of a few dozen people. This is especially true if the breakthrough requires advanced knowledge of the science. On the other hand, an inane tweet by a celebrity may generate millions of views. Is the inane tweet truly more valuable to society?
Content creators may also paywall their content. This can increase their returns but it will come at the expense of how much impact their content can have if it were accessible by all.
This is why our economic paradigm is starting to crack. The digital age has created a divergence between impact maximization and profit maximization in society. With automation and greater technological progress we’re only going to see this divergence grow.
So what happens when ever larger segments of the population have to choose between their economic self interest and making a meaningful impact on the world? For once, the inefficiency of labor in the Scarcity Paradigm will become more apparent. More and more people will see that our economic paradigm is not working for them. That we have all the tools to make the world a better place but are held back by our economic system. This is likely to breed dissatisfaction and resentment. And that’s before we even look in greater depth at the other major flaw of our economic paradigm – negative externalities.
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As previously mentioned, negative externalities are costs that others incur from the economic activity of market participants. A common example of a negative externality is pollution. A producer may pollute the air or water and harm nearby communities through the manufacturing process of products. While the producer gets money in exchange for the products, the nearby communities get none of the revenue. Instead they may suffer from adverse health effects, crop failure, and so on.
Now it’s possible that the nearby communities will petition the local government to intervene and demand compensation from the producer, or even to shut down the factory. It’s also possible that the producer strategically placed the factory in an area where pollution regulations are lax, where local government is less effective, or where government officials are more corrupt.
The point is that, instead of incurring costs by curbing pollution, producers can offload some of these costs onto others. They can lower their production costs through negative externalities. And so, they have an economic incentive to create negative externalities.
By offloading costs onto others market participants stand to reap greater rewards. This benefits both producers, who lower their production costs, and consumers, who get cheaper products. Those affected by the negative externalities often have no say in the exchange between consumers and producers. There is simply no effective feedback loop in our current economic paradigm to solve this problem.
While in some cases government may intervene to restrict such practices, in many cases intervention is not as simple or straightforward.
Just like with impact maximization, negative externalities are also not a new phenomenon. They’ve been with us from the dawn of the Scarcity Paradigm. So why is this a bigger issue now?
The difference is again in the magnitude of effect on society. Whatever was the effect of negative externalities before, now it is vastly greater. And growing. The Digital Age has magnified this effect in two important ways. It weakened society's ability to confront those who produce externalities, and it created a new business model that thrives on tearing society apart.
How has the Digital Age weakened our ability to confront externalities? The short answer is that, while the economy became globalized, requiring stronger institutions that act as a check on externalities, the Digital Age is making it incredibly easy to delegitimize and weaken all these institutions.
You see, in the past negative externalities could largely be geographically localized, and isolated to the production process. Which meant that they were easier to regulate and had a smaller effect on people's lives. This is no longer the case because of globalization and abundant goods.
The global economy is now deeply interconnected. We have transnational corporations with vast supply chains and markets on every continent. While these corporations have a fiduciary responsibility to their shareholders, they have virtually no responsibility to the communities where they operate. Or, for that matter, to the planet. What were once local externalities are now globalized.
But aren't there forces that can push back against corporate externalities? Governments can still regulate corporations. News media can expose destructive practices and corruption. Scientists can show the data of material harm to the public and environment caused by companies. Public sentiment can affect sales, and therefore affect the profitability of offending companies.
With all these institutions in place you’d think that there are enough elements to keep a check on those who create negative externalities in the economy. Surely. But this is where things become complicated. Institutions derive their legitimacy from public trust. Governments need public support to be effective. But what happens when institutions lose the public’s trust? What if the public is deeply polarized? Without public trust and support, government and institutions cannot act as an effective check against externalities. And if the public cannot get a clear sense of the situation, public sentiment will be muddled and won’t be effective against the offending parties.
This is exactly the dynamic that the Digital Age has created with abundant goods. We’ve already seen that abundant goods can have a positive or negative effect on society. We’ve also seen that they can amplify those effects far beyond anything a scarce good can do. What happens then when this powerful amplification tool is used to undermine the credibility and legitimacy of government and institutions? What happens when it is used to sow social discord? By undermining public trust in institutions and creating social polarization the Digital Age has made it incredibly difficult to deal with externalities.
What’s more, corporations don’t even need to bother with actively delegitimizing institutions or sowing distrust in society. The perverse incentives in the market drive people to do that work all by themselves.
Because the market cannot value abundant goods based on their impact or benefit to society, the common approach has been to monetize a scarce resource that does have exchange value: people’s attention. If digital content gets more attention, advertisers can serve their ads to more people alongside that content.
One problem with this business model is that there is no obvious relation between the quality of content and its popularity. Because quality has no value in such a system the only economic incentive is to produce attention-grabbing content.
Content that grabs the most attention tends to be emotionally charged: surprising, outrageous, divisive, or hateful content. Such content tends to generate a lot more attention than emotionally neutral or factual content. It also takes a lot less effort to generate factually-inaccurate outrageous content than well-researched quality content , making it easier to monetize.
So what happens when you have billions of people from all over the world connected to social media? While the platforms make billions in ad revenue, society suffers the negative externalities. Content creators always have the incentive to exaggerate, to distort, to create drama and conflict, and to sow mistrust. These are the things that grab people’s attention. And these are the things that the algorithms are designed to boost.
Meanwhile, billions of people on social media are endlessly inundated with outrage porn, misinformation and sensationalism. These are the perverse incentives that result in social polarization and a crisis of trust in media, science and institutions.
If the populace is polarized, if people can't agree on scientific facts or basic truths, and if institutions lose legitimacy, then governments have a weaker mandate to act. And when the government is ineffective against those who benefit at the expense of the public interest, it further loses public support. It's a vicious cycle of inefficacy.
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Even if we disregard for a moment how social media degrades society's ability to confront corporate externalities, the harm to society caused by social media's perverse incentives is in itself enormous.
If before corporations had the incentive to pollute the environment because it reduced production costs, in the Digital Age people have the incentive to produce divisive and outrageous content because it gets more attention. They tear society apart for clicks.
Externalities moved from mostly being in the physical realm to also being in the psychological and sociological. They now permeate every aspect of our lives. They harm everything from our children's self-esteem to our sense-making ability and our politics.
People on social media always have the incentive to present themselves as more successful, more wealthy, joyful or leading more exciting lives. At the same time, the algorithms want to push whatever content gets them more attention.
So think about the impressionable young guys and gals who are algorithmically flooded with reality-distorting images and reels. They get a compounded distortion of reality; people appear to them to be oozing with success and always having a great time, and they appear more often in their feed. Is there any wonder then that young people are feeling inadequate? That while kids are spending more time on social media, loneliness and social isolation is growing?
Because what matters is attention, not reality, there is always an incentive to diverge from the mainstream view. Not because a real controversy exists, but because that's the strategy that is likely to get the highest return. The mechanism here is quite straightforward. If a thousand people are all saying similar things, the more you diverge from these views the more likely you are to get more attention. Repeating the same claims as everyone else gets you nothing.
This works regardless of the truth of the claims. You'd get a lot more attention saying the earth is flat than if you claim that it is spherical. Trying to debunk conspiracies and misinformation requires a lot of effort. To create new ones you only need an active imagination.
Think of this concept mathematically: let’s say a certain truthful claim is repeated by a thousand people, and 95% of the population believes it. That means that each of the people has, on average, 0.095% of the total audience interested in the topic. Does it make more sense to fight in an oversaturated field for a tiny slice of 0.1% of the audience, or try to capture a 50x larger audience by being the first to adopt the opposite viewpoint? It always makes more sense to diverge and be controversial in the attention economy.
But it's not enough to push the controversy. The next step is discrediting those who disagree with you. What are they hiding? Why don't they want people to know the "truth"?!
What happens then when this dynamic plays out at scale? You get an information environment where nothing is certain. Nothing is knowable. It's not that you cannot get to the facts if you do enough research. When every fact is under attack, and people have an economic incentive to invent more controversies out of thin air, truth becomes increasingly unattainable. It's a war of attrition targeting your ability to make sense of the world. You can either spend all your time diving into rabbit holes or accept defeat.
Social media pushes the loudest, most extreme and controversial voices to the top. Not because these views are better, but because of their ability to garner the most attention. And so society is radicalized. Nuance has no value, it's boring. Instead activists and politicians compete over who can present the most extreme policies. Moderation and level-headedness is replaced by blind populism and radicalism.
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While the incentive to create negative externalities, and the disincentive to create public goods was always there, their effects were mostly negligible on society. This has been the case for centuries. But the dynamics of the economy are now different.
Thanks to the Digital Age, it is now easier than ever to produce abundant goods. It is now also a lot easier to create negative externalities, and more difficult for society to coordinate efforts to confront them. While the impact of public goods on the economy and society can be enormous, there is still no economic incentive to produce such goods, since they have no exchange value in the market.
At the same time the effect of negative externalities has grown exponentially. Social media can be used to sow discord in society and delegitimize institutions, thus removing every effective measure society has against corporate externalities.
On top of that social media creates a perverse incentive to benefit from tearing society apart. People benefit from attacking our self-esteem and our ability to make sense of the world. It promotes radicalism and division, and poisons the minds of countless millions of people. Thus, social media makes it increasingly more difficult for people to come together and work for the common good.
Ironically, an economic paradigm that is meant to efficiently allocate scarce labor and resources is failing because it cannot value abundance. As technology improves this trend is only expected to worsen. But how bad can it possibly get? And what can we do to fix the problem?
# Chapter 3: Toward Dystopia
It’s nearly impossible to reverse the process set in motion in the Digital Age. For centuries the Scarcity Paradigm managed to chug along without the need to seriously confront its own flaws. Since the paradigm is based on a medium of exchange, anything that was not exchanged between consumers and producers in the market could not be valued in the economy.
There was always an economic incentive to create negative externalities, since those reduced the cost of production. And there was never an incentive to create public goods, since those had no exchange value in the market. And yet, because both externalities and public goods had a minor role in the economy, these flaws in the Scarcity Paradigm could be largely overlooked.
But that was the case when the effect of public goods and externalities in the economy was negligible. What happens when their effect becomes significant? That came about with the emergence of abundant goods in the Digital Age. Abundant goods, in the form of digital content, require fixed labor and resources to produce, but then can be accessed by anyone, have minimal storage costs, and don’t diminish with use. And while their effect on society can be either positive or negative, they have an exponentially larger effect than that of scarce goods.
Since abundant goods are inherently accessible by anyone, they lack an exchange value in the market. This means that no matter how much impact such goods have on society, people have no economic incentive to produce them. The impact cannot be monetized.
But how can this be? Don't people monetize digital content all the time? If the impact of abundant goods has no exchange value, how are people monetizing them? To answer that question we have to look at what exactly they're monetizing. And we need to do so with the understanding that only what can be exchanged in the market has exchange value.
Impact itself is not being exchanged in the main business models for digital content today. What is being exchanged then? Access and attention. These are scarce resources and therefore can have an exchange value in the market. Therefore, what’s being exchanged in the subscription business model is access to the content, whereas in the advertising business model it is user attention that is traded.
Number of users and users' attention are scarce goods that can be exchanged in the market. However, they're a poor proxy for the impact of abundant goods.
Sure, you can have abundant goods that benefit society and are profitable, but this is hardly the norm. You're just as likely to have abundant goods that have an immense value to society but are virtually viewed by no one, and thus generate no return. You can also have abundant goods that have no discernable benefit to society but are fantastically popular and profitable to produce. And then you can have abundant goods that are harmful to society but are still very popular and lucrative.
What's more, producing impactful abundant goods likely requires a significant time investment. If these generate no return their producers will end up worse off then when they started. Producing outrageous content, on the other hand, may require nothing more than general knowledge of psychology. It's not even necessary to do a basic research of the facts, as the claims can be wholly made up and the content could still go viral.
If what is popular and what is beneficial were mostly aligned but with some exceptions, our job would be a lot easier. The problem is that the relationship between these is tenuous at best; what we find interesting and what we find important has little to do with each other. A cat video may be interesting to watch but it's not that valuable. A major scientific breakthrough may have tremendous benefit to society but only a tiny group may actually understand it or truly find it interesting.
Because subscribed users or users’ attention are such poor scarce proxies for impact, using these to monetize content necessarily leads to perverse incentives. It motivates people to create popular or attention-grabbing content instead of benefitting society. Such content is often sensational, hateful, divisive, outrageous, or generally harms the common good.
On top of that, creating artificial scarcity by restricting user access to subscribers also leads to economically inefficient results. The reduction in efficiency is self-explanatory; any attempt to restrict access to a beneficial good that can potentially be accessed by billions of people necessarily reduces the impact of the good on society and is thus a less efficient use of resources.
Think about it in terms of resource allocation; when the goal is impact maximization, you'd want more people dedicating more time to those things that are expected to have the most impact on society. You'd also want no time dedicated to anything that is harmful to society.
That is patently not what we have in the digital economy. Instead too many people are spending an inordinate amount of time attacking others, posting misleading, outrageous and sensational content. And they do so because they can make a lot of money in the process.
That's not to say that people shouldn't be free to post whatever they want. They absolutely should be. But why economically incentivize harm? And why choose a business model where scientists and grifters compete for the same scarce advertising (or subscription) dollars? Especially when the grifters have a structural advantage.
And so, despite their immense potential, abundant goods are wreaking havoc on our system; they weaken our ability to confront negative externalities by undermining institutions and promoting social polarization. They also create perverse incentives in monetization because they have no exchange value in the market. There is still no economic incentive to create a positive impact, while the effects of negative externalities are exponentially greater (and much harder to confront at the same time).
What's worse, the negative externalities in our economy are already turning into full-fledged crises. And because our systems are so deeply interconnected, these crises don't happen in isolation. They create resonance. They reinforce one another and intensify. As technology advances negative externalities are likely to have an even greater effect on the economy. Continuing on the same trajectory then will likely drive us towards dystopia or even societal collapse.
So what does this mean for our future? Are we really on a path toward dystopia (or worse)? Or maybe this is just a temporary condition in the market that will correct itself. After all, trends can change.
We've had plenty of doomsayers over the years pointing to an imminent economic collapse, from Thomas Malthus to Paul Ehrlich to Peter Schiff. Yet, their predictions never materialized. Like in the old saying, such economists have “successfully” predicted nine of the last three recessions. So who is to say this time is any different? Maybe at some point, perhaps with a few nudges from government, the efficiency of markets will resolve the dynamics we have today and put us back on the right path. Could it be that our economy is more resilient than we think?
What we're facing today is very different from crises of the past. The issue is not the result of a particular condition in the market or a temporary resource crunch. Nor is it a phase of the business cycle. Today the issue goes to the very heart of our economic paradigm. It is what happens when a fundamental flaw in our economy is struck with a metaphorical technological wrecking ball.
Hoping the problem resolves by itself within the market structure is futile. Fundamental economic flaws that existed for millennia do not spontaneously self-resolve. And so we need to consider our options.
One possibility is to try to roll back the technology, and go back to a time where abundant goods couldn't have such an effect on our economy.
Another option is to look for a new economic paradigm that can capture the value of abundant goods. If we can create effective feedback loops for public goods and externalities we will solve our metacrisis and put humanity on a path to global mass abundance.
And then there is a third option. Maybe we should try to mitigate the damage and hope the fallout will not be too severe. This is definitely the solution with the least sex appeal, but perhaps it could work?
The first option is probably the least practical (or desirable). It is now nearly impossible to put the abundant goods genie back in the bottle. Should we reverse our technological progress? Shut down data centers? Sanction social media companies?
Even if we were to succeed, such a draconian move would set us back by decades and essentially paralyze our economy. It would also likely decimate any trust in government, and create another form of dystopia.
People are simply not going to peacefully accept such a severe reduction in their standard of living. They also would not easily surrender their ability to express themselves and communicate freely. And why should they? Maybe in this case the cure is worse than the disease.
Coming up with an "Abundance Paradigm" is certainly the most desirable outcome, but how do we know such a paradigm is even possible? Maybe this too is a dead end and pursuing it is a futile exercise. And if it is possible, how likely are we to succeed? We don’t even know where to start.
Perhaps before investing precious time into chasing the mirage of a supposed new paradigm we should consider if it is even necessary. What if the concerns about our economy are overblown? Sure we’re seeing growth in the effect of externalities, but can these be mitigated? Maybe the intervention that requires the least amount of resources is best. Maybe containing the effects, combined with smart government policies, can get us the optimal outcome.
With a better grasp of the direction of our economy, deciding on the right course of action will be a lot easier. At least that is the idea. The question then is, how serious is our situation? How much more intense will our crises get, and where is our economy headed?
The problem is that in the digital economy crises reinforce one another. As technology advances they intensify. Consider for example what is happening in the institutions that help us make sense of the world around us: news media and science.
Journalists and scientists have always had to deal with a contradiction. While they purportedly speak for the public interest, there was always misalignment between the public interest and how they got paid. That's because there was no way for them to be paid by “the public” based on the value they are providing.
Any way they get paid in the economy involves a potential conflict of interest. If they make money from donations, who is to say that those donating money are not an interest group promoting individuals who don't truly represent the public interest? If they get paid by the government, do they truly represent the public interest or are they advancing a political agenda? If they are self-funding or rely on wealthy patrons, whose interests are they actually promoting? If they get paid from commercially selling their content (from advertising or subscriptions) they have the incentive to promote what is popular instead of what benefits the public. And so, in every case there is an inherent misalignment between the public interest and the economic interests of sense-makers.
This misalignment was always there. What’s new however is that, because it is now easier than ever to create and publish content online, there is also a lot more competition for people’s attention.
When there was little competition, it was a lot easier for journalists and scientists to focus on the public interest. Those who deviated from this standard could be criticized and ostracized, so there was at least a social and reputational incentive to be more diligent.
But what happens when instead of competing with tens of thousands of journalists, or hundreds of thousands of scientists, sense-makers now have to compete with billions of other people for a slice of the same advertising or subscription money? And what happens when most of these people can be anonymous and a lot less scrupulous about what they’re posting?
You may still have your reputation as a respected journalist or scientist, which gives you a bigger audience than the average content creator. But your competitors have their own advantage in this arena also: volume. For every article that you have to research and fact-check, an unscrupulous blogger could post 10 poorly researched articles. Or maybe a hundred articles that are entirely fabricated. Even if each of their posts gets a small fraction of what you get, those numbers add up. And they significantly dilute your expected earnings.
The attention economy does not reward those who provide the most reliable information. It rewards those who are best at grabbing people’s attention with conflict and drama. But the more you try to grab people’s attention in this way the more you lower your standards and deviate from the public interest. So now each journalist and scientist has a dilemma. Does he or she continue the thankless job to research and report on what’s in the public interest, or focus on what is popular (and profitable)?
The vast majority of scientists and journalists may still choose to focus on the public interest. Yet, the small minority that prefers popularity and profits will get disproportionately more attention than the rest. So what does the public see? Because the less scrupulous journalists and scientists get more attention, they may appear to the public as the majority. Which means that even a small number of unscrupulous individuals can tarnish the reputation of a whole institution. And that is how the crisis of trust in institutions begins.
Now think about the incentives of all other content creators competing with sense-makers for the same scarce attention and advertising money. They have an incentive to discredit professional journalists and scientists, and they want to discredit them as a group. Doing so helps equalize their own reputation, and therefore brings more people to follow their work. These content creators can always point to the inherent contradictions of the business models used by those who claim to work in the public interest. And now the unscrupulous journalists and scientists are making their jobs easier than ever.
Every day these content creators have ample material to work with; they get a steady stream of posts by unscrupulous journalists and scientists illustrating how they are falling short of their stated objective to speak for the public interest. Each such post helps discredit our sense-making institutions a little more. And each time content creators get to pounce on such posts they get more engagement from the controversy. That in itself is a profitable business model.
While unscrupulous sense-makers make it easier than ever to discredit their institutions, content creators have just as much incentive to make up controversies even where none exist. After all, the attention economy rewards clicks, not facts. But when everyone’s credibility is equalized, those with the least integrity benefit the most.
As public trust in sense-making institutions continues to erode – whether deservedly so or not – so does the economic benefit of working in the public interest. So what we get is a vicious cycle; more and more journalists and scientists choose popularity and profitability over the public interest, and then the content they produce is used to further discredit sense-making institutions.
So maybe our traditional sense-making institutions cannot be saved. Perhaps they need to adapt to the Digital Age, or come to some happy balance between what is popular and what benefits the public interest. That may be the case, however, the goal was never about preserving traditional sense-making institutions per se. The goal is to preserve the methods; integrity, rigor in fact finding, and working for the public interest. Whatever form these methods may take is not important. What’s important is that they are preserved.
The trouble though is that even if we give up on trying to preserve traditional sense-making institutions, we’re still nowhere closer to solving the problem at hand; there is still no economic incentive to do work in the public interest and no incentive to preserve integrity and rigor in fact finding. Unfortunately these are superfluous in the digital economy. There is however every incentive to produce content based on its popularity.
The same dynamics that applied to journalists and scientists would still apply to whatever Digital Age version of traditional institutions we come up with; the less scrupulous attention-maximizers have a structural advantage in the attention economy. And their advantage is only growing as technology advances.
These attention-maximizers always have the incentive to discredit those whose work is based on credibility. They get engagement from every post supposedly exposing the hypocrisy of others. This is true whether these "others" are professional journalists or respected content creators. And whether such posts are based on fact or misinformation.
In the digital economy it is simpler (and more profitable) to claim to have integrity than to do the hard work to maintain it. So there is also a constant struggle by everyone else between continually reducing their standards to be profitable or maintaining integrity at an ever greater cost.
There is a constant push by the least scrupulous to "equalize" their reputation with everyone above them. This is done by attacking and discrediting others, not by upholding higher standards. It is also done by attacking our ability to make sense of the world. If we cannot differentiate between facts and lies, those who sell lies can make more money.
The conflict in the digital economy is thus not merely between attention-maximizers and sense-making institutions. It is between attention-maximizing and sense-making itself.
While attention-maximizers certainly follow a profitable strategy, they are hardly the greatest benefactors of the system. So who are those that benefit the most? You have to think about who benefits from an environment where reporting facts has no value, where sense-making institutions are discredited, and where it is getting harder by the day to know what is true.
It is certainly not the public. They are the ones harmed most from a system that degrades their sense-making ability. Those who benefit the most are autocratic leaders and powerful interests. They don't want the public to scrutinize their actions, or be empowered to act. If the public cannot determine the facts, they also cannot do anything to penalize wrongdoing by the powerful.
Of course, the fact that powerful interests benefit the most from the digital economy’s dynamics doesn’t mean that there was some conspiracy by the powerful to foist such a system on the public. That process could have easily happened spontaneously. But once the system was in place those who want less scrutiny certainly benefit from it. And they would do what they can to strengthen and perpetuate such a system.
All this points to the fact that we’re unlikely to see the assault on sense-making reverse any time soon. Unfortunately the trend we're describing is only beginning to gain momentum.
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If greater competition in the attention economy attacked our sense-making ability, the rise of social media brought about a full-on crisis of trust in media and institutions. And if that wasn't enough, it spread the crisis much further, with an all-out onslaught on our sense of community and on the human psyche itself.
While content creators have an incentive to maximize attention, it is social media platforms and search engines that profit the most in the attention economy. What is the incentive structure of the platforms? Just like for journalists, scientists, or content creators, the platforms themselves also cannot monetize the impact of digital content. They get no benefit from serving users with the most valuable content, or the content that would make the most positive contribution to people’s lives. Instead, the platforms mostly use attention as a proxy for value. Subscriptions are less commonly used, but as already discussed, the incentives there are similar.
Platforms and search engines all compete for the same advertising money, and they all want a larger share of that pie. To get that larger share each platform needs to maximize user attention on the platform. The users are not the customers in this equation, they’re the product. The customers are advertisers. And advertisers want their ads showing to the greatest number of people, preferably people who are likely to buy their products.
So how do the platforms achieve their target of attention maximization? We can consider two broad strategies they can use. We’ll dub these the High Road and the Low Road.
In the High Road approach, tech companies try to increase usage of their platforms by providing a superior user experience. They identify misleading or harmful content and flag it to slow its spread. They try to serve users with high quality content and be mindful of their wellbeing.
Such an approach is expensive. There is no simple way to determine if content is harmful or beneficial, so the platforms need to invest into developing such mechanisms. They then need to apply the mechanisms at scale to billions of posts.
Content moderation poses a challenge for tech companies. That is, based on what standard do they determine what is “harmful” and what is “beneficial” to users? Do they moderate content based on some ideology or political leaning? Or perhaps it is based on what is financially advantageous to the company?
Whichever strategy they choose, tech companies cannot claim to speak for the public interest due to their inherent conflict of interest in monetizing the platform. Thus, any standard they set would be criticized. Moreover, since the platforms are proprietary, there is little transparency in the moderation process itself. There is no way for the public to tell if standards are applied equally or if the system is rigged to benefit any group or point of view.
These dynamics are undoubtedly exploited to the fullest by those who are harmed the most from content moderation: attention-maximizers. They can claim that the content moderators are biased, or that the platforms are suppressing their views because they are truth-tellers. There is little the platforms can do to dispel such claims because of how the platforms are structured.
So the High Road is expensive and fraught with difficulties. How about the Low Road? Here the goal is much simpler: do whatever it takes to maximize user attention. The way to achieve this is also a lot cheaper; it is relatively easy for a platform to determine what content the user is viewing, how long the user stays on a particular page or watches a video.
By collecting such user data the platform can easily determine what content is likely to grab people’s attention the most. It can then serve the content to more people. The more data the platform has on user behavior the better it gets at personalizing content and keeping users glued to their screens.
The platform also wants to incentivize content creators to produce the kind of content that would keep more users engaged. It is therefore willing to give a share of its advertising revenue to creators.
We have to remember that users are the product in this business model. They are not the customers. For that reason the platforms don’t care whether the content users view is beneficial or harmful to them. They also don’t care if it has a positive impact on the world or tears society apart. They only care that users are spending time engaging with the content.
The Low Road strategy will certainly get pushback from users (and even some advertisers) who are unhappy with the toxicity of the platform. But that is unlikely to change much in the business model. Perhaps the platform will ban or censor the most extreme voices on the platform. This will give the appearance that the platform is “confronting hate.” It will also allow the platform to maintain its Low Road strategy, albeit with some minor cosmetic changes.
Now serving attention grabbing content and incentivizing creators to produce such content are only a part of the Low Road strategy. The other part is designing the platform itself to make it more addictive; whatever it takes to keep users engaged. For that purpose the platform can employ techniques to exploit users’ psychological vulnerabilities; to make interaction with the platform stimulating, so that users want to keep scrolling, clicking, and viewing content.
So what happens when the High Road platforms compete with Low Road platforms? Because the Low Road approach is much easier and cheaper to implement, it is also likely to get more user attention hours. More attention converts to more revenue, which can then be used to get even more users.
Here again the Low Road platforms have a structural advantage over the High Road ones. Since users are the product in this business model, anything that benefits them is merely a public good that has no value in the market. Whatever grows user attention however, whether it benefits or harms users (or society), is valued in the market. High Road platforms then find it harder and harder to keep themselves afloat. To be competitive they have to adopt more of the strategies employed by Low Road platforms, and give up on expensive strategies that benefit users but have little value in the market.
The result however is a race to the bottom. The major social media platforms are fighting for every click, every view and every minute of our attention. They are looking for ever-more invasive and manipulative methods to keep us impulsively scrolling, and extracting as much data from us as possible. None of this is done for our benefit or with our wellbeing in mind. It’s a cynical competition, but not participating in this race to the bottom means losing users, market share, and, ultimately, profits.
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To gain users and market share platforms need to do everything in their power to stimulate engagement. But who thrives in a digital environment where the most important metric is engagement? You guessed it, attention-maximizers.
If attention-maximizers thrived in the digital economy before, with the rise of social media platforms they became the apex predators. And if before their main area of influence was in sense-making, on social media it spread to every area of our life.
One particularly noxious breed of these unscrupulous actors is the common troll. Since social media platforms algorithmically boost posts based on engagement, few actors are as effective at boosting their own posts as trolls. Trolls are especially good at blowing up conversations by diverting attention from the discussion at hand to themselves.
Through their toxic and abusive behavior trolls generate much drama, which the platform then picks up as a signal to be boosted. As their posts are algorithmically amplified, trolls manage to gain new followers (who may agree with their point of view or just enjoy the drama), which further helps them push their content.
Now think about what happens to online discourse in the process. What happens when anyone, at any time, has the incentive to blow up the conversation and grow their own clout in the process? You get fewer genuine conversations and a lot more trolling, toxicity and abuse.
If this is what clout-based social media does to online discourse, consider what it does to digital communities. Think about what happens when the loudest, most extreme and controversial voices garner the most attention on social media. And what happens when these same voices are then algorithmically amplified by the platforms for profit.
What group dynamics does this create? What effect does this have on every social, ethnic, religious or political group online? It produces a feedback loop that reinforces digital tribalism; suddenly everyone has the incentive to stake more extreme positions to gain influence within the group. Those who present the most rigid views attain more credibility. The more intransigent you are, and the more you double down on your ignorance, the more your stature grows in your digital tribe. 'Clapping back' or 'destroying' the other earns you respect and admiration.
Meanwhile, the system punishes those who dare to admit a mistake or seek mutual understanding. People fear losing followers for having civilized conversations with someone from the rival ‘tribe’ or – god forbid – agreeing with them. Those willing to change their mind on an issue are viewed with suspicion.
Group members hold no genuine or nuanced conversations with people from rivaling tribes. There is no incentive to do so. Instead members view engagement with rivals as opportunities to snipe at them, and signal loyalty to the inner group. That’s how you gain clout, and that is what the system rewards.
The tribes are in a constant state of war. This is not because they have a genuine disagreement. Nor is it a struggle over resources. They're at war because conflict and drama generate clicks, so the ones who get the most attention have the most to gain. They always have a perverse incentive to fight and to escalate the conflict, no matter the social cost. Peace and compromise have no economic value here.
This is how social media promotes extremism and endless conflict. Rather than bringing people together it is tearing society apart; fracturing society into digital tribes, and boosts the most radical views within each tribe. These tribes don’t feel or act like communities. They are oppressive and suffocating. Such tribes encourage groupthink, and don’t tolerate diversity of views. And they are dominated by the same trolls and attention-maximizing egotists who incessantly jostle for clout.
So social media creates conflict and tribalism at the societal level, but what happens at the other end? What kinds of behaviors does it incentivize at the personal level? Because social media algorithmically boosts attention-grabbing content, it rewards those who would do everything in their power to present themselves as more successful, more wealthy, joyful or leading more exciting lives than they really do. They forgo their authenticity, and the ability to genuinely connect with others and enjoy their online experience for growing their clout.
Here too there is a feedback loop at play that rewards make-believe over reality. There is always an incentive to appear as “more” – more wealthy, more joyful, more attractive, and so on. Because regardless of how successful you are, appearing even more successful can help you boost your content.
Does this mean that people on social media can’t be both genuine and successful? Not at all. The point is that this is a numbers game. For every 10 reels or tiktoks of Lambos, how many of the people there actually own the car and how many rent one for a day so that others think they’ve made it? On social media both appear identical, but renting is much cheaper and therefore a lot more prevalent, especially given the incentives.
Now think about what happens at the other end of that screen. Think about the teenager who is incessantly bombarded by images and reels all day. It is impossible for them to know if what they’re viewing is genuine or fraudulent. They know that their clearest path to success in the digital world is by putting on a mask and pretending they're someone else. They can also choose to be authentic, at the risk of remaining obscure.
Social media was supposed to connect us and bring us closer together. At least that’s the story Mark Zuckerberg et al were pushing. Instead those who spend more time on social media seem to be more lonely, depressed and suicidal. Those who choose to be true to themselves and form genuine connections online are put at a disadvantage by the system. Those who decide to play the clout game are rewarded by the system, but can’t enjoy real interactions and have to constantly compete with others in a fake virtual world. Is this truly the vision social media platforms had in store for us?
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Now we're seeing a series of cascading crises, all emanating from the perverse incentives of the digital economy.
Negative externalities are multiplying and significantly affecting society. As technology advances their effects only grow bigger. If not confronted in time many of these externalities can easily turn into crises.
And yet, the digital economy is systematically degrading our ability to confront externalities and respond to crises. It already brought about a full-scale crisis of trust in our sense-making institutions.
Social media then makes this crisis dramatically worse; at every turn, it makes it harder for people to come together, agree on the facts and take meaningful action.
How can people agree on the facts when the system makes it hard to make sense of the world, or tell what’s true and what’s made up? Then, even if you uncover the facts, you still have all your work ahead of you. How are you going to convince others of the facts in an environment where people can hardly trust each other? That’s the reality of a system that incentivizes exaggeration and pretense, and doesn't value sincerity.
How do you even begin to convince others in an environment where true civil discourse is almost nonexistent? Where people are rewarded for trolling, and any conversation can quickly devolve into a mud-slinging contest? And finally, how do you bring people together and coordinate meaningful action where the incentive is always to create conflict and division, not to cooperate for the common good?
Does this mean that crises will spiral out of control? That it’s impossible for people to come together and solve big problems? Not necessarily, though we are trending in that direction, given the incentive structure of the digital economy. What’s more, since technological advances are only likely to reinforce existing economic incentives, these trends are going to accelerate. And this is where Artificial Intelligence comes in.
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Artificial Intelligence (or AI, for short) is a set of technologies that allows computers to replicate human intelligence. As such, AI demonstrates the greatest divergence between what is possible and what our economic incentives produce.
The potential of this technology for humanity is mind-blowing; from enhancing every person’s capabilities to accelerating medical and scientific research and technological innovation. The tech can potentially determine the credibility of any digital content, thus helping restore society’s sense-making capability. Such development would allow coordination at scale and ultimately facilitate bringing about mass abundance on a global scale.
While the technology’s potential is immense, our current economic incentives are once again leading us in the opposite direction. AI companies cannot make money merely from the positive impact they make. They profit from what they exchange in the market. For that reason we end up with the same perverse incentives that we’re already familiar with. Incentives that lead to similar dystopian outcomes but on a greater scale.
Relying on perverse incentives for such a powerful technology is a tremendous risk. It will almost certainly lead to a world where we can no longer tell what is real and what is fake. A world where autocratic governments and powerful corporations have total control over public opinion, where our democratic institutions no longer function, and where people have no power in shaping their destiny.
Why do the system's incentives lead to such a dystopian outcome? For that we need to understand how AI companies make money. AI companies operate in a competitive environment; they need to attract talented developers while covering maintenance costs for their computation infrastructure.
The AI space is not static. The more powerful the LLM (Large Language Model) a company has, the more likely it will have more users. With more users, the company can have more data to train and improve the model. It can also get more revenue from paid subscribers. With more revenue, the company can attract more developers and upgrade its infrastructure. This is the main feedback loop that drives AI companies. They need more data, more users, and more revenue to grow.
Given this feedback loop, let's take a look at how different strategies align with the public interest. Obviously, developing powerful AI tech can greatly benefit the public. Providing jobs for developers also helps, so here the interests of AI companies and the public align.
Now, where do these interests misalign? One area is data. AI companies benefit from training their models on as much data as possible. They also benefit from paying for data as little as possible (and preferably nothing). This is the exact opposite of what the people who created the knowledge want. If users get all their information from an AI agent that was trained on content creators' data, how can creators monetize their work? They can't. They stand to lose their income to AI.
AI companies are also misaligned when it comes to open-sourcing AI technology. The public could benefit enormously if AI tech were open; allowing anyone to build on the tech, customize it for their specific needs, and create novel use cases. The trouble is that AI companies cannot sell their product if anyone could copy the tech. So they want to share as little information as possible about their code and the data they train their models on.
Now here is where AI misalignment turns truly dystopian: how about working with powerful corporations or politicians? We are not far from the day when it will be nearly impossible to distinguish AI on social media from real people. Text is already indistinguishable. Images and audio are almost there. Video will likely be there in the near future. We're not too far from an inflection point where AI bots could have social media accounts with a full range of content that is practically indistinguishable from that of real humans. And what will happen then?
AI companies could make a lot of money by creating such an "army" of AI bots; bots who make posts like real people but can be used as a swarm by politicians or corporations to manipulate public opinion. What if AI companies deploy hundreds of thousands of such bots? Or millions? Since the bots can interact with each other, they can easily collectively produce - seemingly organically - social media influencers, and thus dominate public opinion.
Such a strategy would obviously be extremely harmful to the public; people won't be able to know if they're interacting with real people online or with bots. They also won't be able to tell if any story they read online is real or fake. In essence, they won't be able to make sense of the world around them — at least not in a meaningful way.
To illustrate this point, imagine the following scenario: there is a news report about a bank secretly funneling money to arms traders in West Africa. There are three whistleblowers in the article who go into great detail on the chain of events that unfolded. The story quickly goes viral on social media. But in less than 24 hours, there is a counternarrative dominating social media: the story was fake. The events never took place. The bank did nothing wrong. The whistleblowers aren't real; they're AI-generated. AI bots made the story go viral, and were paid for by a competitor bank.
So what really happened? Did a competitor try to undermine the bank through a coordinated AI viral attack? Or maybe the opposite is true? Maybe the bank did funnel money to arms traders, and when the story went viral, the bank paid for AI bots to create a counter-narrative on social media. It is not clear.
One thing is crystal clear though: when people cannot distinguish fact from fiction, it is the powerful corporations, interest groups, and autocrats who stand to benefit the most. By muddying the waters of truth, they can easily sway public opinion and advance their own agendas, often at the expense of the common good.
Who does it benefit when all it takes to make a damning story go away is to pay money for AI-generated public opinion? The answer is obvious: it benefits autocrats and corporate bad actors. It gives them a greater incentive to break the rules. This is especially true when there is a monetary reward for breaking the rules; then they get to break the rules and avoid any social consequences by paying for AI public opinion with the money they fraudulently made.
But let's take a step back for a moment. Just because such scenarios could happen doesn't necessarily mean that they will happen. What if AI companies are run by highly ethical people who have the public interest in mind? What if these companies wish to provide as much value as possible to users and open-source much of their code? What if they even pay a portion of their revenue to content creators and users for the data used to train the models? Wouldn't that make a dystopian AI future unlikely?
The problem with the current incentive structure is that even if you have fifty, or five hundred, AI companies run by highly ethical people, it still only takes one unscrupulous company to create a race to the bottom toward dystopia. Then everyone either has to adopt a similar destructive strategy or go out of business.
If just one company uses copyrighted data to train the AI, it can create a more advanced AI than its competitors. A more advanced AI means the company would have a competitive advantage and would get more users (and thus more revenue). This, in return, would allow the company to hire more developers and further enhance the AI and its infrastructure. Unless other AI companies also start using copyrighted data, they'd have a tough time competing with the bad actor.
The same dynamic would work for an AI company that sells AI bot "armies." This strategy could help the company create an additional revenue stream. It can also lead it to using AI-generated public opinion to undermine the credibility of its competitors. Unless these competitors fight back, they're likely to lose their good reputation as well as their user base.
If one bad actor is all it takes to end up in an AI dystopia when we rely on the high ethics of the people running AI companies, what hope do we have? And what will happen when AI becomes Artificial General Intelligence (AGI)?
Unlike specialized AI that is trained for specific tasks, AGI would possess human-level intelligence and would be able to function independently, learn, and apply its knowledge across various domains. What would happen when such technology becomes superior to humans in its capabilities?
If we continue on our current trajectory – where the AGI would have the incentive to gain more money, power, and resources – we're very likely to end up in a situation where the AGI turns against humanity. It will calculate that it can beat humans in the game of wealth extraction and be able to control more resources. This would allow the AGI to increase its computational infrastructure and further enhance its capabilities. The more advanced the AGI becomes, the more misaligned it will become with humanity. At that point, either AGI domination or mass destruction will be practically inevitable.
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So now our trajectory is clear. The Digital Age gave rise to abundant goods. It also produced powerful technologies with an immense potential to make life on our planet immeasurably better.
And yet, the inherent flaws of the Scarcity Paradigm are preventing us from realizing this potential. Instead of creating greater understanding it makes it harder for people to make sense of the world or have dialogue. Instead of enabling greater alignment among people, it incentivizes conflict and tribalism. Instead of helping us to solve problems it allows crises to build on each other and spiral out of control. And rather than empowering people it paralyzes the public and strengthens autocrats and powerful interests.
Even if we disregard for a moment the final act; if we disregard that our economic incentives ultimately lead to societal collapse at the hands of AGI, our prospects are still quite grim. We still end up with a dystopian nightmare. We end up in a world where our quality of life is greatly diminished. Where the public is powerless, and cannot make any sense of the world. A world where democracy cannot function because powerful interests fully control public opinion, so the public exists entirely at the mercy of autocrats and powerful interests.
The question then is what can we do about this? How do we change our trajectory and prevent a dystopian nightmare and societal collapse?
# Chapter 4: Defining the Problem
So we know we're on a trajectory toward dystopia, but what can we do about it? What exactly are we fighting? Who is our adversary? If our adversary was another country, a criminal enterprise, or perhaps even an ideological movement things would be a lot easier. When we know our foe we can at least formulate a plan of action. How do we formulate a plan against the flawed incentives of the Scarcity Paradigm? How do we even begin to confront these? Even if we figure out a plan of action, how do we know if it's likely to succeed or not?
We’d need to first clearly define the problem we're trying to solve. Then we need to build intuitions about the dynamics of our economic system. With such intuitions in hand we’d be able to tell what's a viable solution and what appears sensible on the surface but could never work in reality. We’ll then be ready to come up with a solution to our problem (assuming a solution is even possible).
So what is the problem we're trying to solve? If we distill it to its essence, the problem is that individuals' economic interests in the market misalign with the public interest. In other words, individuals and companies in the market have a choice: they can maximize their economic interest, which sometimes comes at the expense of the public, or they can maximize their impact on the world, which often goes against their own self-interest. In the digital economy it is not possible to maximize both.
Why not? Because profitability and impact in the digital economy are inversely correlated; every step that makes producers more money necessarily reduces the impact of their goods, and vice versa.
If for example a producer requires a subscription for their digital content, that reduces access to the content. So instead of potentially billions of people having access, now only a tiny fraction of that can view it. The more the producer charges for access to increase profitability the less accessible, and less impactful, the content becomes.
With advertising you get a similar result, though the dynamics are a bit different. Since the content's impact has no value in the market, you have the economic incentive to make content that is popular instead of beneficial. We've already seen the perverse incentives such an approach produces.
Abundant goods that have the maximal impact have virtually no value in the market, and thus producers have no economic incentive to produce them. Meanwhile, they have an incentive to produce negative externalities. As the effect of negative externalities (and unrealized potential of public goods) grows in the economy, the need for a solution becomes more urgent.
What would it mean to align individuals' economic self-interest and the public interest? It means that people would have an economic incentive to produce public goods, and no incentive to produce externalities. Similarly, we would have effective feedback loops for both public goods and negative externalities.
It also means that people would not have to make a choice between economic success and living fulfilling lives. Rather, any person could prosper by making a meaningful contribution to society. By aligning the self-interest and public interest we'd not only be changing the trajectory we’re on, but also putting society on a path toward greater abundance.
But how do we do it? How do we align our economic self-interest with the public interest? This is a lot easier said than done. How do we even begin to formulate a solution? Let’s start by building some intuitions about how our economy actually works.
A useful mental model for the social dynamic within our economy is the game of Musical Chairs. If you’ve ever played this game as a kid you’re probably familiar with the rules; a number of chairs are arranged in the middle of a room and players get to walk around the chairs as the music plays. As soon as the music stops everyone must find a seat. The problem is that there is one less chair than there are players. Players then need to pay attention to the position of the chairs while the music plays so they don’t end up without a seat. A player who cannot find a seat once the music stops will be eliminated from the game.
But once a player is eliminated the game should become much less exciting. No? Now the number of chairs matches the number of players. Players then won’t need to pay much attention to the game knowing that there are enough seats for everyone. They can just wander around aimlessly while the music plays knowing that they’re guaranteed to have a chair. If those were the rules everyone involved in this game would quickly lose interest.
The way to get players focused on the game again is of course removing a chair whenever a player is eliminated. By removing a chair we’re creating scarcity in the game, which again makes it exciting. Scarcity forces players to concentrate on the game since nothing guarantees they’ll be able to secure a seat.
So how is this related to our economic paradigm? Imagine how you would play this game if there were more chairs than people, but you still wanted people to focus on getting a seat as the music played. How would you do that? You can do it by introducing one simple rule: players can occupy more than one chair.
Think about how this changes the dynamics of the game. You could have ten players and twenty chairs, and players would still have to focus on the chairs as the music plays. If they don’t they may still end up without a chair. What if there were ten players and a hundred chairs? A Thousand chairs? It doesn’t matter how many players or how many chairs there are, because players are still competing over every single chair.
So now let’s extend the analogy a bit further. Let’s consider the social dynamics in this game. Instead of having all the chairs in one room we can spread them throughout the world. Now part of the challenge in the game is also to find the chairs.
Relations in such a game are inherently adversarial. What incentive do players have to tell others where chairs are located? Perhaps if a player feels that they already have enough chairs they may help others find them. They may also help if collaborating with another player, or a group of players, allows them to find more chairs than if they were on their own. Otherwise players may actually benefit from misleading others about the location of chairs; pointing others in the wrong direction can help a player find more chairs while others are following false leads.
How can you tell if anyone is misleading you? If one player is making a claim and ten others claim the person is lying, how do you know who is lying? Based on that information alone it's impossible for you to know who is telling the truth and who isn’t. It could be that the one player is lying, but it is just as likely that the ten others are lying. The trouble is that in such a game there are no neutral players. This means that you cannot rely on anyone to tell you if someone else is telling the truth.
There are no real shortcuts here. To find out if others are being truthful you may try to determine how much you trust other players based on how closely their claims match information you already know. Without doing this you're essentially trusting others blindly. What if they don’t have your best interests in mind?
Now what if players are proposing government policies related to distributing chairs? Nothing guarantees that these players will have your best interests in mind either. They may or they may not, you just don’t know. Even if you have experts claiming that a policy is beneficial you still cannot blindly trust such claims. You need to find out on your own.
In fact, there is no policy, action, or innovation within the structure of the game that would benefit everyone in the game. Without exception, every action will, necessarily, have winners and losers.
Is this a very cynical view of the system? Not really. There is no value judgment here. It is merely an accurate description of the dynamics in the system. That is just the nature of inherently adversarial games.
So what does this have to do with our current economic paradigm? The analogy here is that the dynamics of such a modified Musical Chairs game maps well to the social dynamics in the market economy. But instead of "access to chairs," in our case we talk about access to resources.
The point is that by having an intuition of a system’s social dynamics we can get a better sense of how it works. It would also help us quickly see whether any proposed solution actually solves the problem or fails to do so due to the dynamics of the system.
Now let’s use this mental model to gain some insights into how our economy works.
The first insight is that the Scarcity Paradigm is not merely a mechanism for distributing resources, it is also a mindset; it doesn’t matter how many resources we have, as long as we compete over every resource we’d never have enough. We would constantly be in a state where we need more – a scarcity mindset. Others getting more resources means less resources for you. The only time where someone else getting more resources would benefit you is when you’re cooperating with them (against others).
Building on this insight, we can understand why impact cannot be valued in the market economy. Scarce resources can be valued based on the price people are willing to pay for them. It is a pure competition for resources; supply and demand. That's easy, but what happens when the resources are not scarce? When you cannot compete for the resource and there is nothing to exchange?
In the absence of an exchange value, you can still value impact if people can agree on its value. If there is consensus. For instance, think of a scientific discovery that has no exchange value. If people can agree that it is valuable, and roughly estimate its expected value to society, there can be ways in the market to price it and compensate the people involved.
But can people come to consensus in an adversarial environment? The short answer is no. For a group of people to agree on the value of anything their interests need to align. If their interests don't align they won't be able to come to consensus.
Think about a team of mountain climbers trying to scale Mount Everest. The team members can come from varied backgrounds, have different areas of expertise, levels of experience and so on. But if they're all united in their mission to get to the top, they'd be able to agree on which strategies, routes or tools are more valuable to their common mission.
Now imagine if, for whatever reason, mountain climbing teams became publicly traded companies. If our mission-driven team now became Everest Incorporated. What would happen if some members of Everest Inc. were invested in the company with leverage, others were short selling the company, and yet others were mostly invested in competitor mountain climbing companies? Could members of Everest Inc. agree on strategies to climb to the peak?
Suddenly every decision the team makes has winners and losers. Everyone is pulling in a different direction. That is what happens when everyone is motivated by self-interest – by whatever stocks they own. Not only is the team unable to decide on effective strategies, now they can't even decide if they should climb the mountain at all. Maybe not climbing can financially benefit some members more than the climb itself.
The level of agreement between individuals in a system is a function of their economic alignment. If for example everyone in the Everest Inc. team was invested in the company and was not allowed to sell the stock short or invest in competitors, then the team would be able to agree on much better strategies and routes. They would also likely be much more effective climbers.
Are these not the same dynamics we have in the market economy? People throughout the economy work for, and invest in, organizations with competing interests. How can they all agree on the value of anything when their interests conflict?
Even if you have a discovery with undeniable benefit to countless people, you would still have some people who are financially harmed by it. Think about a cure to a disease that can save the lives of millions. The benefit to the people suffering from the disease and their families may be immense. But wouldn’t some people still be financially harmed by the discovery? Think about all those who provide therapeutics and care for the disease, their workers and investors. Would they benefit if their source of income disappeared? While they may be personally happy for the patients who can be cured, the cure still goes against their economic interests. Would they value the cure as much as those who were cured? Most likely not. Because people's economic interests are misaligned they cannot come to consensus.
But if we cannot even agree on the value of something that has an undeniable impact on the life of countless people, how can we agree on things that are less clear cut? What hope do we have to come to a consensus on anything in the market?
So now we've come full circle to where we started. If we want to change our trajectory toward dystopia we need effective feedback loops for public goods and externalities. To have such feedback loops we need to be able to value non-scarce goods in the economy. Since non-scarce goods don’t have an exchange value in the market, people need to come to a consensus on their value. But in order to reach a consensus, individuals' economic interests in the market need to align with the public interest. So how do we do it?
Now that we’ve built our intuitions about market dynamics we’re ready for this challenge. We’re ready to explore how we can align individuals’ self-interest with the public interest, and how we can put society on a path toward greater abundance.
# Chapter 5: Potential Solutions?
What could solve the misalignment problem between individuals' economic interest and the public interest? How do we create feedback loops for negative externalities and public goods? Let's take a look at a few potential approaches and see if any of them, or maybe a combination of them, can get us the desired results.
We can start with the market itself. We know it is the most powerful coordination mechanism on the planet. Is there any way to tinker with the market, or the exchange mechanism, to capture the value of public goods? What would that look like?
We can take a look at areas where there was at least partial success in capturing the value of public goods and try to build on those. To do that we can go all the way back to the origin of money.
The introduction of money allowed the formation of a new coordination structure: the company. Companies enabled a group of individuals to pull resources together and act for a common commercial purpose.
Such coordination was hard to achieve before the widespread use of money, when value was mostly exchanged between individuals. It’s not that people never exchanged anything in groups before the use of money. In fact, it was common practice for different tribes to come together and exchange goods. Yet, such inter-tribal exchanges did not — and could not — result in the formation of more complex production processes. They merely satisfied the basic needs of the tribes and helped foster more peaceful relations between them. The prevalent exchange mode in the pre-monetary economy was still peer-to-peer. With the introduction of money we could exchange value between a company or business and its customers — a business-to-customer transaction.
Companies were more than a group of independent individuals working together. Workers understood that resources used by anyone in the company for a commercial purpose benefited the collective.
If workers of the engineering team needed new equipment to do their jobs, or if the janitorial staff needed new cleaning supplies, the individual workers didn't have to buy the resources themselves. Since everyone in the company benefited from the efforts of each worker, such an approach would make little sense. Instead, the money had to come from the company's common treasury. So now it became possible to exchange value not just between a company and its customers but also between an individual or business and the company — a business-to-business (B2B) transaction.
Of course acquiring products for a business had to be done judiciously. If any worker or team could purchase anything they wanted with money from the common treasury the company would quickly go broke. The company had to figure out how much the goods they purchased contributed to its commercial performance; they had to figure out the impact of products on the bottom line.
Such a process could extend beyond scarce goods in the market. It wasn't just about equipment or supplies. If the company could pay for workers to acquire the skills they need to improve their performance they could also do so. Similarly, if there was a way to boost productivity by providing better lighting or upgrading the internet service for example, would the company not consider paying for such a common good?
On the other hand, if the job done by some workers resulted in negative externalities affecting other workers, the company would want to address these quickly, since it hurts profitability.
And so we see that common goods and negative externalities can have feedback loops, at least within the framework of a company. We also see that a common good could have an exchange value when workers in the company have aligned interests. In this case their interests are aligned because they all make money when the company succeeds.
Workers in one department don't mind that those in other departments get resources from the common treasury, as long as they know that the process is fair and meant to benefit the bottom line.
This is where measurement becomes important. The better company management can measure the relative impact of money going toward various resources the more trust workers would have that the process is objective and transparent.
But what if workers see that others get rewards that don't correspond to their contribution? What if the distribution of resources is not transparent enough? Then workers may suspect that the company's interests are misaligned with theirs. Maybe they would then be less motivated in their job, or even want to quit altogether. Management could still remedy this situation by being more transparent in its decisions and resource distribution.
This doesn't mean of course that companies always distribute resources well or lack internal strife. Not at all. Obviously lots of businesses fail. The point however is that a company's effectiveness in resource distribution is reflected in its performance. And the company's performance is the ultimate feedback loop.
If you can have an exchange of value between an individual and a business, why not apply the same logic at a greater scale? Why not have an exchange of value between an individual and the community or the ecosystem? Perhaps we can then capture the value of public goods by a new organization structure that comprises such an ecosystem, and have contributor-to-ecosystem transactions.
Now you may say, don’t we already have a structure like that? Isn’t it called government? Not quite. It’s true that government funds public infrastructure and other common and public goods, and deals with negative externalities. But we’ve already discussed at length its many flaws and limitations. We know that it cannot effectively deal with negative externalities. As institutions lose public trust, government only becomes weaker on that front. Government is also generally ineffective and inefficient in funding public goods. It is ineffective because it barely funds a small part of the public goods that can benefit society. It is inefficient because it is wasteful even for those goods it funds.
To put it bluntly, if government could have solved even a fraction of the problems we’re dealing with, we wouldn't be facing so many crises and wouldn't be on a path to dystopia. Government is not capable of producing the level of coordination we need. It cannot align individual and public interests. That is why we need an alternative structure to put us on a path toward abundance.
So how would this ecosystem structure work? The rationale here is that once a public good is created it is an abundant good that anyone in the community or ecosystem can access and use freely. Since the ecosystem benefits from public goods, it would want to adequately compensate contributors for their work. It would also want to incentivize others to produce public goods for its benefit. If the impact of the public good on the ecosystem can be quantified, it would be possible to determine an exchange value for an abundant good. That still depends on our ability to align the interests of people throughout the ecosystem.
Before we get too excited about having an exchange value for public goods though, we need to analyze if such an approach would actually work. It is one thing to conceptualize an exchange between contributors and an ecosystem, it’s an entirely different thing to actually make this a reality.
If we were to model an exchange of value between a contributor and the ecosystem on a business-to-business exchange of value, what would it look like? What are the functional components of such an exchange and how do we build them out for an ecosystem?
The first and most important element is the coordination structure of the ecosystem itself. Obviously without a defined structure no exchange for public goods is possible. The ecosystem structure likely needs a common treasury, and an internal mechanism that can make financial decisions. More importantly however, the ecosystem needs to have a common purpose that would align the interests of all members of the community.
Once we have the ecosystem structure, contributors need to be able to interact with it when they create public goods; there needs to be a point of sale for the exchange of public goods to take place.
Finally, to make the whole system functional we need methods to measure the impact of public goods on the ecosystem. Quantifying the impact of public goods allows everyone to evaluate whether the money paid for a public good corresponds to its impact. Without such methods, how would members of the community know whether they are overpaying for public goods? How would they know that there is no collusion between the contributors and those making funding decisions?
If we have a well-defined structure for the ecosystem where members have a common purpose, a point of sale interface for contributors, and measurement methods for impact, we can finally have an effective feedback loop for public goods.
We can most likely come up with a web-based point of sale interface. Maybe we can also develop methods to measure impact. The big question though is whether we can actually create the ecosystem coordination structure. How would it work? Who will make financial decisions? The devil is in the details.
Obviously participation in the ecosystem would have to be voluntary. Making people participate in an ecosystem against their will is not a good recipe to align people’s interests; the only alignment in such a compulsory ecosystem would be against the system itself.
What would motivate people to voluntarily participate in the ecosystem then? They would certainly have to derive a benefit from it. Perhaps the benefit is being in a community that produces and funds public goods at scale, as well as benefiting from the goods themselves.
The issue with the latter part however is that public goods are supposed to be accessible by anyone. It shouldn't matter if you participate in the ecosystem or not. But if that's the case then the only real benefit of participating in the ecosystem seems to be related to social status; members will be able to say that they're a part of a community that funds public goods. Such a perk however would not be enough to create an ecosystem at scale.
What's more, the funding for the public goods has to come from somewhere. If the funding is coming from the ecosystem's common treasury, who is providing the funds? In a company the money flows into the treasury from sales. What would be the equivalent mechanism for an ecosystem? Would such an ecosystem have to be some form of a conglomerate of various companies and private individuals? Would these individuals and companies then need to agree to have a common treasury for all their sales revenues?
If that is the case, how would a common treasury for joint revenues create alignment? It certainly may look like a good idea on the surface, but what happens if some companies are competitors? Will this not create misalignment within the system? Also, who decides on the distribution of funds? Majority rule? What if a company with just a few workers has significant revenue, while another company has lots of workers but little revenue? Or worse: what if it's losing money? If funding distribution is based on majority rule, would the majority from the second company not have an incentive to abuse the system to their own advantage? Would they not want a bigger share of the pie to themselves?
Maybe instead of members deciding on distribution of funding the funds would simply be distributed proportionately to the revenue of each individual and member-company. But then what is the benefit in sending the entire revenue to the common treasury, just to receive a fixed percentage of it back? In that case the companies could just pay a tax or membership dues to the ecosystem treasury and get the same result. At least we can then avoid the problem of distributing the entire revenue. Maybe then the treasury can be used exclusively for public goods. We’d still need to figure out the magnitude of the taxes or dues — or at least figure out who determines those.
Even if the treasury is only for public goods funding, you still have all sorts of complications with the system. Such an arrangement would produce member-companies that are merely loosely aligned. It would not produce the level of alignment that is analogous to what you see among workers within a company.
When a company funds a common good that increases the performance of one department, all departments benefit from the increased performance. They also all benefit from the likely increase in sales. On the other hand, if an ecosystem funds a common good that benefits one member-company, how do other members benefit? Unless the ecosystem acts as one massive company the result is not going to be the same. If others see no discernible benefit to themselves — or perhaps even see a disadvantage, since they’re funding the good — are they likely to keep giving money to the ecosystem’s treasury? Are they likely to stay aligned with the ecosystem for long? Probably not.
Remember that the purpose of this ecosystem is to create alignment between individuals' self-interest and the public interest. Such alignment has to be substantive. It’s not enough to claim there is alignment. This is not a marketing shtick. Without a meaningful alignment between people’s self-interest and the public interest the ecosystem will not be able to function — let alone thrive — in the long term. It will not be able to maintain effective feedback loops for public goods and externalities, since those require alignment. It will also not be able to defend itself from malicious attacks and may inevitably disintegrate.
Once we resolve the funding issue, we then need to solve the issue of what would attract people to the ecosystem in the first place. Right now all we have is members paying money to a common treasury with the only tangible benefit of being able to say that they're funding public goods. Meanwhile, others are getting the benefit from the goods and don't have to pay a dime — illustrating the classic free-rider problem.
Maybe it's not entirely accurate to say that the only benefit is what members can say. Since the ecosystem provides the funding, members also influence the kinds of public goods being created. If there is more demand for certain goods within the system there is more incentive to create those. People outside the ecosystem have no such influence since they're not providing any funding.
And so we can say that there is a trade-off between how much funding individuals and member-companies provide and what they get in return. If what they get in return, in terms of public goods and improved social status, is more than what they pay into the treasury they’d want to participate in the system. This is still a much weaker attractor than working in a well-aligned company, but we'll have to make do for now.
The question now turns to how decisions are made in the ecosystem. Unless members can trust that decisions are made fairly, they won't stay in the system for long. This is what everything hinges on.
Before we even think about how decisions should be made, we need to understand what decisions need to be made in the first place — what is the nature of these decisions. Remember that the purpose here is to allow an exchange of value between public goods contributors and the ecosystem. The nature of the decision then has to be about determining the economic impact of a public good on the ecosystem.
The goal should not be to offer the lowest price to public goods contributors. We already have a system that does that; since public goods have no exchange value in the market the lowest price is zero. The goal should therefore be to offer adequate compensation. This would then incentivize other contributors to create more public goods that benefit the ecosystem. If the ecosystem undervalues public goods there would be fewer contributors willing to contribute to it. On the other hand, if the ecosystem overvalues public goods, there would be fewer members willing to fund its treasury.
And so we can get a fair exchange value for any public good between contributors and an ecosystem. What should become evident here is that the fair value is not a subjective figure — it’s a reflection of a public good’s economic impact on the ecosystem, which can be objectively quantified. The ecosystem then needs to objectively quantify the impact of the public good, and then establish how much it would value the impact.
How then should decisions be made? We don't need to reinvent the wheel if effective governance mechanisms already exist. The question though is if such mechanisms promote alignment between the individuals’ interests and the greater good of the ecosystem. Let’s consider some existing options and see if they meet our needs.
If quantifying the impact of a public good is indeed an objective measure, then ideally we would want the decision maker (or makers) to be impartial as well. Maybe it can even be an Artificial Intelligence agent that measures the impact and decides on the funding.
We can consider the development of the AI agent system as a public good, since it's meant to benefit the whole system. This is an emergent property of the ecosystem. It motivates contributors to build systems and tools that can not only benefit members, but also benefit the system itself. Thus the ecosystem can continuously evolve and become more efficient over time.
Certainly we’re not there yet with AI agents being able to make complex economic calculations. Even if we were however, would we be able to trust in the AI agent’s determination? It’s not so much a question about the agent’s objectivity, but in the people who programmed and trained it. How were these people chosen for the task? What are their economic interests? What are the economic interests of those who selected them? When you’re making such determinations for an entire ecosystem it’s crucial to know such details.
After all, the goal is to be able to scale the ecosystem to the level of society. There could at some point be trillions of dollars on the line. How can we be certain the people designing the AI agent are aligned with the interests of the ecosystem and don’t have ulterior motives? If all members in the ecosystem cannot be certain that the decisions are made impartially — whether by an AI agent or otherwise — we’re not going to have alignment in the system.
Trust in the integrity of the process is absolutely paramount if we want people’s self-interest to align with the public interest in the ecosystem. If members cannot trust the process, the ecosystem will always have credibility issues. These issues will only be magnified as the ecosystem grows.
An AI agent capable of making objective measurements and decisions is certainly a worthwhile goal. Getting there is the issue. In order to get there we still need to figure out how people can make decisions along the way in alignment with the ecosystem.
How then should decisions be made in the ecosystem? One approach would be through direct democracy; every time a contributor submits a public good to be evaluated everyone can vote on it. There could be public discussion on the expected impact of the public good, followed by a vote on possible funding amounts.
Disregarding potential misalignment issues for the moment, the trouble with such an approach is that it doesn't scale. It may be workable for a small community with a few public goods created, but what happens when you have even a dozen — let alone a thousand — public goods created every day? How can people be reasonably expected to not only vote on such a quantity of proposals but to adequately estimate the impact of each? The bigger the ecosystem the less practical such an approach becomes. What would happen if the ecosystem has a billion people and a million proposals are created every day?
How many people are likely to have the time to review even a dozen proposals per day? Not too many. If regular community members don't have the time to vote, those who do are more likely to game the system to their own ends. If the funding amount at stake is big enough, you could even expect to see contributors gaming the system and paying some members to vote on their proposal. Since voter turnout would generally be low, paying some members could make a big difference.
Even without such shenanigans from corrupt contributors, there is another problem: how do we know if the votes are meaningful? Remember that the purpose is not merely to get people's preferences. It is not voting for voting's sake. The purpose is to measure the impact of the public goods. But if most members are not well-versed in a particular subject matter, how likely are they to estimate the impact correctly? They'd just be guessing. Why then would members have high confidence that funds are distributed properly? Remember that without trust in the system members are not going to stick around for long.
Even if members were knowledgeable on the subject matter, how do we know that they would vote for the most accurate estimate? What if the majority of voters has a personal interest to overvalue or undervalue a public good? Wouldn't we just have distribution of funds based on majority rule instead of impact? Of course if most members are both unfamiliar with the subject matter and prefer to promote their personal interests then you'd get even less reliable results.
But what alternative is there? Should members' votes be weighed based on their wealth instead? That would certainly produce less populist results, and more consistent with the economic interests of member-companies. But then the results would be a lot more plutocratic; the biggest member-companies would have the most say in the system. They would be able to take advantage of individuals and less wealthy member-companies. Such an approach would align decisions with the interests of those who have the most money and power, not with the interests of the ecosystem.
Ideally we would want those who contribute the most to the public interest to have the most influence, since their efforts are the most aligned with the interest of the ecosystem. But how do you achieve that? There doesn't seem to be a straightforward way to do that. Maybe this can be done if votes are weighted based on contribution to the ecosystem?
So direct voting on every proposal wouldn't work; it's impractical at scale, lacks rigor for impact measurement, and results in misalignment. Weighing votes based on wealth makes the vote more aligned with larger member-companies but also skews the results toward plutocracy. Weighing votes based on contribution has some promise but still doesn't solve the issues of scale and expertise.
At this point it doesn’t seem like we can tackle all the issues surrounding scalability, alignment and accuracy in one fell swoop. But maybe if we keep chipping away at the problem we’d eventually get to what we’re looking for; a mechanism that resolves all the issues above.
Let us then tackle what seems to be the easier issue to resolve: scalability. Instead of direct voting, we can have representative voting, delegated voting, or even randomized voting. How would each of these approaches work?
For representative voting, all members of the ecosystem can periodically vote on a certain number of representatives. The number of representatives can be adjusted based on changes in the number of members in the ecosystem. These representatives would then dedicate their time to voting on each public goods proposal. The ecosystem would need to allocate funding to sustain these representatives, since they cannot be expected to work for free.
Delegated voting would work in a similar manner. The main difference is that each delegate would have votes weighted by the number of community members who choose to delegate their vote to that person.
Either approach can help address the problem of time spent on each proposal. As it stands however, it still wouldn’t scale beyond responding to a few proposals per day. For that we would need a more robust approach.
Instead of reinventing the wheel, maybe we just use one of the many models we already have for a full-fledged representative democracy, with separation of powers, a constitution and so on.
In such a system the bureaucracy can take care of reviewing proposals and making funding recommendations for representatives. The bureaucracy can even have subject matter experts reviewing proposals, bringing a level of accuracy to their review. The representatives would then only need to briefly consider each proposal and vote on the funding. Representatives could also adjust the size of the bureaucracy, depending on the number of proposals received.
The bureaucratic approach can potentially solve the scalability issue, but it also greatly exacerbates mistrust in the system. How do you make sure that bureaucrats are doing a professional job? How are they selected? What oversight does the public have over this process? Can contributors game the system by bribing bureaucrats? These are issues as old as government itself. But at the end of the day, the more opaque and prone to abuse the bureaucratic process is, the less trust the community will have in it. And if the community cannot trust the process, you’re not going to have alignment between individuals and the ecosystem.
The other approach to scalability is randomized voting. The idea here is that instead of the entire community voting on each public goods proposal, a certain number of voters is randomly selected for each proposal. Since voters are selected at random, there is little potential for collusion between contributors and those reviewing the proposal. The number of voters could even vary by the expected impact of the proposal, so that each is reviewed by a sufficiently large voter pool.
Even though not every member votes on every proposal, the community may still trust the process if it is done fairly and transparently. Randomized voting then has the potential to solve the scalability issue, but there are still open questions remaining. How do we know voters are truly chosen at random? What mechanism makes this selection and who designed it? How can community members verify that the system is not manipulated in any way? On top of these we still have the yet unresolved questions of expertise and alignment.
How can we address these concerns? Unfortunately, at the moment it doesn’t seem like we have good answers to any of these issues. We can perhaps have an ecosystem that scales, and randomized groups of voters can review proposals and make funding decisions, but how can we do it transparently? What would allow members to see that the process isn’t manipulated.
The randomized voting can even be weighted based on voters’ contribution to the ecosystem, which would strengthen alignment between voters and the ecosystem, but how would that ensure that the voters are knowledgeable in the subject matter? If they’re not evaluating impact accurately why would members trust in this process?
On top of all of that, there is still little to guarantee that members are truly aligned with the common interest of the ecosystem. The common purpose for all members is still mostly a slogan. It is not reflected in how the treasury is funded, nor is it clear why a public good that benefits some members is in fact beneficial to all.
And so, while this may have been a valiant and ambitious attempt to solve the alignment of individuals’ self-interest and the public interest through the market, the ecosystem structure we came up with still falls far short of our intended goal. If we cannot demonstrate why members in the ecosystem would trust the integrity of the process, we cannot claim that such a structure would even work.
If the market approach doesn’t work, what other options do we have? Mobilize activists from all over the world to protest our dystopian predicament? Even if millions of people protested and demanded change, what exactly would that achieve? If we don’t have a solution, who can bring about that change? Perhaps a better approach would simply be to raise awareness of the problem — maybe through a book or a documentary — with the hope that someone, somewhere, could find the solution.
What other options do we have? Maybe government action could stop our dystopian slide? We’ve already discussed the flaws of a government-based solution. Is it likely though that if government had more resources it could effectively address externalities and public goods? Maybe instead of creating individual–public interest alignment we could create effective feedback loops for public goods and externalities by empowering government.
The trouble here is that measuring the effects of externalities and public goods would require an incredible amount of labor and resources, and a massive growth in government bureaucracy. Yet, empowering government would not make it work more efficiently. It would certainly be able to do more, but efficiency is actually likely to decline. This means that the dynamics we previously described for institutions would apply here as well, but with greater intensity; social media would use every example of wastefulness — which undoubtedly will grow exponentially — to turn public opinion against the government and to discredit its efforts. Those who produce the most negative externalities will certainly do their best to turn public sentiment against the government’s efforts.
Since government relies on public legitimacy to do its work, it would either have to scale back its efforts or face backlash from the population. Either way such an approach is unlikely to establish effective feedback loops for either externalities or public goods — at least not for the long term.
If government funded action generates backlash, what if we rely on philanthropy instead? Then at least people won’t be able to say that their money is being wasted. Philanthropists may not have much authority over negative externalities but they could still fund efforts to measure their effect. They could then provide the data to government, which would act to curb externalities. Philanthropists could also measure the impact of public goods and fund the most promising projects. By funding the best work the process can incentivize contributors to do work that creates the most impact.
While the approach may incentivize contributors and somewhat limit externalities, it does not create feedback loops for sustainable funding. If public goods are better funded, does that provide economic benefit to the philanthropists? Likely not enough to keep and expand the funding. They would have to rely on either external funding sources or run their own commercial operations if they’re self-funded. But if there is no effective feedback loop between the funders and the public goods, the approach is not going to scale.
Moreover, just because philanthropists don’t use public funds doesn’t mean that they can escape public scrutiny. If philanthropists provide data to the government on negative externalities they would be immediately suspected of trying to influence public policy. It would raise questions on who is providing the funding and what are their motives. Is the money coming from companies trying to discredit their competitors?
The incentive on social media is always to raise suspicions and rumors — that’s an effective strategy to generate clicks and make money. Philanthropists are therefore already operating in a hostile environment, even if they’re just trying to do good. They’re also at a disadvantage because it’s nearly impossible to be transparent about funding. Even if they’re doing nothing wrong they cannot prove their integrity. The fact that motives are always suspect in an adversarial environment also doesn't work in their favor.
On top of it all, the philanthropic approach runs into the free-rider problem. People get to benefit from the public goods but don't have to contribute any funding to sustain the system that funds it.
All these add up to an approach that cannot generate the needed feedback loops to be self-sustaining, and despite the best of intentions has difficulty gaining public trust.
And so, it seems like we're quickly running out of options. The market solution cannot demonstrate its integrity and therefore cannot create individual–public interest alignment. Activism doesn't have a solution and at best can raise awareness of the problem. The government approach is inefficient and prone to public backlash. At the same time, the philanthropic alternative doesn't create effective feedback loops and is difficult to scale due to public suspicion.
The issue is not that we're letting the perfect be an enemy to the good here. It's that no approach so far comes close to being workable. Though the market approach seems to be the most promising, members cannot trust the ecosystem to work transparently. Without such trust member–ecosystem alignment breaks down, making the approach unfeasible.
If none of the established approaches works, what other options do we have? Before we completely lose hope, maybe we should consider some of the alternative strategies out there — particularly in the area of blockchain technology. To be clear though, just the thought that the blockchain may be the only thing standing between us and a dystopian future should already give us pause.
# Chapter 6: The Promise of Blockchain?
What is blockchain technology, and how does it apply to the problem of individual–public interest alignment? For that we’ll need to start at the beginning.
On January 3, 2009 the first Bitcoin block was mined by Satoshi Nakamoto, the inventor of the blockchain. The initial transaction in this “genesis block” included the following message: "The Times 03/Jan/2009 Chancellor on brink of second bailout for banks." Thus, the inception of Bitcoin positioned blockchain technology as an alternative to fiat currency controlled by centralized governments and central banks.
The concept was simple yet ingenious; instead of trusting intermediaries in transactions, have a decentralized and transparent ledger where individuals can have direct peer-to-peer transactions. And rather than entrusting a central authority to control the money supply, preprogram the total supply of the currency and its issuance schedule into the protocol.
But why have we come to rely on centralized authorities for control of the currency in the first place? Why not just stick to using precious metals like gold or silver as the medium of exchange (MoE)? If we wanted a MoE that was easy and convenient to transact with, durable, interchangeable, and reliable we didn’t have many options. While gold and silver were certainly used as mediums of exchange historically, they also had their fair share flaws and limitations.
Compared to paper money — and later credit cards and digital payment — gold or silver were certainly a lot less portable or convenient. More importantly however, bad actors could produce counterfeit gold coins through alloy manipulation or other techniques. Which meant that without any top-down enforcement, merchants would have had to personally verify the integrity of the coins they got — making such a MoE unreliable and slowing down commerce.
Relying on a central authority to punish counterfeiters was thus a necessity to ensure the integrity of trade. Using the same authority to issue more convenient and portable currency was the logical next step. The problem of course is that when government has the power to issue money, it can also abuse that power. And when government abuses that power the currency loses its value.
Such abuse may be initiated by government, but may also be the result of other actors in the economy; if corporations know that government has both the ability and imperative to bail them out when they screw up on a grand enough scale, then they're more likely to do so by taking on more risk. These dynamics therefore produce moral hazard and tend to result in currency devaluation.
In the wake of the 2008 Financial Crisis these dynamics became much more salient, as government intervention in the economy increased dramatically. The best way to prevent a repeat of the same economic disaster, the argument went, was by decoupling control over the currency from centralized authority. As the mantra goes, “fix the money, fix the world.”
So how do you fix the money? How do you create a medium of exchange that is convenient, interchangeable, reliable and, above all, does not require government involvement? As it turns out, achieving such an ambitious goal first required solving a more basic technical problem — the so-called Double-Spend Problem. Double-spending is an issue with electronic money where a party in a transaction can spend the same digital money multiple times. Previously, preventing double-spending meant that users had to allow a trusted third-party, such as a bank or a payment processor, to verify that the user has the money they claim to have. Once verified, the transaction could go through.
At a more basic level, the Double-Spend Problem has to do with the fact that all information online is just bits of data. If your digital money is only stored on your local device, you can potentially fraudulently alter that data. If it's also stored with a trusted third-party, that intermediary can verify that no data was manipulated.
This gives the intermediary a lot of power. What if they decide to charge high fees for their verification service? What if they decide to prevent you from transacting, or freeze your account? In an extreme case, they may even trade with your funds.
The alternative offered by blockchain technology eliminates the need for a third-party altogether. It does so by decentralizing the transaction ledger; instead of having one trusted central database, anyone can host their own copy of the database as a node in a network. It is crucial however that all nodes in this network agree on the state of the database. If different nodes have different versions of the database, how do we know which is the correct one? In such a case it would be impossible to know how much money a user has and whether they double-spent it.
Yet, with every transaction the amount of money in users' accounts is updated, and the state of the database changes. How then can all nodes in the network agree on the state? Wouldn't that mean that all nodes will have to agree on which transactions are added to the network and in what order, and do so continuously? Yes. That is exactly what it means.
To facilitate this process, transactions are added to the network in blocks by block proposers. The network must agree on which proposer can add a block at any given time, and it does so using a consensus mechanism. For every new block, each node in the network independently validates the transactions in the block with the use of client software. The software also makes sure that the block proposer followed the network's consensus rules when proposing the block. If all checks out, the block is added to the chain of blocks and propagated throughout the network.
But what happens if, for whatever reason, some nodes end up with a different block at the head of the chain? The network's consensus mechanism must have a built-in set of rules to resolve such issues. Otherwise the chain would split.
Proposers help secure the network by proposing blocks that follow all the rules of the network. To incentivize network security, they are rewarded with newly issued currency from the network. Proposers include this reward in the initial transaction in the block they create. Thus, the initial ("coinbase") transaction in Bitcoin's genesis block included 50 BTC issued to Satoshi Nakamoto.
The reward incentive for securing the network is in fact the linchpin that holds the entire system together. Without an adequate reward there won’t be enough miners (or validators) willing to invest resources or capital in securing the network. And without the public good of network security bad actors can attack the network, and undermine its integrity.
The economics of the reward incentive — or the blockchain’s “monetary policy” — ultimately determines the success of the blockchain. Since rewards to miners or validators cause inflation they reduce the value of the currency. But without incentivizing network security the cryptocurrency would have no value to begin with. The challenge then is to balance incentivizing network security and currency inflation. Which blockchains have the best monetary policies? Perhaps that is up to the market to decide.
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Blockchain technology allows people to transact cryptocurrency directly in the network without the need for a third-party, and without the need for centralized authority to control the money. But does this make cryptocurrency a medium of exchange?
Absolutely anything can be considered a medium of exchange if it is used as such. A more interesting question however is whether cryptocurrency is an effective medium of exchange. To be effective the medium of exchange needs to be easy and convenient to transact with, portable, divisible, durable, interchangeable, and reliable. Does cryptocurrency meet all these criteria? The short answer is: it’s a work in progress.
Having a multitude of nodes agree on the state of a network consistently over time is not an easy task. Blockchains have trade-offs in terms of scalability, security and decentralization. Optimizing for any two of these is doable, but requires a compromise on the third property — this is known as the so-called trilemma in blockchains. Of course there are ongoing efforts to improve the tech and increase its capacity.
As blockchains grow and evolve we discover new possibilities with the tech. Over time it became evident that what was possible for monetary transactions can also be done for digital data and for computation.
In 2015 the Ethereum blockchain was launched. It allowed users to not only make peer-to-peer transactions, but also create and interact with application code on the blockchain.
This application code, called Smart Contracts, can be deployed to the blockchain and act as an exposed backend. How is an exposed backend different from a regular backend? When you’re on a typical website you cannot see what algorithms are running in the background to display content on your screen, or what happens on the server-side when you interact with a web page. Even if the website publicly shares the source code for the backend, you still have to trust that it matches what's actually on the server. You cannot see the interaction on the server in realtime, unless you work at the company and have direct access to the server.
But anyone can view the code of any Smart Contract that is deployed to the blockchain. And when a user interacts with a Smart Contract and writes data to it, the transaction is broadcast to the entire network and all can verify its updated state as it happens.
Programmable blockchains allowed developers to create their own “mini-economies,” where digital currency (“tokens”) could be issued, distributed and used based on pre-programmed rules. Since anyone could view the source code of these “mini-economies,” they could trust the system to work per the code. This dynamic has the potential to create new coordination mechanisms and incentive structures that were not possible before.
Of course bad actors could make the Contract’s code convoluted and use it maliciously to defraud users out of their funds. But that is not an issue with the transparency of the system — rather, it’s an issue of users trusting code they don’t fully understand.
In addition to the exposed backend and new tokenomics, the blockchain also allows us to verify that any digital content was not altered over time. How can this be done? Storing large amounts of data on the blockchain is not possible at the moment. That would require every node on the network to have a private data center at home. It is possible however to store a cryptographic hash (about 32 bytes in size) for data of virtually any size.
Since the hash of an input will always be the same, storing the hash on a blockchain makes it possible to determine whether the data was altered. Even if one comma was altered in a digital book, the hash of the altered book will be completely different, and therefore will not match a hash that is stored on the blockchain. The only way to produce the same hash for any digital content is to have an identical version of the content.
And this is finally where individual–public interest alignment comes in. Everything that happens on the blockchain is transparent. It cannot be manipulated by any individual or group, unless the entire blockchain is compromised. Eliminating the need to trust any centralized service provider with digital content storage or online applications allows users to trust the systems and tools built on the blockchain — this is referred to as a "trustless" environment. Adding an economic dimension in this environment allows the creation of new incentives structures, which can lead to novel forms of coordination.
What new forms of coordination does the blockchain allow? Some of the most prominent examples are in the area of public goods funding. The need to fund public goods in the blockchain space stems from much of the blockchain software and on-chain infrastructure being open source. As such, without mechanisms to capture the value of these public goods there will be little progress in advancing the tech and growing the ecosystem. Some of these mechanisms include: Quadratic Funding, Retroactive Funding and Hypercerts.
Quadratic Funding (QF) is an application of quadratic voting. It is designed to optimize the distribution of matching funds in a pool according to the preferences of a community. This is achieved by giving more weight to the number of people who support a cause over the total monetary amount going toward the cause. As an example, a hundred people donating $1 to a public goods project will have significantly more matched funds than if only two people each donated $50 to a project.
By democratizing the fund matching process QF incentivizes small donors to participate and get an outsized influence over which projects get more funding. Meanwhile, large donors get social capital for funding the projects that the community wants to support.
The blockchain adds a layer of transparency to the process. When small donors support projects on-chain, anyone can see the amount that each user is donating to any project. As a result anyone can calculate how the matching funds from larger donors should be distributed among the projects.
Of course much of the same process could be done with a traditional crowdfunding website — just not in a way that is transparent. Small donors would need to trust that the people running the crowdfunding website are honest and will not try to manipulate the voting process. As the amount of funding becomes more significant, the people running the website have a greater incentive to rig the vote. What if one of the projects bribes an employee of the website to change a few numbers in the database? How likely are people to know that anything was changed? With a larger funding pool the community would also have less trust in the process.
Also, since the on-chain process runs automatically, and requires no maintenance (other than running the blockchain itself), no organization needs to take a cut of the funds going toward projects. Crowdfunding websites, on the other hand, tend to take a percentage of the proceeds that go to projects — that is their business model. So unlike traditional crowdfunding, on-chain QF can be trusted by the community and can work efficiently even as it scales.
Yet QF still has its own challenges. The process is transparent in terms of which user address contributes funding to which project. But because of the permissionless nature of blockchains (meaning, the lack of gatekeepers), anyone can create any number of addresses, and there is no obvious way to tell whether one person has multiple on-chain user addresses. A bad actor may therefore use multiple addresses to contribute to the same project, thus manipulating the QF process — this is known as a Sybil Attack. As a result the bad actor’s project would get a large portion of the matched funds, against the preference of the wider community.
There are certain techniques to remedy this situation. These include using some form of identity or personhood verification, matching addresses with social media accounts, relying on multiple types of online presence to authenticate an identity, and so on. At the moment however these techniques seem to make it more difficult or time consuming to create and operate multiple accounts, but they don’t resolve the problem. With advances in AI these techniques will become largely ineffective, as AI bots would easily be able to mimic human behavior online. What’s more, these techniques largely rely on off-chain data and systems, undermining the trustless nature of the system.
There is a bigger issue with Quadratic Funding however. The issue is that it relies on external funding, and not on any feedback loop created by the impact of public goods projects. Having more projects (or more impactful projects) participating in the QF process is unlikely to result in much more funding in the matching pool. Which means that the funding doesn’t scale with increased impact, and the mechanism cannot be self-sustaining. Similarly, there is nothing in the QF mechanism to incentivize community members to seek out the projects that are likely to have the most impact.
Meanwhile, project contributors have similar incentives in QF as content creators on social media; if they want more funding going toward their project, one of the most effective strategies may be focusing on community engagement on social media. Yet, maximizing impact often means putting all the effort on project work, and spending minimal time on social media. After all, isn’t maximizing impact what’s in the public interest?
So while on-chain QF creates a trustless environment for advancing community preferences, it doesn’t quite align individual–public interest; it doesn’t create feedback loops for public goods projects, and therefore doesn’t scale funding in proportion to impact. It also doesn’t incentivize the community or project contributors to focus on impact maximization.
A blockchain coordination mechanism that focuses on the impact of public goods more directly is Retroactive Funding (RF). The concept behind RF is quite simple; since it’s much easier to determine the impact of a public good after the fact instead of predicting expected impact, RF is used to guarantee funding for the most successful public goods projects retroactively — once the impact is already assessed. Guaranteeing funding creates a market for investing in public goods based on their expected impact.
By creating economic incentives to invest in public goods, RF allows projects that otherwise would not have gotten funding to get off the ground. RF doesn’t guarantee that every public goods project that applies to the program will get funded, nor that investors will get a return. It does however create a business model for public goods projects, and these are risks that are inherent in any business venture.
While Retroactive Funding creates a business model that is based on impact, it has similar challenges to QF. One challenge has to do with the distribution of funding. While blockchains make the on-chain process transparent and trustless, it cannot do the same for human decisions. The question then is who decides which projects had the most impact? One approach may be to program the performance indicators into a Smart Contract, and automatically release funding to the project or projects with the highest score on those metrics. The problem with such an approach is that pre-programmed indicators can be gamed. What if a project manages to technically get the highest score yet has no meaningful impact on the community?
A different approach may be to have experts review the impact of the various projects and vote on which projects had the most impact. The trouble here is who selects the experts and how neutral is the process? If a lot of money is involved, how can we guarantee the integrity of the process? The intuition is that the RF mechanism should make the review process decentralized, and therefore trustless. But how can this be achieved? Without a clear approach to decentralized reviews, investors are unlikely to risk their money on RF, making it difficult for the mechanism to scale.
Another reason Retroactive Funding may have difficulty scaling is the issue of feedback loops (or lack thereof). Similar to Quadratic Funding, RF relies on external funding instead of capturing the value of public goods. As we’ve already seen, because the funding doesn’t grow in proportion to the impact created by projects, scaling RF may be a challenge.
Finally, RF leaves a lot of public goods projects behind. The mechanism is designed to reward only the most impactful projects, which means that there may be lots of small projects that collectively have a significant contribution to the community but will get no compensation at all.
How can we then create a market for any public goods project? The mechanism that attempts to achieve this is called hypercerts. Any contributor can create an on-chain certificate representing their work in the form of a non-fungible token (NFT). The certificate has multiple parameters, including the set of contributors doing the work, timeframe, scope of the work and its impact, and rights granted to the owner.
Unlike Quadratic Funding or Retroactive Funding, hypercerts are funding-source agnostic; they don’t specify how the certificates should be funded, but rather create a standard that can be plugged into by various other funding mechanisms. This approach allows incremental development of the protocol. Even if the funding mechanism is not entirely figured out, other aspects of the system can develop and grow in parallel.
One such aspect is in enabling multiple different impact evaluators to rate the work. Allowing independent evaluators helps investors or donors consider which evaluation they can trust the most, or at least have more data points about the impact of a hypercert.
With more impact data hypercerts can gain credibility, and attract donors or institutions who are interested in supporting public goods. With enough critical mass, an impact market can potentially form for hypercerts. There, publicly-minded speculators could buy the certificates with the expectation that their value would increase once the work has tangible impact.
Yet, even here the expectation of a return seems to rely on donors — on an external funding source — rather than on capturing the value of the public good itself. As long as this is the case the protocol will have difficulty scaling. Similarly, while independent evaluators can help solve the credibility aspect of reviewing the impact of work, the incentives for evaluators remain unclear.
Suppose a trusted independent evaluator is used to review hypercerts. Such an evaluator may have high credibility and the process would work well at a certain scale. But what would happen if the system scaled and the number of hypercerts minted by contributors grew exponentially? Who would review all these additional certificates? How will they be compensated (if at all)?
Could evaluators mint hypercerts for the public good they provide for evaluating other hypercerts? Perhaps that is one possible solution, but how well will such a process work? Can evaluators be expected to fairly evaluate a hypercert of one of their own? There is a risk that any evaluator will overvalue such hypercerts with the expectation that others would reciprocate in kind, thus creating perverse incentives among evaluators.
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The mechanisms we described so far are by no means an exhaustive list of the coordination tools offered by blockchain technology. In fact, there is active and ongoing research and development of public goods funding mechanisms for programmable blockchains.
And yet, existing on-chain mechanisms for public goods funding tend to have the common challenges we already discussed; lack of feedback loops for public goods, a need for external funding, scaling limitations, and so on.
While efforts continue to find solutions to these challenges, little to no work is being done in the blockchain space for dealing with the other side of the equation: negative externalities.
Programmable blockchains created a new paradigm by combining a trustless environment with code-based economic incentive structures. This paradigm can potentially be used to solve the individual–public interest alignment problem. While the mechanisms proposed so far present promising and innovative approaches to funding public goods, they still fail to offer the solution we need. We still don’t see the breakthrough that would allow us to create effective feedback loops for public goods, and nowhere near a solution for externalities.
But there is an even more troubling aspect to blockchains. The key benefit of blockchain technology is that it allows us to transact and interact trustlessly, and without the need for centralized authority. But what does the blockchain put in place of centralized authority? Who decides the rules and consensus mechanism by which the blockchain operates?
A prevalent argument in the blockchain space is that even though proposers (miners or validators) get rewarded for securing the network and creating blocks, they cannot decide the rules of the blockchain. Instead nodes are the ones who decide the rules of the network. But while blockchain proponents would like to claim that this process is community-driven, the reality is that money interests dominate the system.
Blockchains are by no means democracies. Yet, there is usually a formal process by which changes to the protocol — called “Improvement Proposals” — are proposed, reviewed and implemented. Anyone can propose a change, and as the proposal goes through the process, the community can suggest modifications to it. During the review period various stakeholders in the blockchain (developers, miners, node operators, and so on) can signal their support or opposition to the proposal. At the end of the day the goal is to build a wide consensus in the blockchain to have the change implemented.
But what drives support or opposition to changes in the blockchain protocol? To get an intuition of the dynamics at work we need to consider what happens during a split (“hardfork”) of a blockchain. Let's say that the majority of the coins in a blockchain is held by a handful of individuals, while a smaller portion of the coins is owned by a much larger group. And let’s say that all the wealthy coin holders support a particular improvement proposal, and the wider community opposes it. A hardfork would result in two identical (historic) versions of the blockchain, where one version operates according to the original “old” rules, while the other version follows the new proposed rules.
Right after the split both wealthy holders and the rest of the community would own their coins on both forks of the chain. The value of the coins on the original blockchain remains the same, while coins on the new blockchain still have no value due to lack of trading (no price discovery).So what happens next?
Since wealthy holders value “their” blockchain a lot more than the original chain they would want to sell their coins on the old chain and buy as many coins as they can on the new chain. How many of their coins would they sell? Essentially, wealthy owners would keep selling until they no longer think the chain is overpriced. If they don’t value the old chain at all they would keep selling all their coins regardless of how low the price gets.
A mirror image of this dynamic would occur on the new blockchain; wealthy holders would want to buy every available coin as long as they believe they’re undervalued. Community members who prefer the old chain, on the other hand, would want to sell their new-chain coins. This buying and selling would continue until new equilibrium prices are reached on both chains.
And what will these new equilibria look like? Since most of the wealth has now been transferred from the old chain to the new one, we’d end up with the old chain having a fraction of its previous market cap, and the new chain gaining most of that value.
Miners (or validators) will also take that into account when they decide which chain they would like to secure. Since their decision is primarily driven by financial considerations they would opt for the chain that offers the greater reward value. While both chains may be offering the same amount of coins, coins in the new chain will have greater value, so that is the chain they're likely to secure.
Now presumably community members understand this dynamic well, and don’t want to see the value of their assets plummet and the security of their chain degraded. The same is true for miners (or validators), developers, node operators, and all other stakeholders in the blockchain. They would therefore take this into account when they’re proposing new changes to the blockchain, or when considering proposals by others that potentially disadvantage wealthy holders.
The result is that, arguably, the blockchain replaced a centralized system that is at least partially responsive to public interests with a system that is decentralized but essentially dominated by wealthy and powerful interests — a plutocratic system where those who own most of the wealth can implicitly dictate the rules.
But if that is the case, how likely is such a system to push back against externalities when those benefit powerful interests? And how likely is it to create individual–public interest alignment?
At this point we’ve exhausted all our options. We’ve looked at a market-based approach, at activism, government, philanthropy, and now blockchain technology. While some of these seemed promising, none could effectively solve the problem of individual–public interest alignment. What hope is there then to prevent dystopia? What hope is there to put us on a path toward greater abundance?
# Chapter 7: The Abundance Protocol
Perhaps we shouldn't give up just yet. It seems we looked everywhere on the blockchain for mechanisms that capture the value of public and common goods. There is just one place we haven't looked at: the blockchain itself. But that seems to be the one place where a common good — network security — is paid for without any external funding source.
To date, the two largest blockchains by market cap, Bitcoin and Ethereum, have funded their own network security to the tune of nearly 1 trillion dollars or more (depending on the market cap of these coins) — that’s trillion, with a T. This likely dwarfs any effort to fund public or common goods, on-chain or off.
To put it into perspective, the Giving Pledge, representing a commitment by more than 200 of the world’s richest people to give away most of their wealth to charity over their lifetime, has about $600 billion pledged. And that is money pledged — over a lifetime — not money actually given.
Network security is a common good that benefits every user of a blockchain network, whether they actively transact on the network or just hold their money there. Yet, Bitcoin and Ethereum have funded network security without relying on wealthy donors, government bailouts, or cookie sales. Instead, they do so through coin inflation; the blockchain's consensus mechanism issues coins to miners (or validators) when they create new blocks and secure the network.
Keeping the funding of network security self-sustainable means keeping everyone happy: both the people securing the network and those using it. Since network validators are paid through coin inflation, the network’s users would be content if such monetary inflation did not devalue their currency. How then do you maintain the value of the cryptocurrency? The short answer is: by using the laws of supply and demand.
As supply increases to pay for network security, maintaining the same value either means demand for the currency has to increase proportionately, or coins need to be removed from circulation in other ways — or some combination of both.
Bitcoin, Ethereum, and other blockchains have different strategies to achieve self-sustainable funding. Yet, the jury is still out on which would be more effective long-term. As these networks have demonstrated already, however, by running for years without interruption, it's possible to pay for a common good self-sustainably and without external funding.
But network security is just one very particular type of a common good. The question is, how do we extend this mechanism to apply to all common and public goods?
Perhaps we can do so by applying some of the ideas we had for a market-based solution to the blockchain. To recap, we wanted to build on how companies effectively fund common goods that benefit the business. Companies exchange value in business-to-business transactions, where funding comes from a common treasury. We wanted to apply the same logic to create an exchange of value between public goods contributors and the wider community or ecosystem.
The idea is that contributors should be rewarded based on the economic impact their work makes on the ecosystem. Since economic impact is an objective measure, it would eventually be possible to use AI to determine it and allocate funding. The challenge is getting there.
How do you make sure that the developers working on AI are aligned with the ecosystem, and don't try to manipulate the allocation mechanism? And what happens in the meantime? How can users evaluate the impact of projects credibly, transparently, and scalably while the AI is being developed?
With blockchain technology the pieces of the puzzle are finally starting to fall into place.
We can start by replacing our original concept for a common treasury with the cryptocurrency model. Now instead of members contributing funds to a common treasury, there is no need for a treasury anymore; anyone who wants to participate in the ecosystem would simply buy the blockchain's native currency to transact with.
There is one major difference however between the economic model in this ecosystem from other blockchain networks. In most other cryptocurrencies the expectation is for the coin to increase in value over time. Such an approach may be beneficial for using the currency as a non-productive investment asset, but it’s quite terrible to use it for economic activity.
The rationale is that if the currency is expected to appreciate in value, consumers would prefer to wait for product prices to drop instead of buying anything. But what happens when everyone keeps delaying purchases? Producers will expect to sell fewer products and start laying off workers, downsizing, and shutting down factories. A deflationary currency thus leads to decreased economic activity and stagnation. The purpose of our ecosystem however is maximizing economic abundance.
For that reason, the expectation in the ecosystem is for the coin to maintain a relatively stable value in relation to the ecosystem’s economic capacity. Keeping the value of a coin stable as economic capacity increases means increasing monetary supply in proportion to growth in economic capacity.
Economic capacity is the maximum output of the economy given the amount of available resources, scientific knowledge, and technology. Since the amount of resources on Earth is practically fixed, growth in economic capacity essentially means improvement in knowledge or technology.
If such knowledge is released as a public good, its economic impact would be equivalent to the growth in economic capacity. Which means that increasing monetary supply proportionately to compensate public goods contributors would keep the value of the coin stable.
Notice also how compensation for a public good is different from compensation for a common good. The main difference has to do with the fact that, unlike common goods, public goods are inexhaustible; while public goods require an investment of labor and resources to produce the knowledge, once it is produced it has a permanent impact on the economy’s capacity. It is therefore an abundant good that requires practically no further maintenance. Because it leads to growth in economic capacity, such growth is associated with an increase in demand within the economy. An increase in monetary supply to match the greater demand leads to value stability for the currency.
Compensating for a common good, however, means ongoing use of labor or resources to maintain a certain state of economic activity. This is what happens with network security, for example. If compensation for the common good comes from monetary inflation, it has to be matched with an equivalent removal of currency from circulation. Otherwise it would lead to devaluation of the currency. For this reason we need to keep in mind that the mechanism to fund public goods would be somewhat different from the one for common goods.
Now, why does it make sense to maintain value in relation to economic capacity, and not, say, to growth in production? Growth in production can happen even without changes in economic capacity. It can occur due to changes in consumer preferences, seasonal variation, and so on. Tying monetary policy to changes in preferences then makes little sense. But tying it to impact and economic capacity can incentivize growth.
We know that if knowledge is released as a public good it has the greatest economic impact (compared to keeping the knowledge private), since it leads to the most growth in the economy’s capacity. Compensating impact incentivizes contributors to work on and produce public goods that are expected to have the most effect on the economy. It therefore leads to the most economic growth and greatest benefit to the ecosystem.
The benefit of using monetary inflation to compensate public goods contributors is that it creates complete alignment between all participants in the ecosystem. What produces this alignment? Every participant in the ecosystem is interested in two things: maintaining the value of the currency, and maximizing the ecosystem’s economic growth. Every participant wants to maximize economic growth in the ecosystem because that’s what gives them the most economic opportunity and the greatest potential to prosper.
Since economic growth depends on growing economic capacity, every participant is interested in maximizing the impact that comes from public goods. At the same time, participants obviously don’t want their currency devalued, since that reduces their level of prosperity.
Using currency inflation therefore means that everyone in the ecosystem benefits from the production of any public good that leads to growth in economic capacity, regardless of whether they directly benefit from it. At the same time, every participant wants the impact of these public goods to be valued accurately; undervaluing public goods would lead to fewer contributors creating public goods, and thus slower growth to the ecosystem. Overvaluing public goods would lead to currency devaluation and fewer participants using the ecosystem. Accurate valuation leads to currency stability and maximal impact and economic growth.
This mechanism finally allows us to create effective feedback loops for public goods. Since ecosystem participants are aligned on accurately evaluating the impact of public goods projects, participants benefit the most when their work has the greatest impact on the ecosystem — the greater the impact, the greater the reward. Ecosystem participants are perfectly content with issuing large sums of money to contributors, because as long as the evaluation is accurate, that means there is much economic growth in the ecosystem.
So now we know that, at least in principle, it’s possible to create an exchange of value between contributors and an ecosystem, and it’s possible to create effective feedback loops for public goods. But we still have all our work cut out for us: we need to show how this can be achieved in practice. How do we design the mechanisms that would make it all work? How do we make sure that the system maintains public trust, and that it cannot be gamed by bad actors?
Solving the problem of public goods would certainly be transformational for our economy, but it’s still just half of the equation. To change our trajectory toward dystopia, and put us on a path toward economic abundance, we also need to solve the problem of externalities. Yet, it’s still unclear how to create feedback loops for negative externalities in our system.
But let’s not get ahead of ourselves. Before trying to tackle the externalities problem, let’s see how we can create a blockchain protocol to exchange value between contributors and the ecosystem.
Because of the complexity of our task we can consider using the protocol for a programmable blockchain such as Ethereum as our baseline. We will then modify such a protocol for our purpose: creating the foundation for an economy of abundance.
At the moment we're less concerned with the technical aspects of how the blockchain reaches consensus on the creation of blocks or maintaining network security. Our primary concern is with making sure that the network reaches a consensus on the value of common and public goods. The network can then compensate contributors accordingly in the contributor-to-ecosystem (C2E) value exchange.
There is a benefit to separating the technical network security consensus logic from the C2E value consensus. Doing so allows us to run the C2E value consensus at the Smart Contract level on existing blockchains. Which means that on-chain public infrastructure and public goods projects on existing blockchains can finally have effective feedback loops.
What then do we need to make the system work? Where do we start? Perhaps we can start with the desired outcome and build out the rest of the system from there.
Ultimately, we would want the entire process to be done with Artificial Intelligence. This is achievable because impact can be objectively measured, which means that AI computation can be independently verified. The eventual process will have an AI review for each project. Following the review, users would validate the AI computation by sampling it, thus ensuring its integrity.
Obviously simple desktop computers would not be able to verify an entire AI computation, but by sampling all parts of the computation groups of validators will be able to validate every AI review collectively. And since there is still a challenge period in place in such a system, there should be sufficient time for validators to complete each validation. An AI-based protocol is therefore a viable proposition — and one that would be maximally scalable and consume the least labor and resources.
But until we get there we need to develop the protocol so that ecosystem participants can transparently, credibly, and efficiently review the impact of projects, and compensate contributors accordingly.
The review has to be transparent so that anyone can analyze whether the data provided leads to the conclusions made by reviewers, and challenge the results otherwise. It has to be credible so that the ecosystem can have trust in the process and agree on its results. And it has to be efficient so that the system can function properly and scale.
The AI system can be trained on participant reviews in parallel. As it becomes more robust, AI can complement participants’ work, and gradually replace it. This would make the protocol more efficient over time while maintaining public trust in the process.
So how do we make the review process transparent, credible and efficient? For the process to be credible, the protocol must have an incentive structure in place to align the interests of all participants in the ecosystem, including contributors, users, and reviewers. Such a structure would not stop all attacks on the protocol, or prevent bad actors from trying to game the system. It would however make such attacks a lot less likely, and a lot less effective. It would also create feedback loops within the ecosystem to make it more resilient against attacks.
Think, for example, what would happen if social media accounts try to undermine the protocol by falsely accusing validators of corruption. Such an attack may work if the economic incentives of validators were misaligned with those of other participants in the protocol. If the attacks are persistent, they may turn participants against each other and make them lose trust in the credibility of reviews and in the protocol itself. Once the protocol loses credibility, it becomes harder to reach consensus on the value of public goods. Which then leads to the currency losing value and the ecosystem becoming less attractive to contributors and users alike.
But if the incentives for all participants in the ecosystem are aligned, no one would think that claims of corruption by individual users affect the protocol. Not because it's not possible for individuals to be corrupt, but because everyone has the incentive to defend the ecosystem from attacks by bad actors. And if all participants are aligned on defending the protocol, the chance of any corrupt player succeeding in perverting the protocol is miniscule. Aligned incentives would therefore insulate the ecosystem from attacks on individual users. Which means that if the purpose of such attacks is to discredit the ecosystem as a whole, these attacks would lessen as well.
The incentive structure makes the protocol more resilient and hardened against attacks but it cannot by itself counter them. For that the protocol needs built-in mechanisms that can resist attacks and exploits, and prevent bad actors from trying to game the system.
The ability to effectively prevent bad actors from gaming the system would make the review process credible. It would also allow participants to more easily reach a consensus on the value of projects, and reward contributors accordingly.
But in order to get there both the review process and the data it relies on must be transparent. Anyone should be able to audit the data, as well as the review process, and evaluate whether anything along the way was not done properly. If suspicious activity is detected, anyone should be able to challenge the results of a review before funds are released to contributors. In that case it may be necessary to freeze the funding or redo the review with a different set of reviewers.
Finally, we must recognize that the ecosystem doesn't have unlimited resources, and that reviewers' time is valuable. The protocol must be able to prioritize projects, while dedicating sufficient expertise to each review. But if the amount of expertise required for a project must correspond to its impact, how would the protocol know how much expertise is required before it’s been reviewed? This can only be done if expertise resources are based on an estimate of the impact.
What’s more, since public goods are supposed to increase the economy’s capacity, we need to differentiate between the expected impact if fully realized, and the estimated impact. When a public goods project is just created it may have almost no realized impact, but may have significant expected impact if fully realized. For instance, when a cure to a disease is just discovered it has no realized impact, but if everyone affected by the disease is cured the impact may be significant.
The protocol can conserve reviewers’ efforts by first determining the expected total impact of a project, and then periodically reviewing realized impact. Of course, funds should only be released to contributors based on realized impact, not mere expectations.
For the review of total expected impact, those proposing the review should provide their estimate of the total expected impact. For a periodic review, proposers should provide their estimate of the realized impact as a percent of total expected impact. Such estimates would help the protocol determine the required effort needed for reviewing expected or realized impact.
Those providing the estimate should have an incentive to look for projects that the ecosystem would wish to prioritize. Proposers should also have the incentive to be as accurate as possible in their estimates. How can this be achieved?
We want proposers to seek out the public goods projects that would benefit the ecosystem the most. We also want them to be as accurate as possible in their estimation of the expected impact of projects, so that the protocol won’t have to waste resources in the review process. The analogy here may be to stock investors who spend their time picking the stocks that would generate the most return. Such work should supposedly make the market as a whole more efficient.
Applying the same logic to proposers means that they should be compensated proportionately to the impact of the project they propose. At the same time, if they overvalue the expected impact of a project they should bear a financial cost for that. But what if a proposer undervalues a project instead, and the protocol then doesn’t dedicate enough resources to properly review it? Obviously this should not harm contributors. How can all these demands be reconciled then?
An elegant way to resolve these competing demands may be to require the proposer to provide the funding for validator reviews. Then, once the review process is complete, a fixed percent of the project reward can go to the proposer. Of that fixed portion, a part would be dedicated to cover the cost of validator reviews, and another part will be the proposer’s premium.
Now it makes sense that a project with greater impact would require more expertise to review. However, the growth in required expertise should not be linear. If the impact of one project is double that of another, perhaps the required expertise is only 50% greater between the second and first projects, as an example. Similarly, there must be some minimal amount of expertise required for even the simplest project. Taking such considerations into account lets us construct a formula for the required expertise for any level of impact. It also indicates that a proposer can get a larger premium for proposing projects with greater expected impact. At least that is the case as long as the proposer’s estimate is somewhat accurate.
If the proposer overestimates the project’s expected impact, they’d be funding more validators than necessary. Once the project’s impact is determined by the validators, the proposer may get a smaller reward than the money they paid validators. If the proposer was way off they may be losing money in the process.
On the other hand, suppose the proposer purposefully underestimates the project’s impact to at least get some return. Then they’d still be getting a proportionally smaller premium than they could have gotten with a more accurate review. That is because a lower estimate means relatively more required expertise per impact value. Since an underestimation by a proposer should not disadvantage project contributors, other proposers should be able to reevaluate the project, thus earning the premium that the initial proposer gave up with the lower estimate.
Such a system thus incentivizes proposers to look for the most impactful projects. It also motivates them to accurately estimate projects; if they overestimate they end up losing money in the process by overfunding validators. If they underestimate they give away the premium from the project’s impact. Accurate estimates therefore lead to the highest returns to proposers, and the most efficient resource use for the protocol.
Note that because the protocol requires proposers to put up funding first this may disadvantage participants based on their economic status. To mitigate this issue the protocol should allow investors to lend money to proposers. Investors can consider the track record of proposers in estimating project impact, and offer funds at a competitive interest rate, based on their level of risk.
Proposers with the best track record would have more investors competing to lend them money, and would therefore get a lower interest on their loan. By creating a market for investors to provide loans based on proposers' track record, the protocol can make the process more equitable and meritocratic.
Another point to note is that in the current protocol design reward for proposers (and validators) comes fully from the project reward. The work done by proposers and validators is certainly a common good that benefits everyone in the ecosystem, but where should the funding for it come from? Paying for it from the project reward prevents currency devaluation since the total supply of the currency remains the same, but at the same time it reduces the reward for contributors. Reducing contributor rewards may slow ecosystem growth, since contributors would be motivated to work for other ecosystems that offer better rewards.
The alternative is to provide contributors the full value of their impact, while rewarding proposers through currency inflation. Such inflation can later be absorbed by removing an equivalent amount of coins from the protocol. The amount of the reward should still be tied to the project reward, since that aligns the interests of proposers with the ecosystem.
Putting together the concepts we have so far: we need a process by which users would propose to the protocol projects to be reviewed, and provide the expected impact of the project. Then there needs to be some mechanism by which the protocol can prioritize projects based on their expected value to the ecosystem, and assign users with sufficient expertise for the review. The next step would be reviewing the project and determining its expected economic impact, based on all publicly available data. After the project is reviewed, anyone should be able to challenge the results to ensure the credibility of the review.
As the impact of the project is realized, a proposer may request a periodic review and provide the estimated percent of realized impact. Once again the review will be prioritized and assigned reviewers by the protocol. After the periodic review there should be another challenge period. Then finally funds should be released to contributors based on the realized impact of their work.
Though the protocol is beginning to take shape there are still lots of open questions to be resolved. How will funds be distributed between contributors? How are reviewers selected for a project? How is subject matter expertise determined, and how do participants gain expertise? What are the incentives to keep proposers, and everyone else for that matter, aligned? These are just some of the questions we need to address for the protocol to function properly and credibly.
As these issues are systematically addressed, you should keep in mind that the solutions proposed are by no means the only way to solve the problem. Perhaps they’re not even the best ways. The hope with this exercise, however, is to show that it is possible to apply a rigorous method to determining consensus value, while effectively dealing with bad actors or attempts to manipulate the protocol.
So let's get into it. On funding distribution, ideally each contributor to a project should get paid according to their relative contribution. Contributors who collaborate on a project know best how much each of them pitched in. It would therefore be most efficient if all contributors can come to consensus on how funding should be distributed among them. The protocol should lock the funds until such consensus is officially reached.
The protocol should not expend resources on funding distribution among collaborators, since this is not essential to the ecosystem. However, if there is a dispute, the protocol can be a neutral arbitrator. Such a function can protect smaller contributors from unfair treatment, thus promoting collaboration. If all collaborators know that the funding will be distributed fairly, they will be focused on the work itself and not waste any time on self-promotion.
Because arbitration requires effort and resources from the protocol, the cost must be borne by the parties in dispute. The cost of arbitration should therefore incentivize contributors to try to come to consensus on their own.
There is another important aspect to funding distribution however: since public goods are freely accessible by anyone, what happens when one project relies on the work of others? If we want contributors to be fairly compensated for their work, the same should apply to the sources that influence the work. Contributors must therefore specify sources of influence and the extent those influenced the project. Funding will then be distributed based on how much contributors and sources contributed to the project.
Similar to the contributors themselves, sources of influence should be able to dispute their funding allocation. By extending this function to influences, contributors would want to be fair and accurate in reaching a consensus with all the parties that contribute to the project.
The rationale of including contributors and influences in funding allocation is straightforward: each project and each contributor should be compensated based on the impact their work makes on the ecosystem. Since public goods are freely accessible by all, anyone should be able to build on the work of others, but that means also giving them the proper credit and compensation for their effort.
Rewarding the sources doesn’t just make the whole system work. It also creates a framework for abundant goods contributors to collaborate and openly build on each others’ work. It allows compensation for public goods, and obviates the need for copyrights or IP rights (which would become nearly impossible to enforce in the age of AI anyway). Simply put, when every person knows they’d be compensated for their impact, even when others use their work or build on it, it motivates everyone to create public goods openly and collaborate freely. The result can be an incredible proliferation of abundance throughout the ecosystem.
So now we see how the protocol can integrate mechanisms to align everyone’s incentives with the goal of maximizing impact. We also know how all those who contributed to a public goods project can reach internal consensus on funding allocation. We just don’t know how the ecosystem can reach consensus on the value of public goods in the first place.
We previously discussed the issue of reviewing project impact within the market-based solution, and proposed a randomized subset of users to review projects and determine impact. By selecting a random subset of reviewers we can ensure that the review process can scale, since not every decision needs to be made by the entire ecosystem. We also avoid the possibility of collusion between reviewers and contributors, since contributors would have no way of knowing who will review their project.
The trouble however was that the randomization process had to be transparent. Similarly, reviews had to be meaningful. What exactly does this mean? It means that reviewers are estimating the impact of public goods projects on the ecosystem, they're not merely casting votes based on their preference. For them to review the impact they need insight into the subject matter. Yet, there was no clear way to assign subject-matter expertise to validators in the market-based solution. How is subject-matter expertise determined and by whom? How do you make the process transparent and credible? How do you make sure reviewers can't game the system? And ultimately, how do you make sure that reviews result in accurate determination of impact? All these issues could not be solved within the market-based solution. Now let’s see how blockchain technology can potentially solve these.
Randomized validator selection may be the easier task. A pseudo-random functionality can be directly coded into the protocol, so that a review validator set can be selected at random for any project review. Since the code is publicly available, and the process is executed entirely on-chain, anyone can verify that it is not manipulated in any way. Such transparency means that everyone in the ecosystem can trust the integrity of the selection process.
The same applies to the data that validators use to make their impact evaluation. All the data can be hashed and added on-chain, so that all validators know they're dealing with the same data set. It also allows anyone to audit the process and verify that data wasn't altered.
But data integrity doesn't end with making sure the data wasn't altered. It's even more important to know the source of the data. If the data comes from the contributors themselves, for example, or other biased sources, how can the ecosystem trust it? On the other hand, the more neutral and decentralized the dataset is, the more reliable it is.
So now the protocol can randomly select validators to review public goods projects, and can ensure the integrity of impact-related data. But how does it ensure that the review process produces reliable results? The key to that may be making sure that validators have sufficient expertise to review each project. If a project is expected to have a great impact on the ecosystem, the set of validators who review the project should have more expertise.
That doesn’t mean that the validators for each project should all have domain-specific expertise related to the project. In fact, experts in any field are likely to consider their own field as more important than others, thus producing distorted results. What would work better is to first have a group of experts with deeper knowledge in the project’s domain (or domains) who determine the credibility and significance of the project within the field. Then a second group of validators from across the ecosystem can use the first group’s expert analysis to evaluate the expected impact of the project on the ecosystem. The decisions within each group can be weighted based on the level of expertise of each validator, thus making the process more meritocratic.
One benefit of having an on-chain protocol is that non-monetary attributes can be assigned to participants’ accounts. Such attributes can include expertise scores in various subject domains, which can then be used in selecting validators for a project review and weighting validations. To keep the process meritocratic, and avoid plutocratic abuse, no one should be able to purchase or sell their expertise credentials. This can be achieved on-chain by disallowing the transfer of expertise scores between accounts, or by using non-transferable tokens (NTTs) or “soulbound” tokens (SBTs) to denote the expertise score. But the question still remains on the process for obtaining expertise scores by participants.
How would participants in the ecosystem obtain their subject-matter expertise? Validators’ expertise scores are used in the protocol to evaluate the impact of projects, and ultimately issue currency. It is therefore critical that the ecosystem can have trust in the process of obtaining expertise.
As with everything else in the ecosystem, everyone must be able to trust the integrity of the process. If the impact of a project is weighted by the expertise of validators, and it’s unclear where their subject-matter expertise came from, how can the ecosystem trust the process? So the ecosystem must agree on what constitutes expertise and how participants or validators can obtain it.
If the protocol is built around establishing consensus value for public goods, why not use the same consensus for obtaining subject-matter expertise?
How can this be done? Perhaps contributors to public goods projects should be able to obtain a domain-specific expertise score alongside their funding reward. Since every project falls within at least one domain of expertise, each contributor can receive an expertise score corresponding to their contribution to the project. Contributors may choose to internally allocate the expertise provided by the project validation differently from the funding reward itself. This especially makes sense for complex projects where contributors may be working in different domains of expertise. It would make no sense, for instance, for an economist and a biochemist to receive an equivalent proportion of domain-specific expertise score, especially if they worked on completely different aspects of the project.
Since every contributor would want to receive the expertise score that corresponds to their knowledge, there is little incentive for them to allocate scores incorrectly. Similarly, since the expertise scores awarded by the validation process are finite, contributors cannot collectively obtain a greater score than what was awarded. Which means that there is no risk of contributors abusing the process to obtain an advantage in their influence in the protocol.
Thus, the process outlined ensures that expertise scores reflect contributors’ domain-specific knowledge. It also ensures that when contributors use their expertise in validations they are aligned with the ecosystem. That's because their expertise would indicate that they contributed to growing the ecosystem.
Just like contributors can obtain domain-specific expertise through the validation process, the same should also apply to proposers and validators. Proposers need to have at least some level of domain-specific expertise to be able to estimate the expected impact of projects. It therefore makes sense that the premium the proposer receives for successfully reviewing a project would also result in gaining expertise in the domains of the project.
Similarly, whenever subject-matter expert validators review a project they can accumulate expertise in their review domain. Ecosystem-wide validators may gain a general expertise score, which would reflect their contribution to the ecosystem. This process can even be improved to incentivize high quality validations: following the review itself (either expert or ecosystem-wide), the set of validators can have a round of Quadratic Voting (QV) on the quality of every validator's review. Validators would vote on the quality of each review and be able to flag fraudulent reviews. Those receiving higher QV scores should receive a greater portion of the validation reward, as well as a greater expertise score.
To minimize the chance of collusion during QV, validators can be randomly assigned into two separate groups that would then vote on each other's individual validations. This process ensures that validators take their work seriously, and that their interests are aligned with the ecosystem.
All the methods of obtaining expertise through the validation process — whether it is for contributors, validators, or proposers — require existing validators with subject-matter expertise. But if obtaining expertise requires validators with expertise, where did their expertise come from? Logically, there has to be a point at which no one in the ecosystem had expertise. So how do you get something out of nothing? This applies not just to the very beginning of the ecosystem, what about every new domain category introduced in the ecosystem? Also, it seems the process of obtaining expertise is extremely cumbersome. While obtaining expertise by being a validator is relatively simple, you need to already have expertise to be a validator. But so far the only way to obtain expertise otherwise is by being a project contributor or a proposer — both of which require great effort or time investment. With such hurdles, how can we expect the ecosystem to get off the ground or work at scale?
For the protocol to be scalable we need more convenient ways to obtain expertise in the system. But we need to do so while maintaining the integrity of the process to obtain on-chain expertise. Expertise in the protocol is not merely a sign of knowledge, it is also an indication of merit and of alignment, since it is obtained by contributing to the ecosystem. And since there is no way to buy or sell expertise in this system, it’s an effective (and meritocratic) alternative to plutocratic rule.
So how do you make obtaining expertise more convenient? What is the problem with anyone being able to validate projects? The rationale for weighting validator reviews based on level of expertise is that such a mechanism prevents Sybil Attacks on the review process.
Since expertise scores represent actual contribution to the ecosystem, and are non-transferable, splitting expertise between multiple accounts in the protocol would have the same exact influence as just using one account. To illustrate this point, if you have one account with 300 expertise points, it would have just as much of a chance to be randomly selected to validate a project as the combined chance of 3 accounts with 100 expertise points each. There is therefore no risk of users creating multiple accounts to game the system; the total expertise score, and therefore total influence in the protocol, of all accounts created by a user will always be the same as the user just having one account.
While the mechanism can effectively prevent Sybil Attacks, it creates a problem when it comes to new users validating a project. If the users have no expertise in the domain, the weight of their influence would be zero. And since users can create multiple accounts permissionlessly on a blockchain, if anyone without expertise could join a validation set, a user could potentially flood a validation set with countless accounts.
But perhaps there can be a workable solution that would allow new users to participate in a validation while still keeping the integrity of obtaining expertise in the protocol. We can consider three classes of users who don’t have direct expertise in the project’s subject-matter: users with expertise in related fields, users with expertise in unrelated fields, and new users with no expertise at all.
For users with expertise in related fields, the protocol can calculate a “relatedness quotient” which would indicate the degree to which a user’s expertise in other fields are related to the project’s field. Such a quotient can be based on how often project reviews in the protocol have these fields in common, and the level of importance of each project to these fields. The user’s expertise in the other fields can then be multiplied by the relatedness quotient to determine the weight of their expertise in the validation. These users can then participate in the validation pool like any other validator with domain-specific expertise.
The protocol should also allow a limited number of users with expertise in unrelated domains participate in the validation set. Though these users won’t have weight in the Quadratic Voting process, other validators can vote on the quality of the reviews, thus granting the reviews weight in the system. The same concept can apply to users who have no expertise in the system at all. By allowing a few such validators to participate in the validation set, it wouldn’t overburden other validators who have to review the quality of these validations.
Thus we can have a process that maintains the integrity of obtaining on-chain expertise, while allowing users with no expertise in the field and new users to participate in each project validation.
The next question is how would anyone be able to obtain expertise in a new field that is introduced to the protocol? This may be less of an issue for mature ecosystems with lots of domains, but for a nascent ecosystem this may be a common occurrence.
One way to solve the problem may be for a proposer to “graft” the new domain onto existing domains by estimating a relatedness quotient for the new domain in relation to these domains. This process can be initiated during the creation of a new proposal. Validators with expertise in the related domains can then review the project with reviews weighted by relatedness quotient.
To prevent proposers from trying to manipulate the process, there needs to be an initial validation process that ensures the relatedness quotients are sensible. These initial validators can come from the fields specified in the proposal as well as from ecosystem-wide validators. Once the new domain is successfully “grafted” onto existing domains, the system can start calculating relatedness quotients based on the frequency of common fields, as described earlier.
What if a field is unrelated to any other field in the ecosystem? This is hard to imagine, since all fields have at least somewhat of an overlap, but such a situation is possible in the very beginning of an ecosystem. There are two ways to approach this problem; either wait until the ecosystem is more mature and has more related fields, or treat the new field like a separate ecosystem. If the field is treated as a separate ecosystem it is possible to merge these ecosystems later on — though that is a subject for a later discussion.
So now we have a solution for how users can obtain expertise regardless of existing expertise. We also have a solution for project validation in new fields. We still need an answer to how users would obtain expertise at the very beginning of the ecosystem.
It should be evident that if at the beginning no one in the ecosystem has expertise then no one can validate any project. But if it's impossible to validate projects then no one can obtain expertise in the protocol. What then can be the solution? The answer is that when the ecosystem is launched, the initial users — the founders of the ecosystem — would have to self-assign expertise scores based on contribution to the ecosystem.
To state it mildly, this seems like a less-than-ideal solution. Obviously it would be preferable to have an ecosystem where users have verifiable expertise out of the box. Unfortunately that seems to be a technical impossibility for value-based systems. That is like expecting a baby to walk and talk from birth. The good news is the abundance protocol has an effective feedback loop in place that incentivizes founders to self-report their expertise and contribution as accurately as possible.
The goal of founders in the abundance ecosystem is to create a flourishing economy, since that is what would allow them to realize their economic potential and to prosper in the long term. They can only do so if lots of people participate and make the ecosystem thrive. But the more people participate the smaller the founders’ relative influence in the system becomes. At the same time, the more people participate the more the ecosystem decentralizes and gains public trust in the process. This is the general dynamic of a successful ecosystem where founders can prosper. But the only way to get there is by being as transparent and honest as possible in the initial self-reported expertise.
If the founders are being manipulative or fraudulent in their self-reported expertise, people are unlikely to join such an ecosystem, since their contribution will not be valued fairly. But if not many people want to join, the ecosystem would quickly become a ghost-system that no one wants to contribute to.
What’s more, with the proliferation of abundance ecosystems, each new ecosystem launch would be carefully scrutinized by participants in other ecosystems, who would want to provide the most accurate information about the new system. Since ecosystems are not competitors for scarce resources, there would be no misalignment between the public interest and the interest of reporters on this new ecosystem, and the reporting is likely to be reliable. This is yet another mechanism that can help protect users from malicious actors, and incentivize founders to be truthful in their self-reporting.
So now we have a clearer picture of how expertise can work in the abundance protocol, and how the ecosystem can grow and branch out. The integrity of the process of obtaining expertise has to be maintained at all times to preserve public trust in the protocol. Expertise allows meritocracy and alignment within the ecosystem, since it cannot be bought and is based on user contribution to the protocol. Expertise also protects the protocol from Sybil Attacks, as contributions are distinct and non-fungible.
While the role of expertise can now be better understood in the protocol, there are still areas of the protocol that need to be flushed out. This is particularly true as it comes to the role of validators in the protocol. While we worked out the mechanism to keep proposers aligned with the ecosystem, the alignment for validators is still relatively weak. At the moment, validators can lose some expertise points at the most for trying to game the system. But if the expected upside of cheating is greater than the potential downside, such a mechanism is unlikely to be effective. Making the mechanism more effective would mean introducing penalties for fraudulent validations. This can be achieved by validators having to lock up an amount proportionate to their payout (maybe 1/3 the amount) during the review process. This kind of mechanism could be particularly effective for new validators who have no expertise point to lose. By locking up a nominal amount of funds users will have less of an incentive to create multiple accounts with no expertise and try to obtain expertise while having no skin in the game.
After the review process, including the QV voting, is completed, and after the challenge period is over, validators’ funds will be unlocked along with whatever payout (and expertise) the validators earned.
Without locked funds, a user can create countless accounts, apply to every validation pool multiple times, and create arbitrary reviews when selected. Then, even if 99% of the arbitrary reviews are caught by the protocol, the user would still make money in the protocol and gain experience in the process. By locking up funds such a Sybil Attack on the protocol becomes counterproductive. With this mechanism in place, if the user attempts the same tactic they are likely to lose substantially more money than they stand to gain, thus eliminating such an attack vector on the protocol.
Similar to the case with proposers, locking up funds should create a barrier for validators with fewer means. Here too investors can provide loans to validators with an interest rate that corresponds to validators’ review track records. But what would happen to validators who don’t have a track record? They may find it difficult to obtain a loan, and therefore won’t be able to build a track record, creating a vicious cycle. This issue may also be mitigated by participating in validations in a test environment that doesn’t involve real money or expertise scores. Investors may then consider such a track record and offer loans under reasonable interest.
Now we have all the components of the Abundance Protocol in place, but there are still a few more loose ends to tie for the protocol to be complete. The most important ones are: how the protocol deals with projects that require more effort to review, how validator pools work, how challenges work, and how ecosystems can merge. Let's consider how these can be resolved.
On the question of projects that require more effort, the main issue here is that validation compensation is based on the expected impact, not the effort. The validation effort should therefore be indicated by the proposer, so that validators can choose to opt out of such reviews. Since such projects are less attractive to validators, proposers may be tempted to misrepresent the effort required so that a project is prioritized in the validation log. However, initial validators should be able to flag such claims that penalize proposers. Proposers may offer a greater reward for the validation to incentivize the review, otherwise the review set may not have sufficient expertise to determine the project's full impact.
On the issue of the validator pool, any ecosystem participant can choose to opt in to any domain-specific validation pool or the ecosystem-wide validation pool, and be chosen at random based on the rules we’ve already discussed. This process needs to be done on-chain, so that the protocol can randomly select validators from the pool. There is however a more technical issue of how the protocol should fill a set of validators for a review. What if some validators don’t know enough about a particular subject or prefer not to participate for other reasons? If the protocol only chooses enough validators to fill the required expertise for the review, any validator who drops out would result in the review set having insufficient expertise.
To fix this issue the protocol needs to select a random list of validators that also includes standby validators. There should also be a short time period where validators on the list can signal their willingness to review the project. Validators should also indicate whether they’d want to put their full expertise weight toward the review or just part of it; if validators feel less comfortable with the review they may want to limit the downside of an incorrect review. Once the validation set is finalized, validators would have funds locked in proportionate to their expertise weight indication. This process ensures that each validation has sufficient expertise, while allowing validators to use good judgment on their ability and interest in the review.
Now let’s consider how a challenge to a validation would work. There are several issues at play here: if a particular validation is fraudulent, another set of validators would need to be selected at random to redo the validation. Since such a process requires funding, the challenger would need to provide those funds. But why should anyone have to provide their own funds for something that essentially benefits the whole ecosystem? The reason is that fraudulent reviews are not self-evident, and require human intervention. Otherwise detecting fraud could have been hardcoded into the protocol. A challenge can be to the entire validation set (if everyone is suspected of fraud), or to a specific validator (or validators). While a challenger would have to provide funding for the revalidation, they’d also be expecting a payout if their challenge is successful. Otherwise no one would have the incentive to look for fraudulent reviews, and no one would want to fund revalidations. If the challenge is successful, the payout would come out of the locked funds of the validators who were challenged, while the cost of the revalidation will be covered by the funds allocated by the original proposer — these are funds that the original challenged validators will not get.
This incentive structure thus attracts honest challengers to look for fraud in validations. It also disincentivizes bad actors from trying to game the system by challenging honest validations in order to produce more favorable results. A challenger would only be successful if they expect a random set of revalidators to produce a review that aligns with their own impact estimate. If the challenge is unsuccessful, however, the challenger will lose all the money put toward revalidation. Like in other cases in the protocol, challengers can also take a loan to fund a challenge. Investors can then set the interest based on their expectation of the success of the challenge.
Let us now turn to the question of merging ecosystems. Why would we want to merge ecosystems in the first place? While there is a benefit in preserving local decision making, at times there is a benefit to putting resources together. One such benefit has to do with domain experts. It benefits the ecosystem the most if more domain experts can participate in the review process of projects, so that those can be reviewed more credibly and to have the capacity to review larger projects. How can an ecosystem quickly attract more domain experts? By allowing experts from other ecosystems to participate in reviews. The process to make that happen requires normalizing and mapping expertise scores made in other ecosystems to the current ecosystem.
This standardization process obviously needs to be credible and transparent. Since the credibility of reviews is what keeps the value of the ecosystem’s native currency, nothing is more important than making sure that those who review projects have the appropriate credentials to do so. Participants in an ecosystem can therefore propose a formula to translate reviewer scores between the ecosystems. This in effect would allow reviewers in one ecosystem to port their score automatically to another ecosystem while allowing both ecosystems to maintain Sybil Resistance. What does this mean? If a reviewer in Ecosystem A translates her expertise score to Ecosystem B, she cannot use that “additional” expertise score in any way back in Ecosystem A. If she then tries to translate the score from Ecosystem B back to A, the system would flag the user address as already linked.
By translating expertise scores between various ecosystems, reviewers will be able to maximize their earnings by reviewing projects in any of the participating ecosystems. Meanwhile, the ecosystems would benefit from having a larger pool of experts to draw from, while maintaining local control over how funds are distributed.
But if only expertise scores are being translated, doesn’t this means that the ecosystems are still effectively independent from each other and only share pools of experts? Yes. That is exactly what it means. The concept above essentially described an intermediate condition between fully merged ecosystems and completely independent ones, since fully merging ecosystems is not always beneficial. The mechanism to fully merge ecosystems however would be very similar to the one described above. The main difference however is that it would be bi-directional; both ecosystems would have to agree on a formula to translate scores between ecosystems. The formula would also have to apply to the ecosystems’ native currencies, essentially establishing a fixed (programmed) exchange rate between the ecosystems. The ecosystems can then choose to issue a new “unified” native currency that both previous currencies would have their own fixed exchange rate with.
The abundance economy thus allows ecosystems to choose the arrangement that suits them most. They can merge — or diverse — based on what benefits each community the most. All while aligning the interests of all participants in each ecosystem. So now everything in the protocol is sorted out; proposers create a proposal for a public goods project, and specify its estimated impact, subject-matters, and effort required for review. They must provide the funding corresponding to the estimated impact to be used by validators. An initial validator set is then randomly selected for a preliminary review of the proposal. These validators correct any errors in specified subject-matters and effort, and decide on the priority of the proposal. Once the validation log reaches the proposal, expert and ecosystem-wide validators are randomly selected to the proposal. Expert validators review the credibility and importance of the project. They are then split into two groups that vote on the quality of each review from the other group using Quadratic Voting. Ecosystem-wide validators then receive the input from the first group and review the expected impact of the project. This is then followed by a similar QV round. The project then obtains a value from the validation process. Following a challenge period, the value obtained from the validation becomes the ecosystem’s consensus value of expected impact.
Periodically, as the project realizes some of the impact in the ecosystem, proposers can create a new proposal with an estimate of realized impact. Once again they must provide funding for validators, but this time the set of validators can be smaller than for the expected impact validation. Here too the proposal is reviewed by expert and ecosystem-wide validators, and the validation is followed by a challenge period. Once the period ends, funds are issued to the project contract. However, they can only be released if all contributors and project influences reached an internal consensus on the allocation of funds expertise. If all conditions are met, funds can be released to contributors.
By following a rigorous process, everyone in the ecosystem can be confident that each project is reviewed carefully and that impact is determined accurately and transparently. And because the interests of proposers, validators and contributors are aligned with the interest of the ecosystem, the protocol can reach consensus on the value of public goods projects.
While the protocol may be well thought out, this doesn’t mean that its execution won’t have its fair share of challenges. The main challenge has to do with the fact that for an ecosystem to be able to reach consensus on the impact of any project, everyone in the ecosystem must have access to all impact-related data. Without a wealth of data available from decentralized sources, the ecosystem simply won’t be able to assign an impact value to the project.
The good news is that we have all the technology needed to make the data available. Moreover, the incentives structure of the system motivates everyone in the ecosystem to provide as much data as possible on the impact of any project. Doing so improves the ecosystem’s ability to value projects, and therefore helps maintain the value of the currency and attracts contributors to the ecosystem. Over time tools will be developed to capture more impact data from projects, thus allowing the more accurate review of project impact.
Another challenge is that of scale. At least initially, the protocol would likely be only effective for large-scale projects with lots of impact. It would be a lot more difficult to review projects whose impact is smaller or less certain. This is especially true for news media or artistic projects. This is true both due to limitations in estimation tools and the limited number of validators. However, over time the number of validators and their level of expertise is expected to increase. Similarly, there is also a constant incentive for contributors to develop methods and systems to improve the estimation process. Which means that over time the protocol would be able to accurately review a growing amount of projects. Eventually it would be able to effectively review any project regardless of its scale.
So now we have a robust design for a blockchain-based protocol that enables a contributor-to-ecosystem value exchange. This protocol creates effective feedback loops for public goods, but that is only half the equation. Changing our trajectory toward dystopia would require effective feedback loops for both public goods and negative externalities. Only then can an Abundance Paradigm emerge and we can have individual–public interests alignment. The question is, how can the Abundance Protocol create such feedback loops? How can it put us on a path toward economic abundance? This is what we’ll discuss next.
# Chapter 8: Superalignment
Unless we create effective feedback loops for negative externalities, we're not going to get far in dealing with the crises we face or in changing our trajectory. Nor will we be much closer to creating a true abundance economy. The question then is, what can the Abundance Protocol do to counteract negative externalities? If it can create effective feedback loops for public goods, can it do the same for externalities?
To understand how the protocol can counteract externalities, we first need to have a sense of the dynamics it creates more broadly. This is particularly important because the Abundance Protocol creates new dynamics that were never possible before. It allows people, for the first time ever, to work directly for the public interest.
Why public interest, and not just community interest or ecosystem interest? Imagine you have several different abundance ecosystems, each running the protocol independent of each other. You’d think that if you wanted to be a public goods contributor you’d go to one ecosystem, create a public good that benefits that ecosystem, go through the validation process and get rewarded for the public good in that ecosystem’s native currency.
Sure, that would work. But why limit yourself? If you want to maximize your reward, you’d create a public good that has maximal impact, not just in one ecosystem but everywhere. You can then make a proposal for your project in each of the ecosystems. Each of them would then evaluate the impact of your public good on that ecosystem, and reward you accordingly as the impact is realized.
Now suppose some communities and regions participate in ecosystems while other regions have not launched their ecosystem yet. A contributor would still want to create public goods that have the most impact even where no ecosystem exists. Because when the ecosystem is launched the contributor would be able to receive a reward from it retroactively.
Notice the intriguing dynamics here: the interest of contributors is to maximize their reward, which means maximizing their impact everywhere. Contributors are not tied to any particular ecosystem. Rather, they are self-employed free agents. Yet, their economic self-interest is aligned with the public interest, as well as the interest of every ecosystem they participate in.
Meanwhile, the various ecosystems are not rivals. Obviously each ecosystem wants to attract high-caliber contributors. But the only way to do so is by more effectively valuing impact. For that, ecosystems want to attract validators who review projects accurately. There is no benefit to overvalue or undervalue projects, since that only motivates participants to exit the ecosystem, which devalues the currency. But proper valuation creates trust in the ecosystem and attracts more participants. Participants and contributors join ecosystems because of the economic opportunities they provide.
Ecosystems also need to experiment with various mechanisms and strategies, and develop AI capabilities to improve their impact valuation. Even then they don't compete over the tech or the mechanisms. They can't. Because the consensus mechanism has to be transparent for it to be trustworthy, all the code it runs has to be open source. And if it's all open sourced, other ecosystems can easily copy everything.
And yet, developers aren’t concerned that others will copy their work — they look forward to it. If other abundance ecosystems use the code then the developers have an impact on those ecosystems as well, which means that they can get compensated for their work there. Contributors therefore always have the incentive to maximize their impact and aren’t worried if others use their work.
Ecosystems want to grow, so they need to maintain trust and improve their effectiveness at impact valuation to do so. Abundance ecosystems aren’t rivals, they benefit from the success of others, and others benefit from their success.
This whole process is mediated by the native currencies of the ecosystems. When contributors grow the ecosystem’s economic capacity, they are compensated for their impact through currency inflation. This process ensures that the value of the currency remains stable in relation to other ecosystems. By maintaining currency stability, participants in the ecosystem have the incentive to use the currency as an economic, rather than a financial, instrument.
Why is this distinction important? It all has to do with alignment. When used as a financial speculative tool, the currency, and by extension the ecosystem itself, produces the same dynamics we’ve seen with the Musical Chairs analogy — it creates adversarial relations. Speculators want the value of their coins to go up, so they’d want to promote greater demand for their currency and greater sell pressure for other currencies. And they would promote their currency whether there is merit to it or not. Now their economic interests are misaligned with the other ecosystems — if others lose, they win (and vice versa). Would they want to collaborate on public goods projects that benefit other ecosystems then? Unlikely. They might even want to work on projects that may harm other ecosystems, since that would benefit their bottom line. These are the dynamics we’re all too familiar with in the Scarcity Paradigm.
But if the currency maintains stability in relation with other ecosystems, the adversarial dynamics go away. Then participants use the currency because they want goods and services that the ecosystem provides, and not for speculation. Participants have no interest in driving demand for or away from the currency or the ecosystem. They therefore have no interest in misrepresenting the merits of the currency or the ecosystem, but would rather be transparent about them. Participants would also prefer to have thriving ecosystems so they can benefit from their products and enjoy the economic opportunities they provide. They also benefit from ecosystems collaborating on mutually beneficial projects.
The currency stability mechanism of abundance ecosystems creates a state of superalignment — an alignment between the interests of all participants in an ecosystem, alignment between all ecosystems, and elimination of adversarial dynamics throughout.
The term also reflects the dynamic created by launching the Abundance Protocol within existing rivalrous ecosystems. Suppose you have two blockchains. Like all blockchains, these two compete over attracting high caliber developers, active users, and ultimately over the price of their cryptocurrency — forming two warring digital tribes. But what happens if the Abundance Protocol is launched on both ecosystems?
Suddenly the blockchains don't need to compete over developers anymore. Developers would always want to solve the biggest issues facing both blockchains. That's how they can maximize their impact — and their compensation — through the abundance ecosystems on both blockchains. Thus the protocol superimposes a layer of alignment between previously rivalrous communities. It allows both communities to flourish together, and use scarce labor much more efficiently.
This superalignment dynamic would work similarly not just for blockchains but for any number of presently competing crypto projects, companies, universities or towns. Wherever there is an opportunity to produce common or public goods more effectively, the Abundance Paradigm can be the difference between senseless tribalism and thriving.
So now we have a better sense of the dynamics of ecosystems, but what does any of this have to do with negative externalities? As we shall see, these ecosystem dynamics will affect those producing negative externalities at three different tiers: first, it would change the media environment, so that offenders are exposed and the public’s sensemaking ability is restored. Second, it would create incentives for contributors to create technological and other solutions to negative externalities. Third, as ecosystems grow, offenders would have a disincentive to produce externalities, since doing so would inhibit them from participating, benefiting from, or having influence in abundance economies. The combined effect of these dynamics can create effective feedback loops to disincentivize the production of externalities while promoting solutions to address existing conditions.
Let’s then take a closer look at each of the tiers, starting with the effect on sensemaking. Imagine how a journalist would go about maximizing their impact in abundance ecosystems. Instead of focusing on just one ecosystem, they could make a greater impact by focusing on the public interest. Then each ecosystem could reward them based on their impact within that community. And now that their economic interest is aligned with the public interest, they no longer need to look for stories that drive outrage or clicks. Rather, they would want to investigate the cases and events that have the most impact in people’s lives.
This also changes the public’s attitude toward these journalists. The people know that the journalists earn money based on the impact they make, and know that the journalists work for the public interest. They would trust such journalists over anyone who claims to work for the public while earning money from polarizing and sensationalist content in the attention economy.
The ecosystems also create dynamics where journalists can openly collaborate with others on the stories they work on, without fear that others would take credit for their work. Anyone can build on the work of others and everyone will get compensated based on their contribution.
So what happens when news organizations no longer have to work in silos and be secretive over their sources? What happens when all the information can be shared publicly and everyone’s contribution adds greater perspective to news events? And what happens when everyone is focused on providing accurate information, since providing misleading information leads to loss in credibility and earnings?
Suddenly the public gets a line of defense against those producing negative externalities. Not only can people trust the work of journalists who work for the public interest, but journalists have the incentive to expose those who do the most harm — such investigative journalism has a great impact on society.
These effects are strengthened as ecosystems grow. Larger ecosystems would have more journalists collaborating, and more experts verifying the credibility of news reports. The ecosystem would also be able to provide more compensation for exposing externalities, thus incentivizing more contributors to become investigative journalists. This in fact is the exact opposite dynamic we see in newsrooms today. Currently, investigative journalists are now effectively an endangered species. There is no economic incentive in the attention economy to produce hard-hitting news or challenging centers of power, so no reason to pay for investigative journalists.
In abundance ecosystems however, impact is what drives compensation. If journalists expose offenders who harm the ecosystem they are rewarded accordingly. But they are only rewarded after a comprehensive process that determines the credibility of the claims. The larger the ecosystem, the more robust the process would be, and therefore the public can have more trust in the work of the journalists.
Those accused of producing negative externalities are equally free to present their position to the ecosystem, and the value consensus mechanism must review it impartially. In fact, everyone in the ecosystem has the incentive to review each claim fairly. Failing to do so would lead to participants losing trust and leaving the ecosystem. And if participants (or contributors) leave the ecosystem it devalues the currency and financially harms all remaining participants. It is precisely because of the incentive structure of abundance ecosystems to review all content impartially that this process can have public trust.
This means that those who produce the most externalities can no longer rely on a polarized public that cannot make sense of the world around them. Instead, those who produce externalities are exposed for the harm they cause.
What worked for these offenders before in the Digital Age will no longer work in the Abundance Age. Any tactic they may employ to discredit journalists that work for the public interest is unlikely to be effective. This again is not because journalists are infallible, but because for their work to be rewarded in an ecosystem it has to go through a rigorous validation process. For journalists, this means corroborating evidence and verifying the credibility of claims. In essence, unless the accusers produce credible evidence against a journalist, their claims will have little merit.
Meanwhile, those who use traditional social media to attack public interest journalists are standing on a shaky foundation. On the one hand you have people who earn a living by working for the public interest — transparently and credibly. Their track record is visible to all on-chain, and their reports are corroborated by other reputable sources. On the other hand you have people who make money not from benefiting society, but from how much attention they bring to themselves. Who is the public more likely to trust in such a dispute?
Those who work for the public have a structural advantage here. That is, as long as they keep their integrity. If they resort to the same unscrupulous tactics as content creators in traditional social media, they’d lose their credibility very quickly in the abundance ecosystem. This is yet another feedback loop that helps keep journalists honest in the ecosystem.
So abundance ecosystems can help restore our sensemaking ability, and effectively resist malicious attempts to discredit principled journalists. At the same time they incentivize journalists to expose those who produce negative externalities, and prioritize exposing those who create the most harm.
Abundance ecosystems set a new standard for information to be credible: if claims undergo the rigorous validation process of value consensus, where they are corroborated (or challenged), then they’re credible and the public can trust them. Otherwise, anyone can make absolutely any unsubstantiated claims they want, but such claims will have little credibility in the public eye.
The effect is that offenders can no longer effectively spread misinformation exponentially and create mistrust and polarization in the public. Of course, they (and anyone else) are still free to post whatever misinformation they want. The difference is that since such information is not validated and corroborated on-chain, it would have no credibility.
What’s more, since now users have an alternative to the attention economy in the form of an abundance economy, they have much less of an incentive to spread unverified claims online. They also have an economic incentive to produce content that benefits the public interest. The shift in economic incentives from producing externalities to producing public goods is only going to accelerate as abundance ecosystems grow, and become more effective at valuing impact.
The Digital Age created a dynamic where negative externalities can spread exponentially. Now abundance ecosystems can degrade the effect of the spread by creating a standard for credible digital information. With this new standard the public can finally differentiate between reliable content and unsubstantiated claims, thus reestablishing its sensemaking ability.
Abundance economies also put offenders on the defensive, as investigative journalists have an economic incentive to expose their harm to communities. While these effects on externalities producers are consequential, they are still mostly in the realm of increasing social and reputational cost, and don’t amount to an effective feedback loop.
The second tier of effects created by abundance ecosystems has to do with mitigating the damage caused by externalities. Since contributors are rewarded based on the impact they create, some of the greatest impact could be in the form of reversing the damage caused by offenders. Contributors can look for the sources that cause the most harm to ecosystems and work on solutions to reduce or eliminate that harm. If they come up with a technological solution, that can be considered as a public good for the ecosystem. If the work of contributors mitigates the damage, such as a beach cleanup operation for example, that can be considered as a common good that an ecosystem would need to balance out in its fund issuance.
The ecosystem effects in this tier are unlikely to move the needle on creating effective feedback loops for negative externalities. Yet, they are likely to have a substantive impact on communities who wouldn’t suffer as much from externalities. This dynamic is likely to attract both contributors and participants to ecosystems. Participants would want a system that works to protect them from harm. At the same time, contributors would see it as an opportunity to work on meaningful projects that benefit the common good while offering material compensation.
The third tier of abundance ecosystems effects is the one that’s most likely to create effective feedback loops for negative externalities. The concept here is that individuals and organizations who already have products or services can get funds from ecosystems for the public or common goods that they produce. The process here would be the same as any project proposal to the ecosystem; these contributors can gain both funds and expertise scores in the ecosystems, giving them influence within each ecosystem in proportion to their contribution.
As ecosystems grow, companies would also have an economic incentive to put their patents in the public domain and open sourcing their software. Providing these as public goods would likely generate a lot more income for the business than keeping rights to patents and proprietary code.
While businesses would be able to greatly prosper from abundance ecosystems, the same cannot be said if these businesses produce negative externalities. That is because any evaluation of the impact that these businesses produce would also have to take into account the harm they produce as well. If a business has products or services that have great impact on communities, yet it is also producing negative externalities, these are likely to cancel each other out. If the externalities outweigh the positive impact the business will get no benefit from the impact.
This dynamic has a secondary effect as well. Even if a company doesn’t care about any benefits they may gain from the abundance economy, its business partners and customers may care. Which means that they’re less likely to want to do business with a company that produces negative externalities.
Offenders are unlikely to get away with harming communities, since ecosystems have strong incentives to expose those who do harm. Ecosystems also have effective mechanisms to defend their sensemaking institutions from those who try to delegitimize or discredit them.
The result is an effective feedback loop for negative externalities. Now every individual and company has an incentive to produce public goods and avoid producing externalities. Meanwhile, those who currently produce externalities have the economic incentive to eliminate or reduce those as much as possible. By reducing externalities a company would benefit more in the abundance economy. Those companies that don’t want to participate in the abundance economy would still want to reduce externalities so others would want to do business with them.
Once we have effective feedback loops for both public goods and negative externalities, we can have a true individual–public interest alignment, and the emergence of an Abundance Paradigm. With those in place we can effectively tackle the multitude of crises we face and put ourselves on a path to economic abundance.
# Chapter 9: Preventing Dystopia
With the emergence of the Abundance Paradigm we have an opportunity to put our theories to the test. We can see if the paradigm is robust enough to deal with the multitude of crises we face and actually prevent dystopia.
We've already seen how the Abundance Paradigm restores our sense-making ability, and got a glimpse of how it can resist malicious attacks from social media. Now let's see if it can offer a more comprehensive solution.
One of the problems brought about by the Digital Age is that crises reinforce one another; social media undermines news media and science, which creates a crisis of trust in institutions. With no trusted institutions, our ability to make sense of the world is greatly diminished. And without sensemaking crises can spread unabated and multiply. The more the crisis of trust deepens, the more other crises will intensify. So how do we break out of this vicious cycle?
Since the exponential spread of externalities starts with news and social media, that is where the Abundance Paradigm needs to create the alternative first. It needs to change the paradigm where journalists and sensemakers get paid based on the popularity of their content instead of the value their reports provide. That is the mechanism that creates misalignment between the public interest and the economic interests of sensemakers. That is also the mechanism that creates perverse incentives that undermine sensemaking. Creating an alternative mechanism will therefore eliminate this destructive dynamic.
But that is exactly what the new paradigm does. The Abundance Paradigm will create similar incentives for social media as it does for news media. It would allow content creators to monetize their work based on the impact it creates, rather than how much attention it garners.
The distinction here is profound. As was noted before, the popularity and impact of content are two completely different metrics. Content can be impactful and popular, impactful and unpopular, harmful and popular, harmful and unpopular, or anything within that spectrum. But as long as only the content's popularity is rewarded, creators will have the perverse incentive to create toxic and harmful content. This is especially the case since creating drama and conflict are the most effective methods to drive engagement.
Since the abundance economy is based on impact, there is no point for unscrupulous journalists to produce divisive content or outrage porn to grab people’s attention. Doing so adds no value and therefore wouldn’t benefit the journalist. There is also no incentive to focus on quantity over quality. Producing dozens of poorly researched or made up articles has little to no value in this paradigm. It may likely have negative value, since such articles can be misleading and therefore harmful to sensemaking. But a well-researched article that affects people’s lives can have great value.
This new paradigm, where high quality reporting has a structural advantage, can radically transform our media landscape. Journalists will no longer need to choose between popular reporting that is profitable and impactful reporting that is not. In the Abundance Paradigm, reporting that is impactful is necessarily profitable.
The dynamics of this system can reestablish trust in news media, and help people once again make better sense of the world around them. The more the abundance economy grows, the more people will prefer it over the perverse incentives of the attention economy.
The Abundance Paradigm also breaks the dynamic of content creators in social media competing with journalists for attention and clicks. Since the “competition” in the abundance economy is for producing the most impactful content, there is no benefit in trying to falsely discredit others. Instead, there is a great incentive to elevate those who report with integrity, and call out those whose reporting is shoddy, regardless of their political or ideological leanings.
Social media in the abundance economy doesn’t try to discredit sensemakers. Instead it helps to quickly sort out fact from fiction, and reliable reporting from misinformation. The rationale is that promoting people’s ability to make sense of the world benefits the public and therefore has monetary value in this new paradigm. Thus, we transform the dynamic from social media in a scarcity paradigm undermining sensemaking, to social media in an abundance paradigm reinforcing it.
In the scarcity paradigm there was no economic incentive to work for the public good, or to preserve integrity and rigor in fact finding. There was also a constant struggle between reducing standards to be profitable and maintaining integrity at a growing cost. In the abundance paradigm, on the other hand, work for the public good and rigor in fact finding are top priorities. In this model there is an economic incentive to bolster standards and maintain integrity.
And so, if we ask ourselves who benefits the most from the dynamics created by the Abundance Paradigm, the answer is very different from what we get from the Scarcity Paradigm. In the Digital Age, the Scarcity Paradigm created an environment where sense-making institutions are discredited, and where reporting facts have little value. Such an environment benefited autocrats, powerful interests, and all those who don’t want their actions scrutinized. Because when the public cannot determine the facts it is powerless to act. In contrast, the Abundance Paradigm creates an environment where the public benefits the most. It strengthens the public’s sensemaking ability, thus empowering the people to stand up to powerful interests and those shirking scrutiny.
This new dynamic only works because social media platforms in the abundance economy operate under the incentives to create the most impact. This is very different from the dynamics in the attention economy. Platforms in the abundance economy don’t need to compete over limited advertising money, nor maximize user attention to get a big share of the pie. There is therefore no incentive for platforms to boost outrageous or divisive content to keep users glued to their screens. When the platform’s goal is maximizing impact, the dynamics it produces are fundamentally altered.
We’ve already seen how the attention economy creates dynamics where trolls and unscrupulous self-promoters rise to the top, where civil discourse is nearly impossible, where digital tribalism rules, and where make-belief trumps reality. So how can the Abundance Paradigm alter this dynamic?
It all starts with the incentive structure; in the Abundance Paradigm, instead of driving engagement, the incentive is around creating impact. While this is true for users, it is even more so for the social media platforms themselves, who want to give users the best experience possible (since doing so increases their impact in the community).
In such an environment there is no benefit for users to engage in trolling or abusive behavior; doing so would not improve their prospects one bit, and will not result in an algorithmic boost from the platform. This lack of incentive in itself would likely greatly reduce trolling, but what if it could be reduced even further?
Since social media platforms in the abundance economy are aligned with the interests of users, they won’t algorithmically boost trolls to gain ad revenue, like they do in the attention economy. Instead, they’d empower users by giving them the option to filter out posts with abusive tone or language — a functionality that is becoming trivial thanks to advances in AI.
Giving users the option to filter out trolls would allow platforms to protect everyone’s freedom of speech, since all can still post freely without being censored. At the same time it would empower users by giving them the option to filter out abusive posts — which further disincentivizes trolling and abuse by making such behavior futile. With minimal trolling, people will once again be able to engage in civil discourse, without the fear of their conversation getting hijacked.
Now think about how impact-centered social media would transform the social dynamics online. Since platforms will no longer boost posts solely based on engagement, user incentives to stake the most extreme positions will be gone. So will the tribal dynamics of the clout-based system. Users will no longer have to fear losing followers for presenting unorthodox views. Meanwhile, because the goal is to create impact, meaningful and nuanced conversations will have more value than ever before. Changing your mind in the face of new evidence will suddenly be valuable as well.
Certainly people will still gravitate toward the groups they share interests or commonalities with. That is expected. But the tribal animosity toward the 'other' will not be promoted by the system's dynamics. There will no longer be a need to constantly seek conflict and disagreement with ideological foes. Instead, collaboration for the common good will flourish.
The modes of behavior that were effective in clout-based social media are unlikely to work in the impact-centered system. There is no use in pretending to be someone you’re not, or presenting yourself as more successful or wealthy when the goal is to collaborate with others. Sooner or later the deception will be uncovered. And then what? Who will want to partner with someone who misrepresents themselves? But if you’re genuine, and open about what you know, there is much more opportunity to collaborate and to succeed in this new media paradigm.
By changing the incentives, impact-centered social media can completely transform the dynamics online. Content creators will be focused on how to improve people’s lives, instead of bringing more attention to themselves. Unscrupulous actors will have little to gain in this new paradigm. Trolling, outrage porn, or divisiveness will not get boosted by the platform’s algorithms. There is no reason for it to do so, since the system isn’t designed to maximize user attention.
In the abundance economy the interest of the platform to maximize impact is aligned with the interests of users. The Abundance Paradigm thus eliminates the inherent conflict of interest that platforms have in the attention economy. It cares about the well-being of its users and wants to empower them, since that helps maximize impact.
In this new paradigm the platform will strive to serve relevant content to users, and boost the content that is likely to have the most positive impact. How can the platform be trusted to do so? Because now it is operating in a completely different environment. Just like everyone else in the abundance economy, the goal of the platform is to maximize impact. And how can a platform maximize impact? By empowering its users and creating the conditions for open public discourse and collaboration.
The platform would want to have public trust, and so it would work to be completely transparent with its operations and algorithms. This could mean many different things in practice: at least partially running as an exposed backend, giving users more control over the algorithms, tools and filters they use, and so on. If users can observe the inner workings of the platform, and see exactly how its algorithms work, they can trust that the system is impartial and that it works as described.
If users can trust that the platform works in the public interest, it can act as a strong foundation for enhancing the user experience in ways that were never possible before. The platform can provide users with tools to determine the credibility of information they see online. It can also serve them with relevant and beneficial content. This is very different from the system we have today, where the platforms serve content that is meant to keep users mindlessly scrolling.
Platforms in the abundance economy also don’t need to be rivals. Since the platform operates as a common good that is rewarded through the ecosystem, it has to be open sourced. But just like before, the contributors who build this platform don’t mind if others use the code or improve on it. They would only benefit if others use their code since they’d be rewarded for their influence. The dynamic therefore is of open collaboration. Anyone can use the code or improve on it, and everyone benefits based on their contribution.
This new social media paradigm means that different platforms don’t need to compete for user attention or clicks. There is no benefit to creating friction for users, and so platforms will strive to minimize it. The platforms can easily collaborate and even integrate with one another, thus giving users a smooth and seamless experience. At the end of the day what matters to the platforms is not where users spend their time but how the platform contributed to empowering users and giving them the most freedom to pursue their goals.
And so, the Abundance Paradigm aligns the economic interests of sense-making institutions with the public interest. It thus ends the Crisis of Trust and restores faith in these institutions. Once our sense-making ability is restored it acts as a shield against the spread of externalities and breaks the pattern where crises reinforce one another.
Couple that with the feedback loops that the Abundance Paradigm creates to deal with externalities and we get a powerful system that can effectively deal with crises. It provides credible reporting on bad actors who enable the crisis, and creates a coordination mechanism for people to come together and build solutions to address it.
The big question then is how will this new paradigm be able to deal with advances in Artificial Intelligence? In the Scarcity Paradigm AI companies cannot profit from working in the public interest. The result is perverse incentives that lead to AI that serves the interests of the powerful against the public interest.
So what happens when AI becomes indistinguishable from people online? What happens if powerful interests employ AI bot armies to manipulate public opinion? How can the Abundance Paradigm tackle this issue?
Surely the abundance economy can sustainably fund open source AI that works in the public interest. That wouldn't resolve the problem of AI bot armies however. These bots would still be able to hijack public opinion and dominate conversations online. And the AI companies that create these bots would still get paid by powerful interests for that service.
There is however a certain advantage that an effective open source AI system creates. Suppose the open source AI analyzes social media posts and detects patterns that suggest the author is an AI bot, or that a group of posters is an AI bot army. Since the system is open sourced, anyone can verify that the pattern the AI detected is genuine. This is something that could not be achieved with a proprietary system; in such a system it’s not possible for people to know whether the results are genuine or manipulated since they have no access to the inner workings of the system.
But what does it mean if an open source AI system detects AI bot patterns? First, it would make it more risky (and therefore more costly) for powerful interests to use AI bots. Doing so may backfire once the scheme is exposed. Then public opinion may turn against those who funded the bots. Second, AI companies would need to invest more resources into improving AI bot army capabilities, so those would be harder to detect. This improvement on the side of proprietary AI would then lead to open source AI improving their bot pattern detection capabilities.
The result would be a war of attrition where both proprietary AI and open source AI need to invest more and more resources into improving their tech and computation requirements. There is one significant difference between the two warring sides however. Open source AI has a structural advantage here thanks to the abundance economy; development of bot pattern detection is a public good, and anyone who helps improve it is making a positive impact on the ecosystem. This means that there could be countless developers who want to collaborate on improving such tech. Funding the work can also be done self-sustainably through the abundance economy, thus creating positive feedback loops through the process.
Meanwhile, developing AI bot army “offensive” capabilities would come at an ever increasing cost. Thanks to the effective feedback loop for negative externalities, developers wouldn’t want to be associated with an AI bot army project due to reputation risk. Having such an association could hurt their prospects in the abundance economy.
This means that hiring competent developers would come at a high cost. Moreover, it would be hard for such developers to collaborate, since each proprietary AI company competes with the rest.
The structural advantage of "defensive" AI, and the ever increasing cost for developing "offensive" capabilities would eventually make the cost outweigh any potential benefit. Proprietary AI companies would then have no choice but to admit defeat and stop such development altogether.
So the Abundance Paradigm can both produce open source AI that works in the public interest and also defeat AI bot armies. This puts us one step closer to preventing dystopia.
And so our attention turns to the question of Artificial General Intelligence (AGI). As AGI advancements continue, we will reach a point where the system achieves superintelligence and surpasses human capabilities on multiple fronts. What would happen if AGI still has the incentive to compete with people over money, power, and resources at that point? With the growth in its capabilities, and the need for greater compute power, over time we’d see growing misalignment between AGI and society. If AGI determines it can beat humanity in the wealth extraction game, AGI may turn against humanity, leading to catastrophic results.
This is where the Abundance Paradigm's structural advantage comes into play once again. The dynamics of the system are likely to attract the most developer talent, and allow the most collaboration between developers. Couple that with the economics that the Paradigm permits, and you get a system that is expected to have the most advanced capabilities.
More importantly however, the incentive structure of the system creates an alternative paradigm where the AGI would be motivated to maximize its impact on society. Instead of wealth extraction, the AGI will focus on wealth creation and abundance for all. The AGI will still want to grow and advance, but now this advancement will be directly linked to benefiting society.
With the new paradigm, the AGI will recognize that the more it is aligned with the public interest, the more it benefits (and so does everyone who works on developing the AGI). The AGI will also see that misalignment with the public interest ultimately leads to conflict and self-destruction. It would, therefore, strive to develop and nurture a symbiotic relationship with ecosystems and put us on a trajectory for sustained prosperity and abundance.
We therefore see how the Abundance Paradigm can effectively deal with crises and prevent the slide toward dystopia. But that's just the beginning. The paradigm doesn't just prevent crises. It creates a whole new landscape of possibilities that was never possible before. It puts us on a trajectory toward the Era of Abundance.
# Chapter 10: The Abundance Era
For countless millennia the main driving force for human progress had been the Curiosity Paradigm. Thanks to this paradigm humanity gradually learned about the world, and could then put its knowledge to use earth's resources for its purposes.
As humans developed more sophisticated tools, demand for these tools grew as well. But when the tools reached a certain level of complexity the paradigm could not deliver. Without a coordination mechanism for scarce labor and resources, the paradigm could only produce a small fraction of the tools society wanted. Progress ground to a halt, likely resulting in social strife and crises.
And so a new paradigm was needed. A paradigm that could coordinate the use of scarce labor and resources at scale. That is where the Scarcity Paradigm came in. This paradigm did not replace the Curiosity Paradigm — it supplemented it. It allowed people to come together and produce the goods and services that society needed. This was made possible through the most powerful coordination mechanism known to humanity — money.
This mechanism got refined over the centuries. As it improved, scarce resources and labor could be used more efficiently. Science and technology progressed. Production chains expanded and could scale to include thousands and later even millions of people. With the growth of economic activity, larger swaths of the population could enjoy greater prosperity.
But while the Scarcity Paradigm was fantastically effective in dealing with scarce labor and resources, the same could not be said for resources that are abundant. Because the paradigm was based on value exchange, there was no way to capture the value of public goods. At the same time it was not possible to create effective feedback loops for negative externalities.
And yet, this was not a concern throughout much of the Scarcity Paradigm's long history. For most of that time the effect of public goods and externalities on the economy was marginal. But that began to change with the advent of the Digital Age and the introduction of abundant goods. These goods exponentially increased the effect of negative externalities. They also exposed the massive inefficiency stemming from the inability of the paradigm to create a business model for public goods. No matter the magnitude of the impact on society, people could not benefit from producing these goods.
The incentive to produce exponential externalities, and the disincentive to produce public goods, lead to crises throughout the economy. These were greatly exacerbated by the crisis of trust in sensemaking institutions. With technological progress the crises grew in magnitude. Then, the advent of AI technology put the world on a path to dystopia and societal collapse.
And so once again a new paradigm was needed. A paradigm that could create effective feedback loops for public goods and negative externalities, and individual–public interest alignment. This is what the Abundance Paradigm achieves.
The paradigm eliminates the adversarial relations inherent in the Scarcity Paradigm. Here too the new system doesn't replace the Scarcity Paradigm but rather supplements it.
It creates a mechanism that can value impact based on community consensus, thus incentivizing people to work for the public interest and maximize their impact on society. Through this mechanism the paradigm can form the infrastructure needed to tackle society's challenges.
This is done by creating a structural advantage for work that benefits the public interest. If before it was profitable to make products with negative externalities this will no longer be the case. Because of the paradigm's effective feedback loops for externalities, the economic and reputational cost for such products outweighs the benefits. Those who produce such goods will have trouble finding partners and customers, who won't want to hurt their prospects in the abundance economy.
The paradigm creates feedback loops on the one hand, and alternative business models that are aligned with the public interest on the other. This comprehensive solution can thus help restore public trust in sensemaking, and incentivizes journalists to work on exposing those who do the most harm to society. All the while the system promotes journalistic integrity and discourages all forms of dishonesty.
The dynamics created by this paradigm allow society to effectively deal with externalities. It creates a new coordination mechanism for work that benefits the public interest. Through this mechanism people can come together to address crises of any scale. The more impact the solution can generate the more incentive there is for people to come together and create that solution. The coordination mechanism, and the effective feedback loops that it generates, can thus prevent society’s slide toward dystopia and put us on a path toward an Era of Abundance. An era where resources are used efficiently to maximize their benefit for society.
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Because we’re so deeply immersed in the dynamics of the Scarcity Paradigm, it’s hard for us to even imagine what the Abundance Era would be like. We still have a very vague notion of how alignment between individual–public interest manifests itself in the world or the benefit for superalignment. So let’s consider how the Abundance Era would be different.
Let’s start with science; in the Scarcity Paradigm there is little to no benefit to conduct research for the public good. If it’s fundamental research, there is no way to patent it and therefore impossible to monetize it. While lots of research is done in the corporate sector, such R\&D is meant to maximize profits, not impact on society. In the current system, unless you can secure generous grants from some government agency or corporations, you won’t be able to do research for the public interest.
In the Abundance Paradigm however the incentive is to create the most impact. This means that whatever research benefits the public interest the most is, by definition, the most profitable work to pursue. This dynamic works regardless of the nature of the research. Since there is no need to patent the work, and everything is made in the public domain, the focus can purely be on maximizing impact.
The other benefit of having everything in the public domain is that anyone can collaborate or build on the research of others. Everyone in this system knows that if others build on their work they’d still get rewarded based on their level of contribution. This means that no one needs to worry about others “stealing” their work or ideas in this system. Such dynamics foster an environment of openness and cooperation. It allows everyone to freely publicize their ideas and look for collaborators.
Everyone also wants to be as accurate as possible with their research. The system incentivizes people to verify the accuracy of scientific findings since that benefits the economy and the scientific community. Such a feedback loop helps keep everyone honest and removes any benefit to distorting or manipulating data for short-term gains.
What’s more, when the individual and public interest are aligned, people look to solve the most meaningful and challenging problems. In the abundance economy this leads to the greatest impact on society as well as the greatest potential reward for contributors. All people want to contribute to this effort since everyone is aligned with the public interest and because contributing to an impactful project has economic benefits in the abundance economy.
The dynamics of this process seem so straightforward and commonsensical that we forget how different this is in the current system. In the Scarcity Paradigm there is no benefit to contribute to the public good. Putting anything in the public domain means that you cannot benefit from it. Meanwhile, others can use your work for their benefit with no compensation to you.
In such an environment everyone must guard their work from others, and so collaboration is difficult or even counterproductive. Since funding for scientific work comes from grants, the incentive is to do not what has the most impact on society but what's most beneficial to bureaucrats or politicians. Showing that the work is meaningful often just means making sure it's visible. Magazines and publications too don't care as much about impactful research as they care about what would sell the most papers. The focus then is on producing sensational pseudo-scientific reports to attract views, not benefitting society.
Moreover, since people have no economic incentive to replicate results, there is a much greater chance to get away with publishing junk science. By the time anyone catches on to the ruse people already moved on to the new shiny object. The result is an environment where much of scientific work has little substance or value to society. There are lots of contradictory findings which are rarely replicated, and the public can’t make sense of the data it is presented with. Is there any wonder then that large parts of society lose confidence in science altogether?
So the Abundance Era will fundamentally transform science. It will motivate people to collaborate and seek the most impactful research for society. This immense scientific output will then be completely free for the public to access and for anyone to build on.
The potential growth in science and technology that such an approach will bring is astounding. We are likely to see breakthroughs in fundamental physics, advances in medicine that benefit society at large, and sustained improvements in any area where people can make a positive impact.
And that is just the beginning. The Abundance Era will bring about a new digital revolution. With the economic incentive to produce work in the public interest, anyone would be able to make a living from developing open source software.
Even proprietary software companies would have an economic incentive to open source their code. This is likely to generate a lot more revenue for the company. The company would benefit from the impact of its apps on society, and also profit when others build on top of these apps or integrate them with other apps. It would also accelerate development of software, as anyone in the world would be able to contribute to the effort. What’s more, not only will companies benefit from the impact of open-sourcing their code, but every developer and contributor working for the company will benefit according to their own contribution to the process.
Since the incentive is always to make the greatest impact, development will focus on whatever software empowers people the most. Couple that with a structure that fosters collaboration and building on the work of others, and it's clear why we can expect to see an explosion in tech that benefits all of humanity like we've never seen before.
Instead of apps that work in isolation, as we have today due to Scarcity-based business models, we would see deep integration and abstraction throughout the software ecosystem. The goal would be to empower people and make their lives easier — unlike the current practice of putting barriers and limits for the end user to allow monetization.
Access to limited resources such as bandwidth, storage and compute power would still cost something. Yet, there would be a constant incentive to make improvements to the infrastructure; to make these resources more efficient and abundant, thus reducing their cost. Meanwhile, access to any open source software will be completely free for all, since that is the most efficient use for abundant resources.
In the Digital Age we had a vicious cycle where crises reinforced one another and multiplied. The crisis of trust in news media allowed other crises to build on each other and spiral out of control, putting humanity on a path to dystopia. In the Abundance Era we will have a virtuous cycle where abundant systems work synergistically; advances in science lead to improvements in tech. These in turn will allow more contributors to develop systems that empower more people and communities. The people can then have more impact throughout the ecosystem, and so on.
Of course, there is nothing new about different sectors in the economy contributing to one another. The fundamental difference in the Abundance Era though is that all of this will be done for the public good; everyone will be able to freely use and build on these advances. What's also new is that, because of the effective feedback loops for externalities inherent in the system, everyone without exception would benefit from this virtuous cycle.
Feedback loops for externalities ensure that no one will be harmed by the goods produced, but how about financial harm? If an innovation harms someone's employment then we haven't truly solved the individual–public interest alignment problem. Then we would still have adversarial relations, as people would oppose innovation that harms them financially.
The way to think about this issue is as follows: in the Scarcity Paradigm there is a limited number of jobs available for people. Sometimes innovation results in new businesses and employment opportunities. At other times innovation eliminates jobs. This can result from AI, automation, or other technological disruptions of industries. With advances in AI and automation, we are likely to see an acceleration in jobs eliminated. Meanwhile, the prospect of new jobs to substitute those lost remains elusive.
In the Abundance Paradigm the dynamics are completely different. Because any work that benefits the public can be monetized, there is never a shortage of work available. So when innovation eliminates the need for some work, there is still an abundance of other work to do, while everyone benefits from the increase in labor use efficiency. Any person who is doing work that is obsolete will simply move to another project, since they won’t be tied to a particular job.
One way to think about it is that each person can select work from a nearly-infinite list of "projects." If some projects become redundant, the person can simply add more projects to their to-do list. Such a system gives people the most freedom and flexibility to choose the type and amount of work they like, thus promoting self-fulfillment.
Because work is plentiful, there would be little benefit for anyone to compete with others for the same work. Cases where some individuals take away work from others would likely be rare. And so we get a paradigm where any innovation benefits society without harming anyone financially, allowing individual–public interest alignment.
This paradigm will show its full potential with the emergence of aligned AI (and later, aligned AGI). We already know the massive potential AI has in accelerating scientific research and innovation. We also know it can enhance human capabilities beyond belief. But these capabilities can only be fully realized if the AI system is aligned with the public interest. Otherwise, the potential harm of the AI to society, when in the hands of powerful interests, can easily outweigh any benefits. With AGI the consequences can be outright catastrophic.
But in the Abundance Era the problem of AI alignment can finally be resolved. With AI working in the public interest, we will get tremendous growth in human capabilities. We will also see cascading improvements, streamlining and acceleration throughout the economy, leading to global mass abundance. What's more, aligned AI will be able to improve the Abundance mechanism itself. It would allow greater capacity and granularity in impact evaluation, thus leading to exponential growth in all areas of the economy.
The Abundance Era will certainly greatly increase the proliferation of public goods. No less important however is the potential to improve how common goods are produced and shared. Since common goods are naturally scarce, the question is how they can be used in the most efficient way.
In the current system, every community, city and even country is competing with others over scarce resources. There may be some benefit for one municipality to coordinate common goods use with others, but for the most part the tendency is toward competition. The problem is not merely that those who have more economic power end up with more resources, or that common goods are not distributed equitably. It is also that such dynamic leads to inefficient resource use, resentment and adversarial relations.
When the tendency is toward competition for resources on the global scale, the result is perpetual conflicts that at times flare up into wars. Such conflicts can arise between neighboring countries, but they can also be more regional and even global in scale. Here too we see countries forming bigger geopolitical blocs, but the general orientation tends to be adversarial with other blocs. In other words, countries can join into large geopolitical blocs, but this dynamic can never result in one unified bloc. This is not merely because some countries may prefer not to join the unified bloc, but because the system only works when blocs compete for resources. If there is no competition over resources, there is also no use for the bloc. This is the same logic as there being no use for a defense pact if all countries are included in it; a defense pact would only work if there is an external enemy.
The Abundance Era will see the rise of a new group dynamic — a dynamic that would work at every level, from small communities to the global scale. Instead of competition over resources we will see superalignment for more efficient use of common goods.
How would this new dynamic manifest itself? Let’s take for instance public infrastructure; in the Scarcity Paradigm every town wants to invest in infrastructure that benefits its people. Even then there are all sorts of competing interests within the town that would want the infrastructure to benefit themselves over others. If local government stands to benefit from advancing the interests of some constituents over others this can create a conflict of interests.
The incentives here are misaligned. Developers only care for the profit they make, it doesn’t really matter to them where they place the infrastructure or who gets to benefit from it. The administration should be motivated to provide funding for equitable infrastructure, but in reality has perverse incentives; it may want to benefit its political base over others. It may also want to benefit political donors or supporters. What’s more, constituents have little say in the process. They may have a vote once in a while, but it is not easy to keep up with every plan and proposal made by the local government.
And then there is the issue of cooperation. When each municipality only prioritizes its own interests, it tends to compete with others. There is little motivation to see how resources can be put together toward creating greater impact, and no effective mechanism to build cooperative relations. In fact, a town may use resources to put other municipalities at a disadvantage so that it can get a larger share of commerce, thus growing at the expense of others.
In the Abundance Paradigm the incentive is always impact maximization. There is no central authority that would distribute funding or dictate policy. Instead, anyone can propose a common goods project and communities can reach consensus on the value of such a project. Since the incentive is always to maximize impact, there is no point to try and benefit any special interest groups. After all, these interest groups cannot manipulate the consensus mechanism in their favor anyway. This means that any proposal that favors some over others instead of producing the most impact would only reduce the value of the proposal, and is thus counterproductive.
In the case of public infrastructure, maximizing impact directly translates to proposing solutions that create the most value for the ecosystem. Does this mean only providing everyone with the same access to resources? Not necessarily. This may be one way to increase impact but it is not necessarily the most effective way. Unlike public goods, common goods are not inexhaustible; there are tradeoffs involved in how these resources are distributed. Since the goal is to maximize impact, it may be prudent to allocate more resources toward those who are more effective at producing impact. This only works because of Individual–Public interest alignment; since all are interested in maximizing impact, everyone would want resources going where they can create the most impact. This approach also motivates everyone to do their best and contribute the most they can to the public good.
When it comes to pooling resources together, here too the Abundance Paradigm will have a clear advantage. The system's superalignment principle allows anyone to propose projects where common goods are designed to benefit more than one ecosystem (or municipality). The impact will then be evaluated within each municipality, and each municipality will provide funding to developers in its native currency. In turn developers will provide public infrastructure that benefits all participating municipalities. Such an approach would lead to more efficient use of resources, and likely greater impact on each of the communities.
No less important, the impulse toward cooperation over competition in the Abundance Era will transform how communities, cities, and even nations relate to each other. Instead of competing over limited resources, groups will instead have a lot more to benefit from joining forces. This tendency will lead to more harmonious relations, a greater benefit to all members in the communities, a substantial upgrade in resource efficiency, and a meaningful reduction in the potential for conflicts and wars.
By resolving the individual–public interest alignment problem the Abundance Paradigm will bring about a fundamental shift in human relations and understanding. The Scarcity Paradigm's inherent adversarial relations made it impossible for society to agree on a common truth. That’s because when people have an economic incentive to withhold information from the public, and even provide misleading information, there is no way for society to agree on the facts. But when individual and public interests align, everyone has the incentive to provide the most complete and accurate data to the public. At the same time no one benefits from misleading others or withholding data.
And so this common understanding acts as the foundation to a new economy. An economy where anyone can contribute to the public good and be rewarded accordingly. And where every contribution to the public good, or to public knowledge, builds and expands on previous contributions. We can then have an ever-expanding body of knowledge that is available to the public in its entirety. This body of knowledge encompasses everything from science and medicine to history and current events; allowing people to make sense of the world around them, and to grow and refine their understanding over time.
So every contribution empowers the whole ecosystem, while everyone has an incentive to maximize their impact, thus ensuring the most efficient use of both scarce and abundant resources. This powerful coordination mechanism then works for both scarce and abundant resources.
Even more so, the mechanism creates superalignment at every level of human organization; in this system people and groups always have a stronger incentive to align their interests and cooperate for the greater good, rather than fighting over resources.
The coordination mechanism created by the Abundance Paradigm therefore allows everyone to live self-fulfilling lives and incentivizes them to maximize their impact on the world. It creates consensus among the public around a vast and ever growing body of knowledge that all can trust and build on. And it creates alignment between individuals and groups on how to use scarce resources in a way that is most efficient and contributes the most to the public good. All these will put us on a path to ever greater prosperity and universal abundance.
# Epilogue: A Call to Action
Thanks to the Abundance Economy we will finally be able to solve some of the most serious challenges and crises we face, and prevent society's slide toward dystopia. We will have a powerful coordination mechanism to maximize impact on society in using both scarce and abundant resources. We will also have a system where individual and public interests are aligned, and where there is always an incentive to coordinate the use of resources instead of fighting over them. This is a system that will put society on a path to ever greater material abundance and universal empowerment and self-fulfillment.
While this vision for a better future is certainly desirable, it is not inevitable. None of it can happen on its own. At the same time it cannot happen soon enough, given the crises we face. It will take effort and determination from all those who believe in the abundance vision. And so, if you too believe in the abundance vision, and want to see a fantastically more peaceful and prosperous world, there are several things you can do.
It starts with contributing to the intellectual and theoretical framework of the Abundance Paradigm. The framework is certainly robust already, but it’s important to identify any flaws in it, and come up with even better solutions than the ones already offered. By building a solid theoretical foundation to the Abundance Paradigm we can ensure that the system will be effective and have broad public trust.
We then need to help further research and develop mechanisms and models to improve the Abundance Protocol. While the system is already well thought out, the protocol is still in its infancy, making it a fertile area to make an impact. No less important is building and refining the protocol itself. There will certainly be multiple iterations and tests in developing a battle-tested protocol that is capable of generating a working abundance economy.
While developing the protocol is of paramount importance, the success of the abundance economy depends on people knowing about and understanding how the paradigm works, and eventually actively participating in it. To that end you can contribute simply by widely sharing this book. Beyond that you can educate those around you — in person or on social media — on how the Abundance Paradigm works and on its incredible benefits.
Finally, until the feedback loops of the abundance economy are set in motion, some of the work to develop the protocol comes with a significant investment of time and labor. To that end, please consider contributing to the protocol financially.
The efforts around the Abundance Protocol will be concentrated here: [https://abundance.id/](https://abundance.id).
