Pennings, A.J. (2025, Nov 30) The Seven Phases of Global US Dollar Transformation: Past and Future. apennings.com https://apennings.com/crisis-communications/the-seven-global-phases-of-past-and-future-us-dollar-transformations/

Introduction

Below is a brief historical overview and future preview of the US dollar’s transformation from a gold-backed sovereign currency to a debt-backed global standard for central bank reserves and trade invoicing. President Franklin Delano Roosevelt (FDR) built vaults at Fort Knox in Kentucky during the Great Depression and moved gold bars there from New York. He wanted to protect them from a foreign invasion of Manhattan and to be extra safe, it was a tank training ground. As gold poured into the US during World War II, the stage was set for the US dollar to take the reins from the declining British pound as the world’s predominant currency and source of liquidity.

This post traces the US Dollar (USD) from a global gold-backed currency to a fiat currency to a programmed, algorithmic instrument of global power, with stablecoins on blockchain, and in multicurrency collaboration with several AI blocs. It outlines the transition through seven distinct phases, highlighting how the US has repeatedly adapted its monetary infrastructure to supply with world with a liquidity solution, and yet maintain economimc hegemony

The Bretton Woods system pegged the global economy to the USD, which was backed by gold. However, the Triffin Dilemma revealed a fatal flaw: to provide international liquidity, the US had to run deficits, undermining trust in the gold peg. This tension led France and others to drain US gold reserves, labeling the system an “exorbitant privilege.”

A shadow banking system emerged in London to hold dollars outside US jurisdiction (driven by Soviet and Chinese fear of seizure). This Eurodollar market operated via telegraph, telex, and later SWIFT, creating a proto-digital “ledger money” that bypassed physical gold constraints to supply USD worldwide as bank credit.

The “Nixon Shock” (See video) ended gold convertibility. The dollar floated, anchored only by state power, the Group of 5 (G-5) and the Group of 10 (G-10), and the technological innovation of Reuters called Monitor that provided a virtual market for FX.[1] The petrodollar deal with Saudi Arabia cemented the USD’s dominance and increased demand for US debt. Citibank’s Walter Wriston argued that gold had been replaced by an “Information Standard,” where currency value was determined by market judgment and financial news processed through global telecommunications.

Under President Ronald Reagan, the Euromarkets anchored the dollar to US government debt. Massive US deficits produced US Treasuries, which became the “prime collateral” for the Eurodollar market. The world’s central banks shifted reserves from gold to US debt, effectively turning the US deficit into the global savings account.[2]

The debt-backed system was insufficient for the growth in global trade. It caused volatility and shortages, antagonizing the world, especially the Global South. The BRICS nations (Brazil, Russia, India, China, South Africa) sought to capitalize on this unease and pushed to de-dollarize finance and trade using alternative payment rails (CIPS) but failed to displace the USD due to a lack of deep, liquid collateral.

The US has responded to de-dollarization with a technological offensive. Riding on the back of cryptocurrencies, the Genius Act mandates stablecoins must be backed 1:1 by US Treasuries. This policy creates a “sovereign digital dollar” that moves instantly on blockchains but is anchored by US debt. Penetrating globally, it challenges local currencies with a programmable, US-controlled asset that relieves the US debt pressure.

Artificial Intelligence (AI) fundamentally alters the friction of global trade. AI agents automate compliance, hedging, and routing, making a multicurrency world computationally feasible. While the USD remains the backbone, AI enables an “algorithmic polycentrism” in which payment paths are optimized dynamically, potentially reducing the dollar’s exclusivity while maintaining its structural power through private stablecoin networks.[3]

Phase 1: The Golden Anchor and the Triffin Dilemma (1944–1971)

The modern story of the global dollar begins at the Bretton Woods Conference in 1944. The Allied powers established a system where the US dollar was the world’s reserve currency, pegged to gold at $35 per ounce, while other currencies were pegged to the dollar. This connection created a system of fixed exchange rates that provided post-war stability. Japan, for example, was brought in at 360 yen to a US dollar. Why that number? 360 degrees in a circle, the rising Sun.

However, this system was doomed by a structural contradiction identified by Belgian-American economist Robert Triffin. Triffin recognized that to facilitate global trade and post-war reconstruction, the world needed US dollars to provide “liquidity.” It needed a currency to facilitate buying and selling. This required the US to “export” dollars to Europe and Asia. They needed to run constant trade deficits (buy stuff from other countries), increase foreign aid, and build military bases to avoid dollar shortages.[3]

However, the more dollars the US printed and exported, the less trust the world had that the US actually held enough gold to back them. Bretton Woods contained the promise that US dollars could be traded for gold at that price. In December 1958, Europeans made their currencies convertible, including for the US dollar, for international transactions. This move dramatically increased the US Treasury’s demand for gold.

The “Triffin Dilemma” exposed the following contrast of values:

If the US stopped printing and exporting dollars, the global economy would suffocate (liquidity shortage).

If the US kept printing dollars, the gold peg could collapse if other countries tried to redeem them for gold (a confidence crisis).

On February 4th, 1964, President Charles de Gaulle of France made a historic statement about the US dollar during a press conference at the Élysée Palace. He criticized the US dollar’s special status as the world’s dominant currency, which his Finance Minister, Valéry Giscard d’Estaing, called “exorbitant privilege.” He began sending ships to the US with their dollar holdings (many of which were siphoned from the growing US military involvement in Vietnam, which had previously been a French colony). Germany and Spain did the same, slowly draining the gold from Fort Knox over the next few years.

Phase 2: The Shadow Rise of the Eurodollar

While Bretton Woods governed official channels, a shadow system emerged in the 1950s, the Eurodollar. These were simply US dollar deposits held in banks outside the United States (primarily London), and thus initially outside the jurisdiction of the Federal Reserve. It began mainly with the Soviet Union and China, who wanted to hold dollars for trade but feared US seizure.[4]

European banks began lending these deposits in USD. They used LIBOR, a survey of London banks’ recommended interest rates, to target interest rates. This market exploded in the 1960s, fueled by multinational corporations. It was a network of “ledger money” transacted not by physical transport, but by telegraph and telex. The telex machine allowed a bank in London to transfer ownership of dollars to a bank in Tokyo instantly via coded messages.

It expanded with the “petro-dollar” during the oil crisis, when oil prices quadrupled and surpluses needed to be invested worldwide. The spread of Eurodollar created the “Third World Debt Crisis, which Reagan used to mobilize the IMF to pressure debtors to open up their economies, and Clinton/Gore used to internationalize the Internet.

This technology produced the globalized proto-digital dollar. It became system of electronic spreadsheets and pure information transfer that bypassed the physical constraints of the dollar and its connection to the Bretton Woods gold peg. It has continued to grow as demand for USD increased, but slowly brought back under the observations of the US Federal Reserve and SOFR (Secured Overnight Financing Rate). The big question: Will it survive the USD stablecoin?

Phase 3: The Nixon Shock and the “Information Standard” (1971–1980)

By the late 1960s, the Triffin Dilemma had reached its breaking point. Foreign nations were demanding gold for their surplus dollars, and the US gold vaults were bleeding dry.

On August 15, 1971, President Richard Nixon went on national TV and announced he was temporarily suspending the convertibility of the dollar into gold (which was revalued to $42 per oz). The “Nixon Shock” ended the Bretton Woods system. The dollar became fiat. US Treasury William Connelly in the Nixon administration famously gaffed, “The dollar is our currency, but it’s your problem.” to the G-10 room of European finance ministers.

To stabilize this unmoored system, the US relied on the coordination of the G-5 nations (US, UK, France, Germany, Japan) to manage exchange rates, but ultimately allowed them to “float.” At the same time, Reuters Money Monitor Rates went online and became the de facto virtual market for global currencies. Banks paid a subscription to view currency prices, and banks paid to have their prices listed. Traders got on a phone or two, to negotiate a FX transaction. It was a relatively simple setup at first, but built on Reuters’ long history of providing financial news over telegraph and other media.

It was aided by the volatility of the Arab-Israeli War, the depreciation of the USD, and rising oil prices that were now priced in US dollars. Going off gold meant a devaluation of gold, and the oil-producing countries (OPEC) were not happy. Prices quickly quadrupled from $3 to $12 a barrel. The Ford administration worked out a deal with Saudi Arabia. The Saudis would price all oil sales in USD, and the US would provide military protection in the volatile Middle East. They also agreed to hold large amounts of US Treasuries with the proceeds from their oil sales.

While Bretton Woods governed official channels, a shadow system emerged in the 1950s, the Eurodollar. These were simply US dollar deposits held in banks outside the United States (primarily London), and thus outside the jurisdiction of the Federal Reserve. It began mainly with the Soviet Union and China, who wanted to hold dollars for trade but feared US seizure.

European banks began lending these deposits in USD. They used LIBOR, a survey of London banks’ recommended interest rates, to target interest rates. This market exploded in the 1960s, fueled by multinational corporations. It was a network of “ledger money” transacted not by physical transport, but by telegraph and telex. The telex machine allowed a bank in London to transfer ownership of dollars to a bank in Tokyo instantly via coded messages. This technology produced the proto-digital dollar. It was a system of pure information transfer that bypassed the physical constraints of the dollar and its connection to the Bretton Woods gold peg.

Chairman of Citibank and financial visionary Walter Wriston (ATMS, CDs) famously declared that the gold standard had been replaced by the “information standard.” He argued that in a world of telex and satellite instant communication, the value of currency was no longer determined by bullion, but by the information available about the issuing country’s economic health. The market’s judgment, processed through telecommunications, became the new economic discipline. Capital would go where it was well treated.[5]

Phase 4: Reagan, Regan, and the Collateralization of Debt (1981–2008)

In the 1980s, the Eurodollar found a new anchor: US government debt. Under President Ronald Reagan and his Treasury Secretary (and former Merrill Lynch CEO) Donald Regan, the US drove massive budget deficits through tax cuts and military spending. In conventional economics, this was reckless. In the logic of the global dollar, it was structural brilliance. By running deficits, the US pumped trillions of dollars of US Treasury bonds into the global financial system, facilitating the liquidity needed for the global trade that filled the coffers of China and Russia.

The ever-expanding Eurodollar market needed high-quality “prime collateral” to secure loans and derivatives. US Treasuries provided this. Trust was no longer based on gold; it was based on the depth and liquidity of the US Treasury market. US debt was the cheapest collateral used to borrow Eurodollars. You could use other collateral like corporate bonds, but would have to pay a more expensive “haircut,” an insurance policy against price volatility.

The Reagan administration began a process to computerize US Treasury operations, eliminating bearer bonds. This helped US export debt, and the world’s central banks eagerly bought it to use as the “safe asset” backing their own banking systems. Bank reserves shifted from gold to dollars and treasuries. The US deficit became the global economy’s savings account.

Phase 5: Shortages, Tension, and BRICS De-dollarization (2008–2024)

This debt-backed system maintained US dollar global hegemony. It provided a liquidity solution for the world, but it also created problems. Because the global economy relies on the dollar for trade (oil, commodities) and debt servicing, any tightening by the Federal Reserve creates increased global dollar demand and eventually shortages. Spreading dollars meant increased trade deficits, foreign direct investment (FDI), military bases, foreign aid, and Eurodollars.

Crises cause additional demand for US dollars. Global elites protect their wealth by buying USD and USD-denominated assets, the so-called “Milk Road.” This weaponized volatility led to Global South and the rise of the BRICS nations (Brazil, Russia, India, China, South Africa). They embraced the new term coined by Goldman Sachs economist Jim O’Neill in 2001 to describe the four emerging economies (South Africa joined later) that had grown tremendously with the spread of neoliberal-based trade, fiat dollars, and US Navy shipping providing international protection for commerce. They began pushing for de-dollarization.

China and Russia sought to escape from the sanction capabilities of the West, where the US could confiscate their reserves, cut them off from SWIFT (the successor to telex messaging), or set price levels for their oil sales.

They attempted to build alternative payment rails (CIPS, SPFS) and trade in local currencies (Yuan/Ruble) or gold-backed tokens. However, they lacked the deep, liquid collateral markets that the US Treasury provided.

Phase 6: The Genius Act and the Supercharged Dollar (2026–Future)

This brings us to the present transition. The Genius Act represents the US response to de-dollarization: not a retreat, but a technological offensive. By mandating that stablecoins be backed 1:1 by US Treasuries, the US creates a non-CBDC “sovereign digital dollar” that combines the characteristics of all previous eras:

Like the Eurodollar, it moves instantly on digital rails (blockchains), bypassing clunky correspondent banking.

Like the Reagan Era, it is backed by US debt, creating structural demand for Treasuries.

Unlike the BRICS alternatives, it offers “pristine” collateral and liquidity that the Yuan and any other BRICS currency cannot match.

The Genius Act weaponizes stablecoins to penetrate markets the traditional dollar couldn’t reach. It provides the Global South with digital dollars that are easier to use than local currency and safer than unregulated crypto. Instead of losing the world to de-dollarization, the Genius Act ensures the dollar’s “Information Standard” is upgraded to Spreadsheet Capitalism’s “Programmable Standard,” re-anchoring the global economy to the US Treasury.

From 2027 onward, every time the Federal Reserve’s FOMC changes the fed funds rate, hundreds of millions of people outside the United States who hold USDC, USDT (compliant version), or similar stablecoin tokens will feel it immediately in their phone wallets. The Fed will have the same tools it always had — but now those tools control a much larger, truly global dollar system that lives on blockchains and reaches places traditional banking never could. The GENIUS Act doesn’t give the Fed new powers; it magnifies their old powers on a planetary, real-time reach. The Fed once again becomes the global lender of last resort, this time for global digital dollars.

Phase 7: AI, USD, and Emerging Multicurrency Regimes (2028-Future)

Historically, alternative currency blocs struggled due to liquidity fragmentation, complex FX risk management, costly compliance overhead, and information asymmetry between participants. AI dramatically reduces these frictions by automating FX clearing and pricing, cross-border AML (Anti-Money Laundering) and KYC (Know Your Customer) (KYC/AML), trade documentation analysis, and risk assessment for invoicing in local currencies. It lowers the operational barriers to non-USD settlement networks (e.g., CNY, INR, AED, EUR multicurrency corridors).

AI-driven “autonomous financial agents” dynamically choose settlement currencies based on fees, liquidity, and regulatory risk, as well as execute multicurrency arbitrage or hedging in real time. It can also route payments across emerging Central Bank Digital Currencies (CBDC) networks.

A world of AI-mediated multicurrency routing resembles packet-switched networks in telecom, where the “best path” may not always run through USD but routes around it. Currency choice becomes computational rather than political. This fluency pushes the system toward algorithmic polycentrism.[6]

Countries developing national AI infosystems (China, UAE, Singapore, India) can use AI to improve risk underwriting and enable more trade in local currency. Countries deploy AI-enhanced capital controls and efficiently supervise financial institutions, creating alternative compliance regimes that are not necessarily reliant on Western standards. This technological transition reduces dependence on USD-centric regulatory infrastructures.

For countries trading energy, minerals, and food, AI enables predictive logistics and supply chain optimization, automated escrow and settlement systems, smart-contract invoicing in multiple currencies, and automated FX hedging. These innovations make it easier for commodity exporters (Saudi Arabia, Brazil, Russia) to price and settle trades in non-USD currencies without significant operational risk.

AI acts as a risk-mitigation technology, making de-dollarization more feasible. AI does not eliminate the dollar’s strengths, such as liquidity, deep bond markets, the rule of law, and global payment rails tied to US infrastructure. But AI does erode the dollar’s exclusivity.

Global finance is shifting from monocentric (USD) to a multiplex system with multiple currencies routed algorithmically. The USD remains the backbone, but not the only spine. Currencies are increasingly driven by algorithmic trading models, sentiment classification, real-time monitoring of commodity flows, and predictive macro models. This makes FX potentially more volatile but also more dynamically optimized.

Multicurrency systems become computationally manageable, reducing the bias toward a single dominant currency. AI fosters a struggle between state-based monetary sovereignty (central banks, CBDCs) and platform-based private monetary infrastructures (stablecoins, platform credit systems, AI liquidity brokers). The USD system may face challenges from private networks (such as USDC and PayPal’s PYUSD) operating atop AI-based payment routing. Yet these mostly reinforce US monetary power because they remain dollar-denominated.

AI turns currency competition into a real-time, highly automated strategic contest. Instead of slow diplomatic or macroeconomic adjustments, currency pressures can be amplified and executed by autonomous agents that route payments, reprice collateral, trigger liquidations, and exploit latency arbitrage across networks and tokenized markets. The result: faster, more profound, and more systemic shocks to liquidity and prices.

Global finance is shifting from monocentric (USD) to a multiplex system with multiple currencies routed algorithmically. The USD remains the backbone, but not the only spine. Currencies are increasingly driven by algorithmic trading models, sentiment classification, real-time monitoring of commodity flows, and predictive macro models. This makes FX potentially more volatile but also more dynamically optimized.

Conclusion

The history of the US Dollar is not a story of static dominance, but of ruthless technological adaptation. The US successfully maintained its hegemony of liquidity by shifting the substrate of its currency from Gold to Information and to Code (Stablecoins/AI).

The “Genius Act” represents the culmination of the USD policy and technological trajectory. By fusing the liquidity of the Eurodollar with the solidity of US Treasuries on a blockchain, the US is attempting to resolve the Triffin Dilemma through stablecoin technology. It allows the US to export liquidity without losing control (via programmable compliance).

However, the impending AI era introduces a wild card. As money becomes purely computational, the “exorbitant privilege” of the US may be challenged not by rival states, but by rival algorithms that route value around the US STAC stack. The future battle for the global economy will not be fought over interest rates, but over the architecture of the network itself.

Notes

[1] The G-10 consisted of governments of eight International Monetary Fund (IMF) members—Belgium, Canada, France, Italy, Japan, the Netherlands, the United Kingdom, and the United States—and the central banks Germany and Sweden. This group was, by default, in charge of maintaining the economic growth and stability of international currencies. Although in effect, its powers are limited, it still presented an important image of national sovereignty.
[2] This research project started with my MA thesis on the deregulation of finance and telecommunications in the post-Bretton Woods era. Thesis: The Message is Money: Deregulation and the Telecommunications Structure of Transnational Financial Industries.(1986)PhD Dissertation: Symbolic Economies and the Politics of Global Cyberspaces. (1993)
[3] Moffit, M. (1983) The World’s Money: International Banking from Bretton Woods to the Brink of Insolvency. NY: Simon & Schuster. This is a classic on monetary events leading up to the Nixon devaluation.
[4] Moffit, M. (1983) The World’s Money: International Banking from Bretton Woods to the Brink of Insolvency. NY: Simon & Schuster.
[5] Wriston, W. (1992) The Twilight of Sovereignty: How the Information Revolution is Changing our World. (New York: Charles Scribner’s Sons).
[6] Tyson, K. (2023) Multicurrency Mercantilism: The New International Monetary Order. Independently published. ISBN 9798864645031.
[7] Initial Gemini Prompt: Provide a historical overview of the globalized US dollar. Please start with the USD-gold standard established at the Bretton Woods conference and how the Triffin Dilemma challenged it. Explain how Nixon stopped the convertibility of the dollar and gold. Be sure to mention the shadow USD, which came to be known as the Eurodollar, and how it was transacted by telegraph and telex. With Nixon’s decision, the USD became fiat, anchored only by the G-5 nations and by what Walter Wriston called the “Information Standard.” Soon, Reagan and Regan organized the dollar and Eurodollar around national debt. They grew the US deficit to produce US Treasuries that would serve as prime collateral for eurodollar borrowing, providing trust among transacting financial institutions worldwide. The USD continued as the world’s primary transacting and reserve currency. Still, shortages heightened international tensions, leading to debates about “de-dollarization” among the BRICS countries, dominated by China and Russia. Finally, explain how stablecoins, weaponized by the Genius Act, will supercharge the global USD and AI will bring into effect new multi-currency regimes.

References

Dordick, H.S., and Neubauer, D. (1985) “Information as Currency:
Organizational Restructuring under the Impact of the Information
Revolution.” Keio Review, No. 25.
Eichengreen, B. (1996) Globalizing Capital: A History of the International Monetary System. Princeton, NJ: Princeton University Press. I am indebted to his fifth chapter, “From Floating to Monetary Unification,” which contains a good overview of this process. Pages 136-141 were particularly useful.
Giddens, A. (1983) The Nation-State and Violence. (Berkeley, CA:
University of California Press).
Goux, J. (1990) Symbolic Economies. (Ithaca: Cornell University Press).
Gowan, P. Global Gamble: Washington’s Faustian Bid for World Dominance. London: Verso.
Greider, W. (1987) Secrets of the Temple. How the Federal Reserve Runs the Country. New York: Simon and Schuster. pp. 337-338. A very extensive treatise on the economic conditions of the 1970s.
Harvey, D. (1989) The Condition of Postmodernity. (Oxford: Basil
Blackwell, Ltd).
Levy, S. (1989) “A Spreadsheet Way of Knowledge,” in Computers in the
Human Context: Information Technology, Productivity, and People.
Tom Forester (ed.) (Oxford: Basil Blackwell).
Roberts, John (1995) $1000 Billion a Day: Inside Foreign Exchange Markets. London: Harper-Collins.
Shapiro, M. J. (1989) “Textualizing Global Politics,” In Der Derian, J. and Shapiro, M. J. (eds.) International/Intertextual Relations:
Postmodern Readings of World Politics. Issues in World Politics Series. (MA: Lexington Books: D.C. Heath and Company).
Smith, R. (1989) The Global Bankers. New York: Truman Talley Books/ Plume.
Strange, S. (1997) Mad Money: When Markets Outgrow Governments. Ann Arbor: University of Michigan Press.
Tehranian, M. (1990) Technologies of Power: Information Machines and
Democratic Prospects. (NJ: Ablex Publishing Company).
Tyson, K. (2023) Multicurrency Mercantilism: The New International Monetary Order. Independently published. ISBN 9798864645031.
Wachtel, H. (1986) The Money Mandarins. NY: Pantheon Books.
Wriston, W. (1992) The Twilight of Sovereignty: How the Information
Revolution is Changing our World. (New York: Charles Scribner’s Sons).

© ALL RIGHTS RESERVED

Not to be considered financial advice.

---

Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, blockchain, crisis communications, cryptocurrencies
Tags: algorithmic trading models > Bretton Woods Conference > eurodollars > Fort Knox > gold standard > Stablecoins > US Treasuries > USD

The Genius Act, USD-backed Stablecoins, and the Death of the Eurodollar

Posted on | November 23, 2025 | No Comments

Recommended Citation APA (7th Edition)

Pennings, A.J. (2025, Nov 23) The Genius Act, USD-backed Stablecoins, and the Death of the Eurodollar. apennings.com https://apennings.com/data-analytics-and-meaning/the-genius-act-usd-backed-stablecoins-and-the-death-of-the-eurodollar/

Introduction

With the passage of the US Genius Act of 2025, the era of “wildcat” crypto experimentation is over.[1] In its place rises a state-sanctioned, gridmatic architecture that fundamentally alters the plumbing of the global economy. By mandating that all US-compliant stablecoins be backed 1-to-1 by short-term US Treasury securities, the Act has transformed the humble stablecoin from a casino chip for crypto-speculation into the primary vector of US monetary sovereignty and hegemony.

This innovation is the ultimate realization of the Semiotic-Abstraction-Computation-Telecom (SACT) stack analysis of spreadsheet capitalism that I have been developing.[2] If the Excel spreadsheet privatizes corporate and government value, the “Genius-compliant” stablecoin nationalizes global liquidity. It is a technology that does not just represent the dollar but weaponizes it, challenging and ultimately replacing the Eurodollar system that has governed global finance since the 1950s.

Introduced on May 1, 2025, by Sen. Bill Hagerty [R-TN], a former hedge fund co-founder and Ambassador to Japan during the first Trump administration, the US Genius Act envisages stablecoins as a technological upgrade to the USD, and it’s shadow variant, the Eurodollar. The law, which stands for Guiding and Establishing National Innovation for US Stablecoins Act, requires stablecoin issuers to back their digital assets with corresponding assets and comply with standards for capital, liquidity, transparency, and risk management. Instead of viewing stablecoins as a threat to the dollar, the Act allows the US to export its currency more efficiently than ever before, while locking the issuers and purchasers into financing the US national debt.[3]

In the context of my SACT framework, they function as a pure semiotic Substitution (S-Layer) vehicle, as exemplified in this USD example.

Signifier – The digital token (e.g., USDC, USDT).
Signified – The US Dollar held in a bank vault (or Treasury bill).
The Promise – The substitution is 1:1 and reversible at any time.

The Semiotic Shift: From Bank Promises to Sovereign Tokens

To understand the magnitude of this shift, we must look at the Substitution (S) layer of our stack.

For 70 years, the world ran on Eurodollars. A Eurodollar was a semiotic signifier that stood for “a US dollar held in a bank outside the US.” Crucially, this was credit money. It was a liability of a private bank (like HSBC or Barclays) backed by deposits but with no reserve ratio. When the world needed liquidity, these offshore banks “printed” it by extending credit on their spreadsheets.

The Genius Act destroys this logic. A Genius-compliant stablecoin (like the new “Clean” USDC) is not a bank liability. It is a tokenized claim on US Sovereign Debt.

By forcing stablecoin issuers to hold US Treasuries, the Act creates a massive liquidity vacuum. Capital is now fleeing the risky, fractional-reserve balance sheets of offshore Eurodollar banks and flowing into the full-reserve, “risk-free” vaults of US stablecoin issuers.

Old Semiosis: Dollar = Private Bank Promise (Credit)

New Semiosis: Dollar = US Treasury Bill (Equity of the State)

The result is the defunding of the global banking system to finance the US government. The “offshore” world is no longer a place where money is created (credit); it is a place where US government debt is distributed (collateral).

The Abstraction Layer: The Bifurcation of Value and the “Pristine” Dollar

In the Abstraction (A) layer, the Genius Act creates a new, ruthless hierarchy of value. We are witnessing a bifurcation of the global currency supply into “Clean” and “Dirty” dollars.

“Clean” dollars are Genius-compliant tokens. These are programmable, KYC-compliant, and backed by the full faith and credit of the US Treasury. Because they act as “pristine collateral” in the new Cloud-Spreadsheet Capitalism stack, they trade at a structural premium.

“Dirty” dollars are legacy offshore deposits and non-compliant algorithmic stablecoins. These are viewed as “sub-prime” money, trading at a discount due to regulatory risk and lack of direct Treasury access.

This distinction will likely drive the price of the US dollar upward. As the Global South and international corporations scramble to access “Clean” dollars for trade settlement, demand for the underlying asset (US Treasuries) skyrockets. The Genius Act effectively turns the US dollar into a global membership fee for the digital economy.

The Telecom Layer: Weaponized Diffusion in the Global South

It is in the Telecommunications (T) layer where the geopolitical consequences are most acute. The Genius Act does not just export currency; it exports a new monetary operating system. In the Global South, the diffusion of these Genius-compliant stablecoins is replacing local currencies not just for transactions, but as the primary store of wealth.

Consider the following transaction: A merchant in Nigeria no longer needs SWIFT or a correspondent bank to pay a supplier in Vietnam. They use a Genius-coin rail that settles instantly. The “T” layer friction is gone, but so is the local central bank’s visibility into the trade.

Regarding wealth, by holding a Genius-coin on a smartphone, a citizen in Argentina or Turkey is effectively holding a US Treasury bond. They are bypassing their domestic banking system entirely to lend directly to the US government.

This change creates a form of “Hyper-Dollarization” that challenges national bank systems. It strips developing nations of their monetary sovereignty. Their central banks can no longer manage their money supply because their citizens operate on a parallel, superior grid governed by US code and backed by US debt.

Conclusion: The Spreadsheet Becomes the Sovereign

The Genius Act proves that stablecoins are not a deviation from spreadsheet capitalism; they are its apotheosis. They complete the transition of the dollar from a passive store of value to an active, computational object. This object is:

Written (Substitution) as a tokenized Treasury bill.

Abstracted (Abstraction) as a “Clean,” premium asset.

Computed (Computation) via smart contracts that enforce US sanctions and tax compliance.

Synchronized (Telecommunications) across a global cloud that renders local borders irrelevant.

We have moved from the “gridmatic” governance of the corporation to the “chainmatic” governance of the planet. The US government has effectively “IPO’d” the dollar on the blockchain, turning every stablecoin wallet in the world into a brokerage account for American power. The Eurodollar is dead; long live the Sovereign Spreadsheet.

Notes

[1] Hockett, Robert C., “Money’s Past is Fintech’s Future: Wildcat Crypto, the Digital Dollar, and Citizen Central Banking,” 2 Stanford
Journal of Blockchain Law & Policy (2019)
[2] Pennings, A.J. (2025, July 24) Stablecoins, Blockchains, and the Semiotic-Telecom-Computational Stack of Spreadsheet Capitalism. apennings.com https://apennings.com/artificial-intelligence/stablecoins-blockchains-and-the-semiotic-telecom-computational-stack-of-spreadsheet-capitalism/
[3] Issuers of stablecoins are required to buy US treasuries. They can hold these securities and collect the fixed interest payments. As well as some fees. Additional information on Genious Act regulations are available.

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© ALL RIGHTS RESERVED

Not to be considered financial advice.

---

Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, blockchain, cryptocurrencies, data analytics and meaning
Tags: Stablecoins > US Genius Act > US Genius Act of 2025

Remediating the Blurred Lines of Human-AI Collaboration in Disaster Management and Public Safety Communications

Posted on | November 14, 2025 | No Comments

This is a follow-up to my prepared presentation for the Asia-Pacific Economic Cooperation (APEC) meeting on July 31, 2025, on Disaster Leadership with Saebom Jin from the National AI Research Lab at KAIST. We used media theory to talk about the possibility of “healing” the Common Operating Pictures (COP) used in disaster-oriented situation rooms and command centers with Artificial Intelligence (AI) and Application Programming Interfaces (APIs). By healing, we drew meaning from the theories of remediation by Bolter and Grusin (2000), which propose that new media forms integrate older forms to create a more “authentic” version of reality.[1]

Operating Systems (OS) coordinate the flow of applications within a RAM-limited digital environment.[2] They facilitate the flow of software data like traffic cops manage the movement of automobiles in a busy intersection. Artificial Intelligence (AI) can function as a sophisticated “operating system” to coordinate APIs, enabling the seamless gathering of multiple streams of data and video to achieve both hypermediation and transparent immediacy in critical information displays like Common Operating Pictures (COPs). This involves AI acting at an orchestration layer, intelligently managing data flow like a maestro, and dynamically shaping the user experience.

This post uses remediation theory to offer ideas about the multimediated experience of connecting different data stream and windows in an individual device or common screen like the COP. Remediation is the process by which new media refashion old media to create a more “authentic” experience. In this case, it “heals” COPs by intelligently merging legacy media (TV, maps, dashboards, spreadsheets) with digital innovations (AI, APIs, streaming video). AI becomes the coordinator and translator of these media, enhancing their functionality and intelligibility.[2]

Drawing on Bolter and Grusin’s theory of remediation, with its two logics of transparent immediacy and hypermediation, we can piece together how AI can function as a next-generation operating system for Common Operating Picture (COPs) in disaster management and public safety command centers, dashboards, and mobile Personal Operating Pictures (POP). More importantly, we can see how media can stake a claim on mediated reality. The two logics work together. Transparent immediacy creates live experiences, making the medium “disappear” and enabling a direct, real-time experience. AI auto-selects, filters, and narrates live feeds or alerts for immediate situational awareness (e.g., live drone feed of a flooded area). Point-of-view (POV) perspective in visual art forms contribute to this experience. This combination enables fast, intuitive decision-making in the control room and the field.

Hypermediation uses multiple windows of statistical and indexical representation (e.g., temperature, wind speed, traffic data), allowing users to see and interact with the complexity of an incident. The AI OS organizes and synchronizes diverse sources (e.g., tweets, GIS layers, 911 calls, camera feeds) into COPs and visual dashboards to help leaders and analysts see patterns, anomalies, and priorities in multi-facted crises like a hurricane with flooding.

The Critical Role of Common Operating Pictures (COPs) and Dashboards in Disaster Management

In the demanding landscape of disaster management and risk reduction, Common Operating Pictures (COPs) and dashboards stand as hopeful pillars for effective surveillance and response. These sophisticated information systems are central to command centers, providing real-time monitoring and management of complex situations, for incident management, emergency response, and the protection of critical infrastructure. Their fundamental utility lies in their ability to aggregate vast amounts of surveillance data and diverse information sources, synthesizing them into a unified, real-time, immediate view of ongoing activities and unfolding situations. This comprehensive display is expected to significantly enhance situational awareness for all parties involved, from field responders to strategic leaders.  

The strategic value of COPs extends beyond mere data display; they are instrumental in fostering collaborative planning and reducing confusion among multiple agencies operating in a crisis. By providing a consistent, up-to-date view of critical information, COPs enable faster, more informed decision-making across the entire response structure. A prime example of this is the Department of Homeland Security (DHS) Common Operating Picture program, which delivers strategic-level situational awareness for a wide array of events, including natural disasters, public safety incidents, and transportation disruptions. This program facilitates collaborative planning and information sharing across federal, state, local, tribal, and territorial communities, underscoring the vital role of integrated information platforms in national security and public safety.  

AI as an Operating System for API-fed Media Management Layers

AI orchestration refers to the coordination and management of data models, systems, and integration across an entire workflow or application. In this context, AI acts as the “maestro” of a technological symphony, integrating and coordinating various AI tools and components to create efficient, automated informational and media workflows.  

The role of the AI-OS is to ingest data via API from sources such as satellite feeds, IoT sensors, weather models, traffic systems, and social media chatter. AI uses algorithms and techniques, often from machine learning and neural nets, to process visual data, recognize objects, and analyze scenes. It tags and contextualizes data using NLP, computer vision, and geospatial AI to collect, label, and align data across multiple users. It can also manages narrative flows by creating text summaries, video captions, or alerts that guide public broadcasts or Personal Operating Pictures (POP) user interpretation. AI adapts interfaces and adjusts the dashboard to user roles (e.g., responder vs. planner vs. public).

AI-first API management integrates machine learning (ML), natural language processing (NLP), and predictive analytics to gain deeper insights into performance and usage trends to forecast weather patterns, detect fire and water anomalies in real-time, and automate response governance. This means AI can intelligently manage the flow of information and tasks between different media components, ensuring the right data reaches the right COPs and models at the right time, preventing data bottlenecks and optimizing predictive resource utilization.  

AI-coordinated data streams draw in various API-enabled data and video sources to “heal” the COP experience. For instance, real-time streaming (RTSP/Drones) provides live visual feeds and real-time object detection, as well as thermal imagery for immediacy. GIS/Map APIs gather hyperreal terrain, zoning, and infrastructure information and then overlay evacuation zones and hazard proximity models that may involve chemical leakages, flooding, traffic, and other factors. Social media APIs draw on the writing of public posts and conduct sentiment analysis, while geo-tagging locations for search engine optimization (SEO). They also have to factor in panic signals and filter out misinformation from mischievous posts. IoT Sensors (MQTT) provide (infra)structural and environmental data that can trigger alerts based on thresholds and can be used in predictive modeling. EMS/911 feeds draw on voice and text emergency dispatches and may require transcription and triage classifications for harmful accidents. Additionally, Weather/NOAA APIs collect storm forecast data and generate path predictions and risk zone maps.

AI-powered API integration solutions automate and streamline connections between disparate software platforms, enabling seamless communication and real-time data flow. This eliminates manual data entry, reduces human error, and provides unified access to business-critical data, allowing systems to scale faster and adapt to market demands. AI systems include computational resources, data stores, and data flows/pipelines that transmit data. Data engineers design these pipelines for efficient transfer, reliable quality, and easy access for integration and analysis. AI orchestration platforms automate these workflows, track progress, manage resources, and monitor data flow.  

Gathering Multiple Streams of Data and Video – Transparent Immediacy in Practice

In the high-stakes domain of crisis management, transparent immediacy is an indispensable principle for designing intuitive COP and dashboards that facilitate rapid decision-making by minimizing cognitive friction. Real-time data visualization tools are specifically engineered to present complex information in a highly usable manner, enabling decision-makers to quickly grasp the unfolding situation without being distracted by the interface itself. Achieving true immediacy in data delivery is critically dependent on low latency within the underlying network infrastructure, particularly in mission-critical environments where split-second decisions can have profound consequences.[Diffserv]  

The integrative interface of AI-assisted media “vanishes” into the COP to provide a multidimensional hyperrealized and contextualized display. It blends multiple inputs (e.g., real-time GPS + bodycam) while maintaining the im-mediated telepresence “touchpoint” to events in the field. It synthesizes spoken alerts from text messages and narrates the changing situation (“A levee breach has been detected 3 miles south of Sector 3”). The result is that decision-makers read the mediated displays as if they are seeing the world with a healed blend of immediate and hyperreal perspectives — without delayed video feeds or digging through massive amounts of raw data.

The principles of transparency and trust are fundamental to effective crisis communication. Providing clear, accurate, and timely updates through dedicated platforms and channels helps to build confidence and establish credibility with affected populations and stakeholders. This approach aligns directly with the human desire for direct, unmediated information. Proactive communication, a commitment to telling the truth, and consistently adhering to factual information are essential strategies for maintaining transparency and regaining control of the narrative during a crisis. These practices mirror the pursuit of immediacy by delivering information that feels direct, honest, and unadulterated, thereby reinforcing public trust.  

In the high-stress, information-rich environments of crisis management, operators frequently encounter information overload. The core objective of transparent immediacy is to make the mediating technology disappear from the user’s conscious awareness, thereby allowing direct engagement with the critical information. By meticulously designing COPs and dashboards to include an “interfaceless” quality, the cognitive burden associated with navigating complex interfaces is substantially reduced. This reduction in cognitive friction enables faster assimilation of critical data, expedites the identification of patterns or anomalies, and ultimately leads to more rapid and effective decision-making, which is of paramount importance in emergency response scenarios. The less mental effort expended on understanding the tool, the more attention can be dedicated to understanding the crisis.  

While transparent immediacy strives to erase the medium and present information as unmediated reality, the integration of AI introduces a new, inherently complex layer of algorithmic mediation. AI can indeed create the appearance of greater immediacy by providing real-time insights and indexical predictive analytics, seemingly cutting through complexity to deliver direct understanding. However, the internal workings of AI processes — how they learn, process vast datasets, and generate their outputs—are often opaque, frequently referred to as a “black box” problem.

This creates a fundamental paradox: the desire for an “interfaceless” and seemingly unmediated experience directly conflicts with the ethical imperative for transparency and explainability in AI systems. Disaster management leaders must carefully navigate this tension, balancing the undeniable benefits of AI-driven immediacy with the critical need to understand and trust the AI’s “judgments,” particularly when human lives and safety are at stake. This complex challenge may necessitate the development and integration of new forms of “explainable AI” (XAI) within COP interfaces to ensure that accountability and trust are maintained, even as the technology becomes more sophisticated.  

Gathering Multiple Streams and Windows of Data and Video – Hypermediation in Practice

When a new AI-assisted digital COP remediates older, perhaps less dynamic, informational sources, it carries an implicit promise of higher fidelity, greater accuracy, and real-time relevance. Fulfilling this promise can significantly enhance comfort and trust in the information presented. For instance, an interactive Geographic Information System (GIS) map that updates in real time is inherently perceived as more reliable and trustworthy than a static, outdated map.

However, the very process of mediation, by transforming data and introducing new digital layers, can also introduce new forms of hyperreality. If these indexical layers are not transparent, or if the transformation process itself is flawed or introduces biases, it could inadvertently undermine the very trust it seeks to build. Therefore, disaster management leaders must ensure that the “improvement” offered by new forms of remediation is genuinely beneficial and does not obscure the underlying data’s provenance, potential limitations, or inherent biases.

This transition demands a critical understanding of the transformative pitfalls and potentials of digital remediation. AI operating systems can call for displays that are layered and windowed that offer diverse media representations, each with its own source, credibility, and relevance. It stacks weather maps, traffic flows, shelter capacity, and 911 calls. It shows social sentiment spikes alongside physical sensor alerts. It also tags uncertainties, such as “unverified reports” or “possible false positives.”

The result is that it enables strategic coordination by exposing the full complexity of the crisis landscape. AI’s core strength lies in its ability to ingest, process, and fuse massive volumes of data from diverse sources in real-time. AI then infers from the large datasets that are collected to inform human-based guidance and decision-making.

Multimodal AI systems are designed to integrate and process multiple data types (modalities) such as text, images, audio, and video. By combining these various data modalities, the AI system interprets a more diverse and richer set of information, enabling it to make accurate, human-like predictions and produce contextually aware outputs. This is achieved through multimodal deep learning, neural networks, and fusion techniques that synthesize different data types.

Real-Time video stream analysis draws on AI-powered video intelligence APIs that can recognize over 20,000 objects, places, and actions in both stored and streaming video. They can extract rich metadata at the video, shot, or frame level, and provide near real-time insights with streaming video annotation and object-based event triggers. Advanced APIs like Google’s Live API enable low-latency bidirectional voice and video interactions, allowing for live streaming video input and real-time processing of multimodal input (text, audio, video) to generate text or audio.  

AI and knowledge graph capabilities automate data ingestion, preparation, analysis, and reporting, significantly reducing manual tasks. This allows for quickly connecting internal data sources with case-specific data like device dumps, license plate readers (LPRs), financial records, social media, and public records for a comprehensive view.  

Coordination for Hypermediation

Hypermediation foregrounds the mediating function, explicitly enhancing the multiplicity of information and exposing the limitations of direct, “unmediated” transparent representation. AI enhances this by intelligently managing and presenting diverse, fragmented data streams in coordinated spreadsheet grids and windows. AI enables hyper-personalization of content by analyzing user behavior and preferences, tailoring content to individual needs on a granular level. This extends to dynamic user interfaces that can adapt based on user theming, curation, and real-time feedback, moving beyond static software to more organic, rapidly changing displays.  

AI in OS mode synthesizes and presents complex disaster information in usable, interactive multimodal dashboards that feature multiple windows, dynamic visualizations, and drill-down options. This allows for enhanced interactivity and navigation, enabling users to explore data in depth and filter information based on specific criteria.  

AI acts as the technological enabler for sophisticated hypermediation, allowing for the intelligent management of interconnected media at a scale and speed previously unattainable. It helps transform a potential deluge of data into a coherent and actionable common operating picture by connecting related information from fragmented sources and streamlining complex analyses.

Coordination for Transparent Immediacy

Transparent immediacy aims to make the user “forget the presence of the medium,” fostering a belief in direct, unmediated presence with the represented information. AI contributes to this by gathering and simplifying complex data into clear, actionable insights and enabling seamless, real-time interactions. [1]  

AI-powered data visualization transforms complex data into clear, dynamic visuals, identifying patterns and relationships that would take humans hours to uncover. It provides real-time insights, automatically updating visuals as new data flows in, allowing for faster, more informed decisions. This simplifies the complex, distilling mountains of raw data into actionable visuals that can be understood “at a glance”.  

AI-enhanced interfaces offer natural language processing, allowing users to ask questions and receive results in clear, interactive charts. By making the mediating technology disappear from conscious awareness, AI-driven COPs reduce the cognitive burden on operators, enabling faster assimilation of critical data and expediting decision-making.  

AI can power immersive VR and AR environments that extend traditional 2D displays into a third dimension, enabling novel operations and more intuitive, collaborative interactions. These environments aim to create a shared, real-time, and seemingly unmediated understanding of a crisis, akin to William Gibson’s “consensual hallucination” of cyberspace. AI-powered insights assist in selecting appropriate hardware solutions for these immersive technologies, streamlining their integration.  

AI Black Box’s Paradox and Ethical Considerations

While AI strives for transparent immediacy by simplifying complexity, it introduces a “black box” problem where the internal workings of AI processes are often opaque. This creates a paradox where the desire for an unmediated experience conflicts with the ethical imperative for transparency and explainability in AI systems. For critical applications like COPs, ensuring data quality, mitigating algorithmic bias, and maintaining human accountability are paramount. The effectiveness and reliability of AI models are directly contingent upon the quality, diversity, and cleanliness of the data on which they are trained, as substandard data can propagate and amplify flaws.  

AI acts as a central nervous system for a hypermediated and transparent immediated COP by orchestrating the complex interplay of data streams, video feeds, and user interfaces. It enables real-time data fusion, dynamic content adaptation, and intuitive visualization, but requires careful human oversight to ensure trust, accountability, and ethical deployment. AI as an operating system doesn’t just manage media, it interprets, structures, and presents it as meaning. In the process it strives to enable leaders and users to move from data overwhelm to narrative clarity.

Concluding Thoughts

The strategic application of media theory concepts, primarily Remediation and its logics of Transparent Immediacy, and Hypermediation, works in conjunction with advanced AI capabilities. This process is paramount for optimizing the “authentic” healed experience in Common Operating Pictures and dashboards in disaster management.

This post has demonstrated how these frameworks provide a critical lens for understanding, designing, and enhancing the information systems and COPs that underpin modern crisis response. From transforming chaotic and static data forms into dynamic visual flows (Remediation) to fostering seamless situational awareness (Transparent Immediacy) and orchestrating complex multi-source indexical and graphical information (Hypermediation), media theory offers a profound guide. AI, in turn, acts as the indispensable engine, the OS enabling these transformations through API real-time data fusion, formulas for predictive analytics, and automated communication displayed on COPs.

The future of crisis response lies in intelligently navigating the increasingly blurred lines of human-AI collaboration. This demands a nuanced understanding of AI as a powerful co-author, operating system, and assistant, one that enhances human capabilities but never fully replaces human accountability and intent. Fostering trust in increasingly mediated information requires unwavering commitment to transparency, data quality, and ethical AI deployment. By consciously integrating theoretical understanding with technological innovation, disaster management leaders can leverage these converged media landscapes to create COPs and dashboards that are not merely displays of data, but dynamic, intelligent platforms capable of shaping perception, informing decisive action, and ultimately building a more resilient and responsive global community in the face of escalating threats.

Citation APA (7th Edition)

Pennings, A.J. (2025, Nov 14) Remediating the Blurred Lines of Human-AI Collabollaboration in Disaster Management and Public Safety Communications. apennings.com https://apennings.com/crisis-communications/remediating-the-blurred-lines-of-human-ai-collabollaboration-in-disaster-management-and-public-safety-communications/

Notes

[1] Bolter, Jay David, and Richard Grusin. Remediation: Understanding New Media. Cambridge, MA: MIT Press, 2000. They followed up on “probes” by Marshall McLuhan, that the content of any new medium is always an older medium. This means new technologies integrate, repurpose, and older media. McLuhan’s main message was to point to the fundamental change these new forms create in human scale, pace, or pattern. These ideas were primarily expressed in The Mechanical Bride (1951) and Understanding Media (1964).
[2] By RAM-limited digital environment I mean the workspace needed for multiple applications to be supported. “RAM” can also be used as an analogy for a human’s ability to deal with multiple streams of information coming into their perspective.
[3] I used two prompts to address the issues I was thinking about.
Prompt 1. How can AI be an operating system to coordinate APIs gathering multple streams of data and video for hypermediated and transparent immediacy? Prompt 2. Drawing on the concepts of remediation and its two logics of hypermediation and transparent immediacy, how can AI be an operating system to coordinate APIs gathering multple streams of data and video for Common Operating Pictures used in Disaster Management and Public Safety?

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Not to be considered financial advice.

---

Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, crisis communications, data analytics and meaning, disaster risk management
Tags: double logic of remediation > Hypermediation > remediation theory > Transparent Immediacy

Taylor’s Fab Rescue: The A16 Chip

Posted on | November 2, 2025 | No Comments

My home in Austin, Texas, is about 25 miles from the newly built Samsung microprocessor fabrication “fab” factory. The fab has been running behind schedule since it switched its plans from 4-nanometer chips to 2-nanometer chips. But Tesla recently signed a deal with Samsung to build its A16 (and maybe A18) chips in Texas for future use in its data center, EVs, and robots.

While the Taylor location has many advantages, the demands of producing such small devices are numerous. While very few earthquakes occur in Texas, 2nm chip production operates at atomic levels that cannot tolerate vibrations from trains, traffic, or settling of the earth below and near the fab. Unlike the new TSMC fab in Phoenix, Arizona, Samsung had to invent many solutions to problems with electricity supply, ultra-clean water and air, and a workforce not used to 2nm production.

These problems and Samsung’s heroic attempts to overcome them are discussed nicely in this video, including a crucial factor for the fab’s success—a customer to point its development towards. This is why Tesla’s decision to work with Samsung on its A16 chip was a welcome relief for both parties. Samsung locks in a much-needed customer while Tesla avoids competing with Apple, NVIDIA, and a myriad of other companies for TSMC’s chips.

This video explains the situation in more detail.

What will Tesla do with these chips? The internally designed chips will be used for both inference and training in a variety of products, including self-driving systems for its EVs, the Optimus humanoid robot, and AI data centers.

Citation APA (7th Edition)

Pennings, A.J. (2025, Nov 02) Taylor’s Fab Rescue: The A16 Chip. apennings.com https://apennings.com/digital-geography/taylors-fab-rescue-the-a16-chip/

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© ALL RIGHTS RESERVED

Not to be considered financial advice.

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Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, democratic political economies, digital geography
Tags: 2 nm chips > A16 Chip > fabs > Taylor > Taylor fab > TX

The AI Prompter as Auteur? Examining LLM Authorship Through the Lens of Film Theory

Posted on | November 1, 2025 | No Comments

I’ve been teaching film classes on and off since graduate school. It has never been my primary focus, but the historical and theoretical depth of film studies intrigued me as well as the technologies and techniques that shape thinking and understanding. I incorporate much of it in my EST 240 – Visual Rhetoric and Information Technology class from Stony Brook University, which looks at the camera and editing techniques that persuade and signify in different media.

This year, we added generative AI as a topic to address logo design, photo-graphics, and video synthesis. Here is an example of the basics for a video prompting plan. In this text, I explore the concept of authorship in the age of generative AI, drawing parallels with established theories from film and media studies, particularly auteur theory.

One of the interesting questions in film studies and media studies is authorship, or “auteur,” from its French roots. Who is the “author” of a film? Is it the screenwriter? The producer? The director? How much do the actors contribute to the creative process? A similar query asked where the meaning in the film experience is produced. Is it in the author? In the content or “text”? How about in the audience or the viewer? A related perspective asks how much authorship or meaning is limited or organized by the “genre,” such as a comedy, drama, or science fiction?

The proliferation of Large Language Models (LLMs) capable of generating sophisticated text and imagery has introduced a novel figure into the creative landscape, the AI prompter. This individual, through the crafting of textual instructions, elicits responses from AI systems, thereby participating in the creation of content that often blurs the lines of traditional authorship and creative control. The question is whether a human AI prompter can be considered the “author” of text or imagery produced by an LLM? This discussion has become very relevant in fields such as education, law, and publishing. It invokes debates around creativity, control, and the very definition of authorship, portending friction between emergent AI tools and social institutions.

Or is it more akin to a “curator?” As understood in museum studies, this person is a figure who makes decisions about what to include, what to exclude, and how to narrate the story an exhibition tells. In the realm of AI-generated content, the “prompter” is the individual who crafts the audio, pictorial, or textual instructions (prompts) that guide the LLM to produce an output, be it an essay, a poem, or a digital image. The quality, specificity, and iterative refinement of these prompts significantly influence the final result. Alternative analogies that may capture different facets of the prompter’s interaction with LLMs include the “chef,” the “collaborator,” the “instrumentalist,” or “partner.” But these mostly fall outside the realm of film and media theory and will be mentioned only briefly.

This exploration is not merely an academic exercise; the increasing ubiquity of LLMs in diverse fields, from literary creation to legal analysis, necessitates new conceptual tools to understand this new form of human-machine interaction. The US Copyright Office ruled in May 2025 that the results of prompts can, for the most part, be copyrighted. The US Constitution recognized early the social value of such arrangements in Article I, Section 8, Clause 8 when it codified the following:

“To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.”

The “Copyright Clause” is meant to promote innovation and economic development and the developer’s of AI LLMs do not seem to have much incentive to oppose the Copyright Office’s “Copyright and Artificial Intelligence Part 3: Generative AI Training.” This is not to say that other content creators have agreed, including people whose likeness has appeared in AI produced content. The Copyright Office has stated its intention to “monitor developments in technology, case law, and markets, and to offer further assistance to Congress as it considers these issues.”

Understanding Auteur Theory and AI

Originating with early film critics in the 1950s, Auteur theory posited that the director is the primary creative force behind a film. She or he is the “author” whose personal vision, recurring themes, and distinct stylistic choices are imprinted on their body of work. An auteur, like Alfred Hitchcock, Akira Kurosawa, or Steven Speilberg is seen as exerting a high level of control over all aspects of production. So some film theorists argued they make their films recognizable and uniquely their own, regardless of the participation of other collaborators like screenwriters or actors.

American critic Andrew Sarris later popularized and systematized auteur theory, coining the term in his 1962 essay, “Notes on the Auteur Theory” in The American Cinema. Later, he proposed three concentric circles of criteria for evaluating directors: technical competence (the ability to make a well-crafted film), a distinguishable personality (a recurring stylistic signature evident across their films), and interior meaning (arising from the tension between the director’s personality and the material). It refers to the often unconscious or ambiguous, personal vision, themes, and preoccupations of the director that permeate their films, even when working with diverse material or within the constraints of a studio system. The theory celebrated directors like Alfred Hitchcock, who, despite working within the constraints of the Hollywood studio system, managed to produce distinctive and deeply personal works.[2]

French film critics associated with Cahiers du Cinéma and “la politique des auteurs” championed the film director as the primary artistic force whose personal influence, individual sensibility, and distinct artistic vision could be identified across their body of work. This approach shifted critical attention from studios or screenwriters to the director, viewing them as an artistic creator of the work. Auteur theory includes determining whether directors have a personal vision and style based on a consistent aesthetic and thematic worldview, demonstrated mastery of the medium, recurring themes and motifs, and a willingness to push the boundaries of the medium. Do they maintain significant control or influence over the final product?

A key aspect of the auteur was the identification of a particular film style encompassing: visual elements, narrative structures, and thematic preoccupations that could be consistently associated with a director, serving as their “authorial signature.” Auteur theory drew a critical distinction between “true auteurs,” who infused their work with a personal vision and artistic depth, and “metteurs en scène,” who were seen as competent but workmanlike directors merely executing the details of a script without a discernible personal stamp. For instance, Michael Curtiz who won Best Director for Casablanca (1942) was often placed in the latter category, while Nicholas Ray, known for the (1955) Rebel Without a Cause, starring James Dean was celebrated as an auteur.  

The “method actor” analogy reinforced the prompter is a director who guides the suggests the LLM that can be treated as an actor. The LLM “act outs” a specific role or persona to solve problems or generate content. By “casting” the LLM in a role (e.g., “You are a historian…”) and providing a “script” (the detailed prompt), the prompter can elicit more structured, context-aware, and human-like responses. This framework emphasizes the prompter’s role in setting the scene, defining the character, and guiding the performance.

A skilled prompter often has a specific vision for the desired output. They use carefully chosen words, structures, and iterative refinements (prompt engineering) to steer the AI towards this vision. This can involve defining style, tone, subject matter, and even attempting to evoke specific emotions or ideas, much like a director outlines a scene.

Experienced prompters can develop recognizable patterns or approaches to prompting that yield particular types_of results from specific AI models. They learn the nuances of the AI and how to elicit desired aesthetics or textual qualities. Prompting is rarely a one-shot command. It often involves a back-and-forth conversation, a process of trial, error, and refinement, akin to a director working through multiple takes or editing choices. Even if the AI generates multiple options, the prompter often makes the final selection, curating the output that best aligns with their initial intent. This act of selection can be seen as a creative choice.

However, the US Copyright Office notes that AI models operate with a degree of unpredictability but concluded that content produced with AI can be copyrighted.

Key concerns of an auteur theory include determining who is the primary creative force, whether they have a personal vision and style based on a consistent aesthetic and thematic worldview, demonstrated mastery of the medium, recurring themes and motifs, and a willingness to push the boundaries of the medium. Finally, do they maintain significant control or influence over the final product?

The US Copyright Office’s stance that AI-generated works can be copyrighted with substantial human input further complicates this relationship. If the generated work is not copyrightable in the prompter’s name alone, or if the AI’s contribution is deemed substantial enough to negate sole human authorship, the prompter’s status as an “auteur” in a legally recognized sense is undermined.

However, current legal and creative consensus, notably highlighted by bodies like the US Copyright Office, generally holds that AI-generated works are not copyrightable unless there is substantial human creative input beyond mere prompting. The reasoning is that even detailed prompts can lead to unpredictable outputs from the AI, meaning the prompter may not have the same level of direct, granular control over the final work as a traditional artist. The AI model itself, with its complex algorithms and vast training data, plays a significant, if not primary, role in the generation process. The LLM’s output is inherently shaped by the massive datasets it was trained on, a factor far beyond the prompter’s control and introducing a vast external influence on the “style” and content.

By “carefully crafting prompts,” the user provides the model with context, instructions, and examples that help it understand the prompter’s intent and respond meaningfully. This includes setting clear goals, defining the desired length and format, specifying the target audience, and using action verbs to specify the desired action. Such detailed instruction suggests a high level of intentionality on the part of the prompter, aiming to steer the AI towards a preconceived vision.  

Arguments supporting a significant authorial role for the prompter often emerge from discussions about writers’ creative practices with AI. Studies indicate that writers utilize AI to overcome creative blocks, generate starting points, and then actively shape the AI’s output into something they consider useful, thereby maintaining a sense of ownership and control over the creative process. This active shaping and refinement, driven by the writer’s “authenticity” and desire for “ownership,” can be seen as analogous to an auteur’s imposition of their vision onto the raw materials of filmmaking.  

The Auteur Analogy Falters

Despite these points of correspondence, applying the auteur analogy to the AI prompter faces significant challenges. LLMs’ construction complicates the notion of a singular, controlling vision. These models are trained on vast datasets of existing human-created text and images and function by “mimicking human writing.” This training design makes it difficult to disentangle the prompter’s “pure” vision from the inherent capabilities, biases, and stylistic tendencies embedded within the LLM’s architecture and training data. AI may not be a neutral tool but an active, albeit non-conscious, participant in the generation process.  

This trajectory leads to the “black box” problem. The prompter rarely has full transparency or control over the internal workings of the LLM. While a film director ideally orchestrates various human and technical elements (cast, crew, script, camera), the prompter interacts with a system whose decision-making processes are often opaque. The output can sometimes be unpredictable, with LLMs even known to “hallucinate” or generate unexpected results, challenging the idea of complete authorial control.  

Intellectual property law presents another major hurdle. Current legal frameworks, particularly in jurisdictions like the United States, generally require human authorship for copyright protection. AI, as it stands, blurs the lines between authorship, ownership, and originality. If the generated work is not copyrightable in the prompter’s name alone, or if the AI’s contribution is deemed substantial enough to negate sole human authorship, the prompter’s status as an “auteur” in a legally recognized sense may be undermined.

The debate over prompter-as-auteur is thus deeply intertwined with these evolving legal definitions. The lawsuits involving creators like Scarlett Johansson and organizations like Getty Images and The New York Times against AI companies for using copyrighted material in training data further complicate the picture, as the very foundation upon which the LLM generates content is itself a site of contested authorship.  

Moreover, many instances of AI prompting might more accurately align with the role of the metteur en scène rather than the true auteur. A prompter might be highly skilled in eliciting specific outputs from an LLM, demonstrating technical competence. However, they may be seen as proficient technicians rather than visionary artists without a consistent, distinguishable personal style, thematic depth, or “interior meaning” traceable across a body of their AI-generated works. The inherent weaknesses often found in AI-generated writing—such as blandness, repetitiveness, or a lack of overarching logical structure — can also limit the perceived artistic merit of the output, thereby challenging the prompter’s claim to full auteurship if they are simply instructing a tool with such limitations.  

Furthermore, a significant aspect of the prompter’s role involves navigating the LLM’s potential for bias, inaccuracy, and “hallucinations.” This requires a curatorial-like responsibility to ensure that the information or content presented is sound, ethically considered, and appropriately contextualized. A museum curator has an ethical duty to research, authenticate, and provide accurate context for artifacts. Similarly, an AI prompter, especially in professional or public-facing applications, must critically vet and potentially correct or contextualize AI output to prevent the dissemination of falsehoods or biased information. This positions the prompter as a gatekeeper, quality controller, and interpreter of the AI’s output—all key curatorial functions.  

Despite these compelling parallels, the curator analogy also has its limitations when applied to AI prompting. Traditionally, curators work with existing, often tangible, artifacts or discrete pieces of information. LLMs, however, generate new content, albeit derived from their training data. The question then arises, is the prompter curating “generated” data, or are they more accurately co-creating it? This ambiguity blurs the line between curation and creation.

The “collection” from which an AI prompter “selects” is also fundamentally different from a museum’s holdings. An LLM’s latent space represents a near-infinite realm of potential outputs, not a predefined set of objects. This abundance makes the act of “selection” by prompting a more generative and less bounded process than choosing from a finite collection. The prompter is not merely selecting from a catalog but actively shaping what can be chosen or brought forth by the structure of their prompts. This suggests a more active, co-creative form of curation than traditional models imply, where the collection is dynamic and responsive to the prompter’s interaction.  

Acknowledging that the AI prompter’s role is not monolithic it’s crucial; it varies significantly based on the level of skill, labor, and “intellectual expression” invested, ranging from a simple user to a highly skilled co-creator. At one end of the spectrum, a user might issue simple, direct instructions to an LLM for a straightforward task, acting more as a client or basic operator. On the other hand, a highly skilled individual might engage in complex, iterative dialogues with AI, meticulously refining prompts and outputs in a process that resembles deep co-creation. The level of skill and labor or “intellectual expression” invested by the prompter can differ dramatically, and this variance directly impacts how their role is perceived and classified. A casual user asking an LLM to “write a poem about a cat” is performing a different function than an artist spending weeks crafting and refining prompts to achieve a specific aesthetic for a series of generated images or a legal expert carefully structuring queries to extract nuanced information for a case.  

Structural and Post-Structural Theoretical Challenges to Auteur Theory

Structuralism and its successor, post-structuralism, launched a powerful critique of the traditional notion of “authorship,” particularly challenging the idea of the author as the sole, authoritative source of a text’s meaning. Two of the most influential figures in this critique are Roland Barthes and Michel Foucault. Roland Barthes, in “The Death of the Author” (1967), argued for the “death of the author” as a key concept relevant to literary and textual analysis. His key arguments included that focusing on the author’s biography, intentions, or psychological state to interpret a text is a misguided and limiting practice. He believed that the author’s life and experiences are irrelevant to the meaning of the work once it is produced. He famously claimed that a text is not a linear expression of an author’s singular thought but rather “a multi-dimensional space in which a variety of writings, none of them original, blend and clash.” A text is a “tissue of quotations drawn from innumerable centers of culture,” meaning it comprises pre-existing linguistic conventions, cultural references, and discourses.

For Barthes, the meaning of a text is not fixed by the author but is produced in the act of reading. The reader is the one who brings together the various strands of meaning in the text. “The birth of the reader must be at the cost of the death of the Author.” This move liberates the text from a single, imposed meaning and opens it up to multiple interpretations. Barthes viewed the author not as a “creator” in the traditional sense, but as a “scriptor” – someone who merely sets down words, drawing from an already existing linguistic and cultural archive.

Michel Foucault’s “What is an Author?” (1969) shared Barthes’ skepticism about traditional authorship but approached the issue from a different angle, focusing on the historical and institutional construction of the “author-function.” His main point was that the “author” is not a natural or timeless entity but a specific function that emerged within certain historical and discursive practices. The author-function is a way of classifying, circulating, authenticating, and assigning meaning to texts within a given society.

The author is not a person, but a discursive principle. The author-function is not equivalent to the individual writer. It’s a set of rules and constraints that govern how we understand and use an author’s name. For instance, the author’s name serves as a mark of ownership (property), a way to hold someone responsible for transgressive statements, and a means to unify a body of diverse works.

Foucault linked the emergence of the author-function to systems of control and power, particularly the rise of copyright law and the need to regulate speech. Attributing a text to an author became a way to police discourse and assign responsibility, especially for subversive or dangerous ideas. Foucault emphasized that the author-function has not always existed or applied equally to all types of texts. For example, scientific texts often gain authority from their content rather than solely from their author, whereas literary texts are more heavily tied to the author’s name.

In summary, Barthes and Foucault challenged the romanticized, humanist view of the author as a solitary genius from whom all meaning emanates. Instead, they argued that meaning is either generated by the reader (Barthes) or shaped by complex social, historical, and institutional forces (Foucault), effectively “decentering” the author from their traditional position of authority. This critique had a profound impact across the humanities, including film studies, by shifting focus from the individual creator to the structures of language, discourse, and reception.

By applying Barthes and Foucault, we can argue that the LLM’s output is not a direct, unmediated expression of the prompter’s “genius” or sole intent. It is a complex interplay of the prompt, the LLM’s massive training data (a vast, unauthored “library” of human discourse), and its internal algorithms.

The “meaning” of AI-generated content is fluid and negotiated between the prompt, the LLM’s “knowledge,” and the audience’s interpretation. The emphasis on the prompter as “author” is often driven by practical, legal, and social needs to assign responsibility and fit new technology into old frameworks, rather than a true reflection of the creative process. The prompter is more accurately a skilled orchestrator of existing information and patterns or a “curator” and “editor” of potential outputs rather than a solitary inventor. This critical lens helps to move beyond a simplistic “human-as-master-of-machine” narrative, acknowledging the distribution of creativity and meaning-making in the age of AI.

Summary and Final Thoughts

Furthermore, as a visual studies and AI professor, I now incorporate generative AI, prompting questions about who the “author” is in AI-generated content. Traditionally, film studies grappled with authorship (the “auteur”), considering if the director, screenwriter, or others were the primary creative force. Similarly, the meaning of a film could be attributed to the author, the content, or the audience.

The emergence of the AI prompter—the human who crafts instructions for Large Language Models (LLMs)—creates a new debate. Is the prompter the “author” of the AI’s output? The text considers applying auteur theory to the prompter, noting similarities like the prompter’s vision, iterative refinement (prompt engineering), and selection process. It even uses the analogy of a “method actor” for the LLM, guided by the “director” (prompter).

However, the post argues that the auteur analogy could ultimately falter. LLMs, trained on vast human-created datasets, operate as “black boxes” with unpredictable outputs, making it difficult to attribute a singular, controlling vision to the prompter. Legal frameworks still largely require human authorship for copyright. The prompter might often function more like a “metteur en scène” (a competent technician) rather than a visionary auteur.

Instead, the post suggests the prompter’s role might be more akin to a curator, responsible for vetting, correcting, and contextualizing AI output due to the LLM’s potential for bias and “hallucinations.” However, this analogy also has limitations, as prompters generate new content rather than just selecting existing artifacts, making their role a blend of curation and co-creation.

Citation APA (7th Edition)

Pennings, A.J. (2025, Nov 1) The AI Prompter/Conversationalist as Auteur? Examining LLM Authorship Through the Lens of Film Theory. apennings.com https://apennings.com/books-and-films/the-ai-prompter-conversationalist-as-auteur-examining-llm-authorship-through-the-lens-of-film-theory/

Notes

[1] The notion of a prompter is being challenged because it is able to store conversations and responses.
[2] Okwuowulu, Charles. (May 2016). Auteur Theory and Mise-en-scence construction: A Study of Selected Nollywood Directors.
[3] Copyright and Artificial Intelligence
Part 3: Generative AI Training. Pre-publication version
A REPORT of the Register of Copyrights, May 2025

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© ALL RIGHTS RESERVED

Not to be considered financial advice.

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Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches Visual Rhetoric, AI, and broadband policy. From 2002-2012 he taught digital economics and digital media management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, books and films, intellectual property, social media, visual literacy and rhetoric
Tags: Auteur > Auteur Theory > author-function > Barthes > Cahiers du Cinéma > LLMs

Spreadsheet Knowledge and Production of the “Modern Fact”

Posted on | October 25, 2025 | No Comments

Mary Poovey’s A History of the Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society is one of my favorite books on the rise of the modern economy. A professor of English and director of the Institute for the History of the Production of Knowledge at New York University, she pursued how numerical representation became the preferred vehicle for generating useful “facts” in accounting and other business and financial knowledge.[1]

This post strives to apply Poovey’s insights to the rise of digital spreadsheets and the development of human-machine knowledge by examining how these technologies replicate, extend, and complicate the historical trends she identifies. The digital spreadsheet has been instrumental in shaping modern meaning-making practices by creating a seamless, interactive environment where humans and machines collaborate in the production and interpretation of data. Furthermore, this collaboration has been instrumental in forming a global economy and social organization based on networked spreadsheet rationality.

“Spreadsheet capitalism” is a term mentioned to me by one of my mentors in graduate school and refers to an economic system in which financial models, data analysis, and algorithmic decision-making, often facilitated by digital spreadsheets and related software, become the primary drivers of economic activity, social organization, and value creation.[2] Its origins lie in the increasing financialization of economies, the widespread adoption of personal computers and digital tools, and the belief in data-driven and gridmatic objectivity.[3]

Poovey’s book provides a critical background and framework for understanding how certain forms of knowledge became authoritative and seemingly “objective.” She argued that the rise of double-entry bookkeeping and statistical sciences in the early modern period was not merely a technical advancement but a profound epistemological shift. These systems created a new way of seeing and organizing the world through numerical representation, presenting complex realities as quantifiable and manageable facts.

Poovey emphasizes that this process involved a conflation of descriptive accuracy in emerging accounting practices with moral rectitude and truth, giving numerical data an impression of neutrality and unquestionable authority. And in the process, establishing the authenticity of merchant commerce.

Poovey’s analysis of double-entry bookkeeping highlights its role in standardizing economic information, fostering a sense of accuracy and accountability, and enabling the aggregation of individual transactions into a coherent, verifiable whole. The digital spreadsheet, in many ways, is the direct descendant of this legacy, but with exponentially increased power, interactivity, and reach. This power comes through automation, accessibility, the creation of objectivity, and the production of acceptable facts.

Just as double-entry bookkeeping streamlined manual accounting, digital spreadsheets automated formulaic calculations and data organization, making complex financial and statistical analysis accessible to a much broader audience beyond trained accountants. This accessibility has democratized data manipulation, allowing individuals and small businesses to generate reports and models that previously required specialized expertise.

Spreadsheets, like their analog predecessors, present data in a clean, tabular format that visually reinforces a framing of order, precision, and objectivity. The grid structure and instant calculation updates give the impression that the numbers are “just there,” reflecting reality without bias. However, the data entered, the formulas chosen, and the interpretations drawn are still products of human decision and are subject to potential error or bias.

Poovey argues that facts are not simply discovered but are produced through specific methodological and representational choices. Spreadsheets vividly demonstrate this, as users actively construct “facts” by inputting raw data in lists and categories, applying formulas, and structuring tabular information. The “fact” within a spreadsheet is a result of this human-machine collaboration where the spreadsheet is both the receptor and shaper of knowledge.

Summary

This blog post uses Poovey’s (1998) A History of the Modern Fact, as a framework for understanding the rise of spreadsheet capitalism. It explains that Poovey’s work shows how early modern representational practices such as double-entry bookkeeping created a new kind of knowledge: the quantifiable “fact.” This process gave structured (lists, tables, cells) numerical data an aura of objective truth and moral authority, laying the epistemological groundwork for the modern economy.

In the globalized, financialized economy of today, digital spreadsheets and their more advanced progeny (algorithmic trading platforms such as Bloomberg and Wind, AI-driven analytics) are the direct descendants of what Poovey calls the “modern fact.” They don’t just record economic activity; they actively constitute it. Spreadsheet capitalism operates by taking these quantifiable facts, often collected through networks, and generated by highly abstract financial models, and using them as the basis for global economic decisions.

The post emphasizes that spreadsheets are not neutral tools. Instead, they are digital environments where humans and machines collaborate to actively construct facts through the selection and use of data, placement, and formulas. The post concludes that spreadsheet capitalism is the modern culmination of the historical shift Poovey described. Digital tools don’t just record economic activity; they actively constitute and shape it by generating abstract, quantifiable facts that are used in formulas and functions to facilitate global economic decisions and capital accumulation.

Citation APA (7th Edition)

Pennings, A.J. (2025, Oct 25) Spreadsheet Knowledge and Production of the “Modern Fact”. apennings.com https://apennings.com/technologies-of-meaning/spreadsheet-knowledge-and-production-of-the-modern-fact/

Notes

[1] Poovey, M. (1998). A History of Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society. University of Chicago Press.
[2] Majid Tehranian, a Harvard trained political economist and author of Technologies of Power (1992), was a member of both my MA and PhD committees. He guided my MA direction by suggesting that I focus on deregulation of finance and telecommunications. At some point he mentioned the term “spreadsheet capitalism” and it stuck with me.
[3] The post defines spreadsheet capitalism as an economic system where financial models and algorithmic decision-making, powered by spreadsheets, become the primary drivers of value and social organization. The digital spreadsheet is presented as the direct and far more powerful successor to the bookkeeping systems Poovey analyzed.

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© ALL RIGHTS RESERVED

Not to be considered financial advice.

---

Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and holds a joint position as a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, books and films, data analytics and meaning, democratic political economies, digital spreadsheets, enabling frameworks, enterprise systems, financial news, financial technology, meaning makers, public administration, technologies of meaning
Tags: A History of the Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society > Double-entry bookkeeping > lists > Mary Poovey > Spreadsheets

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