Originally published privately on January 13, 2021. Updated, revised and expanded for public release on December 29, 2024.
New to the Sorcerer’s Apprentice series? For essential context, start with our foundational posts on the Financial Matrix, Hyperreality, Skynet, and HyperPrices, or explore the full Table Of Contents.
“A fool is someone who knows the price of everything and the value of nothing.”
Oscar Wilde
What If The “Big Data” We Feed Our AI Is Corrupted?
The past year [Ed: 2020] witnessed one cloud-based software company after another debut to soaring share prices. Snowflake—the largest software IPO of all time—immediately doubled, as CNBC reported:
Altimeter’s Gerstner has amassed the second-biggest stake [in Snowflake] by leading a $45 million round in 2015... Gerstner told CNBC on Wednesday that he first asked Speiser about the company seven years ago, before Snowflake had a product on the market or a dime in revenue. He was inquiring about the size of the potential prize if everything goes right.“He looks at me without missing a beat and says, ‘Brad, it’s the biggest prize in software,’” Gerstner recalled. “The cloud fundamentally rearchitects where all data sits, how it’s processed and how we glean insights.”
Snowflake’s meteoric rise exemplifies the premise underlying the Big Data and AI revolutions more generally: scale—more data, processed more intelligently—drives better decisions and improved economic outcomes. On the surface, this premise seems indisputable: what we have accomplished over the past several years would have been considered the stuff of science fiction not long ago.
Yet this entire AI and Big Data revolution ultimately rests on a foundation of quicksand. As companies and governments worldwide join tech giants and AI startups in the race to capture, store, and process ever-larger quantities of data, we fear that they all overlook the most fundamental question: what if we are degrading the most important information required for a functional society—including that which is required for further AI development itself? And what if this corruption is being accelerated by the ways in which we are deploying algorithms and AI into the economy and markets—as replacements for, rather than complements to, human judgment—even as financial markets surge to unprecedented heights, feeding off the very distortions that threaten to undermine their success?
Hidden Knowledge: The Missing Foundation
“You could even go so far as to say that data is the fossil fuel of AI”
Ilya Sutskever, Former OpenAI Chief Scientist and founder of SSI, December 2024
The success of companies like Snowflake is fueled by the conviction that if we can but capture and structure enough Big Data as the “fossil fuel” for our AI, we can model and predict virtually anything. This belief drives investors, economists, and AI researchers—indeed, our entire society—to treat information as a codifiable commodity to be digitized, warehoused across a sprawling network of hard drives such as Snowflake’s Data Cloud and Amazon Web Services (AWS), sent around the internet, run through ChatGPT, and bought and sold in the marketplace.
Yet there exists a second type of information—seldom discussed yet far more foundational to society—that we call “uncodifiable knowledge” because it defies such quantification and algorithmic processing. While we have mastered the generation, storage, and analysis of virtually limitless amounts of the first type of information, our Financial Matrix and HyperPrices emanate from a profound misunderstanding about and corruption of the second type of information. This uncodifiable knowledge is required to coordinate society: even in an AI-driven techno-utopia, without it we as a society will be flying blind, and if this knowledge becomes sufficiently corrupted we risk losing ourselves in a hyperreal labyrinth—or worse yet, we risk veering off into hyperinflations or economic collapse.
A Fatal Error, Digitally Remastered
The dangers of our misplaced faith in Big Data at the expense of uncodifiable knowledge come into sharper focus when we examine Skynet’s intersection with government policy. In the post-2008 era, we face a historically unprecedented situation: central banks and governments are implementing a “whatever it takes” philosophy of interventionism while Skynet—from ‘passive’ indexing (mindlessly buying and selling based on simple algorithms) to sophisticated AI systems—increasingly dominates price formation in financial markets and the broader economy.
This convergence of interventionist policy and algorithmic dominance is synthesizing a Financial Matrix—a hyperreal simulation of markets—whose very existence is an ominous portent that we are somehow corrupting society’s most foundational information system. This compels us to confront fundamental questions about the nature of economic information—questions that resurrect obscure, century-old debates about central planning, communism, and socialism.
While we today maintain the façade of a decentralized free market, by surrendering human judgment and price discovery to Skynet within an environment of pervasive fiscal and monetary intervention, we are beginning to recreate the inability to generate authentic economic information that dooms centrally planned economies—infusing these longstanding economic debates with renewed and heightened relevance in an entirely novel, modern context.
Information Wars: The Forgotten Economic Debate
In the early 20th century, as the spectre of socialism haunted a Europe still reeling from war and revolution, a small group of economists stood against the rising intellectual tide championing top-down governmental control over economies. In the process, they arrived at revelatory insights into how markets function—and malfunction when economic information and price signals are corrupted by central planning and government intervention.
From within this Austrian school of economics emerged Ludwig von Mises, a polymath and one of history's most penetrating minds. His contributions to what we would now recognize as social information theory remain unmatched, and are all the more remarkable for predating both computers and Shannon's information theory. Mises’ framework is the Rosetta Stone that allows us to understand the Rise of Skynet and unravel the mystery of our current age’s hyperreality.
Mises’ critical insight was that economic information exists in a form that defies our conventional understanding of the term. This special kind of information—uncodifiable and immune to algorithmization—lies at the heart of investing, economics and even society itself. Our intensifying hyperreality ultimately stems from a failure to grasp Mises' discovery—the fundamental distinction between codifiable information and uncodifiable economic knowledge.
Economic Blindness: The Calculation Problem
A century before ChatGPT captured our imagination and fueled an AI gold rush, economists waged a fierce intellectual war over how to best manage an economy: through top down governmental control, or through the spontaneous, decentralized, bottom-up coordination of countless individuals. The central planning debate to this day stands as one of the most significant clashes of ideas in the history of economics—and its lessons remain equally relevant in our age of algorithmic markets.
Prior to Mises, the debate primarily centered around human nature and incentives. Critics of socialism argued that it would fail without private property and profit motives. Socialists countered that human nature was malleable—that through revolution, humanity would be purged of individuality and selfishness, and emerge remolded in the Utopian image of New Socialist Man.
The intellectual battleground was entirely redrawn in 1920, however, when Mises exposed central planning’s fatal flaw. In his essay Economic Calculation in the Socialist Commonwealth, he took what we would now recognize as an information theory approach to proving that central planning as a form of economic organization was doomed to fail in principle “even if we…grant that [socialist] Utopian expectations can actually be realized”. Without the free market that underpins and generates prices, central planners were economically blind: socialists lacked access to the unique type of information required to coordinate society in a way that aligned with even the socialists’ own needs.
Economic Calculation refers to the process of using market prices and profit-loss signals to determine the most valuable use of scarce resources—something Mises argued was impossible under socialism: without authentic markets and prices, central planners are unable to rationally allocate resources between competing uses. The essence of the Calculation Problem lies in the process of correctly discovering the relevant, uncodifiable knowledge of consumer demands, which in turn guides production designed to meet these demands.
Even central planners with access to quantum computers and advanced AI—and possessing perfect knowledge of all resources and all technologies—would be unable to know the economic value of goods. Without genuine market prices, they could neither compare different options nor make informed decisions. Therefore, they had no way of determining which resources should be allocated to meet the desires of society and consumers—including the central planners themselves. Economic coordination is not merely economically inefficient under central planning—it is economically impossible since such a system lacks the ability to ascertain and calculate the difference between the costs and benefits of various courses of action.
The issue is not merely a technical problem of gathering information—it cannot be solved simply by placing data sensors and Neuralinks on everything and everyone to produce Big Data, even if the central planners were somehow able to procure such advanced technology from a capitalist country. Nor is it a technical problem of processing information—it cannot be solved using advanced AI algorithms. Rather, Mises focused on central planning’s most fundamental flaw: the categorical absence of a special type of information that is generated by individual humans operating only within the context of a free market.
Prices: The Original AI-Big Data System In The Cloud
“Economic calculation in terms of money prices is the calculation of entrepreneurs producing for the consumers of a market society. It is of no avail for other tasks.”
For Mises, the price system was the original Big Data-based AI system in the cloud: humanity’s most sophisticated information processor, unmatched in principle even by the most advanced future AI and Big Data centers. This system is the result of a dynamic process—not a machine—that discovers and conveys uncodifiable human knowledge through money prices. Its power lies not merely in the numerical prices themselves, but rather in the continuous process of human interactions that form these prices.
The price system performs a truly miraculous feat: transmutation. Analagous to a human nervous system converting diverse sensory experiences—touch, temperature, pressure, emotions, pain, sound, light—into standardized signals the human body and brain can process and act upon, prices transform the full spectrum of humanity’s codifiable and uncodifiable knowledge into simple numerical signals that everyone can observe and meaningfully act on—without needing to understand or decompress the embedded knowledge that produced them.
Neither a coffee shop owner nor his customer need understand why coffee prices rose—whether from Brazilian droughts, labor strikes in Colombia, consumer preference shifts from tea to coffee, shipping constraints, or increases in the money supply. They simply respond to the price signal, adjusting their actions according to their individual circumstances. The price itself captures and expresses the complex web of global factors at play, without requiring us to decode them individually or even be aware of what they are. This makes the price system humanity's greatest information processor—it encodes infinite, ever-changing complexity and otherwise uncodifiable information into just the relevant decision-making signal.
Economic Calculation: How Markets Generate Knowledge
Mises showed that this essential knowledge communicated via the price system can only emerge organically under very specific conditions: the interaction of private property rights, voluntary human action, and sound money.[1] The price paid within this 'free market' institutional context conveys critical information to consumers and producers, enabling both economic calculation and societal coordination across space and time—facilitating the determination of what needs to be produced, when and how to produce it most efficiently, and which scarce resources to use in production.
As consumers, we each place our own value on the things we want to use. As producers, we look at what consumers are willing to pay, assess the worth of what's needed to create those things, and compete with other businesses to obtain the resources needed to make those products. When consumers decide what products to buy and businesses decide what materials and equipment to use to make those products, their choices affect each other recursively.
This interplay between consumer desires and the competition between businesses to best serve them determines the prices of everything from raw materials to the finished products that people buy. The market process interweaves all these decisions: when people choose to buy or not buy goods using money, their personal valuations and knowledge are transmuted into measurable prices that can be used to make economic decisions.
This process therefore solves two distinct yet related problems and thereby allows us to engage in economic calculation. First, it transmutes uncodifiable human knowledge and preferences into codified, meaningful numbers. This transmutation is indispensable because people possess unique knowledge into their own individual, localized circumstances at any given point in time—much of which they can’t even articulate, let alone quantify—and they make deeply personal, subjective assessments of value that vary widely between people. What one person values highly might hold little worth to another, and there is no way to directly measure or compare such subjective assessments—yet through this process, these uncodifiable factors become expressed in codified, numerical form.
Second, the price system allows us to compare different types of goods across a global economy. While the old adage that "you can't compare apples and oranges" remains true, we can compare the price of apples and oranges: the price system establishes an essential common measure that allows us to compare different goods and their potential uses relative to our needs, and enable us to make a decision to buy one versus the other.
Spontaneous Order: The Process Coordinating Society
The seemingly magical ability to coordinate and sustain a modern, industrial society therefore emerges spontaneously through the independent actions of individual members of society—each one possessing and voluntarily acting on a unique type of uncodifiable knowledge within a specific institutional setting. Central planners and algorithms operate within an entirely alien institutional context and are therefore categorically unable to access this same knowledge.
The essential information needed for economic calculation isn’t simply data awaiting collection by central planners or algorithms, nor can it be manufactured, automated, or conjured into existence by decree: it is only brought into existence under very specific conditions—like a rare crystalline structure that forms solely when temperature, pressure, and chemical conditions align perfectly.
When people trade and compete, they discover vital information about what things are worth and how to use resources wisely. The essential engines of discovery are human entrepreneurs who personally own and control the means of production (like factories and resources), actively participating in market competition through their businesses and learning directly from their profits or losses—signals that reflect what human consumers voluntarily choose to buy or abstain from buying—all anchored by sound money that preserves the integrity of these signals. The knowledge necessary for guiding entrepreneurial decisions—and therefore building and sustaining a modern society—emerges only through human entrepreneurs’ active participation in the market process.
The information contained in money prices and the profits and losses that arise from competition—in this specific free market institutional setting only—act as indispensable tools for economic calculation, enabling entrepreneurs to produce goods and services that align with consumer desires. These tools are rendered impotent, however, outside of this framework—whether in the fully centrally planned economies of the past or the algorithmically directed economies of a tomorrow just about to dawn.
The Financial Matrix: Crossing The Edge Of Reality
HyperPrices—artificial prices unfit for the purpose of coordinating society—have grown increasingly prevalent since 2008, when central bank and government interventions accelerated the progressive corruption of the price system's ability to generate and transmit authentic economic knowledge. When central banks artificially suppress interest rates and flood the system with easy money—as they’ve done to an unprecedented degree since the Great Financial Crisis—they propagate deceptive signals throughout the economy.
Consumers, investors, and businesses are left wandering through a hall of economic funhouse mirrors, where every reflection distorts reality until they can no longer trust what they see. Is that business opportunity real, or just a mirage created by cheap money? Are those profits sustainable, or just a temporary sugar high from stimulus? Does it make more sense to invest savings to start a new business, or to speculate in AI-generated memecoins hoping to ride the hype train? Are these rapid swings in prices and interest rates a temporary blip or the new normal that requires rethinking every household purchase? Does it make sense to speculate on another round of artificial rate suppression by buying a house now versus waiting?
In this labyrinth of disorted reflections and treacherous paths, how can anyone be confident in their life decisions? By divorcing prices from the sound money, free market context under which they naturally arise, central banks are dismantling the special kind of information necessary not only for functional financial markets, but for coordinating the economy and for tethering society to reality.
HyperPrices Are Economically Blinding Us
Compounding the issue, the very algorithms that provide unparalleled access to price data—more than any civilization in history—are themselves distorting and undermining those same prices. While the Calculation Problem demonstrated that central planners could never effectively tap into the price system, there’s a certain irony in our current predicament: it's as if we've created a sophisticated radar system that ultimately jams its own signal.
In our digital age, our decentralized algorithmic systems provide real-time access to the price of virtually any asset, good, or service, anywhere in the world, denominated in any currency, down to the cent. However, this unprecedented visibility masks a deeper corruption of the price mechanism. Central banks distort the fabric of our price structure through their interventions, while algorithms further degrade the signals by systematically excluding uncodifiable knowledge from the price formation process—most conspicuously in the financial markets but to a growing extent in the broader economy, as well.
The key issue we face today is not that algorithms are unable to generate prices—they clearly have the capability to generate infinite prices. Rather, the fundamental problem is that when we relinquish human judgment and decision making to pricing algorithms we remove the system from its institutional context—in doing so, we divorce it from the human needs, values, and process that give prices their meaning.
AI Slop, HyperPrices, And Zombie Markets
Source: 404 Media, Archive Link
To better intuit how this algorithmic corruption of price signals may undermine markets, consider a parallel phenomenon that has become all too familiar to internet and social media users. Social media's long-running plague of automated social bots has evolved into something far more sophisticated with the advent of ChatGPT and large language models: “AI Slop”.
Algorithms now flood our digital spaces with a torrent of content ranging from obvious junk to ‘deep fakes’ to other uncanny mimicries of human expression, threatening to drown out authentic human voices in an ocean of synthetic speech that ultimately rings hollow. As we’re bombarded with this artificial information—much of it disconnected from any “base reality”—we start to see an erosion of meaning and truth reminiscent of Baudrillard’s hyperreality.
Source: Unknown
As one critic of Facebook's ‘AI slop’ observed:
All of this, taken together, is why I think we should not view Facebook’s AI spam through the lens of the “Dead Internet.” The platform has become something worse than bots talking to bots. It is bots talking to bots, bots talking to bots at the direction of humans, humans talking to humans, humans talking to bots, humans arguing about a fake thing made by a bot, humans talking to no one without knowing it, hijacked human accounts turned into bots, humans worried that the other humans they’re talking to are bots, hybrid human/bot accounts, the end of a shared reality…
While society frets over the corruption of our information landscape through AI slop and bot-generated content, similar technological forces are unwittingly unleashing an even more dangerous disease. The symptoms of SkyNet’s HyperPrices may be less obvious than Facebook's algorithmic content plague, but transpose that earlier diagnosis slightly and see how perfectly it describes the hyperreality of our Financial Matrix:
The financial system has become something worse than algorithms trading with algorithms. It is trillions flowing mindlessly through index funds, algorithms trading with algorithms, high-frequency trading algos front-running human and algo orders, humans blindly following algorithmic signals, humans reacting to artificial price movements, humans trading against phantom liquidity that vanishes, tradebots feeding off social media sentiment, retail traders coordinating meme stock squeezes through social media while algorithms amplify the momentum, hybrid human-algo strategies, AI chatbots transmuting conversations into memecoins worth billions, human-algorithm interactions that trap markets in self-reinforcing ant death spirals, algorithmic feedback loops that create nonsensical e-commerce prices, cascading flash crashes and glitches rippling through markets in milliseconds, armies of bots optimizing around government and central bank interventions, the end of economic reality…
Just as AI and algos generate artificial content that mimics but fails to capture real human communication, our algorithms unleash a torrent of artificial prices, or “price slop”. The chaotic flash crashes and price spikes we explored in the Skynet series are merely the visible ripples atop the surface of a more pervasive phenomenon—HyperPrices—that superficially resemble genuine market activity while lacking the essential human knowledge and judgment that give prices genuine economic meaning.
Underneath, Skynet is not only generating the occasional artificial chaos of flash crashes, but rather continuously weaving an artificial order throughout the markets—a constant, subtle restructuring of financial reality itself. From Wall Street to Main Street, the enmeshment of central banks and SkyNet is generating endless streams of convincing yet ultimately meaningless prices that obscure rather than reveal true market signals.
Worse yet, price slop is inextricably intertwined with AI social media slop via a triangular relationship between the social media communication system, algorithms, and the financial system. Humans and algorithms are not only enmeshed within the markets themselves and within social media apps, but these former information silos are now able to automatically cross-communicate, entrap, and infect one another via memes and automated AI/NLP systems that scrape and act on social sentiment: this can produce chaotic and volatile results in our hyperreal Financial Matrix, as well as alter ‘reality’ in the ‘real world’ as a result.
Conclusion: The Remastered Calculation Problem
When asked to identify a criterion for classifying an economy as essentially ‘socialist’ or ‘free market’, Mises replied, “The key is whether the economy has a stock market.” If Mises were alive today, we’re confident that he'd revise his statement in light of our argument here and in the chapters ahead. While the existence of a stock market was once a reliable indicator of market freedom, today's markets—though technically 'free' and decentralized—increasingly function more as a videogame simulation of a market than a genuine one.
While markets soar and prosperity appears abundant—as stock indices hit records—a deeper problem lurks: once monetary debasement becomes entrenched as permanent policy—which has effectively occurred worldwide since the Great Financial Crisis—all economic information generated by society becomes unreliable. The importance of this fact cannot be overstated: authentic market prices, born of human action and expressed relative to sound money, are the sole mechanism capable of coordinating an advanced industrial economy. Though drowning in Big Data, we are systematically corrupting the one unique category of information essential to our civilization.
Into this warped monetary landscape—an environment ripe for algorithmic exploitation—we unleashed tradebots that not only amplify these monetary distortions, but also strip away the essential human knowledge and judgment that give prices their economic meaning. The great irony of our age is that while Mises proved the infeasibility of centrally planned economies, our highly decentralized algorithms—the fruit of free market technology advances, operating under the guise of free market principles—feed on monetary manipulation to further corrupt the price system.
The toxic interplay between central bank interventions and algorithms creates a pernicious illusion: together, they synthesize a Financial Matrix that maintains the appearance of a decentralized market and price system. This Financial Matrix manifests as a hyperreal Hall of Mirrors in which humans and algorithms endlessly react to each other's reactions and to central bank interventions, without much reference to underlying reality. The HyperPrices and “price slop” that results from these recursive interactions moves us ever further from genuine human meaning that prices are meant to encode.
This modern distortion of price signals is an incipient recreation of Mises’ Calculation Problem in a novel form. Unlike the Gosplan administrators of the Soviet era—who worked in almost total ignorance of the true condition of the pig-iron foundry or the collective farm—we have retained the façade of the price system and market institutions and now know the ‘price of everything yet the value of nothing'. The modern challenge is no longer the absence of market prices, but rather navigating an overwhelming deluge of artificial HyperPrices masquerading as authentic market prices.
This modern manifestation of the Calculation Problem persists despite—if not because of—advanced technology that would have been unimaginable in Mises' time. Nevertheless we have failed to overcome the insurmountable Economic Calculation Problem that he identified a century ago. Both "no prices" and "infinite HyperPrices" lead to similar Calculation Problems: the Financial Matrix is beginning to reduce economic calculation to a form of guesswork, making economic calculation and rational long-term planning nearly as difficult as if no price signals had existed at all.
As we race to build increasingly powerful AI systems, we depend on functional markets and prices to coordinate the massive resources required by this undertaking. AI systems depend on intricate global supply chains and enormously complex production processes: from the fabrication of sophisticated semiconductors (requiring hundreds of specialized steps and precision equipment), to the construction and operation of massive data centers (involving everything from advanced cooling systems to reliable power infrastructure). Each of these elements represents layers upon layers of interdependent production processes that can only be coordinated through authentic price signals.
Yet ironically, replacing human judgment with AI and algorithms within the price formation process is corrupting the very information required to sustain our AI progress. Our drive to process ever more data through AI could end up destroying the most sophisticated information processing system humanity has ever created—the very price system that enables modern civilization, technology progress, economic development, and even AI itself.
While the price system is inherently resilient, HyperPrices now pose existential risks to economic stability and national security. Like ship captains lured towards the rocks by the sirens' song, our society follows these corrupted signals to its peril. We are recreating the Volmageddon ant death spiral, but on an economy- and society-wide scale. Real, consequential decisions flow from these illusory signals—affecting everything from what we invest in, to what we build, to what we buy, to where we live, to whether or not to have children.
The danger of the Financial Matrix, therefore, is not limited to financial bubbles or market crashes—those are only the most obvious symptoms of the underlying disease: the systematic corruption of our ability to make economic calculations and coordinate society. This is not mere theoretical speculation, nor is the issue confined to Wall Street.
The resulting problem—what we call Multiflation—manifests in myriad ways: the widening divergence between financial markets and the real economy, recent inflationary pressures, disortions in global housing markets, declining fertility rates, and the social unrest such distortions are fomenting. At their core—while acknowledging their complex, multifactorial nature—these phenomena reflect markets increasingly driven by synthetic rather than genuine price signals.
The Calculation Problem therefore takes on more pernicious dimensions in the digital age. As algorithms increasingly optimize around and amplify the effects of government and central bank intervention in markets—while simultaneously generating novel distortions of their own—we are forging a path towards a new variant of technosocialism. More immediately, the Financial Matrix shows ominous signs of an incipient breakdown in economic coordination that, if left unchecked, threatens to eventually metastasize into a failure as profound as the ones that plague centrally planned economies
Like a malware rootkit that stealthily embeds itself into an operating system's core functions—making the computer itself untrustworthy while appearing to function normally—when we recursively feed flawed monetary policy, erroneous worldviews, and artificial data into our algorithmic and AI-mediated price formation systems, they amplify and perpetuate these errors, embedding them more deeply into the fabric of tomorrow's economy and sowing the seeds of future crises.
The true prize thus lies not within Snowflake's data cloud or our AI frontier models, but rather within a genuine price system. Remove or sufficiently corrupt the process that generates prices, and there would be neither data for Snowflake nor hard drives on which to store it, nor would ChatGPT even exist.
Therefore, just as we must protect the integrity of our computers with anti-virus software, safeguard our digital infrastructure and power grid, and ensure the quality and reliability of training data used by AI hyperscalers as inputs for foundation models, we must be equally vigilant in restoring and maintaining the integrity of our market price system.
[1] Property rights in this context refers to the ability to exercise actual control over resources, not merely legal title to them—a distinction crucial for understanding how genuine price signals emerge.
Regardless of one’s view on ESG policies, when such requirements force companies to allocate capital and structure operations based on government-mandated metrics, this effectively transfers a portion of de facto control from shareholders to regulators while preserving de jure private ownership via legal title. This phenomenon has historical precedent in more extreme forms; Mises elaborated on the distinction between legal ownership versus practical ownership in Omnipotent Government, particularly in the sections on the German National Socialist economic model (p. 56):
This is socialism in the outward guise of capitalism. Some labels of capitalistic market economy are retained but they mean something entirely different from what they mean in a genuine market economy.
While the Soviet model of communism abolished private ownership entirely, the Nazis preserved nominal private ownership alongside the appearance of normal prices, wages and markets. Entrepreneurs were effectively sidelined, however, replaced by government-appointed 'shop managers' who followed State directives for production and distribution rather than market signals.
This pattern of government control beneath a veneer of private ownership surfaced again recently in
’s discussion of "debanking" and Operation Chokepoint on the Joe Rogan Show. Andreesen described financial institutions serving as instruments of government control while maintaining the facade of private enterprise. While one might debate the scope or intensity of his claims, his core observation is salient and mirrors a well-documented historical pattern; Gunter Reiman explored similar behavior under Nazi economic policy in his book Vampire Economy:
Within Germany itself, the banker's activities are likewise circumscribed. He plays a dual role, a fact which creates many unpleasant and even risky situations for him. He is the head of a "private enterprise," yet he must always act like a representative of the State. A private investor would be naive if he continued to rely on the advice of "his banker" whom he has known for many years and who formerly advised him how to invest his money. The advice he would get now would consist only of the instructions the banker gets from the Government…lt is not illegal to refuse, but inadvisable. If he withdraws large funds for private invest ments or otherwise remains stubborn, the banker will have to send a report to Government authorities informing them about the case. They will then check on how the money is used. The local Party leader will keep in touch with the bank manager, too, and learn of with drawals or of the existence of liquid assets and make use of this knowledge... These big banks are today… under private ownership. This fact easily misleads the foreign observer. For under fascism "private banks" are as much under State control and are as co-ordinated as ordinary State banks. The transformation of the big banks from protectors and pillars of private enterprise to the whip of the authoritarian State to be used in controlling private enterprise…
#multiflation #hayek #mises #economic_calculation