Anthony J. Pennings, PhD

WRITINGS ON AI POLICY, DIGITAL ECONOMICS, ENERGY STRATEGIES, AND GLOBAL E-COMMERCE

Remediation and Spreadsheet Logic: Energetic Interpretants to Organizational Structures

Posted on | June 9, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Jun 09) Remediation and Spreadsheet Logic: Energetic Interpretants to Organizational Structures. apennings.com https://apennings.com/technologies-of-meaning/remediation-and-spreadsheet-logic-part-five-energetic-interpretants-to-organizational-structures/

Introduction

This essay positions Rose and Pennings (2022) as the bridge between Peirce’s semiosis and (my) Pennings’ theorization of operative mediation, showing how spreadsheet-generated energetic interpretants become institutionalized organizational structures.[1]

One of the challenges in extending media theory into organizational analysis is explaining how representations become actions. Marshall McLuhan’s work focused on how media extend human capabilities. Jay David Bolter and Richard Grusin (1999) showed how new media continually refashion older media through the dual logics of transparent immediacy and hypermediacy.[2] Stuart Hall (1980) demonstrated that meaning remains contested and negotiated rather than fixed. Yet these approaches leave a crucial question unanswered.

How do meanings generated within spreadsheet media systems become the decisions, routines, and institutional structures that govern organizational life?

My concept of operative mediation provides an important step toward answering this question. Operative mediation extends remediation by focusing on how media organize action. Rather than merely presenting reality, operative media structure pathways through which signs become decisions, interventions, transactions, and behaviors. However, to fully understand how operative mediation functions within organizations, it is useful to combine it with Charles Sanders Peirce’s theory of semiosis and the structuration framework developed by Rose and Pennings (2022).

Peirce’s theory begins with the idea that meaning is not contained within a sign itself. Meaning emerges through a triadic relationship among a sign, its object, and an interpretant. A sign stands for something in the world, but its significance is realized only when it generates an interpretant within an observer. Peirce argued that interpretants can take several forms. Some are emotional, producing feelings or impressions. Others are logical, generating habits or rules of thought. Most important for organizational analysis is the energetic interpretant, which produces effort, response, or action.

Consider a familiar organizational example. A dashboard reports that inventory levels have fallen below a predetermined threshold. The dashboard entry functions as a sign. The inventory shortage is the object to which the sign refers. A manager interprets the sign as indicating a supply-chain risk. The resulting energetic interpretant is a decision to place an emergency order. Meaning has become action.

From a Peircean perspective, the process could end there. The sign has generated an interpretant, and the interpretant has produced behavior. Yet organizations are not merely collections of isolated actions. They are systems of recurring practices, routines, and structures. This is where Rose and Pennings’ application of Anthony Giddens’ structuration theory becomes especially valuable.

Giddens (1984) argued that social structures are neither external forces imposed on individuals nor merely products of individual intention. Instead, structures are continually reproduced through human action. This is the crucial theme of the duality of structure, which points out that people draw upon existing rules and resources when they act, and that those actions simultaneously reproduce the very structures that guide them.

Rose and Pennings (2022) apply this insight to spreadsheets and organizational technologies. Their central argument is that spreadsheets should not be viewed as neutral information tools. Rather, they function as what Orlikowski in 1992 called structurational technologies. They contain embedded assumptions about categories, measurements, priorities, relationships, and decision rules. These assumptions shape organizational behavior, but they also become reinforced through repeated use.

Viewed through this lens, the energetic interpretant occupies a crucial position within the structuration process. Each time a spreadsheet-generated metric leads to a decision, that decision contributes to the reproduction of organizational structures. A budget variance triggers a cost-reduction initiative. A performance indicator triggers a personnel review. A forecast triggers an investment decision. Each action reinforces the categories, assumptions, and relationships embedded within the spreadsheet itself.

The sequence therefore, extends beyond Peirce’s original formulation:

Sign -> Interpretant -> Energetic Interpretant -> Action -> Routine -> Structure

This expanded sequence reveals how operative mediation functions within organizations. Operative mediation does not merely generate action; it generates recurring actions that become institutionalized. The spreadsheet organizes signs into pathways that repeatedly channel interpretation toward specific forms of behavior. Over time, these behaviors become routines, and routines become organizational structures.

This perspective also deepens Stuart Hall’s observations about meaning. Hall emphasized that meaning remains contested and negotiable. Organizations, however, cannot function if every interpretation remains permanently open. They require mechanisms that stabilize meaning sufficiently for coordinated action. Spreadsheet Logic provides such a mechanism. Categories, formulas, tables, and dashboards do not eliminate ambiguity, but they narrow interpretive possibilities. They establish preferred readings that are repeatedly reinforced through organizational practice.

A category such as “customer profitability” illustrates this process. Initially, it is simply one possible way of understanding customer relationships. Once embedded within a spreadsheet, however, it becomes a measurable variable. Managers begin making decisions based upon it. Reports incorporate it. Incentive systems reward it. Strategic plans reference it. Through repeated action, the category becomes institutionalized. What began as a sign becomes part of organizational reality.

The significance of this process increases when viewed through the broader framework of Substitution-Abstraction-Symbolic Computation-Telecom Synchronization (SACT). SACT emphasizes the role of telecommunications in synchronizing organizational activity across time and space. Spreadsheet Logic stabilizes meaning. Telecommunications synchronize meaning. Together, they enable distributed organizations to coordinate action across geographically dispersed networks.

When a dashboard updates simultaneously for managers in New York, Singapore, and London, each manager encounters the same signs at roughly the same time. Similar interpretants emerge. Similar actions follow. Structuration itself becomes synchronized across organizational space. The organization reproduces its structures not solely through local interaction but also through globally distributed systems of spreadsheet-mediated semiosis.

This perspective also extends Anthony Giddens’ concept of time-space distanciation and David Harvey’s concept of time-space compression. Modern organizations increasingly operate across global networks characterized by rapid information flows and compressed decision cycles. Under these conditions, the challenge is not simply transmitting information but converting information into coordinated action. Spreadsheet Logic and operative mediation solve this problem by compressing interpretation. Categories, formulas, and dashboards pre-structure meaning so that energetic interpretants can emerge rapidly and consistently across organizational networks.

The rise of artificial intelligence further intensifies these dynamics. Traditionally, organizational action followed a sequence in which signs generated human interpretants, which in turn led to human decisions. Increasingly, signs generate computational interpretants that trigger automated actions. Inventory thresholds initiate purchase orders. Risk scores trigger interventions. Predictive models allocate resources. The spreadsheet’s underlying logic persists, but energetic interpretants are increasingly delegated to algorithmic systems.

From this perspective, Rose and Pennings’ theory of spreadsheet structuration and Pennings’ concept of operative mediation are highly complementary. Operative mediation explains how representations become actions through the generation of energetic interpretants. Rose and Pennings explain how those actions become routines and how routines become organizational structures. Together, they reveal spreadsheets as socio-technical systems that do far more than represent reality. They organize semiosis, channel behavior, reproduce institutional arrangements, and increasingly coordinate action across globally synchronized networks.

The spreadsheet is therefore not merely a calculation tool, nor simply a medium of representation. It is a structurational infrastructure of meaning. Through operative mediation, it transforms signs into energetic interpretants. Through organizational practice, those interpretants become routines. Through structuration, those routines become enduring organizational realities. In this sense, modern organizations increasingly govern themselves through the recursive interaction of signs, calculations, actions, and technologies. This process lies at the heart of Spreadsheet Logic and the SACT framework.

References

Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
Giddens, A. (1983) A Contemporary Critique of Historical Materialism.
(Berkeley, CA: University of California Press). p 117.
Giddens, A. (1983) The Nation-State and Violence. (Berkeley, CA:
University of California Press).
Giddens, A. (1984). The Constitution of Society. University of California Press.
Hall, S. (1980). Encoding/decoding. In S. Hall, D. Hobson, A. Lowe, & P. Willis (Eds.), Culture, media, language (pp. 128–138). Hutchinson.
Harvey, D. (1989). The Condition of Postmodernity: An Enquiry into the Origins of Cultural Change. Blackwell.
Peirce, C. S. (1931–1958). Collected Papers of Charles Sanders Peirce (C. Hartshorne, P. Weiss, & A. Burks, Eds.). Harvard University Press.
Pennings, A.J. (2025, Sep 03) Peirce and Derrida on the Logic and Power of Spreadsheets and Dashboards. apennings.com https://apennings.com/technologies-of-meaning/peirce-and-derrida-on-the-logic-and-power-of-spreadsheets-and-dashboards/
Rose, P. A., & Pennings, A. J. (2022). Knowledge, Decisions, and Norms: A Framework for Studying the Structuration of Spreadsheets in Social Organizations. Information, 13(2), 46. https://doi.org/10.3390/info13020046

Notes

[1] My first inquiry into Peircian semoitics came in research about Substitution and become a key component of the SACT analysis. Pennings, A.J. (2025, Sep 03) Peirce and Derrida on the Logic and Power of Spreadsheets and Dashboards. apennings.com https://apennings.com/technologies-of-meaning/peirce-and-derrida-on-the-logic-and-power-of-spreadsheets-and-dashboards/
[2] I remain eternally grateful for the research by Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
Prompt(s) Operative mediation extends Bolter and Grusin’s remediation by explaining how representations become actions. Drawing upon Peirce’s theory of semiosis, operative mediation organizes signs into pathways that generate energetic interpretants that trigger decisions, interventions, transactions, and behaviors. How does this fit into Rose and Pennings (2022) analysis of spreadsheets in organizations?

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

Renewables Give Us Power, Batteries Give Us Control

Posted on | June 7, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Jun 07) Renewables Give Us Power, Batteries Give Us Control. apennings.com https://apennings.com/digital-geography/renewables-give-us-power-batteries-give-us-control/

Introduction

Energy is a major focus of my classes, especially ICT for Sustainable Development, but even Engineering Economics. Renewables are now becoming a major threat to hydrocarbon combustion. Oil is an amazing extractive commodity. It packs a lot of energy, is easily moved, and distills into several amazing products. But oil’s future as an electricity producing chemical is limited.[1]

We spend a lot of time looking at massive wind turbines and fields of solar panels, but the reality is that the weather is intermittent—the sun sets, and the wind stops blowing. Without a way to store that power, renewable energy is like a grocery store that only has food when the delivery truck is parked outside. Batteries are the missing link that transforms intermittent renewable energy into a reliable, 24/7 baseload power source. They are effectively rewriting the rules of the global grid and transportation.[2]

What Are These Batteries?

The revolution is driven by two highly specialized classes of large-scale battery technology.

Battery Energy Storage Systems (BESS)

These are utility-scale giants. They look like massive rows of shipping containers parked next to electrical substations. Instead of powering a single machine, they store hundreds of megawatts of electricity directly from the grid.

Electric Vehicle (EV) Battery Packs

These are the structural foundations of modern electric cars, trucks, and buses. Unlike batteries for electronics, EV packs are engineered for extreme thermal management, structural safety, and high power output.

Chemically, the undisputed king of this revolution is Lithium Iron Phosphate (LFP) technology. While early EVs and smartphones relied heavily on Nickel Manganese Cobalt (NMC) chemistries because they could hold more energy in a tiny space, LFP has taken over the grid and mid-range vehicles. LFP batteries don’t use expensive, controversial materials like cobalt, they are significantly less prone to catching fire, and they can be charged and discharged thousands of times over decades without degrading.

Who Makes Them?

The battery manufacturing landscape is a highly concentrated, geopolitically tense oligopoly. Building these batteries requires immense capital, hyper-advanced automation, and absolute control over chemical supply chains.

A handful of mega-companies dominate global production of the new batteries.

CATL (China) is the undisputed heavyweight champion of the battery world. Controlling nearly 40% of the global energy storage market, this Chinese giant supplies cells to almost everyone, from Tesla and BMW to massive regional power grids. BYD (China) is famous both as an EV automaker and a battery pioneer. Their proprietary “Blade Battery” (an ultra-safe, dense LFP design) is widely considered the gold standard for vehicle integration. BYD is heavily vertically integrated, meaning it mines its own minerals and builds its own chips, allowing it to undercut competitors worldwide.

LG Energy Solution & Samsung SDI (South Korea) are the primary democratic counterparts to Chinese dominance. They operate massive “gigafactories” in the US and Europe, serving as the primary suppliers for domestic Western automakers and data centers looking to comply with local sourcing laws.

While Tesla (USA) buys a massive number of cells from CATL and BYD, they are also a tier-one integrator. Their “Megapack” (a massive utility-scale battery container) has become the go-to choice for Western energy companies looking to stabilize their power grids.

If Tesla’s electric vehicles are the company’s public face, the Megapack is its financial and infrastructure powerhouse. It is a utility-scale, containerized battery system designed specifically to stabilize electrical grids, integrate massive renewable energy projects, and replace fossil-fuel peaker plants.

A Megapack is not just a bundle of battery cells; it is an all-in-one, turnkey energy micro-station. Delivered on a flatbed semi-truck, each unit arrives fully assembled, pre-tested, and ready to plug directly into the grid.

Tesla’s Master Plan 3 laid out the possibilities of moving to a global sustainable electric economy.

What Do They Actually Do?

To understand how batteries revolutionized the transition, you have to look at the crucial, behind-the-scenes jobs they perform on the electrical grid every second.

“Time-Shifting” Energy (The Duck Curve)

Solar power peaks at noon, but human electricity demand peaks between 5 PM and 9 PM when everyone gets home from work. This mismatch creates an infamous structural problem for utilities called the “duck curve.” Massive battery banks solve this by sucking up cheap, excess solar power during the day and “time-shifting” it—releasing it back into the grid at night when demand spikes.

Grid Stabilization (Frequency Response)

The electrical grid is an incredibly delicate machine that must maintain a steady frequency of 60Hz (or 50Hz in Europe). If a traditional power plant suddenly goes offline, the grid’s frequency drops, potentially causing catastrophic blackouts. Historically, utilities kept fossil-fuel “peaker plants” running on idle to step in during emergencies. Batteries can inject gigawatts of power into the grid in a fraction of a millisecond—faster than the blink of an eye—perfectly balancing the grid without burning a drop of oil or gas.

Unlocking the AI and Automation Boom

The sudden explosion of AI data centers and autonomous manufacturing facilities has broken standard utility projections. These facilities require massive, uninterrupted baseload power that old grids simply can’t handle. Large-scale battery installations are being deployed directly alongside data centers, serving as a structural shield that keeps the facilities running continuously without overloading the local power grid.

In short, renewables give us clean energy, but batteries give us control. They turn a chaotic mix of weather-dependent generation into a highly precise, software-managed machine.

Summary

While wind and solar generate clean energy, their intermittency requires robust storage to create a dependable, 24/7 power grid. Large-scale batteries—specifically Battery Energy Storage Systems (BESS) for the grid and Electric Vehicle (EV) battery packs—serve as the foundational link in this energy transition. Chemically, Cobalt-free Lithium Iron Phosphate (LFP) technology has become the industry standard due to its safety, durability, and cost-effectiveness.

Production of batteries governed by a highly concentrated, geopolitically sensitive oligopoly dominated by Chinese titans (CATL and BYD), South Korean manufacturers (LG Energy Solution and Samsung SDI), and the American integrator Tesla. Mechanically, these batteries stabilize the infrastructure by “time-shifting” peak solar power to match evening demand (flattening the “duck curve”), providing instantaneous millisecond grid stabilization to prevent blackouts, and shielding localized grids from the massive power demands of the expanding AI data center boom. Ultimately, while renewables provide the power, batteries provide the control.

Notes

[1] Pennings, A.J. (2023, Sept 29). ICTs for SDG 7: Twelve Ways Digital Technologies can Support Energy Access for All. apennings.com https://apennings.com/science-and-technology-studies/icts-for-sdg-7-twelve-ways-digital-technologies-can-support-energy-access-for-all/
[2] Pennings, A.J. (2021, Mar 15) Will Offshore Wind Power Print Money?. apennings.com https://apennings.com/digital-media-economics/will-offshore-wind-power-print-money/
Prompt(s) We talk a lot about renewables but its batteries that have revolutionized the transition. What are these batteries, who makes them, and what do they do?

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

Remediation and Spreadsheet Logic: Meaning and Certainty Through Hypermediacy?

Posted on | June 6, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, June 06) Remediation and Spreadsheet Logic: Meaning and Certainty Through Hypermediacy.apennings.com https://apennings.com/technologies-of-meaning/remediation-and-spreadsheet-logic-meaning-and-certainty-through-hypermediacy/

Introduction

This post continues my examination of the capabilities of spreadsheet logic and its impact on modern society and its organizations. I do not ask what mistakes it makes, although that would be a valid concern.[1] It asks how spreadsheets perform productivity in a way that produces coherence and certainty in a messy world?

Remediation is “the formal logic by which new media refashion prior media forms.”[2] Bolter and Grusin argue that part of that logic is hypermediacy, which foregrounds mediation. Multiple windows, dashboards, menus, hyperlinks, notifications, and interfaces keep the user constantly aware of the media environment. In what ways does hypermediacy provide new ways to access and represent reality? How does it help organize and “fix” meaning? Given their argument for hypermediacy, how does the spreadsheet combine various media? And what kind of authenticity or certainty does it create through its operational capacities?

These questions attempt to get to the heart of why Spreadsheet Logic can be understood as an extension of remediation rather than merely another example of it. Bolter and Grusin (2000) often present hypermediacy as the opposite of transparent immediacy. If transparent immediacy seeks to erase the medium and create the illusion of direct access to reality, hypermediacy foregrounds mediation by multiplying representations. Yet hypermediacy is not simply a celebration of complexity. It is also a strategy for producing a different kind of knowledge and certainty.

The key insight is that hypermediacy seeks to address the limitations of any single representation by surrounding it and connecting it to other representations.

Hypermediacy as Epistemological Compensation

Bolter and Grusin argue that hypermediacy offers access to reality through the accumulation of various remediated technologies. A single photograph provides one perspective. A website provides images, text, video, hyperlinks, maps, dashboards, and interactive elements. The assumption is that reality is too complex to be adequately represented by a single medium, so media is “healed” through multiple perspectives.

Hypermediacy, therefore, seeks authenticity through multiplicity. The logic becomes “If one representation is insufficient, then many representations together will provide a richer and more reliable understanding.” A financial news channel like CNBC does not merely show gold and stock prices. It presents an array of representations, including the immediate transparency of the anchors and guests, some of whom are remote and may be shown in a split-screen. Live programming ties the viewer into a stream of continuous media, often splitting the screen into windows of indexical information gathered from various data sources. Reality appears more accessible because it is represented from multiple logics and angles simultaneously.

Hypermediacy thus provides a new epistemology. Truth emerges not from a single window but from the coordination of many windows.

Hypermediacy and the Organization of Meaning

Meaning-making is where Stuart Hall becomes relevant. If meaning is inherently unstable and subject to multiple interpretations, hypermediacy can be viewed as an attempt to manage that instability through complexity. Multiple representations do not eliminate ambiguity. However, they can constrain it. For example, a news article may be accompanied by photographs, charts, maps, timelines, and expert commentary to solidify the intended meaning.

Together these elements encourage particular interpretations while discouraging others. Meaning becomes organized through the relationship among representations. Hypermediacy, therefore, performs a subtle form of meaning stabilization. It does not completely fix meaning. But it narrows the range of plausible interpretations.
The user encounters not one representation but an entire grid system of mutually reinforcing representations.

The Spreadsheet as Hypermediated Environment

The spreadsheet may be one of the purest examples of hypermediacy ever created. Consider what appears on a typical spreadsheet screen:

text labels,
numerical values,
tables,
formulas,
charts,
pivot tables,
color coding,
comments,
dashboards,
external data feeds,
worksheets,
macros.

Each element represents reality differently. A sales figure appears as a number. A trend appears as a chart. A calculation appears as a formula. A category appears as a label. A relationship appears as a cell reference. The spreadsheet, therefore, combines multiple media forms within a single operational environment.

From Bolter and Grusin’s perspective, the spreadsheet is a massive act of remediation.

The Spreadsheet as a Convergence Medium

Identification of the spreadsheet’s five remediated forms makes this even clearer. The spreadsheet combines:

Writing – Text defines list items and labels that identify categories and concepts.

Lists – Sequential records, organize information.
Tables – Rows and columns create relational structures.
Cells – Discrete addressable units isolate meaning.
Formulas – Logical and mathematical operations establish relationships and return structured results.

Each of these existed long before computers. The spreadsheet refashions them into a unified computational medium. The spreadsheet is therefore not one medium. It is a convergence medium that incorporates multiple historical systems of representation.

Why the Spreadsheet Creates Stronger Certainty

However, the spreadsheet introduces something that hypermediacy alone does not explain. The various representations are not merely displayed together, although that often supplies a strong visual understanding. They are computationally linked. A change in one cell automatically updates formulas, charts, dashboards, and reports within a single computer, or through telecommunications synchronization with a network of linked spreadsheets.

The representations reinforce each other dynamically. This recursion creates a powerful sense of certainty. A manager sees a number, a table, a chart, a forecast. All appear consistent. The agreement among representations creates trust.

The user concludes, “The data must be correct because every representation tells the same story.” This summation is an important epistemological shift. Hypermediacy creates confidence through computational consistency and multiplicity of representation.

From Authenticity to Coherence

Bolter and Grusin primarily discuss authenticity in media. Remediation works to provide richer access to reality. The spreadsheet produces something slightly different. Its authority comes from coherence. Users trust spreadsheets not because they directly represent reality but because their internal representations agree with one another.

The spreadsheet creates what might be called computational coherence.
A chart confirms a table. A dashboard confirms a formula. A forecast confirms historical data. The resulting consistency generates organizational confidence. This is not authenticity in the photographic sense. It is authenticity through calculative agreement.

Fixing Meaning Through Structure

Hall would likely note that spreadsheets accomplish something unusual. Instead of persuading users to adopt preferred meanings, spreadsheets embed preferred meanings into technical structures. Categories define what counts as relevant. Tables define relationships. Formulas define causal assumptions. Dashboards define priorities.

The spreadsheet’s hypermediated environment therefore does not merely represent reality. It organizes interpretation. Meaning becomes structured before the user even begins analysis. The spreadsheet narrows interpretive possibilities through architecture.

Hypermediacy Across Time and Space

The significance becomes even greater when viewed through the theoretical gaze of Anthony Giddens and David Harvey. Organizations store information over time. They also operate across global networks, requiring the retrieval of shared systems of data and interpretation.

The spreadsheet’s hypermediated structure provides storage and synchronization. A manager in London and a manager in Singapore can pull up and view the same tables, the same formulas, the same charts, the same categories – even at the same time. Meaning becomes synchronized across distance. The spreadsheet, therefore, transforms hypermediacy into a technology of time-space coordination. Its multiple representations and connections allow actors separated by geography to share a common understanding of organizational reality.

The Authenticity of Spreadsheet Performance

The most important conclusion is that the spreadsheet creates a distinctive form of authenticity. A photograph promises “This is what happened.” A virtual reality system promises, “This is what it feels like.” A spreadsheet promises, “This is how reality is organized.”
Those promises are enormously powerful.

The spreadsheet does not claim direct access to reality. It claims direct access to the relationships that govern reality. Its authenticity derives from three sources:

– Representational multiplicity
– Computational consistency
– Organizational synchronization

Together, they create a new form of certainty. The user was convinced not because the spreadsheet looks realistic in the sense that a photograph reflects a scene. The user is convinced because the spreadsheet’s categories, tables, formulas, charts, and calculations all align to produce a structured, coherent account of reality.

The spreadsheet thus extends Bolter and Grusin‘s hypermediacy beyond representation and into organization. It is a hypermediated environment that not only displays reality through multiple media forms but also fixes, calculates, and performs meaning. In doing so, it becomes a central infrastructure through which modern organizations create certainty in a world characterized by contested meanings, time-space distanciation, and accelerating time-space compression.

Notes

[1] Ray Panko at the University of Hawaii did excellent research on spreadsheet mistakes.
[2] 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).
Prompt(s)

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

Beyond the China Price: Can Democrats Shape a New Industrial Strategy for the AI World?

Posted on | June 3, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Jun 03) Beyond the China Price: Can Democrats Shape a New Industrial Strategy for the AI World? apennings.com https://apennings.com/global-e-commerce/beyond-the-china-price-can-democrats-shape-industrial-policy-for-a-new-globalization-strategy/

Introduction

China is changing and slowly going away. The Democrats need to do something about it. Democrats need to stop relying on billionaires for industrial policy. They need to define an agenda for labor in an AI world, materials in a high-tech world, and democracy in a social media/behavioral modification world.

For nearly four decades, the global economy benefited from what became known as the “China price,” the ability to manufacture vast quantities of consumer goods at remarkably low cost. China’s combination of abundant labor, export-oriented infrastructure, industrial clustering, and relatively inexpensive energy enabled multinational corporations to build supply chains that delivered affordable products to consumers around the world. The result was an era of low inflation, rising consumer purchasing power, and unprecedented access to manufactured goods.

Today, however, many of the conditions that made this model possible are changing. Rising tariffs, geopolitical tensions, demographic decline, increasing labor costs, and higher energy prices have led many observers to argue that the age of cheap Chinese goods is coming to an end. If that assessment proves correct, the United States faces an important challenge. It must ask how can it continue to provide affordable consumer goods without relying on the same global labor arbitrage that defined the first era of globalization?

The answer will not be found simply by moving factories back to American soil. Rather, it will emerge through a profound transformation in how goods are produced, distributed, and consumed. New trade relationships, artificial intelligence, additive manufacturing, advanced materials, and automated production systems are collectively creating the foundations of a new economic model—one in which computation, energy, and machine intelligence increasingly replace low-cost labor as the primary drivers of manufacturing competitiveness.[1]

The first response to the erosion of China’s manufacturing dominance has been the search for alternative production locations. Countries such as Vietnam, India, Indonesia, and Mexico have become increasingly attractive destinations for manufacturers seeking lower labor costs and reduced geopolitical risk. Yet replacing China is not as simple as relocating production. China did not become the world’s factory merely because wages were low. It developed an extraordinarily sophisticated industrial economy composed of suppliers, logistics networks, ports, engineering talent, and manufacturing expertise that emerged over several decades.

No single country currently offers the same combination of scale and industrial depth. As a result, many companies are discovering that diversification rather than replacement is the more realistic strategy. Production is spreading across multiple countries, creating more resilient supply chains but often at higher cost.

This raises an important question. If labor costs are increasing everywhere, how can manufacturers continue to produce inexpensive goods? The answer increasingly lies in recognizing the diminishing importance of labor itself. Not creativity and organization, but the reliance on the human body as a major source of energy and site of productivity.[2] The original globalization model depended on moving production to places where workers were inexpensive. The emerging model depends on transforming production through automation so that labor becomes a smaller component of total costs. In this sense, artificial intelligence may become the successor to labor arbitrage.

AI-enabled manufacturing systems can coordinate production schedules, monitor quality, predict equipment failures, optimize inventories, and continuously improve factory performance. Rather than employing large numbers of workers to supervise industrial processes, manufacturers can increasingly rely on software systems that analyze enormous volumes of operational data in real time. This transformation shifts competition away from wages and toward computational efficiency.

The implications are profound. For much of modern economic history, nations competed by offering cheaper labor. In the future, nations may compete by offering cheaper computation and more abundant energy. Manufacturing competitiveness may become less dependent on labor markets and more dependent on access to advanced digital infrastructure, data centers, semiconductor technologies, and electricity.

This transformation is already visible in the rise of highly automated factories. The long-discussed concept of the “lights-out factorym,” a production facility capable of operating with minimal human intervention, is gradually becoming a reality. While complete automation remains rare, many advanced manufacturing facilities already rely heavily on robotics, machine vision systems, and AI-driven process management. In such environments, labor costs become a much smaller factor in determining the final cost of production.

At the same time, additive manufacturing, commonly known as 3-D printing, is challenging some of the basic assumptions that have governed global supply chains for decades. Traditional manufacturing relies on economies of scale. Companies invest heavily in tooling, produce large batches of products, ship them across oceans, and store them in warehouses before they reach consumers. This model rewards centralized production and extensive logistics networks.

Additive manufacturing offers a different approach. Instead of producing goods in massive quantities at distant locations, digital designs can be transmitted electronically and manufactured closer to the point of consumption. Products become information before they become physical objects. In this sense, manufacturing begins to resemble software distribution.

While 3-D printing is unlikely to replace conventional mass production entirely, it has the potential to transform significant sectors of the economy. Spare parts, medical devices, aerospace components, customized consumer products, and industrial tools can increasingly be produced on demand rather than stored in inventory. As a result, transportation costs, warehousing requirements, and supply chain complexity can all be reduced.

The broader significance of additive manufacturing lies in its potential to shorten supply chains. For decades, globalization sought to minimize production costs by extending supply chains across continents. Emerging technologies may achieve similar cost reductions by compressing supply chains and producing goods closer to where they are needed.

Nanotechnology introduces another dimension to this transformation by changing the material foundations of manufacturing itself. Throughout industrial history, productivity improvements have often resulted from better machines. In the future, productivity improvements may increasingly result from better materials.

Advanced nanomaterials can create products that are stronger, lighter, more durable, and more energy efficient than conventional alternatives. New battery chemistries may lower energy costs. Lightweight composites may reduce transportation expenses. Self-healing materials may extend product lifespans. Programmable materials may eventually enable entirely new manufacturing processes.

These developments matter because they reduce costs independently of labor. If products require fewer raw materials, consume less energy, last longer, and require fewer replacement cycles, affordability can be maintained even when wages rise.

Energy itself is becoming a central factor in manufacturing competitiveness. Industrial production in the twentieth century was often constrained by labor availability. Twenty-first-century production may be constrained by access to reliable and inexpensive electricity. Artificial intelligence systems, robotics, semiconductor fabrication plants, and advanced manufacturing facilities all require substantial amounts of energy.

This reality creates new opportunities for the United States. Unlike many advanced economies, the United States possesses significant energy resources, including natural gas, nuclear capacity, solar generation, and wind power. As production becomes more automated, access to low-cost energy may become more important than access to low-cost labor.

This shift helps explain why investment in semiconductor fabrication, data centers, and advanced manufacturing increasingly focuses on regions with abundant electricity. In a highly automated economy, energy effectively becomes a substitute for labor. The nation that can generate and distribute electricity efficiently may gain advantages similar to those once enjoyed by countries with large supplies of inexpensive workers.

At the same time, the United States is likely to deepen economic integration within North America. Mexico is particularly well positioned to become a critical manufacturing partner, combining lower labor costs with geographic proximity and established industrial capabilities. Rather than relying on trans-Pacific supply chains, firms can increasingly organize production across a continental manufacturing system in which design, engineering, software development, and advanced technologies remain concentrated in the United States while selected manufacturing activities occur throughout North America.

Such arrangements do not eliminate globalization; they reorganize it. Production networks become shorter, more resilient, and less dependent on geopolitical rivals. The objective is not autarky but strategic interdependence among trusted partners.

What emerges from these developments is not simply a new manufacturing strategy but a new economic reality. The original era of globalization was built upon the equation of cheap labor plus global shipping. The emerging model is based on a different formula. It combines cheap energy, artificial intelligence, advanced automation, digital design, and smart materials.

In this new environment, goods remain affordable not because millions of workers are paid low wages but because production systems become extraordinarily efficient. Algorithms optimize factory operations. Robots perform repetitive tasks. Additive manufacturing reduces inventory and transportation costs. Nanotechnology improves material performance. Energy replaces labor as a central production input.

From the perspective of digital disruption, this transition represents a movement from labor-intensive globalization toward what might be called computational production. Just as the information economy transformed communication by converting media into digital information, advanced manufacturing is transforming production by converting industrial processes into computational systems.

The countries that successfully integrate artificial intelligence, automation, additive manufacturing, advanced materials, and abundant energy into coherent production ecosystems will shape the next era of economic development. Their competitive advantage will not rest primarily on low wages but on their ability to transform information into physical goods with unprecedented efficiency.

The age of cheap Chinese labor may be ending, but the age of cheap goods is not necessarily ending with it. Rather, the source of affordability is shifting from human labor to machine intelligence. China could easily stay in the game. The next chapter of globalization may therefore be defined not by where factories are located, but by how effectively computation itself becomes a factor of production.

By building an economy where clean energy replaces cheap labor, and sovereign material networks replace fragile global logistics, we can lower consumer costs while strengthening worker power. This strategy positions the Democratic Party as the true champions of working families, delivering an economy built by American ingenuity and owned by the American people.

Notes

[1] I having been teaching Technological Systems Management and ICT for Sustainable Development for the last ten years for SUNY in Korea.
[2] Zuboff’s early writing on work and IT was very instructive. Zuboff, S. (1990) In the Age of the Smart Machine: The Future of Work
and Power. (New York: Basic Books).

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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 management at New York University. His first position was at Victoria University in New Zealand. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Digital Disruption in the Film Industry – Part 5: Generative AI Platforms for Video Synthesis

Posted on | June 1, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Jun 2) Digital Disruption in the Film Industry – Part 5: Generative AI Platforms for Video Synthesis. apennings.com https://apennings.com/media-strategies/digital-disruption-in-the-film-industry-part-5-generative-ai-platforms-for-video-synthesis/

Introduction

It’s the end of the semester, but my TAs and I have been working these days to integrate AI-generated video production into our Visual Rhetoric and IT course.[1] We’ve spent most of the semester learning the vocabulary and grammar of televisual production, and the students have gone from analyzing movies to music videos to television news, and most recently to a larger project analyzing YouTube video projects.[2] Now its time to turn to generative AI video production and the popular platforms. This post is not about the prompting process but more about the industry and the disruption we are seeing.

Generative AI video production uses text and image prompts to initiate the synthesis of high-quality images and motion sequences. This process is drastically accelerating workflows from pre-visualization to final editing. Leading platforms allow creators to animate product shots, generate B-roll, and edit scenes simply by typing instructions.

This series started by recognizing the transition of film to digital video and introduction of new digital cameras. It went on to non-linear digital editing, and new types of special effects (FX). For last year’s course, I wrote a introduction to the technical aspects of prompting and generative video. Now I want to focus on the disruptive and democratizing aspects of generative AI platforms for video production.

AI Robotic Control Room

Early digital video synthesis relied on procedural animation, motion capture, and compositing systems requiring substantial expertise and expensive hardware. Systems in the 1990s and 2000s depended on keyframe animation, CGI rendering pipelines, manual editing, green-screen compositing, and specialized 3D modeling software. These workflows were dominated by large studios such as Pixar, Industrial Light & Magic, and major broadcast networks because rendering and production costs were extremely high.

The emergence of deep learning changed the situation. Convolutional neural networks first improved image recognition and classification, but later generative adversarial networks (GANs) enabled systems to synthesize realistic images and short clips. GAN-based “deepfakes” demonstrated that neural systems could generate convincing human likenesses, lip-syncing, and face replacement. Although controversial, these deepfake systems proved that audiovisual representation itself could become programmable.

The next major transition occurred with transformer architectures and diffusion models. These systems no longer merely manipulated frames; they learned latent representations of movement, lighting, perspective, and narrative continuity from massive video and image datasets.

Generative AI for video is fundamentally disrupting traditional video production by collapsing the industrial structures that historically organized filmmaking, television, advertising, animation, and digital media creation. What once required large crews, expensive equipment, specialized labor, and institutional financing are increasingly accomplished through software interfaces, cloud computing, and natural language prompts. The disruption is not merely technical; it is economic, organizational, aesthetic, and geopolitical.

The Traditional Video Production Model

For most of the twentieth and early twenty-first centuries, professional video production operated through a highly centralized industrial model. Producing film, television, or commercial media required coordination among multiple layers of specialized labor, including screenwriters, directors, cinematographers, lighting technicians, actors, set designers, editors, visual effects artists, sound engineers, and distribution networks. Large capital expenditures were necessary for cameras, lenses, lighting rigs, studio facilities, editing suites, rendering farms, travel, and post-production infrastructure.

This combination of talent and resources made media production dependent on major institutions such as Hollywood studios, television broadcasters, advertising agencies, and streaming platforms. Companies like Disney, Warner Bros. Discovery, and Netflix controlled not only distribution but the means of audiovisual production itself. Generative AI disrupts this vertically integrated structure by automating many of the functions that once justified these institutional hierarchies.

From Cameras to Prompts

The most radical transformation is the shift from optical capture to computational generation. Traditional filmmaking depended on recording physical reality including actors performing, cameras capturing, lights illuminating, sets being constructed. Generative AI systems instead synthesize audiovisual scenes statistically from training data. A creator can now type instructions such as:

“Create a cinematic drone still shot of a futuristic Seoul skyline at sunset with rain reflections.”

The system generates imagery, lighting, and atmosphere without physical locations, actors, weather conditions, or expensive equipment. You can add movement as well, but I’m keeping it simple. Looking at the picture, I should have added realistic as well. But its a nice picture and includes Seoul’s iconic buildings and scenes.

Seoul Skyline

Popular platforms such as
Runway
Pika Labs
Google Vids/Veo
Kling AI
Hugging Face
Meta AI
Qwen AI
Luma AI
OpenAI Sora

increasingly substitute computation for production logistics. This transforms filmmaking from a process of organizing material resources into one of orchestrating generative systems.

Automation of Specialized Labor

Generative AI disrupts traditional production because it automates tasks previously performed by highly trained specialists. AI systems now assist or replace storyboard artists, concept designers, animators, rotoscope technicians, voice actors, translators, editors, compositors, and VFX teams.

For example:

– AI voice systems can synthesize multilingual narration.
– AI editing systems can automatically cut scenes.
– AI motion systems can animate still images.
– AI avatars can replace presenters and actors.
– AI dubbing can localize content globally within minutes.

Companies such as Synthesia and HeyGen already allow corporations to generate spokesperson videos without hiring actors, crews, or studios. This destabilizes long-standing labor structures within film industries, advertising, broadcast television, educational media, and corporate communications. The disruption resembles earlier industrial automation, except that it targets symbolic and creative labor rather than purely manual labor.

The Collapse of Production Costs

Traditional video production involved high fixed costs that acted as barriers to entry. Even low-budget filmmaking required cameras, editing software, actors, lighting, microphones, and physical shooting locations. Generative AI dramatically reduces these costs by converting media production into a cloud service subscription model, which also serves customer captivity.

A small creator with a laptop, Internet access, and a monthly AI subscription can now produce content approaching professional quality. This economic compression is deeply disruptive because it weakens the scarcity model that historically protected established studios and agencies.

The same disruption occurred when desktop publishing weakened print monopolies, digital photography disrupted film processing, and OTT streaming undermined broadcast scheduling. Generative AI now applies similar pressures to the audiovisual sector.

The Rise of Synthetic Production Pipelines

Traditional filmmaking proceeds sequentially from pre-production, shooting, editing, post-production, and distribution. AI collapses these stages into fluid computational workflows. A single creator can generate scripts, create concept art, synthesize voices, generate scenes, edit footage, add music, translate dialogue, and publish globally from one interface. The distinction between production, editing, animation, and distribution becomes increasingly blurred. This creates what might be called a synthetic production pipeline, where media assets are continuously generated, modified, and personalized algorithmically.

Hollywood’s Structural Vulnerability and New Concentrations of Power

Large studios remain powerful because they control intellectual property, franchises, distribution, and financing. But their production advantages are narrowing. Independent creators increasingly gain access to cinematic visual effects, virtual environments, AI actors, automated editing, and synthetic sound design.

The result may resemble what digital music production did to recording studios. Desktop applications started lowering barriers, decentralizing creation, and multiplying competitors. Hollywood is unlikely to disappear, but its industrial dominance is being challenged by distributed computational creativity.

At the same time, generative AI recentralizes power around cloud infrastructure and AI model ownership. Training advanced video systems requires massive GPU clusters, proprietary datasets, cloud computing, and billions in capital expenditure. This strengthens firms such as NVIDIA, Microsoft, Google DeepMind, Amazon Web Services, and Meta AI. Thus the disruption is paradoxical. Production becomes decentralized, but computational infrastructure becomes more centralized. Creators gain expressive power while becoming dependent on platform-owned AI systems.

Disruption of Advertising and Commercial Media

Advertising is especially vulnerable because AI dramatically reduces production time and cost. Traditional commercial production required agencies, creative directors, location shoots, actors, editors, and expensive revisions. AI systems can now generate multiple advertising variants instantly for different demographics, languages, geographic markets, and social media platforms.

Brands increasingly use generative AI for product visualization, synthetic influencers, automated localization, and personalized campaigns. This threatens traditional advertising agencies while favoring data-driven platform companies.

Streaming OTT Platforms and Infinite Content

Generative AI also disrupts streaming economics. Over-the-top (OTT) Platforms such as YouTube, TikTok, and Netflix depend on continuous content production to maintain engagement. AI radically expands content supply by enabling rapid clip generation, automated editing, personalized recommendations, and eventually customized entertainment experiences.

The long-term implication is a transition from mass-produced media,
to dynamically generated personalized media. Instead of millions watching the same film, AI systems may generate individualized narrative experiences in real time. Will people want their own versions? Or do people consume media, at least in part, to participate in a group experience?

Traditional video possessed evidentiary authority because it was linked to photographic recording. AI-generated video weakens this assumption. Deepfakes and synthetic video systems make it increasingly difficult to distinguish recorded events, simulated events, manipulated footage, and entirely generated realities. This disrupts journalism, documentary filmmaking, political communication, legal evidence, and public trust. The authority of the camera declines when images no longer require physical referents.

Computational Cinema and the Future

Generative AI marks the emergence of computational cinema. Media is generated dynamically through probabilistic models rather than mechanically recorded from reality. Future systems will likely enable real-time AI films, interactive cinematic worlds, persistent synthetic actors, personalized streaming narratives, and AI-generated virtual environments. The disruption extends beyond filmmaking into the broader transformation of visual culture itself.

Notes

[1] Shoutout to my TAs, especially Sumin Cho.
[2] Analyzing a YouTube channel is a challenging visual analysis project because of the new innovations and the need to keep the viewers attention without traditional narrative techniques.
Prompt(s) Describe how generative AI for video has evolved and democratized. Rewrite with an emphasis on how it is disrupting traditional video production.

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

The ICT4SD and World Order Assignment

Posted on | May 25, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, May 26) The ICT4SD and World Order Assignment. apennings.com https://apennings.com/technologies-of-meaning/the-ict4sd-and-world-order-assignment/

Note: This is an introduction to a collection of student articles for a pdf book tentatively called ICT4D and the Future of Global Sustainability.

Introduction

A foundational course like EST 230 – Information and Communication Technologies for Sustainable Development (ICT4SD), and increasingly project initiatives such as AI for Good (AI4Good), cannot operate in an ecological, economic, or political vacuum. True mastery of international development assistance requires a grounding in the realities of global power and the organization of the world order. Specifically, understanding the politics of the US dollar global infrastructure (USD), energy (oil and its alternatives), and technological competition is crucial to the unfolding of sustainable abundance and development.

To understand these dynamics, students in the ICT4D specialization of our BS in Technological Systems Management benefit from analyzing different perspectives. They were asked to compare and contrast two highly influential, yet philosophically opposed thinkers: Jeffrey Sachs, a university professor and institutionalist champion of global cooperation, and Peter Zeihan, a geopolitical analyst and structural determinist focused on demographic and geographic realities. Both are very popular keynote speakers and YouTube guests. Both are accomplished writers on geopolitical issues and tensions.

Biographies & Views

Jeffrey D. Sachs is a world-renowned American economist, academic, and public policy analyst. Born in 1954, he earned his BA, MA, and PhD from Harvard University, rising from a first-year student to a tenured full professor in 11 years.[1] In 2002, he moved to the faculty of Columbia University, where he served as the Director of The Earth Institute.[2] He currently serves as the President of the UN Sustainable Development Solutions Network.

Early in his career, Sachs became famous for advising governments in Latin America (such as Bolivia), and later Eastern Europe (Poland), and the former Soviet Union. In the wake of the “Third World Debt Crisis,” these countries experienced severe hyperinflation and economic collapse. The oil crises of the 1970s drove many countries into excessive borrowing from the global Eurodollar markets. The subsequent interest rate hike by Paul Volcker’s Federal Reserve sent interest rates skyrocketing in the 1980s making repayment difficult. Sachs aided macroeconomic stabilization in Latin America and the transitions from Communism to market economies with government cuts and austerity programs that came to be known as “shock therapy.”[1,2] The most dramatic transition was the end of the USSR in 1991 as oil prices collapsed and debt repayments ended. Sachs absorbed many bitter lessons.

Over the past two decades, his focus shifted fundamentally toward poverty alleviation and sustainable development. Sachs became a Special Advisor to United Nations Secretaries-General Kofi Annan on the Millennium Development Goals (MDGs) and Ban Ki-moon on the transition to the Sustainable Development Goals (SDGs).[2] He was one of their primary architects and their most prominent global champion. His work spans from the early conceptual transition out of the Millennium Development Goals (MDGs) to his leadership in tracking, financing, and modernizing the 17 SDG goals using digital and data-driven solutions.

We use Sachs’s “ICT & SDGs Final Report: How Information and Communications Technology can Accelerate Action on the Sustainable Development Goals” book in our course.[9] It was written with support from Swedish telecommunications company Ericsson and views broadband and ICT integration with the SDGs enabling solutions such as mobile money, digital health clinics, and localized energy grids as crucial tools to bypass traditional, slow “Business-as-Usual” steps. The goal is to use ICT to allow impoverished nations to leapfrog into sustainable economic and energy systems.

Sachs provides a crucial framework for sustainability, ICT4D, and AI4Good because he argues that global poverty and environmental degradation are solvable through deliberate institutional investment and targeted technology transfer.[3,2] Sustainability, ICT4D, and AI initiatives are highly capital-intensive. They rely heavily on investments through the architecture of the global financial system, which is fundamentally underpinned by the USD (US dollar plus Eurodollars) but suffers from persistent shortages and capital flight.

Sachs argues that the current international financial architecture fails the Global South. He argues that developing nations are trapped in structural disadvantages because they have been largely excluded from USD liquidity due to excessively high interest rates compared to wealthy nations, preventing them from financing their own green transitions or digital infrastructure.[4] He has advocated for reforming the IMF and World Bank to unlock concessionary capital that would be vital for any realistic execution of global ICT4SD and AI4Good initiatives.

Peter Zeihan is a contemporary American geopolitical strategist, author, and speaker. Born in 1973, Zeihan took a non-traditional academic route to global prominence, unlike career academics. He earned his master’s degree from the Patterson School of Diplomacy and International Commerce at the University of Kentucky and has a post-graduate diploma in Asia Studies from the University of Otago in New Zealand. He spent over a decade (from 2000 to 2012) at Stratfor (Strategic Forecasting, Inc.), a premier private intelligence and geopolitical analysis firm in Austin, Texas. He eventually rose to the position of Vice President of Analysis before starting his own consultancy.[5] Stratfor, which was started by George Frieden (Geopolitical Futures) and other professors from LSU, is now owned by the RANE Network.[6]

In 2012, he founded his own independent firm, Zeihan on Geopolitics, where he advises corporate executives, military leaders, and government organizations. He has authored several bestselling books, including The Accidental Superpower (2014) and The End of the World Is Just the Beginning: Mapping the Collapse of Globalization (2022), which accurately predicted major shifts in global supply chain disruptions and energy security that appeared after the COVID-19 pandemic and the Russian invasion of Ukraine.[5,7]

Zeihan offers a stark, cold, and highly pragmatic counterweight to institutional optimism. His structural determinism focuses on geography, demographics, and physical security, presenting hard truths that tech developers must consider.[5,7] His core thesis is that the global free-trade order (the Bretton Woods system) was a historical anomaly guaranteed solely by the US Navy to fight the Cold War.[5] As the US pulls back from policing global sea lanes, international trade will fragment.

For an ICT4D/AI curriculum, these are indispensable lessons. If global supply chains break down or transportation becomes threatened, the localized utilization of semiconductors, digital devices, fiber-optic cables, and green-energy hardware will stop or become prohibitively expensive for most nations.

Zeihan emphasizes that we still live in a world in which things must be physically moved from where they are produced to where they are consumed. He tracks how nations with rapidly aging populations face terminal economic contraction, and how localized geography dictates whether a country can survive an energy crisis.[7]

Populations economically provide consumption, investment, and labor. His perspective forces AI and sustainability students to confront demographic and physical limitations. You cannot implement an “AI solution” in a country that lacks a creative and energetic workforce needed to maintain a high-tech economy. It includes avid customers, investors, and intelligent know-how. Also, you cannot adequately develop without the naval capability or protection to secure energy imports or commodity exports.

The Tension

For Sachs, a multipolar world is an opportunity to build a fairer global system. He points out that the rise of China, the expansion of the BRICS bloc, and the economic dynamism of East Asia mean that technological and financial capacity is no longer consolidated in the West.

Sachs believes that if nations move past the quest for dominance, this new multipolar landscape can leverage global cooperation to deploy AI, digital public infrastructure, and clean energy to address systemic issues such as climate change and poverty. He strongly advocates shifting international governance within the UN to a super-majority voting system, removing unilateral vetoes, and opening multilateral finance to the Global South.

Zeihan treats Sachs’s vision of peaceful multipolar cooperation as a dangerous fantasy. He asserts that without the US Navy policing the high seas, global trade lanes will fragment, and maritime piracy or state-sponsored resource hoarding will return. Zeihan warns that true deglobalization will lead to localized “de-industrialization, de-urbanization, and depopulation.”

He points out that countries with terrible demographics (like rapidly aging populations in China, Russia, and parts of Europe) and those structurally dependent on long-range imports for food and energy face systemic collapse. In Zeihan’s multipolar world, only a few geographically secure, demographically stable, resource-abundant nations (principally the United States, alongside localized and preffered partners) will maintain advanced industrial capabilities. In contrast, the rest of the world faces severe resource scarcity.

Why This Matters for Sustainability, ICT4D, and AI4Good

This clash highlights the exact friction points students in international development must anticipate. If Sachs is right, the primary barrier to AI4Good and global sustainability is political will and institutional design. Developers should focus on global policy frameworks, open-source technology transfers, and lobbying multilateral banks to fund clean energy and digital infrastructure in the Global South.

If Zeihan is right, the primary barrier is physical vulnerability. An AI system or localized tech intervention is useless if the server farms lack electricity due to regional oil blockades or a lack of solar panels and windmills. ICT will not present sufficient solutions if the country cannot import the physical semiconductors and routing technologies required to maintain its data centers and wireless broadband networks. Advanced AI infrastructure requires specialized hardware such as GPUs manufactured via highly consolidated, multi-country supply chains (such as TSMC in Taiwan using ASML lithography from the Netherlands). These transactions are settled predominantly in USD. A country’s access to “AI for Good” models is gated by its balance of payments and macroeconomic stability.

AI and ICT4D are deeply anchored in the physical world and bound by energy and financial constraints. Sachs argues that advanced software architectures for health, education, and public administration must be treated as “Global Public Goods,” because software can be replicated at nearly zero marginal cost.[8] To make his tech-enhanced world a reality, he advocates for a radical shift in UN voting and multilateral funding (via the World Bank, IMF, and international private capital) to grant low-interest loans directly targeted at expanding digital hardware, 5G towers, and compute capabilities across the Global South.

Either Sachs’s vision of global institutional redesign is achieved or developers must design “low-infrastructure, hyper-localized, and decoupled” systems that can survive the structural breakdown of global trade.

References

Berg, A. (2024). Jeffrey D. Sachs (1954–). The Palgrave Companion to Harvard Economics, 999-1021. https://doi.org/10.1007/978-3-031-52053-2_40
Farhat–Holzman, Laina (2017) “Peter Zeihan, The Accidental Superpower: The Next Generation of American Preeminence and the Coming Global Disorder. Twelve, Hachette Book Group, 2014.,” Comparative Civilizations Review: Vol. 76: No. 76, Article 24.
https://scholarsarchive.byu.edu/ccr/vol76/iss76/24
Galjak, M. (2023). Peter Zeihan – The End of the World is Just the Beginning: Mapping the Collapse of Globalization. Demografija, (20), 115-118.
Kahn, M. E. (2015). A Review of The Age of Sustainable Development by Jeffrey Sachs. Journal of Economic Literature, 53(3), 654-666. https://doi.org/10.1257/jel.53.3.654
Sachs, J. D. (n.d.). ICT & SDGs Final Report: How Information and Communications Technology can Accelerate Action on the Sustainable Development Goals in our course.
https://www.oneworld.net/sites/default/files/resources/2016-06/ict-sdg.pdf
Snowdon, B. (2005). A Global Compact to End Poverty: Jeffrey Sachs interviewed by Brian Snowdon. World Economics, 6(4), 11-58.

Notes

[1] Berg, A. (2024). Jeffrey D. Sachs (1954–). A Global Compact to End Poverty: Jeffrey Sachs interviewed by Brian Snowdon. World Economics, 6(4), 11-58.
[3]Kahn, M. E. (2015). A Review of The Age of Sustainable Development by Jeffrey Sachs. Journal of Economic Literature, 53(3), 654-666. https://doi.org/10.1257/jel.53.3.654
[4] Pennings, A.J. (2026, Apr 12) USD Liquidity: A Tiered Hierarchy Model and Implications for AI4Good and ICT4D. apennings.com https://apennings.com/characteristics-of-digital-media/usd-liquidity-a-tiered-liquidity-hierarchy-model-and-implications-for-ai4good-and-ict4d/
[5] Farhat–Holzman, Laina (2017) “Peter Zeihan, The Accidental Superpower: The Next Generation of American Preeminence and the Coming Global Disorder. Twelve, Hachette Book Group, 2014.,” Comparative Civilizations Review: Vol. 76: No. 76, Article 24.
https://scholarsarchive.byu.edu/ccr/vol76/iss76/24
[6] Kullik, Jakob. “George Friedman: The Next 100 Years. A Forecast for the 21st Century. Anchor Books/Random House: New York 2010, 253 Seiten” SIRIUS – Zeitschrift für Strategische Analysen, vol. 8, no. 2, 2024, pp. 244-246. https://doi.org/10.1515/sirius-2024-2015
[7] Galjak, M. (2023). Peter Zeihan – The End of the World is Just the Beginning: Mapping the Collapse of Globalization. Demografija, (20), 115-118.[5,7]
[8] This study was commissioned by the Development
Financing 2000 project within the Swedish Ministry for
Foreign Affairs https://eba.se/app/uploads/2021/04/2001.2-Financing-and-Providing-Global-Public-Goods-Expectations-and-Prospects.pdf
Prompt(s)
[9] Sachs, J. D. (2015). ICT & SDGs Final Report: How Information and Communications Technology can Accelerate Action on the Sustainable Development Goals in our course.

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

Epistemological and Philosophical Implications of Spreadsheet Logic

Posted on | May 25, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, May 25) Epistemological and Philosophical Implications of Spreadsheet Logic. apennings.com https://apennings.com/digital-geography/epistemological-and-philosophical-implications-of-spreadsheet-logic/

Introduction

Epistemological and Philosophical Implications of Spreadsheet Logic

This post explores the thinking of Alex Edmans and Anthony Giddens and applies them to the implications of digital spreadsheets and what I call “spreadsheet logic,” the layers of SACT (Substitution – Abstraction – Symbolic Computing – Telecommunications Synchronization) SACT (Substitution – Abstraction – Symbolic Computation – Telecommunications Synchronization).

Spreadsheet logic is more than a tool; it is a distinctive epistemological and philosophical regime. By filtering corporate strategy, investment allocation, public policy, and even personal career planning through rows and columns of assumptions, cash-flow forecasts, discount rates, net present value (NPV), internal rate of return (IRR), and sensitivity tables, it imposes a specific way of knowing and valuing the world. This regime privileges calculative rationality, reduces complexity to financial variables, and reconfigures our relationship to time, space, value, and judgment.

Drawing on Alex Edmans’ critique of quantification and Anthony Giddens’ theory of time-space distanciation (and the related concept of time-space power), we can see how spreadsheets do not neutrally represent reality. Spreadsheet logic actively reshapes it, often with profound consequences.

Hard Metrics, Soft Losses, and the Illusion of Precision

At its core, spreadsheet logic enacts an epistemology of quantification. Knowledge is deemed valid only insofar as it can be rendered numerical, projected forward, and discounted back to the present. This creates a systematic bias toward “hard” information—verifiable numbers, contractible targets, and modelable forecasts—while sidelining “soft” information such as contextual judgment, tacit knowledge, organizational culture, or serendipitous innovation.

Alex Edmans argues that most important outcomes cannot be quantified. His analysis of sustainability metrics, ESG ratings, and incentive systems shows that over-reliance on hard data produces an imbalance. Metrics increase total information but distort its balance, leading managers and decision-makers to optimize for what shows up in the spreadsheet rather than what truly creates value. The result is “hitting the target but missing the point”—a form of epistemic distortion in which the unmodeled is treated as nonexistent.[2]

Philosophically, this echoes a deeper reductionism. Spreadsheet epistemology assumes the world is (or can be made) sufficiently stable and predictable for reliable forecasting. Yet complex systems are rife with Knightian uncertainty, not mere risk.[1] Small changes in discount rates, growth assumptions, or correlation matrices can swing NPV from positive to negative. The apparent precision of a formatted table and Monte Carlo simulation masks profound epistemic fragility. Most complex financial models contain errors, and the very act of modeling can create self-fulfilling prophecies through herding. What counts as “knowledge” becomes whatever survives the sensitivity table, crowding out narrative understanding, historical context, or ethical intuition.

Temporality, Ontology, and the Colonization of Value

Spreadsheet logic enacts a distinctive philosophy of time. Discounted cash flow techniques rest on the “time value of money.” This idea is the assumption that a dollar today is worth more than a dollar tomorrow. The issue is not merely technical; it philosophically privileges the present and heavily discounts the distant future. Long-horizon investments (basic research, infrastructure, cultural capital) rarely survive the spreadsheet test unless their near-term cash flows justify them. The future is not an open horizon of possibility but a contingent stream to be colonized and brought back to today’s required rate of return. This phenomenon yields what critics call pathological short-termism. Quarterly capitalism prevails in which the spreadsheet becomes a temporal disciplining device.

Ontologically, spreadsheet logic flattens reality into financial flows. Value is redefined monistically: an asset, project, policy, or career choice exists meaningfully only insofar as it generates positive NPV or meets IRR thresholds. Non-financial dimensions such as social cohesion, environmental resilience, intrinsic purpose, or human flourishing are either ignored or awkwardly proxied into variables that the model can digest. This process is not neutral accounting; it is an ontological commitment that what cannot be cash-flowed is less real.

Ethically and existentially, the regime subordinates judgment to formula. Leaders no longer ask “Is this the right thing?” but “Does this pass the model?” Purpose and intrinsic motivation are crowded out by instrumental compliance. Edmans’ warning applies directly. When everything must be quantified in a spreadsheet for incentives or evaluation, genuine stewardship gives way to box-ticking and greenwashing. Spreadsheet logic thus erodes the space for moral reasoning, replacing it with algorithmic governance.

Giddens and Spreadsheet Logic as Time-Space Distanciation and Time-Space Power

Anthony Giddens’ framework illuminates the structural power at work. In modernity, social relations are increasingly “stretched” across time and space through “time-space distanciation“, the disembedding of interactions from local contexts of co-presence and their reconfiguration across indefinite distances and horizons. Mechanisms such as money, standardized time, expert systems, and information technologies make this possible. Giddens explicitly links both ancient (lists, tables, writing, zero) and advanced information technologies to the achievement of time-space power. This capacity of social systems (and those who control them) draws on the ability to store, coordinate, and exert influence over distant times and places without physical presence.[3]

Spreadsheets are a quintessential disembedding technology. They substitute and abstract local operations—factories, communities, ecosystems—into global cash-flow models that can be manipulated from distant headquarters or investment funds. A private-equity firm in New York can model the future cash flows of a manufacturing plant in Indonesia, apply a discount rate reflecting global capital costs, and decide to restructure or divest—exerting time-space power over workers, suppliers, and environments it never physically encounters. Decisions are lifted out of immediate context and stretched across global space and multi-year time horizons. The spreadsheet becomes the expert system that coordinates absent actors, much as money or clocks did in earlier phases of modernity.

This produces Giddensian consequences, such as heightened reflexivity (constant remodeling of futures), the globalization of capital allocation, and new forms of systemic risk (correlated models amplifying bubbles or crashes). Yet it also creates a paradox Edmans would recognize. While time-space power grants financial elites immense coordination capacity, it systematically erodes the soft, local, and contextual knowledge required for sustainable value creation. The very mechanism that stretches power across time and space blinds it to the qualitative realities that cannot be adequately represented in spreadsheet cells.

Broader Ramifications and the Need for Re-Embedding Judgment

The epistemological and philosophical implications converge in a subtle but powerful ideology. Spreadsheet logic naturalizes a world in which only the modelable matters, the future is discounted, and power flows to those who master the formulas. Personal career choices become exercises in self-financialization (“What is the NPV of this degree?”). Public policy is reduced to cost-benefit spreadsheets that undervalue the unmeasurable. Societies underinvest in the very sources of long-term flourishing—culture, trust, resilience, breakthrough innovation—because they resist clean projection.

Giddens’ analysis suggests that such distanciation is not inevitable; it can be countered by re-embedding mechanisms that restore local context and qualitative judgment. Edmans offers a practical path. He suggests supplementing metrics with assessment, narrative, and principles-based decision-making rather than mechanical quantification. The challenge is not to abandon spreadsheets; they remain powerful for discipline and scale, but to recognize them as a limited lens, not the final arbiter of truth or value.

In the end, spreadsheet logic reveals a deeper tension in late modernity: the extraordinary expansion of time-space power through calculative technologies has come at the cost of epistemic humility and philosophical depth. Reclaiming judgment, purpose, and the unquantifiable is not anti-rigorous; it is the necessary corrective if we wish to build economies and societies that actually grow the pie rather than merely optimizing what fits in the cells.

References

Edmans, A. Grow the Pie: How Great Companies Deliver Both Purpose and Profit. Cambridge University Press, 2020.
Edmans, A. May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases—And What We Can Do about It. University of California Press, 2024 (or Penguin UK edition, 2024).
Edmans, A. “The Dangers of Sustainability Metrics”. VoxEU, 11 February 2021.
Edmans, A. “No Stakeholder Left Behind: The Dangers of ESG Metrics”. Medium, 21 November 2021.
Giddens, A. The Consequences of Modernity. Polity Press (Cambridge, UK) / Stanford University Press, 1990.
Giddens, A. The Constitution of Society: Outline of the Theory of Structuration. University of California Press / Polity Press, 1984.

Notes

[1] Knightian uncertainty is a concept that establishes a clear theoretical divide between the predictable and the unpredictable. Understanding this difference is critical for analyzing market volatility, entrepreneurial decision-making, and regulatory policies.
[2] Edmans’ works focus on the practical distortions of quantification in business and policy. Edmans, A. Grow the Pie: How Great Companies Deliver Both Purpose and Profit. Cambridge University Press, 2020.
This is the core book developing the argument that most important outcomes cannot be quantified and that purpose-driven companies ultimately deliver superior long-term value. It was coined by economist Frank Knight in 1921, who stressed quantifiable risk by acknowledging the unmeasurable, and the unknown variables where statistical probabilities cannot be assigned.
[3] Giddens provides the broader sociological framework for understanding how calculative technologies like spreadsheets exercise power by disembedding decisions from local contexts and stretching them across time and space. Giddens, A. The Consequences of Modernity. Polity Press (Cambridge, UK) / Stanford University Press, 1990.
This is the primary source for the concepts of time-space distanciation, the “stretching” of social relations across time and space, and the role of expert systems and abstract mechanisms in enabling time-space power in late modernity. Giddens, A. (1983) The Nation-State and Violence. (Berkeley, CA: University of California Press) is also very important for its integration of technologies.

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.



AnthonybwAnthony 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.

The Global Spreadsheet Goes Distributed: Blockchain, USD Liquidity, and Time-Space Power

Posted on | May 23, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, May 23) The Global Spreadsheet Goes Distributed: Blockchain, USD Liquidity, and Time-Space Power. apennings.com https://apennings.com/global-e-commerce/the-global-spreadsheet-goes-distributed-blockchain-usd-liquidity-and-time-space-power/

Introduction

Anthony Giddens‘s concept of time-space distanciation provides a powerful framework for understanding how blockchain extends the telecom synchronization layer of spreadsheet capitalism into a new regime of global coordination and power. Giddens was one of my favorite authors in graduate school because he theorized how technologies like lists, tables, and double-entry bookkeeping are tied to time-space power and its ability to control the trajectory of societies.[1]

In Giddens’s sociology, modernity transforms social relations by lifting interactions out of local contexts and reorganizing them across vast distances through abstract systems such as money, bureaucracy, and telecommunications. Blockchain intensifies this process by creating a globally synchronized computational ledger capable of coordinating value, ownership, contracts, and trust across time and space without requiring continuous physical co-presence or centralized institutional mediation.(Giddens, 1990). In effect, blockchain has the potential to transform the spreadsheet from a centralized institutional artifact into a multinational monetary coordination machine.

The contemporary global monetary system is increasingly organized not merely through states or banks, but through computational infrastructures that synchronize value across global networks. The rise of blockchain technologies, Treasury-backed stablecoins, artificial intelligence, and distributed ledgers marks a profound transformation in the architecture of global finance.

Earlier phases of capitalism coordinated economic activity through industrial production and territorial institutions. Later phases relied on computerized accounting, digital spreadsheets, financial terminals, and telecommunications systems. Today, blockchain extends this logic into a globally distributed synchronization layer that continuously coordinates liquidity, contracts, ownership, and settlement across time and space.

Within my SACT framework (Substitution, Abstraction, Computation, and Telecom synchronization), blockchain represents the next transitional stage of spreadsheet capitalism. It extends the telecom synchronization layer of financial capitalism into a new regime of recursive coordination and programmable monetary governance.

From Spreadsheet Capitalism to Distributed Ledgers

Spreadsheet capitalism emerged through the convergence of digital accounting, financial abstraction, and telecommunications infrastructure. During the late twentieth century, applications such as VisiCalc and Lotus 1-2-3 transformed the personal computer into a decentralized planning engine capable of modeling debt schedules, interest-rate risks, commodity prices, foreign exchange exposure, and portfolio valuation in real time.

At the institutional level, platforms such as Bloomberg L.P. and Reuters integrated spreadsheets, databases, telecommunications, and securities trading into continuously synchronized global systems. Walter Wriston famously described the post-Bretton Woods order as an “information standard,” in which information flows rather than gold reserves increasingly anchor monetary coordination (Wriston, 1992).

But the “information standard” was, more precisely, a spreadsheet-interest-rate standard. Value became recursively computable through networked balance sheets, pricing models, derivatives calculations, and synchronized terminals. Global liquidity emerged through the continuous recalculation of future expectations, especially as interest rates change.

Blockchain extends this architecture by decentralizing the spreadsheet itself. Traditional financial systems depend upon centralized databases, correspondent banks, clearinghouses, and messaging infrastructures such as SWIFT. Blockchain introduces distributed ledger systems in which synchronization occurs across multiple nodes simultaneously through cryptographic consensus mechanisms. The ledger becomes globally persistent, continuously updated, and recursively accessible. This innovation transforms the global spreadsheet into a distributed temporal infrastructure.

Blockchain and Giddens’s Time-Space Distanciation

Blockchain strengthens all four layers of the SACT framework simultaneously. Substitution is the process by which the “messy world” of physical assets, currencies, contracts, and identities is transformed into tokenized digital representations. Abstraction is when economic activity becomes reducible to ledger entries, metadata, programmable rules, and computable relationships.

Computation uses consensus mechanisms, smart contracts, AI optimization systems, and algorithmic governance to automate validation, pricing, settlement, and liquidity coordination. Blockchain enables distributed ledgers to synchronize these computations globally across telecommunications networks, cloud infrastructures, fiber-optic systems, and orbiting satellites, and increasingly with AI-enhanced routing architectures.

The result is the emergence of a globally synchronized computational-financial infrastructure that replaces static, heuristic-based network paths with dynamic, machine learning-driven systems. They continuously predict traffic, balance loads, and optimize Quality of Service (QoS) in real time by embedding localized AI inference or reinforcement learning agents at network edges, core routers, and centralized software-defined networking (SDN) controllers.[2]

Telecom synchronization distributes and reconciles compute globally across nodes and networks. Blockchains allow stablecoins to function as “programmable Eurodollars.” These tokens circulate globally on a telecom substrate that bypasses the friction and “haircuts” of traditional correspondent banking. Adaptive actors use the sub-millisecond latency of the blockchain to fine-tune liquidity access. Because telecom synchronization is near-instant, AI agents can respond to a “sustainability transition” or a “geopolitical shock” in real time, buffering Tier 5 economies before a traditional crisis can occur.

From the Eurodollar System to Treasury-Backed Stablecoins

The twentieth-century USD system operated through a combination of Federal Reserve liquidity, Treasury securities, Eurodollar banking networks, SWIFT messaging, and offshore correspondent banking systems.

The Eurodollar market allowed international banks to create offshore dollar liquidity. They went beyond direct US territorial boundaries while still relying on US Treasury collateral and Federal Reserve backstops, including emergency lending facilities and temporary monetary mechanisms designed to prevent systemic financial crises. These tools allowed banks and financial firms to swap eligible collateral for cash when funding markets experience severe stress.

Treasury-backed stablecoins may represent the next stage of this transformation. These instruments potentially merge blockchain synchronization, offshore dollar liquidity, programmable settlement, AI-managed liquidity coordination, and short-term Treasury collateral.

In effect, stablecoins may transform the Eurodollar system into a continuously synchronized blockchain-based USD network operating globally across digital infrastructures and into digital wallets.
This “retail Eurodollar” could dramatically increase USD liquidity penetration into Tier 4 and Tier 5 economies currently underserved by traditional banking systems.

At the same time, these systems deepen US monetary influence by extending Treasury-backed dollar infrastructure directly into digital payment systems worldwide through smartphones’ digital wallets. Mobile devices connected to blockchain settlement rails would provide access to dollar-denominated liquidity without requiring conventional correspondent banking relationships.

AI, SACT-AI, and Programmable Monetary Coordination

The integration of artificial intelligence into blockchain coordination systems introduces a further transformation. Under a possible SACT-AI framework, AI systems would optimize cross-border liquidity management, reserve balancing, foreign exchange routing,
collateral allocation, trade settlement, and dynamic adjustment mechanisms.

This coordination begins to resemble a technologically updated version of John Maynard Keynes’s proposed Bancor and International Clearing Union (ICU) architecture from Bretton Woods. Keynes envisioned a multinational clearing system that would reduce dependence on a single reserve currency while distributing adjustment burdens more symmetrically across surplus and deficit countries.

Blockchain synchronization and AI coordination potentially supply the computational infrastructure that earlier twentieth-century institutions lacked. In this scenario, blockchains provide synchronized global ledgers, AI systems optimize liquidity flows, stablecoins provide programmable settlement instruments, and multi-currency reserve baskets reduce USD dependence for coordination. The result would be an AI-enhanced multinational clearing architecture operating continuously across distributed networks but based on USD stablecoins.

Time-Space Power and Financial Sovereignty

Giddens emphasized that modernity expands administrative and surveillance capacities alongside abstraction systems. Blockchain does not eliminate institutional power; rather, it redistributes and reconfigures it. Whoever controls protocol standards, reserve assets, settlement architectures, AI coordination systems, validation layers,
as well as liquidity infrastructures exercise immense structural influence over global finance.

The industrial economy depended upon factories synchronized by clocks. Spreadsheet capitalism depended upon terminals synchronized by telecommunications. Blockchain capitalism depends upon distributed ledgers synchronized through cryptographic consensus and increasingly coordinated through AI.

The central political question of the twenty-first century may therefore concern not simply who controls money, but who controls the synchronization infrastructures through which global liquidity itself is computed. This marks the emergence of a new form of time-space power that is globally distributed, computationally recursive, continuously synchronized, and increasingly autonomous.

References

Giddens, A. (1990). The consequences of modernity. Stanford University Press.
Wriston, W. B. (1992). The twilight of sovereignty: How the information revolution is transforming our world. Scribner.
Keynes, J. M. (1980). The collected writings of John Maynard Keynes, Volume 25: Activities 1940–1944: Shaping the post-war world: The Clearing Union. Macmillan.
Simondon, G. (2017). On the mode of existence of technical objects. Univocal Publishing.
Deleuze, G., & Guattari, F. (1987). A thousand plateaus: Capitalism and schizophrenia. University of Minnesota Press.
Cetina, K., & Bruegger, U. (2002). Global microstructures: The virtual societies of financial markets. American Journal of Sociology, 107(4), 905–950.
Castells, M. (2010). The rise of the network society (2nd ed.). Wiley-Blackwell.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
Bricklin, D. (2022). VisiCalc and the rise of the spreadsheet. Software History Center.
Lotus 1-2-3 Campbell-Kelly, M. (2003). From airline reservations to Sonic the Hedgehog: A history of the software industry. MIT Press.

Notes

[1] Two of the most influential texts were Giddens, A. (1983) A Contemporary Critique of Historical Materialism. (Berkeley, CA: University of California Press).
Giddens, A. (1983) The Nation-State and Violence. (Berkeley, CA: University of California Press).
[2] Most of my work has been on telecommunication policy and I teach a course about broadband technologies.
Prompt(s) How does blockchain extends the telecom synchronization layer of spreadsheet capitalism across time and space, creating time-space power. Expand the analysis of blockchain power in spreadsheet capitalism by using Anthony Giddens concept of time-space power.

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Chat GPT and Gemini are the primary LLMs as they have been trained for the last year. Links are used for some citations. Results are for educational use only.



AnthonybwAnthony 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.

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