Category Archives: The wandering economist

Seeing the world with the eye of an economist-ish.

Designer economies. How much freedom do we really have when imagining believable economic systems?

1. Introduction and lecture outline

Worldbuilding is hard, as authors well know. In this lecture, we are going to take a look at the part of worldbuilding where you give your planet, eldritch dimension, fantasyland or post-climate change polity a believable, though obviously fictional, economy. In the time-honoured tradition, I have good news and bad news. The good news is that we have considerable latitude in designing your fictional economy, just as in inventing rituals, dress codes, weapons, and other technologies. The bad news is that coming up with a good design is nontrivial. But this is also good news, since overcoming difficulties with a creative act is what authors do, and it is perhaps the most fun humans can have.

In the lecture, We are going to reflect on some basic choices that we need to make when designing an economy. To help reflection, we invoke concepts from social sciences and economics.

Concept in real life	Related concept in economics
Designing credible economies	Incentive compatibility
“Human nature”	Value theories
Institutions	Economic anthropology
Plausible histories	Subgame-perfect equilibria

2. Suspension of disbelief

I am no author, just a lowly reader. But, when I read, I take pleasure from diving into an immersive, textured world that I can explore. This pleasure is enhanced by suspension of disbelief, the psychological state of someone who, willingly, suspends certain functions of critical thinking in order to enjoy the narrative. Emphasis is on “certain”. If I read The Lord of The Rings, I can allow myself to get worried about Sauron’s armies eating what’s left of the free peoples of Middle-Earth (though there is no Middle-Earth: if I look out the window, Brussels is right there). But some parts of my critical thinking are harder to switch off. If, in order to prevail in the Battle of the Pelennor Fields, Gandalf had called in a drone strike on Mordor’s siege machines, that would have broken the suspension of disbelief, at least for me, and made my reading much less pleasant.

So, worldbuilding is a balancing act: the more space the author claims for imagining things that do not exist, the higher the potential entertainment value from the exoticism and mystery. But that space needs to be highly organised to avoid inconsistencies that puncture the bubble of the reader’s suspension of disbelief.

Careless depictions of the economy of fictional words can also rupture suspension of disbelief. A favourite example of mine is the use of coins cast in precious metal as currency in J.K. Rowling Harry Potter’s saga. The book makes it clear that the coins are precious and fungible (i.e. there is an incentive to steal them). They are kept in high-security vaults, guarded by goblins with great magical powers. This, however, does not quite gel: one imagines that, if the coins were really precious, wizards would simply magick them into existence. This would quickly lead to hyperinflation (depending on the magical cost of making new coins), which would drive the real value of those coins to near zero. I cannot justify those goblins.

Of course, this is a fantasy world, and you could always salvage it by inventing exceptions: for example, the wizards can magick up all sort of stuff, but not gold coins. Additionally, that no tracer spell can be put on coins so that they “know their master”, which would make them more like balances in a bank account, valuable but impossible to simply steal. But you see how this looks a bit contrived and “just so”.

Granted, readers can sometimes ignore the nagging feeling that something is off, and concentrate on the characters and the drama instead. This is easier in some subgenres than in others: readers of high fantasy novels are unlikely to pay close attention to economics, and indeed the Harry Potter’s saga is a huge success. So is Tolkien’s, and he got away with a complete disregard for anything to do with economics: who’s feeding, clothing and arming all these standing armies? Where are the breeders and stables to produce all those horses in Rohan, and would they not create some environmental damage? How is it that in besieged Minas Tirith there is no trace of a black market, like our grandparents experienced during World War 2 in most of Europe? On the other hand, if you are writing solarpunk sci-fi or cli-fi, alternative modes of organizing society are likely to be at the center of attention.

3. Incentive compatible mechanisms as a template for credibility

I propose that a good compass for figuring out whether a fictional economy is believable or not is its incentive compatibility. It goes through the concept of mechanism, associated with the names of Eric Maskin, Leonid Hurwicz and Roger Myerson. The basics are as follows:

A mechanism is a manufactured environment for agents (normally people) to interact with one another (auctions, tax systems, social media…). Within the mechanisms, agents act according to their own objectives. [Maskin 2008]
Designing a mechanism is about making its rules so that people in it spontaneously take the system in the direction that the mechanism designer wants to go.
A mechanism is incentive compatible when every agent’s best strategy is to follow the rules, no matter what the other agents do [Hurwicz 1960].

In his Nobel lecture, Maskin asks three questions about mechanism design:

When is it possible to design incentive-compatible mechanisms for attaining social goals?
What form might these mechanisms take when they exist? Auctions, tax systems, elections, ritual combat, social media?
When is finding such mechanisms ruled out theoretically?

They apply fairly directly to worldbuilding for science fiction authors! We can use them as a guide to imagine an economic system, for example that of a space economy. The authors can choose the system’s goals, then take on the mantle of the mechanism designer, and ask herself what form might incentive-compatible mechanisms take for those goals.

4. Incentives to do what? Introducing value theories

Incentive compatibility is about mechanisms being compatible with people’s incentives. Ok, but what are these incentives? What is it that people (be they humans, aliens, elves or robots) want? What do we value?

This is a philosophical question, and let us never forget that economics grew out of moral philosophy. Adam Smith was a professor of moral philosophy, and he authored a Theory of Moral Sentiments before The Wealth of Nations. The branch of economics dealing with it is called value theory. The most important thing to know about value theory is that it is inherently political and highly contested. Human communities in different times and places have adopted different value theories.

Consider, for example, the concept of production boundary, the imaginary line that divides activities that produce wealth to activities that merely redistribute it (I am borrowing this terminology from Mariana Mazzucato’s The Value of Everything). For example, imagine that Alice, a farmer, leases Bob’s land to grow wheat. Most economists would agree that Alice, when farming, produces wealth, whereas Bob, when collecting rent for the land, is simply redistributing to himself some of the wealth Alice has created. Another way to say this: Alice’s work (farmer) stands within the production boundary; Bob’s (landlord) stands outside it.

Mazzucato’s point is that the production boundary has shifted over the centuries, amidst much debate and political battles. A common pattern has been that, when a class of people managed to attain some power, it fought to get legitimized as productive, declaring whatever it was doing as “production”. If what counts as value depends on who you ask, it follows that value is not some kind of universal measurable property, like mass. It’s a convention, that results from a political process.

An interesting question around value theories is this: who is supposed to be doing the valuing? The physiocrats and the classical economists disagreed about where the production boundary lay, but they agreed that value was an objective characteristic of things. Both schools believed that Alice the farmer creates value, while Bob the landlord does not. Furthermore, they believed this was just a fact, that depended on no one’s personal opinions and views. The objective nature of value implies that societies can and do make collective choices that they believe to lead to producing more value. If the value of care work (feminist economics) or nature (ecological economics) are objective, then surely we must reward their production, just as we reward the production of food and manufactured goods (you could state that value is, instead of objective, intersubjective and historically determined, and the argument would still hold).

Marginalist economics has a different idea. Starting in the second half of the 1800s, this current of economic thinking maintains that value is subjective. If anyone is willing to pay for something, that something has value for that person. Marginalism posited that people wanted something called “utility”, and that different people would derive utility from different things. Its founders (Walras in France, Menger in Austria, Jevons and Marshall in the UK) had mathematical training and were keen to show that economics was “a real science” like physics. In order to make this idea of subjective preferences mathematically tractable, they assumed that, for each person, utility is an increasing function of her individual consumption of goods and services. Then, using calculus (invented in the 17th century), they could model individual choice as maximising an “utility function”, the arguments of which were individually consumed quantities of goods and services. Any collective dimension of value was discarded.

The founders of marginalism were well aware that theirs was a simplification, a thinking tool. Later, however, neoclassical economists started to think of this individualistic, subjective notion of value as true in itself. People, they thought, actually acted selfishly and in isolation: that was just human nature. Much sci-fi dystopia goes with the notion, and you can do so too. The main point of this lecture is that you don’t have to.

5. Underpinning the neoclassical theory of value: homo economicus

Neoclassical economists, then, posit that they have built a barebones model of human nature that (1) makes collective human behavior mathematically tractable and (2) despite its simplicity, captures the essence of how humans function. If both these claims hold, economists can build relatively simple models that will, nevertheless, capture the essence of human economic behavior and have explicative and predictive power. The idea of creating such a simplification for the purpose of doing economics goes all the way back to John Stuart Mill in 1836, and has later crystallized a model known as homo economicus. Looking at the conventional assumptions behind the economic theory taught in most university, homo economicus can be characterized precisely.

He is motivated by self-interest alone.
He has unlimited computational capacity (for example, computes the expected value of relevant stochastic variables).
This model underpins Pareto-efficient general equilibrium theory, in which the individual pursuit of self-interest by each economic agent achieves socially optimal outcomes. This is the philosophical foundation of Ayn Rand’s “Virtue of selfishness”, or Gordon Gekko’s “Greed is good”.

The history of economic thinking has seen several critiques of the homo economicus idea. Among others:

Most traditional societies are based on reciprocity (economic anthropology: Sahlins, Polany, Mauss…)
Bounded rationality (Veblen, Keynes, Simon…)
Inconsistent preferences for risk in investors (Tversky)
Unstable, poorly defined preferences (behavioral economics: Kahneman, Thaler, Knetsch…)
Not confirmed by experiments (experimental economics).
Not confirmed by experience in actual societies (Sen).

In general, homo economicus is not a good simplification of “human nature” on which to hang a theory of value. It creates more problems than it solves.

6. Underpinning other theories of value: group selection and the evidence from ancient societies

A more plausible theory emerged in the 2000s from the work of a a school of biologists interested in cultural evolution, the interaction between evolutionary pressure, the human genome, and human cultures. The main idea is that, unlike other species, humans are subject to evolutionary pressure on two fronts: the individual level (from Darwin’s natural selection, common to all species), and the group level (from group selection) [Henrich 2016]. The fitness of a human individual depends both on the individual’s own fitness (for example his or her resistance to pathogens) and on the success of the group to which he or she is a member.

It turns out that successful groups are groups that are good at cooperation, which is intuitive and confirmed by plenty of ethnographic and archaeological evidence. So, evolution pulls humans in two opposite directions: it wants us to be more competitive, to obtain a better position within the group; but it also wants us to be more cooperative, to benefit from the success of the group with respect to other groups.

The main driver of the group’s success is the scale at which it can manage cooperation [Wilson 2012]. Given cognitive limitations, this means the successful human in a successful group must be able to cooperate with complete strangers (unlike, for example, chimps: troops of chimps that encounter a lone chimp from a different troop will typically kill the stranger on sight). This poses the additional problem of free riding: non-cooperators in a cooperative group will reduce the group’s performance, because they benefit from the “public goods” created by the group without contributing to them. For this reason, humans appear to have evolved methods to detect and expel the strangers in their midst. Experiments on infants as young as six months – and so untouched by education and value transmission – show that they react more favorably to others who speak their own dialect (even though they themselves have not yet learned to speak!). Biologists thinks that this “primary xenophobia” is innate, hardcoded into us at birth [Henrich 2016].

The late E. O. Wilson, perhaps the most accomplished scholar of cultural evolution, sums up his idea of “human nature” like this:

Individual selection is responsible for much of what we call sin, while group selection is responsible for the greater part of virtue. Together they have created the conflict between the poorer and the better angels of our nature.

So far the theory. Is it borne out by the data? An influential 2021 book by economic anthropologist David Graeber and archaeologist David Wengrow claims it is. The Davids combine ethnographic and archaeological evidence to confute the mainstream narrative about the invention of agriculture. Such mainstream narrative is associated to scholars such as, among others, Francis Fukuyama, Steven Pinker and most recently Yuval Harari, and goes like this: for a long time, humans lived in small hunting-gathering bands. These early societies were free and equal. Women were not oppressed. But then, ten thousands years ago, agriculture was invented. Early farming societies had a decisive advantage, as they could hoard stocks in good years to weather the bad ones. But farming meant inventing and enforcing property rights, which meant top-down management and therefore hierarchies. Stratified classes appeared, themselves allowing cooperation on a larger scale and giving farmers further advantages over hunters-gatherers. Patriarchy also ensued.

According to the Davids, this story is largely a fantasy, originated (much like Hardin’s tragedy of the commons story) by deductive thinking from philosophical premises originated in the Enlightenment. We have evidence of socially stratified hunting-gathering societies; egalitarian farming ones; societies taking up, then abandoning farming; even societies that farmed in the summer, and hunted-gathered in the winter, changing their leadership structure and political order with the season. The Davids refer back to Marcel Mauss’s 1903 studies of Inuit societies, which indeed changed their societal arrangements in sync with the season (hierarchical and patriarchal in the summers, egalitarian and free-love practicing in the winters). Given the harshness of living conditions in the Arctic regions, Mauss expected that these variations could be explained by the material advantages they brought, but he had to conclude that they could not. In the words of the Davids:

“Yet even in sub-Arctic conditions, Mauss calculated, physical considerations – availability of game, building materials and the like – explained at best 40 per cent of the picture […] To a large extent, he concluded, Inuit lived the way they did because they felt that’s how humans ought to live.”

7. Implications for authors of economic science fiction

So, where does this leave us? In a fascinating place. Powered by group selection, all kinds of societal and economic arrangements seem to be possible. In fact, very many are certainly possible, and we know that because we have tried them before. Remember Maskin’s definition of mechanism, “a manufactured environment for agents to make decisions”? That definition also describes a society. A society is a mechanism, because it is manufactured – via a political process – by its members. This gives authors a license to use their imagination to design new and fascinating economies we, the readers, can try on for size. It also gives them a library of arrangements that have been (or are still being) tried, to take inspiration from.

To conclude this lecture, I want to briefly point to some historical and contemporary examples.

Monastic economies

In the 6th century, St. Benedict of Nursia codified in his Rule the “protocol” overseeing the interactions among monks in a monastery (6th century). He did not found an order, but the Rule went viral and was adopted by the nascent monastic movement. People who used it were more likely to run a successful monastery than people who did not; and so, by the time of Charlemagne all Europe was infrastructured with successful monasteries running on the Rule.

Benedictine monasteries were units of production, because, in order to be effective places of devotion, they needed to be autonomous from the secular world. Benedict was aware of this, and his Rule contains some economic prescriptions:

Monks must price a little lower than seculars – doing otherwise would be avarice, a sin.
Everything monks do must be high quality. It is work, and work is dedicated to God and leads to Him.
Any profit you can make within these constraints is good, and you can use it to fund work that does not generate revenue.

This could not be more different from the neoclassical theory of labor supply, where a self-interested worker trades leisure for income; as well as from the theory of the profit-maximizing form. And yet, it worked extremely well. Monks made and ran inns; farmed the land; built water mills; created schools; copied and preserved manuscripts. At its peak, the famous Cluny Abbey served 10,000 warm meals a day to people in need. Also, this model is very stable, having been around (and prosperous) for 15 centuries straight. Even now, as a Benedictine superior told me, “we tend to to get prosperous, because monks work hard”.

Seasonal economies

These were described above in reference to the Inuit. The Davids again:

“In the summer […] property was possessively marked and patriarchs exercised coercive, sometimes even tyrannical power over their kin. But in the long winter months […] Inuit gathered together to build great meeting houses of wood, whale rib and stone; within these houses, virtues of equality, altruism and collective life prevailed.” [Graeber and Wengrow 2021]

Another traditional society with a similar arrangement are the Nambikwara in Northwest Brazil, studied by Lévi-Strauss in 1994.

Systems of cooperatives

Most economists, and most of the rest of us, think of the for-profit corporation as the “natural” form to organize economic activity. And yet, cooperatives are widespread all over the world. It is estimated that there are at least 280 million cooperators worldwide, and cooperatives have at least 27 million employees. Cooperatives lend themselves to self-organizing into “layers” to solve the problem of “make or buy”: a typical example is a group of farmers who grow grapes who join forces to commission a facility that will process the grapes of all of them into wine. This way, farmers can appropriate the added value of the transformation of their primary produce. One level above, you can find that the wine-making cooperatives of the same region can create a second-level coop to organize the distribution and marketing of the wine produced by all of them, and so on.

Europe has entire regions where most of the economy is cooperative. The most famous one is the Mondragon valley in Northern Spain, where an entire cooperative ecosystem of automotive manufacturing has come into being; several northern Italian regions are characterized by the prevalence of cooperatives in industries as diverse as agriculture, construction, insurance and banking.

Commons-based peer production

These are arrangements whereby non-hierarchical communities can maintain common resources (forests, fisheries, irrigation system) over time. They are very well documented: Elinor Ostrom won a Nobel for a 1990 book, Governing the Commons, where she not only looks in depth at case studies from Spain to Japan; she also comes up with 8 principles for designing the governance of a common resource. Principle 1 is “clearly define the group’s boundaries”, which goes back to Wilson and Henrich’s point about groups in competition needing to expel free riders. If you want to imagine a space economy, you could do worse than starting from here.

Notice that Ostrom’s book proves conclusively that “tragedies of the commons” do not always occur. Indeed, the 1968 paper by ecologist Garrett Hardin which introduced the “tragedy” concept was based not on evidence, but on deductive thinking: if homo economicus is a good model for human behavior, then tragedies of the commons should occur. Historically, the English commons (common lands) were eliminated not by “tragedies” of overconsumption, but by violent evictions and enclosures. Hardin himself was a white supremacist who cultivated a “lifeboat” vision of society.

War economies

A war economy is an economy the purpose of which is exogenous to the economy itself. Typically, this purpose is winning a war: the enemy is at the gate, and all economic efforts are directed to defeating them. War economies are field tested, and have proven to work extremely well: the most famous example is that of Germany during World War 1. Germany’s technocrat-in-chief, Walther Rathenau, pivoted the Empire’s economy in a matter of weeks as the war started [Scott 1998]. The state became the master planner, and, for many businesses, the main client and a sort of uber-CEO, with entire conglomerates strongarmed into pivoting overnight into new products.

This move was very successful in keeping the German army in the field and equipped, well after external observers had predicted its dissolution. And it was widely copied, which is why Rolls-Royce makes airplane engines as well as luxury cars.

Red plenty

This is more conjectural: the idea is that, as computing becomes cheaper and more powerful, central planning might emulate the efficiency of markets, while overcoming the latter’s blindness to externalities such as pollution or care work. We do know that the Soviet Union attempted cybernized central planning using linear programming techniques [Kantorovich 1939]. The centerpiece of the effort was the idea to use something called shadow prices (the marginal advantage of releasing a constraint) to simulate market prices. This fascinating story is told in a very strange (history? Fiction?) book by Francis Spufford, Red plenty.

8. Thinking up a fictional economy with subgame-perfect equilibria

In an attempt to grapple systematically with these ideas, a few years ago I was part of a group of people that were interested in the intersection between science fiction and economics, and called ourselves, cheekily, the Science Fiction Economics Lab. We started turning these things around in our heads, and came up with the idea of an open source world where we could explore these thoughts. We were doing worldbuilding, rather than writing actual sci-fi (though eventually some sci-fi stories set in our imaginary world did appear).

The overarching concept was that of a floating megacity, adrift in the oceans of a vaguely post-climate change Earth. We called it Witness. Witness had launched as a unitarian project, inspired by the floating city concept of UN Habitat, but then it had fractured along ideological lines. It was large enough to sustain several splinters, called Distrikts, each of them with its own economic system. We structured the work on Witness as a wiki (Witnesspedia), with major entries for the most important Distrikts: Libria, a hypercapitalist economy with minimal state intervention, reminiscent of much cyberpunk dystopias and, well, us. The Assembly, a cooperativist society with super-strong anti-monopoly culture. Hygge, a Nordic social-democracy on steroids; the Covenant, characterized by the presence of many monasteries and other religious institutions, with a strong manufacturing vocation (pun unintended).

This all is fun, but quite hard to take it down one level, to imagining institutions like markets, central banking, antitrust enforcement agencies, and indicators of economic performance (because, seriously, what kind of self-respecting sci-fi piece of work would still be mentioning GDP?). The incentive compatibility constrain kicks in. Perhaps the hardest nut to crack is the presence of trade across different systems: if Libria can trade freely with Hygge, will not its products – made with alienated labour – outcompete the fairer ones in Hygge? If not, why not? You find yourself designing policies that reproduce the economic systems you want – which is more or less what Graeber and Wengrow tell us real societies do.

In trying to make these imaginary economies credible, we felt the need to come up with an origin story. If these systems are in our future (which makes them more relatable) there should be an incentive-compatible path from here to there. So, maybe a Distrikt in Witness has robust trustbusting policies. Problem with monopolists is that they tend to capture their regulators, because they typically have more money and woman- or manpower than them. So, a society with strong anti-trust policies that never had any large monopoly is in equilibrium, because no firm becomes big enough to capture the regulators. But a society that starts with incumbent monopolies (like we do), and then somehow introduces antitrust policies, is not, because monopolists have the power to prevent effective regulation. To be credible at all, an imagined future needs to be connected to the present by an unbroken series of changes, each of these is incentive compatible. Game theory has a formalization of this concept, called a subgame-perfect equilibrium [Selten 1988].

Subgame-perfection is a test for the believability of the origin story of an economic system. To pass the test, the story needs to respect the incentive-compatibility constrain at each step of the way.

9. Coda: the role of science fiction in developing economic thinking

For the first time since I am intellectually active, late-stage capitalism is being seriously contested; and neoclassical economics with it. The battering ram of these contestations, alas, is not the economics profession, but climate change. But the profession is stirring, flexing muscles that had not been used for almost 100 years. New concepts are afoot: degrowth. Commons-based peer production. Universal basic services. Modern monetary theory. And some old concepts, like mutualism, and cooperativism, are making a comeback. They can inspire you as you build fictional economies in your head, and I hope you have as much fun as I did.

It is fun, but it also is important work. Like Cory Doctorow says, science fiction stories can function as architects renderings, making these models come alive, showing us what our lives would be like, if we lived in these systems. What would our jobs look like on a planet (in a galaxy far far away) that embraced degrowth? Our schools? Our romantic life? I am convinced that we need science fiction to inspire democratic debate on the urgent economic reforms that await. So, let’s get to it.

Essential bibliography

D. Graeber and D. Wengrow, 2021. The dawn of everything: a new history of humanity. London: Allen Lane.
J. P. Henrich, 2016. The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter. Princeton: Princeton University Press.
L. Hurwicz, “Optimality and informational efficiency in resource allocation processes,” in Mathematical methods in the social sciences, K. Arrow, S. Karlin, and P. Suppes, Eds., Stanford: Stanford University Press.
E. S. Maskin, 2008. “Mechanism Design: How to Implement Social Goals,” American Economic Review, vol. 98, no. 3, pp. 567–576, May 2008, doi: 10.1257/aer.98.3.567.
M. Mazzucato, 2018. The value of everything: making and taking in the global economy. London, UK: Allen Lane, an imprint of Penguin Books.|
E. Ostrom. Governing the Commons: The Evolution of Institutions for Collective Action, 1st ed. Cambridge University Press, 2015. doi: 10.1017/CBO9781316423936.|
R. Selten, 1988. “Reexamination of the Perfectness Concept for Equilibrium Points in Extensive Games,” in Models of Strategic Rationality, vol. 2, in Theory and Decision Library C, vol. 2. , Dordrecht: Springer Netherlands, pp. 1–31. doi: 10.1007/978-94-015-7774-8_1.|
E. O. Wilson, 2012. The social conquest of earth, 1st ed. New York: Liveright Pub. Corporation.|

Sociopathic innovation: how we are investing most in the most evil technologies (LONG)

TL;dr

Artificial intelligence and the blockchain are the two main technological hypes of the past fifteen years. Both were hailed as technologies with the potential to solve many problems and change the world, for the better. It now looks like their impact is overwhelmingly negative. Though they could be used for the common good, it turns out they are not very good at that. They are better, far better, at harming humans than at helping them. They encode dystopian, sociopathic world views; and tend to attract developers, investors and entrepreneurs that share those world views. So, once deployed, they tend to bring the world closer to them. They are sociopathic tech. This is disturbing, because mostly everyone fell for them: investors, developers, entrepreneurs, academics, government officials. I call for a re-examination of the achievements of these technologies and the impact they are having on our life and our societies. I would like to see support to innovation systems depend on how new technologies improve the well-being of humans and of the planet, and only on that. In what follows, I review some of the facts as a discussion starter.

Of how Artificial Intelligence excels at everything, except solving problems that matter

I recently had the opportunity to be exposed to the work of Juan Mateos-Garcia, a leading data scientist. Juan and his team had been looking at a large dataset of science papers published on the topic of Artificial Intelligence (AI). Their results look like this:

AI has been undergoing a revolution since about 2012, when deep learning started to systematically outperform established techniques.
Scientific production (papers) is booming. AI is shaping to be a general-purpose technology, like electricity or computing itself.
Industry interest is evident. Many top scientists have been recruited from academia into industry. Venture capitalists have moved to invest in AI startups. Major governments are underwriting large public investments. There are talks of a “AI arms race” between China, the USA and the EU.
AI is dominated by a small number of organisations and geographic clusters. Diversity of its workforce has stagnated.
AI has had no impact on the effort to beat back the COVID-19 pandemic. In fact, all other things being equal, a paper on COVID is more likely to be cited by other papers if it is not about AI.

This final point gave me pause. Something was off. Why would AI not make a valid contribution to fighting the COVID plague? The conditions all seemed to be in place: there was, and still is, plenty of funding for research on COVID. There is a large, inelastic demand for the applications of that research, like vaccines. There is plenty of training data being generated by health care institutions the world over. And, if AI is a general purpose technology, it should apply to any problem, including COVID. The most exciting technology of the moment somehow failed to contribute to solving the most pressing problem of the moment. Why is that?

I can imagine a world where AI is deployed to help in the fight against a pandemic. We would use it to engineer a more targeted response to the risks of contagion. Granular risk scores could be associated to individual people and different situations, allowing society to protect the most vulnerable people from the riskiest situations, while leaving low-risk individuals in low-risk contexts free to get on with their lives.

Sounds good, but that world is not the one we live in. In our world, AI-powered, individually customized COVID restrictions would run into non-tractable problems. First, the algos would seize the high correlation between different socio-demographic variables, and decide that poor people, people of color and (in America) trumpists are more prone to the contagion, and should stay at home more than white, affluent liberals. Discriminated groups would react fighting back, challenging the algos as biased, starting litigation and inviting to civil disobedience, as is happening time and time again. Even if there was no conflict and everybody trusted the algos, it is not clear how we would use effectively the predictions they make for us. First of all, there is the cognitive challenge of understanding the predictions. You could tell someone something like this: “the risk of catching COVID on public transport for someone with your demographic profile went up 20% today, avoid the bus if you can”. But that is unlikely to work, because

Most people do not understand risk. For example, they are more scared of terrorist attacks than they are of car crashes, though the latter are far more frequent (hence more dangerous) than the former.
AI is Bayesian statistics, and as such it makes prediction not on you, but on somebody who is like you in a quantifiable way. It leaves out everything that makes you unique, putting it in the error term. For example, imagine you are a 45-year old living in the Netherlands who is also an ultra-marathoner. The algo computing your risk factor processes your age and the country you live in, because it has thick enough data in those dimensions. Your ultramarathons stay in the error term, because there are not enough people doing ultramarathons for that activity to be tracked in its own variable. And yet, when looking at the overall resilience of your organism, this is clearly an important information.

Given this situation, I suspect most people would end up following their own belief system rather than the algo’s recommendations. People who fancy themselves strong and resilient might say “yes, this gizmo is predicting high risk, but it is not talking about me, I am healthier and stronger than most!”. Or, vice versa, “yes, a low risk is predicted for outdoor mingling, but with my history of allergies I still don’t feel safe”. This is de facto happening right now with how people process scientific findings about COVID-19. Some people prefer to trust their own immune systems over the pharma-punditry complex. Others [made COVID restrictions into some kind of weird religion], following them “above and beyond” even when science is calling for their relaxations. Even if a good AI-powered risk prediction system were in place , many humans are way too irrational to take full advantage of it. They prefer simple rules, applicable to all: “1.5 meters”, “wash your hands” and such. The promise of AI, providing personalized recommendation to each and every one of us, clashes with the human need for stability and security. In conclusion, AI had no grip on COVID, and is unlikely to have any grip on any similar high-stakes problem. So, what is AI good for? We can start with the applications already being developed:

Boosting consumerism: recommendation algorithms, targeted advertising. This has substantial socially detrimental spillovers. Algos maximizing engagement on social media are known to push their users towards ever more extreme, radicalizing content.
Surveillance: facial recognition, predictive policing, remote invigilation of children (with notoriously racist algorithms), bossware.
Machine translation.
Deepfakes – whose main applications, according to Wikipedia, include “blackmail”, “pornography”, “politics” and “sockpuppets”.

With the exception of machine translation, these applications are all detrimental to human well-being, for world-eating values of “detrimental”. We are seeing yet another example of Kranzberg’s First Law in action: AI is not good, nor is it evil, nor is it neutral. It could be used for good, though I am unconvinced it would work very well: but it is when you use it for evil, dehumanizing purposes that it really shines. That such a potentially toxic technology is attracting so much attention, public funding and private investment is a spectacular societal and policy failure. And that brings me to the blockchain.

Of the blockchain and its discontents

The blockchain, as by now everyone had to learn, is the name of a family of protocols that allow data storage not in a single repository, but in many. Using cryptography, the different computers who adhere to the same protocol validate each other’s copy of the database. This prevents a “rogue” participant from altering the records, as the alteration would only be present in a single computer and not be validated by the others. This system was first proposed to solve a problem called double spending when no trusted, centralized authority is present.

That was in 2008. In these 13 years, blockchain solutions have been proposed for many, many problems. To my knowledge, none worked, or at least none worked any better than competing solutions that used a more conventional database architecture. This makes sense, because blockchains are self-contained systems. They use cryptography to certify that in-database operations took place, but cannot certify anything that exists outside the database. Any system based on a blockchain relies on external sources of information, known as “oracles”. For example, if you were to build an identity system based on the blockchain, you would have to start by associating your name, date of birth etc. to a long string of digits. Once stored on the blockchain, the association is preserved, but some external “oracle” has to certify it before it gets stored. In the absence of a credible external certification, the system could work technically, but it would produce no impact. I could create my own identity system, but no one would use it, because I am not trustable enough when I issue a digital ID to your name. There are entities with the trustability to start such a system, for example major governments. But, because they are trustable, they do not need the blockchain at all. I have lost count of technologists who told me:

Any technology which is not an (alleged) currency and which incorporates blockchain anyway would always work better without it. (source)

But the blockchain is not just another clever technical solution in search of a problem to solve. I argue it is a major source of problems in itself. Consider this:

The distribution of Bitcoins is extremely unequal, with a Gini coefficient estimated at 0.95 in 2018 (theoretical maximum: 1; Lesotho, the most unequal country on the planet for which we have data: 0.65). In fact, inequality seems to be a feature of blockchains, not just of Bitcoin – for example, it is estimated]that the bulk of the monetary value conjured by Ethereum-based non-fungible tokens (NFTs) is appropriated by “already big-name artists and designers”.
Blockchains use a lot of power. Every update anywhere in the system needs to be validated by network consensus, which includes a lot of computers exchanging data. Bitcoin alone consumes about 150 Terawatt per hour, more than Argentina. Providing computer power to the Bitcoin network is rewarded in Bitcoins, through a process known as “mining”: this provides the incentive to underwrite all this computation. In bid to make what they see as easy money, Bitcoin miners have resorted to malware that infects people’s computers and gets them to compute SHA-256, incorporated into the builds of open source software projects; resurrected mothballed power stations that burn super-dirty waste coal; installed mining operations in Iranian mosques (which get electricity for free) and engaged in plain stealing. Their carbon footprint is enormous: one Bitcoin transaction generates the same amount of CO2 as 706,605 swipes of a Visa credit card. Some blockchains have less computationally expensive systems of verifications, but they are still more energy- and CO2-intensive than traditional databases.
Fraud – especially to the detriment of less experienced investors – is rampant in crypto.
Crypto has provided a monetization channel for ransomware attacks. Ransoms are demanded and paid in Bitcoin, untraceable by Interpol. Some observers go so far as to claim that the price of Bitcoin is tied to the volume of ransomware attacks. Hospitals and other health care institutions are among the main targets of these attacks: not only do they have to pay money, but their IT systems shut down, threatening the lives of patients.
In 2021, tech companies that used to donate CPU power to legitimate projects have had to stop doing so, citing the constant abuse from crypto miners. It is worth quoting the words of Drew DeVault:

Cryptocurrency has invented an entirely new category of internet abuse. CI services like mine are not alone in this struggle: JavaScript miners, botnets, and all kinds of other illicit cycles are being spent solving pointless math problems to make money for bad actors. \[…\] Someone found a way of monetizing stolen CPU cycles directly, so everyone who offered free CPU cycles for legitimate use-cases is now unable to provide those services. If not for cryptocurrency, these services would still be available. (source)

In return for this list of societal bads, so far, all the blockchain has to offer is a plethora of speculative financial assets: a casino. Which is also a societal bad, if you, like top innovation economist Mariana Mazzucato, believe that the economy is overfinancialized, and that policies should be put in place to roll financialization way back.

The blockchain is, overall, a net societal bad: it consumes resources to deliver a casino. Humanity would be better off without it. The picture gets even grimmer when you consider the opportunity costs: blockchain startups have gobbled an estimated 22 billion USD in venture capital funding from 2016 to 2021, very likely matched by various forms of government support, and that money could have been used in more benign ways. So, what’s going on here? Kranzberg’s First Law, yet again.

The original group of developers that rallied around Satoshi’s Nakamoto White Paper had a libertarian ideology: they dreamed of a trustless society, where contact is reduced to a minimum and anonymised, and were obsessed with property rights. So, they built a technology that encodes those values, which in turn attracted more people than believe in those values. Code is law, they said. If someone can technically do something, that something is allowed, even moral, under some kind of tech version of social Darwinism. When the DAO was hacked in 2016, exploiting vulnerabilities in the Ethereum blockchain, the perpetrator bragged about it: if I stole your money, it’s your own fault, because code is law. I am just smarter than you, and I deserve to walk away with your money.

Trustless societies do exist – the mob is one of them. But they are not a good place to live. Economists and social scientists think of trust as social capital, and seek ways to build it up, via accountability and transparency. Again, the blockchain could conceivably be used for something good, but in practice almost all of its uses contribute to making the world a worse place, while making money for the top 0.1% of crypto holders. This is because the tech itself embodies evil values, and because the social coalition behind it upholds these values. Don’t take it from me, take it from open source developer Drew DeVault:

Cryptocurrency is one of the worst inventions of the 21st century. I am ashamed to share an industry with this exploitative grift. It has failed to be a useful currency, invented a new class of internet abuse, further enriched the rich, wasted staggering amounts of electricity, hastened climate change, ruined hundreds of otherwise promising projects, provided a climate for hundreds of scams to flourish, created shortages and price hikes for consumer hardware, and injected perverse incentives into technology everywhere. (source)

Or writer and designer Rachel Hawley:

NFTs seem like an on-the-nose invention of an anticapitalist morality play: a technology that delivers exponential gains to those already at the top by convincing everyone to collectively imagine that free, widely distributed artwork is actually a scarce commodity, all while destroying the _actual_ scarce resources of our planet. (source)

Or economist Nouriel Roubini, testifying to the U.S. Senate:

Until now, Bitcoin’s only real use has been to facilitate illegal activities such as drug transactions, tax evasion, avoidance of capital controls, or money laundering. (source)

Of how and why we are bad at supporting the right innovation

Why are the two most hyped technical innovation of the past 20 years, the blockchain and artificial intelligence, diminishing human well-being instead of enhancing it? Why are we investing in things that make our problems worse, when the world is facing environmental collapse? My working hypothesis is that the financial world will put money into anything that promises returns, with little humanitarian concerns. They lead the dance; and governments the world over have been captured into supporting anything that promises GDP growth. If I am right, it is important to decouple support to innovations from their growth implications, and throw our institutional support behind technologies that uphold human well-being over capital growth. Jason Hickel has some interesting thoughts in his book Less is More, and Mazzucato has forcefully made the point across the arc of her work. Time will tell; and I am confident that better minds than mine will cast more light onto the matter. But this question can no longer wait, and if you are working in one of these two tech ecosystems, you may want to ask your employer, and yourself, some hard questions.

Update 1

Thanks to all the fine folks that reacted to this piece, and gave me useful suggestions. Many people pointed out counterexamples (I owe this particularly nice one to Raffaele Miniaci). But of course, it is not a problem of finding counterexamples, but to assess the overall net impact of this particular bit of technological development on society. My answer may be wrong, but I am fairly confident that my question is right.

Another objection comes from Yudhanjaya Wijeratne, who says that, without giving a definition of AI, the whole first part is meaningless. I went back to Mateos-Garcia’s definition, which he borrowed from Brian Arthur:

Machines able to behave reasonably in a wide range of circumstances.

Depending on how you interpret “reasonably” and “wide”, this indeed captures everything from deep learning for facial recognition to the individually trained spam filter in my personal install of Thunderbird. The reason for this choice is probably that it enables a statistical test for structural change: in 2012 everything changed, more or less at the same time as an influential paper by Krizhewsky et. al was published. Output of AI papers went way up.

I am looking for a socio-economic definition, not a technological one. These technologies each catalyzed a “scene” of researchers, companies, investors, governments etc. What values and visions do these scenes embed? What do they want? The libertarian streak of the blockchain gang is clear. With AI, this is less obvious because AI has a much longer history, and you cannot define it technologically. I guess when I am talking about “AI” in this article, I refer to its post-2012 scene, with some fuzziness but still quite identifiable. This excludes the spam filter on my e-mail client, and should take care of Yudhanjaya’s objection. It also raises concern, for the surveillance-authoritarian streak that this scene has.

Update 2, 2024-11-04

Some time has gone by since this post, and we now know a bit more about real-world use cases of AI. Cory Doctorow has provided a helpful summary. It is a bit of a black book, unfortunately. An excerpt is copied below; or you could read the entire post on his blog.

The real AI harms come from the actual things that AI companies sell AI to do. There’s the AI gun-detector gadgets that the credulous Mayor Eric Adams put in NYC subways, which led to 2,749 invasive searches and turned up *zero* guns:

https://www.cbsnews.com/newyork/news/nycs-subway-weapons-detector-pilot-program-ends/

Any time AI is used to predict crime – predictive policing, bail determinations, Child Protective Services red flags – they magnify the biases already present in these systems, and, even worse, they give this bias the veneer of scientific neutrality. This process is called “empiricism-washing,” and you know you’re experiencing it when you hear some variation on “it’s just math, math can’t be racist”:

https://pluralistic.net/2020/06/23/cryptocidal-maniacs/#phrenology

When AI is used to replace customer service representatives, it systematically defrauds customers, while providing an “accountability sink” that allows the company to disclaim responsibility for the thefts:
https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

When AI is used to perform high-velocity “decision support” that is supposed to inform a “human in the loop,” it quickly overwhelms its human overseer, who takes on the role of “moral crumple zone,” pressing the “OK” button as fast as they can. This is bad enough when the sacrificial victim is a human overseeing, say, proctoring software that accuses remote students of cheating on their tests:

https://pluralistic.net/2022/02/16/unauthorized-paper/#cheating-anticheat

But it’s potentially lethal when the AI is a transcription engine that doctors have to use to feed notes to a data-hungry electronic health record system that is optimized to commit health insurance fraud by seeking out pretenses to “upcode” a patient’s treatment. *Those* AIs are prone to inventing things the doctor never said, inserting them into the record that the doctor is supposed to review, but remember, the only reason the AI is there at all is that the doctor is being asked to do so much paperwork that they don’t have time to treat their patients:

https://apnews.com/article/ai-artificial-intelligence-health-business-90020cdf5fa16c79ca2e5b6c4c9bbb14

My point is that “worrying about AI” is a zero-sum game. When we train our fire on the stuff that isn’t important to the AI stock swindlers’ business-plans (like creating AI slop), we should remember that the AI companies could halt all of that activity and not lose a dime in revenue. By contrast, when we focus on AI applications that do the most direct harm – policing, health, security, customer service – we *also* focus on the AI applications that make the most *money* and drive the most investment.

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