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Why Our Economic Intuitions Are Often Wrong

Blog Post | Progress Studies

Why Our Economic Intuitions Are Often Wrong

Such tendencies stem from our evolutionary psychology.

Summary: Many common economic misconceptions stem from evolved psychological instincts shaped in small, zero-sum tribal environments rather than modern market systems. These “folk-economic beliefs” lead people to misinterpret trade, immigration, profit, and regulation in ways that conflict with core economic principles, often resulting in support for counterproductive policies. Because these intuitions are predictable products of human evolution, they help explain why flawed policy ideas persist. Recognizing their origins can help counteract misleading instincts while reinforcing those that support cooperation, openness, and exchange.


Economic models, rooted in assumptions of rational agents maximizing utility under constraints, have long provided elegant frameworks for understanding human behavior in markets and societies. Yet, a persistent friction exists between these idealized portrayals of human beings and the ways humans actually navigate economic choices. People frequently champion policies that contravene basic economic principles, including minimum wages presumed to boost income without increasing unemployment, rent controls expected to enhance housing affordability without reducing supply, or tariffs that run counter to comparative advantage and affordability. 

People also often harbor counterproductive intuitions, including a belief that markets erode social bonds, despite evidence that markets foster cooperation and thus generate wealth. Those tendencies stem not primarily from information deficits or irrationality, but from our evolutionary psychology. Our economic intuitions were shaped over thousands of years in a world of tight-knit coalitions and zero-sum intergroup rivalry, rendering modern market dynamics counterintuitive. As such, markets are often rejected even when they are beneficial.

Perhaps the most parsimonious theory explaining why people often behave in economically harmful ways is the evolutionary cognitive model of folk-economic beliefs, proposed by anthropologist Pascal Boyer and political scientist Michael Bang Petersen. Folk-economic beliefs are those convictions about economics held by laypeople untrained in the discipline, which frequently diverge from fundamental economic tenets. These encompass mental representations of varied topics, from prices, taxes, and tariffs to welfare and immigration policies. 

Economists have traditionally critiqued those as irrational beliefs or mere byproducts of ignorance, but an evolutionary lens reveals them as predictable outcomes. Ensuring fairness in trade, sustaining social ties, forming stable coalitions, and resolving ownership disputes are all responses to ancestral challenges.

If this theory is right, both actual economic behavior and theories generated to explain one’s own economic behavior are predictable outputs shaped by evolution. When folk-economic beliefs are wrong, they are wrong in predictable ways. We talk about impersonal markets as if they were tribal conflicts. We treat economies built on innovation and surplus as if they were competitions over a fixed pile of resources.

Consider the intuition that international trade is harmful because another country’s gain must come at our expense. From the perspective of standard economics, this belief contradicts the well-established principle of comparative advantage. People benefit from specializing in what they produce most efficiently relative to other goods, even if a trading partner could produce everything more cheaply in absolute terms. For example, a surgeon who happens to type faster than his or her secretary still benefits from hiring the secretary and devoting more time to the operating room. Likewise, America could manufacture its own consumer electronics, but every dollar and worker devoted to assembling phones is one not devoted to designing the software, chips, and financial services where American companies dominate globally. The result is more total output and mutual gain. 

But our evolutionary psychology wasn’t built for comparative advantage, especially not across nations or tribes. Human groups historically competed for territory, food, and status in genuinely zero-sum ways. If a rival coalition grew stronger, it often meant danger for one’s own group. When modern individuals read that another nation is exporting more goods to us or running a trade surplus, our tribal instincts activate automatically. Nations are cognitively represented as tribes, and the success of one tribe is interpreted as a threat to another. The idea that both sides could benefit simultaneously—one of the central insights of the founder of economics, Adam Smith—runs against these deeply ingrained intuitions.

The same coalitional logic helps explain folk intuitions about immigration. People opposed to immigration often claim that immigrants steal jobs from native workers while also claiming that immigrants siphon welfare benefits without working. At the level of policy argument, these beliefs are apparently contradictory. But at the level of psychology, it is an expression of a single concern: Outsiders are draining scarce resources, whether the resource is employment or benefits. Humans evolved in groups where membership conferred access to shared resources—food, protection, or status—and where vigilance against free riders was essential to sustaining cooperation. Newcomers were therefore automatically treated with suspicion until they proved themselves contributors rather than exploiters. 

When this ancestral heuristic is applied to modern societies, it produces the intuition that outsiders must be consuming resources that properly belong to the in-group. Whether the imagined resource is employment or welfare benefits—or even whether the resources are truly being drained at all—matters less than the perceived threat that group boundaries are being crossed without reciprocal contribution.

The psychology of free-rider detection also helps explain the peculiar ambivalence that many people feel toward welfare programs. While people readily endorse the idea that society should help those who fall on hard times through no fault of their own, they also often worry that welfare encourages laziness or dependency. These views appear inconsistent only if one assumes that the public is applying a unified economic theory. In reality, they reflect two separate intuitions inherited from ancestral exchange systems. 

Communal sharing evolved as a form of insurance against bad luck—injury, illness, or an unsuccessful hunt—where helping unlucky group members benefited everyone in the long run. But the same systems also evolved to punish individuals who accepted benefits without contributing. Modern welfare debates, therefore, activate both intuitions simultaneously: compassion toward the unlucky and hostility toward perceived free riders.

Another common folk-economic belief concerns the relationship between labor and value. Many people feel instinctively that hard work should determine how much something is worth. In the hunter-gatherer economy that prevailed throughout most of human history, where the value of goods was closely tied to the labor required to obtain them, strenuous physical effort was intrinsically linked to value production itself. Hunting, gathering, building shelter, or crafting tools all involved visible effort, and individuals who contributed more effort typically produced more resources. When applied to modern economies, however, the same intuition can generate confusion. A programmer writing code, an entrepreneur coordinating supply chains, or an investor allocating capital may create enormous value without performing visible physical labor. Yet because our ownership psychology is sensitive to effort and physical transformation, profits earned through organization or innovation are often framed as morally suspect, particularly in socialist ideology, as if they are thought to represent extraction rather than creation.

Some common opposition to the profit motive itself is explained by evolutionary psychology. In face-to-face exchange within small groups, unusually large gains might indeed signal exploitation or hoarding of limited resources, especially since producing anything of value typically required communal effort. Someone who consistently benefited more than others from trades might be suspected of manipulating information or violating norms of fairness. Modern markets, however, often reward individuals precisely when they discover new ways to produce value—whether by inventing technologies, improving logistics, or coordinating complex networks of production. Because these gains arise in impersonal systems where the beneficiaries are distant strangers rather than known partners, the profits they generate can appear less like the rewards of innovation and more like evidence of exploitation. Our evolved moral intuitions struggle to track value creation in dispersed and opaque market economies. 

Likewise, many popular beliefs about regulation reflect ancestral intuitions that authorities can directly control outcomes. If the chieftain declared that food should be shared in a particular way, the order could be enforced through social pressure or direct monitoring. Everyone knew everyone else, contributions were visible, and deviations from the rule could be punished immediately. This experience makes it intuitively plausible that governments—which our minds intuitively represent as tribal coalitions—can simply command economic results. If rents are too high, they can seemingly be capped. If wages are too low, they can seemingly be raised. In naive folk economic theories, prices behave like promises: If the authority decrees a new price, the outcome should follow.

Take rent control. The intuition behind it is straightforward and morally compelling. If landlords raise rents beyond what tenants can afford, people may feel exploited: The owner of a scarce resource is extracting more money without providing more housing. A government rule limiting rents, therefore, appears to be a simple act of fairness. Ostensibly, the authority steps in, declares that rents may not exceed a certain level, and housing becomes affordable again. But in a large market economy, rent is not just a moral claim between two parties; it is also a signal that coordinates investment and construction of new housing. When rents are capped below market levels, the signal changes. Developers build fewer apartments, landlords convert rental units into other uses, and maintenance becomes less attractive when returns are limited. Over time, the supply of housing shrinks, and the shortage intensifies the very scarcity that drove up rents in the first place. The policy fails because the mechanism through which housing supply adjusts is invisible to the mental model that produced the intuition.

The same dynamic appears in debates over minimum wages. If workers are paid very little for difficult or unpleasant jobs, the situation feels unfair. But in a modern labor market, wages also function as signals that coordinate hiring decisions across the entire economy. When the legal wage floor rises above the productivity level of some jobs, employers do not simply pay the higher wage and continue as before. They reduce hiring, substitute machines for labor, or restructure tasks so fewer workers are needed. When the price signal changes, behavior adjusts in ways that the regulation does not anticipate. That often results in the direct opposite of the desired effect.

Our minds are not utility-maximizing computers that simply deviate from optimal choice due to insufficient information or computing power. They are toolkits. Our brains have evolved specialized cognitive inferences, or intuitions, that solved specific recurrent problems in our ancestral environments: “Who is trustworthy enough for exchange?”; “Who belongs to us, and who is a rival?”; “Who is contributing, and who is free riding?”; “Who owns what, and by what right?” These intuitions can be triggered by modern economic situations that resemble ancestral ones, even when the actual circumstances are entirely new. 

Folk-economic beliefs persist not because people are irrational, but because they are reasoning with tools that evolved for cooperation in small bands rather than coordination among millions of strangers. The challenge for modern societies is therefore not simply to correct mistaken beliefs, but to build policies that work with—rather than against—the grain of human psychology. 

Modern market societies represent one of humanity’s most remarkable cultural achievements. They sprang into existence by harnessing a set of different ancient social instincts—ones that enable cooperation on an unprecedented scale. Systems of property rights, contract enforcement, and voluntary exchange allow millions of strangers to coordinate their efforts in mutually beneficial ways. 

The claim here is not that markets are infallible. It is that our evolved intuitions often misidentify the nature of the problem and thus point us toward remedies that make matters worse. In modern economies, visible losses are concentrated, immediate, and emotionally salient, while gains are diffuse, gradual, and spread across millions of consumers and workers. A serious defense of markets should therefore acknowledge adjustment costs and real harms without conceding the larger error: namely, the belief that mutual gain, price signals, profit, and exchange are themselves forms of exploitation.

Some of our evolved instincts—like valuing reciprocity, rewarding contribution, and building reputations for trustworthiness—remain essential foundations of prosperous societies. Markets themselves depend on these deeply rooted norms of cooperation and exchange. Other intuitions, however—such as zero-sum thinking about trade, suspicion toward profitable innovation, or faith that authorities can simply command prices—reflect cognitive shortcuts suited to environments of scarcity and small-group control rather than decentralized abundance.

Recognizing that distinction should not slide into a blanket dismissal of public concern. Not every market outcome is benign, and not all economic anxieties are mere illusions. Trade, technological change, and broader shifts from manufacturing to services can impose real, concentrated losses on particular workers, firms, and regions, especially on lower-skill laborers whose jobs are exposed to offshoring or displaced by new forms of production. A person who loses a job to foreign competition is not simply trapped by faulty intuition. He is often responding to a real personal setback, even if the economy as a whole still becomes more productive and prosperous. The same is true in recessions or cases of fraud and negative externalities. 

The question, then, is how societies can address those real costs without defaulting to the very intuitions that misdiagnose their causes. 

Human beings are unusual among species in our ability to revise intuitive judgments through abstract reasoning and accumulated knowledge. Economic theory, empirical evidence, and institutional experimentation provide ways of testing whether our intuitions about markets actually match the systems we inhabit. Over time, societies that learn to distinguish between intuitions that promote cooperation and those that misread economic signals tend to design more effective institutions. 

Much of the progress of the last two centuries reflects this process of institutional learning precisely. Expanding trade networks, protecting property rights, encouraging innovation, and allowing prices to coordinate decentralized decisions have produced levels of prosperity that would have been unimaginable in the environments where our economic intuitions evolved. Understanding the evolutionary roots of folk-economic beliefs, therefore, helps explain why certain policy ideas remain politically attractive despite poor outcomes—and why sustained progress often depends on institutions that counteract some of our most natural intuitions while reinforcing others that support cooperation, openness, and exchange.

This article was originally published at The Dispatch on 4/21/2026.

Wall Street Journal | Health & Medical Care

Anti-Tumor Device Placed in Brain, Boosts Survival

“Brain tumors are one of the most devastating consequences of cancer’s spread—hard to treat and highly deadly. Scientists have found that using a radioactive implant precisely where a tumor was removed in the brain can help patients get their cancer treated more quickly and in many cases, live longer.

A new study showed that GammaTile, a radioactive wafer the size of a postage stamp, nearly doubled survival rates and nearly eliminated tumor regrowth in people who had it placed in the spot where brain tumors were surgically removed.”

From Wall Street Journal.

Blog Post | Science & Technology

The AI Debate: Extinction Versus Salvation

Why have AI doomers embraced an ominous H. P. Lovecraft meme?

Summary: The AI debate reflects a deeper philosophical conflict about the value and risks of knowledge itself. Some AI critics fear that greater intelligence could unleash uncontrollable and catastrophic forces, echoing a longstanding tradition of skepticism toward scientific and technological progress. Others argue that intelligence and knowledge are humanity’s primary tools for overcoming existential threats and improving the human condition. At its core, the dispute concerns whether the expansion of intelligence should be viewed chiefly as a danger to be restrained or a virtue to be cultivated.


In December 2022, just a month after the release of OpenAI’s Large Language Model ChatGPT, an ominous meme began circulating that is still with us today. It is a cartoon illustration of the Shoggoth, a mysterious and deadly cosmic monster from the early 20th century classic horror author H.P. Lovecraft.

Image source: An X post by @TetraspaceWest, 12/30/2022

“The Shoggoth meme has gone viral in the small world of hyper-online A.I. insiders,” explains New York Times tech columnist Kevin Roose. He documents in his article “Why an Octopus-like Creature Has Come to Symbolize the State of A.I.” that the meme has become a popular symbol in AI-related essays, X posts, and message boards. Elon Musk even posted the meme and then deleted it, Roose reports.

The “RLHF” on the meme stands for “reinforcement learning from human feedback.” Roose explains that the initial version of the meme, posted by @TetraspaceWest, is “an image of two hand-drawn Shoggoths — the first labeled ‘GPT-3’ and the second labeled ‘GPT-3 + RLHF.’ The second Shoggoth had, perched on one of its tentacles, a smiley-face mask.” Other later versions of the meme have just depicted one Shoggoth with RLHF and a smiley-face.

Image source: An X post by @alexandr_wang, Chief AI Officer at Meta and and founder of Scale AI, 3/27/2023

“It’s the most important meme in A.I.,” Roose quotes one AI executive as saying.

So what is the meme’s significance?

Roose gives a simple account:

In a nutshell, the joke was that in order to prevent A.I. language models from behaving in scary and dangerous ways, A.I. companies have had to train them to act polite and harmless. One popular way to do this is called “reinforcement learning from human feedback,” or R.L.H.F., a process that involves asking humans to score chatbot responses and feeding those scores back into the A.I. model. …some argue that fine-tuning a language model this way doesn’t actually make the underlying model less weird and inscrutable. In their view, it’s just a flimsy, friendly mask that obscures the mysterious beast underneath.

This explanation is illuminating as far as it goes, but a broader message can also be gleaned from a closer look at the work of H.P. Lovecraft. His cosmic horror monsters such as the Shoggoth represent an anti-Enlightenment anxiety—a general pessimism about the consequences of the growth of knowledge—that strikingly resembles the fears of modern AI critics. Lovecraft’s underlying assumptions about the consequences of scientific and technological discovery are relevant to the AI debate, making the Shoggoth meme’s salience far broader than mere R.L.H.F.

Yudkowsky’s Fear of Technological Knowledge

Perhaps the most prominent extreme AI critic is Eliezer Yudkowsky. Widely regarded as a founder of the field of artificial general intelligence alignment, he is the co-author (with Nate Soares) of the 2025 instant New York Times bestseller If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All.

The book argues that, “All over the Earth, it must become illegal for AI companies to charge ahead in developing artificial intelligence as they’ve been doing.” This proposal is hard to argue with if you accept the central claim of the book: “If any company or group, anywhere on the planet, builds an artificial superintelligence using anything remotely like current techniques, based on anything remotely like the present understanding of AI, then everyone, everywhere on Earth, will die.”

The problem, as they see it, is that AI will not automatically care about sentient life such as humans. They argue that such a caring must be specially built in, which we don’t currently know how to do. “The AI does not love you, nor does it hate you, and you are made of atoms it can use for something else,” Yudkowsky argues in a 2023 Time Magazine article titled “Pausing AI Developments Isn’t Enough. We Need to Shut it All Down.”

Yudkowsky’s vision of the fruits of technological advancement strikes, as we will see, a rather Lovecraftian tone. “To visualize a hostile superhuman AI, don’t imagine a lifeless book-smart thinker dwelling inside the internet and sending ill-intentioned emails. Visualize an entire alien civilization, thinking at millions of times human speeds, initially confined to computers—in a world of creatures that are, from its perspective, very stupid and very slow.”

This reflects some of the symbolic intent behind the Shoggoth meme. Roose write that he was told by @TetraspaceWest that, “I was also thinking about how Lovecraft’s most powerful entities are dangerous — not because they don’t like humans, but because they’re indifferent and their priorities are totally alien to us and don’t involve humans, which is what I think will be true about possible future powerful A.I.”

To ensure that we “shut it all down” as Yudkowsky demands in his Atlantic article, he proposes that governments around the world:

Make immediate multinational agreements to prevent the prohibited activities from moving elsewhere. Track all GPUs sold. If intelligence says that a country outside the agreement is building a GPU cluster, be less scared of a shooting conflict between nations than of the moratorium being violated; be willing to destroy a rogue datacenter by airstrike. … Make it explicit in international diplomacy that preventing AI extinction scenarios is considered a priority above preventing a full nuclear exchange, and that allied nuclear countries are willing to run some risk of nuclear exchange if that’s what it takes to reduce the risk of large AI training runs.

Powerful political figures have expressed fears of similar magnitude. For example in the Wall Street Journal, US Senator Bernie Sanders published an article in which he asks, “How can we rush forward when leading scientists warn that AI poses an existential risk to the human race?” He announces in the article that he has “…introduced legislation, with Rep. Alexandria Ocasio-Cortez, to impose a federal moratorium on the construction of new AI data centers until strong national safeguards are in place.”

Lovecraft’s Fear of the Growth of Knowledge

Lovecraft is widely regarded as one of literary history’s most significant horror authors. Stephen King has called him “The 20th century’s greatest practitioner of the classic horror tale.” His work contains a bizarre and phantasmagorical pantheon of interrelated cosmic sci-fi/fantasy monsters. Cthulhu is the most famous one, and the Shoggoth is one of dozens that are more obscure.

His work is so unique and influential that it created an entire horror subgenre, known as “Lovecraftian horror” or “cosmic horror.” This subgenre focuses on fear of the cosmic danger and vastness of the unknown. I regard Lovecraft as an anti-Enlightenment figure, because most of his stories are about science uncovering horrible truths that should never have been discovered and cannot be unlearned. The unmistakable moral of Lovecraft’s writing is that the universe’s most profound knowledge should remain unknown.

The opening passage from The Call of Cthulhu(1928), probably Lovecraft’s most famous story, illustrates his anti-Enlightenment ethos well:

We live on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto harmed us little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age.

These sentiments can be almost perfectly analogized to Yudkowsky’s fear of AI. Yudkowsky acknowledges that we live on a placid island of ignorance—hence the delta between our intelligence and that of superhuman AI, and our ignorance of how to control or withstand superintelligence. Yudkowsky presumably acknowledges that AI has hitherto harmed us little, but agrees with Lovecraft’s narrator that “some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein” that cataclysm will strike. Therefore, Yudkowsky advocates anti-Enlightenment policies such as outlawing vast swaths of technological research and being willing to bomb datacenters—explicit calls to destroy knowledge and halt its growth. It is therefore apt, if hyperbolic, to note that Yudkowsky would have us “flee from the deadly light into the peace and safety of a new dark age.”

These same basic messages are present in almost every story Lovecraft wrote. In his 1936 novella At the Mountains of Madness, which contains the first appearance of the Shoggoth, he writes that, “It is absolutely necessary, for the peace and safety of mankind, that some of earth’s dark, dead corners and unplumbed depths be let alone; lest sleeping abnormalities wake to resurgent life, and blasphemously surviving nightmares squirm and splash out of their black lairs to newer and wider conquests.”

Illustration of a Shoggoth
Image source: ElioSoSavage Posted in
Shoggoth,Shoggoth/Gallery at https://lovecraft.fandom.com/wiki/Shoggoth/Gallery?file=Screenshot_20171022-085959.jpg

Lovecraft also wrote nonfiction in which he expressed precisely the luddite sentiments that you would expect from a thinker so focused on the horrible consequences of discovery and science. His 1933 essay “Some Repetitions on the Times” laments automation leading to mass unemployment, closely reflecting contemporary AI animosity.

“For several generations the man-displacing effect of the machine has been realised by a few, yet the momentary ability of new industries to absorb displaced labour was enough to blind nearly everyone to the consequences inevitable after the end of this plainly temporary absorption,” Lovecraft claims. He goes on that, “It is by this time virtually clear to everyone save self-blinded capitalists and politicians that the old relation of the individual to the needs of the community has utterly broken down under the impact of intensively productive machinery. Baldly stated—in a highly mechanised nation there is no longer enough work to be done, under any conceivable circumstances to require the services of the entire capable population if each individual is worked to his maximum (even an humane and rational maximum) capacity.” Invoking the frightful mentality present in his fiction, he concludes that the government must dispense with laissez-faire “political and economic orthodoxies, if the peril of an unfathomed revolutionary abyss is to be averted.”

Essentially these exact fears are presented as novel dangers of 21st century AI by powerful Republicans and Democrats. US Senator Josh Hawley has advocated for banning self-driving cars to protect the jobs of car and truck drivers. In addition to the existential fears expressed in Senator Sanders’s abovementioned Wall Street Journal article, he also declares that AI “kills jobs” and he has posted on X that, “Trump wants to deregulate AI and let the richest people on earth do whatever they want. Unacceptable. It will make the oligarchs richer while millions lose jobs and income.”

Nick Bostrom’s White Balls

The disastrous outcomes of mass death, destruction, and economic disruption predicted by AI critics are real possibilities. But they are not unique threats of artificial intelligence. Rather, they are examples of the danger of intelligence generally.

Long before the breakthroughs that put AI at the center of anti-technological rhetoric, people thought up countless possible destructive consequences of the growth of knowledge. Many feared that nuclear scientists would bring about technological Armageddon by creating a chain reaction that would destroy Earth. Throughout the cold war and subsequent war on terror, media and government institutions spread numerous fears about governments and terrorist groups causing mass destruction by creating chemical or biological weapons. There were several widespread hysterias throughout the 20th century that economic development would cause apocalyptic resource collapse before the end of the century. While most of these fears turned out to be unfounded, it was never impossible that they might come true.

By its very nature, the discovery of new knowledge can accomplish amazing things, for good or for ill. As science and technology continue to overturn the stones of reality, new possibilities will be revealed and old barriers to action will be outgrown. The consequences of these new discoveries can never be fully predictable in advance, because to predict them you would have to already possess the knowledge discovered, and all related knowledge. Therefore, there will always be a nonzero chance of mass destruction resulting from new knowledge.

The question is: Is intelligence worth the risk?

Nick Bostrom, University of Oxford philosopher and founder of the Future of Humanity Institute, embarks on a frightening exploration of this question in his 2019 paper “The Vulnerable World Hypothesis.” In it, he offers an analogy called “the urn of creativity.”

One way of looking at human creativity is as a process of pulling balls out of a giant urn. The balls represent possible ideas, discoveries, technological inventions. Over the course of history, we have extracted a great many balls–mostly white (beneficial) but also various shades of gray (moderately harmful ones and mixed blessings). The cumulative effect on the human condition has so far been overwhelmingly positive, and may be much better still in the future…

What we haven’t extracted, so far, is a black ball: a technology that invariably or by default destroys the civilization that invents it.

Such black balls may include the genocidal AI of Yudkowsky’s nightmares, the cosmic horrors awakened in Lovecraft’s phantasmagorical visions, or any number of other yet-unimagined catastrophes.

The longer we keep pulling new balls out of the urn, Bostrom argues, the more likely we are to eventually stumble upon a black ball, ending the human project forever.

But while Yudkowsky, Lovecraft, Hawley, and Sanders all share this fear of the growth of knowledge, there is another perspective—an Enlightenment perspective—which contradicts them. Defenders of the core principles of the Enlightenment hold that, for generalizable reasons, the costs of scientific and technological advancement are well worth the benefits.

Intelligence Is a Virtue, Whether Organic or Artificial

The renowned University of Oxford physicist David Deutsch argues that the urn analogy only captures one side of the coin of the effects of knowledge on existential risk.

In his book The Beginning of Infinity, Deutsch explains that knowledge, rather than merely being dangerous, is what allows humans to survive their ever-changing environment. He refutes the “Spaceship Earth” conception that many tacitly hold, according to which Earth’s natural environment is a life support system: hospitable by default, unlike outer space or an Earth drastically altered by anthropogenic change.

“…I am writing this in Oxford, England, where winter nights are… often cold enough to kill any human unprotected by clothing and other technology,” Deutsch writes. “So, while intergalactic space would kill me in a matter of seconds, Oxfordshire in its primeval state might do it in a matter of hours – which can be considered ‘life support’ only in the most contrived sense.”

He explains that, “There is a life-support system in Oxfordshire today, but it was not provided by the biosphere. It has been built by humans. It consists of clothes, houses, farms, hospitals, an electrical grid, a sewage system and so on.”

So how did people and other animals survive for so long without modern technology? Generally, they didn’t. As recently as 1900, and for all of history before that, human life expectancy was around half what it is today. Humans were constantly dying of famine, disease, and other ailments that could have been solved by the right knowledge. Other species almost all got wiped out entirely. It is estimated that over 99 percent of species that ever existed on Earth are now extinct.

But modern technology has only just scratched the surface of solving all the deadly problems that are likely to befall humanity. Like the people of Oxfordshire need clothing and other technologies to survive today, humanity will soon die unless it gains new scientific and technological knowledge to protect against exogenous threats such as asteroids, supernova explosions, the expansion of the sun, and countless others, most of which have probably not yet been discovered. To maximize its chances in the arms race against an ever-changing environment, humanity must constantly expand its horizons of research and discovery into the infinite unknown.

In an interview with Dwarkesh Patel, Deutsch explains the implications of this circumstance with respect to Bostrom’s urn of discovery: “Nick Bostrom’s jar with white balls, and there’s one black ball, and you take out a white ball, and white ball, and white ball, and then you hit the black ball and that’s the end of you. I don’t think it’s like that, because every white ball you take out and have reduces the number of black balls in the jar.”

When an asteroid caused the Cretaceous-Paleogene extinction 66 million years ago, wiping out about 76 percent of all species on the planet at the time, those species effectively hit the inverse of a “black ball”—they needed asteroid defense technology, which humans have recently developed, but they didn’t have it. And similar stories could be told about all the other mass extinction events in Earth’s history, and the future mass extinctions that are bound to come if humans don’t advance technology fast enough.

While increasing intelligence, artificial or otherwise, poses serious threats to humanity, stagnating or declining intelligence is an even surer death knell.

AI of the sort powerful enough to wipe out humans is likely also a panacea for discovering and preventing virtually infinite other existential threats, biological, cosmic, and otherwise.

While existential risks create especially salient examples of the possible upsides and downsides of intelligence, the same logic applies to morally virtuous action generally. If there are moral truths to be discovered and known, general intelligence should be able to know them no matter what substrate it exists on. Knowledge is knowledge, whether encoded in brain chemicals or silicon chips.

As Deutsch argues in an interview with Sam Harris, “…the problem of AIs is the problem of humans. …humans are dangerous, and to that extent AIs are also dangerous, but the idea that AIs are somehow more dangerous than humans is racist.”

I think Deutsch’s racism charge is lobbed somewhat jokingly, but it also points to a deep similarity between bias against the agency of foreign peoples and that of mysterious artificial intelligences. Lovecraft has been widely accused of racism for his fearful treatment of foreign cultures and peoples, which seems of a piece with his general fear and distrust of the unknown. There is no reason to assume that perceptions of AI entities would not sometimes be shaded by the same underlying prejudices, which have their utility as protection against unknown threats but which can also lead people to dark and destructive attitudes and behaviors.

If people should be pessimistic about the consequences of artificial intelligence, they should also be pessimistic about the consequences of intelligence generally. Conversely, if optimism is warranted about human agency, which is fundamentally a matter of human intelligence, then optimism about artificial intelligence is warranted also.

TechCrunch | Science & Technology

This AI Weather Startup Out-Forecasts Government Agencies

“A new AI weather forecasting tool released today by the startup WindBorne Systems offers more frequent and accurate predictions on key variables than the world-leading system developed by European governments, thanks to advancements in how sensor readings are fed into deep learning models.

Founded by a group of Stanford students in 2019, WindBorne began by building a better weather balloon, with the idea of selling weather data. But with the arrival of the weather-forecasting deep learning models in 2022, the team realized they could capture more value by building their own model as well.

Today marks the release of the sixth version of that model, WeatherMesh, which the company says is more accurate than traditional and AI forecasts produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the European intergovernmental organization seen by meteorologists as the leading provider of accurate weather prediction.

One simple way to understand it, WindBorne’s chief product officer Kai Marshland says, is that WeatherMesh-6 ‘is as accurate five days out as a traditional forecast is the day before,’ particularly on surface temperature measurements.

WeatherMesh-6 produces a forecast every hour, as opposed to every six hours, as traditional models do. Its resolution is now down to 3 km in Europe and the continental U.S., where the quality of data is highest.”

From TechCrunch.

Interesting Engineering | Science & Technology

Humanoid Robots Process 250K Packages Without Failure

“US robotics company Figure AI has completed a 200-hour autonomous livestream using its Figure 03 robots.

During the run, the robots processed nearly 250,000 packages without experiencing a single hardware failure.

The firm’s CEO, Brett Adcock, said the milestone run began as a response to an 8-hour endurance challenge issued by industrial automation veteran Dr. Scott Walter.

On May 14, Figure had said its humanoid robots surpassed 24 hours of continuous autonomous work, extending an originally planned eight-hour test.”

From Interesting Engineering.